diff --git a/TODO.ipynb b/TODO.ipynb index fa8503a..6e29954 100644 --- a/TODO.ipynb +++ b/TODO.ipynb @@ -152,4 +152,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/WOS/ai_scope_keywords.txt b/WOS/ai_scope_keywords.txt index 7b408ad..ddc56fd 100644 --- a/WOS/ai_scope_keywords.txt +++ b/WOS/ai_scope_keywords.txt @@ -53,7 +53,7 @@ Bayesian network*, genetic algorithm*, swarm intelligence, cognitive computing, -artificial neural network* +artificial neural network*, convolutional neural network*, recurrent neural network*, ensemble learning, diff --git a/WOS/wos_extract/geckodriver.log b/WOS/wos_extract/geckodriver.log index 083733b..dd045c0 100644 --- a/WOS/wos_extract/geckodriver.log +++ b/WOS/wos_extract/geckodriver.log @@ -1457,3 +1457,306 @@ JavaScript warning: https://www.webofscience.com/D6XP2sNyj/cx/4v/uz3Enbrfd-gQic/ console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." 1680606547191 Marionette INFO Stopped listening on port 51159 Dynamically enable window occlusion 1 +1680772944496 geckodriver INFO Listening on 127.0.0.1:64163 +1680772947553 mozrunner::runner INFO Running command: "C:\\Program Files\\Mozilla Firefox\\firefox.exe" "--marionette" "--headless" "--remote-debugging-port" "641 ... "--remote-allow-hosts" "localhost" "-no-remote" "-profile" "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\rust_mozprofilesDWMVq" +*** You are running in headless mode. +console.warn: services.settings: Ignoring preference override of remote settings server +console.warn: services.settings: Allow by setting MOZ_REMOTE_SETTINGS_DEVTOOLS=1 in the environment +1680772947891 Marionette INFO Marionette enabled +Dynamically enable window occlusion 0 +1680772947895 Marionette INFO Listening on port 64173 +WebDriver BiDi listening on ws://127.0.0.1:64164 +Read port: 64173 +1680772948048 RemoteAgent WARN TLS certificate errors will be ignored for this session +[GFX1-]: RenderCompositorSWGL failed mapping default framebuffer, no dt +console.warn: SearchSettings: "get: No settings file exists, new profile?" (new NotFoundError("Could not open the file at C:\\Users\\radvanyi\\AppData\\Local\\Temp\\rust_mozprofilesDWMVq\\search.json.mozlz4", (void 0))) +console.error: ({}) +DevTools listening on ws://127.0.0.1:64164/devtools/browser/1d3b3fce-be91-4432-84a7-bddadce6f5b9 +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://access.clarivate.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: unreachable code after return statement +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +1680772966618 Marionette WARN TimedPromise timed out after 500 ms: stacktrace: +TimedPromise/<@chrome://remote/content/marionette/sync.sys.mjs:219:24 +TimedPromise@chrome://remote/content/marionette/sync.sys.mjs:204:10 +interaction.flushEventLoop@chrome://remote/content/marionette/interaction.sys.mjs:425:10 +webdriverClickElement@chrome://remote/content/marionette/interaction.sys.mjs:173:31 +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +JavaScript warning: https://www.webofscience.com/uoPa1mhwbY2CW-U5Igz_Reug/pOmYmV4NaGcuiu/A19V/Iwk1OH/EsUF8, line 1: WEBGL_debug_renderer_info is deprecated in Firefox and will be removed. Please use RENDERER. +console.warn: LoginRecipes: "Falling back to a synchronous message for: https://www.webofscience.com." +1680773896434 Marionette INFO Stopped listening on port 64173 +Dynamically enable window occlusion 1 diff --git a/WOS/wos_extract/wos_query_generator.ipynb b/WOS/wos_extract/wos_query_generator_legacy.ipynb similarity index 99% rename from WOS/wos_extract/wos_query_generator.ipynb rename to WOS/wos_extract/wos_query_generator_legacy.ipynb index b351f64..2a86aec 100644 --- a/WOS/wos_extract/wos_query_generator.ipynb +++ b/WOS/wos_extract/wos_query_generator_legacy.ipynb @@ -325,4 +325,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/WOS/wos_extract/wos_query_generator_simplesyntax.ipynb b/WOS/wos_extract/wos_query_generator_simplesyntax.ipynb index 519eede..5dab943 100644 --- a/WOS/wos_extract/wos_query_generator_simplesyntax.ipynb +++ b/WOS/wos_extract/wos_query_generator_simplesyntax.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 65, + "execution_count": 14, "metadata": { "collapsed": true }, @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 15, "outputs": [], "source": [ "country_mode = \"CU\" #CU-country-region AU-address" @@ -27,7 +27,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 16, "outputs": [], "source": [ "# (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"computer vision\") OR TS=(\"pattern recognition\")) AND" @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 17, "outputs": [], "source": [ "keywords_source = r'..\\ai_scope_keywords.txt'\n", @@ -51,13 +51,13 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 18, "outputs": [ { "data": { - "text/plain": "'artificial intelligence*,machine* learn*,neural network*,big data*,deep learn*,pattern recognition,computer vision,image classification,reinforcement learning,support vector machine*,recommender system*,random forest,ensemble model*,image processing,generative network*,ai ethic*,natural language processing,clustering algorithm*,feature extraction,time series forecast*,anomaly detection,identity fraud detection,dimensionality reduction,feature elicitation,chatbot*,clustering,*supervised learning,convolutional network*,convolutional neural,adversarial network*,adversarial neural,adversarial machine,autoencoder*,gated recurrent unit*,perceptron*,feature learning,feature engineering,long short-term memor*,word embedding*,word vector*,gradient descent,k-nearest neighbor*,naive bayes,transfer learning,fuzzy logic,backpropagation,computational modeling,computational statistic*,intelligent agent*,expert system*,decision tree*,Bayesian network*,genetic algorithm*,swarm intelligence,cognitive computing,artificial neural network*convolutional neural network*,recurrent neural network*,ensemble learning,data mining,artificial general intelligence,artificial consciousness,evolutionary algorithm*,self-organizing map*,deep reinforcement learning,adversarial machine learning,machine vision,neural-symbolic integration,probabilistic graphical model*,hybrid intelligent system*,machine creativity,explainable AI,interactive machine learning,artificial emotional intelligence,evolutionary computation*,human-in-the-loop,unsupervised deep learning,deep belief network*,quantum machine learning,artificial immune system*,swarm robotics,autonomous agents,machine ethics,collaborative filtering,content based filtering,pervasive computing,ubiquitous computing,human-computer interaction,cloud computing,Internet of Things,artificial cognition,computational creativity,sentiment analy*,robotics,boltzmann machine*,kernel machine*,Hopfield network*,Hebbian learning,latent factor model*,non-negative matrix factorization,independent component analysis,principal component analysis,data augmentation,image segmentation,autoregressive language model*,generative pre-trained transformer*,smart city,smart home,smart grid,smart health,smart manufacturing,smart agriculture,smart environment,smart energy,smart mobility,smart buildings,smart tourism,smart logistics,smart supply chain,smart retail,smart waste management,smart parking,smart governance,smart education,smart technolog*,smart diagnostic*,data* analytic*,hadoop*,mapreduce,map$reduce,large$ dataset*,data warehouse*,predictive analytic*,no$sql,nosql,no sql,unstructured data*,data science*'" + "text/plain": "'artificial intelligence*,machine* learn*,neural network*,big data*,deep learn*,pattern recognition,computer vision,image classification,reinforcement learning,support vector machine*,recommender system*,random forest,ensemble model*,image processing,generative network*,ai ethic*,natural language processing,clustering algorithm*,feature extraction,time series forecast*,anomaly detection,identity fraud detection,dimensionality reduction,feature elicitation,chatbot*,clustering,*supervised learning,convolutional network*,convolutional neural,adversarial network*,adversarial neural,adversarial machine,autoencoder*,gated recurrent unit*,perceptron*,feature learning,feature engineering,long short-term memor*,word embedding*,word vector*,gradient descent,k-nearest neighbor*,naive bayes,transfer learning,fuzzy logic,backpropagation,computational modeling,computational statistic*,intelligent agent*,expert system*,decision tree*,Bayesian network*,genetic algorithm*,swarm intelligence,cognitive computing,artificial neural network*,convolutional neural network*,recurrent neural network*,ensemble learning,data mining,artificial general intelligence,artificial consciousness,evolutionary algorithm*,self-organizing map*,deep reinforcement learning,adversarial machine learning,machine vision,neural-symbolic integration,probabilistic graphical model*,hybrid intelligent system*,machine creativity,explainable AI,interactive machine learning,artificial emotional intelligence,evolutionary computation*,human-in-the-loop,unsupervised deep learning,deep belief network*,quantum machine learning,artificial immune system*,swarm robotics,autonomous agents,machine ethics,collaborative filtering,content based filtering,pervasive computing,ubiquitous computing,human-computer interaction,cloud computing,Internet of Things,artificial cognition,computational creativity,sentiment analy*,robotics,boltzmann machine*,kernel machine*,Hopfield network*,Hebbian learning,latent factor model*,non-negative matrix factorization,independent component analysis,principal component analysis,data augmentation,image segmentation,autoregressive language model*,generative pre-trained transformer*,smart city,smart home,smart grid,smart health,smart manufacturing,smart agriculture,smart environment,smart energy,smart mobility,smart buildings,smart tourism,smart logistics,smart supply chain,smart retail,smart waste management,smart parking,smart governance,smart education,smart technolog*,smart diagnostic*,data* analytic*,hadoop*,mapreduce,map$reduce,large$ dataset*,data warehouse*,predictive analytic*,no$sql,nosql,no sql,unstructured data*,data science*'" }, - "execution_count": 69, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -71,13 +71,13 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 19, "outputs": [ { "data": { - "text/plain": "'\"artificial intelligence*\" OR \"machine* learn*\" OR \"neural network*\" OR \"big data*\" OR \"deep learn*\" OR \"pattern recognition\" OR \"computer vision\" OR \"image classification\" OR \"reinforcement learning\" OR \"support vector machine*\" OR \"recommender system*\" OR \"random forest\" OR \"ensemble model*\" OR \"image processing\" OR \"generative network*\" OR \"ai ethic*\" OR \"natural language processing\" OR \"clustering algorithm*\" OR \"feature extraction\" OR \"time series forecast*\" OR \"anomaly detection\" OR \"identity fraud detection\" OR \"dimensionality reduction\" OR \"feature elicitation\" OR \"chatbot*\" OR \"clustering\" OR \"*supervised learning\" OR \"convolutional network*\" OR \"convolutional neural\" OR \"adversarial network*\" OR \"adversarial neural\" OR \"adversarial machine\" OR \"autoencoder*\" OR \"gated recurrent unit*\" OR \"perceptron*\" OR \"feature learning\" OR \"feature engineering\" OR \"long short-term memor*\" OR \"word embedding*\" OR \"word vector*\" OR \"gradient descent\" OR \"k-nearest neighbor*\" OR \"naive bayes\" OR \"transfer learning\" OR \"fuzzy logic\" OR \"backpropagation\" OR \"computational modeling\" OR \"computational statistic*\" OR \"intelligent agent*\" OR \"expert system*\" OR \"decision tree*\" OR \"Bayesian network*\" OR \"genetic algorithm*\" OR \"swarm intelligence\" OR \"cognitive computing\" OR \"artificial neural network*convolutional neural network*\" OR \"recurrent neural network*\" OR \"ensemble learning\" OR \"data mining\" OR \"artificial general intelligence\" OR \"artificial consciousness\" OR \"evolutionary algorithm*\" OR \"self-organizing map*\" OR \"deep reinforcement learning\" OR \"adversarial machine learning\" OR \"machine vision\" OR \"neural-symbolic integration\" OR \"probabilistic graphical model*\" OR \"hybrid intelligent system*\" OR \"machine creativity\" OR \"explainable AI\" OR \"interactive machine learning\" OR \"artificial emotional intelligence\" OR \"evolutionary computation*\" OR \"human-in-the-loop\" OR \"unsupervised deep learning\" OR \"deep belief network*\" OR \"quantum machine learning\" OR \"artificial immune system*\" OR \"swarm robotics\" OR \"autonomous agents\" OR \"machine ethics\" OR \"collaborative filtering\" OR \"content based filtering\" OR \"pervasive computing\" OR \"ubiquitous computing\" OR \"human-computer interaction\" OR \"cloud computing\" OR \"Internet of Things\" OR \"artificial cognition\" OR \"computational creativity\" OR \"sentiment analy*\" OR \"robotics\" OR \"boltzmann machine*\" OR \"kernel machine*\" OR \"Hopfield network*\" OR \"Hebbian learning\" OR \"latent factor model*\" OR \"non-negative matrix factorization\" OR \"independent component analysis\" OR \"principal component analysis\" OR \"data augmentation\" OR \"image segmentation\" OR \"autoregressive language model*\" OR \"generative pre-trained transformer*\" OR \"smart city\" OR \"smart home\" OR \"smart grid\" OR \"smart health\" OR \"smart manufacturing\" OR \"smart agriculture\" OR \"smart environment\" OR \"smart energy\" OR \"smart mobility\" OR \"smart buildings\" OR \"smart tourism\" OR \"smart logistics\" OR \"smart supply chain\" OR \"smart retail\" OR \"smart waste management\" OR \"smart parking\" OR \"smart governance\" OR \"smart education\" OR \"smart technolog*\" OR \"smart diagnostic*\" OR \"data* analytic*\" OR \"hadoop*\" OR \"mapreduce\" OR \"map$reduce\" OR \"large$ dataset*\" OR \"data warehouse*\" OR \"predictive analytic*\" OR \"no$sql\" OR \"nosql\" OR \"no sql\" OR \"unstructured data*\" OR \"data science*\"'" + "text/plain": "'\"artificial intelligence*\" OR \"machine* learn*\" OR \"neural network*\" OR \"big data*\" OR \"deep learn*\" OR \"pattern recognition\" OR \"computer vision\" OR \"image classification\" OR \"reinforcement learning\" OR \"support vector machine*\" OR \"recommender system*\" OR \"random forest\" OR \"ensemble model*\" OR \"image processing\" OR \"generative network*\" OR \"ai ethic*\" OR \"natural language processing\" OR \"clustering algorithm*\" OR \"feature extraction\" OR \"time series forecast*\" OR \"anomaly detection\" OR \"identity fraud detection\" OR \"dimensionality reduction\" OR \"feature elicitation\" OR \"chatbot*\" OR \"clustering\" OR \"*supervised learning\" OR \"convolutional network*\" OR \"convolutional neural\" OR \"adversarial network*\" OR \"adversarial neural\" OR \"adversarial machine\" OR \"autoencoder*\" OR \"gated recurrent unit*\" OR \"perceptron*\" OR \"feature learning\" OR \"feature engineering\" OR \"long short-term memor*\" OR \"word embedding*\" OR \"word vector*\" OR \"gradient descent\" OR \"k-nearest neighbor*\" OR \"naive bayes\" OR \"transfer learning\" OR \"fuzzy logic\" OR \"backpropagation\" OR \"computational modeling\" OR \"computational statistic*\" OR \"intelligent agent*\" OR \"expert system*\" OR \"decision tree*\" OR \"Bayesian network*\" OR \"genetic algorithm*\" OR \"swarm intelligence\" OR \"cognitive computing\" OR \"artificial neural network*\" OR \"convolutional neural network*\" OR \"recurrent neural network*\" OR \"ensemble learning\" OR \"data mining\" OR \"artificial general intelligence\" OR \"artificial consciousness\" OR \"evolutionary algorithm*\" OR \"self-organizing map*\" OR \"deep reinforcement learning\" OR \"adversarial machine learning\" OR \"machine vision\" OR \"neural-symbolic integration\" OR \"probabilistic graphical model*\" OR \"hybrid intelligent system*\" OR \"machine creativity\" OR \"explainable AI\" OR \"interactive machine learning\" OR \"artificial emotional intelligence\" OR \"evolutionary computation*\" OR \"human-in-the-loop\" OR \"unsupervised deep learning\" OR \"deep belief network*\" OR \"quantum machine learning\" OR \"artificial immune system*\" OR \"swarm robotics\" OR \"autonomous agents\" OR \"machine ethics\" OR \"collaborative filtering\" OR \"content based filtering\" OR \"pervasive computing\" OR \"ubiquitous computing\" OR \"human-computer interaction\" OR \"cloud computing\" OR \"Internet of Things\" OR \"artificial cognition\" OR \"computational creativity\" OR \"sentiment analy*\" OR \"robotics\" OR \"boltzmann machine*\" OR \"kernel machine*\" OR \"Hopfield network*\" OR \"Hebbian learning\" OR \"latent factor model*\" OR \"non-negative matrix factorization\" OR \"independent component analysis\" OR \"principal component analysis\" OR \"data augmentation\" OR \"image segmentation\" OR \"autoregressive language model*\" OR \"generative pre-trained transformer*\" OR \"smart city\" OR \"smart home\" OR \"smart grid\" OR \"smart health\" OR \"smart manufacturing\" OR \"smart agriculture\" OR \"smart environment\" OR \"smart energy\" OR \"smart mobility\" OR \"smart buildings\" OR \"smart tourism\" OR \"smart logistics\" OR \"smart supply chain\" OR \"smart retail\" OR \"smart waste management\" OR \"smart parking\" OR \"smart governance\" OR \"smart education\" OR \"smart technolog*\" OR \"smart diagnostic*\" OR \"data* analytic*\" OR \"hadoop*\" OR \"mapreduce\" OR \"map$reduce\" OR \"large$ dataset*\" OR \"data warehouse*\" OR \"predictive analytic*\" OR \"no$sql\" OR \"nosql\" OR \"no sql\" OR \"unstructured data*\" OR \"data science*\"'" }, - "execution_count": 70, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" } @@ -94,16 +94,27 @@ }, { "cell_type": "code", - "execution_count": 70, - "outputs": [], - "source": [], + "execution_count": 26, + "outputs": [ + { + "data": { + "text/plain": "138" + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(keywords)" + ], "metadata": { "collapsed": false } }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 20, "outputs": [], "source": [ "scope_country_source = r'..\\eu_scope_countries.txt'\n", @@ -136,13 +147,13 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 21, "outputs": [ { "data": { "text/plain": "'AUSTRIA OR BELGIUM OR BULGARIA OR CROATIA OR CYPRUS OR CZECH REPUBLIC OR DENMARK OR ESTONIA OR FINLAND OR FRANCE OR GERMANY OR GREECE OR HUNGARY OR IRELAND OR ITALY OR LATVIA OR LITHUANIA OR LUXEMBOURG OR MALTA OR NETHERLANDS OR POLAND OR PORTUGAL OR ROMANIA OR SLOVAKIA OR SLOVENIA OR SPAIN OR SWEDEN OR NORWAY OR SWITZERLAND OR UNITED KINGDOM OR ENGLAND OR WALES OR SCOTLAND OR N IRELAND'" }, - "execution_count": 72, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -157,13 +168,13 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 22, "outputs": [ { "data": { "text/plain": "'PEOPLES R CHINA OR HONG KONG'" }, - "execution_count": 73, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -177,7 +188,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 22, "outputs": [], "source": [], "metadata": { @@ -186,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 22, "outputs": [], "source": [], "metadata": { @@ -195,13 +206,13 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 23, "outputs": [ { "data": { - "text/plain": "'CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUSTRIA OR BELGIUM OR BULGARIA OR CROATIA OR CYPRUS OR CZECH REPUBLIC OR DENMARK OR ESTONIA OR FINLAND OR FRANCE OR GERMANY OR GREECE OR HUNGARY OR IRELAND OR ITALY OR LATVIA OR LITHUANIA OR LUXEMBOURG OR MALTA OR NETHERLANDS OR POLAND OR PORTUGAL OR ROMANIA OR SLOVAKIA OR SLOVENIA OR SPAIN OR SWEDEN OR NORWAY OR SWITZERLAND OR UNITED KINGDOM OR ENGLAND OR WALES OR SCOTLAND OR N IRELAND) AND TS=(\"artificial intelligence*\" OR \"machine* learn*\" OR \"neural network*\" OR \"big data*\" OR \"deep learn*\" OR \"pattern recognition\" OR \"computer vision\" OR \"image classification\" OR \"reinforcement learning\" OR \"support vector machine*\" OR \"recommender system*\" OR \"random forest\" OR \"ensemble model*\" OR \"image processing\" OR \"generative network*\" OR \"ai ethic*\" OR \"natural language processing\" OR \"clustering algorithm*\" OR \"feature extraction\" OR \"time series forecast*\" OR \"anomaly detection\" OR \"identity fraud detection\" OR \"dimensionality reduction\" OR \"feature elicitation\" OR \"chatbot*\" OR \"clustering\" OR \"*supervised learning\" OR \"convolutional network*\" OR \"convolutional neural\" OR \"adversarial network*\" OR \"adversarial neural\" OR \"adversarial machine\" OR \"autoencoder*\" OR \"gated recurrent unit*\" OR \"perceptron*\" OR \"feature learning\" OR \"feature engineering\" OR \"long short-term memor*\" OR \"word embedding*\" OR \"word vector*\" OR \"gradient descent\" OR \"k-nearest neighbor*\" OR \"naive bayes\" OR \"transfer learning\" OR \"fuzzy logic\" OR \"backpropagation\" OR \"computational modeling\" OR \"computational statistic*\" OR \"intelligent agent*\" OR \"expert system*\" OR \"decision tree*\" OR \"Bayesian network*\" OR \"genetic algorithm*\" OR \"swarm intelligence\" OR \"cognitive computing\" OR \"artificial neural network*convolutional neural network*\" OR \"recurrent neural network*\" OR \"ensemble learning\" OR \"data mining\" OR \"artificial general intelligence\" OR \"artificial consciousness\" OR \"evolutionary algorithm*\" OR \"self-organizing map*\" OR \"deep reinforcement learning\" OR \"adversarial machine learning\" OR \"machine vision\" OR \"neural-symbolic integration\" OR \"probabilistic graphical model*\" OR \"hybrid intelligent system*\" OR \"machine creativity\" OR \"explainable AI\" OR \"interactive machine learning\" OR \"artificial emotional intelligence\" OR \"evolutionary computation*\" OR \"human-in-the-loop\" OR \"unsupervised deep learning\" OR \"deep belief network*\" OR \"quantum machine learning\" OR \"artificial immune system*\" OR \"swarm robotics\" OR \"autonomous agents\" OR \"machine ethics\" OR \"collaborative filtering\" OR \"content based filtering\" OR \"pervasive computing\" OR \"ubiquitous computing\" OR \"human-computer interaction\" OR \"cloud computing\" OR \"Internet of Things\" OR \"artificial cognition\" OR \"computational creativity\" OR \"sentiment analy*\" OR \"robotics\" OR \"boltzmann machine*\" OR \"kernel machine*\" OR \"Hopfield network*\" OR \"Hebbian learning\" OR \"latent factor model*\" OR \"non-negative matrix factorization\" OR \"independent component analysis\" OR \"principal component analysis\" OR \"data augmentation\" OR \"image segmentation\" OR \"autoregressive language model*\" OR \"generative pre-trained transformer*\" OR \"smart city\" OR \"smart home\" OR \"smart grid\" OR \"smart health\" OR \"smart manufacturing\" OR \"smart agriculture\" OR \"smart environment\" OR \"smart energy\" OR \"smart mobility\" OR \"smart buildings\" OR \"smart tourism\" OR \"smart logistics\" OR \"smart supply chain\" OR \"smart retail\" OR \"smart waste management\" OR \"smart parking\" OR \"smart governance\" OR \"smart education\" OR \"smart technolog*\" OR \"smart diagnostic*\" OR \"data* analytic*\" OR \"hadoop*\" OR \"mapreduce\" OR \"map$reduce\" OR \"large$ dataset*\" OR \"data warehouse*\" OR \"predictive analytic*\" OR \"no$sql\" OR \"nosql\" OR \"no sql\" OR \"unstructured data*\" OR \"data science*\") AND PY=(2011-2022)'" + "text/plain": "'CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUSTRIA OR BELGIUM OR BULGARIA OR CROATIA OR CYPRUS OR CZECH REPUBLIC OR DENMARK OR ESTONIA OR FINLAND OR FRANCE OR GERMANY OR GREECE OR HUNGARY OR IRELAND OR ITALY OR LATVIA OR LITHUANIA OR LUXEMBOURG OR MALTA OR NETHERLANDS OR POLAND OR PORTUGAL OR ROMANIA OR SLOVAKIA OR SLOVENIA OR SPAIN OR SWEDEN OR NORWAY OR SWITZERLAND OR UNITED KINGDOM OR ENGLAND OR WALES OR SCOTLAND OR N IRELAND) AND TS=(\"artificial intelligence*\" OR \"machine* learn*\" OR \"neural network*\" OR \"big data*\" OR \"deep learn*\" OR \"pattern recognition\" OR \"computer vision\" OR \"image classification\" OR \"reinforcement learning\" OR \"support vector machine*\" OR \"recommender system*\" OR \"random forest\" OR \"ensemble model*\" OR \"image processing\" OR \"generative network*\" OR \"ai ethic*\" OR \"natural language processing\" OR \"clustering algorithm*\" OR \"feature extraction\" OR \"time series forecast*\" OR \"anomaly detection\" OR \"identity fraud detection\" OR \"dimensionality reduction\" OR \"feature elicitation\" OR \"chatbot*\" OR \"clustering\" OR \"*supervised learning\" OR \"convolutional network*\" OR \"convolutional neural\" OR \"adversarial network*\" OR \"adversarial neural\" OR \"adversarial machine\" OR \"autoencoder*\" OR \"gated recurrent unit*\" OR \"perceptron*\" OR \"feature learning\" OR \"feature engineering\" OR \"long short-term memor*\" OR \"word embedding*\" OR \"word vector*\" OR \"gradient descent\" OR \"k-nearest neighbor*\" OR \"naive bayes\" OR \"transfer learning\" OR \"fuzzy logic\" OR \"backpropagation\" OR \"computational modeling\" OR \"computational statistic*\" OR \"intelligent agent*\" OR \"expert system*\" OR \"decision tree*\" OR \"Bayesian network*\" OR \"genetic algorithm*\" OR \"swarm intelligence\" OR \"cognitive computing\" OR \"artificial neural network*\" OR \"convolutional neural network*\" OR \"recurrent neural network*\" OR \"ensemble learning\" OR \"data mining\" OR \"artificial general intelligence\" OR \"artificial consciousness\" OR \"evolutionary algorithm*\" OR \"self-organizing map*\" OR \"deep reinforcement learning\" OR \"adversarial machine learning\" OR \"machine vision\" OR \"neural-symbolic integration\" OR \"probabilistic graphical model*\" OR \"hybrid intelligent system*\" OR \"machine creativity\" OR \"explainable AI\" OR \"interactive machine learning\" OR \"artificial emotional intelligence\" OR \"evolutionary computation*\" OR \"human-in-the-loop\" OR \"unsupervised deep learning\" OR \"deep belief network*\" OR \"quantum machine learning\" OR \"artificial immune system*\" OR \"swarm robotics\" OR \"autonomous agents\" OR \"machine ethics\" OR \"collaborative filtering\" OR \"content based filtering\" OR \"pervasive computing\" OR \"ubiquitous computing\" OR \"human-computer interaction\" OR \"cloud computing\" OR \"Internet of Things\" OR \"artificial cognition\" OR \"computational creativity\" OR \"sentiment analy*\" OR \"robotics\" OR \"boltzmann machine*\" OR \"kernel machine*\" OR \"Hopfield network*\" OR \"Hebbian learning\" OR \"latent factor model*\" OR \"non-negative matrix factorization\" OR \"independent component analysis\" OR \"principal component analysis\" OR \"data augmentation\" OR \"image segmentation\" OR \"autoregressive language model*\" OR \"generative pre-trained transformer*\" OR \"smart city\" OR \"smart home\" OR \"smart grid\" OR \"smart health\" OR \"smart manufacturing\" OR \"smart agriculture\" OR \"smart environment\" OR \"smart energy\" OR \"smart mobility\" OR \"smart buildings\" OR \"smart tourism\" OR \"smart logistics\" OR \"smart supply chain\" OR \"smart retail\" OR \"smart waste management\" OR \"smart parking\" OR \"smart governance\" OR \"smart education\" OR \"smart technolog*\" OR \"smart diagnostic*\" OR \"data* analytic*\" OR \"hadoop*\" OR \"mapreduce\" OR \"map$reduce\" OR \"large$ dataset*\" OR \"data warehouse*\" OR \"predictive analytic*\" OR \"no$sql\" OR \"nosql\" OR \"no sql\" OR \"unstructured data*\" OR \"data science*\") AND PY=(2011-2022)'" }, - "execution_count": 74, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -216,7 +227,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 23, "outputs": [], "source": [], "metadata": { @@ -225,21 +236,8 @@ }, { "cell_type": "code", - "execution_count": 75, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'pytest'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mModuleNotFoundError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[75], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mwossel_miners\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m wos_fetch_entries,wos_fetch_yearly_output\n", - "File \u001B[1;32m~\\PycharmProjects\\ZSI_analytics\\WOS\\wos_extract\\wossel_miners.py:3\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mos\u001B[39;00m\n\u001B[0;32m 2\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mglob\u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mpytest\u001B[39;00m\n\u001B[0;32m 4\u001B[0m \u001B[38;5;28;01mimport\u001B[39;00m \u001B[38;5;21;01mtime\u001B[39;00m\n\u001B[0;32m 5\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01mdatetime\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m datetime\n", - "\u001B[1;31mModuleNotFoundError\u001B[0m: No module named 'pytest'" - ] - } - ], + "execution_count": 24, + "outputs": [], "source": [ "from wossel_miners import wos_fetch_entries,wos_fetch_yearly_output" ], @@ -249,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "outputs": [], "source": [ "# wos_fetch_yearly_output(query_str_list=sub_queries)" @@ -262,8 +260,43 @@ "cell_type": "code", "execution_count": null, "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 27, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hoooold...\n", + "41511 records found! Here we go in 139 steps...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 138/138 [15:20<00:00, 6.67s/it]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "final batch of 41401-41511\n" + ] + } + ], "source": [ - "# wos_fetch_entries(query_str=scope_query)" + "wos_fetch_entries(query_str=scope_query)" ], "metadata": { "collapsed": false @@ -291,4 +324,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/WOS/wos_extract/wos_records_concat.csv b/WOS/wos_extract/wos_records_concat.csv index 17f0ba0..44e0f04 100644 --- a/WOS/wos_extract/wos_records_concat.csv +++ b/WOS/wos_extract/wos_records_concat.csv @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:1b4488a74c6abb033610f3d72af50b4d4aa1f0d455b0fa7399161d8a11cdf085 -size 126669330 +oid sha256:0b7c19c1fc124bcb8803a10547e64e3738792718d1e156ce58369eeb4418608b +size 185094020 diff --git a/WOS/wos_extract/wos_records_concatter.ipynb b/WOS/wos_extract/wos_records_concatter.ipynb index 84cf73e..ee41a26 100644 --- a/WOS/wos_extract/wos_records_concatter.ipynb +++ b/WOS/wos_extract/wos_records_concatter.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 18, + "execution_count": 1, "metadata": { "collapsed": true }, @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 2, "outputs": [ { "name": "stdout", @@ -26,12 +26,12 @@ "text": [ "Concatting records for query:\n", "\n", - "['(CU=PEOPLES R CHINA OR CU=HONG KONG) AND (CU=AUSTRIA OR CU=BELGIUM OR CU=BULGARIA OR CU=CROATIA OR CU=CYPRUS OR CU=CZECH REPUBLIC OR CU=DENMARK OR CU=ESTONIA OR CU=FINLAND OR CU=FRANCE OR CU=GERMANY OR CU=GREECE OR CU=HUNGARY OR CU=IRELAND OR CU=ITALY OR CU=LATVIA OR CU=LITHUANIA OR CU=LUXEMBOURG OR CU=MALTA OR CU=NETHERLANDS OR CU=POLAND OR CU=PORTUGAL OR CU=ROMANIA OR CU=SLOVAKIA OR CU=SLOVENIA OR CU=SPAIN OR CU=SWEDEN OR CU=NORWAY OR CU=SWITZERLAND OR CU=UNITED KINGDOM OR CU=ENGLAND OR CU=WALES OR CU=SCOTLAND OR CU=N IRELAND) AND (TS=(\"artificial intelligence\") OR TS=(\"machine learning\") OR TS=(\"neural network\") OR TS=(\"big data\") OR TS=(\"deep learning\") OR TS=(\"pattern recognition\") OR TS=(\"computer vision\") OR TS=(\"image classification\") OR TS=(\"reinforcement learning\") OR TS=(\"support vector machines\") OR TS=(\"recommender system\") OR TS=(\"random forest\") OR TS=(\"ensemble model\") OR TS=(\"image processing\") OR TS=(\"generative network\") OR TS=(\"ai ethic\") OR TS=(\"natural language processing\") OR TS=(\"clustering algorithm\") OR TS=(\"feature extraction\") OR TS=(\"time series forecast\") OR TS=(\"anomaly detection\") OR TS=(\"identity fraud detection\") OR TS=(\"dimensionality reduction\") OR TS=(\"feature elicitation\") OR TS=(\"chatbot\") OR TS=(\"clustering\") OR TS=(\"unsupervised learning\") OR TS=(\"supervised learning\") OR TS=(\"convolutional network\") OR TS=(\"adversarial network\")) AND PY=(2011-2022)']\n" + "['CU=(PEOPLES R CHINA OR HONG KONG) AND CU=(AUSTRIA OR BELGIUM OR BULGARIA OR CROATIA OR CYPRUS OR CZECH REPUBLIC OR DENMARK OR ESTONIA OR FINLAND OR FRANCE OR GERMANY OR GREECE OR HUNGARY OR IRELAND OR ITALY OR LATVIA OR LITHUANIA OR LUXEMBOURG OR MALTA OR NETHERLANDS OR POLAND OR PORTUGAL OR ROMANIA OR SLOVAKIA OR SLOVENIA OR SPAIN OR SWEDEN OR NORWAY OR SWITZERLAND OR UNITED KINGDOM OR ENGLAND OR WALES OR SCOTLAND OR N IRELAND) AND TS=(\"artificial intelligence*\" OR \"machine* learn*\" OR \"neural network*\" OR \"big data*\" OR \"deep learn*\" OR \"pattern recognition\" OR \"computer vision\" OR \"image classification\" OR \"reinforcement learning\" OR \"support vector machine*\" OR \"recommender system*\" OR \"random forest\" OR \"ensemble model*\" OR \"image processing\" OR \"generative network*\" OR \"ai ethic*\" OR \"natural language processing\" OR \"clustering algorithm*\" OR \"feature extraction\" OR \"time series forecast*\" OR \"anomaly detection\" OR \"identity fraud detection\" OR \"dimensionality reduction\" OR \"feature elicitation\" OR \"chatbot*\" OR \"clustering\" OR \"*supervised learning\" OR \"convolutional network*\" OR \"convolutional neural\" OR \"adversarial network*\" OR \"adversarial neural\" OR \"adversarial machine\" OR \"autoencoder*\" OR \"gated recurrent unit*\" OR \"perceptron*\" OR \"feature learning\" OR \"feature engineering\" OR \"long short-term memor*\" OR \"word embedding*\" OR \"word vector*\" OR \"gradient descent\" OR \"k-nearest neighbor*\" OR \"naive bayes\" OR \"transfer learning\" OR \"fuzzy logic\" OR \"backpropagation\" OR \"computational modeling\" OR \"computational statistic*\" OR \"intelligent agent*\" OR \"expert system*\" OR \"decision tree*\" OR \"Bayesian network*\" OR \"genetic algorithm*\" OR \"swarm intelligence\" OR \"cognitive computing\" OR \"artificial neural network*\" OR \"convolutional neural network*\" OR \"recurrent neural network*\" OR \"ensemble learning\" OR \"data mining\" OR \"artificial general intelligence\" OR \"artificial consciousness\" OR \"evolutionary algorithm*\" OR \"self-organizing map*\" OR \"deep reinforcement learning\" OR \"adversarial machine learning\" OR \"machine vision\" OR \"neural-symbolic integration\" OR \"probabilistic graphical model*\" OR \"hybrid intelligent system*\" OR \"machine creativity\" OR \"explainable AI\" OR \"interactive machine learning\" OR \"artificial emotional intelligence\" OR \"evolutionary computation*\" OR \"human-in-the-loop\" OR \"unsupervised deep learning\" OR \"deep belief network*\" OR \"quantum machine learning\" OR \"artificial immune system*\" OR \"swarm robotics\" OR \"autonomous agents\" OR \"machine ethics\" OR \"collaborative filtering\" OR \"content based filtering\" OR \"pervasive computing\" OR \"ubiquitous computing\" OR \"human-computer interaction\" OR \"cloud computing\" OR \"Internet of Things\" OR \"artificial cognition\" OR \"computational creativity\" OR \"sentiment analy*\" OR \"robotics\" OR \"boltzmann machine*\" OR \"kernel machine*\" OR \"Hopfield network*\" OR \"Hebbian learning\" OR \"latent factor model*\" OR \"non-negative matrix factorization\" OR \"independent component analysis\" OR \"principal component analysis\" OR \"data augmentation\" OR \"image segmentation\" OR \"autoregressive language model*\" OR \"generative pre-trained transformer*\" OR \"smart city\" OR \"smart home\" OR \"smart grid\" OR \"smart health\" OR \"smart manufacturing\" OR \"smart agriculture\" OR \"smart environment\" OR \"smart energy\" OR \"smart mobility\" OR \"smart buildings\" OR \"smart tourism\" OR \"smart logistics\" OR \"smart supply chain\" OR \"smart retail\" OR \"smart waste management\" OR \"smart parking\" OR \"smart governance\" OR \"smart education\" OR \"smart technolog*\" OR \"smart diagnostic*\" OR \"data* analytic*\" OR \"hadoop*\" OR \"mapreduce\" OR \"map$reduce\" OR \"large$ dataset*\" OR \"data warehouse*\" OR \"predictive analytic*\" OR \"no$sql\" OR \"nosql\" OR \"no sql\" OR \"unstructured data*\" OR \"data science*\") AND PY=(2011-2022)']\n" ] } ], "source": [ - "folder_token=\"2023-04-04-12-58-59-994722save\"\n", + "folder_token=\"2023-04-06-11-22-23-982324save\"\n", "workdir_path=fr\"wos_downloads/entry_batches/{folder_token}\"\n", "outfile='wos_records_concat.csv'\n", "try:\n", @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 3, "outputs": [], "source": [ "# df_pre = pd.read_excel(r\"C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\WOS\\wos_extract\\v1_\\wosexport1.xls\")\n", @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 4, "outputs": [], "source": [ "col_vals = ['Publication Type',\n", @@ -151,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 5, "outputs": [], "source": [ "df = pd.read_csv(outfile, sep=\"\\t\",low_memory=False)\n", @@ -185,4 +185,4 @@ }, "nbformat": 4, "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/WOS/wos_processed_data/keywords.xlsx b/WOS/wos_processed_data/keywords.xlsx deleted file mode 100644 index 9f72ff2..0000000 Binary files a/WOS/wos_processed_data/keywords.xlsx and /dev/null differ diff --git a/WOS/wos_processed_data/wos_addresses.xlsx b/WOS/wos_processed_data/wos_addresses.xlsx deleted file mode 100644 index d64ae4c..0000000 Binary files a/WOS/wos_processed_data/wos_addresses.xlsx and /dev/null differ diff --git a/WOS/wos_processed_data/wos_affiliations.xlsx b/WOS/wos_processed_data/wos_affiliations.xlsx deleted file mode 100644 index 3a8a54a..0000000 Binary files a/WOS/wos_processed_data/wos_affiliations.xlsx and /dev/null differ diff --git a/WOS/wos_processed_data/wos_author_locations.xlsx b/WOS/wos_processed_data/wos_author_locations.xlsx index 22ef76f..7b12117 100644 Binary files a/WOS/wos_processed_data/wos_author_locations.xlsx and b/WOS/wos_processed_data/wos_author_locations.xlsx differ diff --git a/WOS/wos_processed_data/wos_nlp.xlsx b/WOS/wos_processed_data/wos_nlp.xlsx index 23ff805..53b80d7 100644 Binary files a/WOS/wos_processed_data/wos_nlp.xlsx and b/WOS/wos_processed_data/wos_nlp.xlsx differ diff --git a/WOS/wos_processed_data/wos_processed.xlsx b/WOS/wos_processed_data/wos_processed.xlsx index 10ad8af..f74c61d 100644 Binary files a/WOS/wos_processed_data/wos_processed.xlsx and b/WOS/wos_processed_data/wos_processed.xlsx differ diff --git a/WOS/wos_processed_data/wos_univ_locations.xlsx b/WOS/wos_processed_data/wos_univ_locations.xlsx deleted file mode 100644 index 78bc9d8..0000000 Binary files a/WOS/wos_processed_data/wos_univ_locations.xlsx and /dev/null differ diff --git a/WOS/wos_processed_data/wos_univ_locations_v2.xlsx b/WOS/wos_processed_data/wos_univ_locations_v2.xlsx deleted file mode 100644 index a9f4c5b..0000000 Binary files a/WOS/wos_processed_data/wos_univ_locations_v2.xlsx and /dev/null differ diff --git a/WOS/wos_processing.ipynb b/WOS/wos_processing_legacy.ipynb similarity index 99% rename from WOS/wos_processing.ipynb rename to WOS/wos_processing_legacy.ipynb index 13044f8..232f93d 100644 --- a/WOS/wos_processing.ipynb +++ b/WOS/wos_processing_legacy.ipynb @@ -1297,4 +1297,4 @@ }, "nbformat": 4, "nbformat_minor": 1 -} +} \ No newline at end of file diff --git a/WOS/wos_processing_pipeline.ipynb b/WOS/wos_processing_pipeline.ipynb new file mode 100644 index 0000000..b845d70 --- /dev/null +++ b/WOS/wos_processing_pipeline.ipynb @@ -0,0 +1,1060 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import os\n", + "import shutil\n", + "from flashgeotext.geotext import GeoText\n", + "import re" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [], + "source": [ + "import hashlib\n", + "\n", + "def md5hash(s: str):\n", + " return hashlib.md5(s.encode('utf-8')).hexdigest()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "record_col=\"UT (Unique WOS ID)\"\n", + "outfile = r\"C:\\Users\\radvanyi\\PycharmProjects\\ZSI_analytics\\WOS\\wos_extract\\wos_records_concat.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of initial records: 41511\n", + "Number of filtered records: 35663\n" + ] + } + ], + "source": [ + "wos = pd.read_csv(outfile, sep=\"\\t\",low_memory=False)\n", + "print(f'Number of initial records: {len(wos)}')\n", + "metrix = pd.read_excel(\"sm_journal_classification.xlsx\", sheet_name=\"Journal_Classification\")\n", + "\n", + "\n", + "metrix = metrix.set_index([c for c in metrix.columns if \"issn\" not in c]).stack().reset_index()\n", + "metrix = metrix.rename(columns={'level_6':\"issn_type\", 0:\"issn\"})\n", + "metrix[\"issn\"]=metrix[\"issn\"].str.replace(\"-\",\"\").str.lower().str.strip()\n", + "\n", + "wos[\"issn\"] = wos[\"ISSN\"].str.replace(\"-\",\"\").str.lower().str.strip()\n", + "wos[\"eissn\"] = wos[\"eISSN\"].str.replace(\"-\",\"\").str.lower().str.strip()\n", + "wos = wos.set_index([c for c in wos.columns if \"issn\" not in c]).stack().reset_index()\n", + "wos = wos.rename(columns={'level_71':\"issn_var\", 0:\"issn\"})\n", + "\n", + "wos_merge = wos.merge(metrix, on=\"issn\", how=\"left\")\n", + "wos = wos_merge.sort_values(by=\"issn_var\",ascending=False).drop_duplicates(subset=record_col)\n", + "\n", + "# drop entries not indexed by metrix\n", + "wos = wos[~wos[\"Domain_English\"].isna()]\n", + "# drop duplicates (based on doi)\n", + "wos = wos[~((~wos[\"DOI\"].isna())&(wos[\"DOI\"].duplicated(False)))]\n", + "wos = wos.drop_duplicates(subset=[\"Publication Type\",\"Document Type\",\"Authors\",\"Article Title\",\"Source Title\",\"Publication Year\"])\n", + "wos = wos[((wos[\"Publication Year\"]<2023) & (~wos['Domain_English'].isna()))]\n", + "print(f'Number of filtered records: {len(wos)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": " Article Title \n60737 Beauty3DFaceNet: Deep geometry and texture fus... \\\n61738 Document Neural Autoregressive Distribution Es... \n47201 Discriminative feature representation for Nois... \n65760 Large-scale hydrological modeling in a multi-o... \n19959 Location Prediction in Social Networks \n... ... \n41680 Altered global brain signal in schizophrenia \n27626 Prediction of Surface Topography at the End of... \n44966 Cascading Failure Analysis of Cyber Physical P... \n38077 Data analysis and mining of traffic features b... \n67492 TBN: Convolutional Neural Network with Ternary... \n\n Keywords Plus \n60737 FACE; COMPUTATION; BEAUTY; SHAPE \\\n61738 NaN \n47201 NATURAL SCENE STATISTICS; SPARSE REPRESENTATIO... \n65760 GROUNDWATER DEPLETION; EVAPOTRANSPIRATION; WATER \n19959 NaN \n... ... \n41680 RESTING-STATE FMRI; FUNCTIONAL CONNECTIVITY MR... \n27626 NaN \n44966 SELF-ORGANIZED CRITICALITY; COMMUNICATION; STA... \n38077 NaN \n67492 NaN \n\n Author Keywords \n60737 3D facial attractiveness prediction; Deep lear... \n61738 Neural networks; Deep learning; Topic models; ... \n47201 Discriminative feature representation (DFR); N... \n65760 Large scale modeling; Multi-objective calibrat... \n19959 NaN \n... ... \n41680 resting-state; global signal; psychiatric illness \n27626 Wear Modeling; Sliding Wear; Surface Topography \n44966 Cascading failure; control threshold; cyber ph... \n38077 data mining; GPS trajectory; Internet of Thing... \n67492 CNN; TBN; Acceleration; Compression; Binary op... \n\n[100 rows x 3 columns]", + "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Article TitleKeywords PlusAuthor Keywords
60737Beauty3DFaceNet: Deep geometry and texture fus...FACE; COMPUTATION; BEAUTY; SHAPE3D facial attractiveness prediction; Deep lear...
61738Document Neural Autoregressive Distribution Es...NaNNeural networks; Deep learning; Topic models; ...
47201Discriminative feature representation for Nois...NATURAL SCENE STATISTICS; SPARSE REPRESENTATIO...Discriminative feature representation (DFR); N...
65760Large-scale hydrological modeling in a multi-o...GROUNDWATER DEPLETION; EVAPOTRANSPIRATION; WATERLarge scale modeling; Multi-objective calibrat...
19959Location Prediction in Social NetworksNaNNaN
............
41680Altered global brain signal in schizophreniaRESTING-STATE FMRI; FUNCTIONAL CONNECTIVITY MR...resting-state; global signal; psychiatric illness
27626Prediction of Surface Topography at the End of...NaNWear Modeling; Sliding Wear; Surface Topography
44966Cascading Failure Analysis of Cyber Physical P...SELF-ORGANIZED CRITICALITY; COMMUNICATION; STA...Cascading failure; control threshold; cyber ph...
38077Data analysis and mining of traffic features b...NaNdata mining; GPS trajectory; Internet of Thing...
67492TBN: Convolutional Neural Network with Ternary...NaNCNN; TBN; Acceleration; Compression; Binary op...
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100 rows × 3 columns

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" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wos[[\"Article Title\",\"Keywords Plus\",\"Author Keywords\"]].sample(100)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "data": { + "text/plain": " UT (Unique WOS ID) keyword_all\n0 WOS:000208863600013 COMPARATIVE GENOMICS\n1 WOS:000208863600013 ANAMMOX\n2 WOS:000208863600013 KUENENIA STUTTGARTIENSIS\n3 WOS:000208863600013 METAGENOMICS\n4 WOS:000208863600013 ENRICHMENT CULTURE\n.. ... ...\n97 WOS:000209724300006 VIRTUAL DISKS\n98 WOS:000209724300006 HETEROGENEOUS SERVICES\n99 WOS:000209810700046 CORROSION CHARACTERIZATION\n100 WOS:000209810700046 FEATURE EXTRACTION\n101 WOS:000209810700046 PULSED EDDY CURRENT\n\n[100 rows x 2 columns]", + "text/html": "
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UT (Unique WOS ID)keyword_all
0WOS:000208863600013COMPARATIVE GENOMICS
1WOS:000208863600013ANAMMOX
2WOS:000208863600013KUENENIA STUTTGARTIENSIS
3WOS:000208863600013METAGENOMICS
4WOS:000208863600013ENRICHMENT CULTURE
.........
97WOS:000209724300006VIRTUAL DISKS
98WOS:000209724300006HETEROGENEOUS SERVICES
99WOS:000209810700046CORROSION CHARACTERIZATION
100WOS:000209810700046FEATURE EXTRACTION
101WOS:000209810700046PULSED EDDY CURRENT
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100 rows × 2 columns

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" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "kw_df = pd.DataFrame()\n", + "for c in [\"Keywords Plus\",\"Author Keywords\"]:\n", + " kwp = wos.groupby(record_col)[c].apply(lambda x: x.str.split(';')).explode().str.strip().str.upper()\n", + " kwp.name = 'keyword_all'\n", + " kw_df = pd.concat([kwp.reset_index(),kw_df],ignore_index=True)\n", + "kw_df = kw_df[~kw_df[\"keyword_all\"].isna()].copy().drop(columns=\"level_1\").drop_duplicates()\n", + "kw_df[\"keyword_all\"] = kw_df[\"keyword_all\"].apply(lambda x: re.sub(\"[\\(\\[].*?[\\)\\]]\", \"\", x))\n", + "kw_df.head(100)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "data": { + "text/plain": " UT (Unique WOS ID) keyword_all\n0 WOS:000208863600013 COMPARATIVE GENOMICS; ANAMMOX; KUENENIA STUTTG...\n1 WOS:000208863600266 ANME; PYROSEQUENCING; AOM; COMMUNITY STRUCTURE...\n2 WOS:000208863900217 DEFAULT MODE NETWORK; EFFECTIVE CONNECTIVITY; ...\n3 WOS:000208972600008 BRAIN-MACHINE INTERFACE ; FIELD-PROGRAMMABLE G...\n4 WOS:000209043200014 CYANOBACTERIA BLOOM; DRINKING WATER TREATMENT;...", + "text/html": "
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UT (Unique WOS ID)keyword_all
0WOS:000208863600013COMPARATIVE GENOMICS; ANAMMOX; KUENENIA STUTTG...
1WOS:000208863600266ANME; PYROSEQUENCING; AOM; COMMUNITY STRUCTURE...
2WOS:000208863900217DEFAULT MODE NETWORK; EFFECTIVE CONNECTIVITY; ...
3WOS:000208972600008BRAIN-MACHINE INTERFACE ; FIELD-PROGRAMMABLE G...
4WOS:000209043200014CYANOBACTERIA BLOOM; DRINKING WATER TREATMENT;...
\n
" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wos_kwd_concat = kw_df.groupby(record_col, as_index=False).agg({'keyword_all': '; '.join})\n", + "wos_kwd_concat.head()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "data": { + "text/plain": "Index(['Publication Type', 'Authors', 'Book Authors', 'Book Editors',\n 'Book Group Authors', 'Author Full Names', 'Book Author Full Names',\n 'Group Authors', 'Article Title', 'Source Title', 'Book Series Title',\n 'Book Series Subtitle', 'Language', 'Document Type', 'Conference Title',\n 'Conference Date', 'Conference Location', 'Conference Sponsor',\n 'Conference Host', 'Author Keywords', 'Keywords Plus', 'Abstract',\n 'Addresses', 'Affiliations', 'Reprint Addresses', 'Email Addresses',\n 'Researcher Ids', 'ORCIDs', 'Funding Orgs', 'Funding Name Preferred',\n 'Funding Text', 'Cited References', 'Cited Reference Count',\n 'Times Cited, WoS Core', 'Times Cited, All Databases',\n '180 Day Usage Count', 'Since 2013 Usage Count', 'Publisher',\n 'Publisher City', 'Publisher Address', 'ISSN', 'eISSN', 'ISBN',\n 'Journal Abbreviation', 'Journal ISO Abbreviation', 'Publication Date',\n 'Publication Year', 'Volume', 'Issue', 'Part Number', 'Supplement',\n 'Special Issue', 'Meeting Abstract', 'Start Page', 'End Page',\n 'Article Number', 'DOI', 'DOI Link', 'Book DOI', 'Early Access Date',\n 'Number of Pages', 'WoS Categories', 'Web of Science Index',\n 'Research Areas', 'IDS Number', 'Pubmed Id', 'Open Access Designations',\n 'Highly Cited Status', 'Hot Paper Status', 'Date of Export',\n 'UT (Unique WOS ID)', 'issn_var', 'issn', 'Domain_English',\n 'Field_English', 'SubField_English', '2.00 SEQ', 'Source_title',\n 'srcid', 'issn_type'],\n dtype='object')" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wos.columns" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "geotext = GeoText()\n", + "\n", + "def extract_location(input_text, key='countries'):\n", + " anomalies = {\"Malta\":\"Malta\",\n", + " \"Mongolia\":\"Mongolia\",\n", + " \"Quatar\":\"Qatar\",\n", + " \"Qatar\":\"Qatar\",\n", + " \"Ethiop\":\"Ethiopia\",\n", + " \"Nigeria\":\"Nigeria\",\n", + " \"BELAR\":\"Belarus\",\n", + " \"Venezuela\":\"Venezuela\",\n", + " \"Cyprus\":\"Cyprus\",\n", + " \"Ecuador\":\"Ecuador\",\n", + " \"U Arab\":\"United Arab Emirates\",\n", + " \"Syria\":\"Syria\",\n", + " \"Uganda\":\"Uganda\",\n", + " \"Yemen\":\"Yemen\",\n", + " \"Mali\":\"Mali\",\n", + " \"Senegal\":\"Senegal\",\n", + " \"Vatican\":\"Vatican\",\n", + " \"Uruguay\":\"Uruguay\",\n", + " \"Panama\":\"Panama\",\n", + " \"Fiji\":\"Fiji\",\n", + " \"Faroe\":\"Faroe Islands\",\n", + " \"Macedonia\":\"Macedonia\",\n", + " 'Mozambique':'Mozambique',\n", + " \"Kuwait\":\"Kuwait\",\n", + " \"Libya\":\"Libya\",\n", + " \"Turkiy\":\"Turkey\",\n", + " \"Liberia\":\"Liberia\",\n", + " \"Namibia\":\"Namibia\",\n", + " \"Ivoire\":\"Ivory Coast\",\n", + " \"Guatemala\":\"Gutemala\",\n", + " \"Paraguay\":\"Paraguay\",\n", + " \"Honduras\":\"Honduras\",\n", + " \"Nicaragua\":\"Nicaragua\",\n", + " \"Trinidad\":\"Trinidad & Tobago\",\n", + " \"Liechtenstein\":\"Liechtenstein\",\n", + " \"Greenland\":\"Denmark\"}\n", + "\n", + " extracted = geotext.extract(input_text=input_text)\n", + " found = extracted[key].keys()\n", + " if len(sorted(found))>0:\n", + " return sorted(found)[0]\n", + " elif key=='countries':\n", + " for i in ['Scotland','Wales','England', 'N Ireland']:\n", + " if i in input_text:\n", + " return 'United Kingdom'\n", + " for j in anomalies.keys():\n", + " if j in input_text:\n", + " return anomalies.get(j)\n", + " else:\n", + " return None\n", + "\n", + "with open('../eu_members.txt',\"r\") as f:\n", + " eu_countries=f.readline().split(\",\")\n", + " eu_countries=[i.strip() for i in eu_countries]\n", + "\n", + "def country_type(country):\n", + " if country in eu_countries:\n", + " return \"EU\"\n", + " elif country==\"China\":\n", + " return \"China\"\n", + " elif country in [\"Switzerland\", 'Norway','United Kingdom']:\n", + " return \"Non-EU associate\"\n", + " else:\n", + " return \"Other\"\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "locations = wos.groupby(record_col)[\"Addresses\"].apply(lambda x: x.str.split('[')).explode().reset_index().drop(columns=\"level_1\")\n", + "locations = locations[locations[\"Addresses\"]!=\"\"].copy()\n", + "locations[\"Address\"] = locations[\"Addresses\"].apply(lambda x:x.split(\"]\")[-1])\n", + "locations[\"Authors_of_address\"] = locations[\"Addresses\"].apply(lambda x:x.split(\"]\")[0])\n", + "locations[\"Country\"]=locations['Address'].apply(lambda x: extract_location(input_text=x, key='countries'))\n", + "locations[\"City\"]=locations['Address'].apply(lambda x: extract_location(input_text=x, key='cities'))\n", + "locations[\"Country_Type\"] = locations[\"Country\"].apply(lambda x: country_type(x))" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "scope_types = [\"EU\",\"China\",\"Non-EU associate\"]\n", + "locations=locations[locations[\"Country_Type\"].isin(scope_types)]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": " UT (Unique WOS ID) Address \n1 WOS:000208863600013 Radboud Univ Nijmegen, Dept Microbiol, Inst W... \\\n2 WOS:000208863600013 Zhejiang Univ, Dept Environm Engn, Hangzhou 3... \n3 WOS:000208863600013 Radboud Univ Nijmegen, Dept Mol Biol, Nijmege... \n4 WOS:000208863600013 Delft Univ Technol, Dept Biotechnol, Delft, N... \n6 WOS:000208863600266 Univ Bergen, Ctr Geobiol, Dept Biol, N-5020 B... \n\n Country City Country_Type Institution \n1 Netherlands Nijmegen EU Radboud Univ Nijmegen \n2 China Hangzhou China Zhejiang Univ \n3 Netherlands Mol EU Radboud Univ Nijmegen \n4 Netherlands Delft EU Delft Univ Technol \n6 Norway Bergen Non-EU associate Univ Bergen ", + "text/html": "
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UT (Unique WOS ID)AddressCountryCityCountry_TypeInstitution
1WOS:000208863600013Radboud Univ Nijmegen, Dept Microbiol, Inst W...NetherlandsNijmegenEURadboud Univ Nijmegen
2WOS:000208863600013Zhejiang Univ, Dept Environm Engn, Hangzhou 3...ChinaHangzhouChinaZhejiang Univ
3WOS:000208863600013Radboud Univ Nijmegen, Dept Mol Biol, Nijmege...NetherlandsMolEURadboud Univ Nijmegen
4WOS:000208863600013Delft Univ Technol, Dept Biotechnol, Delft, N...NetherlandsDelftEUDelft Univ Technol
6WOS:000208863600266Univ Bergen, Ctr Geobiol, Dept Biol, N-5020 B...NorwayBergenNon-EU associateUniv Bergen
\n
" + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ_locations = locations[[record_col,\"Address\",\"Country\",\"City\",\"Country_Type\"]].copy()\n", + "univ_locations[\"Institution\"] = univ_locations[\"Address\"].apply(lambda x: x.split(\",\")[0])\n", + "univ_locations = univ_locations.drop_duplicates()\n", + "univ_locations.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": " UT (Unique WOS ID) Country Country_Type \n0 WOS:000208863600013 China China \\\n1 WOS:000208863600013 Netherlands EU \n2 WOS:000208863600013 Netherlands EU \n3 WOS:000208863600013 Netherlands EU \n4 WOS:000208863600013 Netherlands EU \n\n author_str_id \n0 54c7bc6fe9b77434ca1bf04d763d843b \n1 6a775fcd8d11fcb084671b8cae4d6305 \n2 aa6accfdf7626441fe9191636dab4c35 \n3 b707b51d1ca3b5aa76de6ce6df20e6e4 \n4 df81f9da6c8f5c968c16ef0aab1bb8f9 ", + "text/html": "
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UT (Unique WOS ID)CountryCountry_Typeauthor_str_id
0WOS:000208863600013ChinaChina54c7bc6fe9b77434ca1bf04d763d843b
1WOS:000208863600013NetherlandsEU6a775fcd8d11fcb084671b8cae4d6305
2WOS:000208863600013NetherlandsEUaa6accfdf7626441fe9191636dab4c35
3WOS:000208863600013NetherlandsEUb707b51d1ca3b5aa76de6ce6df20e6e4
4WOS:000208863600013NetherlandsEUdf81f9da6c8f5c968c16ef0aab1bb8f9
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" + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "author_locations = locations.groupby([record_col,\"Country\",\"Country_Type\"])[\"Authors_of_address\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_3\")\n", + "author_locations[\"Author_name\"] = author_locations[\"Authors_of_address\"].str.strip()\n", + "author_locations = author_locations.drop(columns=\"Authors_of_address\")\n", + "author_locations[\"author_str_id\"] = author_locations[\"Author_name\"].apply(lambda x:''.join(filter(str.isalnum, x.lower())))\n", + "author_locations[\"author_str_id\"] = author_locations[\"author_str_id\"].apply(md5hash)\n", + "author_locations = author_locations.drop(columns=\"Author_name\")\n", + "author_locations.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "data": { + "text/plain": " UT (Unique WOS ID) Country Country_Type \n0 WOS:000208863600013 China China \\\n4 WOS:000208863600013 Netherlands EU \n6 WOS:000208863600013 Netherlands EU \n7 WOS:000208863600266 China China \n13 WOS:000208863900217 China China \n... ... ... ... \n441911 WOS:000951829800021 China China \n441912 WOS:000951829800021 Netherlands EU \n441913 WOS:000952055000007 China China \n441914 WOS:000952055000007 China China \n441916 WOS:000952055000007 United Kingdom Non-EU associate \n\n author_str_id \n0 54c7bc6fe9b77434ca1bf04d763d843b \n4 df81f9da6c8f5c968c16ef0aab1bb8f9 \n6 df81f9da6c8f5c968c16ef0aab1bb8f9 \n7 5dfb4f0408a2cc8b7f36f5516938b62c \n13 00e44aa0a23a3fc9571b1053a4453a54 \n... ... \n441911 fc15bf7c800877e1c33f4a7397840faa \n441912 6b8763361150d7c3ceecf9eca9efd83b \n441913 80231479c1502ce8649717236023b6c9 \n441914 0af23824e538b0816c19239079d58c77 \n441916 b77dd6bc0ae30a2f96d43eebb1b3d89a \n\n[387172 rows x 4 columns]", + "text/html": "
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UT (Unique WOS ID)CountryCountry_Typeauthor_str_id
0WOS:000208863600013ChinaChina54c7bc6fe9b77434ca1bf04d763d843b
4WOS:000208863600013NetherlandsEUdf81f9da6c8f5c968c16ef0aab1bb8f9
6WOS:000208863600013NetherlandsEUdf81f9da6c8f5c968c16ef0aab1bb8f9
7WOS:000208863600266ChinaChina5dfb4f0408a2cc8b7f36f5516938b62c
13WOS:000208863900217ChinaChina00e44aa0a23a3fc9571b1053a4453a54
...............
441911WOS:000951829800021ChinaChinafc15bf7c800877e1c33f4a7397840faa
441912WOS:000951829800021NetherlandsEU6b8763361150d7c3ceecf9eca9efd83b
441913WOS:000952055000007ChinaChina80231479c1502ce8649717236023b6c9
441914WOS:000952055000007ChinaChina0af23824e538b0816c19239079d58c77
441916WOS:000952055000007United KingdomNon-EU associateb77dd6bc0ae30a2f96d43eebb1b3d89a
\n

387172 rows × 4 columns

\n
" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "author_locations[author_locations['author_str_id'].duplicated(False)]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "author_primary_region = author_locations.sort_values(by=\"Country_Type\").drop_duplicates(subset=[record_col,\"author_str_id\"])\n", + "# author_primary_region\n", + "\n", + "china=author_primary_region[author_primary_region[\"Country_Type\"]==\"China\"][record_col].unique()\n", + "eu=author_primary_region[author_primary_region[\"Country_Type\"]==\"EU\"][record_col].unique()\n", + "assoc=author_primary_region[author_primary_region[\"Country_Type\"]==\"Non-EU associate\"][record_col].unique()\n", + "\n", + "\n", + "# records that have distinct authors with different country affiliations\n", + "valid_scope = wos[((wos[record_col].isin(china))\n", + " &\n", + " ((wos[record_col].isin(eu))\n", + " |\n", + " (wos[record_col].isin(assoc))))][record_col].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": " UT (Unique WOS ID) Country Country_Type \n0 WOS:000208863600013 China China \\\n304939 WOS:000648878200015 China China \n304935 WOS:000648805900001 China China \n304934 WOS:000648805900001 China China \n304933 WOS:000648805900001 China China \n\n author_str_id \n0 54c7bc6fe9b77434ca1bf04d763d843b \n304939 043a846fd3ea05c308e9944b984b8d8f \n304935 4132592fad8ecaa0bc99a8148c348f45 \n304934 0bcfdc30b9929c5513eaabfe484ffd26 \n304933 3d5c738679e81c68cc67a06ecc686851 ", + "text/html": "
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UT (Unique WOS ID)CountryCountry_Typeauthor_str_id
0WOS:000208863600013ChinaChina54c7bc6fe9b77434ca1bf04d763d843b
304939WOS:000648878200015ChinaChina043a846fd3ea05c308e9944b984b8d8f
304935WOS:000648805900001ChinaChina4132592fad8ecaa0bc99a8148c348f45
304934WOS:000648805900001ChinaChina0bcfdc30b9929c5513eaabfe484ffd26
304933WOS:000648805900001ChinaChina3d5c738679e81c68cc67a06ecc686851
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" + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "author_primary_region.head()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of records: 35663\n", + "Number of valid cooperation records: 31861\n" + ] + } + ], + "source": [ + "print(f'Number of records: {len(wos)}')\n", + "print(f'Number of valid cooperation records: {len(valid_scope)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [], + "source": [ + "wos = wos[wos[record_col].isin(valid_scope)]\n", + "locations = locations[locations[record_col].isin(valid_scope)]\n", + "univ_locations = univ_locations[univ_locations[record_col].isin(valid_scope)]\n", + "author_locations = author_locations[author_locations[record_col].isin(valid_scope)]\n", + "author_primary_region = author_locations[author_locations[record_col].isin(valid_scope)]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "affiliations = wos.groupby(record_col)[\"Affiliations\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n", + "affiliations[\"Affiliations\"] = affiliations[\"Affiliations\"].str.strip().str.upper().fillna(\"UNKNOWN\")\n", + "affiliations = affiliations.drop_duplicates()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "outputs": [ + { + "data": { + "text/plain": "Affiliations\nCHINESE ACADEMY OF SCIENCES 3623\nUNIVERSITY OF LONDON 1729\nUDICE-FRENCH RESEARCH UNIVERSITIES 1421\nTSINGHUA UNIVERSITY 1347\nCENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE (CNRS) 1330\n ... \nFRESHWATER FISHERIES RESEARCH CENTER, CAFS 1\nHEILONGJIANG RIVER FISHERIES RESEARCH INSTITUTE, CAFS 1\nINSTITUTE OF METEOROLOGY & WATER MANAGEMENT 1\nFEDERAL MINISTRY OF HEALTH - ETHIOPIA (FMOH) 1\nTANGSHAN UNIVERSITY 1\nName: count, Length: 6784, dtype: int64" + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "affiliations[\"Affiliations\"].value_counts()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 21, + "outputs": [ + { + "data": { + "text/plain": "Institution\n Chinese Acad Sci 3618\n Tsinghua Univ 1633\n Shanghai Jiao Tong Univ 1372\n Zhejiang Univ 1288\n Univ Elect Sci & Technol China 969\n ... \n Ludwig Boltzmann Inst Clin Forens Imaging 1\n Royal Brampton Hosp 1\n Inst Spacecraft Syst Engn CAST 1\n Sevalo Construct Machinery Remfg Co Ltd 1\n Int Digital Econ Acad 1\nName: count, Length: 14546, dtype: int64" + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ_locations[\"Institution\"].value_counts()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 22, + "outputs": [ + { + "data": { + "text/plain": "31861" + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ_locations[record_col].nunique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "data": { + "text/plain": "31861" + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "affiliations[record_col].nunique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 24, + "outputs": [ + { + "data": { + "text/plain": "138559" + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "univ_locations[\"Institution\"].value_counts().sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "outputs": [ + { + "data": { + "text/plain": "181832" + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "affiliations[\"Affiliations\"].value_counts().sum()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "WoS Categories\n Engineering, Electrical & Electronic 6066\nComputer Science, Artificial Intelligence 4859\nComputer Science, Information Systems 3740\n Telecommunications 3304\nEngineering, Electrical & Electronic 2451\n ... \n Criminology & Penology 1\nArea Studies 1\nMaterials Science, Paper & Wood 1\n Emergency Medicine 1\n Geology 1\nName: count, Length: 415, dtype: int64" + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wos_cat = wos.groupby(record_col)[\"WoS Categories\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n", + "wos_cat[\"WoS Categories\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "Research Areas\nEngineering 12815\nComputer Science 12386\nTelecommunications 3577\nImaging Science & Photographic Technology 1949\nEnvironmental Sciences & Ecology 1887\n ... \nMusic 1\nAsian Studies 1\nCultural Studies 1\nArea Studies 1\nEmergency Medicine 1\nName: count, Length: 145, dtype: int64" + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wos_areas = wos.groupby(record_col)[\"Research Areas\"].apply(lambda x: x.str.split(';')).explode().reset_index().drop(columns=\"level_1\")\n", + "wos_areas[\"Research Areas\"] = wos_areas[\"Research Areas\"].str.strip()\n", + "wos_areas[\"Research Areas\"].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": "['Domain_English', 'Field_English', 'SubField_English']" + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "[c for c in wos.columns if \"_English\" in c]" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "metrix_levels = [c for c in wos.columns if \"_English\" in c]\n", + "for m in metrix_levels:\n", + " wos[m] = wos[m].replace({\"article-level classification\":\"Miscellaneous\"})\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "wos" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "metrix_levels" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "outdir=\"wos_processed_data\"" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "outputs": [], + "source": [ + "record_countries = locations[[record_col,\"Country\"]].drop_duplicates()\n", + "record_author_locations = author_locations[[record_col,\"author_str_id\",\"Country\"]].drop_duplicates()\n", + "record_institution = univ_locations[[record_col,\"Institution\",\"Country\"]].drop_duplicates()\n", + "country_types = locations[[\"Country\",\"Country_Type\"]].drop_duplicates()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 81, + "outputs": [], + "source": [ + "country_collabs = record_countries.merge(record_countries, on=record_col)\n", + "country_collabs = country_collabs[country_collabs[\"Country_x\"]!=country_collabs[\"Country_y\"]]\n", + "country_collabs[\"weight\"] = 0.5" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 82, + "outputs": [], + "source": [ + "inst_collabs = record_institution.merge(record_institution, on=record_col)\n", + "inst_collabs = inst_collabs[inst_collabs[\"Institution_x\"]!=inst_collabs[\"Institution_y\"]]\n", + "inst_collabs[\"weight\"] = 0.5" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 85, + "outputs": [ + { + "data": { + "text/plain": "Index(['Publication Type', 'Authors', 'Book Authors', 'Book Editors',\n 'Book Group Authors', 'Author Full Names', 'Book Author Full Names',\n 'Group Authors', 'Article Title', 'Source Title', 'Book Series Title',\n 'Book Series Subtitle', 'Language', 'Document Type', 'Conference Title',\n 'Conference Date', 'Conference Location', 'Conference Sponsor',\n 'Conference Host', 'Author Keywords', 'Keywords Plus', 'Abstract',\n 'Addresses', 'Affiliations', 'Reprint Addresses', 'Email Addresses',\n 'Researcher Ids', 'ORCIDs', 'Funding Orgs', 'Funding Name Preferred',\n 'Funding Text', 'Cited References', 'Cited Reference Count',\n 'Times Cited, WoS Core', 'Times Cited, All Databases',\n '180 Day Usage Count', 'Since 2013 Usage Count', 'Publisher',\n 'Publisher City', 'Publisher Address', 'ISSN', 'eISSN', 'ISBN',\n 'Journal Abbreviation', 'Journal ISO Abbreviation', 'Publication Date',\n 'Publication Year', 'Volume', 'Issue', 'Part Number', 'Supplement',\n 'Special Issue', 'Meeting Abstract', 'Start Page', 'End Page',\n 'Article Number', 'DOI', 'DOI Link', 'Book DOI', 'Early Access Date',\n 'Number of Pages', 'WoS Categories', 'Web of Science Index',\n 'Research Areas', 'IDS Number', 'Pubmed Id', 'Open Access Designations',\n 'Highly Cited Status', 'Hot Paper Status', 'Date of Export',\n 'UT (Unique WOS ID)', 'issn_var', 'issn', 'Domain_English',\n 'Field_English', 'SubField_English', '2.00 SEQ', 'Source_title',\n 'srcid', 'issn_type'],\n dtype='object')" + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wos.columns" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 87, + "outputs": [ + { + "data": { + "text/plain": "['Authors',\n 'Book Authors',\n 'Book Group Authors',\n 'Author Full Names',\n 'Book Author Full Names',\n 'Group Authors',\n 'Addresses',\n 'Reprint Addresses',\n 'Email Addresses',\n 'ORCIDs',\n 'Publisher Address']" + }, + "execution_count": 87, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "drop_cols = [ws for ws in wos.columns if ((\"uthor\" in ws or \"ddress\" in ws or \"ORCID\" in ws) and \"eyword\" not in ws)]\n", + "drop_cols" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 88, + "outputs": [], + "source": [ + "os.makedirs(outdir, exist_ok=True)\n", + "\n", + "wos.drop(columns=drop_cols).to_excel(f\"{outdir}/wos_processed.xlsx\", index=False)\n", + "\n", + "record_countries.to_excel(f\"{outdir}/wos_countries.xlsx\", index=False)\n", + "\n", + "record_author_locations.to_excel(f\"{outdir}/wos_author_locations.xlsx\", index=False)\n", + "\n", + "record_institution.to_excel(f\"{outdir}/wos_institution_locations.xlsx\", index=False)\n", + "\n", + "kw_df.to_excel(f\"{outdir}/wos_keywords.xlsx\", index=False)\n", + "\n", + "country_types.to_excel(f\"{outdir}/wos_country_types.xlsx\", index=False)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 89, + "outputs": [], + "source": [ + "wos.drop(columns=drop_cols).to_csv(f\"{outdir}/wos_processed.csv\", index=False, sep='\\t')\n", + "\n", + "record_countries.to_csv(f\"{outdir}/wos_countries.csv\", index=False, sep='\\t')\n", + "\n", + "record_author_locations.to_csv(f\"{outdir}/wos_author_locations.csv\", index=False, sep='\\t')\n", + "\n", + "record_institution.to_csv(f\"{outdir}/wos_institution_locations.csv\", index=False, sep='\\t')\n", + "\n", + "kw_df.to_csv(f\"{outdir}/wos_keywords.csv\", index=False, sep='\\t')\n", + "\n", + "country_types.to_csv(f\"{outdir}/wos_country_types.csv\", index=False, sep='\\t')\n", + "\n", + "inst_collabs.to_csv(f\"{outdir}/wos_inst_collabs.csv\", index=False, sep='\\t')\n", + "\n", + "country_collabs.to_csv(f\"{outdir}/wos_country_collabs.csv\", index=False, sep='\\t')" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Basic network layout" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "# Simple NLP part" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 32, + "outputs": [ + { + "data": { + "text/plain": "" + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import spacy\n", + "\n", + "nlp = spacy.load(\"en_core_web_lg\")\n", + "wos_nlp = wos.merge(wos_kwd_concat, on=record_col)\n", + "wos_nlp[\"Document\"] = wos_nlp[\"Article Title\"].str.cat(wos_nlp[[\"Abstract\", \"keyword_all\"]].fillna(\"\"), sep=' - ')\n", + "# wos_kwd_test[\"BERT_KWDS\"] = wos_kwd_test[\"Document\"].map(kwd_extract)\n", + "\n", + "vectors = list()\n", + "vector_norms = list()\n", + "\n", + "for doc in nlp.pipe(wos_nlp['Document'].astype('unicode').values, batch_size=300,\n", + " n_process=4):\n", + " vectors.append(doc.vector)\n", + " vector_norms.append(doc.vector_norm)\n", + "\n", + "wos_nlp['vector'] = vectors\n", + "wos_nlp['vector_norm'] = vector_norms\n", + "wos_nlp['vector_norm'].plot(kind=\"hist\")" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 35, + "outputs": [ + { + "data": { + "text/plain": " UT (Unique WOS ID) TNSE-X TNSE-Y\n0 WOS:000641589600020 131.783783 -4.202979\n1 WOS:000590197400003 74.897812 89.280334\n2 WOS:000510863400004 84.939049 23.416033\n3 WOS:000403039400031 -39.527546 54.230900\n4 WOS:000439363600016 -59.109379 72.877693", + "text/html": "
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UT (Unique WOS ID)TNSE-XTNSE-Y
0WOS:000641589600020131.783783-4.202979
1WOS:00059019740000374.89781289.280334
2WOS:00051086340000484.93904923.416033
3WOS:000403039400031-39.52754654.230900
4WOS:000439363600016-59.10937972.877693
\n
" + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.manifold import TSNE\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "# % matplotlib inline\n", + "\n", + "vector_data = pd.DataFrame(wos_nlp[\"vector\"].to_list(), index=wos_nlp[record_col]).reset_index()\n", + "vector_data.head()\n", + "\n", + "labels = vector_data.values[:, 0]\n", + "record_vectors = vector_data.values[:, 1:]\n", + "\n", + "tsne_model = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, random_state=42, metric='cosine')\n", + "tnse_2d = tsne_model.fit_transform(record_vectors)\n", + "tnse_data = pd.DataFrame(tnse_2d, index=labels).reset_index()\n", + "tnse_data.columns = [record_col, \"TNSE-X\", \"TNSE-Y\"]\n", + "tnse_data.head()" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 36, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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//B2XshIJdrZgfEdn8jM6o1RrOJpWx5ZsfwD2xeYRnV1Gu94jMRj1FftsnqCo6u5Z/OLzKkktrP5Lrq8555KKOZ9Scs8yGo3A5JUXiEgvRanS/EUtE4lEIpFI9G8lBlMi0f+J4/GFfLpxL6vWb+RGXiVSCSzadY3IEj02X68hLreCN3bE8vGBG6SXF7ApIhV/R3Oe6t6a7x7rT0GFgj5etpTtzqastI7VZ9O4ll0B+kbg0o5HRj1CgJM5wz8/zbLDCeRW1N2uvCKbPVfSOXSj4O+5eGU9X/hEsniYzz2LbYnIBAkMC3XE/R/awyYSiUSixtLT05FIJERFRT2wOt555x3atm37wM4v+vcSgymR6D8uMqOU2RsiuZxWyo0aU87XOlJVr+R6biUnk4q5qvFlSJdgPG1NCHIxx8zYgHHezxJkF4Cfgwm7o3NYcuAGrw0JoGugHVM+6I6phRFOFsZ0MEiHpMPUKLXZ/0yN9Onlb0d2aS1ZJbW3G3H6U+aaHGBGeKs//fqUag0Ldl4jp6z27oUaaiD1BDRU3fNcRvpSZoT7ML6jZ4vrV6jUXM0sa3F5kUgkahGNGtJOQ+w27b8a9QOtbsqUKUgkEj744ING23fu3Ink5tIYLdW7d29efPHFP7F1f1xaWhqPPfYYzs7OyGQyXF1dGTlyJPHx8S06fu7cuRw9evQBt1L0byTOmRKJ/uN+PJdOXYOaQcEOOFvLSMmvIjqnkhqFijeHBGJsoEdCYQ1yQ30Wjw5h5elUfjyXTnJRDblFxRibmNLKzoS15zPYE5PL0Zd788EvMQQ4mmGtTGVLXjwbr33Bh56j8QubwisDA5o2YuBi0JO2uM2vbovG1dKY2f39bm/UqJs9R2R6KVsisxka4oSLlbz5E5rYkD94JY99c5H1083vOhdqVDvXFrexQaUhIqOEk/FFrD6XwYlXeuNsKc6xEolEf4K4XXBgHlTm3t5m7gyDPoTWIx5YtTKZjA8//JBnnnkGKyurB1ZPSzU0NGBoaPiHz6NUKhkwYAD+/v5s374dJycnsrOz2b9/f4sXGDY1NcXU1PQPt0X03yP2TIlE/2HbIjIorKxjdHs3WjtZ8Eg7N/oEOtDZy4oGlUBCYSWGBvpU1DVw9EYBi3ZfR63W8OEjoSzqYciSjAnUVRSTWlhFbE4FO57rjpWJlCp1EUWV1ayIrCe09eO84TsB3xOfQO1demgMZCA1aHG7bUwNMTHSp9/SE+yPzeW91XuJXzax2bLtPa35emI7Onnb3POc9mYyhgY78tP59Ba3416eWxfJtydTcTCXse+FHmIgJRKJ/hxxu2DL5MaBFEBlnnZ73K4HVnX//v1xdHRkyZIldy1TUlLChAkTcHFxQS6XExwczMaNG3X7p0yZwsmTJ/nss8+QSCRIJBLS09NZs2YNlpaWjc71616vW0PpVq5ciZeXFzKZDIADBw7Qo0cPLC0tsbGxYdiwYaSkpLT4uq5fv05KSgpfffUVXbp0wcPDg+7du/Pee+/RpUsXXbns7GwmTJiAtbU1JiYmdOjQgYsXLzZq251WrlxJYGAgMpmMgIAAvvrqK92+W0MPt2/fTp8+fZDL5YSGhnL+/PlG5zh79iy9e/dGLpdjZWXFwIEDKSvTvpdqNBqWLFmCl5cXxsbGhIaGsm3bNt2xZWVlTJw4ETs7O4yNjfH19WX16tUtvi+iP4fYMyUS/Ye1q7/IcaUelfVuzN8eQ2ZpHQtHtMHSxAh9/VoMpVLaeVjRzsOK04lFXMutoM7WBHNjAw7mmFDR/kvcpa6cTiqiv78deeV17InJRGpQwIttTbDdvoi8hrP4B02A1uPQoEdKQRW+DmZ/qN230rQHOJkhN9LHyMQCE59Hmy1rINWjd4D9b55TT0/CqDBXyuuUf6htt7haGSM30mdqT+8Wla+oVaIvlWBiJL7sikSiu9CotT1SNLfwuQBI4MBrEDD0vnr7W0oqlbJ48WIee+wxZs+ejatr0976+vp62rdvz7x58zA3N2fv3r08/vjjtGrVik6dOvHZZ5+RmJhIUFAQixYtAsDOzq7FbUhOTubnn39m+/btSKXaa6ypqWHOnDmEhIRQXV3N22+/zahRo4iKikJP77f7Bezs7NDT02Pbtm28+OKLuvPeqbq6mvDwcFxcXNi1axeOjo5cuXIFjab5ZETr16/n7bffZsWKFYSFhXH16lWmT5+OiYkJTzzxhK7cG2+8wSeffIKvry9vvPEGEyZMIDk5GX19faKioujXrx9Tp07ls88+Q19fn+PHj6NWa4d0LlmyhHXr1vHNN9/g6+vLqVOnmDRpEnZ2doSHh/PWW28RFxfH/v37sbW1JTk5mbq6umbbK3pwxHd1keg/SllZxDdxRgxp50lscQ0TOruTX1GPr4MZUVnlLB0XipmRPsXVCmxNjejsbcNnRxPp7W/PxksZtPWw5sXtOXT3KaO7rx3OFsZIJBJic2rxte+Ik28g+/vuwXnvQgwGz8PZ1ZNVp2/w9YkMjszpgbXJ7eEQVfVKpq2N4IPRwXjZtnyYRHcf7RtwmFuPJvtK83JQ1ddj79XyeVje9n/eEI13Rgbp/p+QV4mPgxlSvbvPK/joYDxSCdiWxTD74R5g5fGntUUkEv1HZJxr2iPViACVOdpyXj0fSBNGjRpF27ZtWbBgAatWrWqy38XFhblz5+oez5o1i4MHD7JlyxY6deqEhYUFhoaGyOVyHB0d77v+hoYG1q5d2ygAGzNmTKMyP/zwA3Z2dsTFxREUFPTrUzTb5s8//5xXX32VhQsX0qFDB/r06cPEiRPx9tZ+IbZhwwaKioq4fPky1tbaNRN9fO6etGjBggUsXbqU0aNHA+Dl5UVcXBzffvtto2Bq7ty5DB06FICFCxfSpk0bkpOTCQgI4KOPPqJDhw6NerTatGkDgEKhYPHixRw5coSuXbsC4O3tzZkzZ/j2228JDw8nMzOTsLAwOnToAICnp+dv3gvRn08c5icS/cdEppdRVtOAVCLgL6+iU4A7rw0OpJ27NUOCnalXqjlyo4DS6ga2RWTx+KqLVCuUGOrrse3Z7gwMcqJOqcHeXMa+2T1ZOjaMyV09GNfRDb3ocjzkhtzIq0Sh0jAw1AMrM1PM5dox7QODrejdOZqXtlzm0PV8XZtMjfSZ3NkDR/OmQ+Gyb1xnz/IP7/s6Yy9cZOvB86g1AtsistgTfa8PIC1T26AiuaCKuoaWT/SuV6qZtekqU9dcJjqrjNic8mbLzX3In2fCWzHF7KL2g5BIJBL9WnULM562tNzv9OGHH/Ljjz9y48aNJvvUajXvvvsuwcHBWFtbY2pqysGDB8nMzPxT6vbw8GjSk5WUlMSECRPw9vbG3NxcFzTcT53PP/88+fn5rF+/nq5du7J161batGnD4cOHAYiKiiIsLEwXSN1LTU0NKSkpPPXUU7q5VKamprz33ntNhh+GhITo/u/k5ARAYWGhrs5+/fo1W0dycjK1tbUMGDCgUR1r167V1fHss8+yadMm2rZty6uvvsq5c+J7y99B7JkSif5jvjyRzGOd3ekf6MBTkyY12W9ipM/KJzoCUFOvxFxmQINSA0ba/eU1DVTXqZAbSnG1lvPy1iiyS+v4fKIflT4ZPGXbiWNZJRy4lk//1g54PPqx7tznk5SU5fRh/qAA3KxvJ4OQSCQMDXVutr1Wzi74d+/V7D6NRkCp0WCkLwVFFex4Dga+B1YeqF0C+DY6i0eq61FrNJxPKUMtaBCQ8HDbey/M25yK+greOLSV9Aw/HM1lfDo+jA0XM5nSzROZQeMhId+cTCa3rJ5FDwchM5DyVHdPNl7O4tNDCShUApue6dr0Ok0MQYBlRs8Q0GCCfmQWmy9nMTDIkad6tGyooEgk+o8zdfhzy/1OvXr1YuDAgcyfP58pU6Y02vfxxx/z2WefsXz5coKDgzExMeHFF1+koaHhnufU09NDEBoPX1Qqmw67NjFpuizF8OHD8fDw4Pvvv8fZ2RmNRkNQUNBv1vlrZmZmDB8+nOHDh/Pee+8xcOBA3nvvPQYMGICxccvnvVZXa9dM/P777+ncuXOjfb8eQmhgcHu+8K35YbeGDt6rzlt17N27FxeXxu9pRkbaN+zBgweTkZHBvn37OHz4MP369eP555/nk08+afG1iP44sWdKJPqXqlYoScyvbLL9hykd6R947zfanLJaItPLaONqyeZnumJtaqTbp1BrKKqqY+qay+yLzaWjhyVxeZUkl2VysGw37u4W9Gttz9aILAor6xudd1iIEwtHtCHAybzFc4NMLCzx7Xg7+KhX3u4Ven1HDFNWXdI+0DeGVr2hIoey6+eI+fwNDj7bjjXn0vn2ZCo3CipJzK+m/le9Sm/sPchHx04AkB1zkorqmiZtaFA38PzR53F1KGFmP3eySuu4llPOV8eTOZtY1KS8oVQPfentIX3jO3nw1tDWqAVwt27+zTG1qJp+y44TkV7KpfRyMkpr0QgCIS4WLbpPd/r1BxKRSPQf4dFNm7WPuw0ZloC5i7bcA/bBBx+we/fuZhMmjBw5kkmTJhEaGoq3tzeJiYmNyhgaGurm/dxiZ2dHVVUVNTW3X4Nbsi5USUkJCQkJvPnmm/Tr14/AwEBdgoY/QiKREBAQoGtPSEgIUVFRlJaW/uaxDg4OODs7k5qaio+PT6MfLy+vFrchJCTkrunWW7dujZGREZmZmU3qcHNz05Wzs7PjiSeeYN26dSxfvpzvvvuuxfWL/hxiz5RI9C/19I8RXMksZ98LPfG2azoXqLCqniV743lnRBss5Npvxi6mlbAvJo+27lZcTiulvWfT1LcOJlLe7ONI70B7Zm+MYvuzXQlwNMNCz5vP+30OwJs74hjZ1qVJvSZG+r8ZRGk0Anp3mVt0OrGIJftvsO8FbU/VE61qqNGPBrqCVJ+NpnLkEVGcq/Zk/sefUWNoQlFFFm8NC+D7MxkU1yh4ZVDj1OwuljLMZDJQ1mN7YAbXe3xJ+259G5UxlBriIrcntyKNGSH2PNJejYGehBm9vcmpaBwwAkxtpifJxdqY4aHOjO/o3uy1edmasHF6V1rZmaIv1X6PNWeA/z3v1d3M3HCFfoEOjG5hKvfXtkVjZWLIvMGBv6s+kUj0F9GTatOfb5mMNqC684uTm6+bgz54IMknfi04OJiJEyfy+eefN9ru6+vLtm3bOHfuHFZWVnz66acUFBTQunVrXRlPT08uXrxIeno6pqamWFtb07lzZ+RyOa+//jqzZ8/m4sWLrFmz5jfbYWVlhY2NDd999x1OTk5kZmby2muv3de1REVFsWDBAh5//HFat26NoaEhJ0+e5IcffmDevHkATJgwgcWLF/Pwww+zZMkSnJycuHr1Ks7Ozro5S3dauHAhs2fPxsLCgkGDBqFQKIiIiKCsrIw5c+a0qF3z588nODiY5557jhkzZmBoaMjx48cZO3Ystra2zJ07l5deegmNRkOPHj2oqKjg7NmzmJub88QTT/D222/Tvn172rRpg0KhYM+ePQQGiq/zfzWxZ0ok+pd6plcrts3o2mwgBWBsICXQ2QwjAz3qlWoiM0rZcjmLy+mlKBpUvDakmfWgAGK3YbTrafoFOHJuXl/8HS1YNDKINi7muiIfjw1hZFjzw/buprRawZzNV+n10fG7zivq6GXNx2NDtb1TSUcIzN9DB2cjEvIrUWsEalQ1XLfTI71en/0nT2BipI+XrSnXzpxCpWzgya6eTc75XPdwHm/fGQxk6L0Q1SSQuuXFygZsakrQl2pILKjm6xOpPNfbl8ndmp6zOY7mxncNpED7LWh5rZJXtsWQW1bLugvpjfZX1itZdTqVBpV2+EdpcQEadfNZpJ7t3Ypevi3PjuVsaYyzpeyu++PjYoi4fP6u+0Ui0V+o9QgYtxbMnRpvN3fWbn+A60z92qJFi5pks3vzzTdp164dAwcOpHfv3jg6OvLwww83KjN37lykUimtW7fGzs6OzMxMrK2tWbduHfv27dOlU3/nnXd+sw16enps2rSJyMhIgoKCeOmll/j4449/87g7ubq64unpycKFC+ncuTPt2rXjs88+Y+HChbzxxhuAtjft0KFD2NvbM2TIEIKDg/nggw+azfwHMG3aNFauXMnq1asJDg4mPDycNWvW3FfPlJ+fH4cOHSI6OppOnTrRtWtXfvnlF/T1tV9Kvvvuu7z11lssWbKEwMBABg0axN69e3V1GBoaMn/+fEJCQujVqxdSqZRNmzbd170R/XESQRwvcl8qKyuxsLCgoqICc3Pz3z5AJPqb5VXUse5CBiXVCnzszcgsqSavQgHAJ2Pb6nqtdJT1UFsCFi2fd6RUa1h7Lp1HOrhhYdz8elIKlZqRX5yhu48t8wYHYqjf/Hc5ao1A309OsKVdHA4N6ay3nsnKM2l8/mhb3K1kHH3/eRyGTKNtkB8mljZkFpZz8JvPGT3tKWzsbaG+CkxtW9x2nZI0QAI2npTXNlBUrcDX/veleK9sqOSrC78w3HcwbZxut6Wgsp6rmWUcvJ7H5bRy9szqgYXcAIVKQ1ltA+/vvcGS0cGYyQyo+rQjOR1fJ6DnqN/Vhvux4Ze95FcpmTPp4Qdel0j0d/kr3r/r6+tJS0trtEbS76ZRa5PVVBdo50h5dPtLeqREItH9/S2LPVMi0X/cnM1R5FXUM+chf9q6WxKdXcWCEa3xczBDcucrQFU+qJXaBXZbGEiV1JWwMX4j9Uo1UdnlVNfffQ0njQYa1AIDgxzvGkgBSPUkrHgsDGvP1uRo3PG4vpbF/e0IdrXEECUjzY7Q1lmK1MSS81FxuFsZMf3tt7FxdkERsRb1L8+39NZQXVaCskFBYkEl2HiBjScAlnLD3x1IAay9tpaUohrOJJbw3LpI1pxJA8DBXMagQFtqFGo6eFpxKC6fXdG5zPgpEicLY1Y81g4zmTYYrR21hladh/zuNtyPx0YOFQMpkeifRk+qTX8e/Ij2XzGQEon+kcRgSiT6B0nJzqeiuvZPPefzfVrxykB/7M1kdPCw5peZ3XG1MuGVQQGYy+7oRdr6JNzYrXtYXK3g4S/PkFN29/ZUKiq5VpDI4v1xPBLqjO0diSx+zdhQyrqnOtPJy6bRdkEQ+OlELAUlFbptwa6WGPj0oTLhDNKKQq6ueJfS2CNcW/8aRwbsx8S7Ex/sv8HbB9JpSDqlO25xii8LGyaTkFNKXbE2ZW5SWRIxRTHNtunwdyvYvvkXxn5zgWqFEo2gIbU8Vbd/xbEk9sY0n3K9QaVmW0RWs/scTR2ZEx7OM+H+GEglnEou5FJqqbbX7/MwvusnxVymz7cnU+ntb8/rQ5qOcXfwaoOB4d3vZ/Nt0hCXW/HbBX8nhVrB7rirxOc/uDpEIpFIJPo3EYMpkegf5N0tp/lh54E/5VylualE7vqSbq3scLJoPsPc1YwyKm/1Jo1dDYHDdfus5IY819sHe3Nt9/bMDVe4nN44y5GXpRdTW79MbFYlS48lMnvjFTJKmmbLu8XZqmk71BqBttfep/LUikbbFbW15MuCCZ0wh1EzZ6Jv6Ui+vgt2ttpFIDt6WvPdpDAMAx+iWqEi+fBKZql+wtNIwCbvJIp1EzgWX0hEfgSns083256Bz73EqHEj+X5ye0yNDIgrjmPWlrMs2nOVQ9eziVN+j0Za2Oyxl9JKWbw/nvTi6ib7HvF7BEt9D5Lyq1g4MojWThbkV9aBgQzFqO9osPNj3uBAPh4bioWxAX6Ov6MXLHoTlGU02rT6bBrPrItstvjPkdkcvJbf7L6WupB7ga+vv8+3x1N/u7BIJBKJRP8HxGx+ItE/yJsjQ7G1+R3zfZrRUJaLTUkkakFA7y5pdj86GM8zvVrRO8AezBqvVC/Vk/BQG+22gsp6ahtU2JsZNjlHKztT9szuybWsMvbE5jdK270/dT/5tfk8GfTkXdupL9XDcfQSrC0tdduOp0ZQWZeHlaULemZ2OG0ZhWb45yS5jqGdhTa4GxzkRHJRFUgkPL/+ClZ48nG4P1Nb9QSNmjSbELq52iAzmHDXuuVm2nkTnby0PUBBdkHM72vJ5xd2YFXUAXM9d745noOfvQX+do0nFffwteP0q3102QtrG1TIDW+/pH56JJG0ohq2PduduQO1yT42XMxgX1Iq5o7reNFyNE4JP4PH103adTKhkC7eNmy6lMnDYa5YyA1IKc0mrVBJ/4Cb7ciOAEsPsPLQHfdEN086ezW/4KS+VIJUerd0yy0T7hZOa6u2WMp+/xBIkUgkEon+S8RgSiT6B/Fp5fennavWvi1DU8dytl6JtUnzw8U2TO+iW0Twlnqlmhc3X+XVgQG6TIEyAykdPK1xML/7AoNBblYEuTVOte5s6ozM4PbEzasFVwEIcwhrVM7OWRsgJBVW8fXxFIrranCyNmDhuMnaBXOfOoyeiS1zvCVQV0Hlit4c81/EO+fqOPtaP17s54uHjRyDW8MM9aR4ebVqwV1qqoePK4Euj2Ets6a8tj0fNeylXFUINM3QdCuQSiqo4pGvz7FmaifC3LX3oK2rJVkldY3Kh7haom/UGn/nYHYcy2MEppy/lMbOa4UEOJrx+pDW5JTV8vRPkSwbF8qljDL6BtpjITdgTeRRLl53ob+/J7nbX2da5lA+bdeGO3MyygyktHVvmu4eYOTvWMi4OXam978ulkgkEolE/1ViNr/7JGbzE/2blNYo7hpI3Y1GI7A5IpPBQU5Yypv2RP0R6+PWAzCx9cRm9xdV1rP/ej6tbOWcTy1h3YVMDs8Jx87sjkw6gkDW+W1IfPpibmqOedo+QANtGme9i8stx8PGtMWLB/8RGo3AsiOJ1DSoeXuYdq2VeqWa8toG5Ab6mN+ZMVGt4lJyLp8diGWN4Yd8avUGzh5++DmY0dlb2yuZVVqDm7VJozqUaiUSiQR9PX0u7V7JzroQ3nq4PcaG4qR0kagl/nXZ/EQi0d9GzOYnEokA7juQAtDTkzChk8ddA6l6pZonfrhEUkEVJdUKcstr2XAxo9mytxRV1pNfWcfE1hN1gVRaUTULd19Ho7n9fY6duYzJXT2xMzfGzkzGmqmdGwVSGrWa05t+Qt8+BFd7m5tBikabQvhXZm2M4pOD8bo5YRO+O8+3J5MBOHQ9nxMJhWy8mEHfT05o17VqgcX74kgqqNI9Pngtn1VnUtHTk/DyQ/5087Zi8d44vj2ZgsxASr1STZcPjhCfV0HErflmkavpGPUmgzq1IWbITrqEhRGbXakLpABdIFWpqNRtM5AaoK+nDQyFNqMwNTHjUloJ359K+c12C4JAZV3TTIvpGRlcvZ7QomsXiUQikUjUlBhMiUQPwJJ9cXxz84P7n6GuMIXknYtR3WURV9DOa/riaOI9zyMIAusuZJBa0nhuU0tV16sor1Mwup0LOWXVDFp+mr0xucTmNM7uVnXiC8p+vr0C/KxNV3l757VGZQz19bA1MUQ3ylCjgYo8APwczHiimxdt3Sx15SuvbOPGymlk5BWTGRut267wH8HbKf5URP4MDbczD345sR0J+VW6IKanry1BLtq5PuW1SsrrlPTys2NSF3ftUMIWOJ9SQmTm7SQO1iaGOJjJEASB9JIaLqWVcS6lBGsTbU+Up60pKx5rR1JBDRsvabMLEvwIkgGLqFOqqaxT4udoxpBgJzZeyiAyo5S5W6M5cC2PitoSxmx/gvjUE03a0dnbhteHBmIq0yepsJqTCY2TZHxzIrlRFsbj8UVM+P5Ck/NcuHyJPZeu3/V6TyQU8Omhez+nRCKRSCT6fyYGUyLRA9CgFGhQ/XkjaKurKqkuyqJWcfcelIT8KnZG5ZJXXnfXMiqNwOW8y8w8OZWjmUfvux1v/3KNKasiKKiso2sre6b28GBCR3eWDG881yvCsBNb1b3QaAQ2XMxgdl8f3hraWrd/w4V0Zm64Snx+JTfybva+HHsPfhp517pNvDpB0BgmvPgSnUeN022XSiS0tazF/OBs2PeKbnuAoznrpnWhb4ADAE/19GbWxhiuZJQxrqMbD7d1wcVKztQe3i2+/vVPh/Jp4kRSylOoVqiQ6kkIc7dk6prLjPryLHMe8qd7K2ssjQ356MANvj2ZzMJd1wkmiaVd6gFoMLBgT5YBUz2K6RO3ACcLY3oH2FNdr0KRc51nnZMJdbPEQm6DdekMNsRVoNKoqKhtQKFq/Ptv72FNZy8bLE1uDyOcuvoSF9NKqFPeDry7+9rwxYTG89QAHh09mrcmD2+y/RYHc2N87U3uuv+W2JxyymsbfrNcTHZ5i3sBRSLRf9eJEyeQSCSUl5ffs5ynpyfLly//S9p0y5o1a7C8IyGSSPRbxGBKJHoAFoxsw+x+vn/a+exahdF2+teN5978Si8/O36aFsK6xBXUKhuvDRWVVcZbO2KJzCjjw+HDeKXjK/R07YkgCPy4/hcSsoqanO+5dZG89nN0o20v9ffFzVpGYWU9ERmlPNvbF9PrG2D7dF2ZhPxK5I4+HCyxo16p5nxqIYezd3A65fZ6TfYWMvwdTQl1tcTC+OY1dX0eBn901+uTWrnTpvswpHqNE2boS/UY3bszkmknoMdLt3fUV6CXpl2D6pWtUWy4mMG2GV1p62ZJvVJNdFZ5s/Xsi81j+o+X2XprDan4fVCv7XkzNzJn87DNxJfGczm9kHf3xGFjasSoMBcOvNCTijolF9PLuJZTgbGhPjnl9bwxpDXy/ItUnPoGDrxOcU4qa88kU2bkzElZL5ILtanVp/dqRTeTHFrJqnSp7N8aE0ityUXyKqt5fWskG0/GNmnvmPauhLreTjrRL9CB6T298bE31W0z0pfqkokAxOdVMvqrs9SqBJDe/TkV6GTO8BYkrlh2KImzycX3LCMIAl/vPEZcWvZvnk8kEv31pkyZwsMPP9xke0sDnz/izwxgioqKePbZZ3F3d8fIyAhHR0cGDhzI2bNnW3T8+PHjSUwUe+RFLScGUyLRP9APZ9LYF9v8YrH3UlCfwfHs45TXl1NZr9QNcSuvVVKvUqMvkSDTl9HHrQ9GUiNUDQqU189SW1bS5Fz9WzvQ52avzi1uNiZ8/0QnWjtbci5Ze0x8TgnZ+q4A5CbF8/KmK0RnVaARBBbvv8HltAoKqqopr7s916h/oCMfjAllWq9WuFjJtRtNbKBVbwCKszNvV1qSCtufAZWiUVt+PJfOxDuHrtn7gq3PHTfjOpz+mMT8CrLLavlwfwLmMn309CTM2nCFt3+JRX1zvlZZTYNu7paRvgQDqR6FlQpQq+DiN1CapjutjcyGHUk7cLZR8em4UGQGUka0dcHBwpjLaaV4WpsQ4mrB/th8Fo0MQqIHe/Itoa6UOAcfrDN3saXVQZQyW6aftWLl6VTtGlQAoY/yWnoYb2zXLjIc5OiOq3oKXx/P5G2ni3Qv205sdrmuLSq1htkbrxBzR2A4sYsH3Xzsmvw+7+RhY8JzfXwapXL/I1Y+0YGhIc73LJNaXMMbrEV+bfufUqdI9F+n1qi5nH+Zfan7uJx/GXUzc0NFTY0ZM4arV6/y448/kpiYyK5du+jduzclJU3f55pjbGyMvb39A26l6L9EDKZEon+gy+mlXMkoA7RzoWZuuEJlrZK3dsby4sYrjebDVNUrdY/DHMLYPGwzzmbOxGSV8/HBeAB6+9vz8di2dPjVGkQGRjKmLf6IsBBtgu1Xt0VzMVX7hjO6nSsD29xee6qu4fYb+eh2rrwy6GZSbrdONLj3BqAkO4s3fKt4vKsHs/v5cj2nkrHtnPExGsq+mBoq6xp4+MuzukQQzSnMy2Pday+SkHIzoDIyB6dQ0DOgtkGlGyYW4GhKew8rsvZ+RMzhn5qeyKMbTNnDmeRSbE1lnJ/fF9ubySwmdfHk60kddL1cj31/gfk3A5h+gY58Nak9z/f2Bqk+xYGT0MhvBydzNiVQnPwklxLUfHwwgZjsMiavuohaIzAs1JnlE8Jo42zBvCH+5FeVsuZMGseUIeQPX8f35bEcdfaHQYtxtjRm7wvdsTMzwkDv9kuxm7UcV2s5r22LprCqjie7ezJ/cCAO/WdzxHoiZXcMp6ttUJNVWtdk+B9AVmltk223GBtK6R/ocNf9AFcyylDeY47eLZej81mx7DIVvzHMb8Ev19jn8AaH9fvx9Ynmf/85ZbVMXnWJ8prfHjIoEv2XHck4wsCfBzL14FTmnZ7H1INTGfjzQI5kHPm7mwbAmTNn6NmzJ8bGxri5uTF79mxqam4v2P7TTz/RoUMHzMzMcHR05LHHHqOwsPkF0E+cOMGTTz5JRUUFEokEiUTCO++8o9tfW1vL1KlTMTMzw93dne++++6u7SovL+f06dN8+OGH9OnTBw8PDzp16sT8+fMZMWJEo3LPPPMMDg4OyGQygoKC2LNnD9B8L9kvv/xCu3btkMlkeHt7s3DhQlQqlW6/RCJh5cqVjBo1Crlcjq+vL7t27Wp0juvXrzNs2DDMzc0xMzOjZ8+epKTcTiC0cuVKAgMDkclkBAQE8NVXX+n2NTQ0MHPmTJycnJDJZHh4eLBkyZK73gfRX0sMpkSif6CvJ7XnzWFtAO1aRu3cLTEy1KOgsp7SmgZ2x9zutfryWDKTf7jEGztiUKo1mBlqkyx08rIhyMWCmRuuIAgCcTkVvL8njuisMpYdvj2EYUtEFik3h5qF+9nhbiPX7bvVW5NfUUfnxUfIviOIS8ir4OfILALa9STfqj3v741jdaEd7t36cS65mA4e1gwPceJKVgXXcsrp18qCxfvisZLrY28mo65BzQ9nUkm9Wfcttg6OOM34gLn7MsirqANTW+j6HOjp8cnBBJYdjGPHhUQS8quZ85A/tcbO1BraNLmHp86e4VJULE929+TT8W2xuiOzYbi/Hc6Wt9fMWjC8NY93vb34Leln4LtwFCo1+RG7yEy5nTxjZKgzBnoQ5mHF0nFt8bA2ZWIXd6R6Et7YEcuzP0Xy9q5rvLolhme2f8djPSwZ3tYZR3Nj/K39ya3J5cfI0wz+/CA+dma8/JA/NqZGrDiaxHPrI3m+jy9j27uhUGmY8dMVZm+MQqnSMHNzFD1MyunmdXtIn7mxAe893IaOXo2vv6iqnn5LT5JeXMPvUa9UM39HLJ8eup3pr6K2gdNJhYz9+lyj5CUGhlI0BtrevHv59vEOTH+kAwM6ejKgtWOzZYz19aiuV3Ilo2XfIItE/0VHMo4w58QcCmoLGm0vrC1kzok5f3tAlZKSwqBBgxgzZgwxMTFs3ryZM2fOMHPmTF0ZpVLJu+++S3R0NDt37iQ9PZ0pU6Y0e75u3bqxfPlyzM3NycvLIy8vj7lz5+r2L126lA4dOnD16lWee+45nn32WRISms9CampqiqmpKTt37kShUDRbRqPRMHjwYM6ePcu6deuIi4vjgw8+QCptPhHR6dOnmTx5Mi+88AJxcXF8++23rFmzhvfff79RuYULFzJu3DhiYmIYMmQIEydOpLRUOzokJyeHXr16YWRkxLFjx4iMjGTq1Km6gGz9+vW8/fbbvP/++9y4cYPFixfz1ltv8eOPPwLw+eefs2vXLrZs2UJCQgLr16/H09Oz2faK/gbCv8jJkyeFYcOGCU5OTgIg7Nixo9F+jUYjvPXWW4Kjo6Mgk8mEfv36CYmJiY3KlJSUCI899phgZmYmWFhYCFOnThWqqqpa3IaKigoBECoqKv6MSxL9n7pxLkdITyr5XcdqNJpGj2sUSuHI9Tzhk4M3BKVKLfxyNVtYtv+6MHDZCWFfTK7w8YF4QRAEYePFdGHa6vPCtexy4cfVG4WfT58R3tgeIyzeGydcTG3allWnU4ThX5wW1GptfVFZZbq6L6QUC9PWXBJGf3VGEARBiEgvEVaeThFe3nxVeHHjFWHEF6eFmesjhR1b1gj5x78XFqxaLZR90lE4m3xVmLvttDBjbYRwLadc6PvxcWHn1ewmdavUGuHQ9TxBqVILgiAIaXGRQn3yaaGwsk7YsmO78OSX+4Wvjty46z06Hp8vfPTTL8Km3fub3V+jUArnU4qbbI/OKhMq6hoEQVEtCJkXBEEQhNIaxV3r0Wg0wqZLGUJptbbMpbQS4fD1PCE6o1T48UyKMObrk8KUHy4K83+OEeIK04Tu63sKaaW5QmpJnvDj5YuCIAjC1cxSYf2FNGHgpyeEz48k6M79yFdnhM2X0oUbueVCXYNKWLbqovD9i88JV6PjhKQC7WtWTmmN4PfGPuHw9fwm92/tuTShVqG6a9ubiNkmCBFrdA8j0kuEladShILKOmH6j5eE6T9eFDq9f1hYey6t5ee8T3UKlTDii9PCodjcB1aH6P/XX/H+XVdXJ8TFxQl1dXW/63iVWiX029JPCFoT1OxP8Jpgof+W/oJKfR9/2y30xBNPCFKpVDAxMWn0I5PJBEAoKysTBEEQnnrqKeHpp59udOzp06cFPT29u1735cuXBUD3eev48eONzrl69WrBwsKiyXEeHh7CpEmTdI81Go1gb28vfP3113e9jm3btglWVlaCTCYTunXrJsyfP1+Ijo7W7T948KCgp6cnJCQkNHv8r9vSr18/YfHixY3K/PTTT4KTk5PuMSC8+eabusfV1dUCIOzfr30Pmj9/vuDl5SU0NDQ0W2erVq2EDRs2NNr27rvvCl27dhUEQRBmzZol9O3bt8n7v+jBuZ+/5X9Vz1RNTQ2hoaF8+eWXze7/6KOP+Pzzz/nmm2+4ePEiJiYmDBw4kPr6el2ZiRMncv36dQ4fPsyePXs4deoUTz/99F91CSIRALERBXzwS9zvOlYiaZyAQW6oT7/Wjrz8UAAapQIjZTURSblMDbNhcLATrlbGnEosYoCzhHbnVuBtKcWgIokzN6JwsTRm/pBAOnpasT82l4PX83RDxnr42NI/wB49PQn1SjWR6aXU3RxiJzPQo7uPLfMGBbD5Uibbr2TzVA9vBgU54W0vx9zYgKd6eNGxQ2diHSyIsz1AQf+vWXr5W9zcUmjrLMPe1Ij+bRzoG6Adm55ekc6zm/eyNyYXqZ6EAa0d0b/Z2/HCwRJO3cjGzkzG2KGD+MF1H8/q7dDeAI2GsuiDHL2eA0BxlYLPjyZTZOTB+GGDmty/5UcS2bBjF78cPdUkPfybO6/xwd44BAM5uHUGICqzrEnq8VtqG1RcTCtm8b4bZJfWkl1WR6CTOdamhuyJzcdaLqd7KxvC3MzRKCwYaD+P+no5Lub2KOpsWX8+ncJKBZX1KpwsjVEoVVxILeHw9Xwe6eDGuI4evLIthqjMMlrpm9Bu/Osczdfj0HVtenYrEyMmdnKjnbtlo3aVlZdB7BYyS6oabY/NKWfyqoukFTXeDoCxJchv93C197DmqZ7eGEmlmBoZkFuu4LtJHXi0k3uTQxUqNUsPJVBS3fw3wS0lM5Tyy8weDAhy+kPnEYn+ra4UXmnSI3UnAYH82nyuFF55IPX36dOHqKioRj8rV65sVCY6Opo1a9boeoFMTU0ZOHAgGo2GtDTt/NLIyEiGDx+Ou7s7ZmZmhIeHA5CZmdmkzt8SEhKi+79EIsHR0fGuQwZBO2cqNzeXXbt2MWjQIE6cOEG7du1Ys2YNAFFRUbi6uuLn53fXc/z6ehctWtToeqdPn05eXh61tbdHa9zZThMTE8zNzXXtjIqKomfPnhgYNE34U1NTQ0pKCk899VSjOt577z3dMMApU6YQFRWFv78/s2fP5tChQy1qu+iv8efMPv6LDB48mMGDBze7TxAEli9fzptvvsnIkdr0ymvXrsXBwYGdO3fy6KOPcuPGDQ4cOMDly5fp0KEDAF988QVDhgzhk08+wdm56QRqhULRqKu4srKySRnRf8+N3Aq87UwxauH6Q/dr6LQg/AoaD29LK6rhpwvpvDm0NXq/yljXEgU1BWxa/SFu2DKszWD6xb4Awd9z68/cytmFkXPfYNqGWAzNH0KiJ1B2c27K9dwK5v0cS2cvK/wdzfG0McHP0Rw/R3NAO18qMrOc4W2duZ5TwcHrBbw5rDUTvj1PTx9bTieVUNegxu3cG9i79eB4fSt2RuWQWlpIJ09fXghZyHNbcxnv9DSbT5bwhNkeVitHcCK+kGd63k5Nbm9dg7etaZNr+3F6OBa3MhkaGFPZ5VVkMhnLD8TzZDtzMiIPsh5z+rVxQW4kpW+APZO7ejY6x6Ld1ymorKdfgAOXc8zp6G/dJDBdPj6Uk0f3ceBgFoMHDQOgsEqB9I5yO6/mcCOvkvlDAjmVWExOaT3dfGwx0Nfj4LU8kguLsHe5RohrO6b28OTbk6nsvVZBZZ2S3bMGMu7bC7zY35ek/ArSS+rZ9HQXYnONiM0up1alwdhAj+1X8qmsUzG+ozvvDG9DgJM5JpO1i/q2u6O9AgI1DWpKqhVYm2qHMZ5JKsJRk89A5XHQn0JSQRUvbLyCt70ZdqaG2JsZ8sq2WLY807Xx88ynX7PPKwu5AUvHhVKtUGFqpE9sTikxOdVM6nx7WKRGAxV1SlSaP285AJHo/1FRbdPMqn+k3P0yMTHBx8en0bbs7MYZOKurq3nmmWeYPXt2k+Pd3d2pqalh4MCBDBw4kPXr12NnZ0dmZiYDBw6koeH+50P+OgCRSCRoNPeezymTyRgwYAADBgzgrbfeYtq0aSxYsIApU6ZgbGx8z2N/rbq6moULFzJ69Ohm62lJO+9VZ3W19rPA999/T+fOnRvtuzX0sF27dqSlpbF//36OHDnCuHHj6N+/P9u2bbuvaxE9GP+qYOpe0tLSyM/Pp3///rptFhYWdO7cmfPnz/Poo49y/vx5LC0tdYEUQP/+/dHT0+PixYuMGjWqyXmXLFnCwoUL/5JrEP1zPLv+Cg+HOfNif/8Hcn65sQGhnlaNthnqS7CS37GI7X06m3uWvAB9Rgc9ipujN3i9A5YePNpJ+2J8JiWPU5m59PJ140xyKcvHhmGj0n4DGuRiydE54diZ335jeHVrNO42cmb29cXKxJAvH9N+jL+UWkpkRhmVdUqCXS34OSqHxaOC2BWdS4/246iVeVASnUuQkzkSi9MEe3Qiv0SOm40J3Xt7Ei71IiPbkfRcFZ29rPnkUALtPawY096ThQM8dfXP+CmCEFcLnuvji6WJoXajRgM3djNstzFt7GW42NuiMrKiw9RP+eHmceWKQmb2bZqWPsTVgqIqGaPbuzK6vevN0wlU1Cmxunl+bzszbO1ykN58U0wurCI+r5J5gwL4YN8NHu3kTrCLBXZm2vIyAwkulsbM7OtLUVU9maW1IDHiRoWC5zo6sDUim3dGtKGwqh6FUs3WpPU8McCCrp6dWfDLNR5u64KengSNBgIdzfn6ZCqPd/Hio0fa6trd3tOaLRFZlFQreLZ34w85ckN96pUaLqSV4nsz8D0eX0Rbd2uGP78fAAuVmlcHBfDNqVTKaxuwMNZnSneP+wrYJRIJZjID3ju5msy6awQbzGi039hQSoiLBR/uj+fT8bfbfvLkMTJLqnh8dOP1w9KLa5AbSrG/4/kmEonATn7vTJz3W+5BaNeuHXFxcU2CrltiY2MpKSnhgw8+wM3NDYCIiIh7ntPQ0BC1+sFlK2zdujU7d+4EtD1I2dnZJCYmtqh3ql27diQkJNz1elsiJCSEH3/8EaVS2STocnBwwNnZmdTUVCZOnHjXc5ibmzN+/HjGjx/PI488wqBBgygtLcXa2vqux4j+Gv+ZYCo/XzvsxcGhcYYqBwcH3b78/Pwm6S719fWxtrbWlfm1+fPnM2fOHN3jyspK3YuD6L/ry8fCaGXXtIekJV7/OYbEoiq2zeh+X8e5WMmZdcfaVEVV9fx8JZvorHJKa5R89mhbHC3u/u3WaN/RjPa945szr56N9hdWV3KjKJ1lI9pjbGSIaX0efN0BZseAhXOjQKq2QYWl3AAruTZoyCiu5nBcIdN6eTM0xFmXBvvZcHd83ArxczRm5Zk0ejzcA09DfQykefg7W/CIy7OgVlHvIsHC2IAZ669y8pU++DtZEn8kkQupJXjYmBDsYglAjUKF3FCKRCKhlZ0pPvZmty8g6xKkn4XLq/iy11JKG8CiIR0ni9uLAefX5DN051D2jdqHk6kTcbkVuFjKsZAb8HCYK2qNQEx2OSGuluRX1nE5tZTvTqexe1YPdkblIAgwqt8c1BqB00lFGOlJSKg9zJa4ZNacM0Kp1jAo2JHuN1OPG0ilpJfWUtOgwtHCmHdHBhGdXY6p0QDOJhdzPrUEe3MjJnTS9uJ413ljZmiGicyAcR3ccLfS/j6dLbUL9yYXVrM1IoPHOnsiN5Dy08V0ngv3wd/BlCoL7e/nq+NJlBvtY4BXNzo6deSTcaFEZpRy8Fo+A4MceWu49n6oNQJZpTW8syuOyvoGNkzvyrt7rmNraoTNHck4Golcq82eGPRws7utacfl5FY82s+iyb6rWWW3U7zfZCgzRmakbFL221MpuFvLmwSHItH/u3b27XCQO1BYW4hA055eCRIc5A60s2/XzNF/jXnz5tGlSxdmzpzJtGnTMDExIS4ujsOHD7NixQrc3d0xNDTkiy++YMaMGVy7do133333nuf09PSkurqao0ePEhoailwuRy6X3/OY5pSUlDB27FimTp1KSEgIZmZmRERE8NFHH+lGLYWHh9OrVy/GjBnDp59+io+PD/Hx8UgkEgYNajo0/O2332bYsGG4u7vzyCOPoKenR3R0NNeuXeO9995rUbtmzpzJF198waOPPsr8+fOxsLDgwoULdOrUCX9/fxYuXMjs2bOxsLBg0KBBKBQKIiIiKCsrY86cOXz66ac4OTkRFhaGnp4eW7duxdHRUVxc+B/iXzVn6u9gZGSEubl5ox/Rf18bF0tkv3MNnj4B9nTw+OPfFFXXq0gtqsVKbkAnLyvMZE3HWpdUK6hqaGb+yx3qGtREZpQyOtSfnyY8hYFEzuSunhjZelL45EWuljcN0F7cdBVjQykTu2iDgKtZ5fx8JZuKjFg4rk3HejL7JLNOzWB/zo8oqeDpXl7M3RqDRAIvD/Aj2MWC/bF5TP/mALLYjfRv7cj+F3qSVVjKd1//SDt3c1ytjLmeW8HRGwWM/PIsU9dcZl9sHgCvDArgoZup2ZcdTiQ3KxX09GHqAYK7DaSVgw2KWm22uvnbYzmXXIyjiSMHRh/AyVQ752b+jlg2XsoAoLRawevbY5j4/QXKaht4bt0VDsfl8+XEdsTnVXIkrgBTST3EbiO/vI43d17D086UKZ0C8bV34OSrvbmWU0F83u373cPXju3PdcdcZoBGI+DnaMaT3b1wtDBmZj8/ZvT2wcLYkE8PJ3Doej4uxf7IUrRf6IS6WeJ1c2HduVuiWH4kiUWjgrmcXs668xkUVSvYFZXH9ivZlNUq8bI1oV6p5mh8IYo6U13WRgOpHkVVCnIrGgcyxxMKeXlLNEYGegwJckaqJ+H9USE8HObKq9tiKK351fym6kKI2wVnlt31ufRceCjvPhxCx5sZBe9Myf7+qBA2TO/aqHzXzl0ZO3Rgk/MsGhnEM71a3bUekej/lVRPymudXgO0gdOdbj2e12keUr0HMwS9JUJCQjh58iSJiYn07NmTsLAw3n77bd1UCTs7O9asWcPWrVtp3bo1H3zwAZ988sk9z9mtWzdmzJjB+PHjsbOz46OP7r6A+72YmprSuXNnli1bRq9evQgKCuKtt95i+vTprFixQlfu559/pmPHjkyYMIHWrVvz6quv3rVnbODAgezZs4dDhw7RsWNHunTpwrJly/Dw8Gi2fHNsbGw4duwY1dXVhIeH0759e77//ntdL9W0adNYuXIlq1evJjg4mPDwcNasWYOXlxcAZmZmfPTRR3To0IGOHTuSnp7Ovn370NMTP8b/E0iEX8/A/peQSCTs2LFDt1p3amoqrVq14urVq7Rt21ZXLjw8nLZt2/LZZ5/xww8/8PLLL1NWVqbbr1KpkMlkbN26tdlhfr9WWVmJhYUFFRUVYmD1f+ybEylkldXgY2fKkz28f/uAu8gpqsDFzoL8olLsbSzQa+Eb5KozqVTXK/nyeCo+wRtYNewjnEydyK+sY2tENhZ1OThRxIChY7mUVsLbv1xn/ws9kUgkhH98nMWjgujuY8fPkVnUpZ5nklcNmNhD4gEY+QVrz6VxMa2U14cE4mIlR6XWsDMqm7PxeXQ0LcarTWeC3IzJrs4k0CaQzJIaziQX42ZtzM+ROVQ3qFg5uSNrz6WTkhzPwjEdEeTWaAQN6Rl5HP3xeybOeRkTc3OKquoxkOpxI68SOzMZThYyKuuVvLEjlk/HtsXSxJAfzqTS3ccOf0ezZu/Hvtg8Ql0tOJNcjFQi4ZEObiQWVDJn81XeaKemq1kR2W7D+fRIIo93cSfM3ZqPDtygvbsV/Vo7kn1hO5viFEzR24utXjU8uoHzWTWEyMsxWT8UnjkNZk3XZbqaUcaHB+N5sb8f55KLORxXwNqnOjH+2wv8NLUzLtbaQPXAtTw8bEwwLmqgoV6FXydtkLgtIouUomoGtnHgVFIJ5bUNBLtYsPFSJq8PDqC2QU1bDytm/BRJO3dL3G1MGBHqrEvMAZCQX4mfg1mT+V9qjUBBZR3OlnL6Lz3B28Pb0MtP26MmCEKT8qwZASHjIWAIlCSBWydAmxb/vb1xvD4kEGfL298UR2aU8tLmaI69HN6oPX+1z48kkFRQzRcT2/9tbRD9O/wV79/19fWkpaXh5eXVaD7N/TqScYQPLn3QKBmFo9yReZ3m0d+j/z2OFIlEf4b7+Vv+z4S0Xl5eODo6cvToUd22yspKLl68SNeu2m9Lu3btSnl5OZGRkboyx44dQ6PRNJn0JxLdS2ZpDVkltcTm3D0hSV2DmqLq+rvuj466Ru/lZ4nPq6Dv5xc4ceJii+vv4GFFV2KZ0tGe1zrP1fXE1DdoKKyqx0xuhIlc2+vRycuGvbN76j48//BERzrdXJdoTHs3JnVwAmUtOIeS6TKYeqWayd28MDc2oOhmdrbqehUn44tIK1NyReHCJ4cTeG1bPN4Wfvx09Sg7Ew6RkF9FR08bnC1lzOjVikPX88kpr8PIxgNMbFh/Yz3vnH+HVl6uPP3OQkxufpixM5NhKTekuKoemVSCiZE+lsaGPNTaEROZtndwag/vRoHU7I1Xicoqo6peycmrNxjiK6esVomhvh52ZtohbH4O5nw5sQNdbeupUwqYGOnz6bi2hLlrew3zKxWkFGsn/rpm72NCmDVn3Z5mqeOH1AiGzNoQxY+RpajNXJmzM4ln1zUd8+9qbUw7dyucLGSMau/MjHBv7MxkHJvbm4SCSlYcTQJgUJATgU7mOHib4+x3e65canENRVUKQlyt8LKVM7a9K66WxpjJ9Pn8eDIWJobIDfUxNdJHI8COKzlU199eKPLTQwnM3nSV1KJq6lR1TDkwheQy7YK4Uj2JLvjZOqObLpCCphkh2TNHm83vyo9QkkjDz89yPbuE7LIaUouqySmrw/hXyViCXCz4YkLYbwZSqUXVXM0su2eZW64f+J7kSwdaVPaWqno11Q0Pbq6FSPR36O/Rn4NjDvLDwB/4sOeH/DDwBw6MOSAGUiLRP9C/as5UdXU1ycnJusdpaWlERUVhbW2Nu7s7L774Iu+99x6+vr54eXnx1ltv4ezsrOu9CgwMZNCgQUyfPp1vvvkGpVLJzJkzefTRR5vN5CcS3c3i0SG/WWbNmTSu51WyYmLzY9uDgvz52UBGgJMFmx4PJtDDvmkhQaC5jBShTnLY9wWdhn8OzqG67Z62Jrw7MrhJeamehL3RubhaGxPqZkVmSQ1bI7IwNtTHzcqbtBoHSk6UcipJzgfW5XT2tmHJHddoZmzA5cwynujqSQdPa4ykEk4lFiGVSLien4OZaT0LR45i7eVL5CvSOJEQilojMK2HFwLwytYoTIwDebJHN0C7KOzVuHjC8n9G1u810DfkqxOp9G/twLO9W2FsIOWR9q7svJrD0BBnqnMzyDm7k7YTtfMX+wXa42JhTHJBNV+eSKOnJoK1aaGEuVkQHnb7PnrYmIDNEH46lULc7ussCrck7dwWWj/8CkOCnLAyuTl08pGVuCjryFu/gAq3CZgY6bN3dg9OJBSxzW4NBpml5JfVs+p0Kk/19EYQBPbG5BLub8/0nt5YmRhy8Fq+bo4ZwLXcCs6nlPBQkAOO5sY8t+4KoyzNsVaAzxB3Fu2O49PxbTGTGZBTWsfKU2l883h7UoqrcbaU097DitjscrZGZPHVpPYo1Rq2X8nB0ECPiPRS0opraFCp6etvx+QfLnHopXAeD3wcZ9Obr2W1ZaAnBZm5LsHGXZm5gFNbaNUHLN34LmgdddeLOJVYhK+9GSsea4eViRHnkosxMpDQ3sMGI30poW6WvP5zDOjB4lEhUJ4JSMDSjQaVhnMpxeyJycVMZkCYuzaIrGtQk1xUpZsn10hdOfqVaii8AfaB2m2HF4CtL4RNarbpbwxr3ex2kejfTqonpaNjx7+7GSKR6Df8q3qmIiIiCAsLIywsDIA5c+boxuoCvPrqq8yaNYunn36ajh07Ul1dzYEDBxp1z61fv56AgAD69evHkCFD6NGjB999993fcj2iv9fTay9zPvnBpJcF+PlqDg4Wd5noD0j1DQhp4wNJhwk48wzTD7xFZkXO7QKpJ2HlAEA7NyqtqOb2Pn0jeOYkH8ca8d7e65xIaLwuiVojcDWzjMtpJaTf7H3JKK2lsEqbljanvI7L6WVcyShDI4CtmRGJBTX42JvQ0dOSE2tXUl6QB8lHoDIfpVrD94+3Z0Z4Kzp6WhMsL2Osj4C+VI8PBk/mjZ7atdriSxORm6YyoZM77jYm1CrV2JvL8HMww83CkowCEy6lFPPtyRRWn06ipLQEbk6yXj+9M+Yyfab9GMG+2DxqFCp2x+RRWqPAWJFLaMYyqKsAYFiIM9+dTsXa1JAtz/VknaI7ZkZ6JBVWs+FiBm/tjNXeCI2GxPwqkvIrae1sTkNNKSYVSajUAv1bO9D+zrltGhXexjX0vdlz5GBhTFTxZSrUWXw4ti1dWtlgbqwNvmob1Ky/lMXa8+k8v17b073xUibrL91eQ+X5Pr5U1iuJyarAxEif9p6WKCsO0L3VBaxMjOgbaI/6VjZBUwMyy+ooqWlgWIgLL3f1opOpnK6tbBl4c95YrULF/mt5lNU0UKNQUVHXwGtDWjPnoQCWjW+LiZE+/Tz6ITe4ORTv2CI4d3uOgFJ9j1TC4S9D+8lgqU2uM7N/ILP6+vLp2LZUK1SsPJ2KRiPw+o5YvjmR2uhQP0cz/G8mCrl2bh99v42jXqnmla1RLNx1DSOpHs+Fe0PMVqivZPuVLF7YeJmq8hyoyofMC9osjUCbUa/gKatDHbUZgPKaBpZU9KfaoVOzzV5xLIkpP1xi8qqW9+qKRCKRSPRn+lf1TPXu3bvJIpt3kkgkLFq0iEWLFt21jLW1NRs2bHgQzRP9y2gEbcfPg7J0XAhezayZBJBcWM37e2/w9aR2yNw6cdJkKPqFTrz5cwZrp7poC7m0g4HaTEG/ROcSk13O8nFtWXUmjcp6FS8N8GNMmCsnE4u4llNJb3/tnJ7159P48XwmrZ3MkeqBu40JL/T347k+tzOndW1lS2cvGySS20O+evjYciG5CEn2FQyNjcmpUpETe4U2wXrsr/Jna0QWa6d2Ql8q4eLFsyyId+H7J1xxtzFh2eFEuvvYstjJEGKvgpWcpIIqAp3M8LAxYXqvVuyKymXV6XRcrIxJL65h+aSHcLJ8WNemlzZHodYIDAiwo5evHWbGBvw49eaHaKsexPg9h/7hH2g94iUkaNOC6+tJEAxNOZuSQEmVAg8bY0qqDVEobwYOK/vi2ONtjAzsSCuqwbZXe2x9Gi9AKQgCEkEAIzOsx3+J8R1DxgpLLQi0NSYqq4yiKgWzb2ZbNDHSZ+P0Lqy7kIH/zZTka6Z24sCRg0QdWk/bhyYi1ZPw/eMdcbKUIUncz0tWRTS4BWFooM/Kc+n0DbBn5elUKupUvNTfF39HU6xNDPg5Mpv6pAo8pIZ0G+NDQn4VZbUKKmtVvD4kkOjsCoYEOxHur+2BM5Dq0cnLhroGNb9E5TCmvStqoQFp+HwkUkOkgsDZ5GI+OBDPxqfbsjF+I+P8x2Eps7zn81dmIMXVWs6ltFLeGBrAgl3XWPJwEO08tQFo/fmVbK8JYlCnEGRS7VuJb/+neN+vHJmBlIFtHDHU10MD2MkEiN4ITiEM8zXmdM73qM66auPo+D3w5AGw0c49LAmczNBvrtKnKoYXTA4yqioDQfJSs230tjWhuFqBl43JPa9FJBKJRKIH5V8VTIlEf6aVT7R8+IRCpeadXdd5vrcPrtYtS9ca6mZ113325kYMD3XCSF+PBfuzcJM4sewhT3IM70gVbWRGidSFfS8/Q9v2rpi3egw+b0ePh9aiMNP2IDjJYXJXN6TS23/KlfVqfB1M6eBpycPt3DA10mfjxQw6e9vgbWdKg7qB1bGraWPXhh4uPQDYejmT3Ip6Xgishs1P0m1WBJPWRJFZ1oH14V3obAsNShUd3jvCVxPD6DJoEoEVVzkRX0RbDyUpRVUMCnIExwHUWgWwfO8NXujnh4XcAJVaw0cH4pnUxYMRbRsPpxUEgWPxBfQNcOCVh/y5kFbK0YQC7C2MdenXbzH3aoeJsfbeqzQCL/Tz1a2VVFrdwEsP+eHnYIatqdHtOUGDP6RG1grplWherVjM4s1vMnVQF12K+W9OJlOdn8ILWS9hMOMECdUyJq+6xKuDAhjR1plVk7TzE7JLawl20QZNh5Ovcr04mjFt+jGpiweFlfW6hA4+BqVYVd0eiux8M/U5xcnUJRzliuvjOIcNprQmA0GAqd29yCyrxdzYgAmd3LE2McLTVk6VqSE9/O0pr2lg+ZEkHMyNUKkFnujqzvXsck4kFPLOiDbIDfUh4xwcmE/2yN0cistnUBtHVsR+grXMmotX2uPnYEZ4SAVd2kVwrUSf/en76eveVxdM7Y7KwczYgN7+jYeZ7orK5WRiISde6Y3MQEpmaR0Bzha6hawrlHpcyBX4/tuLOFoas3F6F4wEBV1vpo0fEuJMTz87VGoBDA3h8e0AWADfPPQ5yCxB3QCDPwTpzeGWhQnYrBrAirFnOJxUjeA7iADVaiiJAaem2f+GhDgzJKRlQ7R/uZpNRZ2Syd28WlReJBKJRKKW+FcN8xOJ/i5SiQQ3Kzkyg9+ZjrYoAbY9BWrtmjvmMgNGt3NFItEmu7Xx6YSZRzsCnBpnmLKwd6T7iCFY+Xelm78T5Q8tJapCToirJQCffr+FqSvPNuqxfbaPD++MaMPZlFIqarXD+tJKaimrVZJeXM32+EPsTzuGVKNdK0ihUrPtSg7pJTXa3rAXrnKjqIFnwr35amIYpdUK5myKJiargjHtXQh1s0JPT0J8QTV5lXWYGuljpC9ly+VMnt6SQrWFH2pBQCNo2Bebyxvbo/klOpczycW6NsZklSMIAqcTi5izJYb0okqCzs5imm8ti0eF0i9Q28uWkF/J6K/OUtugwrP9QOxaa9fOeurHy8zfHqs739Znu9Hdx45pP0ZwNL6Q+poqkhOvg1tn8uukKAwtSPKaiKuLC8YGUpILtIlDhgU7ExDQmsxeS8HEDh97Mz4dF8rmiEzKahoY9+05zqcU42otZ1xHdwBWxL5HUkU8FYoKBEHgiR8u8cOZNAB8widgM2yB9ldeUkpxoXb9OkXn54kKX4nCrSeL9l4nqbCK5UcT+elCBq9uiyEqqwwvGxOM9PVQqQXKa5WsOpPKhcJjPNOhClOphqXjgjmRWMTZtBLsTA11iSiuaTz4ye4lHl99ked7+1ClUDI9eDrj/ccT6mpJenENuyNrSU33p4tTF3aM3IGPlTZo33gpg9TiauqVTRM4dPK24rFOHljKDZEZSHn5If9Gc68cek3l88ld+ezRtnzySAgpRdXUfRWOMulmEqCjizC7uhIrE0Oq6xREHFhLWr42EcXWBCUZlWqQmd8OpADs/GDqfpLLBWqyYtCvyoRhn0LQb2da/S1XMsuJyGhZIoxfU2lUzDo6i0t5l/5wO0QikUj03yIGU6L/tLyKOqb9eIm9MbktKh+fV0lRZdMMfPpSPZ7r44Ot2d3nQN2TsTV4dNWuk/Qr74wIYlQ7V93j4/GFvLQ5SluvgQHe4SPxat8fZ0s5eRYduJJze+7UpDEDea5fAI+vush3J5PJKNHu++5UGm5WcgorFby8+SrXcyrwtTNla0Q2+bmt8BdexNnYm29PplBUVU9vPzteH3Jzwr/UgOjsMtZeyKCgsoHHVl1EIoWuPra8NawNMzdcYXd0Dh+NDkZalYObXEX3VraU1Cgoq23AzsyIt4a1RqHSsPxwEqW1SvbO6sljnbVrcuy8ks24786TXFhNTz87djzfFS97C/AbDGZOuNtog9ZqhYorGeXMCG+FoVSPU4lFuqBxdKg9zkIeT66+xJWMMt32RSOD6ORpxcmLEby2OwWAMHcr5gwKwrrjOCb38ONQXAHDVpylslaJq7WcYaFutOo0GCQSpHoS3KzlPNrBHXtzGXMG+NPGpfECtZuHr+fLgR8QZBuERCLhhX5+jArTDs1MOfszhWnaIC/r4GfU7HoFFFVUfdWPdafjicgopYePHfMHBfJwWxeGhzqzZkpH5v0cy/IjieSW1fLCpqsYG+jhYWNCYlkiKaUQnV/LU2siuZFXybyHAiisamD7Ve38ujKlIVeVHihVcDAun1Vn0nEwccDSyJonu3thYqSPi6kbUpUdSfmV1F5YAwXXAW2Wxo6eNgwKcqKyVsngz06RWqSdY+dobkx7z8a9q7eCS9QqaKgFwCzzOGn7vsBabkhU24Worf20hQNHgk8/AC5cS8Q7/jvKC7MBuJFXSVGVNlNkQ4MSza31XSQScGhDT3kGwwItsHMNaPzHcvJDKE7m91g4MojPJ/y+hU71JHpIkJBUkkW14u4ZOkUikUj0/0cc5if619NoBCrrlFg2k7HM1EgfJwtjNJqWTY769mQqHb2sdB/8fy059gJny63p28YFN+vm52nklddRUFlHW/c7khuY2kHHaS1qQ71SRb1S1ey+QGdzPhwTwkcH4unsZU24vwvXY3NRqjW425gw/IszXHi9H9N7elFZpySnoo6qehW2JoaYyPSZO9Cf5MgCzhWrUajVZJXVolAJPHvHfCqARzt5UFLdgIGehGXjQunayhZTI+3LxdyB/myPzEZfraS2JAtFhQtIzHCzNOG53r5IJBJ+PJtOLz87Xh8SQGJBtS4ITSyoZP6OWNY82RFfB23SAm/bmynPwx5j5ZVteFl608+7HaXVDdQnHMaPSA5r3uLjgwlsmdEVW1MjhlpmQuZcDvbdj6eNnKGfn2bRyCBWnUmji7c1k/v0plvHKkqrFURlVXAhtZjIjDKm9vBicJAjnjZyzOXaHhG1RiAyo1SXLr6wUkF0djlGBnq0cTanoraB70+lalPdV9Xz2a8+kA8KdtT9vyHlNMWKeuy9gmk94iXySkqJzG3AqfPzvO7dGvOqNMwsjMDGnPjCKlIKq3GxMqaHjw1yAz3szI05+1o/tkVmEZlRTlu3kUTWljGpiymBTha4WBrjbWdKoLM5Jjd/HxmltbhZyenha4OfvRkjw1yIySpn1sarHHypF8sfDSMqs4xzKcV8dSKVF/UT8HTQDpmbfnPhXLVGYFd0DrP6+OBqdfdhrC8P8Ke1szlc/Bbyo2D09zRUlSLUV2JlYohT/jHiU/bTdtqXjbJM9m0fRE3QcQ6dSCaXXN4e3ka3L37dSxjLTfF99APtBlUDZWd+oP3gmWD7q6F9aiUI90ik8YDoSfT4rO9n9N0yGFWDJU+06/eXt0EkEolE/0xiMCX61zsaX8CXx5PZ+XyPJvvMZAa8+3DTVOF3s3RcqG4eTnN+OHGDQrkvbVxt7xpMfX4siYT8Kp7r40P/wKYLvTZy5B0wsYOuz+s2DQ52ZnCwdh7I9ivZrLl0EUv3vbwU/CFtXW25mFbCjbxKbEwMCfe3p6OHNZbGBnTzsaODhxXv7LpOZy9rDPX12BWVx6AgRz46mKCd16Onh1RfD42eQFFMCW/098fY1JDY7HJSCmsoq20gs7SWZ3p783xfX7ZfySYio5RLaWWU1zbgY2+KzECKvbmMt/cm8dXjI3hkyzWe6WXKywP90dOT8MPZNOJyKjCRSTl4LZ/Xh3myPHI5Y72fws/BnFOv9sHOTJthU6FSY6SvHTpZFbMbn+jt7LN7mH7e7bAyMaDMyJUiMyk2pgY4WRqh1mg4lVjIlQxHXnz2FNZ5KlaeTeO1IYG4WBiz6OE2XMuq4IP9N3gmvBVJBdVsu5LFwNYOGOhJMDaUMubrc7x1M512g0pDdlktszdd5cALvbCUG7LtShY1CjUR6WWYywxwt5GjVGkIcTWnoPJ2z+T6X/bSnSg8BzwHcm0PTuDk5br9ZzIV7LhaRnx+JhumD8fBXAaRn4BdANh4I2gEUotrWLzvBodfCmfYijMEueZrU8ErVLR1NaeHix5Dy/dRF/gcV/LrcLeWczy+gFe3xbBmakfaOFsyPMySS7mXmOXeT7fm07qLGYwMc0Z26UtQVPLile5425igFATmFg/neYU37WqVFN5cB83VUs6Z5GJeGxyInkQ7j2pgkBOJBVW0cTbneHwRPf1s6eytDThp+xgohgHgP2ga/kB5ZQ2aLi9hZWzc5GleWF2PUqXhXEoJx24UYmwopaJOxagwF2x6P4+x7PZQP6VEn+mlk/iSVjTpR+r75r3/nh4giUTCxiFbsDdpfuFokUgkEv1/EoMp0b9eT19b0oprKKlWYGP6O4fh3dRcIFVZq9T1Yrz33BNI9CRNFz29w9vDWrMnJpf9sXmNgqlqhYo1Z9OY2sNLmzgAwMZfO3QpeCyYNl1nqqevLXWqIEwtrHhi1RV+eqozczZHs25aZ7zttJkC7cxl2JnL+PhAPIHOZgwNdsTP0RwnC2OGBjujFgQCnczRl+pxLqWYo1nFzBzqx/YV0dxoUGDnYkZhVT1ZJbWU1TbgrszAYv0sisf/zOh2roS6WhKbU8HemFwySmoxMlRwXb2c8ZbTaGVijKuVnC+PpxLmbkV0dgXWcgPGdXDH3daYI3EFROdnEJmdypfbT3PylT44W2o/bJ9NLuKzI0lsmaFde8rM3pN2waPo1e4RAIz0pZg5+eLXsS9V9Q14WJtgJTfESF/Ktsgc9KV6WBobcjWzHLm+lD3RucwZ4Mc3p1JQqQWOx59n27PdeKGfL69sjWFEqBNnkor5ZFwovjdTeT+/PpLyOiXP9vLmp/PpxGRX0C/AHgN9KWPa3x56+dqtIZB3CHU2wSYpFU5/AgPfJ7e8lo8PJPLuqKCbi+wKdPCwIr+8jrKaBm0wNWiJ7viH2jiiJ4FwP1vmbo0i2MWcNs7mKMpy6OWkxreVN1TmQX0eNRoV351M4ZerORRV1TF3oD/+Dtr5ddlV2ay+tpmzMY68c3ONsWk9vLGUG0DDEFDV84mXE3O3RvPhI6HIDKSsPpvGssPJ9A2wRyKBF/r78c3jHQAoqqpn3cVMrE0NeW17LHP6+rD5Sja2pgbcyK9iXAc3JHIrKjCBWiVXMstIKKhEGnMMZXkxz731OoJaxb79u+ndvQcmVnasPJ2GgZ4ev8zsQY1CxfnUEpQqbQ+Ti/fte1tarWBndC6H54RjekeA1RJHbhTQ09dWF5w/CI5m5r9dSCQSiX6nNWvW8OKLL1JeXn7PchKJhB07dujWURX9vcQ5U6J/PameHrE5FZTeTLbQEudTill6KOE3y9Ur1fReeoLSfe9BXgx6Ur17BlJKtYZj8YV8cyIVG5PGgV1RRR1XMsqoU9wx2T9sAlEBizh98BCbbmxiw4070vanncZOU8zETv6M9O/Ludf6EupmyZl5ffG8IxX01ogsorPKsDYxZNPFLJ5ZdwW5gZS9Mbk0qDUYSPVo7WwBedEExy3FwlhKclE1V1z12Z1WTERGKW5WcowN9QnzsOaVCQPZY/EYkXn15JbXIjeS8nCYC5886kelsoyOHs5MC51MhOe3XFdEMiTICUu5PjamRvwSlUuwhYJOXON6Xj7Fpstp52bPTyM/58iccF0gBdpew+jsCjZdyqBBpQHHYMw7jNUFtIb6ekzv4YmFsQFKlUCDSsOuqFw6e9vQy9eWnPI6JnX1YOP0LjzZw4vXBwdiaqRPaY2SRzu60qARKKtV4u9ozq5ZPZjWqxUvhLvhaa6nSyTy8kP+hPvZcSy+kOu5Fag0AqPbu7A56io7YmIAyCmrJSa7HICkggo+ORhPenE1QR17YzZmOYTPo6Cyjhk/XcHPwRTDm71DoW4WTOriybN9fXVDGhfuus7Wy9q1qN7fF8e3p7Sp0TPLarGQ61PdoCbhl4/J3f8JAKn1ZkSELsTZwZbn+rTC3Fifzye0Z3xHd10vVBvbNrzRYSEDQ28Pz/NzNMPeXAa2PkQpXZm2NoJNz3TFXGaAqZE+L/X3Zf7gAF4c4MfMvr6NEpjYmcnY/ExXOnhaM7iNI7/E5lJeq0SpFjieUIhSLVDboGLkl2d5c2cM6y5mkFVSy+iJ4xg1bToAtXX1/JRkQF6J9r69MtCflwZo51KZCDX0qd5PSWUtJdWKRn8jlfUqLqWV8OnhRN22gso65m+PplqhTd6CRgNnllFbnENhlbZnraxGwawNVzl0PZ97qimBiux7l2kBhappwg6R6L/i/PnzSKVShg4d2uJj3nnnHdq2bfuH6y4qKuKRRx7BysoKc3NzevfuTULCb79XnzhxAolE0mwQ4unpyfLly/9w2/4K48ePJzHx9uvf3e5rXl4egwcP/gtbJroXsWdK9K9nINXji/ucWB6bXc61nIrfLCczkLLp6S5YJsY1SR7RoNJw5EYBD7V20H2wLa5WsPpsOi8N8MHHvvG32GdSStDTk2Aia3weB59Adl/8hRtng3ipW9UdjdwKPv3BQttDcuuberUg8NCyk3w6ri3+jmYs3H2duQP9Gd/Jnap6Jc+Et0KChJWnU9l1I5LXB4fgYe4BhmYUYs2B64UYGujz5aR25FfWsfxwInamhsy/o/dl7GPa+V0Ld10nuaiaFY+1o7CuELXsKoFOYVib9MFMz4l2jn7kGCnJKrPFTFXBW15JJKTZ4122Cadun2BZ8yQHoxuY3hMupJaQVlyjS8Gt0Qi4Wco4fL2A7q3scLORs+3aafalHuSHEe9BbRl82xMe34GXnS8j2rqw/1oevf3tWTwmRNfW2RuuYGViwMKRwajUGt4c2prO3taM7Xhz3lttGRjKQd+I9B3vIEeB2RNfAhDgZE5aSQ3ZpXU80c2TQGft76yDlwkWhpYAnE4qIjankhBXSxbtiaeyTkkPX1s8bU3BSBsk2RhoeKaXNwODHHXPhYnfX+StYa0b9U5W1ispr9MGBS/296OkSoG1qRHbn+vBaz/HcDm1hHKHp5g62hOAiIxSUopq6OBpjaOFMe09rHC1lnM5rZRTSUW8/JA/AJeKD5FemYWd/jPIdz+N7ZiPOV5kzju7rtPH35adM3vgaGHM279cw8/BjLkDA/C4uQbagl+u4WJtjLuVMc6WxoTdnOuXW1bH+ouZOFkYM39IAO09rWl/c40pQ/SY0NENH3tTkgqrKa1RYCA3wcZa+xw1MTVl00vDdNdtpC/lSkYZZsb6+EpLkCQdoswkqEkWQU9bE159KID0klrdtqJKBVczKyiracDUyAAENSfzDPkxMRMDgzy+fbwDViZGHJ0TfjsV/d1cWQOl6TDyi3uXu4eiqnrGrzyEj1sJdkbevD88/HefSyS6F0GtpjYiElVREfp2dsg7tEcifXA9r7esWrWKWbNmsWrVKnJzc3F2vvvyA4IgoFb/eV8uzJs3j4iICPbs2YOjoyNXrlz50879b2BsbIxxM0Olf83R0fE3y4j+QoLovlRUVAiAUFFR8Xc3RSQIQkR6ifDWjpi/pe4NF9OFPh8fF7JLa1pU/uiNfGFfbK7u8aXoWGHt9l8EQRCEhMJcYd+ezYJw7qtmj00trBI0SoUgKBWCIAjC5bQSoa5BJQiCIBRV1QmCIAhfH08Wnl57WXfM5axUYfb2zUJxbbEgFCYIgkYjxGSVCd2XHBGq65XaYyvrhWlrLglfHE0QBEEQDlzLFarrlULm9Rgh48sVQt7xU8LkleeFeetPCULGBd25fzqXIszacEXYfiVb+PJYkvDViWRh+S/nhIMbPhOGf35aOJlQKAiCIFzLLhdSi6oEQdUg5H05TNi4a6+gVKl158kurdFdhyAIQlRukvDxmZ/uuPDTwubzScJnhxMEjUYjPL8+UriYWiwcuZ4v5FXUCoIgCK9vjxYeX3lBuJBSLAxadkrYE5UtCIIglNcqhBc2RgrxG18XhAvfCoIgCBVFOUJVUZbu9LtiY4UF+442ud8f7b8hdF9yREgpqBTmbrkq5Fdo73FBZZ3QcEf7b9lwPk0Y/dVZQa3WCJW1DUJcTrmQU1YjqNQaQUg9JQibJjUqr1Cqhep6pfDWzlhh2aEE4duTSUJhpbYOoSxDELY/KwgNtTevo0FILKjU3p/MMmHyqgvC+eRCYcQXp4XVZ1OFd3ZdEzKKq4SRK04Jq08nCVG7Vgia2jIhr6JW+OJoknDg2u3n3LLDCUJqYbX2wZnlghCxRkgtrBISCwuFsat+Fhbsim7UztTCKuFccpGgUDa95lsupZUIE787L5xPKW66szBREK5t193TdRfShePx+cLW84m657IgCEJl9C5BU5yie/zixivCqYSC5itUq4WC0grh6I08obRa0WyRBpW60fNKR9UgCIrm/15Vao1wJC5f+zu7h3d+uSa8uuOQMG/vMuGlzYvvWVb0z/JXvH/X1dUJcXFxQl1d3R86T8XBg0JieG8hzj9A95MY3luoOHjwT2pp86qqqgRTU1MhPj5eGD9+vPD+++832n/8+HEBEPbt2ye0a9dOMDAwEFavXi2gXYJb97N69WpBo9EICxYsENzc3ARDQ0PByclJmDVr1j3rf+qpp4QJEybcd7tvtausrKzJPg8PD2HZsmWCIAhCWlqaAAhXr17V7S8rKxMA4fjx443OdeDAAaFt27aCTCYT+vTpIxQUFAj79u0TAgICBDMzM2HChAlCTc3t15P9+/cL3bt3FywsLARra2th6NChQnJysm7/rbp//vlnoXfv3oKxsbEQEhIinDt3Tldm9erVgoWFhe7/zd1XQRAEQNixY4fuuMzMTGHs2LGChYWFYGVlJYwYMUJIS0trdH86duwoyOVywcLCQujWrZuQnp5+3/f5/8n9/C2Lw/xE/1prz6Vz7ko8aRk593XcoWu5XE4vue/6Cipq+fTYOeoalOSsnEC4STY/PtkJl7tkPyuubpxCuW+AA4ODnHSP/Qr2Max2p/b/llYM1rsKQQ8DUNegZvRXZ4nPq6SkWsETqy9xZe08Un+aw55f9tLB3UI3VM3WVEZBZT1fHEvkpf5+uvNfKz+JlUMcDZUKdu6ZTkN+DMGuluyc2V2XCc7WzIjvn+jIzL5+1DWoWXk6jeyyWq4dP0y5kQEW9jYMC3Ghg0kxZF4A4KvtR1l3IYOJnd3xtpPT2tkcpUpNiWDGQxNm896oIAIdtT02bVwsyC6t4/PjqQih44mqNGHR7jhqFNpshfN/juWFjVcB2JOyh12pP/FU0AjdPXgt0hT3jLM03DhCQWU9T/fy5oczaXx2LIkr6do1g94fFcK3j3fAw9qYSeaRuCf/BEBkZB4BqQ2UhzylTZgAmNs6E1FsSE6ZtuejsqGaivpqNBqB9OJq3b0b28GVVvamvPHLNTQCGBtq77W9mQwDqR45STHsWvupNkU4kFFWh4mhHhIJLDuSyKyNUThbypHqScAuEMImAZBYmsjiUxv48kQSb+28Rr+2ZQS3KmbFsRQS8rVrX2Fgol1vSc+A6znlPLTsJF8e1aYD97QxYVIXDzp52fLyQ/5U1CipqVdxNrWUfgEOdPSyJXT480iMLXE0N2ZmXx8Gtrn9nHuxvx9ediZUN1SDcztwaIOXnSnWpnoE+sfwdC93FvxyjbIa7fA7ZytjXtgUxe7o239jlQ2VvHt6BTll2p7djp7WrJvehS43k1MUxe2g4tueKNVKKM+gKCuZyasuYlIWh486FY0AmoSDELkG0H6znXB2F6nxt7+BLq5poLaZta8Aas58RcnGZ/CyNUFfenPIbX4sHF6gK/PSligmr7rY9GCpgbaXshnF1fV8cihBN3Twbmb0bkWW5ggSWQWBrupGQyRFoj9D5aFD5LzwIqr8xsNWVQUF5LzwIpWHDj2wurds2UJAQAD+/v5MmjSJH374odnn+GuvvcYHH3zAjRs3GDBgAC+//DJt2rQhLy+PvLw8xo8fz88//8yyZcv49ttvSUpKYufOnQQH3zsh1MiRI9m2bRsHDhx4UJfYYu+88w4rVqzg3LlzZGVlMW7cOJYvX86GDRvYu3cvhw4d4osvbvdy19TUMGfOHCIiIjh69Ch6enqMGjUKjaZxBtI33niDuXPnEhUVhZ+fHxMmTEClaprBd/z48c3e119TKpUMHDgQMzMzTp8+zdmzZzE1NWXQoEE0NDSgUql4+OGHCQ8PJyYmhvPnz/P000/fc8qC6P6Iw/xE/1r5lXVEpVcwr/1vd4kDcGMvFMSy+HJnfO1N6Ohp06TIt6tXI7X3Z9rQbk32pZQWczg+i4Gt3Si37kGAjTs2Ns1/MKuuVzJqxTm+ebx9k3WKbrEovwFtHtI+kEjAwIhb0xiNDaU818cHdxs5ckN9Pn4kFLnSmavXMkjJSWdYbQmY2hOdpQ0oQt2sOPBiOG7Wt9vzRJsnUAtqlh27SoSlJ+fOV5KUd4Y1T3bSlSmuquf9vfG8M6I1FnJDtt5MBuH//BzWX0jn/VM5vP+wGz4dR5F57Tjbj29gRMeueDmU4mNvio2pEc+vj+ThMBc6edmQVVrDjqs5yPT1eLlfK8ryc4nJaeBKVgWzn5zEk96VLD+WRFlNAyZG+jzfpxXSm8PiAqwDuJZ4ni4fn+GTR0IZFOxEmLUBRtHZDPW2QqUW0AgCZjJ9pod4MiTk9tCTz48molBpeLtvR2ry86mraqBDsANytYTOrd2pV6q5mFBEuL8dHx6Ip0srG2b2cYL6Bj4aMYzvTsfz/elszr/WD0N9PTxtTVk6ri0fH7yBn7055r9KhnAgEy7WtGaEVPsS+trgQE4nFvH2L9eZN8ifCZ3cdGWjyqSUC23pDRTWFZJekcrTQYNw6WjMgosvYGZoQYc2PmTUCvRgEBFHt+JnY4ZUBQgwoaM7T3Tz1D5n5Aao1Bo+O5qIrZkRU9pbUnpxP+9Ed6RPgCMfH0rESF+PIBcLevvZUaVQoVIL9PKza9T+5/Z8hLVqEDKpIU8bVeNtZ8PC7u9QWq1gd0wurpbGTA9vhZG+lHdGtKadx+31puqV9VwvTuZGQTnXcmrYHZ1Ha2cLnuvjg0KlRs+pLdeCh9NVTwq+/bHw6suIqBzqCuuQWToR4m2LsfNo3RBJiUSC3bjljVKy//RUZwDKaxpYeSaV5/v46gJaw9CxlBl14PjFLGoUKnq0smOIixzktrrjn+3VioqbwylbysHcmP0v9PrNcicTitAvH054a1Miy/ey4NwCRrQaQQfHDvdVn0jUHEGtpmDxEmguSBcEkEgoWLwEs379HsiQv1WrVjFpkvbLn0GDBlFRUcHJkyfp3bt3o3KLFi1iwIABusempqbo6+s3Gn6WmZmJo6Mj/fv3x8DAAHd3dzp16sTdxMXF8dhjj7Fo0SKmTZvGsmXLGDt2LACRkZF06NCBoqIibG1t73oOV1fXJttqa2ubKfnb3nvvPbp37w7AU089xfz580lJScHb2xuARx55hOPHjzNv3jwAxowZ0+j4H374ATs7O+Li4ggKCtJtnzt3rm4+2sKFC2nTpg3JyckEBDReV8/Y2LjZ+/prmzdvRqPRsHLlSl2AtHr1aiwtLTlx4gQdOnSgoqKCYcOG0aqVdrmJwMCmSZVEv5/YMyX6x1Pc5RvqOQP8mTu6C6G9erfsRJauYN+G9dM689XE5j/4dA1woZPf7Q/pSw/Fs+5cGgDdvNzZ/9x4jlyr5Wdld2wc3Zo9B0BiQTUaILewSDth/qYTCYXkV9ZpH4xZCW0nAFAv6DMmaQA3qrSB4eqzaajVgi7rX2dvG1IU5tgGhfHqc8+AqT2VdQ0cjitg3rZYorLKEQQY/sUZymq0iTgkEgn6evq80Ls9H/X8ioRcGBLshKnMgMo6JY99d56VZ1JIKqyipkHF9aR9rL76ja6tJxOLsTUzxvTmHK8rEZeJTTTBSa8USycPpvxwiZ/Op/NEN0/C3Kz4JSqH9/feYFYfH2aE+3D2zEX2f/EJz/X2ZfWUjgCYGRtw7EYhenoSKuqUuFjJ6XBzHo6d3I5qmYRH2lnjZWeKgVSP0YoNOLuVsUHoxdcnU5Ab6tPWzZKKOhWCILDiWBI/nElFqifhud4+4Nmd07F+pEQVcTGnnPdjtMke0oqr+ehgPHUNatZP68Arg1px4Mo5PjpcTVpxNVsiU3hzlCmG+rdfEm1NjXh3ZAhPdvfi67MXGPv9Qd0+L2cHqvUtG/3OPW3l1DWo+P50KhZyA9ILSjiz8hUuX0smPl/b69XDpQffDX+TDl7WOJrL+Lr/13zc60N87I1ZeTaZi2klZCvk1BpYcTqpiIV743hxgB+y+gI0RdreqSuZ5WSW1nImqZi0zCwUSSeZ1MGJb06m8OGYEIYEO4EgUNegQZa8D9eYL0nKr+ShT09yI0/b+zU19DH0MUahVKOvB2//co2MkhqsTY14oY8vdua3k6cMCXbG0aAe4vdBaRr2JvZsGrWc/gEerLuYSa1STd8A7Ty4GT9d4VS6Id27vYqeRHsvDfX1eKSDG8FBoRzMFBix4gyJdWZgZEZ2aS0TvjvHe3viqGwm+Nkbm0dCfhVVd+wzsHCgW+cuzOzjg4WxPt+dTqHewhO6z9KVaeNiQTef2x+4jscX8mkLEs7cqaymgfHfH+Zafmaj7a5WxjzV3Y9BAW15o8sbdHfujqtp0w9wItHvURsR2aRHqhFBQJWfT21E5J9ed0JCApcuXWLCBO37kr6+PuPHj2fVqlVNynbo8NtfHowdO5a6ujq8vb2ZPn06O3bsaLYH5pZ33nmHwYMH89prr7Fr1y5mzJjBN99o35NiY2MJCAi4ZyAFcPr0aaKiohr93GvO172EhNyem+vg4IBcLtcFUre2FRYW6h4nJSUxYcIEvL29MTc3x9PTE9AGlXc7r5OTduTAnee5X9HR0SQnJ2NmZoapqSmmpqZYW1tTX19PSkoK1tbWTJkyhYEDBzJ8+HA+++wz8vLyfnd9oqbEninRP9r+2DwW7LrO/tk9sLm5NhFoe35MZQaNvjH/TU6h4BSKyz2KhHR9qNGQhtjsCmIlEiZ189Jtu5JRzohQZ+JyK3hndxw/PdWpSTrmdh5WrHisLQH7x7MmahSPPT4DQ309dsfkIQjgaG5MrUpAfnOd4QaVhvLaBpQ3J/LamRphKTfgQsr/2Hvv8Ciq9v//tb2k9947CUlISCgh9Kr0pihNQFBQwAZieSzYsWFDBESwICii9N47AUJL773X3WQ3W+b3x8YAgu3j83zK75vXdeWCnTnnzJmzM7Nzn/s+77sGsyDQO9gFjc6I5Ba3/IcHsjmaVUmsjyNNLW108bBl8eAQ7NUyzGbhNlU8P2crfnksGbAsoJdJxCQFO7HtcjkbZiXgaa/G5Yf3MIUOwGAy8/mxXN4c3xUnawVn82rYfb2CWTOXMPbUSqpSLvB++T3M7RdIqJstFwvrOJ9fxyP9gsiv1rDhTAGDI9x58UIb259bzopfLiNVW5EY4EhSsDNnlg3E0UrBu3szqCvO4I2xEeAShp3Cjtldn+XrM4XsvlpGlJcd+RFz+ehgFu/NjOClPXvIrpVQ2SDm3I1iJvoquFHWhEZnAEQ4Wsnh2DsM7RWGKDKKLgI41Rgpy6knOMCOF0dGoJJL+DJtPQ26Bp7v+zyxvlXcKG3i23nxOKssP9JNOgMvb7/B0uHhFjlzICnQDZPJwM6LhdwT602PQKcOGf7MiibqLtXg6WtHzyAnvjyZz0+XSvhpTixu1NFkK+aepCDajGbe3pvGQ70tsuVTvjjLrD4BpJc3E+MzGO/YNkJdbegyagp5eXkkN+1jmzqS53+6Rk1lCQPsK7l/SjDP32vJkYXZDPuewzzlbcyNtiyIdqR293ICExcwJtYStqlSRkODPRoHFX1CnPFqV1PsHxRORW0Ru69X4OVghbO1AoXMYvw42yqIzV8DenfoOc9yrOz9cOYziL4Pes3vuAbn9w/G016Jn5NFzGLZCMuYncmtwUHSSrj/zQmHlQezkUpETOvpS3C7rL+7nRJPezXlja1cLWmgX5grNc16LhfXM6SLOzqDCTNgMJsRzGZWfrOVSX1i0Ft7UtfSxqLBYVRrrvH5sVwWDw7FZBYsoZW/wd1OiXftaa6teZ+G/q+RHOJyR5nfopJLcHKswyzS3rb9ViMNYFjAsD9tq5NO/irG6up/a7m/w7p16zAajbcZH4IgoFAo+OSTT7CzuxllYWV19zyLt+Lj40NmZiYHDx7kwIEDzJ8/nxUrVnDs2DFksjtTH1y9epUZM2YAEBcXx/bt2xk2bBg1NTXs3buXhx566E+PGRAQgL29/W3bpNKbr7pisbjjvH7FYLi7F/vWPopEojv6LBKJbgvhGzVqFH5+fqxZswZPT0/MZjNRUVG0td2uNPzbdoE7QgH/DhqNhvj4eL799ts79rm4WJ5169evZ+HChezdu5fNmzfzwgsvcODAAXr27PlfPm4nN+n0THXyv5p+oS48MzSsw5DKrGhi2dar3LPyBDO+PE9L2+/PcgGcK85kxJZJlDTU/KXjfZv2LcvPLO/4/NrYrh0JXqubdVQ163hrYldGxXrh62TFw8kBHYbU6exqlv10taNuN19HzGM/xzthVIfH471JMfQOduLNPTfo+84RLhVawvRsVTLWzUgg2sdiHI6M8aRHoBNfnspn9XGLZ+z+RF9GdL25/mXBwCBm9ArgrQnR9A1zRZ97gt4nZyISiZj3dQqfH8vl58vFpJc18tTmVE5mW358V+zL5JuzhcxODuKjKbF4OahJL2uicewmopOfo7yhiYrc69RXFALw6ZFcLhbUcSyzijXmkZTHPYWvo4qmViMSsYiKxlbSypqQSsQ8OiCYayWNVDbrOPBkfy4dOQont2A0mXl+2zVqNXocrRR8c7aQAWGuPOpyGa58D0BWZTNyiRh7lYz8WssLbKi/H29PG4BCKqHUdIQbdSk8MTiYD01XEJuMrJoaT99QFx7pH8i6k/lo7cMQO/pT1tDKlDVnMbQYMLSayK/WsuSHqzTrDNwfdj+zo2YDsCujjjUn8vGw8kAmtvzAqbJ20EeejebkanSpWwGI9gggyi2MPSevkHbiCFYKKTE+9pbv4dvLHKlrQqGWMizSnWdHhLFxVg/kKls0Q97lnqR4AFYfy+FcXj1yqRiVXIq9lQxHKxm9g50Id7fF00HFm7vTuV7ayMELVym8sAtPOwWxvnY8OzGZEaOn3H6xikSgdia3wYD66i8MrduCnb4KpcREZVMrPd88SIkqFLqMxloh41+jItG2GTuuufwqDbFe9kjEIhYOCsFWKUO7fgKDhHN4xd8L/reEunadDLP3dxhSnx3JYc+1cnoFOuGnMlgMu5SvCLUzYqeWUV+QSuh3STSlHmXPewcw6E2smByDlULC2by6DkNfKhHz3uRYvnu4F/3CXKGlHuPpT9iVWgzA7ORA1s1IwMtBTf6NGupaRRgQcaGgjgNplWw6X0Sstx3hbjbUNusZ8O4RVh68RVa9oZVnt17Bw1ZJ1+huENCXrr8TdvtblDIJn024j2i328NhNDoDzx19lXPld1mTdQtHio5Qr6v/S8fqpJNfkbr8uaH/d8r9VYxGIxs3buS99967zatz5coVPD092bRp0x/Wl8vld1X1U6lUjBo1io8++oijR49y5swZrl27dtc2vLy8OHHiRMfnpKQktm3bxvLly8nNzeWxxx77ZyfJTePiVs9MamrqP263traWzMxMXnjhBQYNGkRERAT19f/8/v+9cb2VuLg4srOzcXV1JTg4+La/Ww3gbt26sWzZMk6fPk1UVBTffffdH7Tayd+h0zPVyf9q1AopkxJuzm47qOV08bRlQrw3OVUalFIJlU2tOKjkyGV3xo9HufkxPmgqXnZ3ro8CqKqppr40ixJZMGGnFtG/y2ASI26+tHrfsgZp7Yl8RCLLmomFg4MZEeXJkC4345hrNHqK6yyx2Xuvl3Pm0BH6hDgzJDkGqtLB1fJSZjAJZJdrifSwoav3zQedf7tU9coDmfQJcSXe34HPp3a/LZGwwWTm4b2PMztqBuFO0ey+UcGwKHfc7VQUyQK4yGCm5x1lTLcQlu9Ix89RhZONgonxPh25jl4Y2QW5xJJrqbiuhZ1Xyymo1SIGnh9pw8qrr/GovBC/8j7cULnh46BiwYAg8mtaOJ5Vg1ZnZHycN+/vz0IuFfPkUEuc97ZLJbS0GQl2seZCQSV2KjMvZ6h55YHZXKw2se+Jfh2iGSV1LUR52eIz9pWOc/vmTCHejir6h7uy9VIpOoMJkQgGvHuEL6Yn0FY5gV6RIYikUtZH3kvlqRpW3u+DWiFFKhaTWdlMbeRgrJyscDaaGBHlxpbSBt4e4odUIubYkgGIRCLyaxTYKS1jvWR4OM8MC7vtmpBpK1Bhy9kKEd3sZLT7gegf5ko3x54orWxuK//Zg93wtFd1SNfLJBLkUjHHsqrYmlLEhjm9ABgb64mPo9qS/wn4evbtM4JqhQS5RESQqzXdHhhPSf1wRmja2HG1jEndfaluF0a4WFjP7mvlPDHUl5eNGkRXGxnp6EVyhDviiHvYdL6QS+cz6RnoxKXCOk4XXudqiZ7XRiaz51o5l4vqifOL58FefkjEIhpb2nh3fyZzkwORiO2wkdug9P5NCI9IxAdHCjEYTSwZEUGAsxWe9grI3APHV8BDuyH3EPgng8qBewYOwtBlL/O2FDA1UI+sMQtnhwCW3dOFK8UN/C66Rtw16czu/SCbLxRxX4IvAOWNrZgkYqZ264N/oDs+/gI7r5aSXalBEGDrpTKOZFYxr28QWZVNvLsvk6eHhVFQ30JaeRM6o4mIyFiIjP39Y/9FNpwuwFxuwDPa8Q/LvXPhHSaFTmJW11n/+Jid/L+Duns8Und3jJWVd183JRIhdXND3T3+33rcnTt3Ul9fz+zZs297AQfLWqB169bxyCOP/G59f39/8vPzSU1NxdvbGxsbGzZt2oTJZKJHjx6o1Wq++eYbVCoVfn5+d23jmWee4Z577mHBggU8+uijGAwGjh07hlwup7q6mh07dtxVgOHvoFKp6NmzJ2+99RYBAQFUVVXxwgsv/KM2ARwcHHBycuKLL77Aw8ODoqIinn322X/c7t3GVaG4PYflgw8+yIoVKxgzZgyvvvoq3t7eFBYW8tNPP7FkyRIMBgNffPEFo0ePxtPTk8zMTLKzs5k+ffo/7l8nFjo9U5387+HCl5B/HNbfC7nH7lrE1VbJtF7+dPd35P5EX8RiEVPXnmf5zrS7lreSK5lmY4+pMuuu+1OPbsP51HJSCuvIDpyOdchwfGwC71r2iSGhLOjrx4BAW8LcLPmIDmdUsuqoZS3L6G7efDPH8oIc42OPn4MKK5kYMnbCyQ862rFWSPlyViJfPtQDmeTOW/BcQT2ncy1eJLFYRL22je3tamrfnilEUxOLo9wTF2slm+f2wt3OEroVGejL9OF9KVFH4qiWs2dRMr6OalxsFAyKcON6wykq9z6D7a75KAWLWltOlYbcag3vT44lxNUa24wtPFKvwmH852y1C6H01ALubd7Pe3uzyKnSkOjvgK+TGgSBV8ZE0T/UhZ8uWpKgnsqt4VJRAw/09EOwO8iByvVsXzyAAXEhPD0snCmrz7Dju81oGup59p4I3GyV6I0majR6xn92CidrGXP7BhHmbsO7k2JQyiQopBK+eiiRKE9bvp/bi7SyJvZcK6d3kBMiwRKq8WAPP3oEOvHupBgyKpp5bttVFFIJ90Z74iVIuZJVQ16VpiOc4v39Wey+flOd7khGNe/szbj5BfSaz7c1wVyz7UuXpNG8vz+drM0LMWXtx6S2I/mDU5zLq7F41PYuI9TdtsOQAnh7bwbP/nSVT4/kEOFpz4cHM6HkIj7fDWBstJvlBWnHIqhMQ280cSa3msXfX6ZGo6dvqCsvbLvKlgvFBLvaWPI++dizNaWE6WvPMmnVaTR6A6Gu1ohFYopqjHg5qOjTbyjGkOE899NV2gxmajR6egU44GVvhc6sxYzFyD+QVkm1po2HN1xg59UyvBzUiMUi7NUy7K3keM74EpuIwWj0RnRb5sDVLR3nJRKweMOAH1JKyK7UUiY48q1qCmvOlpE7YBWXWiwGxqbzRXx0TYKTsxvqiF5c+/kDKDiBm62SoZE3JyB25+3m81TLmgiDyczhShVMWEuTUUJDy83Qm7f3ZHC0tgnBV40gCMzecIE1x/NJKahjSs3HfDDSk2ZdG4czKon3dUDbZiC3SkOPACe2P5bccY/8ltXHctl4uuCu+36Peb08ea35BD7631f9O1N2hiD7IB6K+vOwpE46uRWRRILbc8vaP/wmZLX9s9tzy/7t4hPr1q1j8ODBdxhSYDGmUlJSuHr16l1q3iwzfPhwBgwYgIuLC5s2bcLe3p41a9aQlJREdHQ0Bw8eZMeOHTg53X1yc/jw4Rw6dIhr166RlJTEwIEDO9ZxvfLKK8ycOZPTp0//43P98ssvMRqNxMfHs3jxYl577bV/3KZYLOb777/n4sWLREVF8cQTT7BixYp/3O7dxvW3qNVqjh8/jq+vL+PHjyciIoLZs2ej0+mwtbVFrVaTkZHBhAkTCA0NZe7cuSxYsIB58+b94/51YkEkdOq6/i2ampqws7OjsbERW1vbP6/Qye9yraSBI5lVLBzULued8iU4hYBYAm6RoPxr4Tgp+bV42qvw/B2J8ufWbsPbyZZrWjueGRZOYPt6DcDi/TC3oVBYXrY06YfQHHqHlvu23lbuV/JTL3Ly+41Me2slAFeK6ymobWFM7O0rsdLKGrFSSPFzao8rN5ss5wVo9cYOaXKdwURzqwGXdm/FyexqciqbWX+6gP1P9kMhlXC9tIEPD2azelp3vj5TgAA8lHRzDVdBTTOv7cqgZ4AjZU06WvRGLhc1MD7Om0tF9TyY6EvfMFdeO/saw3UmurcZYeCLIJVjMJlpaG3DxVrJjbJGvj+RwY70Bt6fHMtr+87xQfcSms4q8Bw5BoOdlG/OF1HeoMfBSkY3H3tkEhGncmpZ+ZukyfW6ekxmE85qZ3ZcKaO7vwP1za3k//IVsfeOR7B15ZmtV7k/wYehXdxYeyKfviGOxHjbw9Y5kPwkuHflRmkjbSYz3Xwt4Y+fHM5GIoJHB4QAFvn057ddY2KcJ71DXPn+fCFHMqpZPd3iWdm9OYMUqYFirY5J3X0Y2sUdfVMlyw89Rhe3bjzQ+zlOXcpg7ZUmFgwMorufE+fza3ltZzpLh4eRFOLC9xczUBd+xMDuM5F6xLN85w3m9w/Gy1wBzWVQmQYtdTDAMgv59ek8zubXIxZZhBAGhrsR6iiDskvonBIwtbVglf09zxbGo1IoOZFTw+w+AZQ0tGKrlLH5QjFdPGz45EHLzHNZfSujPj3J4/0D2XOjkvcnx3K5uAFrhRRrhZRQdxvmf3ORab38yavSkFnZTGWTDg9bJUX1rfz4aG/L2iNBoM1gorS+lXf3Z+HnpOLlMXeXKf4ltZT8y8dYPGEAn12/ip+zkntD+2EyC4z77CTz+wdjLZdSUVPNldxypHbuxPva897BLA4+2Z+simaa9UYSAxxZczyXFp2ORUMjLY0bWkFmud8yajOobq0m2TuZHVfKeHn7DQ492Rd7q/aZ14zd4BSCxjYAncHEgBVH+fHRXrSZzBxOr6Req2N60xcE9hpPhWtvMn9YjqubJ6+UdOP5UZF09bJH26jHqDdh53rn82HtiTwOpFXwdXwu8uLTMG7Vbfurm3TUtxgIdbe5o+4fcanyEl/f+Jr3B7zfKT/8v4j/jt9vnU5Hfn4+AQEBKJXKP6/wOzTt30/lG2/eJkYhdXfH7bll2A4d+u/oaieddPIH/J17uTPMr5P/MWQSMWr5zdm1F4oTmOjmzY2yRuRVDUxKuN2YOppZSX2LkXHdbjdcugfcfZYLYP63Fxkal0CvYCdKThzmx9wzLHG5qfplCTu7OWt9rMkDfGcxwun2xbVPb0llZLQH/WPj8Qrv0rE9xseBGJ+bIhgH0ypYcyKfUDcbHtF/CUHR0H1mhyFVr9UzYuVxvp7TkxBXGz46lM2GMwWcXjoQO7Wc8/n1+DiqWD2tOwqphDajmSgveyK9rNlz4ywPxndD1n5T51Q289L2Gzzcxx+NzkivICeulzbxyZEcnh0WSt9wN+b1C7o5vj1fILuyGY29Cusjr4FTMDtFA/ghpZjvHu5JSV0rpS1ikkNdyKnRMDYqHFFAMu9ducGA0lr89NakFjeyZno8jlYKtl8uY/OFIjbeHwitDaCy55ktqajlUl4Za5GB/eFgLmfKGvGwU9Ld35EuC57ku3OFfP1zCi8M9MDrwnMM3jONZf1ciNk2CR49BUGDwNrivThXUEdpSTGf7W3j01kDKajV0qRpQ99qRKGSIhJBfq2WtScL6B3iyv2Jftyf6MeBtEpqNTpeupbPioldSVLICHSxfKeK0yuZKffALtwSLlJ0/gRGcRfWHM8nL7yF/qEufPRAN/zbr4HB3kZGpl3AuXUG104X8Pq4X5WYAsEpkMbaSnKr6/jVnBwZ402fUFcOp1dyIK2SWUmBIBWDX2+Kf95FYOaT8NRV6jKv4qcWc/Cp/oDFMJdLJHQ3yWgzmmnSGVj43SWeGhbWIdk+s4/l+zyZU4PKpCWwbB9Kz7k8MyyMsgYd9moZ/cJcKarV4uekRjBbPD4fH86mTtOGk7WMU7l1bJtvkfutadbhZK2gpraGi1vfJ37SElwcHRgd40lrl8mYpRK+OnydiT3U3BsK6SW19FQUEu8TwydHi9l6qZjNc3sS5GqDUiZheFcPGlraCHGzRtrudX24r6XPpfUtvLs/k4bSHN6/1wuHsN6EO4Wzcx+IddXc09WDSE8b7K0UlNa3sGJfJm/bnkGvM1Kod+aN3RkceqovDS1GvO1UbL9Szhvjoig47kru8aNETehNv4EjyNdZk3fFMsFyMrua9CMlBKmUDJx2pxTwoHBXyhtaEQcPBM+o2/a9vjuVqtZKpCY33psce0fd33IgrQKRIDA40oM4tzji3OL+tE4nnfwetkOHYjNokEXdr7oaqYsL6u7x/xE59E466eSf0WlMdfI/RriHLeEeN2cHo73tcLFWkFnWzJGsaqJ9HAhzv7nfYII2g5GDaZUM7uL2p+3XttZiZZ/LN+f0ZFQ0ITd7MiY0+A/reHt50Ojgcts6JQCN3oi2XexCrrQYX9XNOp79KYU3xsXjZmvZ5u2gpouHLcvuiUB+UAXF5yzGVDsrD2Xj52TVoWY2p08AfUOcsGuX9Xty6M2ku5cK61m8OZXDT/VDpCjm3Wuv41K0iB73jgcsIYBKuYSu3g58P89ifAS52iCViEgKdcVGKcNkFvjm8iGOFl1g/bhlvLLjBvcl+DAqdBionbjHwZ3eQZbQrGFR7gyLcienshl7tZzd18pRyiTM6RuIj4PKYjh62QEiFFIJBbUaxGIRZT89j7tvMNZDn0MmFZNbezP5rSallgGR9h3y5wCTu/twMrsaLUpK7bsz2MGBD0/XEzF8NSu3ZjElYTi9rC2KabOSAjh+uZW918oQieDl0VEc2JbN3vU3GDPfEgr47URvBOvbF2PnVmvYf6OCB3v4Mjr2N7LVA54jWCQCucVY8g/zpDXNxIgYF45mVSEVw6Bb1sI5O4dxIPFlCtQhSMSWBcW1X31FW0kZHi88R64iktWN9qxuL9/SZkQsEtE72IUrJU2Y253/VU06ltc4887YTbhLFQS6WDMs8uZ1HOPjwKH0SkwKEV087ZCKRIhEIgQBLhbV8vbuTF4YGYGDWsF9Cb6cTrlE5bVDvFLcjeWTEmlqNaLR3ZxsKG9s5ektV+gb5sKcPoG8tz+DgtpW3p0UA4DZLDD6k1O8d18M3e20DDCdpr4mBxwTEIlEHbL8X81KJNjVhoJaLdO+usRCHy2SlnqW3RPOkuFhLNl6Fb3BxMr7u1lyh313ifmBVZhKL+M65AkiPS2TItYKGR62SsoaPLDy6wZARnkjKYX19A9zQSIWEehi8QA16QyU1LdSOWQp7+7PRhDysFPJUEjEPPXDFSbEe7FseDhxfo58EzwNRysFztYK1l/3pryxlU8fiMPJSoGXg4rGni7063J3aeQAF2teHNXuMbO/fZJGIQNnq0KW9Rlyl5p38sXxXMQiEYNvSZLcSSf/BJFEglWP38/L1EknnfzvoDPM72/SGeb3n0NvNFHVqMfHSc2JrGoSAhw7BAt+5efLpfxr+3VOLhmIrepOadVbqdBU8GXqTzRVJuFpp6KiSccb46P/sM7f4ccrqbx7/VGe7bqOtBIRz93zx0nwNPW1HMrX0GIQMaWH7x83nrUPvW9f0qv1xPrYk1baiJuVDhulLXKlipJ9H3L/pS58MaMHwa42SMSiDkno/TcqqG7WU1DbQmmDlvxqLWFeeh5M6E7X8q0o3EKpc+1JXUsbIa42zFp/jkgPO54aHk5Ti4F+7x7l4WR/qpp0nMipZdYgE7XmGyyMW8hLv1zH0UrO/AHBTF93DpEg0MNbzgM9A3FxckSrN9LY2oanvSWkatqaM/QKcmb+wBCOZlaSUlDP08NuJiZ88edrlDXo8HNS42qjoFlnpDDrCu97H+N9xaPM7BN8x3qXd3akEedph6+3Lct33uBJ0fd06zWYV3MDGdfNExcbhcXbgJjdN8qJ93XA3U5JhIctMomY1cdy8XFUW/IxARx4CUKGdajXPbzxAkMi3Jmc4EPOpUo8g+xR292+4LfyYibaI6vxmfEEMheLQEqdRo+tSsYnh3PQ6I280K4CKQgCIpEIncHE9tQyxnbzQi4VM2fDeULdbegV6Ey9to3RsV68sOoCYcEOTBtyu9GfW9nMZ8dycbVRIBaJWDQ4lMMZlWj1RmQSMSOjPdmfVoGjWkFioCMrD2YR6mbNiEClxaAPH0F+tUWswdVOiXXePsjeR3Gft/CyVyEWTJze9AaGrvcRHx7EY99dYmKcNyNjLEaI2SwgEsGFgjo2nCog3t+BI5nVfD27B7lVzfx0uZTH2hPrljW00JB1kuIb5+k24WkMZjMr9mXy5OBQpE3FHE25wgOTLF7BvOpm3tidwXuTYjomFIwmM8/8eIX5A4IJcbXhfF4Nnx615Bh7UH6UTTWB9O7WjcK6Vp67NwLSd1pCggOSya/RoNWbiLqLYt+pDS/T5hpJTN/ROJ54FTxjIXrSH9+Hf8L10ka+PJnPe5NjaGgx0GIw4GX/55LRnfzP8H8pzK+TTjr5n6UzzK+T/5Mcyajinb0ZrJgYQ3KoCzQUg1QF1jfzuozt5kXvQMe7GlI6o44yTRmB9hYBCXdrd57rM/+Ocn+HzIomQlxt7vBUAVzNl/BU1w/o6u6LrfyPM6wv3v0ZEYfTGTh5CiE9et+2b/WxXLr7ORLv3x4uqGvCfOIDuMeXE1lSvjiWw6GMag4/1Y+GJhNBSnB28eDRbmqCXW14aft1WvQmbFVSlo/tiq7NiI1SSoibNUlBjuy7UUGL3kiL3ohSLge5mr03KjiTW8snD8Qhl0pRyiWYzQJv7cvg7fFRqBUSrpY3EeamZu2BZt6b1pcH1pxl4cBgEvwd0RvN+DlZMb6bJwkBTohMljwaVgopx7OqUUolOForeGtiDPbtL8l5VVqulNSTUpECukBOZNfwwsgu6NpMZFdrKK5t4ZH+3ryrrSAPT+RSKeJb1ptcK2nAw07JklEWIyW7qhlPOxXuSc+AmzsBDcXYKmXsvFLO5gvFfPVQIon+jnjbq3hg7VmGRrrz5vjoDsOtskmHUibBbsgrt30fH0+JQ94eonZ6ay5R/TyJHuDDTysuMWBqOC6+NrjEBmOfZ0DWLuyw+lguhzOquD/Rl4WDQvh1hmr1sVy2pBQzId6L+f1DmBDvzcnsapJDXHi0fwhqmYSvzxZQ2tDK6Fgvhnf3ZHVqMRMKmig59xYXu45lSpcHCHKz4YXRfqQUVvHB3kqSQpwYHmUxBpvbmjEKBsoadZjMAoIgkFJYj0kQGKHKgnOfU+neB3c7NQqpmClrzjI+0I6BPiNoM5kt13ZTFb0j/CEmDL3RhLutEldby/e24VQ+Fwrq+OTBeBKL1pHYfwCby6QdY+Rto+KhSC9UcgmZFU0EulhT6BTHsIcs6zp+vFRMRYMOa4WUcq2OA5U2TDaZkUrEBLrYsHZGQsfYH0qvpKBGS2OLgZwL+wlxqMAr4iHuifLgvkRf9D9vIGJgT5zC/QHLBMzxc5eJDQ/GJQDyqjR8daaQdTMSbkvADOAiaeBIaQ2Pv3OU/WOS8fQMJr9aw6qjuYhFcH8PX2JvCdntYNujED0ZggZwIb+OA+mVHRMnrjYKEgMcEYlErD2Rz45rpXz/cC887W+fABAEgV3XyhgY7tbh8fuVd/ZmMDHe+65rNDvppJNOOvnfT6cx1cl/nPSyRiI8b58pXnM8lwBn69vC9YZ2cUcqFnUs9q7c9RrVEhei7n+NOo0ee7UcsViE6y2eitL6FlYfz+OFe7uw4dI+dhV/x/YJm/92H7dcKOZoRgWfTbv5YvftuQLe3pPJdw/37JjprmrW8fy2a8zuE8CrY28u3g9yvfkidCG/jhqNnqGR7h3eoh6e8RiGJxAUH8euq2Uk+Dt2SGRrdAb2X8wkvvgU9F5EjVHBqKpnWGVyJ60sl2A3G2o0bWRVaHhtdzq/LOiNddx9PNh+vEf7BfHz5VK0BhMFNVqe+ekap5YMwLk9N5e2zYSnvYpob3uyq8cR4mbLA14Ck2NdAfh82k2J3QAnK1ztlHxyOAeduR4/BxWfTe1OhLsd8/tXE+Fpx9qT+RTXt/DWhHYvX/E59u78gZ5TX8XeRs2uq2Vsv1LK0uERVDbr2J9WyUNJAcxKDsTVM41DRTcY4xOKvUpGVaOeRZsvMybWk/03KsisbKbaZI/T8Kd50vb2maBnf7qGs5WMZfd0IdzDlhBXG96eGMPua+XsyC5gbt8gfrxQTHp5E0uGheFpr8TLwXKt/PhIbxp1FnW4X42Qp39IxddRzcJBoTy5JZUxMZ70C3O9zRt6/wuJyFWWx2SPMQHYu6tJK67CyUqK26SP2Xe9gubCYvqFuhDhaUOkux3lja14tYuheNkr6epty+X23E7V9fW47X+EOtUbbLnQxqTu3rwxPhqT2WJ+OXvbIL4mIr/Nip9Fw7DTW+qlFtbzXsoniCVajOYRGIyW8k2tBl449Spx7pHMSpoJLfXs3LMbXwdfnhwSRnZuHTJdI5/sTCUuxGIs1Gj0aJQ+PH5BguZkKjsf74PQXE1NXiUu8aCQSm5+t0CEhy2bzhez62oZ9yrtQapkVKwnvYMtaxX3ni7mxxvlvPZgDDPWn2PxoDA+O5LDjsf7YFd5jgFnl2PffwOO1goco2JZHxULQGGNBhdbJWq5lLSyRjzsVDTpjFQ16/F1VBPs6wPWrng5qLkv0ZfqJh2/OC1mVmgAbUYzYz45yQv3hlMeOhVvPweczAIOVnJsFXf/WQsd8xzBB15kmnMWqm7vU99i4Gp2NRq9kfwaLff3uBmkcSalDI3ZzJBEb+gyBlzCKKzV8um5n7GWOmI2h1ueRbZK7k+0eJkjPG2I9om4w5D69R787lwxXTzsKKjVopRKOpL/yqXi2yYNOumkk046+b9FpzHVyX+UsgYtD647z7sToxkYcdNwcrNV4mB1u3epsK6FzReKSQq2rH+pS15OfYtlndJ9X5xlYLgry34TSieTiHGykiMWwdiwobRpAqnT6HG0vj0s61bqtHqOpldR0tjKwkGhaPRGNp4twM3mZp28Kg0R7rZ8+kBchyH13v5MnK0VVDfp+SGlhJ6Bznx0MIuD6ZVsnZ/UIXOe9e1KtMG9GH0kh3u6erBgQDDOcj9WZ+cxfbCEA2mVuNkqO4ypjIpmdK16kBWBYMLZWsEH98XQ1dOeVdO6YzCZOenrQKCzmiAXK0QiEfVaPVeK6wlqTsHXw5OFg28udj/yVH/yqrUs/P4yA8Nd2X6ljEWDQvnuXAHLd2aw/4m+iEou4HpgAcLCS8gVCg6mVXC+oI7H+odgNJvp4mFLnJ8vCpkUsyAw6L2jfPZgHHYqGaNjPWmqKSOzookwd1tMbjFskhnJOl/CwkGhfDQlji9P5WOtkPL9+UrO5NV1qA+ODBqJIz1pNZiwVkp54ZfrPNjDlx4BjoS52fBzain3dvXAxVZJaX0L350v4onBoUglYgaEunCxqJ79NyoI97Alr0pDlUaPm62CXx2HOpMJmURMF0/bDhW141nVGM1mlFIxU744Q/8w13ZhDlHHS2xFYysl9S1MX3eOtyZ0xdNaCmWX2FHtRd9QF1xslPhFWl5+NYdWYCfRYJryKTKpiNxqDXEeCvoq8tlwzZVzeXUM6eKGq62CkTFejIy5uRbH3c4G95BwcHCkV5ABLwcVJ7OqWXsyn/cnxxDuYcuGWT3QHXyDh/QFeMRaVmK9uTcdD7thPDEghJYeYvydLMb7J4dzKCruz+t9+1oO0FJDc3055W0ulGal06RT86XnRF7sG8fx7DpcbORsezQJa6WUeF97JGIJpfUtvLdLT5eMHky714BCLcNoMrN4cyoT47zoH+7GY4OCcVDLIXouAGpA7Sjlu3OF7MmvZEySD806I8O7eJBZ0dyR00vrGImu19NUNOrYcaWUUb+ORVM5z311golJUYzr1YWVh7IZHunOuDhvuEVgpubCjygdKrEWm8nNLGbT+Rb6BDkS7mlPdz8HDqVVkVJcz0+XSnhyaDh9Q12I87OszzMc/xCZXyL4tXuCbVy53uZFnaCmv0hEekUTO6+UsWJSDOfza4n1ubmur/xKLTq5CBK9IWw4KRUp2MsNdPP2IquygTaTGaVYgqZNQ62uFj9bP0ZG331dFljSIXz3sCVtwuncWtRyCQaTGZlEzOLBob9br5NOOumkk//9dBpTnfxnqS7m3QGO9AlxwWQWePSbFNxtlZQ26Fg3M+G2oo5WcqK8bEkprCU5xJUIX9eOfS/cG0GY253yxK62Sha1v4w42yi5XqrneHYNY3+j+Pcr+9MqeHdvJiFu1oyPswgTHEirxMdeRTcfO3ZfLcfDXsmsry7w0f2xJIfe7EPvICccrORM7+WHueQiFJ9neFQYarkUncHEm7vTeXxgCL1GjsE5IAiHrCZ8ndTkVGlwtJLxwr0RHM+q5vVxXbFSSCmu09KsMzK3bxAu1jJw6ddxrJ6BN0MbZRIxA8JdWfjLBgZEB2OlkPLJ4WxO59ayxPo0PvJo8I6zSMu7hLO9wI2cimb6h7oS4W5DTpUtHx7M4s1xUaye1g1/ZytarBLJNXxMVHvyv8zKJsrqW5my9iyLB4eQV6NFLBYR422HSiJicIQbPu3eFg/q8PiuJ0+4refZ+wfhZqtiVnIIbQVn2Hy4keG9YunqZcdz265yOqeOXYv68Og3KbjZqnh5dCTXSxuxVcqY1N2H/uEujProJE8OCWVAuMUQeXyA5fs0mEzsuFLO0Ag3YlKW8bR3Agyf3TEuG88WYDIJLB/XlcYWS5jhA4l+vLM3g5L6FtLLGpGIxdS1GGhsbePU9UKCxVq8HCwJI18bG4W03QrbOKsHACq5FEcrBVTfQPhpLle81xLmboOLjbJj7VPixKUc/+46u3ZlsqOoBkcrOeX2NQRef4UHZu1nfJw3z/xwFUEwd0wMdKBvgopr0FrP2G6WZMGPfXsJuUzc4cUEUPZdjKfZ0JFX5u2JMbjbKjELAk/vvsKYWA+GRXkilYC7lRs2cos8+j3R7kx5cA5TgO3r1mJU2fFAwn28szcTvVGgi4cNUV72kLaD+nPneaIomZNLBzKklw8DpjojyCwTAhKxiMLaFi4VNdA/3O02Q6G4TsvXZwtZNiICHwc1Ud72DIlwJ72iCSdrBY8PssjWpxY3sGzrdV4eFYezzIDVLeFt5WY7XhjiS1ikZQw+ezD+tvP/lbaL35GnsyHRSUdsyWGivGaw4XQBU3sHsGREOL+klnImv443x0fh52RNTmUTvk7WyKVizqfnYN0oJ8bvZljtd5IxSKQikkxmegc50zvIcp8N/Y1oxPjZFq/z8axqPj+Ww0iXz7EOvZdFfSZT3axj0feXePHeSFLqDrK/YD+fDf7sjr7fSkNLG6lFDfQPd2VqTz+W/JDKVyfzeLhf0E0Ds5NOOumkk/+TdBpTnfxHKU67jp1MjlwaT5vRTEubiWBXG9zt7lzMZ6eSkVbezI3SZpJDXO/Yr5JLOJZZRb+wO/eB5QXw8YHBXP55JU3qAWws9eChpICOnE4AQc7WPJDow7AoDzzaw3HGxnoyLNKNw+mVPPfzNZ4aEsKnD3SjV5Dzbe3f+llSdhFMbYT2TiTU3Q6N3ohSKqa8sYV5++pZP1PEpASLMMHHh7JJLW7A0UpGdqWGV8ZEEeNjz74blRQW5PGS80GkXScC3Smq1fLR4WxeG9sVpUzCaztvoJCKmds3CHtJMCos5/7k0DAebNLhaZcEYhFPbk7FxezDMicT03v50awzkFJQT2KgMzKpGIlYzP4blTTrTfQLdUMsVRCVMBCw5Lb68WIZ03v5oTOaESFgNpsJdbMhtbiRYBcrlt0TgSAIfHAgi4nxXrjNO417xiWKtBm42XbD27UZ4chmGtW9aGmLos1oJsDZiiERbnjZq+gV6ISrrZINpwuQScR087WnuE6LALjaKugb6kJTq4HDT/fv8Cj5Vx7izbBqwjzavS4qWzAZwNCCoLDlUlE9fo5W5FZpGPXJCU4uHYhMIuZ0Xg0hrta8vS+TBH8HPn0wnsYWA/VVVYz0UVMgEbHg20t8+mAcFwvriPG2Z+7GFB7tH8zYWC/e2pOOk9RIgdOHDIr0I9zdlu2nitiVUsKqx3shtnYmblJPRJVNFJkNjI31wttRDT0PgNmIRGJg1dQ42kxmS7/PrwWJDOJngJUTGLRw7jMYZclVtm5GAnZqmeW8K9N45edU+iT1xUat5KfLV3hrQgx+jmqMNTWUoqK8SUerwUS9to1ob3us26/vh/sG4mGnxGAyIxWL+N4cRXcXB0bUbCexbCWfB68mwsOOSatPM89fIDkulr3jk7FWShnR1YPrpQ3M3pDCx/d3IzHQiR2P9+m43vXGNubue5QlPZ4kvVDBtkulPNjDjeRQF9ztlMikInoGOhHn68Cqo7lMSfQhzM0aK4WUGm0b90Z7UlhUSH1DIw72dmxJKSG3Ws5HMRK+PVdInF0LVuc+xHf8q2DjBuk7OJ5ZSVHMezzY058PDmYTF9mLFYHOzFh/jsPpldwobUJnNPPFtHh8nKxYeyKPX1JLSQ52pqxRxyOjXsPL0YqaZj06g4nLxfW0tmoZZDjKhWwnzhVreWJIKIIgcKO0iS6ettRq9CACl/YQ2Rgfe+b39ST2cCZC98cxGE1UN+vR6k089t0lFo12xkFxl3VWQLPOwNS155jXLxAXGyVfns7HVimli5cdZ/LqCXNVk3CLymUnnXTSSSf/N+k0pjr5j9JjzMSO/8ulYr6Z0/MPy789PhrZbxaO12r0LPj2EgsHBbP3RiVJwc6U1Lfy7dlClt0TgVgsollnQCoWE+VlT5duzuSe3UKdbCwGox8o4GBaJdUaHVMS/W5b3wTwxfE8nKzkTOzuQ2WTHnc7Fb1v8SgYTGbKGlpvJuAF6HF75vCvzxSgaTMS7m7H6Bgv3tmXxRftiWN7BjrR1NpGm8nMZ1PjOpTuRsd48G1ZJqKabEueJiwCDtG2OvJ2rsBr8OMEuVix7XIZhXXXmNQ9BF2bkc+O5DCvXxDevyYpPrWSyQopJd6jwd8btcgyHutPFRDjY8cnh3K5J9qDifHemAVY9tNV6lvbePGeSBZvvsw7E7ry1vgodl8rJ8rLlvSKZorrWylvaGVElDuHjxZSKFOi8laz6mguvQId8QkKwbX0II0118GjG88cf4b5QxfhZuxCXrWG5FAXgt2sWfx9Kv0jXJne25J09XppI4IA608XUNmoY8307iwbEYG9Ws7g947xfIKIKaOGWcbdOZIkeRHIJOhGfYreYKZm7ycEaS4jum8jW+b1JrWwnkuFdfy8IMniUQK2P5YMQJyfA/7OVmRXNrN061W+nt0DK4UURW4WSz2vsGKvis0pJXw2tRsFtS1UNrYiEjmglEtxNjdwslXG6zvT8J0aR5dAe3QGY4cQibWDEi+DESdrBeEetvR84zBrZ8RzoeEn8hry+GDQWyiklrVXO0qVtBrFTP51adqAFyxGFZbkzp8fy2P5mCjs1DKw8yIpqJhAd3vSy5ooa9ABoDl5kqq33iZo1062zU9izoYLZFdpeGpIONmVzdwoaaCbrwNP/5BKvdbAupkJPNjDl16BTqiMagpaRLjaeCARi+gb7EyB3I3BMUHI8q+ia1WgdA9jz7UKurjb0tXbnub29WU2Skso7oJvryBmNgF2AdgGtRLtV0JWVSt+TvDWnnRqNG28MS4Kf2drcqubaWkzcr7qJKumx+Ni5URTi4G3fzpFtJcNj0waxehYT+ZtvECtVk+L3sjxjHJW5Y9ln0mNG6CxC+Wwzp7ctCqqNQZKG1qY5FqGPDWPr2fNpFaj5/2KSzzcLwIfJyvKGloYG+vJwHBXRCK4XNRAuKc9AN+czSG9rImegY4s6uNK4NkrfJqWgUwQgFA2nilg84US3hgXxeaUInIqtTw+KJi+oa7YqWT0CfOjyvMojmo5D311gbKGVl4dE0lJvY5rldtJVIfBNxNh7KdgfTOU2VohpZufA7o2E4HOVizoH8wLv1zn4yndGBLhzIBw9zuUKjvppJNOOvm/h/jPi3TSyX8do8lM2ZdTqc44/Yflahu1/Hz4NOWNuts8SWAJ/+sR4EicnyM/zU9CKhEjk4hQ31LurT0ZrD6eC4BY38w1276kVIkwmC3eAYVUjFJ297mDcHfrDgNrdnIgQ27JMXS6IJPPjmQwZ0NKx7YqbQ1v7r1GZnkj6WVNAJzPq8HZSoZYLOLxQSG8Pj4KvdFESV0Lb+9NJ9DFGr3RzDt7M7lW2gBASb2OTTlSTFN+ADtvKDiBk7WCrp42PJYZyyu7ssiu0rJqSgxiTQVpWVmklTVT2tBCVbOOrReLQVsHLuH07J7IxCh7Wt/ryt7Dh9l8vpi3JkSz42oFOpOJCfHeSCVi5FIxIW42jI/1wtVOwcN9A/niRD5v7E6nrEFHQ4uBTeeKeW1MFMOj3FHJJdgjRlOvx8lGydFn+tMz4y0ov8pD2DE44xAAG0dsZHBgP07lVrP1YgkXC+pRSiWsejCOfdcrOJNbzb0fncDZSk53fwcGh7mhM5gwmgUi29ekrR3rwVj5hY5xfnRvA1saQtDqjfR5+zDHsir5tDYe45DXAEvC5W/OF7LnegWhbrfLHD+x+TJncmspa2jl/QOZ9AhwxEohZUdqKUcvpCIrOc2wKA82P9yTbj6OzOnjT4yvPSKRiJL6FnaVW/PhlATGxHrw0vY0vjpXyOT+FpXIV3fc4J296YiArRdLuVzUQM9gR364WIKTuS/m2mHkVTV39KXUoQfZ6hgWfHcJQRAgeCB4WHI9vX8gi+ZWA+Jfn8RKO+yDEihv0HFPtCcbZ1vCD+fckLBnzsto9UZSi+p5ZmgYBTUttJnMrDyUxYoDmQAM7eLG0PbcVfdGe/Lu/kzev9DCispYdAaB1cdyuVzUyJxkSyLdZ3cX88OpGwCczKllbv9AVHIJC7+7zPxvLnacw8hoTx7tG4Zapqa8ooWGZhmOKosxv3xsFF72SqwVUlrbjOy9XonBJLAtawfL9x2ntc3IulN5hAcHMmWExRsa4GzN/icH4GSlYEbvALZkQ4KfA9VaEwCbcmU0YM3Xs3uQ4G9PRnkzTioB2lqQSsRkpF/jjYLJCJoqtl4sYtZXKXxztpC392YAIpJDnHl26xWaW/Tc27KdAHUrWy6WEBAQhG7Sd3yTYSbCypIPrVeQM31DnYj1dWB6rwAcrWRcKa6nsEbLUz9cQWcwMu/rFDZfKOK5EREsGhhCvJ8jv6SWUloWxk+X/BC6jAGlPQCHM6pIL2tCJBLx8qhIRGIxj3xzkUsF9SwZFsbj36dyrayZzReK+fRwNtVNurs+lzrppJN/HwUFBYhEIlJTU/+nu/If4ejRo4hEIhoaGv5ynf79+7N48eL/WJ/+/z7mt9JpTHXyjxEEgX03ytEZTHfsE4lEFNj3wmzz+4uzAdYdTeen63UUVVQDsHTrFZ7YfLmjjXUPJd6W+NXLQc0TQ0IRi0VsPF3AsEh3HuptETlAZc+43lG8Mal7R7hOcqhLRzJTqrOh6CwAh9OrWH08j26+DrS2mXj0m4sU1GgBaG0zMXd9LhcKanl9XBTpl0/DgZd5/dxyqoUTpBTW89ruNErrW1hp/Q2P2pwBLC/5Gp2JXm8cxmA2cU9XDyZ19+GtCTEkBDjiZGWRnI7zc+DwU/35JbWUBTsrIPcoACEhoSQGe/Jov0AWDQ7Fyc6Kj+JreXRABE8MDSOnWsPpnGoOZ1Rh/mkep+vTKbXzBKUN5f3e41yTA6dya8ksbyIlr5bn7A8hS1nLDynFvLbzBiq5BFHzAU4XXEKrNzIkwo0gV2v8kTBcqmbFxBhs1TJe3ZlOSX0LD0yJJLKPJ3M2XOByUT2XtM6sSamlOmg8TPgSQRBQSiwv1k2yw/h7all1LIcLBXXUtRg4m1dHlKc9702OYcb6Czz23SXUCglfTO+OUiZhxrpzvLDtGj3jYlANe6HjO5aIwWxow0ohZcnQcFYeyqFLgDdSx5s5up4dHnGbGuGv12Odtg21XMzUdedo1hkY0dWdDw5k8enRHFrduuFw/+fYqaS8dyCThpY2zIhYvjMdgG7edkzu7sMTW66QXtFMoIsVs5P8AZje3p6ztZwAV2u+n9uT3sHOuNso6RPkxAMJ4RRWyVh3qoAvT+Uzb2MKj/QPIdrLhq6eNohEIs5k13AovRKAifHevDmxKzZKGW/vSSezopnNKUXsulpGZkUz+dVaMsqbmBDvxYAeIbz4yzUWfn+ZupY2nhoaxpm8KiI8bNHoLPfe0EgP7kvwJeWXh7lw7Vsc1XKc1DLenRjD1F7+RHjYIJeK2HC6AIA3pg9i0ugxAPy8oDe92tfqLR0exrMjboq9jO3mRWKAE7TUE3ZqMYsimolvvx/rW/QczqymWW/E2UbJ2WUD8XOy4uGwV6ir82TZT9fYd72CqKAA7Kxvz8F0OL0SsQh2P9GPNQ/1tKznAmb08uetqBKozqK7vxOLh4Si8u8BUeMpqWtB6hxA05QdpNbIeXd/Nv5OKh7o4ceAMFcc1XLkEgne9mpkIhPi8kskekrZ+mgSIpEIZdVVViY2kTx4NAChbjY8O8Iit+9hq8QsgEoqwUohpYu7DTKJhO5+DvQIdCbC05bR3byQScS4WMu5NzwSkUTFD6b+ILV4Ro9nVXE4o7LjHE0mE2bBzIGMCr49V0iivz2P9g1EIobU4nqulDbSSSf/G5g5cyai9mTht/4NHz78f7pr/xgfHx/Ky8uJior6L7dhNptZunQpnp6eqFQqoqOj+eWXX/5S3WPHjjFw4EAcHR1Rq9WEhIQwY8YM2tra/sv9uZXevXtTXl6Ond2dOfb+q5hMJt566y3Cw8NRqVQ4OjrSo0cP1q5d+5fq/zvG/P8KnWF+nfxjtHoja47nE+xqQ9BvcqVIxCJ6j1/wu3WL67RoLmxiUY8kzCNGoJJLuJBdRrSbConc8nJSVF6Fk70dVioFjS1tqBXSDuU8gMrmVsLdbSxhUgA9H6GqqRWf9jArQRB4cssV5vQJsHhBis9CTTb49sTTQckjfS2z9FdLGuju72BRLcOyRmvfE8l42dtwKL2CH660sCLEkZfiFmIlt0IhUXC9tIn6FgNew54FpR259YXIzc5sTy3h1dFdCHC2IaCPDVtTSnC3U/JgDz+qCvLIyaokOLEXP14soUVvxNrWAQa9CFiMuNSSBjRt3rjrTLz37XUm3DsGb6UNS7akEuFuw9dni7BXyfjS9wUqTL/Qsy4dLxsvAhOG8VKCJcmqWCxCLhMTJB0AaiUJSgfsVFJO38jl/uxlbOr+Krl6NVGedtgq5Dzcw4fK/CZyNHo+P2ZZ9/LB9nTOuJbxwMgQnhwayuH0KtbVJGOtkNB8toAnh4bz+HcXaTWYWTcjgXvDuxFoH8icHi60tpnYe6OC9yfHWuSfEdEv1BlXWwWrjuYyOtaTSd19eGJIKJ72SlYfyyXUzZoB4W48tfkyvT3k1H35Em1207CzisHDTkGYm7rje39zdxobzhTy7PBwQlytWbr1Gt8+3AM/Jys2tAtKWClk2KhkrDyYhbutmjfHdyXa2wGJWMT5vHoqmvRYK2Tcl+DD6GhPmloMDI/ywGA2c6nIBqVMQryvAyeyawlwseGhpADifB06rjVvR0t/wj1t6dJuCHw/rxdikYi918vxc7LsX3uyCFdbBZ8fzWH7lTLujfZkUIQbw6M8yKpsRoIOa6UMmVhEjHMrGZo6LhTa0aI3Uqc10C/UGYPJzIxe/vQMsKxNWnf2CnszrzI1NplegU5UN+n45EgOTw0Lw1+ixqRyZly8N+62SlRyy73gaqNkUrw3J3NquJBfS9GFndSJnZl8z1DqW9p4cft1zCYz70yKwas9HHXRpssoZRLenhgNZiNWrgH0TbgpHhPmZseL90YQ7m7xDlq3hwbG+TnyQKIPl4oaeG9ybIcH8lcqGlt5e28m18sauS/BF1cbMdo2E9YKKSUb38dffAysXFBO+pLBEW6Q8hVk7eFS5Pucz6sj29WBnOomnhgcQkFtCwW1GgprtTTp2hCLRCQFO6NUqXGdsZH0Az/TmneWT7LtGaQ9RKLmKAUNw/HP2QhyG4ibCoC91IBeU49Z5IizjYLZyRZv5PP3RgJgMgtMX3eOpSPCKW/UselCKS+O6sLV4gYKajT4O1tTo9Hz8+VSegQ60d3fkZxqLY/2DaZHkBNNrQasFFLGrzrNq6Mj6RPyG4GSTjq5BbNZoDy7AW2THitbBR4h9nfNefjvZPjw4axfv/62bQrF76vj/l9BIpHg7u7+5wX/gG+++YYPPviAjRs30rNnT3Jycv5SvbS0NIYPH87jjz/ORx99hEqlIjs7m61bt2Iy3TkJ/V9BLpf/4/P7La+88gqrV6/mk08+oXv37jQ1NZGSkkJ9ff1fqv/vGPP/K3R6pjr5x1grZfz4aO87DKnfklOl4btzBby+K40TWVUAlDXoEFVeR9FSxceHszmVXc2ZH79HfHkv9ydaVNeErQ9TfGQNAIs3p/LCtmv8eLEIgKsV2ezNuoZr03WMeSd5dccNTuVUc98XZxnz6SnA4tlKDHDA+Vfp87hpkDiX5pW9+eFgCtfbQ/W+PVeIl52q40V51dEcSuoss0Z2KjllRmtIWoijyhGFxNLWmxOiLdLpdt60iCU8sPt+Nl4+xaXCBmQyMW1GMxq9kQ1nCjiebfG6NVZVcvbiNQ6kVaLRG+kV7MxTIzxo1FtmqF1slexd3Jdufo6YNCWcadHy8p50dl0rZ1ZSALXNbXjZqXC2lmNlY88Lvf+FrrEL60/mUd2so6iuhaNZVfyYUsyQLu7Y2DuBRI6NUsbjm1K5PzmaSYrVxEZN4qXRkUzo7s3LYyI5ca4Ug4ucMTGefD4tnnuiPVkc549XrQkTLUR4WBPtbcecPgG4WisIcLbmsW8vYqWQMDXRlyadgZwiTxyVrryzN5NlW6/xY0oJrW0WefuTOdXkVGt5bGAon0+LZ0i7VH7fUFeCXW1xs1V2JPg1CSCztmPC/DnIT69gcLAtScEuhLnbd1xPQS7WvD2+K2O7eRHr48DMJH+8fpPjZ0ikOz0DnTCZRTS0thHlZc/e6+WU1rew9VIxTw8N5WhmFZcLGyhrbKHnWwdZcyKP1cfyEGHJ46SQSShvaqVe20ZyiDPWSimVTToe+/Yi10stPyoPdvfG395y3ShlEuTfjcOQf4bRMRaP7LPDw3lmqOUF/NH+wSwYEEx6WRONLQae3JzKu/uzWDAgmHf3Z7IuRU+D+DD5VVq8HVRcKannTF4N6eVNbE8t47tzRaw6ksPFvDZeHRXPTxerCHZVk1OtIaWwjla9EeeRK3ELHsa/fr7OkYxKZq4/zwNfnOGTozkU1LWQUdFMenkT1lZqUutlNOsMOFjJGRzuiq1KznM/XOHDA1kAJIc6Y6eWcjavFqxdGJ03hjOVAtdqrrHxxkakEjFquRSN3vI9rzyUxc59+wC4lFaDh1hKpJcdFU2tHM2sguYqjmxeyZHrxQyLdCOrUsP21DLWnshj7kZLmKdNUDgm9zh03j3JrbaE4xH7AIxZxegYL14b15U6rZ6i+haGR3ogk4gxGgVO59by8i83+PJUHisPWfpvMJmxqr2KUHENncHMKftR1E3ayrCVx2nJPwenPuy4Xi6eO8oI0RlGBMg7tgnn17Lju89IK2tEozcyNNINPycrlo+Nom+wMxcL6rlS0kBhXQttRjMf3R/HroV9ifezCFM8OyICJ2sFBqMZLwc19mo5vyxIusOQMpjM5FQ239XD/58gtbieXVfK/luO1cnfJ/dyFRufO83PH1zmwLo0fv7gMhufO03u5ar/6HEVCgXu7u63/Tk43BRZaWhoYN68ebi5uaFUKomKimLnzp0d+7du3UpkZCQKhQJ/f3/ee++929r39/fnjTfeYNasWdjY2ODr68sXX3xxW5lr164xcOBAVCoVTk5OzJ07F41G07F/5syZjB07ljfeeAM3Nzfs7e159dVXMRqNPPPMMzg6OuLt7X2bUXi3kLMbN24wcuRIbG1tsbGxITk5mdzc3N8dG7FYjIuLC/fffz/+/v4MHjyYwYMH/+mY7t+/H3d3d9555x2ioqIICgpi+PDhrFmzBpXq5m/Wn42dXq9n6dKl+Pj4oFAoCA4OZt26dcCdYX61tbVMmTIFLy8v1Go1Xbt2ZdOmTX/a11vZvn078+fPZ9KkSQQEBBATE8Ps2bN5+umnO8qYzWbeeecdgoODUSgU+Pr68vrrrwN3H/Pr168zYsQIrK2tcXNzY9q0adTU1HTs79+/PwsXLmTJkiU4Ojri7u7Oyy+/fFu//uwaPHnyJMnJyahUKnx8fFi4cCFarbZj/2effUZISAhKpRI3NzcmTpzIP6XTM9XJfxuNLQbKG/RcKW7Ax0FNcij0CHSCwA/BqCdt3wm8TVXMemQmEqkUQRB468gOauUzmeQTQcaJbSzon8ynR/K4XNjA8ChP3j+9hUHxKvz1DZgabbFVuhDoYs36GQkItxx7SrthBpaXllcPVTMyaD5hLW4cKWsAYG7fQAKcbxqENioZyvYZ/e7+jnx0fzde/OUaD/Xy50x+HeO6efHTpVKqmnQEulgxtps3awZsxtfOHfvBlpex5TvTyKvWsOWRXuiNJj45lM3s5EQyFL54nXkJs0sy3g4T+Ojy+/jZ+jEneg45VRre259h8YL8PIEfx3xEmWMcjlYKdl4tQywRMznOm6zKZsRiEQaTmYLqZk7n1VFY20JKUT3xvg5UNuuY2N0HsvaTU1HLdtsH+Wpmd35JLeXVKX3p4mFLcZ2Wt/Zm8s6EaHT1epqb2hCLRby8/QZh7jbM6R8I/X1ZcHABg/0GMy58HIIgMKKrB6L2XD0X8uuY0VPFhfw6vj5TRFKwMwsGBJFTpUEpk2CnlmMyC8zqE8jEeIvCoZVCyvBVx1g2IoJ+Ya4IgnCbnP2H93dr/58/xA1AAjzaPxiAaWvPsXBQCJMTLOF++9MqCHa2Zk5yINVNOp796SpdPe1ADMnBrsT7O/DOxGh0BhO1Gj3rTxWwZHgoIpGI9afy0RkFtHojZjNsm59EoIs1BTUaTGZ4afsNnh0RwZoTeeRVa9l9rQwrhZRpPf0oqmvleEY1UV4OfLdjF6dLDHz62ARLt3s/zr7DInQljUR529Mr2JljmdU8NSyMHamlvLU7nczKZsbFefH+5BgcreTcKGtk0eAQBEEgUB3EgSwtRpNAtJc9CwaEoJBK+P5CIcX1LdRq9fQNcSPK2Q+p9DzFdS0czqxh2/w+yKVizhRdJdItiC+md0ctl5BXo0UkwPyBIZRoijCoCrAV+zK010SGiUQY25UHp/XyQ2Z3nrqvDhMR8hAAQ7q4U6dpQ9/+kj8w3IXz+XUMiBEwC2YMJjN7b1RwJLOKl0dFYmus5d6LsyHhJGIRuLSHtW44ms6Vyjb6Tw1HqnZAJpPx5NAAjCYzErGIH1JKOgwQl34jgZF8sDeD01uu8POCJGoLrpFf3UT3XgMAGBzhxpHMaq6VNvDEEIuc/nNiEfvSK7lS3EiAs5oNpwqYnOCDpPfj7KjW8PLom8+BU0sGoLYeDrqbYXZhPYah8onDx+emVLrI2g1HFwNO0lau5DdxMK2SNpPApcI6Rsd4cDCjmjfGdeXLkwXsuVbBWxOisW9XZ6xu1tGiN7JifybTe/oxvKul3V+FPW5l77VyPjqcw5REH2b1Cbxj/7+bC/n1HC7fwra0UJ7oM4BIn7urEnby30/u5Sr2rr5+x3Ztg569q68zfF4UQd3urmr7n8RsNjNixAiam5v55ptvCAoKIi0tDYnE8jt58eJFJk+ezMsvv8x9993H6dOnmT9/Pk5OTsycObOjnffee4/ly5fz3HPP8eOPP/Loo4/Sr18/wsLC0Gq1DBs2jF69enHhwgWqqqqYM2cOjz32GF999VVHG4cPH8bb25vjx49z6tQpZs+ezenTp+nbty/nzp1j8+bNzJs3jyFDhuDt7X3HuZSWltK3b1/69+/P4cOHsbW15dSpUxiNxt89/0GDBtHY2MiLL77I8uXL//K4ubu7U15ezvHjx+n7a07A3/BXxm769OmcOXOGjz76iJiYGPLz828zRG5Fp9MRHx/P0qVLsbW1ZdeuXUybNo2goCASExP/cr8PHz7M/PnzcXG5uxd92bJlrFmzhg8++IA+ffpQXl5ORkbGXcs2NDQwcOBA5syZwwcffEBraytLly5l8uTJHD58uKPchg0bePLJJzl37hxnzpxh5syZJCUlMWTIkD+9BnNzcxk+fDivvfYaX375JdXV1Tz22GM89thjrF+/npSUFBYuXMjXX39N7969qaur48SJE39pPP4QoZO/RWNjowAIjY2N/9Nd+f8F+dWajv9r0/YLa559Urh89JAgCIJQqa0UBm4eInx9/rLw/o4UYd26VYKg13aUbzOahJlfnhNWHcm2bMg6IAjV7f/XawWhvlAQDDrhUmGd0GY0ddQzmczC6mM5QmVjq5Bf3Sx8dChTmLcxRZjw2SnhcHrlXftpMpmF/isOCy/8fFXIrmwS5nx1XvjkcLaw/lSe8Pi3F4WHvjwn1Gn1giAIwq6PVghFN64KhWnnhSXfnBDm/bheWJ26Tqhu0glLfkgVjmZUCpWNrcLlA98JIz84JBzOqBAaW5uEb87mCg0tbUKdRi98d65A2HapRNh89JIgmM3CsYwq4UR2lVBW3yL8mFIkCIIgXC9pEBZuuiRUNrYIk1adFKZ8flpYvOmiUFqvFYpLS4WjmTfPJb28QbhSXC+sP5knTF97Vli+47rw5ck8oUHbJqw/mScYbhmf1KI6Yca6s8J9q08L4z87KVwtqRfKNGVCXk2tsOZYjvDOnnTh2R+vCG/sShMEQRDmf5MiHEiruGPMFnyTIjy84byQUd4o9H7zoFCn0QkH0yqED/ZnCA+uOSOsPJAp6A0mYeeVUqH3GweFjw9mCk2tbXcd/5I6rfDliVwhp7JZOJZZJTS0tAlms1kQBEF4bWeacLD9+F+dyhce+OK0sGJPmtD3nUPC1eJ6IbWoXtDqDUKdRi9UNbUKxzOrhFe2Xxe+OZMvLN9xXdAZjEJNs064VlIvmM1mobKxVQh9freQXlovPL3lsnA0o1w4kmEZy8IajVBar72jf3XVFUJOVvpt23QGo/D6zjRhy4UioaxBK9z70XHhTE6VMPz9Y8LTW1IFveHmmJ/KqRIGvnuk4/O+n74UDv+4Wjhwo0JYdzJPuJBfK4z++ISw7niOsPpojvDTxSLhZHaVYNjznFC+602hqrFVOJlVJQx+74iw6nC2MGLLBOHzQ2sFo04j7L5a1nGs7Mpm4b1je4QZ254XBr57RMivbhYEQRDe+v6g8Nb6LYLOqBMWHV4kXMo4JRgNBkEQBOH5rVeFKV+c7ujb9ZIG4UJ+TUebm84VCG/sShM+O5wtVDS2CIs3XRbq6+sEoTJN+H7rD8LV4npBEASh+LMJwtULxzrauZBXK+RVNQtms1nYmVoq7EwtEZp1BuGZHy4Lh26UCZvOFgjppbXC+l/2Cc06g7B3/x7hkdX7bhvjd/dlCCeyqgRBEIQxn5wUdlwpFT49nC1cyK8VzuRWC2/vThN6v3lI+Pp0nvDVqXxLpaosQajKFMobW4Qt5wuFH1OK73rN3cEPs4QbXy0SVmw+KLy567owY91ZYcXedGHOV+eF1jajUNXYKuRXa4RLhXVCwmv7Bb3BJDz+3SXh9Z1pwuLvL/1p821Gk5BW1iBodJZxf39/ppCSX/fX+vZfZEPKMWHIBweE9w9k/Fva07Y2/Vva+U/x3/H73draKqSlpQmtra3/pfomk1lYv/Sk8Mm8Q7/799WzJwWTyfxv7rkgzJgxQ5BIJIKVldVtf6+//rogCIKwb98+QSwWC5mZmXet/8ADDwhDhgy5bdszzzwjdOnSpeOzn5+fMHXq1I7PZrNZcHV1FVatWiUIgiB88cUXgoODg6DR3Hw32LVrlyAWi4WKioqOfvr5+Qkm081naFhYmJCcnNzx2Wg0ClZWVsKmTZsEQRCE/Px8ARAuX74sCIIgLFu2TAgICBDa2u7+m/NbtFqtEBkZKTz88MNCjx49hKeeeqrjN0gQBMHGxkb44Ycf7lrXaDQKM2fOFADB3d1dGDt2rPDxxx/fdh3+2dhlZmYKgHDgwIG7HuPIkSMCINTX1//uOdx7773CU0891fG5X79+wqJFi363/I0bN4SIiAhBLBYLXbt2FebNmyfs3r27Y39TU5OgUCiENWvW3LX+b8d8+fLlwtChQ28rU1xcLAAd11S/fv2EPn363FYmISFBWLp0qSAIf34Nzp49W5g7d+5t206cOCGIxWKhtbVV2Lp1q2Brays0Nf35s+rv3MudnqlO/hlF58A7gZtyZDepatJRUNtCYsDdc6k0tLQxYuVxfnksiVA3W7TeyRzzliCT+BELuKpdOThpX0feIWgXGhAEMBmQSeXM7O3PwbQKjM01GC5tQtV1NFUST+RXvse+eCe6NhPzKxbwch8lpxsceWVMFGKxiLl9g2hqNTDwvWM8kOiDr5OKlVNiO+SsvzyZx7n8ela3ixuIxSK+mNYdf2crZBIxvYKdaWo10MXdkQc8ysm5cprkt+s5/exAQnom4eDhRXP+OTylAsgccFXJcLZR8PbEGBZ/fxmN3kjXbqNR5VyhokGHNMCJAzdquF7azJJhEbd40izemu/OF9HU2sZbE2NYsS+TUDcbLhTU88roSPbdqGDVg/E06wx8ejQPec0NHLdM4PvYnTS3Gon3d+DBtefZsaAPKYX19Atz4afLpSwOcMJOLWNcN2/e2J3OosGh2KlkuNkpCfEyYJYX0cdjMIHO1qjldjyz+wppZY28OjqKs/m1XC5qAODTBy1jdDq3hvxqDUV1rQgCjIn1IqWwHq3ewAf3xaLRmXj/QBbPDg+jsV3BTi4Vkxziwv0JGpQyKT3fOESMjz0bZiWSXt7IrqvlLBvoTesvS8gQT6arlz19Q12o0ejo8/YJEgLsmdDNh25+Dnx9toApiT482MOXOm0bXg5qunrbM23dWeb1DeJsXi3NOiMzk/yJ87NHKhZzIK2S3m8d5r2J0Wy7VEJOdQvv3xfDxlmJtJlh741KpCIRgkhE/zBXfJ2sOJFdTWWTnm6+N2fyW6U2aFWW0M9N5wqxt5IzIsqDoV3ccLSWYzJBd18H4v2dmNsvgA8PZVPZpEMpFfPYd5d4dUwUL4f58PyOb1k8YBTVbv24VtbEw85WDO7iRlWTDrFIRLCbLZGettipZEglYrJbRrP+XDldz14jyVPOI/2COF9Qz5r+n+C+fQ67JF3ZkmUm2tuOxlYDWr0Jo9aPKaExNHgZcbO1hJhM7e4JzaCQKPhwwIe33aceajMVTTfv70AXa9aezOPny2Vo20z0DXFicIQbCe33+Qf3x1oKVpZxn1MeRk9rzIIZ75lr8VbZd7Sz81oZ/k5W2KhkvH8wi0gPa2IbDlCrieDVXRn0DHTkHudKpt6YS0PyJYYNGc4wYPfhI7TqTUwYMZinhoZ1tPf8PRFEeNqilkmo1epxsVHy4cFs7u/uzeQEP67duM6ejW+zujYOeysZLg4tlDa0UtGoZ2ikm8VbVJYKtTnQ1RL28fPlUq4U11PZpGdMxDO4GcuJMDvy7bUmnGzE1GnbkEvFKGUSlA3ZuLiE421Ssf6hRORSMcvHRKKQSVDKJHd9Bt6KzKAhIv1TUDtCwQlcfJejVvx5vb9KQY2Gkzk1TO3p37Ftenxfprc/Vo8c2IF9wR6Cp628q+fsz8guPc/MI4+xd9jX2LiE/XmFTu5KeXYD2gb9H5bR1Ospz27AK+zf700cMGAAq1atum2bo6Pl3k5NTcXb25vQ0NC71k1PT2fMmDG3bUtKSuLDDz/EZDJ1eA+io6M79otEItzd3amqqupoIyYmBisrq9vaMJvNZGZm4uZmCQ+PjIxEfMt7h5ub221CBxKJBCcnp452f0tqairJycnIZH/tWv/qq69oaGjg008/RaPR0L9/fx566CHWrl1LSUkJGo2GpKSku9aVSCSsX7+e1157jcOHD3Pu3DneeOMN3n77bc6fP4+Hh8efjl1qaioSiYR+/fr9pf6aTCbeeOMNtmzZQmlpKW1tbej1etRq9Z9XbqdLly5cv36dixcvcurUKY4fP86oUaOYOXMma9euJT09Hb1ez6BBg/5Se1euXOHIkSNYW9+5JCQ3N7fjurr1+gDw8PDo+B7/7Bq8cuUKV69e5dtvv+3YJgiW3Jn5+fkMGTIEPz8/AgMDGT58OMOHD2fcuHF/a1zuRqcx1cl/HW0NbJ4Ks/aB051hKWdza1l3Mp8lI8JICr7TRWyvlnNiyQCcbZSUV1bi7urKu9N6Y6O8eVmuO5nH8Ch3jOJq/O38Sf/mGZ6uHMyPgXtRjfuAYFdrajQO3Ei7gUNtHc7B9/LulquYakJ5b+46lA1FHGuqQnz6TXRxNxVoTGYBW5WMnxf0JqjpIqKT71PVsgVXW8vD3stBhXe9RbJYEAREIhEhbjYd9XVtJvqFutBmMHG6yYl+gf783Ls3tioZtgm9ALB2GMFYPy0jVh7nqf6W0CTatMwyfI9IPR9Hayn5NRqcjeWo5X68NzmGF3++zlu703lyWBjfni3k/gRvPB2s+HxafIeoxMmlA8mp0lBQo6VG08ahjCrGdrPktqrXtvHCWREenh9TXK0jLlCCu62S18dE8dmRLAaFuVjyCGmaCWw+D4zGYDaTV60hu6IZbycVKpmUIZF2HMiRMiDclWadgQ2n8tHoDAwKd6ObnyNKuYQZvQN46Mtz+Dtb8dLoKL46VYCztRy9wYxZgDajmZM5NSQFO+Nmo6DNaGLXQksOqOTQmyEqtioZjw+2JE/tHuDApcJ6qpt12CjkllxaYgmB3l680SsOiZU9AE5WCu7r7s2JrCrKG1vYvbOc4voWxsR4UVrfypen8nlnouWB3NhiZPe1Ch7s4cvb+zI4llWFv5M1PQKcCHa1oam1jSc2p5IY4MjoWA9cbBTc/8U5nhgSzKP9AmlpM/P0sJsvh2uP52GrlvGxrwMldVqcrJWcyKohtaTBkkRXKeVA5Secr/VkepepBLSvJTQD+66XMy7OhwHhbtir5TS2GLBXy1DKxFh5SriSt53157ywEnuRUaklp6qZIFdrvjyZj61KSlKwM9O/PEv/UBce7hvMnjoRCtdgNt2oJEQmMLp3GBPaQymZvZP9316kWtNKRZOOt/dk8HDfQPqHu/LVqQI+S6iCZgM4BZLZ5ojHjS/xcHFG7BXD9tRSbFUy+oe5MrvpU7Y1u1CricXJWsGi7y+T6O9AaUMrcqmIak0bGr0JEdA9wBGu/gAhg8EtEtwieeXkv/C1CeDhmIdYeyIXkbyMouZSZKZgxl55Evvwzzn0VH9oKidl937UskjGxbohIGFDgYqm0E2oU6oRUcPiIaEohDbMtIfi5ByGXU/ArH0kBFgWOh/LrObFX64xpIs762YkcC6vDq3eSJijhACrKvRREZRlZWJXa+SduT0R3bqgX1MFdfkdH4NdrSlraCG/RkutQcWXV5REe8sIDSghQ7+ND+/5Fo3OSH5GKgFbR8Dia2y6qsG3XXjETi3nbF4tOdUapva4GWL4K4W1Wnwc1BZRAbMJWqrBIQDsvC1Gz10mqf6rHEyrZNf18tuMqVtxdfemviGUf/1ynReDQG1rhfJ3Xlh+5e096fQOciY51IUgj3jWxD+LtVPwv63P/y+ibfpjQ+rvlvu7WFlZERx89+/w1vU9/4TfGjAikQhzexqTf9LG32n3757L1atXiYyMRCaT4eDgwIEDB0hOTmbcuHGEhIQwfPhwPDw8/rANLy8vpk2bxrRp01i+fDmhoaF8/vnnvPLKK396/L/b3xUrVrBy5Uo+/PBDunbtipWVFYsXL/7b6oFisZiEhAQSEhJYvHgx33zzDdOmTeP555//233SaDSMGjWKt99++459t47dH32Pf3ZMjUbDvHnzWLhw4R37fH19kcvlXLp0iaNHj7J//37+9a9/8fLLL3PhwgXs7e3/1vncSqcx1cl/HStneOIGSOV33T0yxpMjWdU0thp+twlnGyVGfStjPr/AJyPdSYzv3rHPbBY4llXNtYp8TuqW8mb3TST2mMAzTbao/B7l82O52Cql9Ax0YsTKFhYOfIeMI8sZmzyYePsEUCvZV1DHhqzPmd37M3p7OVHTrKdW28bUtef4fGoc8f6OHCz1Iqb7YqauPcfDfQOY1N2XYZEeDIv04ERWNTWnN7BfF8nbMwZzLKuKc3l1vDauKwBzN17ASiGj/30T+fXn5/OjOdipZEwJ1OPV0si+xX1xzf8Z7HzAvStOYg1n68+zsfQ8JwcGozBp4PgJnOQ2vD5uFlXNOowmM7o2I4M/OMGmh3sS42NRcfroUDY9/BzpEexE31BnSutb+GJadwwmMwPDnfF2tEKrN+JoFY5CKsLH0QpBEFh9LIdWg5mu3g4Eu9kwxrkMc8q3GBNG8vy2a1Rr2lAodKzYU0iouy1utg4cTnFlcS8jP14s5kxeHTN6+/PYd5d5bFAIT26+Qu9gZ+yt5GRWNJNR3tSRpFijN7LxVD5KuZg3xnUlxseeez86gUIq5qf5Sfx0qYQvT+WzfUGfDmWqszk1XCxqYEC4CzuvluMgh3GJgQS4WGYnxYNf7LguzubV8snhHN4a35UIT1uGdHEns+IGPQOdqC0vYn+ekZyqZgpO/4B11CgWDQ7BYDQhk4iQiEQ0aNqQuog4kV3NhtMFfPlQAvdGezC+mxfBbrbsulrGpjk9OJZdjZutkgnxPgiCwKhPTjIq2oM1MxL49f171oYUegc588ywMDR6IzqDiVExXkjz+rDm6lfkNQwgwMUOvdHE2dwaerR7b+yLDoFnN9RWrlwrbSK3SsPAOH+mGleg1RvxcVSzYECIxfN7+kfGx8+kzWhZWxTnKkWv0/PqnuP8XLOYZ8I3MH9gEunbN3FqzTuEd5WzQSViScISVkyKpahOQ7C9hG8HtiALtRgciQFOsGepxcvrFMgrO9J40VlFiFSBGIuXMcjVmv5hrijHrWSUAUQSCU9uSWVevwC87dWcy6/DQSUj2FVNYa0OkyBASz3sex401ZhzDvCj/0tItYO5UmKGGLCSS7GxduRoRjNd3ayQ9pxDheDAFztusGR4OOYe82g8foazxRKkZgdyq7VsnerPgSIxlRodbUYze+vceWxA+93mEQPRU8DKMlmTW6Xhq1P5xPvYotFZXho2nS9E22ZgVEwk+LzHvSYzeRffo5B7yK5oxtlWgaN1uzhN6FDLXztRXnZEedkxpUcbA989xtrpcaRXaHC0TsCp3pmVh7Jp1rVxKL2O40+kgdqBA2m5mAUBmURM7yBnBEFAMFtWcH5wIJPewU6klTUzJtaL8atO88XUeIvMvNoBRn5AeWMrh/S9mfpvNKQA5vQNonewE1q98Y5cfgCRXbuxDzfqLhST+uMBSs0KDKMndKzfajOakd+SUP3JbXsIU1WRWm7P+gtSlg3py4m2alYfe5LpEdPp5hpneWYdzCIxwIGeQZ3KhX8FK9u/ppz3V8v9O4mOjqakpISsrKy7egYiIiI4derUbdtOnTpFaGhoh1fqz4iIiOCrr75Cq9V2eKdOnTqFWCwmLOzf5/GMjo5mw4YNGAyGv+Sd8vLyYtu2bTQ3N2NjY4OrqysHDx4kOTmZnTt3cvHixT9t41YcHBzw8PDoEEb4s7Hr2rUrZrOZY8eO/SXRi1OnTjFmzBimTrUolZrNZrKysujSpcvf6udv+bW+VqslJCQElUrFoUOHmDNnzp/WjYuLY+vWrfj7+yOV/tfMjz+7BuPi4khLS/vdCQEAqVTaIR7y0ksvYW9vz+HDhxk/fvx/qU/QaUx18k/5HUMKLKFxH9wXe9u2wxlV+DupCbxF+U+qUPHzjFA8fII6tuXXaHh9VzpDu7hzNr+OwNa3+HBfBe9MjKY/F8ChP7E+TWy5UERJXQvLRoRxvfYKYrmSYAdflCoZedXNrNhTTL+uI/BxcOTzY3nIxCKWDA9n0eBgor3tEASB76824DKwGw/0aCDm10XYvyyA4KHEBt1DQVMPmusUqOQSuvk4dOSuAotS16+y01dLGvj0SA7Fda2MivGAgpPsLJCwqbqFhTYZuPhLCfVPwmvQowxp0xJtE4vCIcTSUOlFkMgxCwLjPzvNtgW9eW5kJPEBjkR720HeUdDWsOm8A642cnrgxHfnipCIRfQLc+WJ71PpFezEsCjLi/uv4YobTuexOaWU9yd1parZQJSXZb9/9xEYuw1DKhHj66Ciu589T56cTbLjXGYmjSatrIndi5IxCQJn8+p4/p4IZFIRDmoZ2ZXNvOa4i0v6/tSZ3ekT4oyj1c3r4FpJA3vTKiltaGXpPRGsP5XPwDAXhrUnQ471sWNgmAtn82tpbTOhkot5eXsaCBDjZsPwQGve2pvB0ChPrNSWsV51NBu9wcziIWEoJGKs5GI87JR4O6qp0+rZnFLCrocjqdn8GPKQp3ikux3W51/hvDKWe+NDWbTpMgPCBdY/lAhtWpAqOZlXh5eDColIxMmcWiI87ZBLJfx4sYSHkvxwtlKw9kQe3g4quvs7EepiTUGN9rYXyrcndMXX0Yr8mjoyK3ehb52BUmbLiMAR9PUawk+5m9iRm8tgn3uQSUXUt7ZBayP8/CjcswJZ9CSSgp14fU8GfcNcGdvNiwfWnKVngBMZFU3IKwqY4mnJhfQrC2rf5JIQzBdM4AHfT9lyvpGcyjyeHDkOjr9PW2UebR5x/GvvTlxVPjzePxZKLyE5+jrYesCOhTRN3kp94r/wc7KCK5v5ZYgSReRLSNuv5ermNtRyHYIg0GyUYquW0WY008VNjXXRcazdRtLYaiQuKos9ld/i0TaHjPJmegT6w8KLIJJgkNlwIc+IVGqHtsXi5Z3S7p1xkLsS5GrFoE9K6BGYj71aToO2jYWbLvP+lK4U1DQR6ODB5/tS0G2Zy/fSF5mVFIRULCLM3eamMWDlBAOeZf+NCipK8zmQp8PPzZmHevvx8NeXyChvZFKCN2uO5dPd15HX9qSzbHgYP3nNZWaSP8t3ptE3xKU9L5mEmUkBtz2vlu9II9LTlrHdvFj/UALRXnaEutuy/mQBA8K7gQjqtToqG3XkNEu5dKOIjbN7sPd6OQHOlhfBXkHO9Aqy5O+SisVkV2jYdL6IxABH9i1Kxrn9eWI0mdl0oYguHrZcLWns8IgD1GvbKG1o6cjD9Vcob2hh/reXGKe2IbaPF98fPotOkHJP31gGd7lTrri8sZV/7UgjwdeBjGGTSSmoZ4RSwpQvzuBirSCrqpmvZ/VAozcS4GKNq2sJ913bQKY6kVTxYNrMjeTU52Aymthw5RfebfuIWNsR7LzkSn1LG+HudlgrpUglnSLCf4RHiD1W9oo/DPWzdrDIpP8n0Ov1VFRU3LZNKpXi7OxMv3796Nu3LxMmTOD9998nODiYjIyMjlxUTz31FAkJCSxfvpz77ruPM2fO8Mknn/DZZ5/95eM/+OCDvPTSS8yYMYOXX36Z6upqHn/8caZNm9YR4vfv4LHHHuPjjz/m/vvvZ9myZdjZ2XH27FkSExPvarTNnj2blStXMnr0aF5//XWcnJw4ceIEGo0GtVrNunXriIuLu+uxVq9eTWpqKuPGjSMoKAidTsfGjRu5ceMGH3/8McCfjp2/vz8zZsxg1qxZHQIUhYWFVFVVMXny5DuOGRISwo8//sjp06dxcHDg/fffp7Ky8m8ZUxMnTiQpKYnevXvj7u5Ofn4+y5YtIzQ0lPDwcKRSKUuXLmXJkiXI5XKSkpKorq7mxo0bzJ49+472FixYwJo1a5gyZUqHWl9OTg7ff/89a9eu/UsG959dg0uXLqVnz5489thjzJkzBysrK9LS0jhw4ACffPIJO3fuJC8vj759++Lg4MDu3bsxm83/2FDvNKY6+UvoDCZW7MtkXr9AXG8xJm5l97VSzuXV88oYS9xya5sJbZsRZ2sF+hYtLRotp7JrMJgcOoypjBsZ5GdmMmL87bHCDmo5nvZKxCJ4Zmgov6SWoDcKRDkK8NVLcN/X9AwMQiUT8+PFEgrztYxLtqFC64OfrR/7b1Sw6mgu66bF02I0Eepmx9NDrRGJLEber6Eu2sYGVgzzwsHd4aYhBVxxHMGbx6zpWpDFkQwzA8Mta6W8HdX8nFpKrUbPvdGeBLpYk1ulwWQWcLKS42wlZ0RXd8bGegPBONlVsaj0AvICLYVOyYQCZO7GViwj1e1BCssrKazT4mztzphYL5yAfuEupJc2EuRiQ69AZ8sLlV7DpewiZid1s7wAA4O7uCGTWBTDFg0OwdFKTq1Wz5D3j/PN7ES6eNphp5LhbqPAx9GacA8pL/58jWadkfFx3qhlEroHOPL8SEsOnboDz6MUO2EyCzz74xX8nKzoH+aCn5OaDw5m8dKoSHYuTOaDA1kME0zYW8lIcHBkWi/LWBpNZjQ6I72CnNkyrydNrUZ2Xy3ncHoV8/oFEuVjT32lFlGljieHhrPtUgm1ukrig2R8eH8Me44VU322ihnTo4jwtOkwpACadEb0Boubv5ufA6un38xz5GilYOujvfB1s2FH5MvY2NgzOMGP16s/ZLqfZc3ZyindOsq3bZlFk+8QkpJnk12poVbbxrZHe/PUD1ewVUrxtlfx9Zki5FIRbrYKFnx3me8e7sH77QqD5/NqadQZGNLFnTAPJe+c3EB6dji2zseRFXlB1HgOpFmuvydG+2M0yNhxpYyJ3XwYGOrC4YJm8vzfYUrwaDIK61gyPJy8ag0mQaCp1YC9SkbPQEdyajRkKsMxB0SwbdUL2CTNZXC0L8r4B0iozqLXwEQOpleSVVbM00PD0BobcB71KmfTK2nLrEJum4GztZivzxTQIzCE0LlHwNAKyU+zM6ORI5nVrJneHTJ3Yy9VQswouLAOIsfz+bR4RMDe6xV8ejSHQGdrPprSjQfCJRi/fZ0q325seaQXda1h1OoS0WqcUcstPyeNZiWFtVqiE6axIsHy7GjW3fROZ1U2c6WkkcRAJ+6N9uREVjWfPBiHu72Kk0sHIpWIuZSfjZVEz/wRCbhbr0O1tRR7KznbLhUR46kmp6qZSZ+fZnKCN4/2DyGjogkHqRXDvZu5d2AY9mo5k7t74+mgJtzDlm9O5dGkNxDva4+9lYJl5jVwtI2V93+EVCziclEDEWefgZwH4PLX0G0qBA+irzyTM6mtfFQXj41KxvbLJWjbzAiCGVuVB5VNehzUSgrrWvn0SDYNrUYmJ/gyPOqWcJ/L30KbBnrM4/FBlsmTqe33zK3ojGZOZtcwINSVdyZGYzSZWXk4C5FZIMjNhl1Xy/hiegJtRjMms9AxifN72KnlRHna4au2Qi9As1mGplVHjI/9Xcu72Sjp7mtPzxBHEMQ8OiCEeF8Hdl2rIMjVikndvVlzIp/qZh0fTIhA3nCWYxH38kvVSUa5DcDZRgYiCLUNJKWiBA+1D0GOvizo60m4lx2LNqcyMtqSwLyT30csFpF8X8hd1fx+pc/kkP9Yvqm9e/feEa4WFhbWodC2detWnn76aaZMmYJWqyU4OJi33noLsHgFtmzZwr/+9S+WL1+Oh4cHr7766m1Kfn+GWq1m3759LFq0iISEBNRqdceL878TJycnDh8+zDPPPEO/fv2QSCTExsb+7ronT09Pzp8/z9KlSxk/fjxNTU10796djRs3olarGTJkCEFBQTz55JN31E1MTOTkyZM88sgjlJWVYW1tTWRkJD///HPHGqi/MnarVq3iueeeY/78+dTW1uLr68tzzz131/6+8MIL5OXlMWzYMNRqNXPnzmXs2LE0Nv71JOHDhg1j06ZNvPnmmzQ2NuLu7s7AgQN5+eWXOzxLL774IlKplH/961+UlZXh4eHBI4888rtjeOrUKZYuXcrQoUPR6/X4+fkxfPjw29a//Rl/dA1GR0dz7Ngxnn/+eZKTkxEEgaCgIO677z4A7O3t+emnn3j55ZfR6XSEhISwadMmIiMj//Lx74ZIEAThz4t18itNTU3Y2dnR2NiIra3t/3R3/tvQGUysPJjFrD4Bt3lmbmXj6XzO5NWyaqol3GvdiTyulTXS2GLAQWgmpdLMivviLHLo7fz4ww4qiwpZ8NRjtLQZadGbcLZR8OTmy4Alj1Pi64cYFe3Oa+NuX5QopG2nvqWNFFUfNp4p5Js5Pdh3vQJvBxVdPG1pbjXy8NcXaNaZ2L3IslbncmEd7+7PYvW0eKyVMio3L0ForMB97saOdlvbTNz/xRkGh7siFsPZPIsEcpRRRkVuI4ZuDrjaKDiaVUWb0cQvl8v4aIolZHD5zjS6eNgwId6HJT9eQas30i/UmRe3p3HwiX68suMGUxJ9GRThxqJNl2k1GIn1sSfB34mEAEdMZoGktw4xKtqD4rpWjudUc3LpQNRyKS/8fI3aZh06I2ya2xOAvGoND29M4V8jI0gtbuSxgSFsPJXP+tMFvDYuip6Bzuy8WoZKJqF3kBOv70qjd7Azddo2rBRS7kvw5bMjOVwtaeDzaTdDLB/5+gJVzW08PiCIM/l1uNoocLNVEexqjb+TFZvOFWKnljMh3iI5u+poNtdLm6hq1jM4wg1XGwU/Xizh4wfiKK1rIchVQtkPD2IOXERVqS+2vVwIcbVhV8E2MusyWdD1GazlEox6M0prGYIgsPJgNlZyKaO7eeJme/s1d7Wknjd3Z/LWhK4om4xUFjQRkuTJ+fxa+oXdXTJ41dEchke6c/36Fa7XS1kyNpFlP13lnq4erD+ZR0ubmRWTomlo0TNzfQrLx0ahkktIDnHhnb0ZDI/0ICHAkTd2pXGlpIHN83rTamxl9aVv8BAN5L5IR1DagVhMa5uJvJpmIj3t+enQCfam1RAa2oWx3TyZ9/VFbJVSesRdIDffn3kBdtiUnWZqeg/2LE7mUHoVTa0GXKwVnM+vpaGlDQ9xAz1iuzI08qZHIbsumxUpK/h44Mfk19Xw4L4xvNtrIwOCuzBz/XmCXKxZODCEt3ZcpqJZz+QewYxo/gkEE6Zej1PdrKOx1UBYe7JdDDr4+REY8AI4B1PdrMNKLiWroolWo7nDu2I2C4hEsOd6GXpJJi3NATyQ6N/hRdl3o5yNpwt5d3IMHnaW+PZLhXUU1rUyrpsXRzIqeXd/FtN7+hHlaUtdi4HkUEsI2Ju70/B3smJKDz82nSsktaSRtydEc7Gg3rJ+ad8HeLRkIJ6wmlVHcgh2tWZCmBKaSsEz9q7fO7pmeD8CHtoDHl3hwEugsIW6PLDzhAHP01zbSn3OeVyDQ1E2ZIBrJNi4oj+7hm3lzoR068vFogYuFdZT0tDKU0NCqGxqY+fVcj6e0o2fLhWz81oF03v50at+B3VF6XSZ+ZHl+Ok7Qaq0rCO7G8Y28k5v46lrnmy4LwibhjREwYMprW9h9oYUnhgcwrAoD/QGE2/sTiezQkObtoYfR1shDh5w9zZ/B6PJ3OEZamwx8OaedJYMC+sIc6xobOVU+SEOFu/CQeHCa8kv88GBbKxkEtaezGd4lBtKmZg+XhK2Zi0m3vceLpTZYC+Kpk79LW21w5lmc5FVpguMsY3ApcmTb+sTifO1pX+4G05WCrwc/tlC73/Cf8fvt06nIz8/n4CAAJTKu/9W/hVyL1dxYnP2bR4qawcFfSaH/I/IonfSyf9r/J17udMz1clfQimTsHRExB+Wmd47gOm9b4bJPNjTjxa9kbUn8yioFjEzye6OWVFHTRkR3S3tvr0ng4JaLRtm9aBfmAtGk8AXx/MId7dmYpwPTToDz2y5wkuju+Bpr6a0tolNVxoJ6NnGkuEWF+3ZvBrC3G3wcRGxJWcLr44ZS5tRys+XSzmXX8e0Hr5YK6VIfl2r4/0AQeEG3IHPj+US7+tAQoAjvzzWp6OPCwZa/m2ua8XBzQr3QDtKalv4IaWUwREu/Px4H1ysLTfaiyNvutCn9/LDyVqBh52KeH9HPO1VyCVijO0Zz1dO6cauK6XsS6tEJZeQEOCI2Wzmy5ndMZkFPjqUw+hoT7ZdLmNCkJmBDT+R7jOFxwaH0qBtY/WJXCI9rBgZosLf0YpD6dX8eLGIaq2eJ4aE8s6+TBYNMnMgrRIbpZRzeXVcKKzH21FNr0BnmjIbOZRaho+DqiNR6KJNlwhysaZOa+D5e8IxNVVTUFnP4sE9+PxoHiqZhAgPW2YlB7IlpZgXf77O8rFRKGUSrOUSJg0IoqxBx8gYT/qEOnMiu5q1J/J4a3w42HriFeyDf2IQoz85wYsjuzAxdCJz1x5h9LETHH/Ajobr36Pr/xxWcntSi+sxmgUivWxws/3/2Hvr+KjObf//PS7JJBN3dyVCEoK7UxwKtFip0pa6C6XuQo0qNUoppRR3dwiBuLvrzGSSTEZ/fwwnlNtz7um533vuPff88n695vXK7P3smWdn7/3Ms5611mfJqe2q5WTdSRbHLEYlk+DlJEMmEWKx2tjf2MGJUwb25zeTEep2g3ra5gvVXKjqpLfPxNhoL3bXyRAK4K39RbwxbxCnSlvxVisob+nGV63EXSXn3FPjOVzYTG6djvEx3oTrwNjWAyGuPDAhkgaNPWxNIVbwgG8KePrAibfoUXmzsvUILw17iad/beKBcZHMNu9jWpQcxt6EAAFKqYj7x0aiEeo5c0nJ5yYZHw0exOuxiZyraGdPbiMpgS54Oco5UdHAiCgRjoogJsZ5o23Ko+DUK2RO+RBvR2/mRMxhQ9ZPHKm8yqtpXzM0MIJHP/6ZW1Ij2Fykp1Hby/NepzhuFtCo86PGORml2Ia7UMD50naKzjXyyN2piERCkMhh/kYAPnnrA/bJknlwQhQhknaSXOSAO7uvNuDrYjeqvzhZiZdfLkaNglOl7Wh6TPx4RyaT4nw4cjaLzsIT+AyZBMCGE+VYrTA72Y8x0V4Md9fTs3kpJeO+YkTMdfEaVwcpbx8oIdHfmUUZQWSE6Clq1JEa7ML5ijbSxy9H0NcFcsn18ejAM6Cth/nXC3SC3XBYf6SEeF9nTkZtZ51PAnd/n8UCtwTGJCeBzUK3oZc+fR/ZB6o5Wy1BodXy8ES7gXKp8TJbG5PAKkVTrSG/XsvICHfWHy1HJBTirJCQFOCMi4OUJUOCcZRLmZbgS1fDKHqk186pvRIOPgtTrxffPFnSgkouISnQBZPFirZTw7E6C0qpiMPHDjNGv5vT3THUaQzse8Bekya3TkOrrg9XBylzU32J6m1FeGED/IPG1O9D7Cw2Kz19Zp7ZdQx/nyb8JaNYOjSYK8e9kEujKWi5TGVbF0FuSqw2G09NjcHPRc7e/FpePtrGhsXfE+LlTnK9hvcO5RIsWUquw5vstYwh02kqpU0G9vT6o5XtY3zM/fxwoRYPRxm9RgvTBvmQ6K/+h/r+/zfCkj0JGeRhV/fT9eHgZA/t+2d5pAYYYID/OgPG1AD/NOQSEVcPVHOpupOhEW4UNPf+QSJ47Io7+/+O8HIkwtMe/ueskNLVa+JEcTMZIS4MClTT2WOktqOHJq0BW1cn9a7DifLL50BRI91GK9uz66nt7EUqEdFnVlKpq2RhtAyVVIWrg9TusfJz5uMlqVS3dxPq4cjpql5+NcBSaTOOMjFSiZDsmk6SA1144Mds5qb69SvPqVwVqFztK+3+bkpOPzG2v+9fnaxgd24jv9wzjJ+zyrEBEf46vijYxbOZzyIRCnliWw7v35yM5Hc5N9MG+XG+spMrtRp0vSamvn+CcE9H1oyPRCQSMjXBl+1X6rnVsZxByjZ+qtdytVZDiLsDjZ29JLTu4dauQ3x75V3WzYrn6V9zKWrqYsXwEEZGeXClppNJsV7MSvGny2DCYDZzpUbDhGgvyvI7QODM5OGBnC5vQ9NtxMtZjp+LgiVDgkjwdyH39GkwKrFYbTw0MZKGzh5OFLeQ5q8kpX03EfEz0PeZOVveTk1HL6/PT+K9g8W8ua+Ix6fEMDPJj6q2buQSBd0pr/FDXiuN2nx23DuiP/dorn8X3sOioTOHyvZCmqoPMyN6PhtXZvT/n6xWG6/saMDNTQAxEOLheL2wr9wKJVL25TXx2twECho0rN50hZdnxTEm2gt9n5msyg5enp1AlLeKGYN8KWjQUtjQRVV7NxcqO4n2ciLBz5m6zh7u/DaLm0LcWTUxgmmJvgD0dBmpaNIzDFBKxYR7Xs/5s+1cw/OqtQQ6j2DVID9W+8Xgr/LnpVnuhHo4IohcR0FtJ4YaDTFeKnycFajkIu7dCK/MDCWxZiOanhju/u0yBx4cQbiHinW78tH2mnh7iSuF1SrOlOro7DZSWn+G78R9ZMpVVF24zAhLC/GJ40j0jGd0SAJnylqJ9nMhPsiLLzJ8aNUZOOy6kLhYFVv3FbOxuY+RER6si4axIW4IL7ZTWKulUtvDFycreWV2PHF+atr9knDuFhHopqR4y0u4BjqgmvEqdZpe9Ea7N3XbPcMB+6LDz5dqaOu+voL+uvxraA8B7MaUSi7pD9MEkKj9cR73CGlRwXbPTWcVDL2XO0aGkRSgJsbHGYAvTlVS29nDg+MiWPrVRbbcmcGggDCatAa8na+tFJr6IO0OwO5BX/bVBdbOiMNBJmRXThMlzXp0vWZ+u1LP0swgIr3isSqltHT18vy+fFyVxczP9GdYkpq0UHdMFitFjV0cazhOYVMYU6JjkV2TP188JBgbAp7ensfRh0czJcGHhRvO8PDEKBam2cPX3AJjcVOrQVNLjyqAnoQ7UebtQOmTBA6ufHi0DC8nBR8EuvDOgRKKmnQoBUF4WFqISBzK2/mDKD1XTZS3ExuOl7NyeAjbsmsxtLXQI3flUGEzO++bAcz46wPvn6TPbLV7JoMsnKvP4SZ/+7VcOiQakSAGF4e7cVFKCfNwpq4ln1NZH5IR+xplfQV0Olzgw6IdPKp4lrPlrVQrXkRhuINvkPIDfRxrayJS3MgSHymOCWNxV8mZkqjE29GJ1ZsK6TObifVxGsif+jsIhYJ/ivz5AAMM8N/LgDE1wH8rtR09BLheD+NwdJbz8U2JuPs7YrX+7YhSk8XKd2erWZweCMCFyg78XKTc5HGBWbVHGP76QwwLt3sckgNd+Ondr2i1KXHXVrFo7h10i+VkhLhSr+nhtyuNeCg9eHn4y4B9hdpDJe8PL8mq7uD+H7M59fhYkowVHLUG0qg1cMuQIHJqO1n65QW23pVJW3cfRc16RkR6klOn4fU9hSweEoSLUorJYmVYuDvP/ZbH7SPCiPR2pKzVLhJwuv4cYpGA4VGjSfa0T/hbdAYK6nXUdHbz4/kalmYGs/5wGSOjPHhhZhw2m/2H89U5iUQqdHh5K/j0FnsBmJEeetjwAH4zP2ZwgysbfivinSXJvLcoBQ5s5wPBKk6WtPPQBHjhpjhq9r6JqE6Da/RU9uc1o+k1MSvFn725jfi7KEgJdEEhE9GT4cJtw0PR95nxVSuxCkzIPfcxKmYp7kp7SFfqqJv4bBScrWhn55V6dL1m8hq03J3pQXLVGZKHzsYkFjIkxJUVw+xhMxKRkHqNng59H1su1bLragNh7g4881sev9w9FIlIyLPbc0n0d2bJkGBGj592TTAjkuT4WYiF9mGprauPjm4jkd4qekwWWnVmbk2fcMN9Y6g4h+iX5ay5/yrh3k74OitQycRMifcmLdiVnDoNHxwu4/2FSZwub2PDiXK+WzWEaYm+rD9cyh3fXmJIqBsLB/sz5YOT/HiHmjtHhmA63coDG7P4dPUQBAIBK+9I6g9jM5gs9JmsOCslYLPROH0jV3c04eHjxTelYlKDEujQW3lhZwGf3JKKUiqjoKmbXqOZzFC3ftXDrXcO5VBhM/FBo1hfoeS9hb4EuTlS2apnbLQXla16fjkt42J1M8FuDlS2dZOecjvpcTdjFYjZeuEqd7vlc1Q8kokxdi/N1qx6piYm4O1tT9Su7ujhQH4z0xL9+HxpGi/vzifzWpityk3BYVcrnUdKiPR0JCPEFV+1krLOMtbMzqRDbyTAVcmzkoV0ePpxM9DSZWDn1QaSA9RYKk/QVFPBmIX3MzXB90aFuOjpUH64/+1b85Ow2Wxoe0zIJEK2ZjXhIEtmtlAIDu7suVJDm6CKpZnBZIS6k1XdQbveyOoxYVS3dPLarmy+XJbKoABXLh37jearh0hd/iZCAViGr+sPJ3z8l6uIhQI6evroMYqJ9HLk6WkxXK7W4OYo7Q9V3J3TwOcnKiho7OKJKVG8f6iIWyLNOLq0cqRBzJdHC9lw1yqWRkv45kwlCpmIBdeMpdkpfng7y/sNgTXjI4jx+Q8hYxe/ALORp7Xz0GuimGXI4+reYh6ZHMU3KzOQXDt26dAgeo0WzCYjMz7WsUbtSXFLDs5yKXNT/BALBWh6jeTVaEhtvoAgcQqL0v97co58nBV8c1sGNpuN3LpMEq/ljEZ4qv7QVmvuoQozCEXMi5zHaP/RrN2ZxYPFRUR5yxnik86dg9IpOdGCoziCyY6DGRYbxLvZL1OTtZubvJ8lr+UnIrsHkeCXQEtXH70mC6oBY2qAAQb4N2DAmBrgv41mnYGxbx/jyMOj+w2quOG+/fv/EJ5w8UsQiiF1GRKRkCenxJAW4soLO/JJClRzoPIAbeLLjB/7Gq/bPGnTm5gQ44lAIGDuvfchFIow2QTIJSJe2JFPdm0nj0+OYVyMPa/kwJZP8IpIZV+TI406A4EuDiweEkh6iBt7HxiJWCRk8YolLP5dlyK9nXhxdjxhno68McadK3t+xTb8IVp0fWSEurEnt5G0YFfEYiFCgV16XCkTUd3eQ4PGQLOul6IKT1aNDMXX0RdfR1+yazrwVSvYvWYEbV195NRr2XyxhnptDwJsHC9uJSPUzZ6fE+mB8Yc1vMItSDy68DpxkVlrHsXpll841hNErI+VsSoVSudrsrijHmO2po8UnZXnf8vjiSkxdFHDlfoO5kRPJcHfCRv2Wlk/XqhhRLgbKUEu9BqtNF4LVdueXU9qkBoHmRirzYoNG2fK2gj1dEAiFFLR1o1cLMTdUca4GC9enh3P5WoNFZmvE6nyQgLMTPZj3c4C/JwVrM70BEUEL+3Kp76zl2Hh7rxzqJT1i5IJuzZRc1FKya7V4OnUyKZztfSYzHy5PB1HmZjTZa28fSiXm5LVZFUIWL8omZ4+M509Jrv8NtBYVsJv+07hnDoCTcCrBBS3caGinTAPJYn+Ljw3w55MOihAyurRYWy9XMeKocG4KK+rDk5zrmCkfxYv1I/iWEkLz06L4XhxGylBzvjcGk2kvg+BQMDpslacFRJ6TVZOl7ZQ1KRHKRPzzoIkCs/sJOz8M/z2UA7Vbd2s3ZHPseJWNtw6mGWZQTy0+TIr0rxZkhGC2WJlzicneGJKJFFebsilQkxWKxbfJFKFOrxUUo4VtbDp79Ej4QABAABJREFUfDVyqZhUSSVNXUbuHT2UQDdHUtp2gN6JJv/J3Pv1Gd5ashijQMCPP1xGIhRyc3ogN9v20NA0Ctw0sG0VUXN/YM24CHLqOrFYYOXwUD46UsbnJyv58Y5MEv2d8XFSMCXBnnCu69Nxy2+38P2U79l0tg9nhYRpg/yJ8nWy12aTSwhxV7I9u4EVXlZkKhv1nb1Meu8EBx8aaTdqLn1Ncd4ljP5TSfjds9Wm7+PZX/NICXYhzMMR5V8EFAKHcCbLgVCLlQ+PlDIk1I0mnYH6zl4mxnkjqslioewyg33siwuNRiWhYdF4O8t5Y18hV2u1yMRC3pg3iFgfJ8pFemK8nXB1lJEabDdc/V3soi2HC5v5JasWpVTE+4uSqW7Tc7ykjVfS+vCr2ALHu4jwGst7iq0c2VrJTcse5tHJMeTVazlb3kaAq4ILx2qJjHTj7YPFSIXCflEJADqqMG++hY881+KvyyYmVIlXhDvRfq/ilHMcyadDEDyYD0L7uXs7yfuN9Oemx/DV6UqWJyjI1ynZll3P8ZI27hoZys+rR3ClIpJtOa0szgj+zwfifxCBQNBvSP0t4nzTiPO1i77IAD+VHwGqTvpkVlLjarjYLOTApVaqDWks0H+DVVSJ75BfcEWCq9SDau1RRmoDSNP/wlqHKL5clv7feg4DDDDAAP+bDBhTA/yXMJgs3PVdFg9MCCcpwF47x8tJzsnHxuLh8Lfl0unVgsIewoNrGAhFFDdq2XyxjienxiAVC5kY54VF1MBt6SPwVc3lyS25JAgu89CCiaCyG0oWgZhfsxt4fW8Bu9eMJMRDyeVqDTm1nby2t4iFaYFE95VwIltMRNIUatt7OFHaxtQEH7ydFP2T6u/OVhHl5UR6qP0cWrv6mJXkR1Wbnr3FOrQWd6YKBJwobSEz1J2SFj3xfs4MDra3v3t0GDabjcK2ckZHheLuKGdibABlzddDnp7+NY94P2d6jGaGhroxKsKdyfG+JPipGRPtycINZ3llTgJxvs4cLW4mfsLb9J1twqbTIwuLRqmUg0c6HZfrsFis1FnM9DRoSQt25etLrRwtaMLXVYmnSoaodC9xSSuJ8bFPY/fnt6CQClmaGYKbo4xLNVq+v1DHR4uTyaruxGazIRLYw+g2HK1mafq9eCjlrDl6lng/Z9KCXdl2uZ5Pbkmls6ePoWHuyCUifs6qpdtoYXKQFZx8+fRoBaUteoRl+yH3Y7j9MGEeShYmeRDm68HGM1X8ml2PUiohNdiFJ6bG8PaBYtYfKWPt9DjyGnUoroWA7s1pQOaUT6pFyC3T5gPg5ihjaoIPcX52D4ANG4FuSuIjfQnMjKC2o5ucWi17cptI9L9xYrh4SBAzDCZ81UpCPBx5YWc+j0yMoscMHX0CxkR5olZKeWN/EcPD3Dle0kqouwOvzk2kSdvLVycrEQoFTIz1pr6jm4e638VhyCP2fgQO55z0K0YCQe4OvDYvEalIgFQsJFmhJLenhtLvNjMy4T3WbM7G1b2CXpuC/XkmsspaGRrjxZaLtTwzPZZbvzyPo0zEnSPDcJCLiS4/js1XTOaBUuYP9ifNTw0yR9wdZXzl9DmOl/x4tvdmpiV4c/M1j26stJVAXwm7S7sY6RDIC4ebadbbBUiclBKGhrkzL9Wf6nZ7bZP50T4c+a4IQ4g7XVYLWdXdHJh7ACeZE8uGdvNLVi178xqp1/SyL7eJJ6fGUN3ejc0GHu7R/KVy0E93DOn3DhEyisaSNtJaDtConU11ew9DQt3YcLyCzl4TizOCEADP/ZZHkJuSjWeqyQxyJLeyAV8fPypa9SQGqJl+LcSyKKeEYL9I5I5qzla0USkKos4xhDjggfFRXCxv50RZG4eLmilp7qKwUY9QIOCVPYU4SEWsGW+vRfLdmSp+zqol0d+ZBYMDCHJzwE+t4FR5B9s7/BG7PsKdw4MIEInRxk3Br+v64s/e3EZyG7QMDnZhX0snlWIrTT19rBgafOP45uxHz7AniOiQMFJ3moOKm3AztxJ25TPCxj8PgyJAZK9pc7W2k0d+zuGOYX7MN+5kaPitXKzqYMyFO0kZ+zqWgCFE+agYH+NFcZOOJ3cUM/+a4Mu/AjHeznx4tBSVbzlms5nc7h30Ck2Upw1BIhxOYP42OsxdRIjcqOwcwgGLCNPgmbwUOSCeMMAAA/x7MWBMDfBfwmazT2jhRm+TrraLO/bk8/7KtP46K3+ht/wcK7Y3MColgfmDA3EPG43OYOLQmSp+vlRLerALErGQw0UtqP334u3gTbT7EgJdFGSKDSCSYbPZuO2bi8xO9mXzxRo+WjIYb2cFNw3yY2yUF2KxAAe5mG6jictRDzArxovSFj03ZwQxLNy9fxX43YMlhLg7YO0/D/j5Yg1vHijm8UlRPPNbPs9Pj0UXncjbF99mdFIyQR1eBAd5ExPsSme3kXMVbWRVa5ieIuGA5kmmxv6CSChgVpI/rb/LH9m4Ih2FRMiTv+bSqDVwoKCZABclNyX706o38OMdQ1BKxZgtVh7ZcpU7Robx1MQEZMe3QvspaAiCiAlMTfDhkZ+vcKKkjednxOKnluOilOCokHC+soNvVmYgzd0BAgv421fx30luxrnuKDbbYJ6dFkNHtwlPtRVnuTOPTIqkoq2buo5ebNjYll3P3FR/eo0WXJRSsqo6uTUjkE9uSaVB08PqH7L5aEkKY6O9eO/mZKwWC3ycDLM+4Y7RadwxKhS50AThgwBYJD4Bpw7AzT+wcngI35yp4nxlO7tyGihs0vHNinSWDg3Cw1FOyjXjFCAz3IMlbosJP/Uw2zXhTJ8wHrnAzMOjfEFm98j5hkfhG369LoRcIuKuUWH9qmRFjVrePlDCCzfF4eIgo0nXR369jiA3JQqJEIEAyqSxHBZ68kiSL0FuDkyO90EkFNDQ2YPimsz3S7sLGRLmzqKMQBxlYuYP9ocLI8Hbj5qObhCKGJk2mCe35TJjkA9bLtYS5+vM7SNDkTtKmZ6aiffc4YiEAsZFe3G+UkR2uRK0WkLP6tB6O+Ortuf+PDY5Cn2vmXBvFS/uzGd+2q1khLjxWaCGI0UtaEOn4qyQIAacYidCw2VWjwlD+rvaHI5z3sURkJibyMl8j0c9VRQ3deGhkt0QinahqoO6jh68VTKihngjVYj46Xgl27IbOJIwGgA/tYJfLjcwMdaTxemB2GxwsqSV1GAXas5u44J7MudP6RkZ58mg4X7syqnnYmUHNyX5MXrxowDsuVDN6Yo2dl1tYHFGIAvSAnCUiTGarYR7qlBKxYS4KUnu2El6xzHesD6Lp0qG2GJAhgWJVAbpM6jpNhLRa2LdjkJGRLoxNcEHrmxCGjGJYVEeDIlwp6hRi9Fsw2yFnHoNYEN47Xn/8kQ5my/VMTfFj7tGh9OqN/DA5mxeuCmOp6bGcLVOww9nq3l4ay4WG0yO8+bw8aO4OJ8l9NYPeNTrEn2mHJbsnkmsrxPpUe7s3J6P0FbJ67OiYfNimPQyeMZgCptAs74B5fJfmS0UQFs5mLz4bNtektV9pI2337dR3k4MD3cn2UsG2UUEJgtxlElon7UJ/8BQEIrQ9Zip6ejBUSbGUS5mzr+AMVXf2cM3Z6sJczByx8hQco0H6DX30iFqZ2jAUPbWnWaodCzVDb+Q6ODBMXMxhaXj+SH+CrHpj/1vd3+AAQYY4L+dAWNqgD9FeXMXX56q5Lmb4pBLRCikIr75nUDAXwgKd2FEhAfnagp45vx7fDbxMxwkdqNKETyYFcML2VGmY4y+D3eVjA8OlqDpMfLVisF8cLiMzDBXGjUG0oOXMDvOn/wGLdtyWlh53xJQOqDvNdKoNdBntLHtnmE0tJXQrtGzPddCTr2WN+fHERh8BalkLDVtFsRCIceLW1FIRby6p4hBkc2EqH1J9AvFV60kxtcJU5+FE5uLONSn56EJkUwf5MeoaE/cHeX8WnqZ7WU5lGnLeNw7EbHMPnGtbOtmT24jpS3dTE9MYP+8/bjI7R6RUE9HrM1Wpn1wkldnx/eH0Hy42G7gDA1z53hpKzcBa38rYGiwiiXDIhCLhOx9YATWViM/PH+OpeueQywwUtst5Ic9hdwxKgyBTcDXywbj76Zk4YZzTEvwYdWIMNwdpXiqZDDKPlnR9pi4WqdhZGgiOErYm9fEt2ermJas4u7Tq9g/Zz+xvs4MffUwI6IcUDs58cbcRB7achV9r4m1s2L5+lQ1bXojz+46hn/oMdJDRyFEwNasWg4UNLPhllRYuZ8Gswo3uYhWvYnRb5/m8EOjKCxqRt/qTqdhJLMNJlRyCcuGBmOz2vjtTDURno7IJCI8JCLeOlCMRCCgrE1PcqALK68VTi0Z+QF79hVy7OervBN4BnP9Vd5UPsDzM+I4XdbKmbJ24vyc6ewxcqy4lQhPRwJcFGy+VEusj4ryVj0bjlfg7CBh55UGkoPUzEzy47HJ9vyiz09W8cSUaILcHLBYbVisNkRCAb6/k25+eXYCCokIaU8zbc1alJ5haNwX4qtS89r3WTR3Gfjl7mGMCHcjyFXJCzPjkRva0Z3cQF/cLSQMC+z/rJQgF/zUMgwmK56xCkSJPlzVdtPVa+K2jRf4cnk6t355nsXpgaSHuNLda+R8RTvezjLa9H0YzRae+qWQpUODiE5eDMmL8QXOV7Tz8u4CHpkUhUwsYnduPSMjvQhxd+DxX3JYkBpAjLcKjD10WSVU5V+gsbCaneZUwrycmTjEHuK3ekwEy3+nxikWCTn6yOh+oZC6jm4e+yWHGYk+3NP4ExpUOLsFo3KxG7AXKjupbu+xC52YLMglInZdKGSkcyttzoPxVMlxVkpY+uU5FoRZuXv0UKrauhke7ICvywrOeY6m9KSGV6eHIN52Gy+VrcLZJ5QYXyeMFitOCgmvz7N7cEVYad73K6V6XwZnjOBkaSsfHC5j533DMZgtbLtcf12gBNhX0Mz0RB/uGh1uH4skYsI9HGnWGdif34RMImLp0GAuVLZT3a4nwd8Jl8xIxE21ADSrU/jushVPJxkSAXg7yVg1Ipj0EDcQy2DQInCye9J0vWZy6rSYrFY0ZYWofYOQDV2NYt8pJC5ie85dwT6ci37k+YXf2zsYvAER8NLs64sKABKRAKFQgI9agVohYeulWlaNDOOfRYOmh22X67lndPjfVI0TCMBbWse8U7dSPPMXtpQVEqseTFpIGu4mHxxOJpCwIIKTQhGGTj9uMYuIuy2KIL+J/7R+DzDAAAP8bzJgTA3wp/jgaBlXazX9K71/jXMV7fSZLESGqJGKVKxKWNVvSAEgEjMpI4FJv7PBXB1leDrJuOv7bL5cNhgHmRixUIS7SorRbCXO15lzT41H02PkqW05PDcjjm9WpON5re7Q52dexFviwG0TP8ZosdJj0lPYmUWax0gemhjFpaoOylv1rF+UwuAgF45XmmjWCDl4tYItdw3l69OVFDfpmOOo4r1piShU9vA/2bWQs9kRs5kdMZvdV+vZUH2KwX5RROBFSpALKUGpN5y/rteEk8IewuPppMDXWc57h0r5asXv8gN0jWSqe8i8Juu8ZlwYxsOvgCqFPYKRhHk44u4h43CIiOGaPtxtIpCBSCRArZCwfsn1CuuvzE4gKUCNUibmTGE1j36bx/rbxvH+4VI0PUbau414DTbhp69gTOZETpS28luWjk9mfsXNn17hvZuT+HhJCj9fLiK0q4j4lCjmpPgT66MiQVRHWtQV+vySmJHoTyMejBsWRlZVB0eKWhke5mr38jn7UfvZanSODoQseJWf7xyKt7OCsxUddMsCqFY68XvZkZOnj5F3WszD99lvgu4+M/vymrhrZAg+agUdPcb+tkarFZVcgslixZywiK6gqXhV2BXhOvRGDGYLMrEQX7WcKXGeuDrKsGL3KixKDySnRkNVezczkvy4f6zdWDVbrGw4Xk6bvo/X5yUQfi2H64mtVxAJBLw2P4mHt1whthtyrUYemBmHs7sD5G/n2SxvEiOFKI60MPnRFNbNisNqsZ9diIfDtfA0F2QWPZsqJJzPPchHLpv5JfxNlFIRZyva6Ow2UdGqRygSMDXeh6yaFm7NDCSrRkRRk44JsV70Gi38crkON4UUg9WKSChkZKQ7Hio5Ac2H6CoKA5+Z1Hf2cKVWQ3qwK66OUlZvuszi9ED25Dbz4ZFyzj01nkRfJ+o0es5ezkbWUUjqhMV8nd3DB0OE7LQ5olJIaNPrWLf7Eo9OyEAhEfHoz1d5dlosewuaqO/s5ZlpsRQ06vj0eDkjI9xYNjQItcsvDAGG/O7arpsZT2uXgbu+v8yazVf55a5MYkN8Ke9xo6VFT7POgJujFD+FGffms8BQfjxfhV/tLpZOzCQ1bjhfdv8Au7+DGR/wtNwZJAoEmhrMW2/HEPUDegM8uS2HCbHeVEW9xqXKTl69fIbfVg/Dz1lBZ7cRR5kIba+Jezdd5r2FSYhFQn66I/MG4+DFXfnoek28f6SUQX7O1Gp62ffASLzbZbx7sJQ7RoSTkZoKpKLrNXJO60xU6jimeTpy749XyAx3Z2tWPZPjfEAgQBcxCye5pP9eeHdhEgAnvnqfpMwUImbeza2T7Wp5Wy7WcrFAwZujbixsWdPezenydhalXzfAVw6/Lhu/9qa4G0U+/gkYzTZa9X38Z8Un9+Q0crzaibhp7xPol0hM0zC2HYqnLn0Xt6fcTPeMAk60uXG1ScoD45T0XXTE0/m/Xm9pgAEGGOBfnQFjaoA/RUawCzHejv2r1H+N2vYeekwWlvXnEVyfFNhsNnS9ZvbkNbBwcCAFjToCXJXk1Wtp7jJw+OFRuCilnCptZU6KHw/+dJXWLiOuSim/Xa3nqakx+DorEAsF2ABtj5Ft2fVklc7j+9syEYuEiEVClKh5NPlV9uY1MTkGFPIebOq9WGwJiIQCThdKGBrmhNWmoUVnYH9eI1abjbah7jyxK5835w/C0Gtm+89F3DQvCrWTjC9OVlDWrKNJcYiKLgtgT0j/9mw1b8yzFxK+UNHOmf0/EuftiEvyTQwOduXdm5Np7eqjq72XPRvymH5vIg45P0FHFdoJb3Iwv4k9eY30dk/mx/Ak8k80I5eI8FTJkMpF2MwWfnkrmykPJPHopGgAnv41lynx3vSZrYyL8eKXrDoC3ZTEKnWMVJTjKB7DQo8qNjf6EuflxoXyq4yWafERCojyVGEwWYjziCLIrYcPj5RT09GDl1KIV+MZlAdOsnDss6BSw77XoT4LWdptzHXvhL1bYdVdHMovYGioC3UaA0s+P8cPtw8haOK9vLKnhAc6e4l06KW3upTZyamAHwAnSlpxVopxVLbybO1TvDbiGRTX8uocZGLeW5hEjI8TIqEAg8nC3txGJsd7E++n5v1F141HicqJO/ztRu6MJD9mJNk//+7vL9Gs62NMlCfeznLuHh2OxWrjSl0nO3OaWDQkqF957dGtV2nXG3l6Wkx/sdrcOg3Fzd0Mj7Cr3C0YHEBbaSfFrTr6S5oPuYuX4ntxkMuQjAtn1beXmBRnz1UymCws+fwCIR5KVo8OY2xMOEuWhOK3/wl0kjBUcjEKqZC17sfpGzqDk03etHYbmZLgQ2HfJjotQ3l51ihOl7fRpDEQ6Kqg12QhNtyJC1WdPDwxHJXMPlFflqhArrYblGfL2/joaDkzk3zJqdNy39gIzld2MDvZlzFRdgXKb85V02u0sCLDm8wYL2KCXFBMyyDXYuWma3V+NhVuRuNwErM1HYPJQpu+j25tK+NjvHj3UAkbz1QiADLD3FiSEcTiz8+xKD2AGYFmOPkuhgmvMPfzLN6an0iklxPTErzxcJTya3YtTXoLIW7OrB7vj6uDDIFAwKuLh7PoMzHW8lYenxKL/odXePV0HGf3nEYsCObW5BTcm8RUtbcwKEBNSwd82X0fIy90UNjaTZyvEzHeKmK8VQS7OeKjlrH5Yi2HCpqJ9XXiscnRVLZ1U9HajdlqQyz6o/jNnBR/hMDcFH+GhLqRV6/liV9yWJTuT1qIC0brdRn327+9RK/Rypa7MrHSx+GHRwEwPtaH7JpOZNpe5n16hkMPjcZXkw1qf1Dbx745Dz+GyMnnhu+eneLHqEh3+EuO2TWOFLVwvrLjBmPq9/i5KGntMvzVff9dBLs78MJN8Tdss5r6MOg6UbrZ81XFYiEpgWr218ajaK7ntUmPcXNsPT0WH3YXZVNvvMgT6SO5PSoelL44zJCxrWQbIqGImeEz/6n9H2CAAQb432DAmBrgT7F4SPDfbTM/7a9L9r6+t5BTZW18sCiZY8WtDA93Z9Fn53h5dgJ6g5k1YyNwUUq5b9NljpW0cu+YcN5dOAhnhRRNjxGRSICHSs7y4SF09Bh592Ax/mol8wf74+skx0ltT2g+U3+GNJ801AoJ/i72iYpYaEMkNiIQ2EgKcOGV2QkoJELcHCSMffs4j0+K5HxFJ9HeTrTpjYiFAnp7TfS2G+g1mFA7yXCUicDYzdqMxwgOsE903BylKCVCNp6qZPnwEFKCXJD4C2nVtqMzmEDXhKNRh6N7JFaLlfRpwbQZzbRFrWJ3Th25v+TQ2W1kfpIXM/LWgeYlHh3qBU6e0NfF1/MCQeWCbnk0M789x00RXtw/I4bMax6h9w6VkhHiSlFVPnJpBGkJCay+KwHMfSTmvUFi0mJ+PZ1B/MiRBKTN56ltufio5RjNVppqdAzpEeGe4YnJZkMqFBDvsQIqjmMRyREBTHwZbNcmlJ4xMH4tCEW8PDuBx7fmsGxocH/+mXdwDB/cY/e0Ze/ahEf9IfQ3fcE7B0tYvziZvHotXk5yZiWH8cHo9xjkmwFmIwcu5nOuVcLIKA9EQrsoyfmKdh7acpVB/s74uihp1RtwVcoQCQU8c+B+AiQe3DtuHQAFDVrCPBx5dU4iBpMF72uT05r2Ht49WExLl4Ekf2dq23sIvVYXalF6ID1GS78hBRDv58yHS1IIvKZAGeml4vFfcvh6RRrBbkqMBjNiqYhHtuaxLDOY0dGevDY3AWeF3SCUS0QcfngUHx0tY92uQkZEetqFVKa9AcAUsCcZVtZzvKaHNdurmZ/qR0FjF+/OepLnthfS0lRNg85AsJsD4V4qZif7o5KJmZogx0EiZtw7x9m9Jh3v9CUIpfa+i0VCBvmrOVfRgVohYVyMF+NivPrPy1Eu4aPFKVS26hkZ5dlf4+1MWRv6PnN/0dQR3lNxJ4OFn55lz5oR/JxRAaffhVu28sC4SLZcrGF3biPvLUzCaLYS5uFgf77EZlD7I5dKeWxSNCHujoiEgn5vSuXhUtKDHdAZzOj7zExff5rjj47mQlUHd4wMId5PjUAAlinvcOqHYjJDXVkyJAiTxUpFWzdSsZCCBi2tXUaCg/wxOZ/mrTG38OvlJkrr9pCvy+Pe4Wuvna2AEHcHUgJdaO0yUK8x8Na8Qf3nvONKPe3dRlYMC6G4SccXJ8uJ83Fm88U6Tjw2BneVnNqOXvzUDny1/EaluUnxPoyN8uDqlQO8UpDLdzffhZPMfg02nChnRqIvv9w9DF+1Ak7+DEFD+40poXs4FhvcWF0PHthyheVDg5kUd93Qqtf0YLFYb2h3uLCZPrMFg8nKqEh3Rrx+lM8XxTAiLpj/KTb8dJ6jTSa2PGI3pv4SCqrpMfLunhz2/PAD3zjtYIp6EtMrLvNi763Uu3bTfvgARExk2IIYLjVfQiwQDxhT/0aMHj2apKQk3nvvvX/6dy1fvhyNRsP27dv/x797gAH+DANFHgb4pzMtwYcVw4IJcXdkw62DCXB14NxT49iT28CqESGMjPIkt15Du76Pu0eGMifZDw+VHKlYiNFs5XxFO4UNOn66UMOFX97nHr8qbh8VipujDJdrHg5dn47XLrzG20fO8N6hEvbkNLD+UAlRngF8Nu0lHKQKntuRR0e3kajO49ztkcueNSOQiEUMj3Bna1YdM2rfRJj/C16eDtz5aDpO10JTxkZ7Mc+8C8kR+ySenk46uo30me15WmCf2CZOu5tcz2lEeakoOfotxuPvAiAUCVFHODPx/RN8dqqC8nYDrg4SvrktnbkZoUhHPgCOvpz+6gkay3LIOfQDfbufACApxo11IyOpaulG32NieqIfw8Ld2XnfcJqaGvg+24y/rB5dWy8Hv86npb4Plm6nK2kVE2+PIzTFrrd2+4hQFgwO4KMlqTgqpCSGuJLoIeC7AxcYou6Eb2dD2ipu21zMzI9OUdDUhc5k42BBE8gcea/Mm6Vfnrf3KcCZAFclacGutOSfQLPjaQCyazr5SjOY86lv0qDtpaixC6lISLC7A1Xt3YiEIgb5ZtBlMPHStvN05OxGLBLy44VaHvwpG4CRkR68ukTGL1VfAvDMtjz25TYAMIloIrrc+u+rDZvy+OXzXNRKab8hBbBmczaR3ipWDQ+lVmPgSq2mf196iBujo+zGd4tOj9FsQSAQ9BtSAC4OUl6aFU+Ai5KKK63s/ugqFpsNIQIc5PZpcbveSPbeao7sr+Djo2XsuNrA3aPDuG9cGI2aXpp1Bo6XtFDYqKPPbGH1pstUpD7D++faeXNeIrE+KkxmC9nVOkLcHdh2tQHPgBNMS1ZS1dZNbUc3ot52dLl7kF34kLO3OPH2kbO8emRHfz8NJgt+Lgrmp3nQ2q3FZDUB8O2ZKt7YW0Tmq4fJrdcwMd4HuUTETxdreO9gMa0aA6tHhYGuAXo62ZvTyi8XW4nzdab4aB0kLkA/5QP0fWaC3R0wmK2smxXPszvyOVnayouzEkgOdAWVJ71DHuSTk1UkB6qRVx5h59FT5NRpMFusjIhwZ1FGEHeOCiO8r4hvbo7ASSHhx/M1mC1W7vk+i3t+yMLF3YvX5iawYngI3i4iNl68QE17DwvTAhEKhER5q3hgoi99tnYAsqo7SSw+xNTWDvJayrh14z4MJgvDwt1RSEUopWJSA9XIr0mvv76viDa9Af9ruXBCwN1Bxp68Rn66YwhSsRCr1YZaKbmhRt6Jklbaf32MlaE6glUgPr+ZVIcMXjn/Ct/mf4vZaubTWwYzreM7Ysrs9ysz3oXE+dfv0eMVLPvyPH1mCwC7TlYxY/1Jnp0Wy9BrNa/+QoyPM97qG8PhuvrMNOv62Ha5HgECloUb8O6r4n+S2RPieWxc8B+2y8QiEAiROzjw6ohXSVTFYZU6IRUKSU1MJOOueWTMsispvjLiFdYNX/c/2u8B/nGWL1+OQCDgrrvu+sO+1atXIxAIWL58OQDbtm3jxRdf/B/u4QAD/Gsy4Jka4AYsVhsmi7V/Rfc/42JOHlV9zn/VI9VntlwrxArx/mrir62C/wUHmZhlQ4OJ8lLRru/j+7PVjI7yJK9BS069lnHXcqK0vSZy67V4quQk+DnzU1Uybx8XcnSIiMOFTTz+Sy577x+Gs9KRHbN30PzDnRyQTWSryYte8/VVXqPZSk6thvExniCwgdVCoKuSo2Yr6cGu7Mlt5KzXOKYGpJNbp6FRa+CJX3J47qZYZiX543nzM2A2gsUEH2ew0eMTWi0qnpgSzeofsnhj3iBEQgEd3UZMZisf94zn9hGBFGfVMjHeBxellBOPjsZFKcNstXLP95epauu2e0gixtOsNfCd693MN3nybG4Eq4eNYgkgkYmZNjqEaaPtq8F3fneJBD9nVozwxcNbzd77RxDs6UR7gx5TnwUEUNIlYd4nRzj12FjKauoQ1l/CK2kSTr3V7LyqwkkuYtTEII4deYaHYjtwC5pD3YqL+MscWTUilKt1GgLd7DlAm85XMzbaiyFhbjjI7cPF4BBX7voui2VDg6gv1ZGMC4NNFu7+/jJJgc4YLdBtMPPxLckIBAIqW/WUtXT1XwuBQIDAwY0pE+/FWa1md04DtmvxdHn1WryVnogkITRrezlW0sr4GLtBOHbymhvuoeWTw3G0Xg/f+v5cFRNivPlqeRrOCglCoYAxv/PUAOTXa4jwVCGViLhz/4MkuQ3jalEESzODmZXsR+XVVhxd5AyPsH9nYKwbLt4OCAUCJsR5EexqzwF8clsuQ6ILqNeIEZiSmJvih5ujjLkpgTz3Wx7ujjLMFivVHd2Ee6rwd1WwN6+JtTNiOVfZwZKMIDp7LezOacRosbHuplhOt5Rwz7elJAe4ou+z0KAzIJM74HnlRUQxmUyIjKOqzcDXpyrwdpJzc3oQAE36Juqs+YA9/Kymo5tek5U14yL6vWcALbo+vj5dxYJAD/IuNiHLeQL/8HgqtVOI91NzpLAFrcWKrVfHO2c6kYi0PDk1hienxqDpNfLdygzUSnu4YW17Nz9eqGH58BC2Zzfg7ihjvq2WqnZ/HHz6KG3p4s7vLnH0kTE4yMSYrvzMk5XTWKYV8NnSwZQ265CKBf0hbe09XewrLmFwRA+1wm94eeS3AEglQqRiAe1aOenOy9hwpJKxIQ5ofF7m+V3FzGvuIci7Fz+X68a0psfIqCBp/xhW0tSFR7gbE2Lt98LTv+WxLCMIpUzMnd9dYt8id8I9Y9l0+xC0vSY+OFzKPWPCqevswVvkjptUBTJHUu/+klSRmM2Fjeyp3IO3cCgWswNToqf11436CwaThfcPlTAkzJXy1i42navBreEkusIgnpsTTayvM/+ROSn+zEm5Ua1v1rVQ1hXXRFmeXD77D8f9s/H2csXby/UP2xVSES/MTQGuheImhUJSBplWmz2sUun3P9vRf0OsVgv1hfnoNZ04ql3wi4lDKPz7v83/LwQEBLB582beffddFAr7c2UwGNi0aROBgddDUF1d/3hPDDDA/18Z8EwNcB2bjYNnLvDoz1f/ftu2cnqOr0dbcAgM2j/sXv7xQb7Zse+Gbe8eLKKytQubzcb356qJ8lLh6ijDbLXh5SznlswgPliUwtAwd7Q9JqxWG0HuDnyzMoMFaQG8cfASOdoOFg/1xGK14aGS4ess4+2DZTy0+RJkb8IrZji3jk9n8x2ZPDLxunS2DRuf3ZpiD6uJvYmf+jJo2vIQsrozHCxsYnCIC1NnzAV1AFVt3RQ16Xh2eizj/zIZ79OD0sVeI2bZLp6ZN4z3b05GKhbQ0tVHdXs31e3dZNdoqNV2IpOIaeu28vr+YoqbdWDQQe5WjCYzComI2Sl+eDsraO/uY/zbx3jvUAmt3RbGRHmx6fYhLMiM5NyJ/fSd/gQAU2ctrVnb8VMr8Fcr+S7/O149/yZbsxupbu9G4CShMV6Fm78KJ7mYL5YN5mBhE4evltNXcYrMN07QvvNZ1h8uIatGA0CHbwKayLG06AyM/eASNe3dDI/wYPWYCGrauzGarAyR13Agr44hYe7cPjKMli4Da368QkaIK7E+TkQnpDF84SO8uqeQ1WNC+fSWwfi7KMmq6STeTw3Yk+g/ucVePPXnizUYTRaenhaLs1rN9ux6Bge7Mn2QH919Zh7fmsPqb+r47qAbW7Pq2HrXEGYk/W6CabXCpoVQcZzkeC8iEq/XrPnmTDVHilpwcZBez5GxmKDiuP2W7TLw6rZzmNenQ1sZT2Q+wLLkqYyN9iQtxK642FTdxSe7i/j1ch0A+4uaWHu0mIJ6Db9l1/HK3kIAHpkYSU2XBF9Xdx6aEInVKqCwUcfWrFqenR7L6jHhPDQxColIyMGCZsbHeBPoouCt/SWcLm3jxwu1OCskVHf08tSUGCxWKCkdxM1pQdRpuhELYU+5kQ7XwRiX7eelfHeivFyJ8HQiu1bDl2eqAPj1ch2v7GxkzeA1tOvNHC1q4Znpcbw8O4GpiT4UNOowma18erycYWHuxPs6MWlEAKowJ14XrESfcidOCjEquYRf7x3OVNUmBN9M49YhQcxOtqvTbduznXu+OIKLg7Q/tHNffiP7CpqQiYT8eHsGs5P9IG0l982byOhIT2J8nDn5+Fie3JbDZ8fLMEx4mRERnrx1oJQrNZ1E+zij67XQ3Weh12jh61M15NZpaGkOY8PEDdhs0GUwMSvJj9FRXpQ067lc1UJFZSnFV89iEkgYFOTGrIREXpo8Gx9nBSaLlSe35XD0cgEh36SBphqAL5en3SDk8O6CJDIjPDiU38yzyXrMOx9g4rvHuVzTic1qo7vPjMVqY3FGEK5j14DbtWNFYr7K+wpzbwiPD1qP0aRE02sE73ioy+Kj0y/yYfaHADz8UzYVbXrCPVS8OT8JsUgAnpEk3yRjaMK1MaXsMC1HX4JT7/X3raGr4Q9jKdj/F1PeP0FpcxetXQYMJstfbfc/zevnX+ex4zfKnf8tFcAB/jFKz5/h89W3sWXdU+z54E22rHuKz1ffRun5M//U701JSSEgIIBt27b1b9u2bRuBgYEkJ19XyBw9ejQPPPBA//uPP/6YiIgI5HI5Xl5ezJs3r3+f1WrljTfeIDw8HJlMRmBgIC+//HL//traWhYsWIBarcbV1ZWZM2dSVVX1p/v83XffMXjwYFQqFd7e3ixevJiWlpb+/ceOHUMgEHD48GEGDx6MUqlk6NChFBcX3/A5v/32GykpKcjlckJDQ3nhhRcwm839+2tqapg5cyaOjo44OTmxYMECmpub+/cvX76cWbNm3fCZDzzwAKNHj+5/v3XrVhISElAoFLi5uTF+/Hi6u7v/9LkO8K/JgDE1QD/bT1xk24UyHh8X9Pcbu4cx6panWOVZTPnXj1BXXXPDbg8nBcWG6yFZVquVQ4Wt5NRpMZjMHG/cSb2uE4AjRc3kN2hRXqvt88XJCl7ZU8BvV+u55atjaPu0yLtq+CFdw4cLRrE3t4vOHiOJ/i68szCFe8aE8UBAJVcvHucX62jMTj4opCI+PV7Gnd9dAuDFnQXc/cOV/v64OcjodY+n1uyMVCRgZKTdE9HdZ7arv42LZHaKP/tymyjIPgsfJGHUd/Da3kKKzN50m6w4KyWEeqjYcmcmu3IaEAsFSCQmfr5chpujlOERHhx+eDSpga7Q1YT58iae2nKB9w+VMDPIjMPXYxD3tPH0tBjmpvrhqZLy3I48/NQKLDa4WNlKr6GXH85Vc+j4CVov/cpzM+KYleLHrQ5hZDY4c6VWw6GCJroMZvIbtJgsVj4+WsHJogYuXDxHp9iduFvf5pFJUeSlvcqm24fw0AS7kTkneiFpPtM5W9HOicfG4KNW8NPFWnqNFi7XaDhT2sTttY/T1NjA7I9O06Iz4CgTM2OQD/eOjSDa2cqQrv206/uYHO/N16eq2Xi6ghGRHjw+OYbNF2rYldPA8q8vAGC2WHljfzFfXzMEAI4Xt9LQ2UuP0YxSKuL72zNYnhmMl7OU8TFeJPi79HsYLlV1sCunAQRCfp+Fkl3TSVGTjoMPjWJRRiAmi5U9uQ38cL6KjYeyqNi3Hno6cVfJuXtyKppR69hTK0EtDiTYxZNZyX74qe3hXenTQ7D4KhBcK2QsEtqlqfcVNINAyMMT7GFL0d5OTAmZyM+nFFys7GDLpRo6ugzUtPcgEQlp0vYy7u1juCllBLrI+fpUJeWt3aydGcf6RSncNjwEsQgS/FTM/PgULV0GBAioaO1GIZbQoTcxKdabnTmNlAv8CfZwxFku4XhJG/F+zmy+fQhmi5VhYW6sGhFMj9FMaWM78UdXQGMOAH0mK61dfRg7a6moa2L90VJ+uCOTwREehHmp+Gj5CHbktrNyWAgL0wJ56KcrNEavhEmvsC+/ma9PV2Ox2vD2bmVhjH2BxWi2cKmqg9tHhuPnrOCdgyW4OsrYl9fEjiv1ANz2zUV25dQjE4uQiIQcK2njYlUHT0yL5dCDo2jV91HZpuftBYMYE+2JTCxkYlwAi1ISEQrgrX2VbDxdyayPTrPsi3OYLFZmJfvxYKqMdx2+Zc2s4UT7OPHopOuLJQDbs+tp1/UyMUiK8PZDoL4+jh3Ib+Kdg8VsvlCNl5McVwcpby1IIn3EFMRLf+XVOYnE+jjRbugkp7kcXV8XFquN6etPsvNKPfd8f4leoxm1TE1lo4Dceq099zM9iKo2PbSXM03izvTQ6QDE+jlR1dbDlkt2o/zWzGBuGj2EsJik/j5pr2xiZsNOCnzjACjpLGHmbzMpaS/pb9NlMKHvM6OSS3hicjRujlKe/jWXX7Pr+FegqaeJTkPn/3Y3/u0oPX+GHe+8gr6j7Ybt+o42drzzyj/doFq5ciVff/11//uvvvqKFStW/M32ly5d4v7772fdunUUFxezb98+Ro4c2b//ySef5LXXXuPZZ5+loKCATZs24eVlX1QwmUxMmjQJlUrFyZMnOX36NI6OjkyePBmj0fi3vvIGTCYTL774IlevXmX79u1UVVX1hyP+nqeffpq3336bS5cuIRaLWblyZf++kydPsnTpUtasWUNBQQEbNmxg48aN/Uaf1Wpl5syZdHR0cPz4cQ4ePEhFRQULFy78U30EaGxsZNGiRaxcuZLCwkKOHTvGnDlz+iMzBvi/y0CY3wD9jBqcRISfO/5ebn+/MYBLEIx+gp3fH+Dszgp+uvd6CMBLNw+9oalQKGT3/SMAuFB3iRbbSRyV0wB7eIu0OYdNuw6wePpElg0LxmiyouvtJiLsKpdONjDOsQdZeznhgxZwzygrXQYTL+4sYHiEO5HeKgbFp3G8R8Cp2jxyjV+yduharDb7KjPA3aPCqOnopqa9m5NlbSzJCILYlXRuyyHW1ZGkABc0+j6Wf3OR+0ZHMC7OPtBfqdWgc/NkS9QOVhgkNGkNbL5QjdkKL81OYH9uA28cKGZCjBcioYCbB4dytKiNYUnuHCxoYnL8tQRzj0gUK7fTuTmbEHcHzrYKuSRYSNH+BpIC3ZjQ9SvPh/ryU28qp0taOVbaytpltwAQUt6GST2Bi55DCeozcce3WTwzPo6bMhRIugPRG8wEuTvwTnQpXDzNE1PupKetmpdLjfT09iAQCHBzkGESiXFXXc/JaCy5TK3Ql+PFrcxM8mPLhSpqLh+kIWghtwy5NhEdk09mmwmzohU3R7sQxN3XavXQ3Ij+yna26dJYPSGGFcOCaND28sBPV7hvrD/fX8zl9TlJvDjTrg62OSuPQQEqHhxvN0g4/xnvDkuGABeWfnmeOSn+jIhwp7Kthz6TjTNlrby8txCLxcYnS1Lp6DbS2NUHi37sP4dDhU18dryCGYN8iL4mKlHU2MX6w6Uk+Ktp14so8HiWN5R2z9OwCA9gGt/vK8LBoRc3Rwlj3zrOgQdHEHxNQOG+seGs+uYS7Xojt40IZUqCL5eq28mu6eyXpvZwknNTkh/B7g4cL26lrrOXTRdqSQpSA+DtrGD1mHAmxHjRqu+jSduLh5McR5mYl3/Yw1NDHRkSmkmFvgp3dxsxPs6UNOk5W9nOkBA3TGYLE+O8yQh25ZNj5by7IAmpRMSLM+ORXFOuvPWLc7g6SHl/UQoP/XSFGB8VI0as4kq3C1/+eJn3FyazbmY87H6YZQ6+rNMPpba9mwe2XOXjJcn8dqWevblN1Hf28NyMOGJ8nFC4eIN/ILeHWsmu7uT+Hy8T7T2W+yZFkFOn4ZU9heTXazn66GgmxntzoqSVDn0fEpEQi82GrtdIs86ARW9/7t5ekERFq553DhQzIsIDJ6WEC5UdyCUiRoc4QtE2iJ/H6EgPqtu76eozUdjUxchIN2Yn+VDe1o3oL+UY3CPomb+pf9EFYHt2HZpeE8uHhpAa5EJQdw6eF3+gccoX3PPRad6Yl0iElwpvZzl59VrW7SqkWdvLmgnR5DVo+fhYGV+vSCftWnktlUxGoLsQR6n9Xv9xSRS9DXnkNUjQ9piYETqLORFCtD1Ghr1+lC+WpnD395d5e1QAY01d4BzM0cJm2rpMvDIr4Q8S4+2fz8aYuAT/4YspznicVZ0nOaotwdjiQZJnEvMj57P1xLM8GjQDScotvHuwBIlQiEeLCVWkircOlCCXCDhV2sai9D+x6PVP5t0x7/5vd+HfDqvVwpGNn/2nbY5+8xlhaRn/tJC/W265hSeffJLqart39/Tp02zevJljx4791fY1NTU4ODgwffp0VCoVQUFB/V6srq4u3n//fT788EOWLVsGQFhYGMOH20sF/PTTT1itVr744ot+z/fXX3+NWq3m2LFjTJz49+uT/d4oCg0N5YMPPiAtLQ29Xo+jo2P/vpdffplRo+zh0E888QTTpk3DYDAgl8t54YUXeOKJJ/r7GBoayosvvshjjz3G888/z+HDh8nNzaWyspKAAHtqw7fffktcXBwXL14kLS3t7/azsbERs9nMnDlzCAqyP78JCQl/97gB/vUZMKYG6MfFQYpLeOjf3G+yWJGI/oMzU6Hm9qVzmNnVd8NmJ7kE6i+DUEyWMQCJSNCvHuYqDaOn+i6KauUEqKzIxCK81A6cbVdQ095NoJsDaw/m8eP5Wt5bsIhnd+URN9cF7yF30Vxwlld3dHJHkit9ZjHV9Q1YjS4MCuxilOAqMRPX0GOOQddjYvWYCO4dax+cnZQSMl092JNbT2nT9fydV+Ykcrm6nU+OlTE72Y/uPgslLTqqOru5bXgoSpmIktZuChr7iK3SEO2tor6zlwcnRKI3mHji1zxGR7ixK7eJ2Sn+LErxZf4gH84c+IntTf6MDlMjrzkO+mackm5lfIwXo6M9+fZMFf4ZM1kQ7olCKuLQVgWJnq7cMTiMBZ+eIdpbRaO2Fx9nBbl1WoxmCydK2xgc7MKSjEA6CrM5WFvO1DuHY+wzkFPZRKI6AOQq9p6vJa9Ky5PL53Khyp60f9O13IuiRh2HCpu5d1Qwrr/eTMPQ93ln4RQAPIRdzNK/j0Qykc9OF3CsrJ5Ny+biLdRw+8gw3j1YTH6DjnUz42jvNpLgF4t6xc+sunZLqBRSxvk6Y7HZ8HF2oLFDRFmziZlJ9lpOXcI8woNlN4TgHas2MNTHwuqx4Wh7TLg5yshr1PHUlBikIhuHClsYHe2JUiZiYpw3t3xxnl6jhfvHRVLcpOPrU1UEuiq55ZrapMlgIMHfmV9XD0cuEVHRoudIsT0Mo7JNz44r9SQHuvLY5GisVhsWm40Tj43B21neX2i2tcuIQCAgp1ZL8ZHviWjZS8RNGwnzcESAgMIGLcGuDkjFQhL91RjNVmJ9VGTXasiv1/V7tH7JqkMkFDDTqx3HI2spHvIqieGhRPs4c1bvwZc/ZWPweo10r2FIasIoaNIR7+uEUiakqKObPosVN5WM3HotjToDQW4ObNpfRmd3H48tGsSyocE4XDMsHp4Yybdnq3jfGEdqoJzObiNWmw2jycprhkUsHBKMfGsR7TU6Vg4LxtVBhs0mYE6qH9oeM2arjdtHhrInt5HsGg1LhgSy/mgpCX5q5g22h1lGeqlYNSKYcHcV7o5y5qUEIECAg1zMpHjv/mcqwc2Rs/UaZmKfLLg6SMkIdUMqEnLvD5dRKcQUN3Ux2l0HB9eCWMn6An9KmvU8NyOGpZmB3LvpCjvvHU6IhyNHi1oIclWyP7+JHVcb2PvASKi9ABIlV2qho9vI8qEhhHo4EjpqGlgnI++14O0sQ3nNq5norybRX83sJD+U13L/Qt0daNFdlxq3WC1cbbvIWzMnIxFdqxklbIG6LZx47EsM5zfy3Fkjty1eRLinil/vGcpvOWXEBch48VIwiXcNo7VRx70/XuaFm+JJDfljTol57PP4BEZysKCZjada2bJkClsaTyHBPuFbFreMLaY+CB0DVad53DmLL8xTOG3WMU3szOhIdzp6+vBylJNX3YJfyXe4BMVD5KQ/fNcA/zepL8z/g0fqP9LV3kZ9YT4BcYn/lD54eHgwbdo0Nm7ciM1mY9q0abi7u//N9hMmTCAoKIjQ0FAmT57M5MmTmT17NkqlksLCQvr6+hg3btxfPfbq1auUlZWhUqlu2G4wGCgvL/9T/c3KymLt2rVcvXqVzs5OrNdKG9TU1BAbG9vfLjHx+v/Lx8e+0NnS0kJgYCBXr17l9OnTN4QfWiwWDAYDPT09FBYWEhAQ0G9IAcTGxqJWqyksLPxTxtSgQYMYN24cCQkJTJo0iYkTJzJv3jxcXFz+1HkO8K/Lv1WY39q1a+3J7b97RUdH9+83GAysXr0aNzc3HB0dmTt37g3xrgP859zx7SV+uxbK83scZGJC3B3+eED5YXIvbeBY9SnKKsqheC8A4Z4uvDkvmcd/ySGr2h4ikjF0NPUGOQ1a++Qm0VfNs9OimZLoy5rwdkpy7aFivg6wM7MMobs3nk5yHul6k4XuVeCfCpNfxlnmRlGtjOVfn+dwkT1murS5i2GvHUHbY2R/XgtyMZw4c5rObnsIwcXKTk6XteHpJOPu+ErmF9xHuJsMgNFRHlS36VmcEcCCwQEsTg9CazBjsdlwlEt4cVY8SpmEt+cPItTDEY68hOHUh0TX/Mjj6VIMpz7ky71nMRuNiEVClg0NQSQUsuNqIyYLeDnJcZJLmHPLPYSnTUImFjIzyQ8bNl7fW8SZsjbMVitSsZB4XycuV3fyyfEKBAFRWONGkFXdgf7Q63TtfAKDTxpETqahuZuGbiOeTnKmJ/pxtLCZ2765CEBPn5kDBU08+HMuWTP2kTJ8MidLWtH3mRkzOAHpw7kIXILwENtwkYrp1Xfx7SP30VZXw+BgV2K8Vdz9/WU+PVbO1ToNQmMX5cV5ABQ26jCarWSEuKGUypk/OIh43+s/wHcPXswTo+YCdoGS1oTbuP9wD4eLWujps3CowP4sbr5jCKOiPIj3c0VvtDA+1guxSEiHvo8+k5kEX7sHSogAoUDA09NiEQgEVFzJYvkH+9if19gfGhjq6ciqEWH0mcw8tz2PvAZdf0jFgz9dYcnn5/B2lnO8oJaZ7+ylviyPGF8nvlqRRl6DFr13Oh8Zp3OspIUXZyVgtFi54/ss1m2+yqcfZnGxqoPBwa6Mi/XmkUnRPDY5ivwGew7hhqWDmZHoC0V70Cv88HB1wVkp4Q71JaYHW/jg5mReHfI5d3guZkaEE/eOjmBhWiBrxkXx2pxBqGRiwr1UfLwklc0XagEI8nQgxMMRm83G+Fhv9H0mXt9bhJ+LkoVpgcxM8mV4pAd3jgzjrQPFiIUCPFyccVQoeG1aPNGR7kxL9EUiEnLnqDAS/dU4ysWkv3yIjacrUUqESIRwtLCZyzUaVo8JRykR8eHhUgQCqG7rRSKx/2w0answW2zIxCJ69UZ+efMS9aWdvLwoibWz7KFrFyrb2ZPTyK2ZwQiFAvxdFAS7KonwUoFbOJ3zf2ZdsS+NWgMquYifL9aRFuTG3BQ/XB3tz+CZ8nZKWrqYneLHPWOueUXLj0LNWdbeFM/T02K4/ZuLtOv7aND0YLIJcHGQ8sktg/FzVcKRl+Bruxc8xNMRLyc59WW5xLvC49eKZ/9wrpqvzuTxTf63nK+uI7deA4DFJ5kfA5/DarXxTJ43fj7ehLg7XhvHHDnZ9QZLRzhw35hIsqs7ifFxYv+Do5g3OABOvw8thZwrb2fhhrMYTRa+KXfk84ttTIz14q00HWxaiG9xF/M/LKVR24uXgxf3DXsWidoPRFJkciWN2l4mRsp5ckc+Ie4OLBgcSH5FFW2nNqKs2Avtf27COcD/DfSaPxc2+Wfb/VdZuXIlGzdu5JtvvrnB8/PXUKlUXL58mR9//BEfHx+ee+45Bg0ahEaj6Rex+Fvo9XpSU1O5cuXKDa+SkhIWL178d/vZ3d3NpEmTcHJy4ocffuDixYv8+uuvAH8IE5RIJP1//8UL9hfDS6/X88ILL9zQh9zcXEpLS5HL/1zRaaFQ+IeQPZPJ1P+3SCTi4MGD7N27l9jYWNavX09UVBSVlZV/6vMH+Nfl38qYAoiLi6OxsbH/derUqf59Dz74IDt37uTnn3/m+PHjNDQ0MGfOnP/F3v7fQddj4pGJUYyO9Pybber19dx/+H70Rj05dRqyg1dxwSeN3NYrCOQ5kLOlv21SgJqf78okI9QeUljRqudiVQdxvk58dLSUZn0vt2TaY2+UvpF813Ftdal0P8reBuanBfPElGhOZHzMflMCL+zI40JFB2VNXbx3qJTnZ8QxIsI+ka8qyeebaSqclVJSgtTkN2j49lIrhbX2yfudo8P5ZmUGHx7NZatmH6KhKwlwd+anCzUMDfNgTqo/r+wuorvPjJNSwgeLkjlX0U59Zw/TE315eU4iGaFuSERCrBl345C2hDc9X6fXPRFNzGJyXMZhSl0JRnuSqaNMzMGHRrEgLZCvTlWw+ZsP+XLHEdr1fQiFAlaNCOGtBckMj3DHRSGhqEnHnstVLFHnsS2rnkgvB57YU0O2Vk6jphfXUfeQfutLGLV2L9S8iWHcNS2KZp2B9w4WIxYLkItFtOoMnChuYWioO9MSfIkI8OWnizW8faCYkr946wQCatt7mC0p5SPXiygcVdy2/nPc/QMZEeHBw5OiifB0pNtgYtmX52k8uxn5oSfR95l5cmoMzgoJZS16AJ6YEkOYpyO/Ztfx6fGy/mt/pLCZpV9dwEMl58hDo5gS74NAYPd8vLGvqL+4q5NSwm/3DrdPXvu60LfXo++zcKrUfp4R3iq+W5XRL48fEBtPSIAXp8va7HWdjr8JnfZcPtPW23nW/zLvLEhi1DVp9NuGh7D62uQ8PdyHO+MFePkEsOlcFTuvNPDhkhS8fPzIGDaBTeeqeWV3AZ5Ocj67dTD3TY4kYrgvIW43LiKcLWsnZPtNfPXDDxTUaxEKBTzaNpnuMS8Q7W8XTkEs43KbkFkfnyXO25cdG7/i0NY9XKruIKdey/uHSpjywQnWHynBaLZS1qzDSyVFrzEwOsmH7K4evjhZQXefmXU7C+noMdLdZ0YhFRF8baLfazLj5iBFLBKS16DlsxPl+ISryW7SctP6kyz/8jxPbsshKcAFmVjItEQfJsZ58/OlenbnNmG1Wa8tFIgxmK00d/ViNFnZcbWBbVn15NZ2smLjRbJrOrHZbIilIpzdFShVUvQGMxPfOUFFq57jxa3syWsEYG9uAzE+Ku4cHc7YaE8e2JzNlkopTXorG24dzCMTozhd3oZSJuL+ib58kfcBveZe7h4VyugoT7ydFYyL8WTX1XrOBa6C9NsBaNba60rZbDZW/5DNgfwmrBe+gKOv2S9K9AxImH/DdVq/P5fNx7P73zvKxKw/1ASN9/Lj2Q4OFdgXYnLrtbxzsJSiJi2x0VH4hKcguuZZFQgEfDl5PRPCB+OkkHCusgMAf6XFLnxiMoDFhNlqpc9sxWKDyvYejhe3kl3TydQdNrTDnkYRmMxrcxLxcVZwrqyFyn0fQmc11qOv8GNnFN1dGhaemsaRlUHMSQ3AYLKyMgaGk41s4deQec9fHY8H+L+Jo/rPeSn+bLv/Kn/JWfpLTtPfQywWM378eN544w1ycnKoqqriyJEjREREoFAoOHz48F89LiUlhdLSUjw9PQkPD7/h5ez8R9XL/0hRURHt7e289tprjBgxgujo6BvEJ/4sKSkpFBcX/6EP4eHhCIVCYmJiqK2tpba2tv+YgoICNBpNv/fLw8ODxsbGGz73ypUrN7wXCAQMGzaMF154gezsbKRSab/xN8D/Xf7twvzEYjHe3t5/2K7Vavnyyy/ZtGkTY8eOBexxuTExMZw7d44hQ4b8T3f1/xQrv7nIyuEhxPndOLhdquqguKmLKC8VDkoJI/1HIhfLuVRdi9FsZVnTSdw6fBg5aSGk/y6BtfIUd23W8dL8NIaFexClhpNLvRDKJWh7zMxvfAOLagLF3tMo7VGxYZVdfnetdjoms5m7O3uY+v5J5qf6U9PRg6bXxMQ4b5yVcqK9VfioFci01eDgzr5SLXP87YaMxQZzUoOZnTL8hvMwWayUNVtZO+4TXDxdqKjqpLzNfszclAAS/dU4yMSUNHchEwm4UNGBQizi6e15vLsgiZw6DZoeI6/tK2b50GDenD+Ij46W0tLVx/vLRkLDFfhhPqy5StaRVjTN3YxbFkuEpwqRNIPW0gvIT52C4DRKzv1GU+Y6RkV6sHZHAS/NSsCl/jjNJl/mR3jycXYt89QNTAp0493sBpICYvl+by6KfR9x54Zv8XF2wMdZQX1nD5peMxkh7sT5OFPV1s2Pl2qRSUSMiHDHQyVnW3YDo6I8aOky8MjPV0kJVPPqniIuPrMEuUREh76Pn7LbmRQvsXvegORAe8hUuKcKhRgswxdj7Fd5a8JZISHc83qcen69juau6+FU2bUa2nT2sFCxUMi6nfmcLG1jZrIPYR4O/dejulWPh5MCo8WCR+l2Agt38vLsTylp7oK+LrsQhdQBdE3Q14XEI4L4MF9UAiE/vniOKf6lqF0uYlMH8KpmAguTUlHJJeh6TTjKxCQGqOky2FcNFVIxc6bZvRf5jTWYzFaWDg1m4YazqJUSAt2UdPWaOV7SQn1HD4uHBOPqpkQuEXEgr4lX9xax+/7hzB0cgNn1FXJOQfC1fL2UIBc8nBR8c6aKnDoNb8y7n2Rgm18Yu3MbYcwS4mJ9mHMtDPZ4cTNFjToyQtyobu9mw4lKPFQyvAq68YtQc9vwEJRSEQ4yMW/OT2RwsCubLtRwuqyNKC8VhY06hAIBTdpeBvmr6e6zoJTaV1/j/Jy5fWQotR3deKjsK8Zxvk6Eejjiq1bwzkQ1uuZOvms0U6/RMDvZH023kd05Tdw0yI8Nt6bS2mVg3qfnmJHozRvzkhAIBEikIsavsHujjtYcZd2cUAJdldw1OoxXdhdS0arni5NVxPiomJlsDxu8bUQIPX1mOvQm5BIRcb5q9q0ZybGSVs5VVaP0MGMqP8Ezl9zJCHZl2bAQvjpVySfHynloQiTaXiNDQt1JDHBhz5oRfHmygkH+zhwuaEKr9GNxUgL6PjMWdSyd0nB6G7REeKkQi4SsvW0uIomEM2VtbL1Uw/AId95ZMIjtVxq4bVgIFquNwgYdNhtsuyuTADcHYn3VWK02Lld3EOCixMNJTk61FS8nHeNjvRh/TXqdnQ+AXwrEzACpElerFAepiLKWLnqMFnydZSilIo49OhZnlZxUQNGgpbq9G01XN+ENJzH2TYC4uVTWKHjkphgExkMEeNgFN3ZeKqVZryTDwwsqj0PyLf/Z0D3A/zH8YuJwdHX/T0P9VG7u+MXE/VP7IRKJKCws7P/7P2PXrl1UVFQwcuRIXFxc2LNnD1arlaioKORyOY8//jiPPfYYUqmUYcOG0draSn5+PrfddhtLlizhzTffZObMmaxbtw5/f3+qq6vZtm0bjz32GP7+/v/pdwcGBiKVSlm/fj133XUXeXl5/6X6V8899xzTp08nMDCQefPmIRQKuXr1Knl5ebz00kuMHz+ehIQElixZwnvvvYfZbOaee+5h1KhRDB5sV6sdO3Ysb775Jt9++y2ZmZl8//335OXl9eePnT9/nsOHDzNx4kQ8PT05f/48ra2txMTE/MP9HeBfi387Y6q0tBRfX1/kcjmZmZm8+uqrBAYGkpWVhclkYvz48f1to6OjCQwM5OzZs3/TmOrr66Ov73o+kE6n+6efw/8kj2+9ipezjIcmRP/NNharjRER7v2FJ3/P0eIWsqo7qfRzJtpbxbxUuxzqymt1UWh/mllyZ3BQ0tZcR3PROeJGzQPveD6dXICvv5qHt1zB31jBg/p3OTH2FyK8HYkYtBpUPkj6hDjIRAiFAj4/UY6b2gk3Bxn+LkoWZwSwcHAQBpOFyjY9mdeKYL46J5HJ751gt8MLSCPH8vbK5/r7OyXOm8vV18MjThyqwtDYy8RbY3h6WgzP/JpLhKcjj02JISlQzet7C9EbzDgqJEyKs/DmvmKcFWKemBJNs87A5DhvHOVifrvaQHefmdVjwvoT5mcn+3Fi1/dcyeomKSkVlmwFqRK5owhLO0x5/wS/3D0UpdQDAhWgqYGgITx1SMboei2D/eSkOuswWaysvujKhYoOXoiHuwf5Elh6FLksgy5DD03aPjyCQpj48gd0WUTIgeq2bjZfrOGRSVE0aXt5fkc+o6M8+GZlBlsu1nC+qoMobxUvz4zH10VBdXs381L9GOSvxt9F0R8m16QzsCOngeLmLt67ORmr1UZWtYaUQBc+OlrKwrQAXB2UrP3oa54PuILUZQ0zB/leu/YVnLuSi1IUxrLMYABadQYCXJS8PteedCsQCFBKBLgoJUgFAkQiIdoeE+uPlHKxqp2ZSb7symnkzhHjmDR3JilyJ1KCXKn5/l5MYiVhN7/But+ymaXMIXHu42zPbmBQgBMLZodx3Pw80+J8EAsErLtzUb9HYeWXp1guO0aW+0xymnp4eGIUpc16zpS1s2HpYF6efT0h+MWZcTjKxPi6KLHZbLy0q4CqdrsxtfSr89w2PIRBfmri/FTsz7fnzYlDh/Pe71IPR0S4oxCLmBLvjb+LnPSXD/L1inQS/dV8fLScm9MC+ORUJf6uCh6ZaK9BJBbVMzHWG1dHGU9MjeTV3cU4TgwiLtITXZ+Je3/M5o15iQQhpuxCEzenBTI9wYePjpaRHKhm4eAA3jlYynfnqnlvYRIXqzs4X9FORqgbMwb50ajpwdPJbnB/eaqS+8ZGANCZfQHX3ku4e91Lm14HDVcJd4vkwQkRxPk5c7qsDQ9HKS/NisNFKUPYUQYOHqBQU9vRg7ezhB8Kf+DB1AfJrtFQ2KRhf34Twe4ObFyZhkouoddoJq9eR9q1nKI4PzW7rzaQW6+hq8/CymHBnChWYM0NR6J/hheX7kHpaG97+8hQJsV6EeqpYvnXF/BxVlDQoKGj226QlbTokYqg1T2U1/Jt1Jy8ggABUd5OaOqKeEyzDvGq/bQZZBzKqmFqhj/OSinP7yjkk1uSGB/jRZ/FygeHS5mW6MOFig7GRHuy++R5Fo8axP7yHr47V80tQ4IYEeHOkYorRHs7Eemd3p9P2jXiWbJbTIzI+hyN1Iu91um4Okj57EQFQ8PcMPf10HLmB+Lm3sUTv+QwPNyVS9UaHGVi7hkTjjZ0I5nrT7Fz9VyeSv1LiFQ0ufUa3B1lPCffglHlBFPe+5tj9gD/dxEKRYxdfgc73nnlb7YZs+yOf3q9KQAnJ6c/1U6tVrNt2zbWrl2LwWAgIiKCH3/8kbg4u8H37LPPIhaLee6552hoaMDHx6e/MLBSqeTEiRM8/vjjzJkzh66uLvz8/Bg3btyf+n4PDw82btzIU089xQcffEBKSgpvvfUWN9100z90rpMmTWLXrl2sW7eO119/HYlEQnR0NKtWrQLsv1W//fYb9913HyNHjkQoFDJ58mTWr19/w2c8++yzPPbYYxgMBlauXMnSpUvJzc3t/3+eOHGC9957D51OR1BQEG+//TZTpkz5h/o6wL8eAtu/kSbj3r170ev1REVF0djYyAsvvEB9fT15eXns3LmTFStW3GAYAaSnpzNmzBhef/31v/qZa9eu5YUXXvjDdq1W+6cHmn9lVm28gLujjNfmDfqbbX7LrmPDiUpWjQj5Q1HJf4TyC7txyPkW71U/9W/T9pp4+tdcFkWJGObcwUOX1FS297Dlrsz+ycnRb7/Azc+fQqdYIjwdSQlypa6jm+/P1TAr2Y9on+vX4bMT5RjNViwWG6ma3VhcIqhzTGDJNXW69YdL2J3TyL4H7Yo+939+nnClgvuXJNJlMPHZiQrae9tQuZTywJBbue/Hy1gsVgLdlGQEu2Ky2ugzWzhS3EZbl4Gtdw8D7DlAUpEQgUCAtsfEOweLeWhCJOYDzyEPzuCoMANtj4mpCT7k1mtRyUVoe+z5S3eNCqXLYOZkWRt3jQqnTWdA7SBF3JILux7CcMtOvj7fyNQELxo0BvKrGljV8ALFgXei1ehImjYboUDAmfJ21u7M58jDo5n14UkkYhFfLk+jpKmLbZfreXZ6LGt35jMn2Y94P2ee2Z5LbUcvk+O9uVDZQWVbN6vHhDEr2Z+O3g42FW3i9sTb6TMKkEvESMXXo4J7jWbmf3qW1WPCmZLgQ2lJIUFdWUw5FsDyoYHcmhkCR1/lYmE5nylW0dhlYtf9IziXV8LZgkoeXDDJLuHtEgxyJ5Z9dZ7MMDdclPaQvcMFzTwxJQaNwcSruwtxkIrYeFtG//fnlZTR1gNfZbXj7yLnjuEhBHtevw9Kmrq447tLvH9zElHOVmoPf0bQ1IeQymRU1tbhVvozD9cOQy4R8/aCJI4WNXOmvJ0XZsZjs9mY+v5Jlg8LYWFaADabrT++/vcUNepQKyU4KSScKW/HWS7Bw0nGwz9d4evl6XQZTHx4rJSSJh1V7QZ+XpBCQJgLX5yqYFi4Gzl1un7VxA8OleCnVjIpQo5Q7syWS3XcnB7IG+c+QdsSz5rRaQS4KhEIBBQ1aHlwy1U2Lk/DUN9DR0M3yRMCqWnX89CWHN5bOAh/1xvDD786VYmLUkKTzgA22HShhlUjQhgX7cXanfncNzacuo5edpyuZlVqIB2OQibEeHPpy/tIHDUfeeQoWrsMrNh4iXgfFSnBrlyu6uSu9lcJTp9OX/zN3PzZWYaEuPH41BgOFzbza3YdtR29ZIa6UtrazcdLUpCJRXx2vJxvz1Zx6olxVLTp2ZZVx+UaDUGuCkQiIc9Oj2XdjnzKWvU8OD6CtBB3hAIwWWzoek3sy29ieoI9eVwkErDg07MEuznwya2p6PvMbDxdgbNCSq/JQpiHAyqZmPRQd6qbNbTlHiBu5GwKr7ZyeVslK14eylenyilr6eZQUQtfL0/D30VJTp2mPxxU223gha9+4ckpscj842nTGegz2/j6dCWN3Y14OSnIrxXw5txEytu68VTJeGJbDqvHhPD1qVoWZwTioZJxuqyNSbFepIrLkJ14FRZ8x4KN+cT7OyMWCjhU2IKTXIKXkxS1UkqouwN78pr4bOlgfJwVLNxwlvRgNQ8PdQWhBBwGiqb+o+h0Opydnf+pv98Gg4HKykpCQkL+dK7NX6P0/BmObPzsBg+Vys2dMcvuICJj6H9y5AADDPDfwT/yLP9bGVP/EY1GQ1BQEO+88w4KheK/ZEz9Nc9UQEDAv40x9Wdo6+rj7QPFqBQSnpr6j7mjNd1Gnvo1l7Uz4/BU/fFmPF7Sgr9aiUBgN6ySA6/Hgd/x7SW8nGTcHSdBoXLGxduHli4DT2/L5WxFO7ePCCXU3YHBwa74qO0ruK/tLcLHWYbNBmarjVhfJwQVx8kcMhxU9jCcXqMZhVTMq3sKEQCrRoTi5iDBoGlF4erFt9kHyWku5a3J92C2WKnp6OG3Kw0IBfa6SEaLlfWLklArpLhY2ji/9z4OCO4gOjSO+WkBdBlMfHKsnLtGh9lVDYG9eY38fKmWABcl7o4y/FwUDI9wZ9kX51k7M46SZj0/nq8h1MORBm0v2+4ZRumFszi6uuMTHgE2G99v3UaCupdBo+bQc/ANyktrkA9dxfNXBTjJJXy0JJUmrQE/FwWnSluI9FLh6XRj8u+WizWMiPToDwPU95kJcnNAbzDx1n57AUMnpZRVo9z4PPdzHhr8EAKblO/OVtNjNJJf38UHi1MoaeoiMUD9h+v5w8kizJVnWLbUnrBc2qSjorWb/AYtDxk/wSSUIzF0wNzP0X06haPOs5g8fxUysYgtl2rJDHVlw4kKAlwU3DkqnLauPnblNFDSrOeVOQk8uS2H2o4eFBIRz8+I4/V9RTwwPoIwT9Uf+nKytJXMUDe0TZW07XgGj5s/wtXl+gTUbLFittr6vXB0NdrzrCa+yBfnmpic4MP5inY+PVbO46lWhg8bZW974i2QOsIQu2fB11nB/eMj2HC8nChvFQaThYmx3pwtb2PdrkIenRyBrctC6946bn4mncLmLp7bnoePs5ynp8eilIgQiYQ4ysRo3x1C4+BHiY5LxebgzmNn3qanJY0Xp42joriDvNMNLL8/FavV1q+KmFuvIcFPzcWqdl7cVcC3K9LRGkw8+1sBb81NwFNiAKULB/Ibadcb6TGZ6TNaWZYZiEPdSQgbC0Ihz/+WR1mLnvvGRbDlYg0tXX0UNnbhoZLx6pyE/mezorkLd2c5923Kxlcl5tW5SfSYrdz6xXlGR3lw37hItmfXU9CgpcdkwWqDucn+HCluRiwUsGZcBFVtPYR6OvLVqUp+zqpl930j2JpVh9lqZXGG3cC8UNHO4GBXntiWg8lspaWrj8QAZ6rbehCJBPYyBXdk0qzpxcdFQWVbN43aXt7aX8Kvq4f1X+cjRS0cK25hzbgIbv/2IjMG+bFiWAg9XUaUKimtOgOdPSb6zFZifOxhgGAPNRVA//vfc76yjdf3FvHBzSnUdvZwtqSRig4jfi5KbhkSSF69jhMlLRQ16fl2ZToqhX0ceGlnPpXVFUT7eaATONBrNHP36HBy67X8fKkGowUyQlwIclHy46U6/NVyHp0czWfHKyhp6GSmYz2Lly36Q38G+HP8XzKmwC6TXl+Yj17TiaPaBb+YuP8Rj9QAAwzwjz3L/3Zhfr9HrVYTGRlJWVkZEyZMwGg0otFoUKvV/W2am5v/ao7VX5DJZMhksv+B3v7r4q6Scf/4CAwmC5+frOCWjCAUUvuAbrPZOFDQxMgIz/5t9Z09rNl8hQ23puIgE5MR4oqjTExnt5Gsmk7Gx3j1y1CfLm1nUICZvXlNCAWQHOiC0WxBKhYxJsoDTycZNmsH1dW11FuURHqpmJrgw8phwSQFujDpvRMsSg/EYrWhVkp4Yopd8rqz24ibSgZWC2TthENHWNk8hzuCmhkyxa4QNCfFH5lYiLtKRsmeb8k6dIRF72xkafIEYAIAZyva2XPoGP7eHqyePZpp8d7c8tUF5GIRMqsBHL34WClhUlAfEyK84Pzn7G0PZkZyOlVt3UR6qZBLREyJ90HTbSSnTkOohwPTEu2hcHuvecgyQt25NTOYXy/XoekxsuF4ObGt1Vyt6IYaiJe20tVcjjF0HEiVSJLmESTei1P6UO53a0MhFSESCvBzUUDpQb45qWTW4BCmJV4zpuovg2soC9ICwWKGnk78lGJwsU8o5BIRkV4qKtu7cXOQ4ungyROpT2LoMdFiNPBrdj0jItzwdJKTU6vhiV9z+e2eoTyxLZcXMmy4n3kZlmxhXqgJcc5n/HBiCJVaG+GeDowyn2HSiCnkZU/gu0ILry+z1w2pm/otG3cWk7+/hKemxZBXryXcw5F1N8WzbmceBwuaqWrrpqhJx9sLksBkYHK8N1drNLR0GXB1lJJV00mf2UpzpRabzcbJji6+OlXJZ0tTGBFhL8Ts5hdG36IveXR7PnJxJVMSfGjWGVBIhMxJDWD5Vxe43bcCN6mFaAd3EIgYHOLKpepORkd5Qlcj711sIdivjPCIKK52iNH06sClhfRgF8bH2ccPtVKCi1KCSq5k3c4CnpsRy/4HR2K5JpVuS/NDcPYj/HuNSMVDkYqFNOsM/HSxFi8nOQ+Mj6RrxmcE+0fAlkUIEm/mzTHP9z+HHS5yFJ4Kfs2uRyTgWk5dPuVtejbcOhhfZwVTE3yRScQIDCYsFiuHT55g4dWVCB/M4/V9xUyO8ybcS8WxuhYMlZeR7nuY7iW7kLv68ux0exL1CzvzmZcaQHFzF64OEmYl+xPi7sDt315i1fBgHt2aw8LBAbw5P9FuXAqFbMuuxVctZ/uVBlaNCAMBFDZ18d1tGfySVYunk5R5Kf4IBAKEQiFyqd0QXDEsmMUZgQiFAhakXZccBkgPdaPXaCEt2IWcOi0CIawcGoKHk5x9uY3symnAarNx27eXSA92ZX9BEy/NiueblXaJ4spWPUFuDoR5OGCxetBrNNPZYyLI1f5MKFVSPjhcgkouYcWwEIyGXvQ6DRKlEy3aHj4+XoG3s5yHJ14Pgd6b28jFqg6emRbLvWMi6a3Mp/vKRYItbRSKx/HQxCT25TWRVd3B5Dgf+sz1HCxoItbXidIWPUuHBnPOVUSvQIarxcbgEFfCPFX8eqWBeD8Xcuo01HUa6Omz4iQX02u0si27DoVMzPREbyZHRtFntiATi8ir11JSmMMc0SkY82R/H5t1vbgoZTd4kgf4v4lQKPqnyZ8PMMAA/338W4+2er2e8vJyfHx8SE1NRSKR3KAoU1xcTE1NDZmZmf+LvfzXZMXXF9hysab/vY+zApVMQm6dFoPpeu5Ut9HCVycrqdf09G8TCWFOmpxPct9EJLSybFgISqmYitZuvjhRzvQPTjLn41OYLVbWjA/nck0ni9IDmBzvzbKvzjNj/Sn76n6cN/4uSjrqainML+a1vYVsz65jdoo/meEeKKRiDj04imkePeTWtNOstXsQf7pYzaT3T1DW0oUFIUx9C9zDWJHiRHSkvWCszWbDho1gdwe01XmYOguYcrtdHezq1Wxe2PgbACMiPIivOUWKuQIsJsJz3+bkPbFYLm2h4MvHQSRGKQ4nt7ELtVJKtSKabXUO7LjSwAeHS7lp/WlmfniKHVfqmTbIl7pOAy/uKkTbY5dsbdUZeOnnk5Qe2wTA7BR/OrqNXKjswGvEVD4rsiARCVAHRHH3kgWkxUdSVXKV35qccZr0KOWtXaQEudKmN/LwlivQq4VDL/Cx4lPGWU7S2mXAYuzjzNE92KrO2i/QuU/gpyXkfvE0jQfsxSGNZivzBgfw4qwE7hwVBkDJhSaOfFNIiLsDe9aM4Mmpsbw0O4H0UDf2rhlBWXMXdZ09tAo9MSXdSnFrLzK/BDqWHeN0TQ9ZNRpOlbaxo7gbyo8SHpNMSHgshW32c48N9CDO15kGrf3eWTcznuRANUKhgFFRXkR5ObJyeAivzEmA3F9g8yJGRXpy//hIXpqdiFIq5sSjY4j1daaxUsuhE7W4OUgZFenB1PdPk13dSZOuF4AvT1XQZTAzx72WwbIaipv1lDTr7VL0yX783OrPlvYQeoY9xtzPsyhs1NGsNeDmKGPuiGR2pWQT7qXCaLZyTDSMHYYUtL0mjBYbQoGAw3u2MCPOlUEBLsglQjydZQgEUNykY+QbR9AbTPZQwfAJOMeOY0lGIHePDuNseQcPjItk5bBgnvwlh6s97sjlCpj/DWUGFZqm689gYVcvg8cGoJAIUUjFWKw2rDYbXb0WpGIhfi5KrtZ2svKbC1yu1fLD7UM4o3Vnf+b3oHRhx+phRHmraNL0sHJYMBZVKEXJ7/P4gWa2XKqlpcvA8q8vcKGykz6TBR9nGadL2yio13KxqoNeo4UANyXvLUzm9pFheKrkmMxWqtq6ya/XYjBamBRrX1QZH+PFCzfZ8yXmpgYQ4GqXdA92d6DPbGHOx2f5+lQFAoGg3zNoqM+j4Mo5ntqWw7dn7VLBL+8uYOvleqraunlnQTIqhYTt2fUcLmrhwyWpfHKsjKQANb1mM4vSA3l5dyHHSlqx2WzM33CWjWcqCXJzYEKsF66OMmYM8iU9xJ1ug5kvT1YwPNydzGuKoj/v2s2j35/g0+PlPLAlhwZNb7+XbH9eIyu/vkCohwOjojwQCgWMjfGkSaCixSmYXcKxTE8KZPUPl/FVK1g3M4Eek5lmnYGipi42nqlm84VaAt0cmDY4nFNlbTRqetF0G7lU2YG2x8QtQ4LYuDIds8VKnaYHH2c5cwf7MyTUjaemxnDriEhcvbxY9flxdu76lc5uI+Hth0GhvmHsvvWLC7y+t/BPjfMDDDDAAAP8v/Nv5Zl65JFHmDFjBkFBQTQ0NPD8888jEolYtGgRzs7O3HbbbTz00EO4urri5OTEfffdR2Zm5oCS318hyM0Bf5cb3ZruKhkfLEr+/9j7yzC5ziTRFl7JnJXFzMykKjEzs2TZsgySQWZqQ7vNzMxsySjZksUWM0OpmJk5s5Lx/ki1PL4z03Pm3G++Oaen1i9Valc9CTvf/caOiBXUdhu51DzAuwdq+Gh1HhF+Sr4/08RdU5LwU0m596fL2F2DzBobiFDgjdd/K2rD4XRz3choLrUOsjArjENVPewr6+BC8yAFMX58d7qZBdkhSCVi5BIRf9tSQnnnELvumUkmkNw0gL9ayrkjLVgcTiZMi+X1fdUEGjq4KxIyp3o/x5kZodjbyyj/5WWqx93FnMxQGP8A44HDld1km+xcaunnju8use/+iVzuUbDJMI1vUr2lQb5qBUm6P3pkFj38FHKNFvDQ5VQTYLdg19cTmxyD1eHi5tQHvBkhwEet5DXr3zigepuH14zgdF0flV0GEoLUaOUSNqwbSbfBgs+V3iCjzUVJr4cEZQBvfXeB+6YmkROp43hdL6E+Cs4+Pg2520Lnlsf4SHMd6yPOEHz+O1qiXgdg/YaLTI9Xcv3kDO+daIUPXL8FyZ6/0uFQsuitY/wypoGHG3PZOjKIgM1rYdYrED8Fy+5dONzeJeCHs02cqe/nw9X5tPSb2FnSybrRMYiilH86B57eVso1BVGkhGp5cns5o2L9SI2L4kStgie/u8i3Nxdy+nQbr83P5I7NlxmbFMg1Betgy+3IxVL2lOrYW97Nx9fnIxOJEAkh44oh8lxDH4/9UsIP60YyOSWItgEzz2wv5Yl56ZA4DYL+KDEd/P0lBpVRxIy/DoCcKVGUnm4iUCPjgRnJ5ET58OWJBiQiAW+uzGVxbiQ3jBYTVfMtCOW8usx7rlidVhbnhuN2e4iUGlG6hrh9UjyO7jra9FIe+9XMS0syYeoTuN0e3vq9EotHwb0L0oi6okXvH9Qzuvo1huKjadGkcLKhj7XjYmnqM3G2oZ+3VuRgtDl5eXclf5uXilwiZl4onKrtpbxDz5rR0SgkIiwOJ78VtTEqzh9/tQ+ysh/ocZjRhUQB8PGROiYmBfLwrD8yJa+vyOF4TS9+V86nII0MATA1xdvv8951+dR0DfHs9lLONg7w0PQkNlT1YHN6MNXoSdMF8fziODacaOR0XR9mu4uPrs1B7IKjTX08ODOZnSWdFLcZGJcYgL9KRpiPksNV3QSoZZxp6KesXc+bK3L+dJ6oZWK6PVDapicj3Ae328Og2Y6fWoZMLOLxealEH3+M7X1zSCycQ0qoFlPRFpo7XVTbpzP5Sr/SXVMTMFocfHemmX6jjQ+P1DFostNvtnOwohuRUIjV6SLaT8W87DCmpwXTb7Ljcnv44Npcsq8YEgGUUjEPTPca8Y5Ud/PNqUbmZI5CKZNQ1q5n8fRJTDVZUPiGIBJ4hwF/dbyRR2an4KOU4AFCfBT4tx+B5kD6/XL57OIgS5SNvJoo5cM2XwJUUnwV3u+Uzemm3+xgSV44/ioZdpebYzU9PL+j3GsHlYvpN9mJD1STF+3LfT9eYt34OMYnBtLUb+REXR9uj4dLzXo+XpNPuE6BTCzi5kIjP3du5Z34uUjbJBD555uBzy/KIC7w35j7N8wwwwwzzH8J/1TBVGtrK6tWraKvr4/AwEDGjRvH6dOnCQz0lvu89dZbCIVCli5dis1mY+bMmXz44Yf/zc/6/0yeXvDva1e3XmrF4XITopPjo5AyLyuUt/dXc7ymhwU54awcEYHeEkK2z0hEV+q7NXIxBrOD38s6WFYQSXaULwcqupBJRKSFagnXKbl2ZBRzs8J4ePNlQrVynlqQTku/kbYBM/JffyDK6SLgtlv5pq2Gkj4TE6bFMjLWD6NNS5ndSYrLzdn6fp7dUcYv83R0qqIRBv+h6P7xTBM7Szq4e2oiU1JC+OYmb1N/pF88M3NjMZgd3L/pEs/NTWbV/CvDQYt/RlWxDWa/Sq/AH1X5D7QrAglt203nos0890sx1V1GHp+bwt6yLvQDHmamXYNWrUIgEDA6IYDRCQFUn++krN5I+rjwP/UxaRRiflo/AY/HQ9eBGp7ZXspbyzIRAR16Cwcqerg+PxCVxpcRMf6QNhJF6jzul3o3Sy+OMBHbsxt/bQEhf/+73RXgMBJZMI+tCRaizuzn2NrRCNTBMDQZ1AGgDqDP/DsKmVcosjQ/khlXytXMdjfdBiunGvq556dLnPnrNKRiIVuL2jBYnahlYi42DZAbpaOq28g3Jxq5YWwM2+4aR32vEWOlns4QPQ/PTkYmFvDK7koeXPgReOCbaBe/XW5DLRXz3ZkmKjuH6DU6aBswkxyoYYZQQWl1P5MLwzlW08PR6l669GYi/HzYXW2i6EI5D85IoU4QRdOghhhg7dfnCPeV8+zCTEw2J2VtemZlhBGiU/DuvhouNg3gq5Ty8/kWFuWs5mLzACvwBlLTNs3isbxXWJo/En65Fb1pAtrAWURXvIpEOwFR3vWAV+BQ3TXEA9OTOFLdg1Ao4HJ1Pb9VmXlyfgbcd441H51EJq7AaHMwLysMvcVBQ5+Z60fH0G2wUtVp4HRdD5NSQjHanOwobicxSEOf0ca+ii5eXJLF2/tq+HsYH7l2I50GC1svtbEoN5yCaF8Kor19S+s3XkApFfHGihwmJQfS3GcmOkDF0wsy+Pm8V+0O8MGhGtwuDyVtem4ZH0tiiBqH0019jxGBVoReaGWxWk555xBKmZiMMC1fHSjhRLsNH5mU1yckkZITTd6IUI7X9vDkb6U8uzCD7qYq0nq+46YV7+AQeIO97iErb+6t5m/z0vB4PLywq5zEIDWtA2Y69RZ2Fnew6YqwZX5WOAdrx3O0V0P8lbZd/7lPMNPjYZZAgMPlprHPSIy/mkGxCJfbQ7/R5s08Jvrz1YkmfBQi8iJ9+PFsEzqFhNN1vZyu7+dCUz/fLQ8jPTKJdw7UkBSiYX9ZNzqV5KqpcWJSELvvnYBKJmZXcTs/nGthw9qRKK9MfrhvejL13UYqOg3sKG7nx3Mt/HDLKHq7TKzYJeetGRai/AVEB6ho6xFSa/CQFe7D4apu+kwOEoAx8QFE+6lICfWh12hFseV2CibfyzvX5P5JmtNntLGruJ1Xl2YRG6jm4yN19A7ZyYvU0TNk54EZiTy7vRyZSMh7vMTIghswpixDIpTA5Mf/1fpceCXTNsx/L//E7ejDDPM/gv/Md/ifKpj68ccf/+H/y+VyPvjgAz744IP/Pz2j/zvpN9nwU/3RJ2Y3DtBfeZSQEQsBeGjmnzXqk1OCmZwSfPXnJXmRfHq0juouI3nRftidbsYmBHDfj5eo6x3EJukDZnK5dZAwnYI7Jycy8bVD/LJ+ND+dbabLYKXfZEcjl3C+Sc+J2l4eTM2hoctAgcmGf4iKR8dEYbY7OVjZxYmaXtLCtMzOCCU/xpfnFqVT4oZDDl9UxR3cNFLG5YZ+Ll7prymM9W42EoI03PndBZ5emE6gWo7R2MP01BCkh5/hnEFAyrUvo4mbgrFkJ90XDvCTZQQBaR+xbkIBgkk3ojJY0SmGeH9VNnFayKr7lLbMtaREjyAFaOk3E+mnBKcdvzN/oVF3A4wL53xDLw43HK3uYXdpBwcenIxIKGDt+DjSdR5Wf3KMt2LPEKIZSU33EBbCCZz7LAXAloutfHemiS9vLERgdZGQMhHdhDl//gDjJnDyQisxNVVEJaXA5McRyK9s3vK8M2mO/fANbrebwSsDBtUyMQ/8VMSdkxMI1ylYnBvG4cpuxsb7c/f3F1leEMm+0g4cbg8BGhlfnmggLVTLrPRQPHjYfKGFw5Xd2FwePrg7D6lYyLWfnSbaX0mXwYbHAys/OUVamJYXFmfy07lmTtf3sX5SPI29ZtRyrxVv3soULjT309Jv4prCaK4pjMZgdfDNyXq2X+7E4XLz0aFaQv0m0NpvxuPxECCHAHsbz24XIRMLOF3fz5Y7x5IZ6sOStBDCfRXoLQ5O1/eTKOiixqSGgijkYjmz/B9D5LiicteGUXP5BO8I4vlyzbdMV2h492AtByq6uHtaAuMsAQRp5Xx+rJ6Gtk52lnQyITn06tu+ODec0rZBnltUiFgkpK7ba6MDkIqFmOwu1HJvBumTI3W0D1rYXtyBn1JK24AFpVTMTeNiOFTVzdJ8b/9Qx4CVAxWdTEgMoHXQQteQt5R1TmYICol3+f7saANfnazn1GPTcLo9/HyuDYlIyJK8SIqa9aSFa1HLJLg9AopbDExICqK8XY+fWkpzv5l95V18fkMBtd1Gqjv1mJt7SbZ2sXDVMvbt3MuHDTImdw2QF+1Lt8FGeZueCx1WRuoiEB5/HRkCmPxXWvrNBGtliIUCuoesJARpyI/25bNjdbg9XLVz/h1P6gJy9NYrJZguPl0z4qo18dMj9fx0vpld94zncHUPJe0GTjd6A6WytkFcbg/bL3dwuLqHW8fHcqlFz+fHG5iRGsS6jCpi+5up1UZR0qYnLUxLQZwvgWoZerOdhzcXMyM9GKfbQ0WHgbEJgXx5o7fPqqnPxMm6PopaBrluZBRzQwxYWy4xYtlCnC43F3c08nRGJIExgRisDh6fm8YruwV8q7ehcwywMDeCV3+v5M0VOby+xyvtMdocPL+zgnv8s5mgCibF/8/ig3cP1JAZ4UO4nxKhUEB2pA+/FbWTEaFFJBSQG+lLfrQfv11qwxx0D7qoLOYr/2sHtg7zv49E4pWNmM1mFArFf3D0MMMM838qZrO3BeHv3+l/xD9VMDXMf56ilgF8FBJiA7wZnPZBC5PfOMyhBycRdsWQ11BZhO7UB3jy5iEQimgo7kGpkSIOkuOrkCIUCug2WNlxuY3fy7v46bYx+KukV+c+vbWvGofbzVPz0zE7B4nUBdBvtCEXi7hpbCzP7Shn022jEAuFHK/rY1FOBG6Ph53F7awqjCI5WMPjv1eSI25HcfE0v17yIJOISA/3QaeU8vGyeDQKGSfr+nC63eiUUr443sCXNxRgc7rYe6yZ9843sumucfQN2Vj20UneXJFDkFZGQYx3flOC3Mrvzz3MLR9/jTHsDrYeqMPyWxlvrsxhd/LzWG12bmx7H0nuYgTNp6C/nnLVDHqMNsL9VPx4vJg5+hZSguRsONVIRrgPKz45xbGHJxOikeGXOwZdeiZPbCnhZH0f01KDOFHbx6LcCERCAc19JvadOItfxREejE0lacqNSJQSEvxU7CnuICXMh4JYP/KjfanrMaKQirh8uA3ToJ2Jq5KwO904L29C6RqCwnUotBoaTEL8HC5kn05GMO8N3m8MI1AjY0luJIcHVUwfk8+o/AwwdIA2lEU5YQjw8Oivl6nrNjIzI5RJyUFEB6iID1BxvqGfmu4h7C43T87/c+aytd/MpaYBek12rkjmeGJuKm/ur2bN6CikYiF3To4n8ko5pFwsZMjqpCDGn0nJfwTiL+wux2RzEaCWE3lF722yOSltG+Krmwqo7BzyDr9tG2T5iEh+PNeMwWrnJv9KPjIFkR2hY/P6MTT2GrE3mbAf7SF4ZBRqhYtXV8QS9N1q4jTLgHwAzMZQTtXpmZURCYW34PTvpnRbN0fqzbQO9jFgcRCqkxGiVXCwogmtXMyWO8eiEAuZEF3KZ0VWfjzbzDWFUeRE+nChcQCb08UHB2s5VtvLQzOSaBmwkBisJsZfhUom4a7vL5IWqkWOjVtH6MiN0rF6tLc3p75niE+O1DM63h+JSEhSiJryjiF+PNfC5zcUXH2f5meHewcqn2tmelog2RHelIrD5UYkgPAr393PbvAOkyzv0BOslrGzpJPEQCU5EVpK2gyMj/enpd87nPqV3RX0m+106N28vWIGT/xWwoKEaN6K6afco6ax18Tb1+TSqbdglQWwsmwkv6+JwUfuDZKe2VbO/dOTkEtEPLK5BIlIwP6KLsK0CmIDlVw/Kgany831X57hsdmpmK0O1HIx87PDsNj/PMPuprExFMTqUMslzM0MRS0TUd5mwOZ0EqpVI8DO4rBBDDZfGvtMPDEvDaVUjEwiwtMtZuHX1TyxcICN60bRbbDSobeQHemL1eEiWCvz9n/5KenQW5GKBEhEQmxOF7VNzYy9/ALajEcI1MjYVazn2+Jgfkyx8NaOywz5qZngOsmHv8cyWt5EdHYkKwtGsfN8JQ/MSsLsFvG0Jh2zzYXb4yHUR47Z4SLCV8ncNQ96X5yhDbrKIHEGbQNmarqGSAoOpVNvRSUVMTYhkD33BSAQCFjx0Uke3HSZz9YUsG5CPE53NFaXgP9vjrhh/isRiUTodDq6u7sB7xylf2u0wjDDDPN/Jh6PB7PZTHd3Nzqd7j8cWg3DwdT/eHZc7iA+SH01mArTKdh1z7irgRRA8ojJGDPHIxCK6B2ysb+6h/ggNW/tKObeaUlMSw3mdHEX5xsGWJwbjsPl5tOj9XQbbKyfnMCNY2Pw4CFEq+D5nQ10Gzp5ZFYyjX0mb6+Kn5JArZxQHwXvrcrl9T2VHKzqZnZGML9ccDI3MxSNQsK1qjJipUYem72I1gELUpGQuyYn8Pn3P9JgURKTmEFcoJrxiYGMTwzku9NNfHuqie9vKWTG6Ejqh1p46sRrrCh8CK1czEObLvOXmcm8sbea/hhfAm59HoVKjcUTyfLJflR1GansMHC5ZZD8UCm6oWKENR4cGYuQOMzYnG6yInT0Dw7R71LSMOYlMmU+FLU0Eh2goiDal+2XO5iSGoQoaQ06sYTqrlrunpzAoryIP+mtxSIBbV092KPzWTh9IhI/LRca+vnseD1/jQvjsttNdqSOHqONawojOdPQx9iZ0fw9Cf3BwSpaG6TcGK0g3qAnd+Z8Zrx1hIcRM9SaylRVInXdPZjsbtweD1WSCGb5hsKFDbDrAbi3mDlZYVxqGqDPaGdRTjhCoYBlIyLRm+3sLu1gakIAt2RHYnW4eHFnBX+bl8ZzO8oYHefPotwIFuZEIBb9oZJODNYwKz2UUXEBNPYNURjrj1Yhod9o44ezzQgEXj35loutXG4d5Kn56Vw3MoZJSYEoZWJa+8043R5a+kyca+zHandREONHjrSdsopqUlPyMVqd5Ef58eFhJYUxflw3Khq7083Sj07xytJMZt6awcm6HsqMO6jVVzJ3wodEBP8RvNmcbqRXnu+OJthW4uTn20bxxNZSpGIReVG+mO0unC43PUV7ODyYQbY2lsQRwYzKyeKLy2e52DzANYVRBGjk+KqldOgt7Cnr5Mn5aYyKD+Donkoi/JR8uDqfDr0FsVBAXpSO+MGTJEbHExfug9nuZPvlDhZmh3LP1AQC1HJu33CeQI2UR2cmMT3Dmz0739jP3tJOHpubSnmHgZ/Pt9LUa6a2Z4jsKB1KqZif13tn0Pxe1kGUn4rUUC0P/VxMUrCaE7W93DUlnh2lnbQOWPjs+hHoVBKe3FrKQzOTcLo8eBDw3I4yFBIxmclpmO0u4gw2zjc28/rvlczNCqW8Tc/WO8fh46Ng1WeneXCGLz/dNhqn21teuCgnnN/LOkgIUjEmIZBTtb2E+yl4a181ZpsLlVREZecQx+p62XbXeF7aVcHhql5eWpLJs9vLWTMmmuM1fRys7CUjTMO4xEAq2ofQKeQ4XB7mx8LLR3u5aUYs/SYn1352hmlpQdw6IZ4ivR+PLR1JaoQfvxW18fnRegI0Mr66qRC5RMSzi/4YyjwxOeiqUGfT+Ra+PFjP2/GJ+PhoCPVRUBkYQ2RQB9QfJNPSzoNVKZjiE+i0y1npa6W7u4MAzSB32r/hzi/n89CScbQPWOgx2flgdT4/nm3mYEUX76zK/WPR7SqHsq2QOIMXd1cyPS2E60bFcM8PFzlZ18fxR6bwzPZS2gatPDY3lQ2nGrE6XNQbKnnkyFO0ld3K4glNjIqJZkbMjD+t5yfresiL8vtD9z/Mfwt/NwT/PaAaZphh/u9Dp9P9Q9v3v2Q4mPofzt+u6JD/JfGB/3pmj1rmPVWqu4bY2zuIvXOA+VlhTEwKxOFy03Sxm7AACX1GBxKRkF33TkAkFGCxu+gZspIRrgMgI0xLu9JCuK+SV68MCr59YjxVnXqe31HOmytyuG96EqXtemq7jFxq0TM1LYjXlmWjlOaDVATlXYyM9eOvW0qo7Bhi080L2FXWg0ajZkpKMP1GGx8crmNiUgDL8sPxV8sx250EqX1J9c1mbkYkcomEEdG+aOUS3h3Rx8e1dgbcGkw2J/f+WMTivHA0MjEt/Sbig9TERfhSEvg9J+t6MVYK+Nu8Scy88t589OSTSCMTESbP54NDNV6NN97NWXHrIANmOzKxkHunJfHT7d6eEbPdybcnGwnxkbMoMwh14++YfBI422mi/WgTzy3KJCfal7cmJ7OpqY81EaFUdRm454dLTEwKpLRdz7a7xl/tr0kO9uH3Mn/OVLZzrvcwN69eyM+3juLBny8zEL6WOf4hvHVN+NXPc+O6K9KV+hjIvwm03nK15gEzcYFqzjb2Y3a46Dba6dJb6TPaGeGrImtAwLgbU/BTSbDYnCQHa3h2RwXT00IojPtjhpPZ7mTAZGdpvrcn667vikgJ1fD68hyUMjHjEgOZnRGCj1JKXrQv4b5KDBYHHxysJTVUw/bjHfxW1EZ2hA9PzEtnbmYIj/5azB2T4nl+WzdvJffRb7JzoKKb1aOi8ZFL0FscAEg7i1hdEM7PF5pp6DVxoLKL+6fPZmXKUrQyLdsvt3Hnz8X8duc4NDIxpe0G9pR0MD4hiJJWA58da+COyQl8fqyeKH8l+dG+tA1aWKk8jzowhIPHlOh1YrQ+MgLUclYWekvygrVyntDshCY/dt+3ltZ+MwMmOw/PSuH9gzX8cKaJd1fl8fY13o11k24pO4o7uBMYMNnZU9rB7PQQNHIJl1sGeXphGo9sLuGXonaiAzUkBWuQiQR8e6aJrEgf5maF0WmwsLukE41cjFQkpKnPRJhOQX23kVf3VLFmVBSpoVrevSYX86HXuX1qIQ1qOYtyw5mRFkL/kY+wS6XIJWMJ0ijwVXnLEJNCNMQFqFBKxUx+/QjfrxuJWCxk9ahownUKNq4bSbCPArfbTYyfEl+5t5xt0uuH+e2OsVgcLlRyMXqLk29PNvHtzYVIREKKWwfJjfTF4XKTFKJhRUEUd31/gRg/Fb4qCQIBBPnIkEuE+Kul/Hi2hR3FbawZtHL31ESOVXfzzI5y1o7LR1jmoMtgp1VvISlIzcqCKKo6h3j3QA0/3zaa4tZBIn2V3Dg2lilXhBxDVgdNfSacbg9qmQSJAFZ+foZXlmSyJC+CkbH+fHoknNBWG8XdtTT2mpiXHcZRTzi/OpvYPrcLRcUmqia8yPunRSS53Nx/eCSDS39AcE7G5eZ+3j5Qy5K8CGxOF9svt5MTpUOCk10nLqEXBzHPX4Onqx6JzcErS7OQXVGYXz86iiC1lI8O1TJN00Zi//eEhH3L0Zpe6nuNZEcksyp1Makj8rGLfYjU/nmAutXh4ult5byxIpvMK+vtMP89CAQCQkNDCQoKwuFw/Hc/nWGGGeY/iUQi+V/KSP2d4WDqn5xN55r57HgDv9834d8tNfB4PJjtLlSyf/t0uPGrs9w9OZH8GF/GJATQ2GciRCsnM9znah9EzMQwJiQG4aP01pbuKG5Hb3YQ5qvgmW1ljE/048UlOSzK/WMDcPNX50gIUvHXuWkYLE70jl6ePf00N/ndztqEEERhKtweD58daWBFQQRrvjjLj7eNorhVz6DZzs1jo2kftGHtqmW+9RjGjDsA74VMIhQwIsaPCUlBNPWZmPPuMZ5dkMFzU27n48O1BIntdDV1oBsTw67ybqa1fMDnspto7g9ndmYIKqmYL080EKSWkhmpI0wrRyZWEe6vAQ98X7yfKbE5hGgCqEqez8zUICQiofeOcOs5kGp4aHoSX51oYPWoKMJ0Six2F6s/P801hVF0GazsKulgUnIQX3c1IWprJMQ/mL/cWIBKLgGHBZHbycQJ0fz8XR/7y7t4fG46R/4ymWe3lzEnI5TeIRvHanroN9kRiQTsuX8CVR05/F5zkWVbbuCO1Oep7jJy56Q4ln50isU5YcQHqjBYncQFqrwBbtx4qlXZJFzJkiUGqVFLxcQGqnj3QA0Gs4PYABWBKikjU4OZmBSISCyktM3Ah0fqeGp+OteOjEYuEfH4lmL0Fif3TInmi1MlNPXI+PFWr2nszRXZ+F/pw5NLRNw1JZGOQTMOp4tofxXR/irONPTx9qocYgPULM0LJyVEw4SkQOQSEVNTQogP0tA6YEapUBIy5XYaekwUt+oBeGZRBk9uLWXnpUbGHV5Pj+/TKBXB7Cvv4pZxcYyUdfLULwNMyE0jK0LHlJQgJCIBSSEaNHIx2VE6fGztPDQlGhsSZrx1lDdWZJMSouWd/dUszA5jh+/9PJGXxoI8rwDCYnfhwUPOlfKxpj4TyTHjcNfso/XMVt5ujCE1VMstE+Ko6R7C5nD/6Xtld7nRW7yaeJfbw5yMUDaebUIiFKBTSsmO1GGyuYjxV9I+aCEpWENmpC9b1o8hNcybzdpxuQOLw8W4hACEQgHrN15kSV44y/MjiAtQ0TroVcMnBKupUvuj0frQ1m8lQCOjrF3PgSoR8cEqSnr1aBUSfjzbzLL8CJ5bmEm3wcrG041suLmAlFAtQWoZzf0mntpWhtPlZvP6sQxaHBS1DnK6oY/rRsWw+fYxBGnknGvs58n5acjFIup6hth6qRWtQsKGdaOo7Tbyw5kmDlZ2s2ZMNEqJmOtHR/Pirgpqe4zcMSmBhh4jHxyq5drCSG4aG3d1bYr0UxCkluH2eBgwOWgatJAeqqWsfYgIXyURvkreW5WLye5ib1kXQVo5N4yJYXdJBwqpkKKWQfaXdxMboKLfbGd5fgTrxsVSGOOHQirGXyVlblYoQVo5Bw4dQCCLJ0gtp75niCGri6gRczmoyOa9fdVIhUI6ZUouzduJXBvHsfozrJ2QzNPz05mc6s1+fnfLlZsWv93FjNaLnJ21DbsuntOBq5gsEPB7SQfjEwORS0V8cqSehl4z+VG+3D8zD4KHQCzkx1tHERugRiQUcF3adVfOnn8tmZBLROy9MrdumP8zEIlE/6kN2TDDDPN/J8PB1D854xIDGLI6/2HN9s7idr4/28L3t/wbivjvr+HBkKlEBuVcfejakdEcqCnlgd+O8vmqJcjEIuZne7MeTf1GgtRyAjUylFIxWrmExCA1U1L/nCrtGbKilosYk+DNZhTE+vNB6Ci+LhrgZIMel13ATROiqe82khGhJdJPyR2T49HIJayK9fCXvdUoJCnMygxjztvlzI9O48OX9nP04cmIBEJWFkShlHpP7yg/JTNSAygZ/J3ZjlV0D9kJ8nGC3YLZ7uLn3hgejxnP2oxcEkN92FfehcPlJsZfiUYuYqi5mI44f17YVcH1I6OYkhrMltpfkIphaswELrUbWTEyhpRQLSmhWhz7vqPKqCBu7v0IhUIsdhcDJjs6pYQJSYFo5GKmpkaxJDec4rZBvj/aSpAinRXZyfiopN4A9fA7MNSJYP7bvLMwBq40nItFQu6dmoS/RkZJ2yCHKrtp7DchFQm5flQMLYMWlmVlcmLHGDxOCS8ti+PXpo9YP+kOTDYhQqGAr082YrY5iA5QszQnnEe2lPLtzYVkRviQFuZDcrCGozW9NPaZ+OvsVFoHLJjsTqIDVAhFAhr6OxgZ68foeH869Bb2l3dx/egYRkT7selCC6+d2EInB7i98I2rn7ekrwLfoEKMNic7L7fTa7Lz68VWpqYEcfvEeL4+1Yh5oJOksAASg9IRWPWElXzMjqHrCfDVcNuGizw1L41+k533VuXywq5yVo+M5u4pCaz89BSfXJfPrarDaI1izkauJWfgMgsXrubHCicBWhlc2sB4q5L4wALEQgH3TE3kzX3VzEwPISFIw49nWxioPUuir5Dro4fYdtcNyCUi3tpfRUmbngg/BQaLg+quIZKCNby6KInPDlQQEeY9r0/W9vHW/mq23z2OxppyNp7v4+ZFE0kJ8fYyvXNNHgCXii4RFOBHeEQ0IoGAh6/IXNr1Vi42D+AR9aLwu8QtI7z9NcFaGSE+CiYlB3G5dZCdl9tRysT4qWUcr+nhgRnJBGqkV4UxY+L9+PlcCynBaiYlBzEhOYDa7iFCfRQkz7sbgHVAS78Jo9XFiKmLKYz1J7HHxJGqLt4/VItWLqIg1p/a7iGOVvcSoVOSGy1Eb3FQ3j5EhE7BdaO8Bj8/lYx7piRSGOv9Hg9aHN6eseu8PWkPbSqivsfE7IwQsiN1ALQOmJGIBCzNi+BS8yCzMkIJ1MgoiPVj0GTH7fbw+t4qVo+MZml+xNUxAoerunn/YA3jEwOp6jLw4PRkRif606W38NXJBkpaB8mM0LH2m/NkRfjw0hLvsNPDlZ28/nsla8bE4K+S4a+RkBnhQ0GMHxG+CgI13g6kV/dU8uO5FjLDNXy5NIa0pnvg9uPgpyU1TMvc7HDaB808u7uG28fF0TRgoqXfwqY6MbdHSFhZEEFmpO7qDaZDld1khGkJ1MohdT7i3DWMifL2kc5dcgMt/SZ2l3SyvbiDV5dmkR3hy9qxsfhr5FSZPZi0M8jDK8r5/0JVp4FbN1xg593jUMv/40bqYYYZZphh/nP8Uw/tHQZCdUpuHh/3D4+ZnBLMswszrv785fF66ruN3h8CksiMj0Z3ZUNT1Wngya2l/HimG4tVjscDDDSCZRCA2769yAs7KxAAVruTCF85C3LCmZYawjcnGvnblhJ2FLdjd7rxV0t5bU8N7x6opr7biI/chzMlMZwxWDltNHL39xeJDVSxIDscp8vDY7+WUN5u4MzWTaTQzQdH6hAKYEqWCEdEA7+uH0uAWs5vRW089OMZOov2At5M1WPzEui3dzFgG+DJPBtLah7ikWsnoTr5Kl8vDCJuzr04ZT7sLe9EJBDQPWRDoxCjs7XzYNMdZPnYeG1pNuMSAnho02WeHfUSy9Km4auU8uUNIyhq1TNkdbCntIOvRUt5oT6RippanpyfzpGqHu76/iICgYBbJ8Tz9LZy2lubCOs/g1wiYklOKI9ODOO+n4qormvH0NoGhbfB+IewVRzB8cUKJB4np/b/yv0/nGfWO0c5W99HZriOV5dnMy8rjFvGx9E7ZOWd/dUIBEp+Wn0beyo6OV5tZLBtDm39DlaMiGRCUhC/3jGWL24swOX2oJCJOPLQJDKvCAx6es189cgJMrUKPro+j/t+KqLbaCU/2pf4IDX7KptZuWsxhdEm8qL9qOkysr/C2xewOC+C728ZzZ0jFjEn4BHSIrzWMkvpbkK3XUtNfQ19QzYOVnVxtKaHWenB3DYxHofbg8vl5tdyEwlqKy63h5XflNNsliHwOBEJhGy5YwxauZjPjtcjQEB9ex8fbztNoEZGtFrOqbIuIhKy0UakMy0ziuW6KmSmTm4YE0NulC+Me4Bxkhp0AjP3/1zE/vIu/JRi9GY7GrmYUB8ZK2ZOYor5dzB1EqCR8+GhWs41DLI0L4JpqcGkh/ngo5Dwe1kHF3Z8xvLmpxmyevttpqQGsaowguLWQeKmreWwJY4Bs5Oajfeye+ObV79bv5xr4FRRKR6Ph6++/46Lm14GYFScPzEBKirbpNTXjcThcmMwOyjvMDA63p/fyzroGDB7M01uNwfKO9lb3sVTv5VxuXmQ3iGvjKK4dZApyYGMig/gdEMfP59t5eHNxWy51Mrjv5Zw/eencbs9/HSuhWd3lJEd6YvB4uDLY/XsKulk4/VpbL7QxvHaPuIC1XxxYwEKmZhX91RS1DxIqE6OyeZiT2nX1dc0OzMUf7U3mCtuGaSi3cC5hn5u+/Y8D89K4ePr8okNUPPrpTZWf36G3Egdj85JY1S8P1aHG7VUzLWfnWFhTjhrvzl/NWA9WtPDko9OUNs9xHdnGtl8oYUleeHcNSWRHqMdu9uNUiomUKMgIVBD0JWg6NkF6dw31WtR3FPSzufH6hkR40dTn5kleRHcOj6ecJ2CozU9VwMpgIRANY/NTmFMfCAinxB4oAL8Yq6ue2u/PoefSsbEhEDKu4ZIDdWgEbup6hyiy2BhV0knQoGADw5W8/S2Un4428yJ2l7e3l8NSTMhqhCA1/dW8d6BGvaUecsztXIxWoWEu6cmMjohkKRgDb9caOPaL07z68WWf7h2/68QG6DmpSUZw4HUMMMMM8x/EcOZqWFQycQkBP0xj6nXaMd8pSmbGc9efbyx14RKKibST0GYLooFOWHIJSLse57gEcdyhnwqeHXZrUTolJxvGmDQ4iBMp8Tm7OWzo3WMTQjA5nThdnsI91Xy1PwMHv+1mEvNg/irZcQFqVk3Po7UEA0Xmwb4+UILAoEAk81JeZuevCgdb++v4o27HyGwu4Ticx/h8oxhaX4ox1prSQnV8ntpJyKBB6tLyGmDL4uAIfsQB2oqaG+cRPjscJD6wlAH9tbLNHcZaFWX8NmZp7gx5g1K2wysHhXF6i/O8PryLAZNgVinl+KR6fh0fznL8iNo6jMjEYmw2J089msJHYMWwnyV2Gw2VFIxIX46ChNDMUu8d+tjApRI3GaKi86TlTOCow9PxnrqCzzN+5l03U9wZitU1HDs4dfo//EZnIYGvov5G5qhi4R2WRnSvMoUQNpTSkZALBkR8cQGqDDanNy+4QJ2p5ufF6q5f1cbq0dFE6KVc7iyi9P1/dw3NQGTzc2klGCu/+IMcQFKnlmYSbiv6qodbsflNg5W9vDmyhwMLhdNyQqcChGhWiU/3DKShzYXU9Zm4L1r85iUGMkjLW8TpYyjpmuIII0M8PDZ0TrSwrSMiQ/g+V3V3DQ2xis1cbtQpM3EpN5EckwSAJ9cX0B5u5639lfjo5DgdHv45WI7H14/ktxYPwRCAR9cl09y6CRk4j9KZPqNViYmBuCrlvLy3ES6DVb++msJ/gh4/Wgt6TePJEgj46XdFSwZ9SbZEf9CHy334Yg7C2OdiY8XRaDRaBj7VjlhPnIemZ3MgcoepizM4LeoB1gzMR0ZcOuEeG6ZEI+vSsrGM00opEK0cgnRfiraM1YwIJnPaPUf5VaDZidNfSbMdhdPzU+joceEOm42BtMfMpfnb1ly9d83jYtHN/RH6d+YeH8qOw106q0cqe5hWmowryzNIjfKl3t+uMSy3HD0Zjv7KoxkReiI9FWQG6WjvHOI7cUdXD86mskpwdw0NhaxSMh7q/J4d28VUpGAc/V9BGjlLMuPRCCANaOj0crF9BisDFoclLYbeH1uJJEbJ+InfYSRsZk8vLmYhTnh2Owueo02/jIzhYJYP4xWJxXtBtZ8eYZrC6OYlRHK41uKGbK6eGx2MqE6Jf1GG8sLIq8GOLE2Jx2DZo5UdnO4qpvkEA0BKimfrhlBXc8Qd01JQC0Tc/Qvk9HZ2knofALflCfIS8ygumuIjw/Xo1VImJQUxMbTjRS36lk7znuTSC0X8+bKHD46XMvMtBDyY7zfu/ON/Xx0pI4lV4Jhs93J96cbkYqFDFocpF7JGv6dJfl/7kF68WAr4T5ybsjV0XNsAzLxaKQiIQ63i+lpIYwc2s884T7u1t1NadsQX9xQ4DVz9lto7jfz0IwkfJQy6npNV/+m1eFiR3E7j8xKYXZGKI//WkJckPqqMOLlXRUE+8j569xUhqwOqruMV3+3uc/I+u8u8fbKHBKDvdmqfeWd5Ef54qeW/em5H63uoUNvYWWB16Q5Jj6QYYYZZphh/msYDqaG+RMX2sv5y8xUvjrRQG3XELMyQ3F7PPQZ7cx46ygb1hUyOSX4T8HXT+GP013VzbXJ08iK8MVqcqCtMZE6JpgntpYwLysUh8tDWpi3jIyWs2DXgVSFXCLm5rGxjE/yXux3XG7H5Q5hbnYYc7O9BrPnd5YzaLYzNVBPg8Ofm78+x6bbx5AS8DJioZgE3wRitX/fWImwOWUsyQ4mpv4zyIvhVO8lhE0b+DQ2HRgDUiUVi37ncHs1h4RCVopyeLggjuygEKamBvPNyQa23zUOmUTE9V+c5kuExAWpUMtEWFsusXNNAnUOKO8wUNKmZ15WCPdPT8H+Vj6e6Dup9xnH/TngEbQwZPFldHwg3x4VYLF4ZxZIxUIWnU9kcnAYYw7tJLbxFGHz/4aochv+ix/A47BhPtiLQ5nO6GkqLG4NiKXkr3qSfOCRzZf56XwLt0+MZ2Z6MIUxOr7d/R03J8XQJA3lui/O8MnqfN5emUN+jB9LR3hLshbnhhH698G++mawmSEohUNVPcjbKzD0hBHk44tJK+bvfvOyjiEywrTcPy0Zp8uNy+1h9qQR7Cvr4uuTjWy5cyyPzEphyOrkoU2X+fWOMXy0Og9fpRTOfwlNp2DpZ6hivGVu9/14CaVUxF9mpjAnMwyxSIhYBB9fn096mJbNr55n9KJ4LDIPG07Us67iJpj7NkSOQCwWcaSml1O1PYxOCEdrdpDS38yNY2KZ0TKAxebE2F9BdauBznh/fPtM3PvjJZ5dmIHZ7qJg6YP4qWX8vOFDxlgO89Gch9GGJBKgVTA1JYh95Z20mER07nqJsJhUdNnL2FvWSbBWRqROgQQX32zeQnDKKBbnRXDjl2fxUQzx6vJseods3DE5gbVfn0MsErAkL9xr+Vs2i1yxkD6jDV+lFKfbw57SDsbEBxCXP40h60Sa+0xE+atIDNZQ0mZg7bgYpl4RJsQHqXliawlvrMjmtg0XmJkeAv11ZAvP0h+3gM+ONTA5JZCmPjP1PSbwwJt7qzFY7CSHapgTGUBRTR/9ZgdyiZiFueHsKm5nw+kmnluQxq0bLpAaouEvE4NJ272MioQbWT9mCU19JqanBrMgJ4yPj9QyOGRibJwORELcHg/hfkqkEhFujwe328PMtBA2X2jhtg0XuGlsLNmROt7bX41MJCQ/xpdzjf1cMzKa0nYDGrmEp7aXo1NIeOeaXJ7YWsa4hACqOoeo6zZyTY6O9JQ5rMlLBaGI9HAto2L9EQuFKGUitl9uQyER8e7+GtZPjudS8yD7K7qQCIX0GK3EBakpbh6kvF3P9LQQrh8Vg1Ao4O39Vewr7yLCV4lDUkaTqIQZvI7d6eLaL44wMyOY0ckeJEIJyX7JDFkcGORiMPcz0n2BxLnX43C7SQrWkhyiYfnvkTw09iEeio5FIxfjr5by/sFqsiN8qOk2khisRauQ8OCMZH4424TN4ebGsbG8f02udx0EnluUcdXoCeD2eDhV18tNY2N5aWnWn9ZmjVxKcrAGnfKPDNOm860ohC76rLAwx1tq3dpv4sPDtUxPC2aYYYYZZpj/eoaDqf+h3PvDRa8CeWHGVWnED2ereLP6Zl4f8wlWh4rPjtezu7QTrULMa8tz2H73WH4614LL7cHt8fD0ggxEQgEzcmJwieRMjotgT2kHwUILdaY6okUhtA5YUMsluNwepr1xmJ33jke280GY+SLEjueJ+Wm43R7WbXuWWfFjeGLeBIasXvvRrxdbqOkaot9oZ152KPPOPMTQyAfpmzCZ8w39eCp24D9lETUGIY99c4lX56YzNs0blNktOoQmIVz4ihmj76LbouCz3SdZM8pCkFZO/aCHXr0Al92Hxl47VZ0SgsaYsThcfHa8gdRQH0bF+/PQjGTqu430mu0EqmTctKWRr6jjpj1WTjw6hX33T7y6GTKOexKNMpUUNNCyh3fbU9jecoL9D0zi69sm43Z7GDTZ0amkfLV2JHvLurC4O+jWphE20ADNZxGmLeSTs3UI/KTcOCGeH34sw2MwcG1aAFaHC7lExKh4fyYmBiIWCcmN1CEQCClSjiKh/RApuQmsGxdLUfMAdT0mcqN8OVbdw5aiNp5blIHm76U+e5/G3VXK2cJ3uGdKFhe+P0R/eysx2UG8uTIHgMubXyFIG8FTC66j32Rj/nuniA1Q4aMQ02u0s2HdSAASjj2CLSCfow+vQyAQsOtyG8KBembaWxCMWEvFFb38NcFtzAgXYVcH4auSsjg3nL9tKcHu8vDqMu/GMSRZx8lTbajy/PjkWCMh49/AzxTKGKAg2o+pyUGo5WI4/yU+QencOsH7HFQyCcs+OcVT0yP5YXQFzpSJjHv5IPPjBHyx/TD90lBWZqiZXnw/U2Pm4HQVkp8YwycXenG7PUxIDuTDQ7WsHhXF+q2jWBsYzlK8wX2IVs5f56Wx7cgZTnR4+GCRNxt1x6R4XB4Xv1xo4ffSDr5ZO5ownZyRsf5kReg4U9+P54q8fuWnp5iRFsLoOH9e3l1JlJ+SD67LY295JwcruvnshgLkEhEfXpdHQqCaT4/WMyk5iCCNjEhfJV8cayA+QMnByh6+nhXI1tPdnCrp5OUlmYT7KrllfDzbLrfRbbBitDroNdlJ8kBCqj9fpo7h6d+K2V3aydnGXgYtdu6YlMBzv54lNkBHmI+cT8/2EpN+Ow+fD+aZfDfl7V6r4crCSO4eHQxFi6F3M0PaBJp7LYxJ9Kdj0MLb+2oIUMuYkBzEuMRAdpd0YnM5+eZkA+MSAnlhZzlZEVoa+izMSg/hlSsGz9RQDb9caOX7040EqqUsyA7F6nSjkom4/ecaPr9hEQcP1XOwqptbxsUS6a8iI9wHp8vNqDh/Rsb58/6hWlxuDwEqKUlBKmZlhDLm5YN8fXMBL+2sRCEV8e3akVfXvJUFUejFB4kP0jA7dh6VvWkMWR0opALEIhdqGZxoO4FCrCDZL/lPwcwvsc+RYRbSatDz5YlGVo+K5o0VOfgqJTy1rQypWMTCnDC2XurgjZXZXDsqhm9PNnC2cYD3r80jPkCDw+2iusvANZ+d4cQjk2nuN3Okuoe7pnhLEvuMNmICVJR1GP60Vv9yoZXYACV50X5Xv5t/59M1Izj86QN0aUdDznIAzjcOIBTADaNj/lPXhGGGGWaYYf73GO6Z+h9KTIAKh9OF0faHtjU1NIgnsjYyMS6bOyYncNOYGNaMiuL+6d7yrK2X2unSWylrNyAA7vjuPO8eqCZYK+fGsTE09Zr57Gg9Hae/Y4xzK6H+KkbF+yMXCxiyOHh+cQZdejPrVW/SH1iIzeHiwZ+LaBs0k6BLIkobytaiNl7fWw1A26CVToOdIK0Mh9PDnrxPuO6IjlAfBUKPk5TWn2GgEdmhvczRDBIR/0fZjlShQjzpUeivA6eNoNTx3H77/YTqFNz41VkO1fTwxOyJfLXkAeZlhtA7ZOeGL8/RZ3Rw9C+T6TZaqe8xYrI5+fFcq1eYoZCQGe7D3Yed7L53HH/bUkqH3sKm8y18daIev4KFpCbFs2H/eWq0I1k2bRwqqZjydj1Ol5s9ZZ2s++YcLreHYK2C60fHEKcTEdu1F6LHYp/2HK39ZkbF+zMqzp/j1T1UKT1I8nz5bGMJy94/gdXhYvOFVnqMNpwuN7d8e4GTtb28sCQbTW8xg32dnK3v48191RS1DOJwu2nqN+H2uLnxy7O0DVwpOZrzGg0xK3nqQC9CoYAl99xPTHYe6FuhZDMAOv9gAvy9wamfSsaDMxJ5eFYKwVo5FoeLr47XA1ChWcw58x9DfHeWdvLWOTP6nhZQBWKwOjh1uQzH6U+Zo2tiWmow5xp6AciN8iXSV47b7Q06fJN1VGJne3EHI2J8GZSH0G50c+d3F6jsGuKJ+ek8v7OCY70acNnZuPsIL28+SlqYlvUzROxvboPc1YhFQnZdF8LaRDPLC6LJCNOSGhnCR5KbcMVPpSxmDWapjhCFm5iqz/ER2lg9KprLLXrSowKZV5jMoMlO66AF6RV1dVRsIpMKcnh3TymbzrcgEgq464fLbLnYjk4p5/2D1SQGa/BVSbA73YTpFFfLFJ+cm8bYeH8uNg+wcW0hvkopA2YHK0ZE8dY1uej1Vla9cwKHxYlQKMDq8M628lVJuXNKIgqpkJRQLXnROgjLJnnkXGxONx16K6IrwXyn3kZdr4kXlmTy+Q0F3Dzuj17JziEH14+Oxen0liOOjvfnc/cTvFc4QOugldeW59AQtoCXrxvH/vIuVo+KZnmakue/3wcKH1rXnMYdmMLJXQ101Q1isDh4ZVk2G9eNvGo8dHs8zM0OpbbbRFGLHqlEQGqoFplYxJDVwbbL7d4B3lfO2+Z+Ez+db+Op+Wkcr+3j5d2VLMqN4KPr8kkL80GnkhCkkVHbY6Sq00Bjr4lPj9Zzzadn8FFIeXFxFkXl1Vyua6J5wMJfNheTG6WjoceMAA/PLEqny2DhoU1FdBusNPWZmRg5ialRU/FX+HOqXMPrv1chFor5cd10Vo1I57bs21iTvgaAhh4jl1sGAOgZsjFkc5If7ce+ByZQ1DzIzuIO6ntNtA2a0VvsJPpJ+GJFHGFaORcaB/BVSfG/opovjPNjbEIgBpODR2cnc++PRZS16ek32a9+Ri/srKCkdRBfhYSjl6t5/Kcz/FbURq/RxpDVCYDb7WHgX/wOwFbFMpIyR1/9eXZWKG+syLk67+3fQm8xcuf+O2nUN/67xwwzzDDDDPO/xnBm6p+cC40DXp2wVv6nx++fnvyvjs2J9CUn0ttn0mWwMjYxkFAfb1nYo78Us3yEV7f8yC/FtA1a8FVJSQxS88uFVuZmhVLWoeej1fl8fcKXh042c/+Rco4YXiW6/15e26nn1/VjWfThcQLVMsRiIQaLg9puI10GK62tSTx+vodR8f7kRekAuPvKHdu/0zZoJrV5gCeOP85rk1/GtW4HFqebgHgHK10upDIxX58/itPtIM0vi7MN/dww4z1+PNvM8Zqqq5riWenBpAR75QjNA2Y+P1ZP+6CFV5dl4nTjHZxZ08v2onaeX5yB3mqn22Cny2DhrimJpIRo8VVJGR3vh0Yh4WLzAE6Xt/dFJhayMlWOWu5PqK/XQCgRChj94gFuGBONVCLi/p8vcdekBJJCtEhDM9iZ/wXXSpUcKuvg3QO1V5Xcyz8+SaJMhquyi+kL48knBLFAwLWFUSSHeJ//J1fK46q7huiY+AoTkoL4YOMFUoM1pF34mp6sm1kzJpMZ6SE89VsZA0YHYToPApU/8fMe5JWsASI0Avj9cShYBxe+BssAZC4jcuINvP57JdllHcxMD2VaWihnyhvJDlUyKyMUm9PbV/dgpZxYfwnPvH6YT6/Pv2pyg5l4PB6klkFEEjl1I54mJT6al7aUsLesk7OPT2NpfgRv7ati1Wen+em20RzvNlDvcVIY7ktcoIZZmd75VyKHG5nBRa/YhlQsIn3cAlDLSGnchr/Ye27HB2noNXWxbdsmFsyYid9AMRR9ydm0tzlW28u1I6OolSYzoIzm9e2XeXB6IpFaIUp3E2dr2vm1ykpikJqmfjMigYCStkG0cjG3T06A8u20NXnYXO3Dk6pthNgExOS/xrY7x2F1OGjus7DhdBN+KikNvUZWjvDq7//OhOQgqjoNKKUi4oI0fHT9lffop+tRZ63EFjOTUYEagn3kDHSauXVUDFYBdA9Z8Qj1zM9TE6z6o2zrye1lBPvIyY3yxWB2YLQ52HCqgQ+uzeexX4uRiIR4PHDbxDicLjBY7MT4KfnlYgvhvkrvRnvlN+ATzajBTtweeGNvFS8szqB1wEJpm57rEh3Y+/vxeDxc88U5XluWRXS4hmuilHxxuZWl+ZFUdAwhFMDmC60szQ1HIBQQ7qtg0+1jeHjzZcRCAQ/MSOFodQ9SkRCtQsTllkEmJAUwITGAeZlOZIN1TEiKI0gjZdP5FiJ8vWvO6lExrB4VA8B7B2ooa2/kgenJZEX4XFWlG2uPo3fpWD5tDp8fq6fL4GFKShApoRpi/dVcau6jvseEzeniaHUPsQFq4nQ+3Lz5LM8vykAu/ve11Z8craffZOfTNSO4+4rQ4lxjP6/9XsXjs1MI08kpiPTllWXZ7DnSxLR3z3AifQcfKNfTprfw2Q0FzM8O55uTDQSpZczOCuN4fS8elweTzcHx2m5Eoj8uwU/OS6PbaOPTo/UEnHyO+2LS8cTd98fa7XLQ99M9vGmexUvrFl79vbevH/On5y0Ti66u2/9vdpd08HtZJ2XtBtZMn4a/4l8r1ocZZphhhvnPMRxM/ZOz4XQj09KCmJcV/h8f/C/4+kQDzX1m8mP9uHlsLDmROsw2J9+eauLxOam4PBCgkbH1Uhs7izsI0kp5bkc5n68ZQUmrgaV5YUT66ggYnERpk5CpKYFIxAKeX5hBQay/926/XMJvd40D4KsTjSzJDaepz4BWZGfD6Ua2XmpjVUEUy0Z4h6KG65Q8OjuJop4ZAHxzsoGiFj2vLc9l6UcneTF8kMr+MswOKwJrDKfq+5ifE05BjC8CvAM7B812rhsVwy8XWvnkWB1PzEtjRIwfrQNmqjqNOFwuyjoM3Dk5gSe3lnrNZfd5Z7d8cqSO03X9vLG3msdmJ/PNySZGxwVwV+BlwvPncaSqG6vDzYScsTz0zUVE/v28dm0uAyY7iSEaNp5t4daxMfxe0c2Luyv5+qZCIvyUOJ1uaroMbLnYSqy/kuZ+MykhWr64sQCxy0N75QC+fnLkRgdHa3p4eHMxOZE+hKsEZF3RTX9wuI5+k52McB96jTaW5UeQnbGOoJg4SloHiPJT88CMJG7dcJ6/zk7lw8O1vJzZSU7uWBAowTwAW9eDOhjmvgV45yCdbRzgaE0vAyYH1xRG4X/iSdSaWFKueebquXLL+Fh6DFZuHhfLvvIujlT3cOuEeACa+s3c9OVZ9t4/AQHejIu3+T6YToMVf5WMtgEzGWHe4DAjXEu33szWyx38eoc3s/LtyUbUPTZOHu0mbkkMG9aOZNBs52RtLx/VBRHuqyS8dZDSeh+kTg3T2u9hsN6P50qjeSp+ARPSo5g2IgWlVMx905L44FAto+L88FFI8dWoKRr9GktyI1gy3jts+FLzAGKREJvDjdvjDUTKLpwjOjiBxbnpjE5bDx2XQeANHE7VmkgK1fDlTYWcb+zjyd/KSQ/1KuYBHv+1BJPdSWyAitsmxKE326/qvhHLQSRDppBw77XesrI9n5UQnuTLr629DNrsKOP3U9Zm4pnxD/HrxTYW5oTzzII0Nl9oRSiAx7YUUxjjx5vLs9lT1kGU0sX5NgOJof5IREKkYgETkoKYkxVGS6+e8IYfaOu6g2ajP0fO1XOsppesMC2Lc8NxOCEzwgeXG2qlqcgjM4gCflw7An+tkjaNhXH+KiYXROBwuXlrXxXPLsrg2CNTAChpG6S83QD54KeS4q+U8M6BagZMDoK1MkJ1CvaXN6CUiRCLRAjbzyP+7V4q5x3g42MtKKQi4gPV5ET60jNkIylEQ3OfmZvGxqCQirHYnQRp5XxwsJaVIyJ4vyOVh2YkE+mn4pqCaDoN3ps8Ho+HL4/VMzsrlF/v8A7KvjYmiNBEHRanm9npoQRrFVezela7kw6DlcqOIS41D/LX8T7cnilkQO49jzn7GdhN1MsWY7Y5SQ/3ITvKlx+eO0NCbhBL0sOYVxCK3Cebpu2tmP5Fxn9XSQdCgYARsX6UtBqYnBSIj1LKgMXFxrUjrh6nU0nRqaS8vjwbSpfA0Vdg9qN/LMoCEb4Ridwdl/2fWsv/JQNmO/O1NTxq+gV53AY0Uun/9t8aZphhhhnGy3Aw9U/O29fk/ofHGG1OPjxUy20T4/FReHtq7p+ezGsHD1M70AXEEu2v5N39NXQN2QjVyViequGHcy00Dbq5dUIc4xOD2H1/Lr5KJfOywzjbOMDElGCK20cxLzsUtVTCF8caqO0eorHPxOK8CJRSMc9tL2d+TiiLciOID1AhOvsL7pIymia8QYhWjk4ppktv4Znt5Ty7KINDlUb2lGqYciO0D1pRS0XIxCIemZlCYpCGMX0L+PFMM4uvjWDtlQ09gWpGxPjzzckG9pd3sWHdKMJ95TjMZvpKzrJ6orcHS6eScqGxj6PVvTw5L50FOWHc9f1Fbp8Uz4LscG6dEMeesk5quocI0sh5cn46Z+s7GX3pJ8zR6egtwVgcLtpdTrKyAvDzkfPT2WauGxXD97eMwu32eHsZxsVhulK2c6Cii5/OtyAQCph4pUem22DjhZ0VPD43laL6LlIy/ak7201rg55vrHr23DeeUAbwvDkOwe3HQRfBS0syEQoEyMRCXlqSRUa4D+AVeNz1w2mmpwZz95REri2MZmScP+cbB4jp3gBdfnQGjaEp/g5GtH2HaOrfQKoCvENAN90+mop2A8E+croMVo5H3cHE9Cj+suky87JCmZgcREu/mZouI3dNTfLaFIVCDlR08fLuSnbdM55Tj03jg8O1XGoeINJXwctLs1HJJMx55zjvrsrhdEM/76/KY3dJB3k6Fd+WDfDF2jzUMjGHq7pAAKEJYr4ZdFNb1sm3pxo5Wt3Lz7eP5pbxcYxNDKS0bRCVTMKCwlD0o35i45luWk0DvDw4CfveenIidZxvHOD60dH0Gm08NDOJkJJPOeVKoTB7Ag6Xm+UfneTJ+WlsK+ogXKdkRIwfI2L8OFTdzQHxInIVOo5WdTNo9iEhcCTGk41kR/nw7M5yCmJ8eXZhJrEBataOi0EjF1Pfa6LbYGVErC99Rm8J3p3fX6RryMaOu8cD0DX9PXqHbERZHTy1rYy/zEhm+o3pCPc+QoMolU2uNF7KuJPKgCFWfHKaG8fEEKiR0j5o5XR9PyKhgCfmpaGWS5CKhOyv7GaBeRNjQqK4/mwcequDl5d6N987ituZneKDs7IYidPImQYnWqUEjUKCww0KqRiJ2KvvP17TQ0X7IGaHB+oOsafWhCiygN/LOnlxSQZjIlVIKrax9Y7lIBRx89dnWTkiAl+VnBcmqGDfX3ly7gvMfu8EWRE+pIRoWD85AYAfzjSTGKzmzu8vsv2uyfTEHSZSrGHQUsfdU9L5+Egd35ysp7JzCIVETNeQjSW54UxNC2biq4d4d1UuTo8biViIRi5h08VmxiUFkhrmnQdV32Pkqd/K0FscaJUSluVHYrM4Ofx9FXPWZ+IXqmZ5gfcGTZ/RhkQk4JZvL2CxO3l/YRTyeH8Mp7/E1lKLau4VrX1YPrisLAqPIC/K72oJ3YjpAQSHiVCE+9FtsPL1xVbaB808PicVAKfLzYTEQHqNNh7dXEx+tB9Wp5NJSQHMzQr/9+f/pcyFkIw/PeQRCGhLX0OEr++//Tv/gE69hdWfn2VZfjiRoRmEJPqCajiQGmaYYYb5/wXDwdQwuNweTDYnHreHkrZBovxU+Cgk9LtLMbiMwBzSwny4Z1oiqSFaHG439h13oukPolKyEKPVydTUYN6+9DplfWWMVz3F9st9PD43hYdnpUBnCRz9mBtnvc2+sk5+udhGebuBF5ZkkRCkwkcuYUdxB74KCeF5q+no7WdSchCTkoOwO93UdBvoNFix2J0Ea+SE67xlL0EaKUapCPobGX/2IYj6khlpIbjcoFVIaOw18dmxOrLCdVxsGeT6sHbipaeAUYyKCyBkjA9CoYqGyz2MyPb2Br24s5yF2V7l+9L8SPqMNuIDvCUzxW16HtlczJm/TmNPaQexgSoQSqme8hmBwYG01DVy09gYjtX0YBcImJ0RyhPbylhsd6KQihDW7oOT7yG6cTsGq50XdlWwJC+Mm8fFsjQ/kpp2PU/9UopMBMvHx+J0uVHuuI2ejDmEj7oReZKGFe1awnVKXt7TRK/sKZ6WBaMFPj/WwLIsP/psIh79pZj52aGE+yqZkBjI1zcUEOwjRykTc9tEb4D5+Lw04FMA1r13DLcbbh57B9aLvRTGutl6qY38aB1BGjmZETo4/Ao/DqbQJUtiR5UJqRB2FncwPjGQh2amYLQ5sTldPH1lXtlnR+uID1Sy5VIrKwqiWFUQSUKQisr2oStnnYdJSQH4KyXsvX8iVZ1D/HKxlSnX5hGe4otE4V2aqjqNBGnljO7bzGi/VtpS16HZ8wjHF3xAiFZCuK+IhzYVMS01mNsnxvPI5svUGo6R7+eiIHYmo+P9vTOLqrrRykX4q6Q8NT+Nx34t4c7wQPbW91BXe5nVhdFMSg7ibEMf8UEqFBIhD/xchNvj4ePrR7A4N4Knt5UhFQsZlxhIZbuBdr2FtFAtG28uvJppenDTZURC+OKGQh74uYgDFV2sGhkNgN7s4O19VaweFc31X5yhMMaPQI2MMw39PDQziZquIfpMNkJ1CoifjLTLhwSVBqFISqS/lhvHxOCrkhLpp6JtwIJCIkIgEHCgootdJZ2EaGW0Dlp5bN2zRAjFfJvZT+uABY/HQ0KQmgC1lOAgLYcnfkC2yof7pikBuGOSt4St4Mrg3Q69ids3XuT+aYlsv9xMsV84D030QREQyZ2TEjjb1M9AdytVxdUcbSvn4TmZ3DAmlm6DlX0VzRTOCAClHyBgw82FVHQaSAv14e4fLjI9NYT3r80lTKegy2AhqGUPVSd/o6rgeW4aE8vYhADePlCNzeFmWX4U2y+38eS8NA5UdrOruIMoPyVj4v2ZlOy1Hb5/XR5DFjsej4cPD9ditDq5fnQ0I2J8WZoXToSf98aATCEmf30aj+wtx+xw8926UThdbv6y+TKRvgomJPnj33aUKBdEpYxnh/U6PiipZlpRMw/OymBjix9+aglzokX02mv4Zm8lf5u8knjXbiz7z7Av92U2nmtGLhFz5+REJiR7SzLXb7xAr9EOeJiXFcqrv1ezYkQEAoGA1f8omBFLIeBflDnXH+GkLZaHt5Xxt+UwO272//L6Dt6exydiy8lOjkIXmgakgd0MEgX8g4HuwwwzzDDD/McMB1PD4KOQ8MyVTfCTWy5w79BbOOY8yRuz7vnTMaPjA7j7h4uUtRnwYyGPL8xBcqwDBODxeEgWrSI/qZMs/yRae+twGvvZsvN3Fk+fAuF5aOQSluRHEqCRXZ2fIpeIeHtfFX0mByOj/dhwpodnF6Tz09lmpqUFs+VSG8Wten69YyxF3UXEhwUyITkTgFsnJjBgslPR20tq+mKcIgU3fXWWG8bG0D5oQSEVkaFzMj5Ow2hxBe7aI/xqWsipHy5z/4pMPqgWcE2kCl3VBvqKLuK//D3unBLPS7uqMNqcaOQSilr17CzpZFleBL1GG9+vG0lZm56ydgMahQSr3UmZ3oavSkpTnxG7w01xi55l+eGIhAIauod4ZPs2xqUoiZdmMG7qkxxo2k+IJIuqriF8lVLONw7w2u+VVHfoEUmEPDI7hZRoXwZMdvznP8Pd21qxV54jIVDF04syOd/UT3KwhsSgaXxypBatQozD5SJ49zoic1eSE5lGafsQR6p76TPaWTsyBL5bAHPfgKBUajuHaDz+PdPGjobQLL64YQRmm4sofxVv76vGYHZwrKYHvdlOoPZKMCXTsLggDoKT2H65HYvDSafBxpQ3DrP73vE8ubWUhCA1d1zJPvSb7DhcHsravWayH8+1EOOvZGxiAB8e8mapHG43/SY7aeFicqN8+fyGAroNVpaPiiJEq6D154dZlTgKbe4SKlvX0qYfYmp0EIy7lTlp0azaeBSFsp87Rs1CLBJw3WenSQnVcntKFhvOwlsrYnnw5yImpwRhc7pp7LPwwq4K4gLUXDcymoCgNB6bouRCUz8t/RZ2lXaQHuoDeBB4oM9kQywUcqSq+4qAIwmXy4NOJeXTI/WE+coZEevHkapumvvNzM8Oo7bLyLMLvTKON5Zn43B5uNzaRXZEMD5KCU9d+Z5VdxnJjNAyJj6QBdlhSO0DbL9r3NWN7RHBCL6oqOWRWaGca+zn14ttPDgjibI2Pc/vKOe2CXH8ZVYKAEnBGopaBukdshIpHgSbkd+qzXx1ooEQHzmXW/X8bV46nxyu4YWdFXyyOp+xrxxk651judw6SEWHgZwIHbtKOvlwVSahYhvb7hpLXKCarEgf8qL8+Mumy6SEdnHrhHh+u9hKc7QvprBVfHOkjrumpTIxKRC9xUFJq55mp47A0Q9it7rQKCS8uKuSdePiWDsulhCtHB+FlLZBM3PeOcFbCyA+ew5Rfgo+P9bI8hGRfLGmALlUhFwioqZniJO1ffirpVS261HJxDT1mzla1cOAxc7tExO4bcMFluZFcKa+nwlJgYTplNw7Lenq2mV1uPj6ZCNCPAxanKy/krH++mQjMpGQU3X9PJRpYkaiC2K9GcOmbj1L/Rqor25iYEISByq7ifRVMCcznA5zJwOONhp6jXxZk0NufxoM2ug12XlzRSoZ4To8V4yny/LC6TPZWZIfiVwiYkJS0NUZUX9nwGTnVF0vc7LC/u0F2u2C/U8zctYrvL86ndTgoH+8oDeegMhCEP2hUJeKhUwUlUKnD/gHg1QJP6+BjGWQc80//nvDDDPMMMP8Q4Ztfv9TGOoEm/E/POylZXkYgwuQagLAosfw/c1UVJR7/7O/gbmqSm4aE8V9C0chkKuZmBxEz5CdrZfaGDApCJHkkxqq47lFmRyr7eezxiD6BD7sVc7lvQM1AAiNnXx5vJ73D9SQG+Xtz7hRfoRLZw4iEQrQW+w8t6OcU3W9rCiI5LE53k3jq2df5ePijzlV18sruytxOex8sb+IVw+2gi4S67mNAByq7GbLpTaCtXJWdbyCrOxnqgZFhMelkJcVilgjpa7bSEqwhugYiLbuxK6JAKmS/Gh/FuaE8fLuSgAen5vCK0uz6BqyUttjYt235/mtqI2qziGEwDenm1BI4YmtpczPCkejkHC4uofSdgM+Sikrowwo7P7InQkcaTBhDknj27JvUSqNbL1zLInBGmq7jRgsDlaOjOGjmwq41GngsV+KueGrs1y0BHPXvEIkYiGF8d5ZPE9tK0MmEfLWvmr6TXaMVhcPz0pFmrMSLm5gUV44pW16vrm5kBvHxMCFbyHnOvCJZNBk52+/lbKxLwUU3nKh/eVdNPSacLk9rBkbzYhYP7bfPZ4XlmSRG6lj0/kWGH0Hh/oDuPv7SyzLj+RSs54ZacG8tiybbZfbaR0woZKJqOs2Ut1lYF5WGCsLIgnTKRgw2VFKRewu7WDDyUZGxPhy//QkOgdt7C3v8p5bjSfA7aLn6Gc0HvwSgLOeJPpkMTDYhmLPvVQ1doJEDpXboOh7Xlo4gidmTGJEjB8hWhkz0oJJD9WSEJyLEDkigYCcKF8uNA7Q1m/moRnJ3DA6hsfmpJIXpWP9xovsPlfJWPdFkkPVFMT4snZ8LBlWEUFtNqalBPPo7BQ6DVYOVHax9WIbd/1wkfON/Tw4PZEZqcEMWR1IJd5S069OeDOTcYEqqjqHEAgEvH/0LLdvuAzApeYB7vvxEgC3TYy/Okj1id9KcX05B2oPUNUwSEVNH4khGu6YlMCIGD8W5oTz6Zp8suS9JJa9Q5fBglImRlSpp/hQC7kROl5aksUX16bzmv8OrBYjIVoZf5ubyn3TElk+IpJXdldwqWWQ1BANfmoZe+6b4M0GXmijS2/jYFU3zQMWRMU/wtbbiAtU8+beapxuEAkFPL0wnRvGxABwzcgoStv03D01kS9uGIHxSslqr9FGRaeB7iE7Xxyr58Vd5UhFQm4eG0t2pJbabiObzrewfuMFEoI0fHVjPuecxVT7yfj5fCtujweB24nk1BvIbf0AtPdbuNQygMfjoaprCJlIQF2XAYvdSduABbVMzIKccNLCffh27UjWjf/DYPjtyUb2lHZgc7ip7hwiWCtDKIDUcG9/3rK8CLIifBgd78+MRB/wi+f5HWU8sbWYtO7tKCUCVi5ayKXGAb66qZCMcB8e/aWYJclz+GDuXzjb0E+PW8KlKD+S43TcPjEOrVzCTV+d5cCeeo58V8XMzDCuHRWDXCLizb1VvLnPayr966/FFLd6TYFv7qvild2VrPj4BF2Dlj8vyIMt7KrfxceFKxFHFZIbHoVc/GeZ0J+wDnl7H3u9a63L6eTHAxc5XNXN7rjHcZ76EFrPe4+d9RKk/OcyXMMMM8www/xrhjNT/wS8tKuCjDAN83Mi/t1jDFseRBY/FtnYO/70eHXnEL4qCYEa7wV6S1EnyphVJGsC2V/dhShwGaO3LQLNdyDT0m6WohALufeny3x6fT43jY0lR2Wh+Jf3sBes4lBVD+8fqsVPJUUogF0PzwEg1OjB6fbAQCOfHqpi7ch0LrQb2F3awUMzkxnY201aVDTnOs38XtbFmcenXTV2ub+cS1PBvbw9+W1UEhXtg24ifBVs2raV9g43N0yZCKIm1DIBP9w6Gr3JjlYpYXdpB/LEx2kYErGnWk/hDddS4HAxTytn6huHeXpBOgE+MkicSuiYu0EsA8DhcuNweU114Y5WCIwhfloSFrsTs92F0epkf0UXl5oHSAxS893pFm4ZF8PWS20UxPjx+Q0juNjk3SitHvqShYkL0BV4G/Sb+ow8nvc+cTrvhq68XU/XkJXlBQmMiQ/k6xMNbL7QilomZlZ6MBkBKlxCIe9em0tpq54DFd1MCfJhw4km3liZTbfBRoD6SrlQ6nxMPvHE+av54oYCJCIhb+6tYPpQHzmTJ4NMTV17H1kRPt5N5xVLWGOficryEk4Xa6k0Kvh2bSFLPzrJ9LRgUkLUuD1ebXlhrB/BV37nr3NTiPFXoZFLUMmEmGwhbLnYxt6yTjwImJcVysHKboasTuZkhrCiIJIvjzVy99QE3tlfw3e3jOKj1bn4q+V4jD3cvKWT9VOrKExMIE3kfT1V6jHInDpihUKipQbuGB+Jxe5CMe8dnip6l/HGIgZ6knlzz0XKOgw8M17Ft0dqmXZNPl/fVACVOwgVhzPk6uRCm5ZJKcFYHE5WfHySawoiuHlcLIWSGqjeS1CklHRHB9uKxBhkLrbU9/C0fwe2cnjrfCDvXJPLLxeayYvyJffMfexXzuWbjiiWW5wszg0nUC1la1Erqwqj2FPaSb/JwV/npjI2Ng6hpwOA+i2NONxmFn1wnM9niOk89TPi6U9z37RE7NbvkQXHcuqrMjwmJzfdmw8eD9uK2ogPVHGqvp8bkz2MiA1gxGSvCdA0YMesd/DM66dpDRbz9qo83uR2osrsuA+0sPDmDL4uasfucuNxOsi3niEuey6v7alkYXYYP59r4s1FyWh8tDy3rQyT1U5j2BxiEqcD4KuUUNmu5+ezzYhFAl6aGw9iDRcbBylqGQS82Z0grYxnF2YSH6hm0+1es5yfSoJC6i1DXD4ikpK2QU7X9tFqsJB5ZWCtj1LK8dMTuOH6EciShjBYnFwqryC7ciskTwN1IBOSA/j+TAsROgW9Rgfzs0O52Gzgr3NTr65hfy9dBdhwqpFfL7by6rwM5JcH8ZkShsnu4KWlmTz0cxGpYT5YDIPgo0CnkrJ+ciIej+dqRjCsswGby8XkKTeCVMU7xzrYVdrJqAR/xiUGMEpUAe50Np5txWJ3cvvEeG7dcIFJKUEsyYvEYHEgEzpxB0Fyeuif1lpfpYS0UA0mm5PGPjMV7QayInx5cEYyhdF+fH6inisTAq6ya8+9/KrSEu2fwLHWY4wMHYlU9OfyQIfNisvhRK5Wg1yD+65LGO1utMBARxuXT51GNmsO9X021JN/ZnxcmLfET64FuQ//Hg6Xm98utV0VAA0zzDDDDPNvMxxM/RPQNmjBTyX5h8es169mij2Gtf+vxz86Ukt+tC+LciMobh0gTCdHLhbR3GfmvUP1fHfLDErVH3OpRsMtk5M5ah4koGmQ6anBnKzr5XhNL7ePieFodAENgw7eW5XJwcouwrRy6vtMbLnYwswwG7H9FWRmzQfg6/W+CJU+rLHbKG/qJTU2EHvMyxisDhz13jvSHx6u5S8zvRmp2sz7iI0roKzNQ6SfB4lQSKfeQg/J3Ls0hOjQYDb/dho0yaS26VnzxWmOPjIVAcBQO6GDHTw8ayHfnWmirttIYawfry7L4kBFN1JxIH6atQzu66VgnoYfzjRxsq6P95em8sGuIm5ruAPxjGf5oTeB8w19hPkqSQhSsbe8i9GxfkT4qpiZHkKcv5IvTjaxt7yTYB85h6q6mZsVhihvNbpzn8GYm3A63TyzrRyBAL64sRCPx8MDP1/m7jFx3izFUBcxGg9+SgmLcsPZV9aFpW6Ig0NDbLt3Aj+cbSbST0mWRYguVIdK6p3fo5CIOF7Tw4HKboSosLtqeG5RJnanm7peK5csU4g6YuWeqWZu2XiRF0cLkB7cwOX8hwnSypmVEYZe3sLvTSZen+rNlizMDvMa75QSippbcLrcfHu6EZFASG60L34qKU9vK+ORWSm8ua+G+TEepgnPs3zBSiQqP3RKKXlROn4+30qfyUGkn4pPb8inodfI2Hivjrms3UBqqABtUCBCXQQnukQUTp+NANh8oYVjtX2MTwoEbSAt875H7IbZL+1n653jmB49h3jfGC7qPahkYr68sQBfoZVJFedRKDVgN+I59QGtonWMF1cRmX0NwVopAuSE+shRySTMzAjlWI0C3xkTCSz5geX9X9A0djMv76/n/axzxPRcAIeJD1d+xOmmfmp7zbQO2Hhg5g1MC8pE2QnjEr3v1yObLuMB9p25zLsLInm/XE5Vp4GRsUGMjPWWZWVODKe/Z5AZgSq0WiM9mhB0Kgk/n2shWCNnZZiYNTdn4bmyo95ysY0dJR20DliYmxmCY2QG4sl/5esTDQxZnYjCxExICmRNbgB2jQiH082llkFUcjFLliTiG6JijW80XXorL+0oxu5S4NPVSm2PAo9AwMakE/Drw3DbUe6fkcTZhgEigwPoNtq448MTrCqM5nhdDxOSAqmoqeXxj7/jlbvWsHp0NFmR3k34lJRAjpU2ctdne3jx+qmIhQJe3VNJWbueawujKWtvYNDi4PXlOfx1bipP/FbK4gw/aC9CbwknM9wHlVzMgNlBRpgaoUfJ57I12CtUGIrKiQ1Q4vF4GJsQyBPzPLy1r5Ytd3rtfD+da8Zid3FNYRTXfHKK2ybGkRupw+pwEaxT4BOp5veyTuqOWViZF84t0SFUGyqI2jCLz0f8hlCpI9xXQYiPnOwIb5b25vGxAN6gXSpiTVoPCwS1KKUTUTr1cORBiP6N5BBfnG4PP5xtpjDGF4fLzbsHy1g/MYn1gi24zg2wJfER1oWqeW5HOfdOS+SmcXG43R5u23iBCF8ZP51rJTlYS060L/Nzw5mf+6+Nqxnjn+PEpVpEgkpeOP0Cr0x8hc6uYAI1UvKivT1uv+77gtP2Yt5a8gl4POw+XcS3ZXa+uS6Vgzu/Jmf6GrpMTmp7hhiyOxifGgZFG6H+GFyz4d+9Zhwo7+SxLSXEBCgZETOsUB9mmGGG+fcYDqb+CXj/2rz/8JiFjiAcTgmn6/sYFffHhfH15Tmca+znhR3lVHQOseWOMQi23M5gzs24PaA32/mlPQCz3Qx4m9TbBq3MSgti3bcXWZAdiqxiEyuztcyMSEYuETE7IxSPBzaeacGNG/+OckK7j/FrWwLhOgWz0kM48dOX+Kp19BYXk/3Y00jFQopaBjnT0M+NY72N9q//XklDr4kPrvNmtw6fLGNEjB+JwWocbs9VSxmAj8QFUgFOlxu1XEpjn4mZ6SEMWY/S2FXPjRsusOfeccilYjYfOI3K0U+3IZq2fhPNFhcpYQqsDhehOq/gorH4EpWDxzg//UNyYzMxtdZztnGQ9dF+xPipeW5RBikhWjbv2Eljo5rlIybx253j0MjFOFweEgPVPLe9jHunzkMb781KrfrsFDqVlIdyo2nuN/PXH4q43dcPRYUeRkbSs+VRYnWJvLXqQfxVMhx7OsiaHs6qhGTcHg8SkZAYfyXPXKplkioQa1UviUFqeo029pV14a+Rcsv4ONRyMfQ3ID3/FR9c+xTP7ihHK5cS5qvk49W5FPrZ+L3Uhx/215ASrOFSyyA/3TaLKQeeg55OiEljzZWSrq+P17PxTBO3TYxjTmYYAgH0736J1s5+WqyLsTpcvH1NLk6bGZVfO5LAIBCKOFXfyxu7yxmwuhkwm6nIqmVhwkKcLg8auffOenGrAR+FlPggNbMywzBY/9BJT04KpKLdQFOfiXGJgdz/UxE3j4vl27UjMdsc3PG5gX0P6pibpSBAI+XZ7WV8tqaAOcvWYhUJKO8ysif0LeZlhTJkm8ye/dVEBWiYYjvEO3GDGBNvZt3X52geMPNYdBXjE/w4FnsP4w/fRWro0+x3T2LuiClsatXharZQ0T5EUrCG4pZBShUjqKo18PRv5Zx8dDJquYSVBZGkh2qxlu8CYTweDzicLu7YeIE7J8ezp6yLsfEB3JKThN3pxoOH3vSb0Dg9FMb6IxUKeHZ7GXdPSeTt/VWkh/lw55RE1o6PZcjqJFAjp9doRSEV0Wmworc4iAlQIcTD3o5+FoeEU9Q6yDML00kJ8cFjdXGxppfgEBXLPj7FljtG89IuGfoeKc8sSCXERwGSpd7MhFDIwcoeytr0LMoNRyEVEROgoiDWl6Z+EyqZEK1bz6okC0iVSD1OXtlThc3pYnpaKH3dbZR3OvC4PbiAAbODvFAfMsN9qO4aIiFQzYXGfp7eVoIHIZ7WC3guPMmL7rdYkBuG3enm3YM1xPgpSQzSMKQdQ5hHSIBGzMTkQFaP9p6L+dH+/G2u96bR8ZoemnpNFMb5IZeISAvXIhGJyIjQkRGhw2J38ciFOtaMjubx0TGESsUc/b6a3CUjKA34npGhcSilYvaUduLxQLZpNyj8IGokdqebya8fZu24GLp7+3hc5R1OjdIf7i3m69NNVLS3EKCW4KuQ8ND0RPotDjZX7CWusYFpjgrMo9aTmh6HUAhhPgrkIm+wu+1yOwuyw0gNUfPoryXsLG0nJ/rfsPM57WzetpWj1nAeWTCCcPVC/uJ+AIlQwunyOlyeP1JYo0bNRnIM9u37nqnZ+Uw5uYa0a35H7hxitqKczwfMVPVYsLvcGIw22LAUZrwI6Uv/4TVjVmYYOwJUJIf++9mrYYYZZphhhoOp/zFMXJzAzpZeBs32Pz0uEgrwV0tJD9OS23mQL38XsSZ6Alr/SG7K6kU1UMNL/yJomZsZisHqICvCl30PTCDaXwVVHZQ09HOwtYKXFo/k3QM1pA/s59aQAIILFlPVGU3gpOVM6Tayo7idrUUXmO2rIjA0ntiU0cx++ygfXJfLuMRAxiUG4vF4SAnRorc6EAoF1PcY+eVEOU8md0FqOgaLg/UTE/j6RD2rR8UgFgn5pj2CUJOc5wKLWRHcTo8+lRXbyth0+0qyRqzk4/o+GnrNRPgq8DVUoBs4T0nPdcQEqHC5PRxq6qfQYuHGsbFMSgwEh4lgwWGUvkKqO418d7aFkbG+iIVQXnyWFc3PIJr1LNMNv2IILmTZhxKmpASilIm50DzIfVMTOVHXR1OvmfVT4smPhrumJCJ3ejj5bTXL/lqA3ebCgZPxa71CjaL0RxBIFGS7PPx1SzG3Xp9Mt8PBvd+c59f1Y/jgunzcbu97Y7Q5+fFsCwarg7QwH5YVRNDS3oX47EdcU5rF/ltTkIikGCwO4gI1pIR4m96TbeXUDUQxc2wBM8Z42FPaid58JYiZ+gQAFxr7OV7by6h4f+r7zNw2IRaFVEzSlcZ5fdpcXD4D/DzGW9JV3m7g/UM1jI7LIbq2j9I2A+snxTMtLYx9FV3MztSxqekkUyKnUBAbzEeHa9l+uY13V+Xx6C/FKKQilicKoXwPnHBB8lz8A+KYIfmFntZ6Tp+/gzcjjyEIz2bj6WaifBWMSwzATyXj+zNNNLd3ESbUIxIKePX3SoK1cnqHbJS06blrSiK7zpTy8MRQEspe4qSykDFx0eAR4HB7uHdKApH9TYhLf2Vy7DhOuBZwoWkAsVDIwRYXPUMd/GVGItPSQnhhZzlvrMgmI9yH1FAtGaFaNAopp+p6+Px4I2NitTze/j7uC83c/UAl9UMCugxWDlZ0c7ahH3+lhFHx/jy/oxSpSITV6WZcQgCBGjkCgQepSIhQAEabC6PVW2Yql4iRS8RUdBhY+tEJjjw0mUW54bjdHtLCfHC7PWy+2IbR4uSDQ7W8tDgThVRE8ZkO7jlSyTUjIzn28BSEApibGYLB6kIhFWN1uLgwoGHsyFtxuz2MTwxg9ahovtr8G5NiZLy+YhYej4eSNj2DFjsnO3yYMWYF4B1M7aMQY3O68VVJWT93DOcb+9EqJAgEAu5JDufpU7W8f6gGlUzMI7NTMdmcrCyIZmVBFAhAUDiVh+uHGBHji0omZsdd47jY1I/N6UYiFpEepiXcV0lTn4mvD1cwI1FFWHgU+TF+HD9+iF53BNIKA5+3DtI2YOHFxVl8evkzPr8QwLr8xRhtDp6al86ivHD+svky68bFMf/uHHC7oa8dAnNBpubOKV5hCic2gzYUGIlULGR2RjDfn2liZUEkTHoBt9tDZZcBH7mE7HAfhiwOyjoMGMwOPjxSzxNz03h70QJ8ZD7YZgRy0uCk8UQ9czJCrw78PVbdw/M7y9l25zgi/ZVsXDfq31+w3U7GCstoDBjgcEsj16Veh0ToDST/ZVkjQHRAHPaUMawtup8Q+1tUm2czVS4GXQiaG35CdrgGm8PEVzNE0HIWFMtAFw4y9X943RgOpIYZZphh/mOGg6n/IQTH+nBz7L99YezSW/nuTDMZ0ihCOuroyJhIqDoEeeMvaE69BQ9VQus5ei5u42frZMqsvtwyPo7fitqwOty8d+1szg+8jljVyInaOJbmRWC/KGVHcSsSZyNHanq5ZUIcYToFD89M4cdzzSwqeRF/30fRB6eSE9WDSuI9FTs33spGUyE54+byw5lmXl6cSXWXEWNnLa7Bn+gImsiMd07y7Pw0PjnagEgo4PrRsdwwJsZ7B1jn4s5xHobC/XlUKbv6GneVdFDcpkdvcfD68uXoQteyvaOIs2YlPWYP2eE+XGjpx2B1cODQAc426nlpvTe4sDldHHhgImcb+6loN5CZmoFUPRe6K/Bb/SV+wDRJLZ0GKxfLu8AjwOOBH28dxZv7qghQSaHuEBMFTkifzogXAxGJhNyVHUlslBap3Pvap+encNf3F/n4eCvBPnJ8A5UcuehtzC9uHeTjI/V8fVMhvUY7u0s7eHlpFgqpiItNA2wpaiPA1Y+PtYTV+TPYVWFCPzSO3d9d4qfbRl99H/aer2CvxcmzK4II9VGSGqph0/kWbvnmHFF+Sp6Yn87F5kHONfQzJyuUzHAfll/pmajbeS+Yuole+h1na2ppPtPI+KRArvv8DBOT/GkZsNBhsFLV4ZUv5MX40mey8cquVuTSFZwPczE3C7oMNlJDvT1jY+L9CdPJoWQDlG0BXRTU7IEbd5Iankao2Z8yvQHf/g7e3FnBqlw/Rp25m2mTX0QiElLWbmChaQeFmm52lqQzOs4PhVTE+we7eXdVNipjM8uOzqJh0VacYiV79FGkRRSgE9r5ZpacOokPfgkrsQyNQS/QcNuHl1g33pc0uZ7Epp9wz3kcg1vEdZ+f4fRfp6I8+iIMJPFodSr1vUaeX5iJxyPglaWZnKzt4xfTjURlxPDJ9+XcMSmBnEgdCqmIJ+amIpOKoLeWA5XdrB4Zc3Xu0qu7K/FRirmmMAqBUMAbK3LYeKaJjacbOV7dy8h4f8J9FIyJ9+NYdRfdJq+GPi3Mh7XfnEMlFZEUquXX9WO454eLpIX5cPvEeN4Ll3Ospo8QHzmv7K7guzPNnH18GnKJiO/ONPHRoVp23zeBig4Dt2+8yKnHpjBK3kLQwBAGy1S0Cglf3VQIQMegBYvDxdx3jvHU/HR+uHU0R6u7qeocwlcp4baNF/jtzrFE+CpRxmgIr1WilElYURCJ1eHix7PNrBoZxYdHaukz2nl0ShLjEwN4eU8llR0GpGIhMQFKrHY3zy/O5N19VShkYvKi/Dha1UlrXQ9/WxsFbjeFZ+6mvvAV1KHxmNQO/FT/D3t/HR3Xla3rw08xSaUqMTOzLLJllJmZ49gBh5mZO9BhThxwyE4cJzHEzMwgy7aYmbGYvz/K7XSfhtv3jt/9xj199IyhYWvXrl1Vqr3XXnPNOd9XitPpQisOY+3JHhYlWXn/4tvoutOYlx1KQoAnr+4o5eaRkYyPUsKlDRCSR1O3k5d+u8obN2bhO/K+vxkTh0VomZ4ezE1rz5ISrMHhdPHmngryoryJ8VNR1ann0cmJbDhTz/GaXt7YVcai3DB8QyScaj3Jjy0nkA8+jtMB902I5avjtaikYo48Voj6mpefXCL65wO2VEnQ3Jd49N8Z3PsbOdomIUf0DmmhWaS9+Ie34CcHq6jq0DM7KwRoBZcdMpf9zdP7Otu43NDJ2Nz/AzNglwvLqTXI0uaBZ8D//vOHGGKIIf4DGAqm/ofTb7Ty/JarRPl5IFXH8LDtbXbub+R40nKssTM4mzaaEWIp1B3B09RMYoAEg0GFUCBAJRXjwo7T6WJF4i20DRh5dlMp2+4bhX74CvaUniOgoQ8hcKmpn11X2/D3lPH41CTI/wWTzUl5ay+vz09n/NuHuXFEBNm+BaREJDMs0oebXPDs1iv4esi4a+4MupXzCVXLeX1eKlNTgugz2xkWrsVkddDcY6S4qR9zYgBBmixu//IMz89M5P295cRUmEnP0mCw2CmI9SUh0JPixl6e2dhGbpSVFoOQOBpR+oThcLjIG5ZLeFgnA0YbJpudDw5UEaiWs/NKG9s07+Gw5VEcfw8ZwZ40fL4UcdYN3DJqBm/vKWdsrB+degsKlxlNdykvz8kH4HKZgeeLVNxrb8dLISUt1IugVB8e23KFrt+tfLp8GAlBah6eFMcPpxpo6jXipZBQ2qbj3nwtiRdeYmbaI5w4ewZ1VxkfLruZ+3+6iBO4bXQ0XnIJHQYffPOXc4e3kkd2tBMm0PH2ojzqu/V8e7KeF2alsPCG27GeqWfR56dZf2seT226gp+HjGCNjMoOAwC3jYnmtjHu/o4wrZJPD1ejkIiYkrYEk7kPu9NFedsgh8pN9BjtHHh4DCu+PsOy3HBUMjGPX+t181bJCNYo+GH1cCw2O4FeSqjYxYvD/CHULR/+6eEaXpydTPCoB6Dg2oTW4pZTd0bPJkwpIVwgwOYYz+KmE+wpO88a6yMkFplZ4aEnM9QLmc/d7OntZ1tRK7OzQojx82REtA9hWg/obECQMJXg2Ez2O8IwVHRitNrpqLjIXbsHCfM3YnO68JCJeWN+GGPj/UjyV+MvkxElTYYgbxCK2HT3CKo6dKRGjUUv1nLryAia+0ycre+lrlvPvKxQBEIBo0yHabYuYMWITPaVdtDYa2RvaQc1XXrSfSHu+FQ2LdmJyy+E138+xMPT00EAnTorr+woZUqyW6zDZtRRWVuH1isYpVREx6CJ8/UDdOqsTEsNprHHXXa7akQkcolbPOFoZSd6i4MYfxUCgYDqLgMp15TrHpmcwKLsMNr69ES1bGdB6iyyw3N4a3cFDqeTu8dFIxOLSJp5L906M2Ne38/czBBa+k18tGwYy786zfLcMEbE+BDl5/anOl3bS2KgJwWxvjxQGEuoVkldl56V35zl3UUZ5Ea5y4n7DVYuNvVT3alDLhFz2+gotn50CZ8cT0ZEexPurWB0rB9aDylquTvQ8DnVT/LUMLIitay5aTi4nO5zQyhEeu9pEmUeJALDzTYK3jhIhI8Hi1KmMj/JhUgowGHxJ97PPblfWRDJ2bpe1p6oY3zSCFi5GQBlr5FQrRK5TExrVR87LrWSNzyYjDAtszLc/UvL88Kp69aREabli5XD8JZLkUpELMkNp33AhNZDxtuLMrja0o/PNd+oMcnLKQgZw6HBYN7YWcHFxl4ae43cOiqaV7aXkhOpdWfo/g85XHKQJJ9IAgKvKReeeJ+Z8ljGjlv5d/t26c1Y7A7mZYUAIRCa+3f7VF45zXflsv+jYOq3c/XEXzxCbPhoFEPB1BBDDPE/lKFg6j+MY5VdaFVSUkO8cLlcCP4XhowapZTf7i6gU2fhaGUXghFfkqmzo1bKaB8088SvbayWtDK98GkkThcnN19hbLw3VoeTp2ckY7Y7+OxAGZuL23l7cRaZYRrWn66na8CMSCjg/vExCEUi0uXdvH9RTofOyicHq7lnfCy2TbfT3avFGvQstyaYyY/1IdJ3JekiIT+crud0TQ/h3krEQhHzPj3FtJRApqb60623cqKmm44BM819ZvzVcg5WdDFgtjI3SUPQN+O4N/Nj6roNHKvqJik+mElZIcwfFXn9c2eEe/PO8hFkRLgnfK4f30UQPZ+L3fH0G2wUpiTy2ZEamlpbuEf4O7L8V7l5ZBT7vtuN2pXMplMNvLc4A4MqjAC5lB1XWinv0PHCrBRi/Dyg5iAcfYeBJZuRSURoUgoJb66grseATGSmuLmfjeeauG1UFI39RsJ93Oai0X6ePDIlkY4BExa7g1fmpmIa7KHYPIPvTjfjzSDjgn0YibvkcsBsJyVIzVfHakkJUkPjaQBiIuORS8II1Sg4XN7B+fo+DBY7ThfsutJBQbQWpxNmZgTT2mfigYnxSMVCunRmFFIxHjIxHx+u4kBpp/u4AihMSOd0UxsTvft5fX46FpuDO9adZ3paIHMzQ6juMvDStlJ+u2s4my62khTkyc2jov/2hOssB62JH5p86DdaeWNBOgkBnqz+9hyLckOZkhJEnbWPUIkni9ac4paRkUxKDqRtwER3n4qcMB/qGmVYhTKKm/rZdbWdH002Vmb709jZR5p/NLf8cJ43ZsXx7dYVTM1/mOBF31JUeYoTVdt5YdaLGK12/OJyecjVwZ7yLrzkEuq6Dfx4tolHJiVwuKqLfouIzOyb6dDbePzXC8zLDGZrcStLcuP54mgta1bKSAz2Yv3pBvqNNh7eeIkb8sNZF/o8hTF+7DrXRLfeQpSPirHxvizKDUcmFkHOFQJUvnTqzDh7a3F1q7hxRBpfH6/j7YXpeCncE/KbCyK5p7yOPD8VqcFerPj6DCuGh+OvlpMf5Y1M7L6uR0So2H2+AlukD9sutyEUwORkt4rc4YpOgjQKvK4ZWMf4eXD/T0UciTuMLGIUP+yv5pagOj7qGobd7mTDmQbyY3yI8vVg010j6dKZ6Ri0olZIWDgslMQgT24b654wP7v5CiabgzvHxbCvpJ1Pj9YyPyeMII2CFfkRnKntoW3AzPikAORSEUtywlh3uh4EbjGStpwG9lmPsDbx4+unhtXuvC6oMnFVEvVWK20DJj47XI1KKmFpXhibi1qYkR5E3DWrpYq2QW4tVBLu63b5+OZ4LXtKO3hh5mIkYvc2mVjEZyuyMVocOJ0uasuL8AxNIsBbyYs3ZgJgkovpcjho7TeT8Vfidc/MTOat3eW8tquM6alBXCzpZFKID8IwJTkR3qQFe7H8q9O8MN+TssFqVNLR2AUafEIDmOhy8cv5ZsbE+9JtqWJYlIiMsAh8Pf/ImJ///TN6/PKZMuK/9L06bCAU/0ND3XdLP2eCsoAHZjzo3jDlDQIEQgJEf387z4vyQfmvMmBA/oR55Bc6/uU+f03x+WP8XOfBqwszSQjW8rzwXn7wS/i3nz/EEEMM8Z/GUDD1H8bFpj7CtEp0Zhtv7innlzsKEIv+tZ2YViVDq5IR4aPiph8u8MzMJAb7TCz67BTjE3w5XtmFj4eM1j4Tsb4quvVWjFYTdV0Gzjf2sTzUjNN2kR/PeOOvlhPrr6K5z0RGiCd/2lHOr3cOh/dG8OCCrygWp1DTqedYdT0bRP3cOG4167ZsZWHXJzRn72bB5yeZnhqE77VV6nP1fawqiGDPg6NRKyTM+eg44xL8udjYR0ufickpAfh5ylmWF0brgJnY8ECKR37ItmpPPhwfDgIhfz5czeTJUTT3GbDbXUT6eSCXiMiI8OHTQ9WYbQ4eXrIOl1DMg28dxksuJsBLwYr8cOZ+0sTwvKXMuSYdP+HWP/HSthLyorxAIKA27SG8I7XMUMnIDNEQrFVw5sQBwmJSCb5pO89tuERqiAdXu6qZkZ7JlJQgihr7eH9fBTcMD2NudihfHKnmsV8u8fENbsnrF7ZepbXfTG6UlgC1nMPlnay5cQqlohqifKNZe6KRsR06RgR6sf/bMnQxvvTqrewv7WD+iqc5UdNDt76f52el8MvPV2jta2Xjykzqeo0kBapZkBNGXZee70838MLsFO5ad4GfzjawqiCKV3eUgcuFEwEPjI9FLBBy17gYBAIBV1r6CalaT29RGVuiX2RuVjA35EcSolHSqbMQ7yFhRIw3WqWMWRnB+FyTbP/pTD0XG/tZnhvB53WjidSpmJOpxWR1srP1U+ySydw7Pg6BpIdNV0/yZvHDfD/9e1aOCGdSciCHKjrZX9pOZaeeOH81L86KRy1xcPRKLW8uymDQZCNcaiC/5xLeHvkUJvgT6a/hkDqYuzbpyYksYWZQKQGSetQKCU9vvkJWmIaZ6UHsLutixfAIHC4Xe0vaKGkbYFKSP2E9J+Hb1Xit3MWMtCDGJvgzOs6Pn883kR7ixaovT7N5eRg3DI/B4XSgkTjID5FypN5B24CF2m4jIRo5CqmYzUWtLMwJZ9Xaszw7I4lPtxUxMz2IZ+66lYuNfby6/iKZ4VqcpgF61t+Bcu57qPwjWT5hODqrjZQQL3Y/OJrLzf1sPNdMqFZBX/U5iDXS193LhrM9FGQlkxmmxeFwQX8TKH34YqU7C3GorINNRc3cNCKKVQURrK5dzagrNiaE2BFX7afeloCnXMS+8k6kYhEuFwwabRQ3DXCldYCMcC/ON/RxvKab0fHuYMpTLuZySz8fH6jk8alJLMj+IwK5Y2wMd/xwntYBEydrevDzkLHrahufLh/GurONbDzXyF3jZzJgGQ0WHff9WkGf0Y4DF2qZhLxob+L8PfnpbANyiZA+g50YPyEdgxaKm/qJ9lMR5yXAIVby/O9XEUssrORTKLif/GhvztT18OmRaj6OOc9gmQV14QOIHWbk+nqOV5cz6ve7uEPzBU8umeBe+AD8wjx5emk6AF8eqUEhFV0XvlieH05Z2yCfH6klM9iLCzYzrUX9+HvKyQhV88IoNSpFD2a7gC/X/8SgKpyXlxdypq6XtBANORE+vLCjFF9FNyvy/rCvOFFXjX/PLrxC/rYHCsC8YRllvlFkTn4Tm92J9K8Coh+mfolaqfpjZ/EfUun13XrO1/exMCcMq93JazvKSAx02wIA0NeA/cTHiKe9Dn8dfAn/dcD117RXFlE7kIXTBT0GG1KR8LqNxRBDDDHE/0SGRsD/MB6YEA/AtyfqUEjE/zSQOlDewfm6Xm4ZFYVaIUEmFlHc1E98gCdbi1rIDNWSFuZFh85MalggaqmQe3aUcfvICBK6BGRMiuBgVScmi43M/Dw2tshp7NKjkompaNOxv6yT1+elMTNDCEIRNQv3EhIcTIZEhNFi5/VdpeQmLUbqEUWPjyd74zewMNiLUbG+WGwOFkTaWJCZTGmHkcRAT67WtxEmdPL6JB+SEuLp0pnZdLGFlGA1HYMmpqcH89PZBhZ9fpKnp+VwfM851p+uZ05mCBHeSgbNdh7YcAkfDxkvzU7hSEUXS/PCMVhtmK1OjA4BxQ097H1oDF06M2HeKpxOF3H+HgSFRuNwujBb7dT2GLgvuBzvttPAuxyr7kIuFrL9cisiEQwLVXP1Sg9jXI0EBwbw5LQELncX8Wv7p9zu5ZYhVkqEKKRiFmWH0zFg4vtTjcz/K1nksQm+6E12pqQGoZCKaO0zseVSK3eOi6NbZ+aXCy3IJEJUnnLSC0Px8pLz/a35nKrpxs9TRmaYF34e7nIpdZQHTVYbJ+sG+fp0BT/dPgKpSMDNo6JQS93nxguzkvG4Vl710uwUtl9ppapDT0yAJ3HdeuZ8fIL1t+WzuaiVyIi5ODwXMj8khLvWX2RCpJRB/3qenTmaX8410muwIhDA7qutLMh2Txxru42UtOkoae9HZ7GTE6kl+ZrXUIcrk0BVIEcbBjjU/huDFgOZvMHG8o08O+JZAIZrDSzou5OqRds43mjkk0PVPBdykbEVW3Hk/IqPSsrj26t5atp9yFVyHs+VYXSaWZHzMpFeXZS160g3GchyuV/ztflpuJxwqLyDfqMNf7Wcx38rZkJiAOfq+zDbHFiCMvEp/DM2s43FuWG8tqOE5j4zw6N92F7XQ56PiZd311Bt7mDQZOPNgH2kXB6g3DEKpQFuzC9g4/kmluaGkRysRiYWMn9YCAFecow2B839ZgASAz15cFIsh8t7eONAPdOU+cQ6VegHzfSb3QHNgMnOtuI21t6US22Xkexwb8Z3NkJtF4EF9/JTggkkclYMjwDA9t189lKAKXUxC1O9GeNn5FCgF2HeclxAisnGDyfreW1BJm+0PIq3zYGHXMqvdxZQ06Xn7vUXyAzTUNw0wNysYDRyMW8vzMDudDJosvHgz0XcPCIKp8tJbbcRu9OFWITbu6hiF1h0rLnxJgA6dWZkIhGPTPlL5sLFmbpeFueGoxArYOt9TFUOp8QnmyMVncxMC6S518SOy208PzMJo9UJAujWWciL8iYvKg90nfDNVESjH+XnO2bgYeuBXT+CzcTekk7CvZW0Dpj5vC0eXUsZjxkfpV6WiLbiJz4K9ybt7nM8hy9KiYgVX53m3vGxDPexQHclxBRS3WVAKf1j3FRIRMQGeDI12Z9jNb2MiffHXy3DQy6mvLGTDcU9/JKVBt4RFNqP4PBxj8EtfSaEuEgI9MQz9Hd8/eYBbi+sALUcraeMj4MKeDuz8O/G5/a8W6nWN7L+l2Iaeo3X/bsAvDz/uSjEscou1p9tYnySP2/uLicv2ocxtuNQXAMZS9Hr9TQVFVPu2se8Wf9nhr1Tlt/PFKC+Y5CYU4/zUsE9/0fHGWKIIYb4T+FfpyyG+H+Oqy0D7LrS9r/cr6S1n8nJ/jidDpyOvy/h2HuljcoOHY//epnNF1uw2B18drgGuUSIRiFBJnZ7DU1KCWRWRhAbLjQT7i3nQEkng/0WWvuNHKjoJESrRCkV8fzMFMbE+fHY1EQkYgljYv0wWB2oFRLsDidP7WriYFkHS784hVQsZOWIKDyc6RjMEN5zAlHLWQZNVurKLhLrp4B1CzGV7UZRsZnGXiNsf5CuTY8SX/Mde0ra+e5kA2fre7l73UWe2XwVgCAvBZlhGrIivMmO0HCmtpdJ7x1F4HJR26Ej0FPO09MSKGkZ4KezjTT06HlsYhzPpXRT3q7j1e1lSEVCdlxuY0tRM0KhgMLEAKL8VNy97jzPbrnKAxsuERSXizx5Oq/vLCM9xIv4QA+a+kwsyg5lcmooi6ZNZMaoXLp0ZmZ9fIIETSa3x7xHlJ8HJc39nGvo5/Mbczhe3cXhyk6GRWi4e3wst313jtu+O0dBtB+zMkMYNNvQme2YjDqijZcpauzDx0PG4txwjFYnu48e4ZOzJ6jrMeByuSiI8eV4VTeNPQZe2FrKy9tK2FzRgyo8ifIuC0KRkLvWX+Tn8010d7Yi+iCdispK3txdjkToLifyUkrx85BT2ubuWzJYHQgFApRSMUtyQokKDeaRHS0EaRR8uSqbFQEnMO58DICEIDXL8tylaOfq+zlZ7fYMm5oSxDsLM8iJ8MHHQ8b4a6vkJ6q7KauKJtQzlElJATySdyfPjXyI2emhnDw3nAGjlcYeA+PX1tI99TMiQwLwVsnpN9sQZy6hbew7PPXbZaRiIRb9AB4/zoTuKrbu28XKAw/w8t59WBwu7i2Mg+H3UB+1DNP7uaiFNn4+38iRym7W3zacyg4d87NCqe/R43K6MFoc3P5jCWurlfx5VzkGi51OnZUuvYVdV1tJD/VCExRFk80LmVjEveNjOK2dC+OfISMqiB1VVsYl+rP+tuEMmGw09BgRCASM8xlALZew5sYcVhVEcrGhD4lIyHv7KmnpM5CqseM37g7u2FDGc1uuMCM9mKdnJBHn50FysCdSsZARMT5cau6DMY9xqnMqjWW9VPbYuWfdBcw293W+N+kV1vTlEKpVQtl2RLse4eW5qbQOWPj2ZD1JwV4EeysZEx+ARCQk2s8DncnGsaouQrVKHp2SyIuzU/lgWRaL2c+a7ac4UNFJgJcCpVTElJRADld2cqV5EJlIyPl69/fMxpugdAu0XHAHVoC/p/xvKtVmpYdwpq4Hu+NaD9T4Z5gxZTqPT03kiWmJTEkJoqJdj8HiwFsl43JzPwMGM7Mj7H8cxKqHwHSIGouHXEIXGgZnfwlKLcEaOcEaJSDAqAzh9mWLsYlVnJePoKzwa36Z9Qta73DCvZWILv2Av9yBn4cc2orh6m8A/HlhOi/MTr3+ct4eMp6ensTwWD/SQr0oTPJHq5Iy6d2jhAX58cvDM8HbHcgKgtLxVLl7ytoGTGg8ZAgEAh7JfZARwW4hGLVCwvHqTn4718UHU59EIvp7j0C/yDF4e8dyy6go7rkmVPLP+Mv3DnBjQRR3j4vCZHUwNczB/SMDmZocAPUnwGrEIywFTUgovgMX/jjAQDNsvgus7p5JdG1w4GWwW/lXWJzQrognIeKfm8UPMcQQQ/xPYCgz9d+M1n4TlZ06Tm7uYkZ6MMNjfP/hfreNjkWrlPDT5oP83AAzhkVR0aHn3SWZlLcPUtllICtMQ3qoF9PSgpCJRczJDGZ8YgD3b7jIj2cbGRvvx7bLbYyM9SUlyItF2aH06G009xnRWOwMGu3kaJr5ec0WJq54nIcmJcBgG9NSAxiI9eHbk/UIXC68VVKsdhc5kd6Utg1istlp6hzgoaDLEL0AeqygdPtEfWR7CZfXtxTP2Mq54ktMa/8FXdwMTCOf5M+Xe6htFbEsUsCk5AASAz3p1plZkBNGj95CS7+JZ2YkA/DVqjyEQgHn6nrZdrmF41XdHHzUvQKskkoI8pLx05lGHsqWINv2AMNWH2DjrRkIcVHSOkif0crcLFia524Un5gcQKCnnNcXpCOQiMAnihmKPmRiEb6ecjbfMxK7w8nab9bQI/Il5+ZFiAQCXgq7hKsvkIXZaTDYimnrs/RFPwBEsP5MI0IBaJUy5BIRXioJnhIxUomQ/WcusbXayrOzMjAYjASaa5j6jZiZ6UFkCypoExrQyWOJ04Ba7KD7y/mcjnsAk2c0HQMWvJQSon2VzEgL5kJjL2OSQyhuM9JjtLPxL6vcC75AovSn31TFwxuL8FPLaeszs2ZlDmPi/bDrBhhtrmDOPeMRCAR4e0iJ8fPgxJMTqGjXERfggWrUI1REL+aGL0/z6Q3DSA/VcLyqi9mZwcxKD+LLo7XcOCKCkzXdaJUSPlrmVhrTm228saucCK27f8RPLcdPLefTY6fx8+nl3sJM1AoJ1V0G9jw4Bn8/DxZ9sIfbs5SERZ/i2+rTjPJegUAwwN6SdjosEmz5d6NQh3DVYwyFvsEsSh2Fr0rD0i9OMSczmElxKVxNe4owk5BRMb7IrmVt2wbM1PcYOF/fT5y/J4nBagQCcDoc1PcYKW7uo1tnxuZw4q2UUdQ0QKjeilQsIMxbSbhWhaGxg807rzJr4UruCszksV+LmZQcQOeghRvyIxhoKaf893d5xb6S95ZmEqJVcs/6i3y5KptALwXjhZdYcO4ZLCNKWJQdwoREf2Z/dJxFOaFkhGlICvLi5W0lpIVqaOkzMjbeH+9gD1ReUhoHjXTpLdevfaXalxHxQhp7TFTZ8vFJzqPuYBW3jo4mPsCDx369zMprJWxL88J4dWc59xbGuMUYfr+TZKeG9V130m+yMrP+BCtHZ6EKdzcp2a9sQivOxsdTinZASoSPkg8PVrnNi6f8iZLycn4vH+Cpz0fBrA+4Kk3nxq/PsHZVLkqZmMMVnex5cCxikZDLLc0kBwZfz56PiXe/xg3DIzhQ3kGYt5KabgMHS5qZ5nwM852n3Qp4PtEw99Prn/etPRW09BlJDFLzyOQElFIx09NNWO1OtD4q3hcu57eTLWyP2sztJ+bz3II8wrxVaJUS3h0r5Uivge3NsSwc8yZam5H9jfvJDSigqrOHMdF/9AKF+6i4fYy7JO9P20t5a2EawRoFgyYbaoWEooY+7vnxIjfGmikcNZpleWH4eLjLg5N8kgAYNNvIidBytrmMdp2Rbl0yd667yFPTE8iO+MP/r1nfzA9lP7Bm0qjrWdx/xI7iZj47XMtT05MYGefHvtJ2fjzTTEmrnlGdm7ANRCEfOAEKLTjdAWnQio85vHkze4+dYvLoEVB92P246Fq5oMsFNgu0XYKSLTD1tX/42glBaljy1D99b0MMMcQQ/1MYykz9N2NySiAPTIinq99CV6/pH+5jdzjZcqmFy5VdfFwOczJCUckleF2T5C1tHWRsnC/Pz0phblYoMrGIIxWdjEvwx0sp4cuEC9wV14/Z7uDIo+NICvJicW44qSFa+op3Ya09QU6kNwtzQvFQyOlwaRELBDy+8QLWz8cS0HeR+ABPbsgLJ8xbyeWmflJC1PS0NTDs7Ft4lB/hoREauPg9DkMvruF38at9JFuvdGK89TivXVEjlHsiD0lne+r7vLm/Fs/AaKblp+Pt5Uldj56xsd6syA1Cb3Wy7nQju0vaWXu8HqvdydWWPm7+5gz1PQZyS1/jidAyJiQFcK6um5d+L0GrkvLAhASOVnVzqk8ND1wClQ/d39xA9f6v+Gj5MJ6anszhik7uWX+RnZdbUUnFjE7w55Fdn7FwzVHO/r6GdGcFhyu6eH6LOzNmtDo4YoikMNvtG6VWSEgW1LHj5BX3F+MZRM60Vdw3JY0vjtawaFio22tpeiISkZAEf09UcgkCfQ8fnezm4cRBYv09+PONY9nRM54P56UxLdzBuMHfsXTUEBaVwN3L5uOnUSNLm8OwpHgq2nS4XE6W5oYxb1gY+8va2Xi+CR+VFKdLwGvz0jhb20O33sK9J5V0DNrpMVjACfVdBqQiIWabA53ZRsvVRvbvdCGwWyhq7OPBn4rYU9JOj97KiUM72Lz/KK9uL0Pr4c/CnDCEQgGP/nLpuojArxeb2VzUgs5s48ujtaw73YDJ6uD3Sy3c/1MRY0OFFLcMUt/tXhF3uVxc1e/k15rv+OrkRU7X9rD1wFGEVzYC8GK2hawQFe3N6Wjto8iL9uXLlbmIhQIKQwXoa04xYLaRG+3HrPjJNHY62XyxmawwDQ09BvpsIiLzpjPmzUMcr+6ifcDMW/vP8WPdy8xp+4CHw6sRiyBCq2RmegBikZDf7iogL9KHALUMpVjAfaZPSfdxkuDngcXupKZLz7bL7fgHRdIqCUMsEhLuoyTWz4MRUT506MxYHQ68QhLpG/44SrkYhUSEUipm5wOj2HKpla5BC1GWSswjH6diQMS4xACCvVVkhXvRq7fQ2NTA/tI29GY787JCuHd8HJeb+vnddIYmQSej4v3ZeGcBctx+YYWJATw1PYkwHyXhPip+Ke7mcEUncomIxh4TarmEycmBXG7uJy/Khx33j2ZaWjDRfh4QN4k2VRpGm4NAtZIvtY8g9Umhp7QP7FaKSsr59EQzZpuTT1ZkExfgyZJr0vn4xRPauocx2gGY/xWE5ZEa4sVzMxJJ797OhqOX0ZntqGRiHE4Ht353mZ8vXcBud3LzN2c5W9sNgELi7sERGLrxM9VzT2E8V6b/RuHbhzlW1QXAazvL+O1CE9sutZAZpmF2Rgi+hmp++/lbSq6Ju6w9Xke/wcrB8i4mJ/mjyl3O2MRgvJTXgobsVdiDs/n8aA0dg2asdie95l521O7gl0sXeHFrzT8dh28ZFcXIWD8GjTZG/fkglR2DpIR4cffoMAYUoewt7WD1dxd4d2/F9ee47FZ+O9/EazvLeXHaWD5cNA6VTOz2vlPI/ub4XsIwvpy0Fpnob7e/s/MKZ2s6rv8+Ks6f5GA1l5r6AcgVlPHgSG+6dBYOapdw1WMkZC6HGe+A3K3siFSJykODQukB+h7Y9wwYuuEvGTJ1MEx9FZS+4B31T/8GQwwxxBBDuBnKTP035RY/H4yVBrimdNvTqqe3xUBcbgAuwOpwsqeuh0itErWXigC1guXXsizzh4UyaHJPvHZfaSMt1IuK07uIEF9Es/x9pAGJLAyVsyAsHaFQwAcHKiluHGDtzbnIB2vI9PfHaLUTolWQkjIRs60QuUTE2IQgLsb+zPCITABSQrxICfHiznGxOJ0uquqbCM0dQ5U8kkxdG8fawynecAjPtAJKWwfRWWwUJqYjEXUT4asiLVTDx/srOV3TS3OuickpQZS2DRLlp4Lj74GulTvHvuHO7qikzEoPRiCA+3+6RHKQms8PV5HksYCV0cEIO4wgEOClENPUayQxyJNf7yqguHmAH880sDw/Aib/ieDAMH4538iGs028Pj+NALWM8w19HCjvpLXfxNiwkZiVTmT1lXR3xJIVFkdLn5FuvQVfDxnrH3D3IbhcLmq69EQs+4gFBjPnP78DXcoNFI4ey6Hai3gp5QRptQwYbby7r5JX56UxPjEAi92Bp6OZl6NKScy9n5/PNZIeokHRacEzGrLSokGxgIauVPpKajld13Ot1OxmvthVhtlm545x0dz67QXqug2MS/Aj3Mf9/U9LDcThtGDd/jj2uU9fK4nUsvWeUXxyyK3clx/tw87LbXx2pIbvb8lj/rBkjpfU8f2JGiakxzIpOZCVa88wXiXmVBuIVRYkIiFigbtfQyISsvZEPSsDG4j39mDZ7VPxlIn58bbh6C12OnRmNpxr4qaRkeSFe1E+cJkrzf1E+qpo7tXTWjuRiEQdMZldZHdvYfjIEAT1te7zadRsAG4y1HC+vod9pe14q6Q893spR+9Jofm3bnw23cz0G9aDy8W7exspax/kwCPjWLLmFEppG49OSeSJqfHkRvkQqlHwyK+XGBUzhzBfIX7qaM6WwpbiVrIjfXhgQzHL8iMw2ZyEeStpGbBQGXsbHbUCMFoI9JKzv6yTwgQ/Lvc7uH/+BA6WdaBRSLA5XXgqJLw6N42vjtWSEaZhanY8U7Pj/+oqFmCw2Ll9dDS/Va6gXOlFw+V2wn2UrPDz4KU57qCcLydgDX2UakEUjT0GtlxqZtPFFqISTxDWIyYzyC1jb1gzGVPBY7T5j8JgcVBwLWsdH+iJxebE4XTx3cl6pqYFuseQb8/x7c15nKvvJSvUi8wIb0hfzHclRaSFirnU3Edp2yBtXQZ6SnoJTvfhR8F03lsST9Q14YaGHiO7S9qZkR7szq7O+hMjpUp6zfDY+mJem5fG/HQ/TD9tQ2daTodIzIGyNiYkBfHd6nQSfYNxuUAoFFxXHn1lRxnZERpw2kiWdlLXbeCMwc7yvDByI705VtVJjL+KrDAttT0GxGIh01KDIFHCR3suYbQ5uXd8HC6XC6FAwKKcEKanBlNvtDAmRYR6842giYRpbyAWCbl3fBzPbr7Ka/PTAQ/WTFqDy+XixiwTRyq62HCkFl+1nPtmJOB/TYgmJ9ItdHGpsZ+Nd4wgzt8TgUDAilHu79dsc+ByOQnR/iEUYf5kFJrwh/nzwoVIROLr5X3vL/3DF+ovrPj6DHkxMl6b80evFF2VVDU04dB1kmc8Dro2vAru5c1Fmdd30RR/RVTcMoI0MTw0McGd9XMlgKkfFBqo2g/tl5k94+E/jjv7Y4gaAxfXgVcIxFzr4fKJdv8MMcQQQwzxLxkKpv6bMGi0cee6C+RHa3lgYgLphWE4rvUddOnMGPssdLfoiMt190E8OyOZ+i49d6+/iMnq4M3d5fSPiWZ2ZghFjX3c9v15Tjw5nuM1XRyr7uL24ZlcPttHkMOJLHY87+wp48yuU/xwaz5ZYRpKWwf49kQdcxY8hJezjyNl9Xx+qpMRsb6MT/Sjrd/M1uJW1tyYzdfH6+g2WHlguA+//vI7iYUzCdUXEbL/Cc7N2MPZym5meGoIF9fjMywanxgfFuWE0txn5OiGd7hz4nz0ZrdK1MqRUcwdFsrp4/uxi+XcZ9gOCSshaiUms56TzReYnVTAC1uvkhmuwdu3htcXJLDnSg8lbTp8PLz5vNhGsEZBbqQPKcFe5L26n7vGxpItGaDZIMThpQUgJDYdk81BWqiY5j4T8YFqnp+VgtPpYuGwUN4/UIVUJGBGmIDPTdPI/UXKuEekOFzw9KbLfLw8m98vtTArM5g9V9p4avNVTj01AYFAiEnmi1bjXhn+6urXRKvyWZKzkn6DleRgNT0GM3tL27khPxy7NIlRS90+TG0DZsJ9bCy/zy2d/NuFZoqaonk2pZl9lzdxJeYOZmcGA3CleQC9xcb6c+WsHhVFuI8Sb5WElBAv3txdwa2jowhQiYlLjUWoUbF68BR0DkBIFjG+KnoiNNw1LpauQTM/nm2gS28hSKNAZ7Yx2lHF8hETEImFfLEiB4U0n1vKtiBu3AUeb4MABEIB9xTG8uv5ZgSDVYhcdgxmO1PeP8KWu0dy749FjE/048fbhl8/r1UyCQNmd2Dvd+bP3BaSwtRx7yBy2WDzHRDzDF2dl7Ff+ZmgtCVcaOjjvQM1TA4x8/kRG7/eWcDDk+I40mxjX7QHT/lNIsCig4/zeP+W/Wxv9qe2U89HyzLZdLEVndlGVacBlUxMV3URKzJCyDz/Op9p78GscvHEtETe2lOOj0rGySfHs/tqG5uLWnlzYRr3jI9HZ7Lxe30xzX1mChP9uW98HCdrukkL1fLZ4Wr2lnbQ3N/HmBFHsPEIu6442XqplW9P1vPT7SMI91bClV/YbMoiOyYIf085IpHo2kT+D07WdGO0OJiYHMDArLWMkXvz+TcXaeoxsL+8iyenxrNixJ+xOZzuoEEo4HjCc2SFZfHz0QYsFisFEZ7QWkSQWAEVuzCPegy7yy2qYrLayYn05qczDdR0G9GZbRhtTk7X9vLirGQUUjFXWga4XSXBanOSOCcSo9VBU5+R1gEjb++p4KU5KSzJCeNSUz/dOgtdeguhGgV+ShkyiQWhsoQ+iy/FzUqOej7HQ7OieXtPJX1Gd7lZkl8Ixc39ZIZp+XrVH/5HL89JIcJHCWoFUxfeyoqvzuAhE7MwOxSxUMCXR+vRKMVMSgxgrI8fLb1G1p+uZ0luOPctmobN4eTE2fPsKq7j9dsXceOIKIqb+3hzdwVysZCvkyeBl/uaYeMq8tKWsebGkX/88Z0OBA4rWpWS+EABY6J8KR804HL97ZgsEwtRycUkXjOg/mvkEhEPTPxbufATyc9Tbgxi3r9Qvus0dHK5+zKrx/mRH/lfepEcFp4JL0FecDuceRP0/6B3dul6vtlZxsXGPhwul/sGf+h1qNkPtx3koxIpcnMEtwHYLdBTA8mz3M912cH570ukDzHEEEMM4WYomPpvgkwqRKMSozO7JyJy1V9K9gaY+8lJjj5eyIiUP2ru391bQW6kN09NT2J4jA8apZSrLQPMzgwhPVTDj7flIxOLmJISxKMbi5mZmsFu4WjqjtRw/4R4pqcG43IJMFodSERChod7sa2onvmSVuxXfmRd/w3MSgnDqpAgE4vxlIuRS4S4XDAuwY9391by4aZiIqpPoxo3icDU8Zj8f6QwOJDCJPfK+JVpb6PuG+DIC4+w+v3PCdOqCOcs5ScdyLqucCTlCdZd1rMoO5RzHQrmVtxHacQyknXt4B3D8f4B3ix6loKwrfQYLBgsRn67/CXPFzxPVacBi82Jp1RMv8lKU4+Rl7aV8ODEeN5fmkl5m44Hj/YxL0jCE+Pdq8kbzzdysLyLj5dnccfYGLj0I1z4HuGSdXx+tIFBo5XUUA1xCWHkirrpyP4ZhzSQJblh3PrtOc7UdPHj2UbGJPjhEsCYOF/UCgltAyZ2qpfwWqo70/Dt9A8RCYVw/hs0TgdTU5djsTlpHzDx0M+XGBPvx6qCKOirZ1VYN0bvULZfbmVmejC13Tp0JhuS6NH4jg8j3S5Ho5Cwb0cNt6SFMKiuYvPlIgrj5tFntPHc1qv4e8pQy8UMmCzc9+NFnpl+FxlqLS0dnTisHjhkBhyuP3xR/dRyHpgYT5y/JwDTMoJg+L30G6xoxFDS3I1koI6c2GGg8uFUTTdFjf3Mzgxh7icneDalG7vEE9/0KXjKxby/JJMAtZypqYEEa+T06C3UdRvIifQmzEdF2zVlO3n+LcwQS3EJBFyoGCB88sf46copbZWrlkcAAQAASURBVB2guqEXfUclFruDR6YmUtNtpqGiAZvDhVwsomPQgr4vjudaYnnAR8axiLeJ6VSxtaiRqk49S3PDqOzQYb7wE29kRUB4Ir99+yOGyIlEhk0hKySGmFD3efnY6AD45UaILmTOyAeZlBLITWvPcvPISJKC1FgdDvw8pEhEYLE7+eZEPb/dVYDd6cJgtmGxexEW0ItUJOfHs+UkBauJ9JETqJaD08HAuZ/Z7fKjYcBBZYeOKSluQY69Je18e7IevdlOmFZOlPEyWsFY1hXrSAq28+Nt+by9u5y7C6OZmByISCjgpR8PMdO4GeOop8gbMYbmPiNpnja+qTfQVnmBoH13w+LvQKZCLhHxw635DJgHOF3dg8lqpyAtmFfnu32pdl1tQyRwseVSCxXtOp6ZmczL20pQSUVE+3myqiCSLfeM4mR1J7XdBvoMVuIC1Xxzcx4Ar+4spd9o56fbhyPTt3FDtJXoorV45DyNWODiXM8+3lg07Xrp2nfnTrPpchnfLL0BHw8ZHx+sQioWXu9L2nG5lQ3Vn7BoXD4LEmZT+NYhlDIx6aGeDI/24cGNl5iVHszB8k7O1rkNhDt0VtafbmBxqhdhGjlv763g0SmJnK7pJT/S26146JH3N2OrROAiPtB9rnd0dNB98jtSHBWw8GuCvBQsmxSD2eYWYvlrMsO0ZIZp/+0xfOKkmUz8L9veOPMG1f3VfDXlKwAadY0cbDzIa6Nfc4tGtPdAsjsjS2AaYTOuZSunvnr9GA6nC6GA65m91QWhfCcEp74XyjaCVygEuAU1fAzVJOjPgH40dJbCzsfg3nPuA2Xf9L/+EP1NcPIjmPLqH2WBQwwxxBD/wxkKpv6bIBOL+PSGnL/Z9tXRGr48Xsf3t+QQ6CXHYnewr6SDUbG+RPl54CkXkxnuvtlvPN+E0eJedRQJBcQHuFdT7Q4Xw6O9qe81UttlIMpHSXFTH7VdBlaOiKCyfZAPD1ajFDhYK34LdfoGyvxGc+/Zb9nfLiYlOIZnN1/m/QlJ3Ds+DrPNgUYlpWPQzLDULJbcMINjO9dzpM4fTVIhz767nw8m+xOWlMrXpyuI9fUkc+oqxEIBx6u6qbWNJT4wjQitlJTwAIb1ifGUS/jglgl8v+l1OhUxJBe9BiIpBRHjWaP8FV8PT96aHU/lqVMk8DJXqiRE+EqpH+hGqfBCLvEgqvJT4gM9EQieYmJSIFlhWrYUNbMiqpLnv25g+oSJzEwPZmSML18cqaWl38Qb4/Khtx7knsQHqFFJTTw7M5nXNl5hTJIfrf2ebLvYxf3jYvhiZQ4/nKon3EeJv6ecOZmhzMkMxWCxE+PnwesL0qnvMVDS2I3l8hYmaY14dh/FNfoRln5xmpdnJ9NrsGG2OfjmRD1pIRqGDV7gzgMKchIbKGrsJ1it4LEp7kb2PoOVBoOYtGAlD2y4hAoBr6Ym4R8xnhkJNiQiCU6nixCNnJ/PNhHurWTThRbEIiEalRTOfsmaWl9MCgUdRVf5/tZ8ZqQHc7i8kw8PVvHDrfmcqOlm58V6JnZ/T87yF5j4SRHvLcmkvqERWf0hcoY9wdYGCSEaAemhGuq69Lw6N5Wc9g3IFSoe+L2ECUn++HvK2VbcyurR0bTUV/LNzlKqrVqUUhG6/h4mpYVR26ln8yUbj0yOpqthkHe2lXNLnp2J5+8gYdUJ1CYJv11sxlMmYlS0N5reo2y9dwxSsZCRcb6IBQIifJZwpXmAqg4d7UYhE3qP8bxoP43tQZhsj/Lukkye/aGbWaYu8sueYcHqt5j07hHCpy/glwtNyEu6eWeCBr6bBRlLIGoMNqeTvVfbeHpaImmhXkjEIt5ZnMnzW65yoWGAm0fGsOehsXQ36QgyupCIhLQMmHkmeSVWu5MHJ8ajlIr44XQ97++v5PGpiTRO/grx0VquNvfTobMS4aPE5XIR4+fBhER/ZGIhTT06loiK+fh8FE/PG8Hh8i5+ONVIYpAXfoZKNGJ3AHbrqCgGDwmRKsTu7+tyO5/fmEtMbA9+EVpIvIDDJcDulcxfum9WfneQ4dEavrulkIsNvXx1rIbVo2PYeL4Ju8PFxAR/ZBIRUpGQaHsdCz3r8cq7H5PVgUIqwkshIUSrcBtN67vg8Gsw8UVem5fOwMEP2LfxIt82BbB+WjDIdYR6K1EoTDx9fBcFQQX06eRUtOu42lNGdqJboRIgKczM8c5dwOMAjIr1w+Cay5qdRpIvvcCvS5aiDvbm++OVxIgG8JZImJUWwKR4b8RSKR5yCUcqOpmQ5M+4zDB6Y8Mx2+ycqul2L44AtV16PjxYzQuzkt3Bx+LvALf0ekX7IH4HH2OrxzISpyziL85LDZXFvHfBRqSfmgcnxnPXuvMsz4tgdLyf+/GBBt489yajhOksHnMrIvHf3la7dRZUMjEK6d97OU2LmkaTrun67zmBOeQEXhvnDV0w2Pqvbwx1x2na/wkXYx9kfmE+AIIL39DdGIw1fRiSir0cUxQyPGEcCmDBklVIDNMYEGnpVecQdduhf338/0r7Vag9AsY+sAxAXwP4xAz1Vg0xxBD/oxkKpv6bYrE7qO/RMysjmPxod29Ex4CFz4/WcKq2h1g/FY8erOL9pVmkhnhx88hIrDbn3x2nMNGfwkR/dGYbcYGe5ER48/HBKr45UU9WmIavbsrleHUPfUYLT+hf4hOpJ6daephZeBeSix2Eeyu5NTuCg9+X87WXmWmpQTwxLYlf775WNtNyCamhlUdKw9g+UskNCTLUGg0SkZBHxhfgIZMhF0kRCAQMC9fSkDwbRYgas2YEi94+zL3jYjhX08ZA/UWqRTE8MykZZBsAOFzcyg9nGri5IIqali7CK0q4oPBGI/ZkVGwAuYbDhPa08U1/Nq/PWoinTIpBIODzw9XIJCI+XJKB+renGBN1L1GB3njKJXjKJdw4PAKzw0G304qkrxOvjqtMSnaXeAFgsmM32OhuGYHD6fbSOVzRSZfOQm6UN4//UsyiXHd/x41fn2FUjA93FcYx9+PjrJ2uIsm0kybhAjxT5iOIm8imu0wcqejG4XIyJzOYAbONtFAvEC3gQVkra0+1EOilYP3ZelJkTRyo3opf6J1sv9zC5ovNzMgIpjDeF64Ze76/vwatQsrqXF+SfhrJi1P+BClzuVDfx6KccCJ8VNCg4HHW4oxbjiBjCT+cqmd6WhByqZAYP3cWo99gJczPi+T8u/DQeLgDpXAt4xL8EQnyQCDgcIW7p+XH24bzxdFahoV5cWPJMB6fksS7WVoUMhF3rbuATCxkdmYIfW119A8a+Xz1RDoGzdw08Bnmq7F8IZxLl97Cy9tLeG5GMl/ePRyVpxRGXyVQLOP73eXMHxZKvL8C44UfeOe8LwuVXYRoPVj19VlGxHjz0pw0vj9VT223gQ/HeNBQWcJVRTY5wwvZWdPNhvNVhEarCQmJBIMYndHM2/NTSA334eXtJayI1MHlXbBqG2gjQSDkbE0v351uYNWISF7fXc6oWF/uHBfDh8uGoZCK+O1CM+fqexgpVRIgEPPAzAT2ri9jz/lmfHwVPLjhEvseHsf9E+KxDXbS21KFn2covXorM6O8ide4WPX5YRYVJLAk04+wEZGsPVFLRoQPYWkf8Odr12moVoHebOPGnAAWfFRCSGADx7rkSMUi9MH3kKx3su5YOV96r+PVn1fyzJJCnE4XB442gdXOD+XtDAhdLM0L4+GJGUT7uLPYVpsTvdmB1e7EYLYzLFzLn/eWc+LJCcglIpbEwQ3HsojqLkKrkvLavHS69VYae43YHE5kIjGo/EAoQSuXskaXRrDUwOOzhkF8FDALTn5ElyuCxSEv4bB7cucPp8mL8mZ8wjSmpwWxs7gVBy4yozVUDAZh3fEE0qAUvIatJMYrlnDvauJjE9jRIaWtrp67vU6T2ngED15Hfuo95MYemP4m1R06DpZ38MKsVIRCAV7XFPZe31nGyFhfhsf4kDZ4jIn2dnZc1tA+aGH1aHdP0KeHq9l4vom9tzzH06GRf5N10R56iin+y8jNXwyA0+XuifwLUpEUp9NJ0+VL6FK60AQEYXPa2Fe/j0kRk3jh96vkRHhz86i/Dzgy/DPI8M/4x4N8ytx/fRMYbINNt6NJWcGo1AguNPTy+6VWFqbM5vVsEWjCsPinEt96FlviPShwL8rhFcT3O4s43mji57/yr/qH7HrSLUhh1YNnMGTdABIZVO2D6n2ga4WIAhj1sFvgwtgHyn8/WzfEEEMM8Z/AUDD13xSXC8QiMatHR10v7wj3UfLjbcNxOV3cue4CeVHemKzussAJSYG09hs5WdWJpOMSmRG+SMKGXT9ecVM/H+yv4pe7Crh3fBw3FURirz4Ex94lxncxO64O8PiUJGwOJ7uutlLbpWZOZgipoRpSQzW0JgpJ7ZUhlYh5ZOMlJicH4OshJ7tuF/nR/hxaNhW7Uc+4pEB8Q8PpHDTxw8lWRkT6cKy2h7cXZlDWPshNI6OY+8lx7hoXw54HR2O0OuguLSP/0jNsG7sN1V/6DXrrmR6nYVxcJqUdJqyB3kye8ADvf3iU2zy9MB1fT1pSKEHeFh6OV/DigRZevWk6vxc1c6yqm4cmxZMc6k3PrUcoVEoRXfNZem7Nz6QHKVg0ezbbdh9B1qNgslxLgo/n9b/VYzeks+liCxcb+pmY5Ef/thcY7RWJKn4qM9KC2FPSzuaiFqJ9VYyL92VmRghWh5OvV+WiOvAEb2me5vmlYwE4UtHFJ4eqeHNBOjlRWrYXt/LhwWomJwcR4WjizO5teAdO4InpSXh7yGip3scvPUW8lyth/rAwztb1cL6ul85BM18eq+PAI2Pp01txOJ2gUMPI+2nzGc7X20t59Jpx6o7zlczIXIRH6nyQKHBcXEdjYyg9UT7kRHgT5KXgYFkHC6+ptBV3FXPbljnsXLCTe9cXMybeD6VUTKBaxvxhoXx8oIIL+zfi02tmR18mU1ICifVXcfeXZ3hxUiLjE/3xvqag9toVNTF+7rKyALUclr4FEgXyvU1khWkRCsBgsXOivpuWAROlrTpWFkQSrFHgKRfzxMaLrDaf5auVL9Ir9uGlbSWMjvPljlHh0NfIe0sy8ZSJQSzi1ROeCKwGovt68ZCF0Ce4SN3AGZYN+5RqXQCCbS/i0A0iWv0pn63IJtJHyQdHExAeruM+yfsg9WD09DfRqiTEBXiiM9tJCfbk6d8uM2Cy89VNuYRqFTT2yllf242fWkYeUVSrXPx+vJZ5WSH8siSbSzvqGDEvlqt7v8PSX0vcbd/y1PQE1HIJ2o5TzPMsY2LCSPgkn7rCLzlc7qDXaCM3wpstxa3cXBCJwWJnf1kHszJC2PLoTAAkLQPUdOlZX95BhI+K24ep8bxaz+qYq0AhXXoztcfaSJsYSlCQBwkeUsbE+eGrlnOpsZ8wDaw9WUfrgJn0UC9sDhdNvUbGxPlhsTnA5cLH1cf9I5M42mgm4VrZ59iEAEbG+iEWCSluEhCe9yhaqfv7HT8iD2+VlFh/jz8GK/8Uvj+voLK4ll/vLuCj5cNIClIjEgqwO5z8dK6R2m4DszOCOVsXTavdzp+GjwLAx1PK5yuyEYlyqdlXwdm6HgLENnyF/sgK/LD63oDYZUMIlLcPcrGxHxdQuX8tqshcsmLTeGxKAm/uqSBYoyDK5aKoaZDcDHcg+hcenZLA/ePj0KikfHOiDovdyZ3XslnqmzYyTariL4ZZa2782wqBII8gPpv8GUz+Y1u/uZ+fyn7iYMNBXp7z6h/j1n/FbgGx7B8/9l/o1lu44/vzfDo3HIdaxJaaLdx1fxEaiYxunZknfjhNfpAEr44SiF0CgGz6q4S4XGw834zJZneXDwMLez8nPXbWP3+xc19D/BQYthIkSth8OyTOBpsZNtwAuath7BOw50louwyXfwHLIBx/B+augaQZ/9ZnGmKIIYb4T+DfDqYWL17MmjVr0GqHVp3+X0AuEfHibLdIQUufkTVHa3lmRhJquXtF9afbR/DjmQZu/e48Z56eiEIqYtPFFs7W9rDUth+jLQSva8FUdacekVDAxjtHsOyL09w/IZYRMb6g9QNbABqFBI1SSnygJ7Vdevw8ZMgkQn4+38ihik7unRDOwu0LWRz4PruLzQRrZFxs6MNTISF7vNuHRAL8vOMwzoqzrHzhT1jtLnp0Vg5VdfPVqhw2nG3k9+JWCmJ8+XJVDqWtbtPY5GAvCJ4DYyayUKqCrirouAK7n0bgF4cqZT65ibNoP3U/tT6P8f0t+Whkdt45UcbZ7lhGxIwjwd5PoqcNiUjIgmGhzEgNgmvtD026Jk7UCJid4Z44zQ3sxTfcXUo3a9JEMOeCyofV354jUCPnT3PT+OZkHR/sr2b3A2MI0SooOjoSk8SHrGAtT266wpsL0gjSKNlxuZWydj33TVCxpaiZbcXtzMt8Bp9BGwaLjQ8PVBHlq+KpaUn8cKYBi83JVPF5dt0zw509csYzc+JEvq0Uo1FKaW5qYHuNN9/M3URNl54rTQNE+iiRtV1ggY+AMbdMIFSr5LUF6dR367na1EdU4ipcLju/nL/M6AgFYoUHj26pIslUhGnwBDKpB7GDXTwz+j7eu9KK3mwnRKPgsyM1SIRQ3DLA3eOT+XD8h7hcLrKTm7lUJ8BqEzArI4SEQA+ivIRsqJXztvBLQjNTqLaJUSul5IZ7Y1EK2XGijWlp7gDK5nShVbkn3neuu8DczGDEQjPdeivPzUxBJBQw86PjDAvzorJDj5dSgp+njBU5gTgdDhJCfFEmvopI68+JohbK2gYYEe2LV9tRjAde5xW/j8gM1zIy1ofzTTr2zrDi2/g7SbM/ZKxuOZuLhvPwz0W0D5oJJp/VoyLZduwCk4ZnIpOICPCQ01bfylltEnnD3UHLY79e5smpiawqiKRbZ6GmS09elA/n6nr59UITIqGA3Cgt+mv9jF5BKmZ4ipmXFUJfux6VlwyXy0XUzMc5U9OBqt/IlqI2/DwlTE0dwZJVEzBaHVgWfEdCUAo/pUsQCAQ09Rq40jyAzeGiqKmPPqOdfqMFX08Zd607T7/RxqLsEGZlhLAgOxSBIAwSvyFA5YPObEMuEXPbc26xj9otxfj4xhGkVVLZ1MnLm4r46d4JPDIlAalQyOXmfkK1Ct5enMHnh2uQioRuMYKGExSOyMPHN4wQjQKzUc/bG3axetoIAoNC+dP2UvKivVmV7U9JfSupCbEIBQI2XWhGLBKQHqImMnY8f4p24biWzUkN8eLjg1UoJCJuHR3ND6uHozfZOF7djUoqojApBXw0gFtx8KVZKfh1HEE+qGLF8AyUslhO9JsY7q3kmQNtDJhszB8mZ2ZGCDMzQgAQthUzoPAnJDaNKy2DrBgewZLccCCSlZlWvJRSrrT0896+SnxUUoI1CiYmu0snHQ4nDoeTjkETx6t6mJEehE5v4d19lTwzIxmH08Vbu8t4YEI8fmr534zLO+t2kuKTgtFmRGD3JViafb2MEeBsbS+VnTp371bxz3BuLaze436wuwr2PQ8L14JEAUBF+yC9egsjYv3QKqU8nW3H/7sC6kc/yNG+c4SLZiASSpmWGkR+uCchrk7CpXqwW7lU10lkiD8apRStSsLbG4uZ6t9PQGwWQbNfJEjxT+7ll36C4g3uPqvwfPf76q2H6HFw+HUIHwnd1e4gKmmOO4vXfgX6G2D6uxA74e+PabdCVzkEpf/9Y0MMMcQQ/835t4Op5uZmUlJS+PLLL5kxY2jV6f8lJCIh3ioph4vbaNvbwrIHs5EpxCzJDSc9xAuFVER52yD3FMZyc0EUCmk+wmuZGLPNweXmfirbdfQbrdw2JoKUEC/e2VtBkJeW5d7+FEb7UJgYgM5s46a1Zxk02/n6phw8ZBJa+o0oJUqezviaujYhwyLkTEkJIC1Eg1go4NZvz/HAxDgAzjpDmT4/h8vN/VS06bh/Yhxn6nox9pqRXu1nbKwPnx2uZvfVNgLVCmZnBhPp68H5hh4EQHaECroroPEUDpUfgw4vtGmLwOVkUOVLv0XAW+c+JTNCxZMPf4EAEVWdep77vZ7F2bmIhAI+/OgCNVohld0G9j48lk+K30dsSWR2xr0AtDYVUNkvZNC3n7gAJZ1mGeEqmJIWiO+1IGB5fiTjEwII81ECkD1uLgAdAyZ69Vae3HSFp6Yl4eshJcpXxcYDpzhY0syndy/gtu/OE+6t4MMDlRyu7CbQS4HJ5iAxQE2qt5OIvetQFoyl/JQDuUqCKiKRiJ5WhEIB+y+UsatRzu2TXHx+pIbEQDXTUgLZ0V3BltJWVq705VRJLZqOk+x15FJc28d8u4KZ92SQHa4mb988FPM+5vQDmXh6qHlhiyc3KC+CbzwEp7Ok8Tfs8Vl4+oZS32OgrF3H6bo+pneZeWFzH+8u1XGucz8DfXO5IScRndlGsJeCmTmxJAQO44ntkUTqlQiFDreX0fBQAjxlZIZrKIi+Jo7iguwILU6niwlqD1J8PDjfPoAAEArA6XTx/pIMQrVKegwW3thZhtHq4OjhnzjUreaiKYAF9S+zI/o25o4fy9ysEOiqZFCYzdehb7BiWAT+ahlCBCQGqukLTWabKYVVThfPby0lWK3glbmpWO1Ofi9uJTLcm5w1mRhCtrC5248JSQF0hMzjYkM/icoQWtsG2XjHCA6WdbLjciuRPirCNEpGWsX0Gy2EahXckB/Bi7+Xct+EWF7ZXkp6qBeLs8N4Z185LX1mPlo+jLXH6yhvG6BLZ2WFSM7waC1rjtRS1alnRLQvv5xvosdgZWRsBfeOi8JfMIha7svhik6a+4z4esjIj9Ly0cEqVo+Jwd9ThtXm5EB5F6sKIjnf0MegycaEpEgAPtpRRq/RwtvXZLPntXxAr+BGmnpDCa34ml+juyneF0dcYSh6i5WCWF/CvFXXZPbjOF7Rysj4IMQLv+ad7aWIhJ08MjkBl82MVAiia6sRaqWYq80DvNTUjM4Kt3r64+sh41h1F0cru7ltTDR3jvXk8V+KmSg8z6hJc3E4nXgpxOT8lUmth0LC6Hg/CuJ8ry8IAfx65wi0Simn+0IwCh3MSHer8P1wqp5LTX3cWxjL1ZYBEgM9+fJoDWWtOt5dmknsjR8A8NWxGuYPCyHQXAvbH4KZ7133mZK7LNR16+kzSGjXmSmI9UEpFRPuo+LI+WLebumkwypmRIw3KqmYALUcsVCAw+lib2knU1KCMFjtmG3O64p+Jd0l+Mh8GBYwjCz1Us7VX6U+tZ5IL/f34sCFyWpnwGTDa7AFAlP/GMhLtoLDBn/lLbXuVD37yrv45qYckoK8yM4bA9q1RJ3/ig1Lf2Tb5XZMdncvbILKSKbhErq0R3l93X66TLBoTBaTUwKZlBxI9hwn3gcfgdiDoA76+xvJYDtc+BbC8kHhDf6JYNG5y/zuOQvl28E8AL1V7kDLaYHOMmi5AAu/BrsJvpwITpu7HPCvabngzm7dfwmEf987NsQQQwzx35l/O5g6ceIEb7/9NosWLWL58uW8//77eHh4/K+fOMT/57RcbUNcu52AGTeDSIy/Ws4kmQdvnm1gbkEgMoUYvdmGh1xCaqjbsHTB5yc58ug4Nl1soSDGh9RQDc19RmZ+eIzdD45hcnIAN3x1EnXgcdafmcCwMG88XQbY9xyuJRsQ+ETiKZfwyVg7rpBs0sO8OVfXyyvbSxkX78+mc3omJPoxOsGfzDAtZ+q6YcfjLA0fT5g2gz6DFacL+ow2PBU2uvUWFqkukpU/ig0XWtErBJS1DNJtsJISrGZqahCHKrpo6DZwvKYbfw8Zvh5yHjyo5ZubXsVaup/Si1cJ6HVS0a5j7pw16C1m4hpbOVtiZ6C1kvJ2PdMz1ASHnmdyWgYHGw+iSlGTIfYiLVzLqrVneHL6CwSp/yjhC4pRc9Jp5kB5J+9d/J6eQSFbV7zMouww3thVxqmaHp6YlkRsgCflZSWEGUopcoSzsVbKO4sz+PWuEey80kaAWs75+l5KWga4fX4i2YZjyGv3Miwihi6dmWERPtw5Ng6tSsqRik4GO2qwaaOwrdrNx6frGStz0tVvZHiKN7eNjqGlz0iZPRCbc5CntxRxw2gxAquGaR8eZV5mJDU2X1YCgoEGgirXEzZsBL/rzJyIUlC7v5IJyYEYg9ajCEvkh8O1TElR46mNZmDgCuaBVuRAcO9pTP7BtBr9eXluGlaznSWJwci1MrRKKWsOtfDRzA8obR1Eq5LwwtZSZGIBj/56ha33FGCxOykqr+OrsJ0M9D7Hos+L2HT3CJxOUMhENPcYsNodBHopMAyYMZ3pQefjwbyRoczLCuX9fRUcr+7m17vc/XZlrTp8VDKMFjuDQaMQuPTYG/U0DnuEfRUuOo7XMjLWD9nBz4i0VPLQ3M841mXnoZ9K+emOAm4eGYkIIZebB7A5nYRqFPQYrARr3EHw/XF99P/6IJWzfychMouz5y6RGaol2s+DtFAN2y+38PO5Zn64NZ+OQTNdOjMz0oN5dWoSFWc68NUoeWtvBfXdRo5UdjEnwY8JSf4EquW8s6+ckzU9vLfY7SE0M8zClXojLQM28iO9OVHThdnuZHZ6CDmR2mvBQC3FTf1Yr+6A0i/wGvs4W8a00371AGfbwhgT70tVh4CdV9o4U9fHK3NSGTTbiPXzYMO5RrYUtdJnNPPuvmpemJHEmmN1AByt7OITnmLDrHEs/uIUEepxPDM8GFGFiw8OVHG4opPDjxVitTvZXNSCV1cRwy89zs4JO0kN8+WucTEIgBkfHuOVuSk8ftOi69dLsFqBn1rGjfmZKAQWhDIPqjt1vLckC73Fjse18racSA3Di3/nk1/lBNma2CUYzbK8CE5UdXGwoouy1n58PRVoFBJenhgI6xdAwf14py0AYET+CGJ0ZmwOJxKRkKLGPup6DExNCWaGr4orzf1su9zGjcMjAOgwdNA40MpvF/VoFVIWxHmBbwItfUa8lFLO1HTx3Z7TjPY30UQaNZ16dGY7SqmYySmBjDIewKL1Rhv7h2T7gxPdqp9yiYhDj7oNd78+Vou1vYSgiSPx0nrjaZjHrgsW8mdJeHBcHnvq+/BX+l8/xohoH34518RPZxu5c+xfeT0BDDa7/Z6EwuubXpyTxriETrehMrhLDeMnMRA6Dl17B7MyQ67vu3LaaGA0JqsD/6AIHstQoQ0MvP64d9ZsSB7/z28sTiuYetyy8d2V0FIE5TtA5ukWmSj6ATwC3T1yhm7oKIeeSli9H7bcDUmzYeb7EJD098eOGAF3nx4KpIYYYoj/SP7tYEogEPDYY48xa9Ysbr75ZtLS0rjvvvsQ/xflovvvv///8zf5P5mvjtWiM9t5aNIfZp9CWy+ati1sOjqSE+0W7hgbg6ePjDcmJxEUo+FkZRdPbbnCqoJIbhkVjVYl5aGJ8WiUUg6UdwKQGqohRKNg7U25vLK9lPvGx/H8zGTeObefKB856882khGiYdbdp7nxq9PcMkrB+DAxSYfv4ujodVC6kzS5LwUxhZyt72XtTe5Jx6/nm/jkYDV5kd5EBI4kKSULrUpKdacebw8Jy/Ldk52RkV6YvnuI160G7H5JjEsPIMTqYFKSP3M+OYlUJOLu0Gq6hIFMt72Ndvy7yL3kPJLciqqvBE3udPxzp3OssovaLgMAl7rPc2bge6aGv4Wfh4w5WSEEezuoLR5EKHBxsOoKk7xDGBucwNfVSnr1Vt7dU49CKiIzTMvNIyNJKAwl59rK+Mn6G2nutfLajjKenpGEwWznRE2Pu9/DpmPdkTJuieojIMgDs80Xp8uFQipmgXkL1ASzIn8uKzK8QKlFE5dMkyAAqUhAhI+K0XG+2Ozusqf2QTNj6j9GxAgOOVO4bDjHstzHuO2jE3wRrSY9VMPTm68SqpFzz/hYLrWX82vVQf406k/8uDAQH28tgUGpFDX0EZdewCstb2Cp7ifG34NwbxV9RivJwV4IZTJ+PF7F/tJugrxkTEsJ4P2DhYTIlUyu7GJU7WEqFPm8VS1g/W3DaSzp4fczTdx6cwZ2p5Meo40rzQPkRnlzvKqLZXlhTEwO4IF+twDH3VMlpHoU4DhZxMeHG9h2bwGh3iqcLhculwuJWEiwVoHABTaJkOxpEcQOC7h+Xjf2GbHYnZQ0DxDlr6Jbb6HHYOV0fQezszRMGhZDRbuO9FANbTSz9VIrO6+0MyHhDmaIzhKk9GXQ3M+g2UHnutVMmvcnUHvw3jR/6K9neX4El5sHQNcOQilIPalTpKIT+ZEkFPDekizK2waZ8M4RnpiWwNzMECYluyejmWFaipv76Dda2VzewY1TIxCLhHx3Sz5n63p5eEwMb31/mdlxFURPnklqiAaNUkrMtd4hf3sbS7X1CEbOZvy7R/jx1jyenJrAfT8V8fniRCK0ElxARqiGFyvFfLV0PdQfR2uqx6/pBHfO+Jl1pxqYlRVCa5+JKF896WFeyAYbcdafZKTdTH9CKpcaB7hjdDRT0oKJC1DT0mdkeLQPIYtyEQgFLMkO5fvTDSjDQ8mOFhFrtDErPQibw0llxyDn6npZlpNLm99nXGk1ofYwMs7fn/f3VzAlJYDWfjM/nWm4fh2/Ms8t1T14eTuWiNFUdPbxyMZiDj9WiLjoeyxXf0G8+FuW5kdC/u8s7NITYm9mZVAiAJ8crsbfU05SkBfjkwJICvQEqQMiR2ELysPyVwHZowdfIMdvOPcXzOO5mSlsPN+IWOTOkCmwMjMKFuWEcaGhj6qBUq4OHmDXA3+6fn5Zcm7juXUXmJAUwIy0ILTjoxBaBrlUNsiaVe4A/sMDVdgcTh6ZfANK3JnSyo5BtCopAWoFncZO/BR+13ugVhVE0vvZ3ZjKDXiNWMqsjGBMNicMtCA48CJTZ7yLS6TAYrQhU0owDg6Q4WmmskfEgk9P8Of5acQGXvOpmvX+343/IqGACckBf7f90OVqZh2YCKv3QoC73Purg1dZXvkg4tH3s9DDiDZwKb2tzZQfPUBBogzSF0FrEcjUEPwPRC804TD9Lff/Z77rVhPsr4d5X7izegB9tRA23C2R3lPl/rf2sLuETx0ME5+HnY9DcBZkLvvb40tVDDHEEEP8J/K/LUCRmJjIrbfeyp133sl77733N8GUQCAYCqb+P2Zsgt/fqfAFZaXQHfcbRZtK6TVZKD7XhqBcx8In3I3ROVFaMsO1xFxbzRw02thX0o5EJOTnO0bgdLq4Z90FkoPV3DM+jpKGTooP/swC/1Z+XPQKDqeL/OgOXt9RSkufkUcmJRDt7wEKCeXLTxGpUNI6oCTYW43rsI6NZxv5865y1t+WT9uAGbFQQFqoF8PH3kpzr5ENZxsYGetLVpiWlj4jQuCjw9U8sHAjogPVjE3w5Y2d5bw6P41+ow2VVMRdhTH4n/oZ/9A8yJkKGh8QizC1bcbcKUYSmI5pxEOkh3lxpLKTui49vZ2RfDDuC8I1bsnil/fuI8E3DFvHXDZf6GTvmRRiE4xkGc8wNXkZGoWUpj4jk5MC8VSI+eRgNfvLO9hyzyjaBkz0DXiSH+FFz6DbHPOVeWnsP1BPRbOO4FBPvr+zELnaXaqUYq7kZE03hQkB0FHinmjghCsbYeE3YO6jWJCO1TLIPeOjeGpbCd0GKx8vH0ZGiAbx/E/xqy4ntdeMOWwsP5f/xk93j6FvwMm0D47y/uIMYvw96TFYmJZaiFB4bYX50GuUqhIJvPkl1hytoTDBj0C1jHu8r3K+tpPI9NvdEtbA7++8hk0TwZLcUajE8Oivl1mSG4bebMfudPBi4McM0wTz5Sp3CdA3De1ccZiZ2KPn3SVZVLQP8vTmy2y+eyRFFXUcbjBREOtLboSWFkMz9x24j81zNnM5eDUqVx8/n2uipd9d5nb8Uhmv7q1l1/1jeONAE4ElBmatSMbocrD449M8NzOJO8fG4sLFi1tLmJ/ux9K8KJbmhfPw1t+pOtrNm9NuwVMmxlx/liSFgj1iIS6HgDlZ4Sz+opWvE+xMSgpALXUirY0FsbvvxFH8C206G+FTHnav8G970D3xG/s4WaveBKCkZYDBy9sZzhW+GZHJvnNV9MXdjLByB0fPnCBl2SvkRcfQ1GvgYlMfS/LC2HiukePVPXy6IhuAu25IJerE11BiYaZ3DFWJBSz49AQLskNYljeOp7Yc4YVYBx8vzyI2UE1soJrHpyYiOPYmFpWQd5d+wJXmfvaWdIBnIBX9Ij5rSOHu+Q+REahmzZFaLtT3ckN+OF8eq6W+20CCrgZn9SGOtURSp4igVe/g/gnuhZf1Zxqo7hxgVnooaYIaUKYyLNKbEzU92BwuOvU9vH32I14e+xDbL7fxy/km1t6US1nbAK2qJJ6Z4c+Gs4009hjo0lnRyCX4ecgwWh28u6eCqi4dn63IAasRjrzFkXgZI0eOYdu9o3C5XOjlwbRKU4iTeCF3udh0sYWJSQHInd7Yfl7N74LR/MB6REv3UtY6SHO/CW8PGQaLnZaMJ3l3dyVOZwdfrHSPazJ5PxovPQB6i43ydj12hwuJCGJpIrb1eXDsZX9pB2HeMfxpVCEtvUaOV3ezJC+cnZfbMNuc6E02enp6GNTpSLGVMt3/j+zOzLQgHLjA1E/R7m84pJzM7pJuzHYHhx4dy9377mZO7BxuTLkRALFIiP8dW0Di7p0K0bqznhadBXtAPiqRjO/2VCKtNbL8niyqzpzEo7SImfNup6i5n5YB8x/B1H/B5rRhspkQC5R06yzXr+NNF5qp6IWpN+9D5J/k7m2zGkgIDWBQdQeOPgObyw2cLjvNC+MCwW6Gq9shcTpXT+9Bpgki7h8FU39NTCHUHAa51q3ON+IeGGiDlnNQdwT8E8A3EQruA2Mv+CWCQuN+bso88AyEpnOw6wm4ZTeIpf/69YYYYogh/hvzvxVMdXR0sHr1ao4fP87XX3/NqlWr/m+9ryGu8Rfj1L+moaSL5VuLkcvEPDE1gXf3VvLnRWnXH5dKxHywNOv676HeSqanBdGtc5ujCgQQoJGjlovBamD56ZlsDXkUh386fY06jCIX5+r6SAnVsOVcLXeYv+KQ91J+rZMQ7CVHJROxrbiH7fcnc2O+EZFYQFN7N8ryTdw3YTEdddU4HX2AL71GCyWtg8zODCEzXMtTm66ikgrxUwkZNNn4+UIz90yIY82NOYR7KxEdfZXf503jfKeB1RWTmSELZlr6aLr7K8gOyGav9DF0qh7mJ4fyxdEaOgbMCIQCytoGeOy3K7y/OJPy3mOcbj9EScMY9IM6grzUbC9uIzdcwzmThuquKPyLWpmcEoDd4SI5xAuArAgvGnsN1HTqWHOkluouPZvuHolaIaGqU0ewl4KqTh3+EgnzhoVSfuw3/OPz8Q4I5VJTP2KhgDFx/rQXvuueVNlMcPlnuPA91B1m5vzZ0PAG7HShkN6LxCTkw/2V7CvtZMu9IzFsLcXLP4CiPjMmpZGpcTbCtEpyI7yJ8PVALBKyZM1pJicFkBmmRiWXErv4EwIVCg4Vt3JbTjj3b73CmHg/hNljGB3Yhk4h5Z5vjvH0tERG3Hofnko57x+o5lhdJWP9pORH+fDWngrmZ4fyzj4r49JkFDX2sfVSKwFqGSuGa0kP1XKkopMTNd28MieVCe8c4VDEV6QGjKHfkEP7gJlZ6elMjN6LHRGv7z7Jx8uGUdU5iEgoYMYHR/l+ZRpvjawFqYp7J8Rxpq+ailONDJsVR6SvCm+VjKhrwb+PSkpC0atgTYExj5Lln4pMKsBsc3Dzt+f4LnAjSSkjeGXOYpZ+eYrabj1HHyukbcDMuLcOsWW2BL/pT/PbhSa2XqokJXAS+yu6uFV6lqWmDZjHv8LcLy8ycvAqc7NCSQvVcKVlgK4uISP8hSRZrpCkLOfKtlZMYjVRydkEqBUcOn6CNpOIj5a5RR0UMhH+and/S7/RCiIh8qXfuBv4TX2ERCqYlxXCqepuUkI0bLxjOD4q2XX1TYDMMA0tkgf46Uo7CcdqKWkZIOpaNstZc4ClYVl4KiQ8u/kKhys7WZ4fztT3j7P9/lEEaxQQOBFx3ETOrz1LqEaOh9JxPZNz/0hfSte/y2eXVnPE2sdH3mVER4/hvaVZ1DcPUHqqnQGxAKcTpqcFkhOhYema01gdTmRiIf1GG5eb+0kM8uTxKQkoZWJE1967CxdWp4O9JW1MTglCcNt+JouE3LnuAiNjfFh7vJ59D08gPXMqb+wqw2Aw06q3kx7qhZfWA13wcLZdCSZvxBOEAVdb+9lwtomUYE+aqq6QcuxeZo3dgEb9R6Dx8cQPkQrdk3JPmYTUYDUSkYCWPiMBQVmIbzvIdyfrOVjeQbSvitmZwewoaeO3880szg1jZkYwSqmIl7eXIjapaGhsZdjKW5n6V0p70X9RIeypI0F/GmnWEm4ZHUdTfydWh50pUVM42nSUK91XeHOsOxBv1bsI1sJrO8rQKCXcXRjLqp8qkYnT+G6klI2VnUyI82PAZCN8xHiiRxXw2vk3WHvLAwR6/FEC+DdY9HRvvZMvtVocxjs4XdvDnofcCqAZYRqCtQrkwdd6zi79BKVbGX3DL8AisOi4qesFHIJkgjwhfs4cUN0GQiHdw+53nzd/TW+d2z+q5DeImQxxE0DlCzUHwetaoFmxE+qPubNXUWOgej8MdkDIMLjwtTvTO/J+aDgFxi53WV9Pnbtkccjcd4ghhvgPR+D6a8OMf8GGDRu49957yczMZO3atYSHh//ffm//TzI4OIiXlxcDAwOo1f94RfH/Np2Ng1yu6yUkXktioBqX04VJZ0WlkdOmb6N2oJYM33xu//48d42LYXScH5eb+zlR3Y2nXOJWkvorrE3nuWgJZXhsIAe+L6NC7aTGYuOZGUn0X9iI5ur3WGd9SrPThxxJPQSk0meBXVdaOVjeibdKxs1JkHT1LVj8PXt//ZXzNZ3cfu9tnKjpZnZGMFuLW/nlXBMvzk5m65lyHryyEMtdp7nUI2FYhBaFVMynB6uIrN/A9FmLeLdIQJfejMNiZcIwIV9UvMYvs36hW29FJhJyus5dbqeUiPnwQBV50d4cKO1wK7J5G0mN0jEpctL1z9its7C7pJWOAQv3TohDJBAgFgmhvwUGmiEinwc2FGG1O1g1IpKD5V3cPCqSIC8Fb+0uZ3xiACdru/m9qJV9j7gnNW1fLUGftpK4/BmUtQ1woaEfXC5+vdDEmHh/VgyPwN9Yg1kRQEdnJ1577kOt9UOQczOmqAl8e6Iek81OUWM/D09OIDPYk88PV9LR3c3MtEByU9wZhop2HWdqu5mYHEBVp57txS209pu40qLjlztHEOGj4udvryB3gCTXh1BvJXlRPixdc4o7x8agOfUqYpedp41LWZQdxtHKbh7KcPDeRTueSiUJQZ7cPiaG2m4DisNv8XPdVMr9RDw8LYnaLj3T0oL46lgtZ2t7CPdWcL6+j5uHBzExLZxNRa0cqujklYI49n5XimJOKEvzIugYNNGlsxColiPY8ww+4UmQt9r9Zei7sB1+F3HJBopm7sKi8GNEtA/nTuwnsnIt+gmvI7LpCffzoUfvydWqbu47VM7hx8ZR06knOViNSiaB/iZa2loITsyntktPlK8HZ47tYrigHMGYhymrbeCbU634+mqZkOBPglKHR9tp2iJn8+qOUgxmO5MSAlg+MvJvL7D6U3D+awZDRmPzTcAnzh08vbR+P00mCZ/fPJoeg4UAtYKnNl0mNcgTuwuOVHaz9qZcLJ01SFrOIsxylzkdr+ri9u8v8OzMJK60DGC2Oajt1PPnhRkkynoxq4LZVdrF2DhfdBYbQoTXxU0A6roMvLDtKitHRFDSPEBcoCeJthb2NZhZFViB3CeU9oCxeMjFLF1ziiAvJV+uuibf3XoJgjKwO12IRUJsvY1IvMN5+adionscLLsrE5FIyIaz9XTprAhw0dJv5rlZKXx+uIZALznDwrW8uK0EX5UUg9VBrL8Hz8xI5oWtV7nQ0Mf2+0fzfcn3RKkTMQyEUtIySMuAiTFx/szNCmH7pRau/vgZ4+bMZni8Fnxj+f5UA60DJp6c5u6vsTuc3P7DeSYlBbAsO4jB0v08fTWAfqOVO8bEMjreD4vdwZGKLgqifRCJhG4T3IEWZn51lfunpiGXSKjv1lPSOoBcLOKZmSlIxUKsdidS8R99SH8xH3a5XKz6+iypoWoem5LI7T+cZ3FO2PXSTgCH3U5bVTmPnL2AnzyctxYMZ0vVFqLUUQwPGY7BYif/tQNsuH04p2u7yQrTkh6m4WpLP3uvdmB29aCR+1CYrOTPp98mUXYTT05N58eyH5kbOxcvmdffnHqHyjt4fVc5L8+MI7vhC/oylyKWR9PUayQjXPOPbwgWvbuHyfvaeWw1wNG33T1M380ClQ/MfA9i/km/VOlWt/iFfzI0nnSXAUaOdEujI3BnmVxOEIrh7JdQfcgtPpG5HALTofEUaCLg1EegCgR9G6zc4r6Ojr7pVif8f8R76v+F+/cQQwzxn8e/nZm69dZbeeONN7jvvvuub7v77rt5+eWX8fX1/b/y5ob4g169hW9O1nFPYRz+4Womhv9xI/ju11LE1QZWPJNHZV8lB6pLkJgT8JCJCLom3ZseqsFgdVDZrgOgfuPbSKKGEZI7nlpxPI//eIEPl8qYsDKJw1uu0GWw4CGXMO14IK/M+ZbJ4SEEOmwc/OI7CuT19Kbdx47LHswSn0TkX4i26QCE5YJQyKi585HV9VHZoWNLUQs6k43l+RFMSPTnT9tLyZF1Ipj2GiaJN7d9f5hl+WGkhXixv7yTBybcDn4B6MxX0ZnseNWfJzm3kG+mfINQIORoZRfDo3z49Xwzd42LRWjtIi1EzdfH6hgV50N1p4Ecj2CaW+zM23mCVSMikUuETI2SsaLpFRj/LFgH4eJ3mLPvwHnwdZRdl+GOozw+JQGFRIS3h4zhJa/QWzmaacf9eGdxJskhXgw4mtnX/y6DlizUMjVBq751SwXr8/h1/3mqTCo+uGE4NoeTbcVtOFxOHut+iaseY9hrH8Z8rwxUaaPZbU7l5+/P88TURIQCAY9MTuTXC00Y9r3BPLmO34IeYkeNldhIK1qVlANl7ewv68TlgqutgzT3mRmb4M+TU5L45UIzZquDl25Ox+V08c3mbfzSpebx2Vk8NjWRuAAP1jfeSqCHmOeDA0gO8qKp10izVItC2YZUIuTjg9WMjfflvf3V2KyTGZvqxWdTk9h2uYXSVneP0urR0dyQH86Tv11hdqICva6fwnfqGBnri1gowD/SE2mOFGHRD5D9FC9vK6XHYGVJbjjzRqwAuRcHyzp4d18lPy8KQGXphcLnKB2UIzMZINoHs2GQfpEf0X4aRIoIXC4XprZ+pH02Tj01AaPFRm7UHwpwVO4hpOMq9b6pzPjoOGtuzEEYlIsxYjIqIOnym7wZFgVjHqWxc4DjRSVMnTAXl95BqFZJZqgX3++rYVygBlWQkpkfHeetBakM33YbH8esoaxWjrrWyOuBneDpzws3TARgX2k7Hx6oZnyiH1XtOroGzcwLM7BmkTsbfGXTWpJkNaiylqG32Hh1ZxmPTo5nVKwvMrGI/WUdLM+PIMpPBZ+MQT7rAxICMvn2VD0ykZCN55s48vh4nE4XXx2rZVi4hu9vyQfcohzxAWrsjS0ca3MwxUuIr13Mm7sruGVUJJ/dkE3fNXPp+m49u6s8uDNYQEXHIK9uL0Gqa+Lb5QM8Mj8FiUhIj8GKv1ruVoWzOnhncTo1XUa2FLUwIsaH8/V9HKnqYtBoZWSMN7H+apKuKde9ODsFi91dgqyUKGnoELL+RDU35ofT3dtNU2sLZIUwLtEf6/SFJCeHwGcZkHcHeckP8thvzfx2sZlwrRJvlYSXZqegVcn4rbgdky2J4qY6lueFkh3pnoiXtNfzw9ly3t/vSUufiV0PjCEYG9+kFOETP56dZV1IREL+vDATgLouPQKBgEhfFSt2rmBM6BhuT78ds83BqztKsTtdhPkoyW75kUungjGpqxgwPvi3425LM7s+fpfnn3gehUKLomIvy5pLOBM3mZe3lfD8rBT2PTSGII2Cn8/VIBO5uGPdBb6/JY+9ZR3EhfXg66nkrR39BPvO4Z6xKUiEElal/OOqjvhATzLDNGRGBCCJe5G/5K28Pf5FmZzMw/3zF6QqmPiCu/xv+jsQmOYuy/tnJM9xS6G3XQKnHRJnQNykP8r2fl0NUiUkzYKy30HpC7o+0LVBxS63D1V0obvUr6MCrDq3f9bZz91ZKflQ0DLEEEP8Z/NvB1OXLl0iLi7ub7atW7eORx99dCiY+v8DdpeLAZOdf5RHjMn0wyvL3aTc1BLJ5RIRlVVVjIn3I9xHxcqvzzA8xpv0EA2rCiIBMPbrsHbo8bU58JCJWJIbSqfeAsDLc1KxO11IREK+vzWfUA8BvWtX4Tn5Yc6Er8ZkOs/Y2Azu10LEwXdok2URmDgNhFLqv1jOyYi7uTDggdXuwGCxc6SyixuGR+AplzAzPYjwznKa+yUoe9vY9cBo5FIRWy+14KUQMzbB/TmGRWg5V99La8gwDlrOUHa2jDdGv8Ghik72l3bw2YpsxO1F2H5cSO34PYyJ92VJbjh7rrZxvKabAE85WoUEu8NJh8lGTXkJUT219JoF+IoN2Ltr+eFMI0fb5/HDDU8zYLT+VfN5Ap6xs7CJvAmVdKBwmnh3bzuh3mpuSnwIlcSTjgETR8vbWORwm5veN1yDRaRCqxBzg7aUA1IfJCIRpgmvkKDwY9fRNrrTnyIoRMPvv1zinnExyCQiqjv1JAap+WB/FUsTZ5KWG8hqv2ie23yVBzcU8djURO4ujOPuwj+uvfN1vSikIrZcaqGsbZA356fTsPVlAuOGIZDKyQgQEe6t5JvyjxgQDGPQ6kOMxhs/TzkHytoxWe0YLQ7uK4zDUy5maV44928oZlJSABOTojlT18Od685T0aHnk2XDmPL+Ub5aOYy391YyPS2YWyzrcJgGMI+5i6V54dz8zTkOVHaxdFwA1s3noHI3r8+bhMtuQe3pgcUeyMeHqpiW4kNelDeKgDjIXklJUxfDYjR8eLCafpON2ybPB+bzyOFHGBUyim/2+KGUivj25lzWnW7k3X2VnHtmImInfHT5fbBFMSz2Kcb7efDNTbnkRHqz+PNTTEpxy/hnhdzN5PRwOPoOYp2Vk02RjNN3IJLIrmdERgRpUXvLEQgFjIrzQS4R0XfjQeSlOrzselZzAA5uhDkf8fWFHQwPTacwIYQYXxV9eivdeguR3gpm1LwASa+DKpOERQ8iViixO5wMGGz4qmRMSgkgzFtFuI+KBfofQRTAjtJJhE/9lbSoGI4ereFsXS8fLskiI0zDjsutfHO8HrFYgNXhJCfKB4vNwbAIjdsQ1z+XuzRdvLjHxLMREdyhf5PnNi3mydk5DItwBx97Sto4VNrFnWNjifBRsSwvgliPIAiKQgUUNfSx5IvT/H5vAetXu7NvPXoLo2OVTPvgGEtzw/BXy0gMUtP0/2PvvaObOrd97UddlmTZcu8d94YNtsH03juhQ0ISSC+k99436YU00iAJJZTQIfQOBtx7773Ialb9/hCHnOydfc++3z373H3O5RmDMdBaS0uvJOt911xzzt+vS49QIGJqkj/61nJ0LQJU/oP46FgFZa39PD9zGhO/P0lGqIZbktyZ0vUtSnMXkMG352opbLLSLdIyc95OWq1KEnxdSQ9xJ9pHyTO7ivBWCPlQ+hnKJd/gpZJic0jYsCKNlh4jR4udqnVXuo/hHdzEA5H3IxQKCNC4AGF4T3kSgOxILzr1Azd+JzuvNSIQCHl4YjRzIueQ5uv01dMaLTT0GJiV4s/QME88uibzzCUBnvIIxsY6558Dn7yL3WZj+gOPcfvLzyL0DKOx28C14jKiuy9TpOwj6Xp58NHSdrRGCyb3LTQ16fjUV0ycKpHtd2dgceiQizScKb1MnHc4HsrrvlTWAYpa9PyY08Rrc5MYMOiRKZQEuit4e8Hf9jNV9VTx+qXXeWvkW3gpnOvt4cJWQj1diPF3+5vjATpyduF6ZQPyu4//vrF0P+Zzn3As/jWmZKX+vn3sc3DidQjKcAZNkn/nnyWRg/9gZ7A05imqrN6EHLsHiUMAgyZDRwlsXggBqYADPCKcHlrJi5yqgEIRNOVCdyUkLfjTsd7kJje5yX9nhP/xIU7+OpAC+AcrBG/yn4CPq5yXZyc6S1v+ipExPiRHOxfY6o5+siI9eXV2PLUdeqx2O3ePiUQuFqO9bioKEL/mJVJnzOLJHfnc++M17h03iEkJftR36blY3YVEJORoSRsPb8mj32DBYJdhF0gY5OvOEwX+CBUa9ha0cizpHbrVcaw7YcHoFsFvPrdyvEVCgLsLCqkIb5WUIA+XG30ifzlcxoa2WJq6rOw7cZnGHgO+ajkjo7xZkvF7+eFvxe24K6RsWDGUeYPmcXfSg9jtDmanBOLAweaL9Vw0hSC5/SDzMqNZEWkiwNFOl95MqKeSx6fG0Wu0IBMLWZEVitfZl/jN51ZmfF9Fg82dRwy3sjmnmZfnpNDVb0U3YCW/qY8gjQKFTMzUX4Wc6ffmYqcQF1c3rDY7QQ4tP+7qoqxVy/dHq9h4vhH7pFdpsKj4qcmbr0vEYOxFdGY9Kd4g0Fv45kctCpU79d0Gfs1tpqS1jzatiW/O1dLYYyS/sZe2ihK2LAxh+cQsviqwYbM7mJbkh8nqDHQBrtb1sG77WeZ//hulrVp81U6fIZFAgKerjGsGb6rM7myrcWHRmDQ8VTKG+A5B0mPm0bypXC2poaCplwOFbeQ29mEwDPDYpnN8crSMI8VtvDM/mdXZYSjlEk5VdOIiFvKXBSkkBrnxWGw3HoXfkBzkTpDGBUY+QlXSOvYVtKI1WnhvgivjxPmINCG0jXsPR9QE1BU7EB14FIAWrZF9Ba3ozVZmDw5EKBRgNRt5r9CFvXktlLb2Mz7Wl2v1PRwubmFt8lpGB41m0ZAgxkR7seFEFe8dKeeVueEoZWIu7a3G1uBPRa2QrLO3QWsRwyK9kIiEPDktlikJfjR1myjvlzpLoHI2EhCZzMt3LeFAxxUe/O1JHt95gl69mScOFHG+pgudycL5qm72FLSy+IcSbs0OB4SIht8Hk52KcKebD3O5sRSxSMj2g1Ws31vCzJRA0sM8YfUB8sRiXj+9kdwu2HSljc9+OcChK6X8cEcmwR5KuLoZSvdDyDAISKW9f4C2ATE0X+Ou0VH8vGYYl+u68XaV466QMGKQJ65SMfoBK0VNfWzNaeDRrfmYLDa6dAPE+qhYLdjL/t9+wyaUsnZMDGmhGlr7nL2RObV9WB3O7JZKJkbbYmDLqR4cDgd93Z247L+P9dP8ifFTc7q8g5IWLY9uy2PntSbGxnqx8UwNfQYzepOVVdnhTEt2lr8V7f2Eov2fAjAkREN6qIYQDwV7F7rxw9BahJ9l8mLrSJ63rGTzxTpuDe0mS1TG2YpOfqhzZ3OpjRPlHeTU95IUpOEvc5Mwmh1URS4HkZjRMT6Mi/Vlb14Ln52q5mBRKwB3JN3BX8Y/z9BwT5J9pTR/uYiO+pIbc8aR4lYe+OkahU29fJrzM2e7f+D+sVEALIhZgNVu5ekzTxPs6cJ3qzOZnx5CiKeSClkCdX0ObAOe9F3P6oWlDiE8NR10HQg/zYLOKjacrOK19mHsiX+HmckB7M1v4WpdD8n2YlL1p7kv7R7WhKWRkRjDmd4y3rj8Io+cfAS1XILNBvvyW+H8J7DnYdi7jpDK729k+X564Qlqcq/8zdxe1+VUKpUIJchFckT/Tlr805NVbLpY/3fXjAq3YfwY+srvGwZ0cPBJHGI5faY/ihohFDmV/kr3wuFnft9u6Hb2T/XVwYHHIXwkF1rsVHqOdar9NVyAlgJnI25LHkx+BWRuUHXUWR6obYWuKtC3QY9Tqt9s/avXvslNbnKT/+b8b6v53eRfm/lpwcgkIqJ8VLy90HmHc1ikF8MincGW0Wxl+5UGQj1VjIr25v6xg9ANWBiw2JBJRHx6opKCZi377h9JrK8rGeEeaDzckN3xBQALAmFUtDcKqZh4fzUBbi7c//M1HpwwCIlIwB1zJjG2Q0dDt4EdV/WsnxGC/NRrYHyZbpuchydEM8jHlUCPFH7Zmsuli/UMCfegqrOf6UkBN96Hh0pCVVsfNrsDV6kr920qId67m4gSI5OGe1GnM9EkE1HrFsTOI2X8mtvFhqxq0kOHc6S4jc0X67gtO4zPT1ZztqqLMcmv0Svx5YVBanoNVroNA6xfmIKpOp8rJ4+y4JlXUEjFCIUCNAopz82IpVNn5pGJ0bx6oASz1c4wfSXrou3Et/6KS8QYJgZ7Utbazy1fnGNOSgAysRiUHozrfY7Xx0cRoXShxNxMfmMvz06P4y9HylHKxDwxOYamPhMny9p5aloc137dhourmhAfp7KezeEgQONCl858o+G/sdvA6TIDd413YefVRq7W9fLSnAR69GbkEhHzV9xLR78Jn9w8VHIJvQYzLoV6XORSqmfvQd3pxvhYP6YmBvDo1mu4u0h4NFJP4phwXFQqug+vRyQ0I0l/iOwob+4dG0XLA7fSPzKTxZkp6Go72XmmmUcmRNGz50lUKbeSFqrBy1WOqPAYlQ3NBLt4MeObTr65XYJIYOGVxtncU9zGxAQ/jj0yhtpOHVM/OMPLU0KwiEJRedWzblIM05IDiPBW8di2POq79WxZO5z3jpSTEKgmJcido6Vt3D6jhiZHORDD0Im+ZG39hi0+6+iLWMnBWhHaqhpuzQ6ntKUfgZ+AF2fFs7+wBYerH4JVe7C6hyEGUj3G8FVvAV4BaqQSIUsyQon1deXj45UM8lGyYmgwK4aFId66nNeH3QN+SdR06AiVOvh25gecLOugpc/IndNjMJitBPr/LhDTbeinpK2NeNUANV167u3dSCXZbLnszJrS3wL718G6ElB4cJs/UHMadj6CYe0JjFYjhU1artT1cMuQYG4fGcF7R8q5WN1NWqiGpRkhZIZrEAjgsxNViIQCnkqOYUTNEYQJcxkUH0Rpi5aVGy/y0qxE3l2UwpW6Li7V9AIQEKhiW1UbJ8o6GBGmROwXx4SkUAQCASfKW5GK7bw5PxmlVMS4v5SxYlgoa0Y7TXGLmrROg2TgF/fbSAl0IxNIUBvw6iih7EIrMfZrIHWlacZmxht8yW/u50x5J5k+3iQGtHK8Cg4VtdKtG+CZaXG8Oiee5V9dRCYWYkVARNp49pae50DjJtJVK5gQH02klwqlXIxN10V/bwfuQbE4HA4cQhn1roMJl7tzurwDsVjIwiHByKUiylt15JV7syptEid+KOGa3IZULWHpWC9SvVMRCoSQ8w15Bg9UCYMJ8FCx4+5stubUk9/YR4S3ijiNFioOgXgIPJQPrn48NsWCWCi4IYu+OCOYcC8lonO/cNUazqUyIT2G0WRGeHKs4UPiO90Y3eec054aYkfZfBZiF8HVTaBtwnXocparnPPyzIeewN33914tgLJWLXM+OcvFpyYQ4hbCJxM++cP+bWuHIfl3vWA0XXNmh67PGcNjAhge8/ucikwFd59HJlOy6K8XDp8YeDAPmq+CJsK5rfIYbFkG9+WAsQ/OrAddJ8uC2iF3F7rBq5G0lSEbtQ7yt0PsVKcJ8Yy//H7e8sPw+Wh4rOJ6DxbM/Pg0L85MuLEm3eDkO9BWCLd899eju8lNbnKTf2n+4czUn9Hf309ERMR/1lhu8g+gMzkDn79HQqAbkd5KjpW0M2CxcaCghQHr78e/caCEtw6U0dhjAJzqVT9fbmDJFxcAZ4nf5uv9GYEeCp6ZHseA1U5Nh471h0sxWWx4qpwKZksyQxkd68ORdaMYPvANrVucZp7rD5XR1GvkwyVpyGVS+lwHsWxTMS9uOU3x6Z0Eeii4WN3J8zMSeGVOIidL23lzfxkdut9LdBbGuHKstJPi2lYKGvtYMzKMnXnNVIdI+DGvmeoOPcWt/ey81kicv5pnpscTlDGTc1WdlLX2k1PXw8R4P9aMDKNLN8CUMaOwiuU09RpICnLn8QlRrNx4iYC04cx+7FmwWVieFcrsVOfFh9Fip67LQLK7kmCrEBeZkYNyDRWaWCjeRXiEiEjXUvrb6/jutqEcLGqnpLWP34rbeOeWFDLDPREUbcQ3tIm9Ba2cq+rk/UWDKWzs41RlF0EaBcWtWlq1JsyhcfxqDKamU09Jqxa1XEK4pxKJSMjJCqcvWLiXkrmpgdyeNYK1oyO4bYTTRNlFKuLNLcdoPvIpNZ06wr0UeLvKeejna3xRK0fo6kFEfBr1XQaOFLfQ1KPHaLHRb3MgSx3Dqs2F5Df08UVzONv6Eylu1nJbdhjYLKiyBqOLSGXxAQeHFbM488RYrjT0MTJvMqh8eXZ6PJsv1PNBx2BmFY2m69IWTjw6Gr2gim+aT/AXv6NkVL3P2V8+BiDMS8WPt6by/rEaeq0nqJN8Qp+5h3h/NTuuNvL8zHhenJlAcXMf8QGuBLsraNGaOFbawQND7uSe1HsAkLu7IRm1juXTxlDgNoa9JVoOXc9eCHBeS2p7O4ks/hSLSY/VPYypH5zmmzPVhHtq2Lr4aR6fmEZZSz8CAXioZARpFDw+NY4wXzVhnkoYcht4x2O22pn36TnePVwKwJ78Zkpb+tF4uiBSiLntm0v0dbXBF+MYq/blhwVPMWdwEK/OSWJ35Mv8NDASfzcXSlu0MPpR5wXrlhVw4GnOVXWyqS0E7jrDa+deYN3xh3liaiwahQSt3kBRs5ZYPzWjo71QSsUs/Pw87x+pYPJ7pxgTpeah0cEQPxPh2KchbQV781t4ZW8xE2K8SDt7F65HHsPfTcGlmi6e2VlAQqSIF+YFUFtVSkOvmai5z2ISKXnylzxkilZam0vwpQdB7k9IREKOl7ZzrKSNdw6VcqGqg6brc8bYWF+GDXLaD3QVHEZVtpWO2hqnTPaQW1m1V4dGJefZ6XF8uHQwTx1spdEtnQVDgnhkYjTrJsfgppAS4+vG+Fhv4v1UBLi78P5v5Xx/0k6yRxbv7OvhTEUn/WYrwyI8qT/5Ha27n6fPYGbk28dp7Bsgc9ET+Pr4U9ulp65TT0FjF2NKXiKm5nvcpd7Mih3N4EmhxHooUDab8FZ4c0vkHDj3MZtqlKw4KuW5vVd4bk8OV+t6ULtI+OlSPWUV5ey7WgHGbnA4ONzgoL5Lj5uLhC7dAK/uLaJVa2RivB8apZT+4HF82RKJj1pGv8nKjxfreWH4C8zoKWFQ/1UAfD01LM1LoVngS1VDIzUWN1B53aju8AwMxmgf4Osz5dR2OeXfY/zUfLc2hI+ufUxdT/vfzPV/CKR07Zh+vo11G3ZQX1kA30x1io/8NbL/hdeTSAzBGXA9wCM4E8Y+7cw+6Vqgu8qp6NdTC+mr6NIaEOua4exHzn1iOfz2Gnw/B3Sd0FYMSfNhxU6Q/K4g+M6CFAaH/IkghVEL9Rec2axf7oCuyr8/1pvc5CY3+RfiH85Mbd26lTlz5iCVOhthGxsbCQgIQHjdrd1gMPDxxx/z+OOP/3NG+v8wzRU9GHUWIgf7cNs3l/FVy/h4mdPbJqemm8oOHYszfldX7DVYeOdwKY09wfyc00i8nyuh12Wnpyb6Mzbah7FxvxtBrhwWSnOvszRIKhax6UIdLX0mnpkez48X6rhY082DEwZxvqqbUM8mPjlezfMz4m70F/iqXbDGzWTALxmA9xanUtddjKl0P/LIcQiy1mKvzGFFdjgJElfatSZe2VfCkFANle06Vg4P4fvbM+g1WPglp5G7x0YRGR7EjwuNxIX5MfLtYzw9NY55Q4JIDHCjNMfExARfdl5t4nx1N9vXDuNkeQcKmYiCxj6Sg904W9nFucpOdlxr4e7RkeRX1bO8bT2IXbD0PcrJVx5gzojVVBZdIOXcwyBVMnfNUUrajfxypYE3DpQw2EeC3KePQE9PfrnWQXqkAIubG+/7vcE0oytdFTWsr3XwwfIIHpwQBTg4WdbGnvxWvl2dgatOz8YGK104RQr6B2xMiPUl2k9NQoCa72/LpKZTR8b5uwga+jTu6ijuGzuIdw+XMyzKk3UTY0gNcSOnposfLtTxwZI0amurqL50gvb4CSQFufPmgRLsJiEOjzCqOvTUdjsveMfHe1PR6sIrV/TMdzhLga429PCXw+WsSFTiJhfz48V6npoSQ3yAmsAF05GKhDy8NZdwbyXxv63C1cWDMo/leLtWEeypQCISsvlCPWtHR+Dt7cO53EImKDsxhDqoNvpyIexeZiukCJrj+XDyVwgEAlrzDtHdIeVEWdv1vy8x6+fFkRg1GkkZCBwyduc1sze3mdRgd/ZcKsUukPDk7DTKWrUk+Kt5cXYCx0s7uXDpEhODHWQNH4UpcjJyiYhJCQoOF7XSfb1XZlqSP1tyGlibrkLloqW0uZvkSBW3ZYcR66fiiV/yuVLbzeKhQWzJaWR0tDfRvipmpQaglkt4dGsuYV5KUoJSyb/Yzb3jPBgd602P3ln+9drcRB7dlodMLCAt1IOpiX7cv6OG+0JWk6EOZOOZakwWG2NjfQnwdKdnQEBLn5FtOQ18tDQNXP3odgkBt5gbgR9iKUs6WtCHZmO22on0dSH0xFqOyqZz2JrKh0sGU9Pag/S6Qe3rcxJJr/oYWQP86r2Wg4UCPl0OUxP9KGnqZX9hG0uis/GJSSVSbGaar4OrOgPbK7bTZ+hEWOdDqbeJCL9JXKjqJK+xjzeGppFgfhsulaFU+fDTXDdevwxd/QMYzXZsDgElLf34ubnQrnV+1uFeKuKmrAXWEo6zfKvXaGbL2iy85A4EHWUgEPP97Rm4SJ1Lzc6rjeTU9iDDxoIoBzF+bhwsbKFLZ8ZLJWVpZiixvkn4ztUS7OHCd+frGRfrQ+CkB+jsu5Vf85p5fqZTFfGZnQWMj/PFZnewdnQYRwob8bEISBgUxFvpzrnIM1DFzMAoHPbrJem2AWjNI2jQY+wZF4BIZKOi1UhObRcF1Q18tzSNgoZu8gQxuKctI0Av4suT1QwN88BNIUWjkFDYpOWRLXlIRAJuHxnB8OFLeC6yn1h/NaOivREIBPQazBQkPEB7WwsNJW2Mi41nw0p//NQycoc+jLurgsaSIo598zkr3voAgUDAS+dfprJBg0y8hDBP53ztIrdT3FPA1qLDLNT3Eubj7uxdChryx0VC5YPk3rPE7zmKUuUOwx5w9i79H2AQClg/UMfainx8xr0IPglw4ROnfLpvLKG1P0LYCGc2rGArSJTOvihjr/NzPr0edG2Qde8fzpsc5A5AY38jPxT/wGNDH0MsFMOUVyD1es5M2+w0BPaM+j96Dze5yU1u8l/BPxxMLVmyhJaWFnx8nPpC8fHx5Obm3shM9ff389RTT90Mpv4JGPst6K4HO49PiXH6Q11nwGbDcD1TVdaqJdrXlV6jGReJEPP1nptQbxXdzTqudPYTHagm1FOJxWqn2+CUd3ZXSFl/uJy0EA3uSinjYn0wmp117fPSg5iY4EuAu4IvVqSjdpHQpbM47ywO9Dslc2VqAv2HcrQvhDvXn2D72mEcv/wha4pPwMSXUKcu5ac7h10fcST6fhNR3irGRntR2a7jXGU3k+IDKGvt51BxK0EeLnTrzJwpbueDzj0cW/cQV+q62Xyxgfdv8WBuehCZYZ70m6zMTPHHQymlqc/IVJUvSruZKGkd09JgUMVHuI17hLcPleLpAp/6u4NAiERbw7hZXpRECfBpr8SqiUQ89jEQSajt7KSuy8Bv68bgXr0P/bkveM72HGOivblc28NQHyM5tTqifV2ZMPVW/tJr4HJtD0XN/bwxL4kXdxfwzLQYUoPdIfgpBFtziXCRMD7Wm1h/N745WcHYUCGfnmjHYLJwuKSDO9I/ZWVGCrs+yCM+2x9ftYwfztbw+JRYvF3llLX2c6ayE5PFhoe2lFvEp3mvJQOD2crbtncQiK281Pg8WRHSG6pvp8u7UcvFrB0djqdSxi1DQ7l38xWWpWi4pfxhdrt8wIuzEnBzkbA9p54X9xTz4x2ZXKrpIb+xl/ikW8AnltzqXmL93Pn+fB02h50eo4XhEV4IAGHOV3i6dBLkG8XM5Lv4y+EyZFIxT+0o5JXZCYyO9qZMPhjvcAEN3UYQCGjsMRKkcSGru5o7E26jpsfErisNTE8JJMxTyZyeb9FJfWjqieXTE1VoXCTEBag5V9XFpBANqfXv0bdzN6MrFrH/gVEEaFx4Y34yFpvz71VvttLQbaCkz5Nl5XMZbGzmu8gQ5g4OwmC2MshHySAfFRlhHmREeJEY4Mbdm3NwkYh4f3Ea3iopXiopbgoJfm7ODGxDl4H066IOpS1aipq0+I2XI7cbucW8m6aAkVR7jOLs8Vp8XGVsPFNDfqOWF2bFMzraB63JzIL0YN7/rZwRUV785vYAU4P9iNYoqO7Q0asfoC78JUbEBnK5pov1v+UxOdKH1cPGkGD0YeXnpxjv1kqgJh61XEpisDsyv3tBIGS4wxUftYAWbQ/+ag1+7i48MyOWxVvN7B+djUfRFRZUfsnqULAmf4PD4WC3+3F+bTjINCZxvqaLaYn+pIZ4QsDLgADEUrRNfSQEtDMzNZCFGSGcKGunulPv7Be8UMeakRF8c7YGb5WMKB8VFW399JUcZV+HDz89MAXKD8HRVxB11/Bx0g6Ku4U8NiWWuWlBbDxbQ6a6m7at75GxZjfDo5zzwPrDpTw2JZZ5n54jzFPBmYpOdt6TTYiHAoFAgJ+HhrzGeu7080Sb/wOToudiHBjgWk0rCT4yPjlZx89rP8BgF1PfoiXWX83mi3WcLOtgXKw3IHDedJr3JWOuz0YGs5Und1xmxdBAxKYePjhexROz0rHK3dh4upqh4Rp81HKnN5NAQIy/K28vSOZ4WTtKmZhYX1dEQgGx/mqu1nXz2Ykq5qcFEeal5MdyIa7yUOZLhFjtDjyUUv5yuJxLNV3MSglkSVoko26750Y/6aPpj+AyzAW19Hf1uySvJL6b9gXPnHyV4u4+jP0aRHXNRP91MAWI5EruWDiLE/t/Jk/nzqpgOe7/B+uOSCjCUxOBdNhScDic0und1eARDPGznAGPh7O3kMHLwS8BvKMhbLjTn8o91Lk+hGX/6fklQgnucncEtWeh6YpTJfDqt6D0gdv2/x+M/CY3uclN/mv5h4OpvxabuCk+8V9HZNrvxo5/kIYGsqO8yY7yRmuwMP/Ts/yyPJLQ8EjuHhPF+Fgflg8LB+DQV0XY/OWIR4oI9VTy5sFSTpd3cHjdaNxcpIR4KPjkeAVPTI0jzEtFc6+Rj49WcKaqk5/XDKOkRUuf0UJWhCezUgJwV0jht9cpqmoB71gSNEaSh6xjZoo/bgopC0Z/wHeiPOZGJeMG8NMSUPnCzPeRCIU09hpxCIS8OieR/gGnMIZK0sQrg/sICZHR1y5A0dPHXWWpyJty+WLlUF6ZncB9P13j9BNj6TFYKGzS8trcJHQNBTw9JRqTFTx8rHRqalkWPQtzny/poRo235HJ9px63uldymOTY6ExB5UIPBSutIXNJnjYApCree9IGUu9apiaHEiz2UqZYgSRC8bwgs7B0bJOtt7qj9eX6ay+/TAfF+nY1Gvil6uN7H1gBDNTnP0kJa39uLpIKGjsIz5Aja9aTqDGhaxIb0RCAYn9p0g8t4tDge8jlYh4blocHx+vICKki3ErYlGopaiNZjaereFgXjV3tbxI1syP2X1vNg4HjPtVykdL1uNe1YlQIEDkGQn6bibE+RIsN/DOxh/JHDGBT5alMWCxcbayk5rSXFKqr/D8zPuchq4jD3BhRzGxzb0EeijJjPDk+RnxJAdr2JzZSEz5zzD/fXDR4NnehKfSgZ9axoH6TcSHpOOuklLdqeOJjinsmdiPy5CFBLf08dZED1KEObgvG05KiDvvHCylvttAu9bEa3OTyW3oYXCIG2Mi3OD98bDkZ7481ES8uxsfHa9gTIw30as+4bndRczqM7FgcACHrpTj4+r1uwn1uXzkDjvfjsigyzCAW80elDYDEs9wTMEjeHx7Hj5qOUnBGg4+MBIftZx+k4WlX17goQmDuD07gqIzzbR2G8hrc8q+h3ooKWrW0tk/wCBfNfPSg/jpUh3NPc4bGLdmh1HY1AeAQiom3EtJgEYBNUcgfwsPr1xGhV7C4SIzCqkYrdHKxDhvXMQidl1r5FJdD6uHh+FwOFDJxDeUBCva+jlY2MbVuh6KW/u51mxiVIw32++YiEY1g4+OlhPsoeORCVGM9PLn3TwR3q4ySlu0fH2mjrtGRTI4VMbLZ76jO6+LTXPfYfUI582tII2CUE8ljBoFI7IpqGtH0Wki3FtBblMT4fblAExP9CdA44LN7uBoeTdlLTruHz+IPoOZouY+Vn5zkVdmJ9GhHUAtExMf4Mbue7NRysQcKmxFo5ByqLiVxm4jY3yD+WC4PwCOQZM4b43BW2an8kIPGoXwRmZt/cJUjGYzEy/ey9w9RZS363hqWhzLMkPZfa2Z24aHcrikjf0PjmLWJ2d4d0Ey56q7SQxQs2hICDEBUghWMXRoKE1dWlK7D5Lil8iDEwMZv208D0V/w6fHm/hg0WAC3OT4uMq4UN2NUiZmccMlMHRBzFSMZhsCBHyxIp1NF+qJS0yhqccIQPn137GnUspD46LQqOT0Gi0oZSL8ZVZW5SyART+AOhiAL05UsiWnkSlJvmRGeKJRSvly1RA+OV5JoLsLH/5WzomyDt5YEEu7vgsPpRSbSMKinQ0s6i5kXfYCfFW/Vwv8e8RCMYvjZ+MzxIdntzUR4qnA47dyrA4Hj0z8XfLcdP5LPm6OZnRwCA098PqBEp4c7YeLiwsuKjWYtLDnIZj4ErgH/+E1BvJ2YPOMRhGUeGObTCS7UVoLwPB7IWYKHH4B2orAc5Azc/TTYlj8s7OUL2qCU0YdnBkrh+1vpdFPrQffRHxjJnN3yt3w6XCnnLtvMvjEQUcZdNeBxx/9EG9yk5vc5F+VmwIU/00xmK3UtGtJaN4OCfNxCNXcGi/kgT2N3DlGjsMhwGS1o7heXjNqaTRqTxdcNXL6DGbuGhXBpHhnkOYiFTEq2pv7frxGQqA7Iwd5MfYvJ/hkSSrK61mw/MZejpe2U97axxsHyrjw1HjkmQ+wpz6fyUHuMMjd2aszwbm4i0USOtEgEEtp0xpxj56BzNVZi++mkDA42J0Xdxfy7epM+o1WPjpawSnd04ywJHNny35CPILwf+hx3MrbKWp2XsjG+rkyLcmPXdeamJbsT5SPCqHVhOKnORxN/ZAx46fyzvwJwAS0BjPjfyjl9lGV3DUqig6d5caF0sHeQGIy38DSruPNU2VsvjMTi8nCwcI2RkTW4Cszs6dTwbGSdkZFe3GppgfDgAU/NznNYZ+wzicOn/oGYv3UzE8PZMPJKqo7dNwzZhBfrBhCcYuWR7flsv6WVFZneGP/YR7d3h/jFZ7INdUI4odNQV1pxdvRw8TEONQKCbH+aj46VoFYKOCJqXHEB6jxclWA1wKO1VnZcDaXF2bG89a8ZIaGaRgWeT2o7qrCHj+XoeEeyMtOs8L6C1+UpvBbURul7f0M8lbhpu+FIDO+ajnrD5USpFHw6fJ06jeuolWdyB7pNKdq4OWvGeJtB4MDTFpevXCYrj4JUwcNp984QFFrF4ESPQcLmukz2fjLRA0LTkn5PFTHTxfridDl4Sup5KSnlKOVcpKCnGarmaEe2BwOarv0aI1Wnvy1jDceuIpALCdzkJXtZRa+WTmY3bnN2B0OXpmbxAMH3sC1zJ1nzR9RPeoMdV16Slu0TB5+LzRcIjVAxV0/5XGPn4VkaSfkHEcUOpJIbxVZ4Z5w5XsCdG28Z54NDgdB7i4kBbpjs9lpLO3mpMpKp9VKwZWzPJAeAOMHUdOp52hpG2a7ne/P13HfdSW4mSmBuLtI6DNYCHB3wcdVSl59D5khw2Dya3yVX0Oybzj3jo0ir6Gb0THeuCmk3PrtJbbfNRyjxcYbB0opbO5DJhHS0mekqkNHgJsLn61IZ19eMwq5mNpuA205TYxa6vxdKg1N7KsRg9SF7OXpLM4w8snxKux2B1KRiLcPlbHpjiweH74Gg2WAwsYeGntNTEn0p7ZTj9lqJz3Ug5aWVp78tZIpCQFYHQMU9p3k/pQh9BnM3PvTNX66M4und+RT06lnfnoQObXd7Mpt5mR5B1+sSOdCdRddejPrJkZzqbqb8vZ+BvmqKGjq5dHJsWREePzN/NRjMPPKkXpemBFPU28zAW5yPjpeyfpbUon2c3WqY2oUZER4MsjPlQA3F/r1Bmx2O5OS/EkJ0hCocWHbXcPYdqmBVq2J0tY+hoR6Ut0h42RNNp9mK9hX0YLvoIUoNd4MU3nwhPkzZiVG46N0J8pbTnKw+41yZDqr2Ha8gBKDmudj4L0j5TiAZ6bHkeIJLce/oD5xJdisrMoMYp9KisvlTxnUYoAZ7/LCr4UopCLeWZjqNMH9dyVoeouNFcNCWTDEh2fPPs4jQx7hu9xDWCwapOIALtV2s2p4GJ2mqzRJvmZGylYAXpgTSJOlAavNzsX6CsK8VHxb9C0TQieQ6Z954/yDfZ03Ez5b4YdYKKC+24DdAddaryGTyIjXxCJpvkykMpTY5OG8mCLAbLHxwrd7SPEWckemD1z+GoKz/tT36YsCG02OTt5c8Vc7uqqhtx5c/Z0iFa5+EJACHpGQ/xPETIOMu+H7mXDrfqfXIMCV75zqgDWnnX1XYmeWF4cD8rdCUBXEOAUpmPgaNF9xmnrbLLDnfugsuxlM3eQmN/lvw81g6r8BBQ29BLi74Okqu7HtUnU3G0+W8b3nRQZCRnP//kqC3fx4aJIPu641MmC1s/1KI2tHRzp7DqI09BkttPUZGbv+BHvuG0lmhDO4MVlsjI7x4eBDI/B3UyAUCjj+6BhnaQvQ0mckPkDNmYpOOvvNfHNrBm4KKd06JUKzCu/QSFCJ4eCTnPZeyrYyK/ePi8BYd4mWLm/e+a2a8XEjWBLtXBzPVHSw6UIdG5anEeKhoN/kDFQ2DP+S9fub2O9tZ8HQMJq0RpQyMZ16y3WJYAeeShn13QZ8XeVYbXZaDA5Uay8SZZGz6PPzeIit3JLsydThScwZHIjN5mD94TLmpQUQ7uVUXvvsRBUZ4R7cNiIMv+tN40eL2/h29VDs9iHk9JlYO8qD5RmhFDX3UVXeQ6thAIGuixHDstmb38xPlxqYmxZAtJ8rp8o7EAkEfHK8gnvHDiLEQ0G4t5Ir9d2sygqDSY/R7x3KS3uKGBPjR0ioDy4Fp1h0bR49CZfYX2Ag3EtBXmMvwyI8cTgcLBoaTFaYJ1caPRjm50pMkIZNF+oJ81Ihdljh8wkw5A5IWsTZDhnf/3SVL1fOo0KSTdDpLcRpHKRlzCcx0A0Ip1U+BT+gtq0bmwN+vlTPLVMfI1jpQSAap2pgbQEIJfTN/BqjxUqoKhaVw4HYtZi9Zx2EuMwj019BhL8GkVBAZH8NY1x7CHB3oaJdR6Q6lbP1sfiHG7BZpU7VNTt8fbaa/QWt/Hr/CPqMFpp6jQgsBvgonYiZe+nPb8MhsPPDhTruHevMrMxLGIYyNoLnD3ix1C7gVHkHr+4r4dgDMgK2rkI/fzOfLUunS5dEmX6AmFFqjhW28ND4aHzc5PRVxaF0DyWyX8Gmi/U8NiaK77af44HIQqbccR8Ut1HRrsN86S36Y4ey1TYWrcnCx0vTmPPpWSbG+5IQ6MrG09WsHhnBq/tKmJ8WzKKhwdR0GXjrUCmjBvng6+HOJ5W38rzye2o7pUTpr/GU7Dc0Ua8SqHFBLBLioZLx8IRoZBIhJS1aeo1WzlR0EhfgxrTkABZlhLAIpwz2kYIWvj9fS2qQO7fpPmda/CwuKkYjFAqo7tBjs9tZlR3OuDgfdl1rprPfhMAuJ9rLg1UbL9GhdQZTzb0mZKZOqPwEv6RbeDYwl8zRw6noMjJT8BG9RgtuCilfrBhChLeKtaMi6dSZyIjwok1rYky0N+FeCn4raeel2c5shcVm59drdYT35eAuiSHYIwyA9w+X0a4b4PV5yXQb+lhxYDUvZb3OgQdH8cWpKiw2B2/MS6FHP8CMj06TGuTOq3OTeHZGHAkBbsglIsjfyvDSXahGfY6bi4T7frpKvL+au0ZFUttlQCSw46dWYHc4WH+knJdmxdOqNXKhuouxrQKalEpapA7eP9TNjHgHo2N9qPtkDsL4WQSPXQ12O+x/lE7FWpQKKT3d3awdHUFjj5GjJW2M9zfjb2/FP0QNR19G31JClWYd986/GyxOefJlmaEYzM6San3gcJRisfO8QiEPT4zhXFUneQ06sgOzcZO74efqiszVDS+VnDuyAhmbGIgo/wxDjL8b8E6LjwfiaTrzIxsackgdFMQQ3yEEqgL/dC2QS5zS6BHeKrQGCys/7OC2CSbiPeMRzf+Cuf/+YJmYFxeNROHiAg4Tex3ZOAyDmCn/W1+q5XNn/0Go6AZ77gd1kLM3yicGzrwPVcfgzqOQNM95TPICcFHDby9BxBgwdDgFKNpLIGnh74EUOJsE77vo/H9bMVz4zJkxE/C7SfCSn//0vd/kJje5yb8q/1vB1KFDh3Bzc07Edrudo0ePUlhYCEBvb+9/+uD+X2br5Xp2Xmvm8xXpPL4jn8HB7rw+L/nG/jGxPgyL8gTxSKQOB3MHNzMu1gc3FwnerlKe3VXEy7OcpVt3b7zM9AR/fqvpICHQjd33jkA/YGVPXhNJge7M/eQsRx4ZRaDmd6UnX5UM7Z63sPpFslufSk2XgY+WpvHD+Vp+ulRPVqQn7gopiUiRWwCBECQqMi4/RJ16Fc3t7pzv9aB512neXjWVvfktlLZo6TdauGvTVb5aOYTs64pg/mo50vK9eIrmMSTcgy7dAPuOXOBiTgFL7lrLa3OTmP7haVKC3G58BlqtlqOl7YhFAu4YGclbOwtICFTj0tvMQGs9b+yFcJmOiUMGc9emq0R4K1HIDSilSrasHYZMLORMRSfFrf14qWTk1PcQH6imsl1PTm03FrudYWEaUky5FFSKCcly5ftCHYvkOkwWK8EaOXF+avqMFpZlhXCyrAMvlZTfSlq5c1Qk/UYrDZ0GEAio8xnLtL+cZkioBpW0kyxPM2MSQ2HENRxiH1zlNUhFQpID3Vk9IoL2fhOPb8vnoyWDeWlPES/NTuStg6WsyAxluqIIKosgcgIIpXD5c0a0FxMy6l2o1tPaH4k0OJ2swR48e7aHs5VdVLT3MzbWhwdTRXxUN4fCzHMcrjMizEjk2sUT1BdfYNj8h8Bmg2ub+bIhlqoOHTNTA5mZ4Emtrpi7RzcxxDUG1ZYpdCavxbt6F6zaw9PBLViFAmakBDA7KYA+7QB7ytq4IzucPfnNXKrtZnK8LwkBarLfPM4XK9P44GgFE+NHIJz8HS8cbWdcrA/9Jhtb1mTh7+7CL1cbKW/15alpIchnuRHr70pKkDuB7i4E+njwdfpOynIGeDtMwJGCBs5ducpH0wP4+JiDGcn+LBwazLBvO7k9O4wrtfW8PCcRV4EAAXocbQWcKGliSKg3kxP8aEv/iGkfnOHrW71xlUvYm9+Mt0rGHSMjeOnXIiradIyL9WHD8jSCPJwKiz+vGUZ5az+Hi1r46Xw/a5O/oaJZQm1bMw+nBKCMSgGpiIOFrQgQ4KmUYHfYGRLmxZAwD46VtOOmkPLElFjYfS+EjqI1fDYT3zvJnb4aQjMCeOKXfF6e/CZDo/yZLZGx7XI9YpGQxybHsvrbS7wxOYD7h3vxwpEqXAw2HpmbyEdLBqM1mrHbHawdHQmdlVAkQ+ATw7AFzoyxtC2fyoJC3mvy5IfF4cT99jTW6IlEjLyPUC/n73/rpXr6zVZWZoWhNTmFN8rb+rlU04XD4WCSugbv4HHE+DhFb3Lqehi47h3kKlUxRL0YHxenzHe/ycrT02JxU0j45WojkV5K5FKnaNHwSG/WHy7DMGAlv1LNPN+pvL3xMlkRHqikYnr0Zp7YUYBIAFG+ah6aGIPFZud2y0/YPcPYfNWAl0pG5nR/vIJUBCokHH5oFGabna9OVDMy8zEiIgfR0mdk16VK7hJJadcaUQu7WfhNHs/PTMBsdXCstJ2+KyeZ27QLwcSXIW0FtgOvkhkgo0foifTYK6hH38fwqES+O1fLmu8vc6mmh7NPjkO5/z6nAt6Q1VR36hEAyzKdqqZ3DL4uplBzmomn10HKZUhZhCpu+t/M94GmSj5PmYgsdtIfvKQ+O1FJh3aA52clAFB36CO0mkSSMsaiVkhYvzCNIWF/lRnsqXX2KwkEeHr7YjTbOFljQuTqj+Pfm/H+OzRK6Z9uZ9GPIHOF60JTpC6F8FEAHC9tZ2i4Byq5+nczXpEUCreC2QRe0XDuI2fGSem8cXetvgcfVxmBGgU0XnMq9mkbYfKbf/76N7nJTW7y34D/rWBq1apVf3i8du3aPzz+t0bam/yfE+uvJrlTh0Iq4ouVaWhcZH9zjNAGiGHzxXpEAnBzkQDOfokhoRqSgzXIJSIyVQrKW3qRioUsHhqMq1zCGwdKKGzsxXOslG9WZ+BlaQOT5kYJyK4LdQSUyOgRSOgUmHlrvjOIWZAeREmLlsZuA0EeCqbd9XuAx/hnkUVP4vh+PXHNZn55aAo9RgseKhndBjM6k5X0MA+2Lg0nofQ1CHsTrUXE01sv8ObAt5Ccyby0Qdz/4xUu94lZM2EYcQHO8bw0MwEHDmjOxdyQg+n8VhLD3nSWMdqsPDnYiiQwGbnEOZ7yXRfZW29i6Tgxv9w9HJpzefbyZqSOJMZJY0iKDiMzQsOj06288P1e7p6QxcsHy5iS6I/GRcw7B8r4bq4vykMPMuGePTywv4klGcEkB7mRHKRBa7Tw0q9F+KrlzB8SyLGSDh6aEIOjvw0cDtaMjiThuiFnqKeSLXdm8e7hcuwIOXXyEJ/XB/LLw1PwAB4d7U/bnqe4WDuOSB8l+Y1ajj86krcPlBPmpWRwiIZX5yQhEjiwN3UjtJtpHvIoR85cYqXFgiAgnVBbPdjUzE8L4pPjRrbVyQh0t9DYo2d2cgArs8PRW2wcGXOQKRHhhIQ4L37rmluJlPfjLgMS50H0ZMRn2ylq7SelrZt3D5fx89os4i//QlPhLzQnPcTTVzy4P24l3h0WntnZwuIMMfvzW1BKROzKbUYsFDAuxovPT1UzJcEXP0sjGbrzBM7LIjZYw/oFydjsUGQLJi2kD7lEyI8X63loQjQV7f2sP1TKQxOicTgcJAW5weWN1PmM4+mdlewNdqPfIkR3vc9uSVYECxW50N/E51ENtEesZl9+C1+uSOf1/aWEeykoa+1nZnIAezpVhCY/y4tbikgKUHP32CiUMjEblg8mzt+VJz/9iRUjYxgT40OP3syUBD/i/PRIxUL83FzYnddMfZeOhm6niMba0VGcr+6isMHGh0tiuFTbhcPbkz3NaryrOtGarBwvbSenrger3c7IaGf5XlKgGrnk+lxp0oFZi5+bnPWDOxnd9AbqzNMIxULsed+Tm6PnF9claA0W6nqMZIV7MineD7eL74G5ExfVk5hb9bTVaQmK1vDxsUqkYiGNPQaemRaHMfFenvnqAm8vSMHf3QWF2o3A3vOsCh3G17/1cpfYFalrAANWG5PfPcWHSwajcpFQ1aUnyENBZU8l314u5kiugMEh7qSF+/Bc3mwsu9vZfHs4CIW8Ni8J23UBkLs3XyWn1o2nvI+BZg6PTIohv7GH9j4j2680Mj8tkNtHRmAwW3nnUBlLMgJRyaS0hdmIOf0Ocx7eRUm3g0hvFc29BvQDVnZca2J8nA9VPVU8cOxhHjfEE2KwcammjyB3F3wjNcgUEsw2M2KhmA7tAKcrOpg+PwVXjQptj4GuARFds74j6FoT89KD8cpvxl0hJTnInZRgNz7b08a0WZ8jl7uC3JX9lf5kJQvIbepDYApmvMIpQBLj54pYCHeOjHD6TQ27zymcACzP/GNZ2ruHyxke5UlW8FCY9+X1SVvonGMtJh7beJAVw8NJTkqBCc+jAKw2O+erOm94MAkEN2yjALAaejBIdbT0GWnuMRHqqcBqc3A9YcWnRy6w9vICRLfuc0q7G3uo8RjPXw6Xs+2uKTcyW3+KxQQ1J2/4QQHg8ldZLI9w8AjHYrPzyfFKnlfFkxzo5swwxc6Ao6+AZcAZYIZnO5/fXgLhIwHYltPA4BANC7vfhIojEJgG6bfBby9CZzmMfPjvj+8mN7nJTf5FEThuKkn8b6HVanFzc6Ovrw+1+m9rz/+r6GjoZ88HuSx/dRiXGnoQCQXY7A6qOvSsGh7G/oIWDhS0EOzhwmOTY/nxYj2NPUbkEiHdejPPzojn+3O1CIUCbssOZ2DTYspdMxEOWY1cIsJLJaPHYCbMS0mf0cKzuwp4cFwU+wtaOVHewTPTY0kPdfbtfHGqis0X6vl8RTqx/mp2XW0kPsCNaD9XmnsNfHmqhienxSITX1/IGy7B3odh2VZsqgD25DczJcHvxkL/W0kb/moZn5+qZvWIMJRSCT9frOPOMZH4Vf5Cb3UuR4sEzJ+bAeUHYfhDsGkOjgfz2HipjcwIT8RCIc/sKmDT7Vlszannlgvzqcx6nlfyvQmztnP7EG+UCWE8duIRRvZPZu646Xx4ro3Ktn4UMhFysYjPVw7l8+MVNGlNXKvvZUqCLyfLOxkW6cnpig50JhvRvipuzQ7n5b1FPD0lhvs3XeLECi+aVAm4ysUs+/IiGWHuRPk6Ax0XqQgZVnQDNkrajfgNgIenA+W199gsvQWT2JWUYHc+zH8eqWUQ00LnoZCISQhSs+yri7w5P4nsKG9a+/T8cKGBh0YFceHSefyjUnh0VwXPTovlkxNVZMibSHKUku89m22XavhqlBHX1Bm8tKeY1+ckctemyzwRXEJq1yFYvg22385DMm8SvKdxe8ZIugwDGPP2sK3SgXvkEILVIoxaLe02BSuEB/isJ43slBB+K+xnzuAA4gPc+PJ0NYcKW5mZ7I9EVcPWi318s2waD397nJX+jYwbksgxfQQfHStnapI/SQFu+KhlRHirOL79U5IGRdCtjuWHs5Xsr3Hw3WqnpHbfma9xicmmSyFlZHgyBwqa+eVKA1qTjbQQD+4ZF8nhwlZmJAfw/tFyLlR1IZeKCdHISQv1YM7gQO778SrB7i48OT2OspZ+BqwWEgM9mPrBKT5YPJiT5e3M6PgKiasnd5Zn4u3qFBwYH+fDtpxGHpowiJPlnUT7KClp7cdgtvHx0jRU597CHpxFq1c2CzecY8c9w/npQh078pq5e1QkdhwIBA5+zW3Bx1VOnL8rNZ16wjxV3JvlCb31WH2TePyXfEYEy3HV1zFxwmSKmvp48pd8xgRYudbrwkuzEvjgaCUhHi6MiPIiS9ECuZsg6x4cZgMC3zgOFrYA4K2S8fLeIt5ZkIJSJuK5XUUMj/JkeVYocokYOsqpMCjpMkFWjLOcrEs3QHWHnn6ThexBXjd+q1/kf+H0pOsdz3MzEjhb0cnBohbcBGbULaeZumA1Eb7uWG12hAIBJ8va8XZ0Enr1LSwzP8TDzZ0Hf77G6GhvRg7yQiiAxV9c5NW5CTy+vYCMMA39Jisjo71Zdj0YyW/sJdxTSeYbR3l2WixWO4yP88FHLeFsw0Ue/E7PpjuyCPNUsPFMDXeOiqCkpZ+XLj7BsuQJTAmZwz2br3Jrpj/Xmg2sHB6Gn9qF1/YVc6S4jV/uGY5CImbplxd4Y14Ssf5qOvtNaI1WInyckuQOh+MPNwf7TH24/Ul53L/f3tTahsQ2gE+gM2O39XI9SUHuxPmr/+Z87FjLVmM6I8dMxl8lhsLtMPwBarr0LPniIi/NjmNyQsDfvN6/sTu3iQtVHeQ2apmZHMA91/v7tl0rxmYoYnH2QijaBU1XYfxzIJL8x4vKtc1w6GmnqfS/iUj8I9htTmGLwDSoOevsf1J6gYsHCEUgkcOCjc5jOytB4QnFe6DuLMTNhOhJUHYQLDpn5uufyL/K+n2Tm9zkfxb/zwZTn3zyCe+88w6tra2kpKTw0UcfkZGR8R8+719lMrbbHRSWdOITqMRNIeNybRf3/XiNaYl+3DkqEgdwubaLoyXtxPqpeXRyDBvPVJNX14O918w7q4YgV0n47lwtdoeDwH4d5/vMDEgl9ButPDElliAPBfbCndSXXGZDRzJq/3BKuuG27DDGxf6uPPXZiUq8lDJmDw5ELBRQ120gxEOBSCigXWvihwu13JYdTn2XkdQQ97/7nj48Ws6QUA3Do7zpN1n44mQVR0vbsVqtbHD/HrdFnzsNgzsrsG65DaGlH336GlxH3gtmA1aRnFUbLxHiLmG1Xw2a1Gl4uSp4cnsefTo9vSYHduCDJYPxc3NeLGy9XE92lBfl7Vry6vsY3LGL8EGJ1KiHkt/Yx6gYb67UdnKtXsvURD9a+wcI1bjgEAgQC+BQcRtvzI6jsNVAjK+K0qpaUHiw8pscTj02hq9OVyF1mNG0naNANRwvlZR7hqr56qqeTRfqeDrMn6REHyJSvDld3oFULCQzwpNl3/7K9MQovjjeRqS3EpFQyEszYiht1RNa+zMR5nKY8yn0t/PKrssIVD4YkfHElDjkp9+keMCDUFfQjL6HquM/EJzzOua1Z1C5eXHx1yo+amrHU27ig6ne4JcInRW8U/Ib8Z7DSQ+M5nxVF7NSAxHhYE9+C35uLmy+VI/EPsBL89P5PPcLeiytvJz9MuCUDHeRiLhS10VL3wCjkweo09YxPWI6t31zCT83OW/MS+ZidRdbchoYH+vDjxfrcVNI+HDxYPrPfY3GK4A6hzeHLhcze+4ifNUufHq8kqOl7UjEVmzyAratcHrWnKvs4EJ1N65yMePjfHllbzEZ4R5Ud+h4Z2Eq+gErFe39eCtllLXpeONACa/OSaDPZOVgfgu1XQamJ/tT0a7j7jERVHcYcJEI+fh4JUqpmBg/VzLCNZgsNirbDSzLDEYpE/HVmTqifJSIBAJGx/hA/jZO9PmgDEokOcgdmUTEC7sL6egfIMTDhSenxbP+cBktfUaWZ4Xw5akaFFIRq7MjCG09iDV3K663bePbc7VkRXjwyt4S1o6KoKN/gAOFrWxYns6t31xkSqIfs1OCWP3tJezArCQvVuQupz5wKgKrmXdttxDm6UK0nxszkv94EV7R1s87h0pp05q4Z0wUkxOdinsDVhsrv77EmpERPLOrgLfnJ/Pa/hI+XppGteEccpGKaLc0/N1+N1ztPrKed0s1PL16ER8dykOCjdVjYnnrRAthnkrWjo6kor2fx7fnE+Qm56NZQeDqnCuaegycrezEV+3C8ChPBjrr6DBLaDKI8O+6QNDQmdR16Zn/2Xl+XpOFxeYgKdANkchZYlbZXUmvuZdwVRJWm5Xi9ibGDooGwGi2cayqkOzwUNzl7hw5e5niq6cZCJ/AshFRBLorMAxYqe3U0am3kBig5lpDLwn+ak5WtPP2wTJCPV34YHE6wR4K2Hm3s09o8DLaSs6w8lQhdw3P4FCunXvHRZEU6M7Flovcd/Q+9s7bi6/Cl/xvH0alqyVi4WvgG3/jM/v6dDU1nXpenZv0+5fSVQ1yN1B6Qkc5XNxA3tAVNOqbMXUn4+UqY7T1HAQMBs3vGa+mHiOBGhcuVXfT3m/iWGkbj02Owd/9evBj7AWZ2pkB03fA93N5x/t1xrs2kNa5mwGFD7K0FRD6u7jFH+hrBre/H8T9L8n9GRqvQl8d6NshcqxT8CI48/cM19ZVED4GWvOcgVVvHSza5BS1+C/gX2X9vslNbvI/i384mCovL6e3t/cPAcfRo0d59dVX0ev1zJkzh6effvqfNtD/TLZs2cLKlSvZsGEDmZmZvP/++2zbto2ysrIbPlp/j/8rk7GuA0trMe2eQ9EarcT6uyIQCJjz8Rlc5WJMFhsDNjvzBwfSqh1gVkogcQFq1m25Ro/ewlPTY4n2VbPpYh0RGhc8+u3EDPZFIBSQU9uNwwHKZhNNQht5fXrK2vvxcZXx6pwkBhpzOXzqLBNlRZQmPszxBgf3qE4jT5xJ28UziLrL8Lrl9+/9mR35HClpJ9bXlQ+Dj6MPGklgQja/Fbfy+v5S1owMY3FmGPTUwU9LYeaHEOw0IP78wm8caP2EzdM3oZQowWbhWkM/z+8pwlch4Ku5gdBeypvVYcyNFNBceJLMrl0o1hxg04VaXGViUoPd8aMD86al3G57km/um47N4eBsRScR3go+PFrJOwtSUMicFa5P78hn0dBgPjtRhcMBtypOcaVPjTB8NGVt/UxJ9OdoaRuDfFxZOzqSPr0ZtUKCQCCgsdtA7sVjZBlOMTZ/PBPifRgV7c2oWBeKO2oYGeL0gmmvvILb0cfRLvyFgpomxhwYx5fx3zNz3EgCrl8Evbq3GN2AlTfnJ1PQ2MuHRytYvygVeUcRwq5yjLFzcT3yKOd7Ney2ZTEvAjJGTYF9j2HtKOM9lwdo1Tt4c3EmbVf38kWxEI/wwTw0LhJDUz772jRMiA+g12jFzQKdDYWcKcrl1lVr+PZcLW1aE09Ni+Or09Vcru3GbneAwMHHss+xpd/GYxcVPD9CgeeP0/gh7SeONpr4ZFkqr+2tIchdQWW7jpHRXmzLacRqtzNNVUmNazqR3koywj1p7nUq2DV2GzFZrIyI8mSg4Rp1ojDajXYuVHXz4uwEjGYbYwIlCEQiXD08mf7BabLC3RgS7sWEeG9OlXWjchHRpTPzzdlatt01nC9PVZEd5YW/mwu5Dd009Zj4Na+Z72/P5I7vcoj2VZEV7oFQKCCvoY8oHyV9RisWm519+S009xrZff8I7vguhxg/FQJAJhZxW3Y4q765xL77R1BycAcGrZajbsMwmq3UdRuZlujHxHhf5m84x9BQD2L8XXlwfDQ2u4O6Lh3HSju4Y2QEbVoTvUYLComQYA8ldocdoUDI5couPjpaytd3DEMiEnKkuJUdVxt5cHw0LlKnhQHAwz9fQy4R8cqcRHJqe9hwsgo/NzlB1ncRhU4gXjORj49X8eSUWKQSAbuuNJHebGf0khgcMiEquTMr8dHRciYl+HKkuJ07RkYgl4g4dzWX2ML1FKqGEzvhNhQyMSqZmDdOfQMOJYXlIWy+Mwvqzjsvii99gSNoCIKgIegHrDz35TYenRDJgGcCCpkIX7WcF/cUoZKKWOrXSMC5F+lecZTvztXya14z2ZGevPpvvZ877nTKYcfNpP/bhZRN/I44pZ4tzZ4szwpDKnYGUTt+2oFVpuKy5xGadc18MekLvr92jF/rvubAwl1QtNspijDrAwCaug14KcW8sPMKRyr6+WLlENJDf+8rumfTFaYl+zEjOZA9eU3svtaMq1yMWibixTlJzgzSibfhykZYe46eLXfxjupugnyC2JnbxLqJMUxN9KfH1MP28u2sTlyNSCjCou9B2FaMKCzLmZG5TlOPAd2AjRg/V46XtBHnr8bP/fcAFUBrtHCt8xx7S/IY4jme+AA3kq68B/GzIcLZo6QzWRj+5jFifF1JDXbHWy0jUdpGlu4Q64R93J16NzG7HgTfRGf2Z+xTANRsvB1BxBg8Oi9jbC1HHZmFy9QX//+vR70NfyOtfoMDT0J7MVgMMOsT2PMATH3792DJYnIGj9tvhblfgCYEVP/rNfc/k5vB1E1ucpN/Bv9wz9QTTzxBUlLSjWCqpqaGmTNnMnLkSJKTk3njjTdQKBQ89NBD/6yx/qfx7rvvcuedd3LbbbcBsGHDBvbt28fGjRt58skn/y+P7k9oyeO+Qzp6xLl06sy8szCZ9FAPPlueTrfOxJHSdo6XdrA4MxSxUEi3fgCAsbG+ZIR7IBEK2JvfxNKhITT3GgmKVtCjN/PO4VLuGhnBmk1X+MuCFLqa+1ArJExP9OP5X4txlYkZEhbArGX3ATAYGBxtg13rIWI4EndPsPrQ3GvgqR2FfLAolZXDw7hlaBAmi4O6K0epa9ITmAAT4v0oa9XeCB5MUg0NihRMZ38laXYEuGhQCnxwEWlo6m8h2iMKtt9GyqApPDxxAj6ucui8iKP6FFfq3bA53HhmxlLocRpCeiplnK7oYPuVJn64I5OBtSfJOl2DQAASBxzOr+eRqUmsGRXJ+sOl5Db08eCEaKegxdmPeD3Wn1UXfOlPX8qdMd609BrJ0nuREuzOmcpOJsX7cqqinXU/5/H8rHjqLPtp1Wp5bdStoIvg7LRBPL0zH4lISF/u9+Tq60nxiMH+7SwqBz/PQd8PeNnDnXS5kgr5YSLw5Zkd+UT7qVk5LIxAjZySZi1X67o5WtqGUCBALhYhHehiQNeJq1wCWffRUabFt6oCZXMJMIXOzMcpbNXzWKQ77FxL4aU+NOW7eHnNVn6+XMfOAweYU7yOhQ/nc/rsCVyqDxG++l36rCFcLTSyzG5HZ7KgM1l46ddC2rQD3DIkmLyGXvIae+hzcUNjNTAkNBCFbxDiNUc4sbsThUSBSqqmucfEyfJOXpwZz4R4P4aGe9LXUkNPtYMWgZD0UA3NvUYOF7fRoTWRHOSGr5salUxEvc5OmcVI/4CNH+/IpLRNh80BV/bvRiqXk71oBZtWJOC+fw0C0UoQzWLHtUY6dWYWDw129qwAv5W0IwBWDA9j84UGeg0Wlg0LQS4RMT7Wm+NlHVR36NGbrcglIlJD3Bke5YVULOL2kRG09pm4Vt/D2lERTErwI6eum9f3lfD6/hKifVS4KaR4pAzH0NVPtEVBoEbB9+erKW/rJ1DjwmOTYskIc0cpcwYtXfoBPJVOEYuNZ2sIcJPT3j/A+aouFo7U8l3RJr6b9jUWnZn+fhtWiw2JSEi8v5pBU+LYcrkekUhIkMYFb5WMu9NDOLezmpd2FjBnaBBrRofjKhNT0X0v46IH8f2ZFqZ6dZLuL+HHa12cqOhkekYUpd06Vn5zmTP3xKPJ3cD945+jXmunukOP1e68h5ap0WORSxk5cgIvHS7Cx9ONe8ZG0d02mFuGBLEm3RX0XbB1FXaPcB4VPs4ziUl4AkqZmHfvWwLAkeI2TGYrV+p7eGFmAgKBgDu+6+eeMd8i7jVQ1qZl5bBQbrvugwVwKeEFtuS1sz47jPyZ+7l88SSpTU9yyz05PPDTVR6dFE2UrxqZVIRELuGB1Af4JP8TDBYDa4ZMY0FilvNEA1oYcNonFDT2sfiL8zw/I449xX18v3oISqmYiu4KvBReaOQavFxl7M5tZlpiADNTApma6I/Jakco+L3vV5/xILvsqUwS2vC+fSuvXx+z2Wrn34r1zGYXzl5JYWqQkSAPFRKlBiKyqeyppFXfyoigEdjtDvzdXBAKnc9642ApY2N8eGpaHG19RtQuElykYu7adIXV2TFke0fxS0EOe4ttfHbLWxwuauXKznxem5uMSi7hxVkJHCtpw2KzIRUK+TLPRIaymNFRw/BV+GLOfozSnGMkOzoBOFDYwlB0bOwOp4tkHr7FHxfN3wkimnKdGSSP8L+/FhVsh+OvwW0HnDLpf03SfGiKgpZr4BkBtx/6436LEQq2wOLNTk+pm9zkJjf5H4DwHz0wJyeHqVOn3ni8efNmoqOjOXToEB988AHvv/8+33777T9jjP+pmM1mrly5woQJE25sEwqFTJgwgfPnz//N8QMDA2i12j/8+2dx+KtCyi60/O2OQRO4ddpInp0ey5cr08EBT+8swN/dhR8u1iMSCNl421BkYhFHS9q447scAKJFLfS2NdDYa2BfXgsPbbnKmu8vY7M7aNOauFbXi95s4/HJcdR06Tld2cmZ3GJmJ/uy5c4sNEoZ1+p7ALDZHfya24TJBsz+BLxj8BgyBo8pd6BRyJiW5I9SLibGT01KsAeZEZ6kLHyaWZMm3Xgb946LZlSM8y6kxMWVI35rCBqodkrjApvO9xBpfcgZSAGMeQphzDTGxfqR6CWGqAkIpr3BsqxQkoPcQeZKSUk++T88ztQkf+wOEIsEYNJitTmo6zZgMts4l3MZ774CVFIhz+0uZJCPK1kRHsT6uVLTqcPWcBGP3kI2ZHQzVnAVmVjE2wfLKGjsRSiAYA8FcqkIX5WMOWmBTIr3JUgxiLPt++gSwPEeLwYsNj5ems6M5AAi+tq4P2wGcrmSsqjbSUoazMtzEilo7EUhE3GiU8XeghbcFDJKW/vpNZrJivCiskPP5doeHp4Qw8fL0pCKheRbgrgrP4L9+c0gUzK5+m3WTs0iYb7zrnN1j4X+/H2g74SlW+jT9lDqOgyAy9XdHOj2ZceovdiEMrLjQkiOjWHVxotszuvm49VjcDhg1uBAXpuXjEomRms0IxGCv7uc2k4jdW6ZSORK1C5iHt+eD56RzE0N5IkpsRwqbKFda+KxCdG891sFzTVlhBVtICUxEXXKHFykIhp7jJitNu41f8t0TuPjKqeoqZdIXzU7WtxZf0sa2+7KJrT9Nzw7zmO12kieu5ThmnIo2olmoJEWs4I1F70wWWx8sHgwX60aQmKgms+XOzOaL89ORCZyZmrXjIrgy1VDmDs4iOq8dmqKuogPcGXDinSGhXugkIpQycU8s7OA1d9cAsDPTU5rn/MGRG2Xjjg/NbdlhxPtq8JLKeOpHQX8WNTPsWY7erMNX7UMf7WCLt0AQoEAgQD25Lew4VQVAAcP7uPw6XMAuErFdGhNjBrkzfMz4vGVxWHtnMzx0jaGp/qz89FRlLTpaO41sDWnkc0X6zhb3op4oIedVxoRCCAkQE1UsheOjgGM9XpCNEqe3VWEWhKCu9ydquZu/PoLwdSHp6uMCXE+DATIOVDUyuQEXzRKOTaHna6SU+zObUYqFtCdv5/G3S9TLElgWNliCqpqebJ6GbdmBXDv5hwG6q8gqj9LfmOfsxTtoXxsIx5h/OAY3Fwk9BrMTH3/FCfK2gC4WteD3mzDR6zn2rVLOBwO5qcFEaUaINlbzIYVQ52B1N5HIOcbAPqsIrr1TiGR3P4dNKrkPOT3HRcbB+jWmzGY7Zyt7MAeNYSZ08YS7BbMmyPfxFvhjUgowlsoc/YFpa2Ahd8CkBCgZsPyNCYn+vPQxGhaewdYu+kKn+V/xg8FO3A4HMxLC8RksfHAz1cBEIuEiIUCik79Qmd3NwANfQP8UGjnSO0J51zUUw82Cw9NjGHK9TJJNxcJdruDJ3YU/mG6fu3ia3x07SN0Zh1/OVzG+7+V39iXGuKGze4U67j1m8u8uq+YjvYmFhp+JtJTypzBgXy5ZCoymx+7rjZyosxpu/BvBHsoGBfnS2mbDqPVRmaImqOGSBIVWbjL3WmUhPNEy2i045zqeCEaBdeyPmRcWhwny9uRqdydPUx/Rv7PUHn0xsMrtT08t6vgj8covZ2CEX8WSAEEDYXMO2DOJ3/ep6VtdgpUuAX9+fNvcpOb3OS/If9wZqqzs5OgoN8nwOPHjzNz5swbj8eMGcMjjzzynzu6fwKdnZ3YbDZ8ff/oNu/r60tpaenfHP/GG2/w0ksv/ZeMLWVCCCrN36r2AQyL8Lzx/6YeA0NCNBQ39/HwhBjcFBJyartp7DGy0HGQoWPCAFDlfEKfOobkeU/Q1m+itsvAW/OSEAkFxPqrGRqu4dV9JTwxNYYtx+sxWW18JPkYQbGJuOSFiETgcDgXcq3Rwo+X6kkL0RB07hksSn/qE+9xqm71GTlV3s6cwc5a++MlbZyv7mJJRjBSsQg3hZRndxYQH6BmzahIAERCAfeMCgXFDzfe109rhqG+XpKEtpmumjwc3ia8Ij349IdNhKkcTFt8N3NSA8lr6KGoqQ+foGi012V7l2eF8ti2PAq+X0fSsKl8EG3galcYP1WKmZySjbtKzi93D0ciFIChm6YBB3d8m8Oq4a+wcng4gQXbQSThQssFeo0O+kwWBAIBK+RdFB8u45EaOQceHMWAxUG4Kpn1Y97BU+HJj5dy6NEPEOXjSnOvES/VYlbJQkkRC9ncE89tPXZoreXJHYW8sDCTpZkh6AYsCBDw0q9FrP3hCl+tHMqOe7JB38XAd3MQzPkEu3sQfTlbWCPvJyF6PAjN7B9IRNUmZKK8G0xiMk7fgV3lx4ZziYxM8ybbfBFG3g1AiKeSLt0A315uZ/3xRr6/PYOo4WsZL6hFq+vnSG4VZoEL31+oY8OKdB6ZHMvu3CY+O1VDsKCLW6OFxI2ZQ7m+jp8O1KGUSXA4HOT3HuVyl42nRq6gqkPPkZI2Yn1V7Mur51RLMuv0P5Axcippk2P56WIdbgop1+SZdMrcCVdLKbnaj4dCyqOTonl8ex5vzk9G1NnCpRIjVxQBhHop8QwfTYnJjaMlLmSNex/7SWeg8vj2fHoMZmq79Lw4M4HRMT7sK2imsrUfD5WML0/XMGdwADF+ai4VtWMxWhELRfxypYEtVxpZnhWKi0TI87MSqGzRYbbaOVJ/ALu6jaywpczfcI7vbstkfrpzrmvs1lPepie64wC9xceJm/UN9xx6DDORfL58LV36AVZ8dYlhkR5kR3lR26ljhvgiRlEonx31ZbrgLGeaHbxTk05muIaVw8OZnzCcSG8VX5+pRiUTc62+l8xwD5ZmBFPU1Ed61x40FhdGTF6Op0PItI/PsPGODEbU6zBX67gvv57lmSEMWKy8+GsBVpEccfpqSgwKvJRWHpoQw6v7inFXSFh/SyoIBGy3jODAUT3PLfPDzUVKfVE3ZrzJCnLn9GNjEGNloHcF2ktbGR45lmmhDnTdhQhaC6HXA7Luxhg6lpKTVey4lsP7i1PJjvIiysfp3ebmIqa514i2q4+rjcV8ViCkoKmPE34fwtCVdIXPRCUXI7OZwWqmtlOHl6ucb25zVjq4SCUMj/LhZIGYbp2ZrXcNB+BgYQt9Rqc8++6K3eyu2s3GKdfFDKqPw861tGvSeXBvIx9E5+OTNpuR0c4L/TszfSnZ8ToLE+YzIf4+VhxeRLb/eNKDw7hjRAS9BvONuedqVStxhR/R6uGPl8coYv3V7L1vAm/uL+PJ4jxebLsX+finIXbajefIJSKenh5Ln8HCi/tPMzFBTXZoCp+M/wSZSIZIKGJ8jDcPb7nKymRXvPwCsFgdhAU4RS5emZtAkLsCQ3cTEZJu/JTi6+eV8vGyTLZebkDpkc/Do2fdeM2hYR4MDfNgXloQdNeCOpndBX70aEvh8HNETHqFA4/+nv1LCHQjIdDZr3TwodG4K/6OBDrA1D/Kk/uqZaQGu/++ob8N/FMgYvTfP8d/xNVvoavcKbd+k5vc5Cb/Q/iHgykPDw9aWloIDg7GbreTk5PDunXrbuw3m838T9SyeOqpp/7wPrVaLcHBf6de/P8Q37B/rIY7UKNgXrqCJV+e57bh4UxK8COvoZdrDT14CHuYNMLZ/By44gsCr9fu/3B7Fu8dLkUu/f0rn5cWRH23gQA3BeNifKju0PNO1yO8Gj8GgAd/zkMuEbLz3hFolFJ+XuPMeHwrmE1lo5XC4jx23ZuNh0JKv8nKOwdLeXZGAvsKmzGa7ezJb6G518QtQ4Jp05p4eGI0LZUV6Ho6GZSeARtGwLwvaNGk4esqJ+9cE+6eLqQO9oPuWkTl+xBe+w7uPkRkeBRe9h6O7D3IxBlTOF3RiUIqYlhkNE/km9gyzEZioBuLM0JQeD3ErivnGDdwlMB5C/FxlePn7cXJsnb2XqnmnYGXwWHH79YDPDs9jlHR3vTozdSox5MWqiE3dwO3jPFjjsAIJ3bikI6kySZFIRFxsaaL46Xt5Df2sfu+EbT0GUkPceNUeReZ4Ro6+l1o7jXSrTNjt9vp0pnRmmwkl3zG2946UiJnUN6mZcXXlziyNpnPEsspGjcdkVBATk03g4Nc+U6fge5CN5NS1Nxblckvdw5Gb7Lwa2kHc1Y8hUouhq+nwJBbwSeWgcz7ubyvk0tHyvj61i8RCAS8dbCEwqY+XpyVSP+xCjLDpWy/0kCUtysrh4XRuPNFBK0t+C/bgNVhZ/6n53hueiwxvq58ujSN6pPfM0h/jZ1FcjaWvcb3i3YRaG1GUHGYQLU3UjGo5BL6B6xE+bniKrRwe/WbuGtWUNjrQobdwtP79qEd6OfjeYv5oSKVtAg3shL9aek1oTNZifVVs8lUh8Vmp3XQUsyWVr4aH01Ln5HtVTHE+6tx1/cyJMyDr8I8MJptqOViEgLUrBoWSkW7jl6DmXXX/Ye0RguNPQZSgjUcLW6jSuXA19uDO68H8JJeK75tNpLHadCatOzKa2JvQTORfv5YHBp0/lZaegeo79YT5qVk+4HD7Ghw5cc1wxjwHoNZ6Y9IKOD+obeikXqx81ojhgEbv96XTUmrlvRQD7Zcqmdry2zm+AbQUXoVT/NW/Dxm8vbsZGS6Ri6U1hEfoEEqERLj68rhq5UMFVTg1+/DxbJI9pf1IRJNwK53cLdEiKdcyu2x/sgFdjpihCwdGUfub2V0agfYerWRlGA3xsT4UNDUBw4b+ecOYp8wmyFhzszr7d9dJtlTwBjtWUZkJjNjwwW+XpXOaV0A80Y6A5l3DpUxUbuLAV03tsAQiix9jMmYRPCIEIIbLtGmNeFhs2M026jvMTIlyR+lTMKzM34XWeg2mBmw2HlpxSRKWzLQFrQxPdkfcexmkKt48vscxsT6cMuMD5CIhPywt5jWPiOfLHNmF1cnrnbOSYl/nOv+LQsEEO4eTrRHNHWdejxdZajiZ0PYKDQyNzzUFbxtyEOYU8wbY951ejXZLMQpdMSOCkag8uaXGXsIcfeju1lPdrgnYqmImk4d7i5ShscGogs/Qdz10tEPfivHZLGhUUjpN1rZm/QRC6KdfT/fnX8TX4GYKVmPEuvnxsDVLXzdJKcuyEQ2oJAo6DNasFitpAapeS28EE+RU+Hv3VtSOVzUis5kZch1NdR2AlltXM1T9QZi/cUYLTYG+biyfFgIj5zIpUU/FI1c87cLwS+3w4h1SCVpWCSu4B7y+/ehH2DhZ+dYNDSYNaOdmf6/DqRMFhsDFjtuij9X+gvyUBDk8e9U/Y6/Bm7BMPqxPz3+HyLzbjB0/f9//k1ucpOb/AvyDwdTY8aM4ZVXXuHTTz9l27Zt2O12xowZc2N/cXExYWFh/4Qh/ufi5eWFSCSira3tD9vb2trw8/vb0gWZTIZM9ufZov9KrDY7n5+qwmS28cjkWAC+uy3zRpN2j8GCRiHlrGw6k8IT2ZXbxLnKTt5e4LwA2J7TwMHidlKC3Shp0SIWCgj3VJEarKGuS8+u3GbcXERYHe7M/eIyb8xN4rXxnkg9Am+M4Y39JfQazARpAhG4mrgzxZ2OfhPernJi/VRolM7P6S8LBwNgsdmx2R30Gc1sviMLoVBASW4eLg2nQVwNK3aCZySL3jnJG/OT2F7fiaBFwEeD/TAFZrJ3UAALUp3GvpPHTyA3t4iizd8yZPRoFgwJoqJNR5inkgfHD+JwUQvh3iqWZISgH7DSfzUf3EORioS060zkNvbSXleGVaiGlCUQkIZIKGBsnDNDuTO3ic0X6tl5z3CWh85FJFWCtgqbJpLe4FQ+u3CJ5VnB3PXDVX5ek8mtw8OobO/npV+LifB0YambOyNCPKnU6fn5WiF5bRXE+E/jvUWplLf1o5n0JBqcFzmPbM3lgfFRqM1tWMsOEZd0C0+dfB/9gIUh4U9hiltIfn0f66a5cXFWL47KLaxrGEaXzsycwUEYzFYUt3znbDJPWYwL8OXKMBZuOMed3+fw1aqhDI/0IkSjINJbxcQ4XzaerSVY48L4WOffeI4wiaT0WQgdVlxqjhDjk8Dluh4EdT2Mi/OjJ3wmK4/F8vnkIYSovqGh24Stt4awrjNsK5nAkowQnt5ZwOvZYmi8RC9q7FUCli69DQew8WwNpn5fMsJC0Q9YOdWj5URvHykJ3vx0qYHWPhM6g4FZPl38eLaKwRE+mMx2BAIBHf0D5NT2MDMlAKlYyN78Zhx2B6NjfRiw2ZmdGsi+gmZ+udLI8EhPYv3VrNuax2OTnOWGu+8bwUt7iojyVWG26Nmac5LTT4xj6tBAjDoLZxvP8szZZ3gu7Ssqm6GkuZ/8hgGWpIl5aEIUA1YHGPuYf2014vSNPPDzNT5YlMqRIitTunQU17jy5ekyRg3yoqxNR1OvkR3Xmjj44Egu6t9n6ei5hCjUrD+iYP7KjWhESmd/168vsaljFlKvcDwUUh6YMIjcKhcSVW68X2DllrQ+Pl7q/LsUCAQ09eixSkUsmx3L1e1vMarzCtbsn7ADET4qXpgZz6BAK+9f+p5nRjyMu7mDif3rEXhNIz0sgHPVnXRqB+hQKmhUJbO5PorfZneioZyHrxkJdFMQbCwnI9yH0gsqwkLjKTH74G5pJfDb8TwW+AOefiHsLdDxjL2VqUkBvDUviR/O1wEOLpU1UH1uB4tvfZCnpzkDq80X6rhc2837iwez4UQlT+zpZP0tqbw2LwmBHUa9fYzvbsugsr2fkddNu3cXFHO59TLPjlmOXCIi/8oFPNvOEDjtUXYc/A153TGmrnmNZO9kLFYBj+06wfioGNaOiYKaE+gaSjlfnsYd42fj562/YXrbYZXjPesDBEBZqxYfV6cIxYfbCklP8WHmmHDWHyonJdiN8XG+dOgGyAx3BjizUwOw27khlf7vCREp0ZiN6EwWXt1XzByNnPYBDRPDfhdn+u5sLS1aI2/MS2bkIqd30vnSelwlNl7ZW83aMZGsyAqjpkNP7tl9bJ7kyZydBcxODaTfZOX1+ckIBULeG/seZtMAHT1aGvttbDhZyYK0YCYm+MHSbVCym6nqAkicBPyu0OfuIuXW7DCyr/tV/QGHA5qv8uRpodMv6npA+x8y+TUQ/gcS613VkPsjjH/2z/d7Rjj/3eQmN7nJ/yD+4WDqtddeY+LEiYSGhiISifjwww9RKpU39v/www+MGzfunzLI/0ykUinp6ekcPXqUOXPmAGC32zl69Cj33Xff/93B/R32H65mR3MnDT0m7hnz+0LUqTPxxPYCPlicyrMz4jld3oHLda8mg8lCc6+RPoMZN4WUQ0WtRHorqes20m2wcaq8g2lJ/kR4K/nsRCVioYAAdxeGhnsiwE6ItJ9vD//Mb7JJuCqkfLc6k2hfFXkNvTT3GlDJpRQ167lcq+XFWQmIRSLa+kz8cL6WFcPCoLceyaFnkcz+mFU/FSJwwMczfTlb08ZwCU4fEm+nrPHPa7PwU7tgNNupbmzhqZ/OYREp6DNZmZzox79V+KemJpCU/DYioYDtOQ1cqu0mLUTDY7/kE+OrIsJbxYDVToK/K7ctXgos5beSNhwOAfePG0T/tUI6XdzoD1uM2Wpnww9Hqe4xc/u4eFYNCyPM04VDRa1kXX2UDrdk0hY9w9zt/cT6lfPczASyo7yYkhTA/VtPMmyQlGhNDLoBK8/OSODHDXmcKmnnjdNVfLo6jBDXaHK7ztHcKeJKuRtScTB781p5elos6aEejBnkA+7BnO91h2v5LAiexpE8LWarnfvHR2O22bHbbVgk3qyvkqKzWdl461AkIgHD3zjKW/OTmRAv5snteYyO8WFCvC+zU/zJbXT2n8UGQoSf8+fd2W/GTy3noYnRhHoqaenqp7bbQJBFBMYexvX+QsrEMeR0yfj6bA2tfSZem5dMuKcSH7ERU+4n7FAsxM87hi/0AUxNkDI+1sdZkmmox9HbxGW/SVzyDSQqp4FxsT58daaGGF9XfFVeaE0WBBIB0d6u2B1wZN1oWvqMHLtWTl1JB1MjtQyOXcbYWB9+PliB7WoP986N4o19xZyp6uKxSdF8frqW6k49b1xXglPJJKweEcbCISFsuVRPcpCajv4BwrwUPHj4BR6bN4KKajUWh4O2/gGsdjulfQbK23UsHDqUx4Y8xqs7Gnh5diKnKjpZnBmCd8km7rHUQfwrAAgeLiTbIkfT2o/dAfVdBlr7TOzJayYxQM2oaC9MHZUsCXXw2JRxyHqqeKm+Akt6JB5qT449Og4PpQy7w9kjo81+ktRftpE9chwR3ipqO/Vcah7gjhUz+GyE83f7+PY8zlV28cs9w7j3x2sEuit4YWY8+xnJ3FHT6arR0t5upE0lo6RVR4ifOw3dBtr6Tbj7BtOy+hL9BjuSAT33bb7KQxNj+OZsDRNmjCNVa8JDdxG7yYV3BmuJDPKDb2cz9e7z7LYtwM/LhTE/ZyNe+DXmceeY2OGKSAhqsYhAF2dWo7BZy7fnahkf74Ogv4Wxuv10dK9g1aZiHp8czaR4X4aEObMoXq4yOvqdpXQnyjrIq+lh7CAfjpS0MyRUQ32XAYC9RSUYXfKwlXrDmdfY47OeUEkSy4EwdzGCdssNYYgDtbvx8FaT13i9TNs9DLVAylZBLVH9dZDtlIrYfKGOb8/V8vOaLJQyMS/8WkS0rysvz05kecA+rPJhQDgjBnnw3bk6tEYLJquDzHBP+k0WjpS0syLLKUneUFyAb0QUUrkLD39xmZmpc0kdEcS9m6/gY+ugw9SAQOBFu24Ab7VztrpzVASW671R/4Z77pd4WFrZtPQlKg7twpp+J2IRhFlrqCmq5pahc+k3WrA5HPSbLFyp7WFMrA9f/nKUQ61ipqZH0NFvxv16Jqm1upAT19oYnuJDoN2BSCigR29mf2ELS4aGsGLYdSGJg89A/EwIyeLkpo34acTEFL6IT+wvjIsLcno8DZr4BwXCP+V/UZpX1VPFA8cf4KWUBxnyvz7LTW5yk5v8j+MfDqbCwsIoKSmhqKgIb29vAgL+6EXx0ksv/aGn6l+ZdevWsWrVKoYMGUJGRgbvv/8+er3+hrrfvxIOuwNHi5Epg3xYkB32h30eShmpIW4s/vICP96RyYhBXlyu6eZSTRd7C1oZsFh5dU8JExJ8WH9LCo3dRl7ZV8LWu4axOCMYqchp4DtqkDc/Xqon1OrC1ER/PjtRyfkqE0+uvpf4VgHNfUYA5qcHo1FKeHVvKU9Pi2XEIG/s10s7H58Sy8NbrlHdqXcOTu4O4SMxIUWnNzE51IbCzRMX7wiqQmZxrNGEpLWKtaMikQiFnNr9FaOHZePTWY/WLmDyqHiSAt3p7tCzbt81HpwYzU+X6ils6mPTHVl8eKySzHANFpud71dnEOvnilgkpKZDz0fHKrhrUw5v3xLN0HA5cnEYNpMeV7GNp67aGNzRwPg4XxqMAmwyJV1nvkTkuwKNwodjJR2Mn72e0hYT8z49Q3aUJyarjRHX76J7u8rxdbegUSqZmhSA2WrjnSNl+KVpiB/kxRt957hwpB3lpCga+xvx0/jx/uLBHC9u41pDDzqzjWemxPHNx1cZMjWISE8prkFq6nvcKGztQWey8PPleqo79NwX4+Doxp9JWPYM8QKw2Rx8faGGmUnexHUfBf0UDBYbbX1GVnx9kUA3F95dlMqVum7qLn2KylHHzkFpjI9YRnKIG6GeSn7NbeKzk1U8MnEcQzz74NIXiJdvw0skR3fme16KcyFh9EJsdjufnajkrgwN3e2dtLobuNhoJsBdTljtBep6FMy9ZRllrWpWXjDww2oFYcNjef+3MkZEeXHuyfHOv9/SAwjKW/hi5Wou/PgqTVs7iV7xPvnnjzBZ2oBXhgrcA7DanFkph8GKOEaNT5iahouVzE4JYHJiALXdRjQKCTuuNmKx2Vk0NASt0YLRbMNVLiantpcQjQtSkYjWzlBi0qL4racHmVjIx0sGU9WuY19+CxUdOpZlhTLeYzCSqMMkBgxnVXYYXkopKMfCQC/k/gSthbRkPYu5rZQQzyhEQgGvzXP6BKUEa5j50Rm69WZe9y/Hv/EilrihnDpzgkHiICRoqO7QoTVZ8FDKuPPAgyQop7Nu9BRG3fIAckMLgt3vEzPqIb5fnXld6ERAQqAba0ZFMCbaG2+VnLfmJ+PmIqFbb6bbpiAuNoGyCy0sD/Qiebiz1LiwqY8PJz+Dj1rOgNXGmwcrqO/SMy3Zn7NPjEcqFtLcYyDaV8Xxsg7axt6OtvwMQTVvsjA3meERW3nVPZCCxiJqOvoJWrYHpU845yq7mJTgDFgazrZRXNZAzN0adCYr555yfreB7kOoCdxOhJuS4ZGeRHir8FbLUcrFfHS0gsUZwSxId44zPcCN3JwWgvxVDI/0pKCxj7yGPtZ8d5knpkwm0mcegktfQNBQug1yfLydQjVpWWMgawwmiw2AZ4c9i93uwGy7HqgEDkYUOJgo/zqwmgC4WN1FWqizlNJTJWPL5QbcXCTcOiwMk8WGxcWbqCB/mnsNZEd54++mQCiApCB3AIwWG7/mNjHIR8XoaG+OfPs1w5feRmxqClE+Sny9nLLmd42K4NGtvcxJcmPnsmy25dRxsaGEn88NsHXNMNyVUr44WcWRkja23TWcuDmPgc2C3iqi0duXx3cUkhKsYdWCJzhT0c6Fo5UsjBYwqexFGhs/48vT7YyK9mbptOFM6DMSE+bP3WOcJXtlrVrsFjFCgZAviyHE5DQv7jGY2X+hkRCrgJHZ18v+fONA6fw840eORSyTw6SFPC1xob+9AdPPL8CVTYjsViTLf3Y+x2z4g3FvU4+BinYdY2L+XMY8RB3CsrhlnN71K47osQwFrHYrW8u2Mn/QfGTi//vVHTe5yU1u8s/iHw6mAMRiMSkpf26u9/e2/yuyaNEiOjo6eP7552ltbSU1NZWDBw/+jSjFvwICoYDpqxL+dJ9cIuLWYeHk1PTw5oFS1k2K4YGfr7HpjgyivFXUdemZEO/Duq15bLo9g8Qgdx6eGM3352uRS0QsyQihtlPP8mFhLBoaDNfv/s5ODcBksSN1U5KhtKG43mdV2NhLcqA7X986lHAvJcXNfVS265iVGojd7uDN+cnIxCJatUaMA0LCM+5EDuwbfBGdIpDvLndy54zJiEVC3jtSTnGLUymwTWtC0pZHf4sfyVnjSY6ppVXuvMObd7geD7sApVTMpDhfQq/X8H+2fDAv7i7ibGUnI6K8eedQKXeMCCfcW8ms1ACKmvr4Ov8rOnT9nLs8ihWpHqQ2XeCRqWn02HWEeUWwYc31TGq1Azwi8Ow1Exegxt07kI+2nUEiEpEWrGZiYiCPbM1lfIwXftIB3po1FZPZRnu/iZa+AZp6jDfKnN7ticRhs6Go7GJu2lLk1zOFLiIh85IC8FLJqGrV4u4lRyyV8EjvfNz3NbB+fC/f3j2Mjn4T7gopHioLfnGDmP7Ey/gFOEvzft5Xxp7yNj6Z4krAr8+Bwk60Tya781vIDPdgdqrzBkdluw65KIpAVw9yrQaifFVIrpfn+LvJ8XOVk178Dri6QsMFSLoFnTKMnZ1BDBfsR9gQSL/3UMra+umyBxF3+2eEnqtF29zH4oxQAoUeKGTOTIWbTMz0RD88VTIOF7cyKcGfz3cc4mXldpj/Nd9fbCTdXU8iUOuRTaG2F3WfiftPS/hpvB9e/x97bxkd17VuaT/FKJWqxMzMsiTLkpmZGcJxYoeZ+YSZOY7j2ImTGGPHMTNbtixmZpaqVKXi70f5OOd29+0+t+Hre2/7GcNjyFLB3qWttde71nznHLGIR34poOngObLDdTy68C+75K9uymLIYqO4pJC7RieBUMjxik5sDif7itr4+EgViQHuvLkolfzGPkK91PyS38KIjBoq+gKQCv0YGrbhoZLy6K9XmZHkx4S4a5PBrkpiu/ZT2TGbQA8Fq7+5wKknJqDyigSZB/0W0G+5BX/hAH8kv4+/JoYFn57mvklRBHkoWJkdTKy/G+6BGQjlEvQGM+V6Ge6e6dz50SlifN3wVsv4YIWWCOFytp01MyWyD7VcwifHm7jZKSFBoiC/vpc/i9tobajkjTwBUWnzee2PMs7U9vC3+a7iTSEVMyc1AKFQQLx3MRT+AnyDyWLnrf3lTI7zRiWT4qeRUdI6wFuLUvjlUjNSiRCJqZs4Lbx3qBKRUMih0g7Gx+ahTvyTV1r6ceJy6qxs15MeouHm7R28tdiXp3cUszxrgPsnx7BqTRI2i52mXiPvHKggK0yLWi5BLhER7+9OS5+RkRE6bA4nAyYrDoeTP0vaaek38tSMeGQSEY5WE2qBgFaHjY1nGliQEcBqXQgbztRzsb6bh365wp1jZtOhmczDo/1RycQUnj+Ms/Y4qSte5p39FYiEAp6aGe+SC1+5hLtaTXh0Avn76onPC0DtLcNss/PFzjJ0Ohm+/q6dlPnpAUyK98FLLWNT0e/saclA3D5MkLaKboORQc0nKHru5ba8SMbH+eDjJmdldghhOiWfHKlib8AChP0i9PW93DP/rz6xxEAPJiUEsKFXy63N/fQ6Kvml4S1emLX5eh/SuBhvJOJrbnxyV0+sCkibMY9tO4rwUknZX9zO2/srCNUpSNVZ0DgG0Pi5MWRpZdO5em7ODUer9fgXY/9Hh6uI9fXiDsdRQnQ63izWkBTozqhIL2aJlZwo6bxeTO1V+7Ljwps8n/cMIaH/0va8V6TjPc9PGKHqwUtsZCZA0wXYvtbl2hrmip4obdNzsbyR8co6V97Yf4FEJGFl/Eo6FDlIr43dzfpmNhRvIEoTRXZA9n/1nBvc4AY3+M/CP11Mvfzyy//U455//vn/6YP5/5N77733362s759hyGxj7Q+XWD8+gnsnRiG4JtM79cREREIBkzzakSanMSrSi2OhHpjtThRSERPifDhR1UWfwUJmqJZHf7vKY9Ni+O50PU/PiKPs/CEy+vYhmPsRr+0t5UpTPz+tzUEmFvHob4UEesiZmRzAmepuArVyaruHOFLewY4zjSQaBNx9fyY7r7TS1GO8vpLPhKfo6x2i4WgNdqcTMfDQlJjr5xLn786RMc+iifel4exvaK98xrzBp3l3SSqB4wN5ylfN+foeZOZeVoyM5Wh5J1nhOn6+KxeRUEB1h54/itoJ8FAwTu2GR8cw90yJRl8YwoJ8BevGRVDQPMBJ5xKWGss50XSCKG08Oy83Mynel4DwsRxqOITcGkdVux6hw8LuxVoOdnvhc/k9YBSLoyOobanh3VIbD09VUNNpoKhlgCCtgjvGa/gg/wOUEiXubtFkBUezaEQQ92y+TGqwhsNlnUy2SDEoBBRG9rHsq/M8Nzseh0SMRi4iRTKI2GjiYGk7G07X89s6l5PZxjN1nK/tRSJu47X5yST4uLNCJmLez9Vsvf0sCSG+BOQ3opaKyInQEe+vgf4mHD1vkTnpUfquVBPujOa5HaX0DFmZnexPpLcKo8XGj6J5xHv6cNq8nBe8YzhZVMEPd09AWj0MumiqOgxMiPVlZIQXZce2srTnHHetfI+p7x9nUpwvUxPcqDpXx9GKbhyA3enkUFkHr85PJjcgh6aidtytQkb4CPDTeHLh6O/YLVJuXTwfkVDAvgfyiPRxTS5HRXrirZaxJDOQx34twGZ38v7ydERCAX8WNPDGvkbO3OGkSx1Fz5CFhRlBvLS7hEUZgYyJ9qayQ8+ijCA2nK7j3aVpyOVBqKU6QkYJeWhrASaLjS9XZPDnljK2iDso79Bz17jxHG/3I8RmJ91Hx4+3ZyPFxoFT+fRK/VkuEyANCOLTgSWsTAi9llnlQ3KQB9+erKOkdYBbR0cw/YMTTEv046EpMWSMmYO3h5xto+CHM3XXg2KfmZ5HZlAbIZ5qNp9rwN07gLgZH9I6MMxdm07x+72jQXMJd1wT8BlJftR2DPDx/iLum5ZMfn0vr+wtxUMpRieNpkQ0EfOVZnYXtNIxMMzFhn7sdgcfLM/g9/tGo5SKOd7zPWs39bFy4GckmnCCg+bx+bFaBECcnzv+HgqcwLnaHsRCAS0DwzwcH82c1EBi/Nx5dX4SaSEuuZ5EJqKl7iqi/hb23n/NwfXQiy7Z15hHro0BXWy92MS0BD+WKPP52wgJz1xy8MS2QjLDdNw0Kox74rVIJEJX3+evBcgkIjbeNpJTVV2MivTkaHkHjb0maruGyIv2IsNdhdXd5UR317gIBNcTnuBgaRsB7lLCIuMxDlpo7zWy91ID90+MZKVOTa0TLrbrcTicXKrvZeeVVt5ekopQ2Mvikf60dLmxaEQQm842Mib5PnICR3Cutp/Osz9RUllFvyiGy+LRSMVCbhsZQNeVPXxXn03SyhyqBkp46cxLvD32bep6htCbLGw4kE9adBgfjP6B5IC/dm9i/d2J9Xdd50aDHodUgEwsQy2TEKCVo5KJCfdUsCZDi5enF7pwT/LddiDtlzAl3peUIA/qu12GKBXtgzyzo5hNt49ELZcwJcEP5bgdJDnEzLvUhJ/GVcSsvCcdq/0vieGhoiHa2+YgyFGz8XQda0aFIRQK2HS2HrFIQOegkU5TJxHjZoCx1xWmPvdjl835NaYk+DJFXkHtwR/QTlGjtXRD5H/t6ucb9pcMPUwTxv7F+xEK/ukElhvc4AY3+A/JP11M7dix41/9mUAgoKKiguHh4f8wxdS/d+74/iL3TowmLcQDgF8vNSESOLnU0M+rC5IRCgRolBLc5VKS/8G+tqnPyK9HzvFw1c04158DoH3QzMLPz7D7njwCdUqenhHHqm/Oc7Wpn1vzwthwuoFADwUX63rpsGpJCxtNuKeS1TmhDNvsvLq3jJfnJfHszHgOlbZjMFsJ1inJDPMkI1THMzuKGHQ4yL1myX7X2Agc/4WxY7BOxRuLXP0uZpud3wtametRhzQiD7FIxNRE1+7L48WBZIa9x1xkfHuyDovdyesLkxHXnyKl8HlssQV8dqyaZ1UJDA1byYn0IsrXjWOPjkckEtJW3Y9CI+VCbS8m8UiWpfYQ6++Gr0bB2auFiNuKeSrzZcrbBtlV0IanWobOTcCW8i2E2+7lSMUA90W04Xb4CX71/AybdS6vDrYwquldshZ+y+jkDoID/Rm22nlnyx7MBk/6LUoa9Y2oxCompoUxNSwIOst42PoVCCZgi8lgVIQXG87VU9w6wIZbMlFIRST6qfhibgBosjhe0cmEKFc+F8ffAWMXiye+SohWwXuHqug1monP8CV42MalzkE8PVwTtPQQHbXdJuL9NWw+38CqeAkNTjNdJ7bSdPwSoYseQRjqz+7CdroMZrqHLHioJByo6WOSfyt5USkUd1bw9PZqDDNFLM+axQu7iujramdMiBSIoVMeikXnxEso4IXZCSQHeVDWPkhl5xAX6nu5a1wEPm5yvr3576vPCtbUp/OEfQsNXmPxbd/C6e5oVEo1okvf0Bq5jJkfneboo+ORHnif8QhYvPQpDpd10NRrZFZyAN2V56gsOs/8efeR4idHEhjIyrePkh3usoV+YW4ihc39HK/s5mxND68vSiYjREtGiJbZHxXjr+nn45XpjIzwRC4VIQQ81TIe8Czm+IAZZ7+c/MKrvNvtwY935JAcpKH24CZkxYdg1AuQuhxZ/AKSKnrxdpOz8Wwdy7KDCdYqeXRaLENmVz7SQ1OiSfB3p33QxM6CFkaEalmQEcSzsxM5XdXFzd9d4NubM8kK0/H96TrWjY+isKmft/eXkxPuSYhOQYBWwVNd6Vja7LybBksyQ3j121+40C/EOCGevGgv8iK9CNGqGDRLqXXPxsfmxAl8tDydbZdbuG9SFFKxkPYeEz1Dekb4jCAm04c01T14e/kyJNWSFOBBaogGsUDInd9fIDNcR6SPGynBHryzOIXeIQurv77IzntzrxuzGC02lFIx/XVXcOsrgZw5DFvtFElH4lexCX/r64yZ+BRjor1p6NbjqZZDcR+pHloemuJyZJQj4I0NlymymJmZFsCteeF8d4vrWjlV1UVN1xAjQrVsvdBEdriWMC8VPmo5dqWcIo2ZUFzy2sOlHZS0DXD/pBgeXzP/+tgybkUs9d1DmCw2jv/4OqmCSgKnfsxdAa5CLNJLxZQEXxhoYdUff4M7j2KPktNY8j1PzVzP5vON7L5QwqW6Pj6fGoavv4nxV57CMesEdVZfOvVmnClTiRwSIRRCqHsoowJGoVPo+Hx1BPQ3Yd95HxuNTyN0+jA4bOWLo9W0DZh5NEtMYFAYVqeYv321ntooC7lBedyfcT/lbS4nwa6mKk4X1vHVw6tAIGD95nri/NS8PD+Znw+c5FDtMN/cPYVgnZK1YyNQSEW8eW0sBVADt+S5dpxOVHbio5YRd+3cAZ4dm0u7WYbNJia/sY/lI0OQlOyipcmLCZkJTPY1kt5/mYzom1xPeLgExDIOV1Wws6CWj5e48iUdYWN4wSHlmz33gsAGkSf/h/exG4XUDW5wg/8X+KeLqStXrvw3v19QUMCTTz5JcXExd9555/+2A/t/ncWZQYR6/qVZN9scGC1WStv0WOwOFFIRn11zYfr5fD3izkIWz5mLViklOCyKzvEFBGhdfT5CgYDHp8YS7evGuh/zmRjvw7tL0nj/z6vEWkrJCsvjt/wm3l6cilgYhVAooFtv5tNj1dwzLhKJWMSw1c7oGG9aBkwk+LuTHOTBW3+WYbQ4SA3yoG/IglUjZVdBCx2Dw6wdG8mF3e9h1Z8iedH3fH+6jaXpAfjp3BgwWdl1pYlxtjfxXvYJ6P6Snrw0N4kLdb3UdBt4fVEKHx+uosdgISZnOpX+ERRebuaXu0ZR22Vg1Tfn+WxlBmNjvXlmwx7G+Awza+5S/Ev30nP+d/aFPsYdk1I5Wt5FrJ+adJ8LlDUU81JTCe8tS2fb+tzr77th+gYsNgcL0wZ458AZZgfOQWwdoF8wyEaHnicXf8e2oqvk9+5mvGEZKqEX9zh+pMVtPAFed2I1Pn69IASg8Sw98mCCAhNYHxENQM+RKi7W9SERiXhjXznfT5eRfP4RjHee5b6frvDcrATmpQeCSAoiKRXtet47VEWQh5zWfhOLvzjLr3eNIsJHjUYh5VxdG99WvcBTI5+k12BhuDGfKu9UnpzxNad/eB6f8TPZbttJvHs8G2+7Gfu1CnfVyBCOFDXyW7OBZ7J9EAl92bxWyR8FBuq6DCQHaujQlzDG0yWTGpeTA+TQ1DvEOwcreHFOIqMiPHl2ZzEPTo5mf0knd8SYOXTiJBv6U8kM1bLpplRMP/yNersPuvKfiE55kKjIVMjfRkDSYvY9MIYADwW9UaMRCFxSyHHR3miVEjJCdfQUVqKy9FLSOkBpu50TDbW8NDcBrdolL7zS2Mfpqm5kEiFfrBnBQEsDM8JcK/MvzE3Ey02KXCLmoel++CgVFJ9opupiB34zJJR227C5+XPrmBgWKQJIDnJNPF9viWVhVjzLc1yr66JvJzAjdSWI72Xz2UZOV3azJNuLjbXP8X7uK3B6D9Oy72BvWR9fn6xFLhZxa14YpXsv4Gs9jTLjZmQiAe2DJsRCIZcaejlX04NaJsJdLiYv2otwb5eJz4hQLRab/frlc/e88Xz4+3mKmgYYGemJWi6mZWCIJ7cV88r8JM7VdDNsseOnUfD0rHg+PlJJTecQKokQO/DGonEQDFQfAb2dqyZ450A5Ud5q4gM09BqtVLYPsn6C69ocEaZj09l6xkR7Ee3jRqd+mC+OVXOmppenZsQxbsbt14+tvnuIN0s0zPBdg8MewN9H/Vf/KKffaOX+SbPJi/JiyjXZsM1qZ2qoJ2MDFeg8BOwoO4ShL5IrTQMsywpGIAClVER6iAdDZjtapQyj1UpHXy1F/RXMBi7vr6dmYIhCkxEG20CqArk7B+sPsq9+H++Nf48Hp8Sy/dQM2oZHEXotmwqzAd3mGUxY8Dm4J8KyTaAJZKD6ALKCn+hOXorJYkUuFnH0sQnXzmQUTFhJaYeZh7bmMzrai90FrZy52/VZ3b+5gqdnruVCjRGRsINJ8cGIbtnJbdeePWC0YrY5CPdSoTz1ImQvRxI/h7ULn6ZbZiDE3WVs8e0trp0fpyOceZnh12XWry9Kxv/aLpNM6CDDyw76DpRuvkR4q7DZHYhFQri6Fdz86PQaSYd+mORADz46Uk2oTsmQxU6sr5qHPE7jefIdPB8sBqGQu8dFcra6m/EXv6Zr+FYul4pZlxOAzbaKjw5XMibKi5RgLTabHZHQiVDkWjSwWx1seekc9y+NoKFhDjGibrDbQPRv6hS4wQ1ucIP/lPxPLxvV1dWxevVqsrKy0Gg0lJSU8MUXX/zvPLb/p5me5I9W9VcuyOqcUNaOjWLnPXnIxP/SdWmcZz8LSx4AYx8eSikpQVqUatcEsanXyGO/FTJsc92AV40MZUSIB09sL2SyVz83DXxJkq+Me8ZH8vHhaoRC1w29smOQEK0SPw8lxyo7eHFnEff8eIllWSGIhUJu/+IQZY2dTEv05mxND3sK2+g2mAnWKoj0dtkJqwLTGPYdgd0hYdeVVgZ/uQvqTuHjJmfTnbl4370HdOEcKu2gptMAQJfeTLfBzEtzk/DTKHh1YQrpoVpO1fbywUULJyq7OFPTzb1brrD7njzGxroKxmUBnYy2nALA2lGCTmJndU44zX0mnttVxKt/lNLqMYG4OV/x1Kx4GnoM3LflMg/v3sL6XV9wqb6XdZvzsTmdzI2VEGpqw9A1zG2xXXQNDXK11cDm0z2IbBH0Dkoob9fjedMmUmatp65niJ0FLQC8sa+MH87U0Ru3iku+y3j0kIGHtxbQOWhi/fhIonzVtPWZOPXYOJI9zLBmBzank5335rEkK5jbvr/Aj4JptIfOI1Yr5MkxXny+JosIXwGfrEilfXCYtgEjD229wrofCsn1nYaP0ocArQL7UD9v7a/CanfQGn8n1tjZPJj2AOPNdvqN3azfnM9P5xuQS0TobSKutgzy7oEKAJL9g2kfGOZCXS+LdXXcM/gRfiNmYas+iuGzSbQNmBAJBdR2DSERCbnvp8s8MCmKm3PD+WltDhK1jpQAd1KCNPya38ygTUzDzC0EJ+UieriE3YIr1Ima+DH0FTZc6edyYx+LPjuDe+potL1HsHdWcqC0ned2lVDbZeDdGn8KQm/l2Z3FDJgs7ClsY8uFRraca6TfaOH2jZeYnuzHnWkqpJe/pfnEbwzufh6Kd5AU4M6luj4ut1YwZ8cc9BY98XkBLHsmi9jJS/j4tglIREJ+qRHz7al6AJr7jFxtGUDpFUzftTBXU9Jqunp74fcHeWleIiqG0EgV3JJ0Cx5iGaamy7R39zI10Y8npsUiEjp4ZkcRnt4O3Kw1pIfo0KpkfHq0hi69GYFAwLChi/riM5S26pGIhJgsdmq7DCweEUx6iI5VX5+jubYCT50n5/vdOVbVCUCARoFaKmX9+EhCtEpsDleoqkYpwWixcaSsi4lxPlR2GmgdcBkx7LjSzK2HBXS11eF0wvZ1ebw0L4k7x0QwKsqT4lYDW8418OGhSgD6hqxoVRKXEYgTWvqH0akkZIe75Io7LjfzwcEK4vzd+W1dLtnZo9A7/+61CYEaBSqpiD1XW5n/ySm2X27i94IWzjf0kjM1nPGJfpR0tPFryWFK2/pp6TNgPPQG/ioRHx2qRioW0WUw02+0MmCykRm/kKdmb3SNC016PIa6WT1xCA6/Aj/MhZbLDA0EsyBiBeDqIW02q0hKHkFupBd2u53qQ9/ymvYlPiwUuYqVkBzoqUa351FaJ/xEdZuA8cpuZjlKwGqC7mrXyUjkJAdpWJwZxOgobzZMlfDo1ktIB5tZmBGIn0aBftiG/toO5fGKTp7dUUTvkIXBYQvPzUnk/snRaFd8DfEuaWRYWDyZ/lmIBpSc+rXq+ufWZmynrO0qhoFeALxEQyh/nAN9DYRGJxPrZobd92F3OFn25Vm+P1PneqJFD9YhTlZ18c2JWgA23z6Stxankh6kwUMhgYFmGPUAXAs2r+k0UNA8QOeEt7lf9juTRfnQfAFHwAgqOww88lsh7+wv557NlxkfGceHC13HLpIImXRrAhnxPsSMXgSDjWAz/bdvXnYrXPgGzPr/9s9vcIMb3OA/Gf/mZaXu7m5eeuklvvrqK0aPHs2ZM2fIysr6Hz/xBv/b2FfURpy/G+FerqLFPyrNJc245r707oEKFmYEMjslEG83GevGRTAzxWVO8Nb+Cp6cEUtepA69WwANyw7SW9/L+4eqWZrlcmP84Uw9nx2rZv34KDoGTHxwsBqNXMztga5eADeFmHh/d+r0kBSoZUmmnfruIX692MQ3t2TR0+IqjBJHTCQRl8nDd7dkE2hUgd8/pHIKBFxt6uOjI5WszA7lUFkHCzMCGRPjTW1xF5WlPWTOCKdDP8zslEBm1ryCKXoOx5qMPDQ5CovDgcXmQCoWkj7zDsA1WXixJIV5IbEsFgrZeKqKm9K0zBsRzuxPz/D1wjrSU5J59XgpHfohPNR1eKt80CmleKqk9A1Z2ao/REz0KPRN+7F5rqK2+iovNx1ifV4oQ8Je5idFIEIAZz+FlKX4uKn5aLkrW2vAZEUiEtBrtHKpoRd/jYzKTj2nqnqID3Bnzcgwtm38gFb3WCIvvoJtzW6e2lZISpCGu8ZF8czMBBrO70JZuBnVqFvIK9wKXk/zTNHnLI5fTmlVGHKRCM1gPgkpkQgMsailarbnN3N4OJYoX3fsDidLslwuaqevlpF24n0KZSpSA9M5WtHpMhzJDmFUpCcWqx3OfQEJ8xkV5UVF2yDm5AzEcz9hZ34zkyNTOBq4Hu8uAxXtetaPj2TYYmdBWhBv76+gS28mwV9DruE4gaHhPBkUz/Qkf9wVEp7ZUcSB0g4OPjSWjyZ+hEwkI58+bE4nSomQtBANIqEAi03A/s9LqEiJwcdNyqYzrkwso9lCXqQnAgQEeMiw2eG1hSlIREJOPzEBhVQMHU3QcgmDezwR5lb+rLWw4fRFTFYbcrGIeeHv4iZ1Y+0PF8mJ8LweHbBiZCj3TIhk0OSaDAdpXfLX1BAt0z84wb60s+iFaj7s8SNWW8PaSC9Gnv2UXw6lkTj7HiRuHrwouZ+Igwe4eeVNxAaKaFa/xOu5n+EbGkVJYDyeAyZWZAfx9v5y3jtQga+7nHEBItoubCQlJBea7GwvVHOlsY84fzfWjg1HoxAh/OMhmP0029blopKJ2XW5mQgfFeHeKl7eU4rZ5iQjREuEl4rjFZ0MWx3suCePR365yk2jwjBYbAxb7cjEAoYFcmq1KTy/o4jdt8Xhaellb60HZS2D7Lwnly+P19CudxVft44Oo6x1kCGzDalIyPIRQVxtGUQick3EK9oHudo8QFPPEIZt9xI29X4enuq67g+XdXCovJPHp8bw86VmIrzV5EV6YTrwChahHKJc8u+uPiUhzuUk9zpZmAje5W2EJfoxNiEAk9nOkMVGoFbJgNFCTYceBxDt60b0vHDeP9JLcLmKlLGvoOs6D55RlBw9w23+9RDiugcFiYwYS8rpdE/ju5PVLGw6z+Ixk/AOdu1+9w9ZePmQgS7V50zusnG08QCrIuSYfOwUHPuFmJpNKO8+REufkae2FfLKjBBONw9zoEnLBK8aBFX7mTdyLQCXGnqRCgXMTwukS2+musvAzstN+NbvwjlhGSFBgf/CEe/viCVCVJq/3O2ONh5l3ykLCZpCHl44hnnflHBP4v0scfMjQytjS4mK+uznCRMK+PqmTL6vfpFvClOI9InkXJma4IEzPDYlA4vdQkvhcVSWbu4a7yowOwcfZ19xG5d/vsKTM+KYk3YtN3DrGuyCXkTxGRCWhxT4ZGUGvUNmfi9oResYgD+ehMA0l+FEyXYCxjzCvtOXQObGjOU//us3KEM35qvbOWlJZPLoUf/6425wgxvc4D8J/3QxNTQ0xDvvvMN7771HVFQUv//+O1OnTv0/eWw3+C+w2R009Zm4UNeLUia6XkwBFHVaiPOXIxEJeXRKLIE6Jd+eqsVksXPvxGiae42cqe1h5z159BktbDnfSEaokGd3ljBktvDsrHhK2wZp7BlCIREwMtyDaD83vj9TR06EjjnhXjTta+bXc/W0GSy0mCQszgyk4tcX6BtSMWfZg2SGeXCxoovLn5aw/PmRaK5ZCAOEeqkAV5/EG/vKqWzX892tWXi5yUgN8mB2sj+v7C1jyGzH2w1a24foLu3jZJCCQ3XdfLpqBMKwsQjqT+HdaWVn300MncrneeG3fB74Go9OjUUiElLfM8RkfwtT41zhmzNsh/FrOYHf9J0ce3wyaqVrEvPI1FhOVnbRafAnwlPNFycLeXVBDgWN/QQyi452MZl+DkyCVp6amkmiLpWfvnqRLmM3x2VZ9PTDku4KMBtY/U0JY5Os3DoqicxQLQeKWviwe4jlWSG4ycWMivTi3QMVWB0O4v3dKRQnYO7WEXnvRb669BMVHd48Mi2WF3eXYLHZeW3hGrAtwykUIYicgPPLcbwy/zO0waORDPdS3DzIfVlO2hjAs+F14EvmpPpzqOszYrynYrM7uP+3qzw4OYYKg4yj0T8zUuBFXXcbrf0mDpW0E+Pnxu5L+zneIOJ73xpUkQOMighi79UWSjotRHkns2v/FToHtASH5dBtsFDUPEBx6wBBWiVzIwR05YZitNopaOonV9EPFi/EVgOZvXsgZBXvLk1j9759dO15iSPSNERhozhQ0sEPt4/E7nDSobfQ0mcicOoz1Fiu0mez896ydMa+dZQPl6XyzoEqHp4SzY4rLbjJJUhFQg6UtLPlfCMeSgnrx0fRNuDJ5AVfIq1r58TQZMZHxeLW3I/BbKOt38S4GNfOZZBWibtCgt+1HKC2ARMnK135Us29Rj46UsXL85KQS0T8fHsmHj/eg3bSC9yaksD55kG4/CPC2e8S2gZR3i7546PZStS/vo9oeDY6tTdfT/8Eo9XAobJ2Pj1ajVoqwc9DjtHqwNtNwWNTY1m/vYjcpDdZI9iLzWxgjKGM2NRs7BIFv19t57PVWWDchGWgg3c27SAzezT7itsRiQS09pmQSwQszAjEZLVjtjo4VdXFkMUlD/R2k3K8qos+o4XJ8b5oFTIcTkgO0nDowTyOfv8CI2WNjF60AYVYhFouuR4A3jtkprHXyHuHKskO05Ea7MEfRa3sK+kkM0zL6GhvlmWH0GesweFw0O8eR5TGi3f+LKfbaOb2vHDCPVUUtg4yNy2ApAANvhoF3emLeXJnGa8MmPDXKLh/Ugyv7i3Fy0NBWqIPElEOB8o6ef9wDdvX5eGhkuJ0Opn98SnCvdQI7HY2rh2FXCIkyTeQsg49eqccXdxMAJ6JqoOao8DdAMy3H8HSUIg+83PE7VewT32bpAhXAbGroIUrDX30D1uJC9RisjqQ2kMo7XSwf/hNHlKNZshzDsXHa7glNwyR1cCh338iZMwKbs4NJzEwDYC1P1xk1cgwfN1k7C1qx2SxszgzmMWZwdjMRnpLT3C+PN1VTP0DA53N2I68huf8N0iZHEx52yBx/u6sSljFrBAzCATIJSKemBHHqEgvELuUCZNbvqRleCQhkYlkhOqQKO/kUvsl9tXvo2cwmXs6vmLrmQiCk1cgbWrD39nF331pG3qGKGruJzVIg1IqZsYHJ4jyUXNf4Cxi3OygDeXdAxUEaeUsywpFp5LR0m9i09lepub6EBaUDXaLy5gCsNWfA204tJlpkwRx/2/lvD1BjDIgGB83f9ebKjQY/EZwttXG5H/rTe4GN7jBDf4D8k8XU5GRkej1eu677z5WrFiBQCCgsLDwv3pcSkrKf+PZN/hf5c+iNroMw3xzqp7jj01gW34T8z49xQMTYwjxlPPi7hLumRhFgIeCZ3cV46+Rc7a2h3cWp1DbZeBCbQ8lbYMszQzGMGxjVIQnQiFY7HbSQ7Uszgzm+V3FlLUP0tBjotvgeswfha3oVFImBrRTkHSGly7k8PyceE7VdDMuxpuWobEILDICtSq+OlHH0YouDrwxGvk1a+BzNT2cq+3kRFUvP68dhVQsZFSEDj93V1ET6KHklWsW0G8udl07m87WERaiZvnT2YilIuaPCqFzcJgnCqK4fcwURs/2Js3moLfHi+FODZNVvjy5vZCUIA9uywvHFDkLhVTE96frGZuzCondJVU539CPxe5gRpI/h0s7+OpkLcuygrnQdYJydtExkEpykIYs92DoKuOgNJoLzdUsic2G0l2sWfMw9YNOmoYcWJxmmPcJAB8uVfJT2fM88vsV7s26mWGjgckBUqK91Ty2rZA7Qy8TU3GF2OXrWPrFGd5fNoG67iFqOg3khsQz8WY5EZ5qArUKbDYbXYPDWOxOln91im3rcnnBewNjewOY4G2nqGWQy039tGevgoEWpDLXroJELGJN8hye+3mY0PL3WOmmQyaJ48ezDWSFe7qKuigv0kM9OFfbQ0OfkQ7LLrICQ/lMdBuKQiGzUp2Utulp7jWREerJ97dmsX5LPocrOgnUKmjrH2Zaoi8jAuQ4PhvBguW/IAvLZtBkxSq9C0nrJZxnPsbSVY0scSENA04KTF4odZlkRCYzJHOjtX+Y78/Uc6qqmyGzlbvHR+HvIeNM/yk8hRlolVJ+vjOHhAB3HAjICtMhEAiI93ejvM2An0aGm1xMcoA7h8s7MJptTE7wZWS4H+DqWdt8vtH1e1mRwsbLB0jV55Df8ydjYxcwOjqYwW4T+zaX84fJgFQsoMtgpmfIzL6q00yKSiPM1wPuzaes106Ul5okBAwceY+91jHkxcPBxr20tyYyJT4Q0fp8Chr7GRsDvgpf5u6fy+rgj5kd40Oom4J2u51dV1p5f1k6HkopC9IDKGjqQzr7Pu7deJbU9i7mT/LhnWNNaCV2Vr67nVeXjiRY5YZGpWDz+QbSQzzo1Js5WdVNv9HKC7tKCNIpKG4Z5KZRofxR5Cow7xwTwaDJdr0Py9tNTqinku35Taz2bSR2uJiWWd+jMFlZ99Nltt+dy4DJSnqIlonvHGNKgi/ucgmt/SYenhrLpHhf7ukyEO7ler0hs40eo5nSdgMHmMoobTC5Ud18fbIWlUzMu8tSkYpEPPprAWeru4nxdePu8el8dF8y53+tQTk1BCcQma8nZJoHEl0A5N5LTLeBMdHe2B0OqjqMRPu68c6SVMT9Zgp/qaPkVAuJowMRCAQ8MyOeM7U9WLrriI6Jh5BRoAm5Pk4qpzyI0mHFQ6LgXutG7GZ/wFXUVHcYGLLaeXluEkE6JUNmG0utQRze/i1PSibj55NCQZuZxh4jdoudT27K5dVdMv48Wc+vd+cyZLZx7OhBVo4cQUqQhrHeRta3foDUnkq3XsHgsJXfTlzhpvi5zBnr6r+6+ZvT+CrgrVV5HKjsQ9svZ7JIQknrALdsuMiZJycil4jwUP+1U1XVYeCrk3X8fM1FtSj7DZ79vZp906xsu9zMgvQYkpOTcTgdOJ1ORILVLBru4429jSQHxpGS/Ffv5qmqLspa+1nhWYNGEcGanFCsdjuC6FmgtvH6zvNUmXUIBa7Ca39JO6FeKn69KwtvzwmgVFBxro3Bnhl4nt1BUrAn4WNnwed5KEe/gK9aQ9DuVWxMGM/ts752valUieecV3ju2jF8eqSaAK2cBen/MXIob3CDG9zg38o/XUx1drq0+2+99RZvv/02Tudfdm0ujb0TgUCA3W7/117iBv8LbD7fQICHnJ3r8zhQ0o5QALkRnmy/0ohcJKam04CXSsoPZ+rxcZPSOzTM1zdlkh6sZVdBC9XdQ7wyPxmj2caKr8/x2oIkxsX64KWW8+mRKnYXtBDmqWRaoj9mqwO5VMT2y80oJCJ6hsycLGvBPDDAr3fnUttl4JlZCQiFAmTR4ykubiPRakcpE/LGgsTrhZTT6eT+n68wJtqTqYm+SMUuuVCsnxvjroU/PvrVRZJCNNwy3WWV3j9kYdvlVnIjPYnyUTF89HuCc5fx0eFW3GQi/ixuJ87PnZ1XWpCIBFxpdOOD5Z74uctxl7su54Wfn+apGXGUtg2QG+XJkERNY88QRouNQZOVvx3ZgnA4ieXZPvxQfz/zQ/JI8opg/eF13BT5HIvdGthe3IcyNp38YjEfGgq4r+MrZLNieOOwgTmp/qyKtsMPC2DpRjIaviejt4v6CU+RX6gnWe7BkrExOEp2sDJ7HE/8Xsqr8RFIpELcFWKUUjEny9sY7OvmxeVjMNuhqcfInWMiWPDpKYpb9Xw81Z3D2leROL5nbJw/m842cKWhj5vzwrl/UgwfHqqkz2hlQuwEfttymY9XZpDpN5Jta41cPlBJbHgwWkcHX92USUFDH1cvn2fe2BGcaYIlWSHE+rkBrn6UP354C61aQYjuXt5dmsZYXT98N52O2T9S0jzAiuwQ3BQS3GLFlLfr+b20j2PCN1EecfDdbfD6jvPcP/gOnbErMXbAi+23cGdxHyaLHY27hpmxAdgvvU3HlE94YkY8nYPDCJxgtNrQm6xcqu/ji8Ur0Sl0mCx2Xthdwt/mJzEm2guZsYNJmi7w8CXAQ8n5X9/lDuEgmRNe4u5Nlxi4JtMr2vURQ9pYcsZOI0ynJFinpM3Qxs6Gb9AII3D3rOd4bS3jYyKwmm3YlCIcYjH+7grcFVKUEjG7G17B30NKTkAOzUNCntxWwKPTYpGKgrCNfAe53oy1ZBvhzRcoUz/HsNVBVYeBDw5VUtrSz90Tojm45CBSkZQrBxsRDDlJyfKjuH0AlVTE2LeOsjonhPI2A1svNpIa7o1v2nLU7hp0qg7uyA3hjNqAr48PYpmEh1YGc7qqi4RADS+feot0bSb+shRK2wc5WdnFnJQAXt9XxnOzE/BQSPBUy/j4SBX9RisfXJOdHirrZMOZeubfO5rfInX0nG/lsamxvLkgmdZ+E3uL2siN8mLbujzCvVTsLWqlS2++Pu68faCChekBTEnwp6CpH6VEzMaz9azJCaW0dQCFVMSijEAe/eUqdiekBXvw3Kx4fstv4mBZByNCdbyyt5S7wv0Qy0RIZCKisn0JitHx4M9XWJ0VQKaqk6dnJvPa3jKOlHdw6JHxjIzw5NLlNupTlNRWd/BEpi8/X2wkSKugtKWfqaVLKRr1Jj93hfHqglkAtG1Ygz16GkGjVwMgv+sgCAQMW+3IJSIemRYLwOCwlXM13eREerG/pJ2IkTNRyaXUDwlZOsePpcCBb0vwCXXj5WV51wOCnX0NTL+8lvaEY7grvBnS21DHTQeZG5vP1NDWb6J/UMCzPWF8M9b1+S0L0aNjEIDMuHCqdI+DREGUuZB3A46z8UwEN40KxeEEs9WOTi0jzEtJRJ/qem/shOQIfg3xRy4VUdI2yOQQIbof1yCc+xHdbgnsLmzlllFhCAXN9BjMzPvkDPdOiMRTLWPdhGjSNQZiBs7x04UGNp1r4K3FKUT7uNFTfoZOI4TpRCgsvZS2uHGxrhcBAkrLuynsk/H7gxMIda9E1vMI7ZLxdEtd8uHyObuIC/Lik2SwGs6wQPQv+3j/kQt1PahaxTeKqRvc4Ab/afmni6m6urr/k8dxg/8Bm+7Iuf51l96MWi7miRnx/JbfyNv7K7lnQiTebjKUUhFysYIX5yVR3qpHKBQgEQm52tjPR4crSQrQkBup41hFJ6equ7lzTARxAe5oFGJkEhGVHXq+OlHL3FR/2vqHCfNSkRvlhZ8mkquemThx8vmxGkQiAWvHRHD3j/mMjNAR6NHJoZJOloz4a5VYIBCwY30u/hrFdWOLgqZ+bvnuPOeenoxcImJMjBehQW7Xn3PX5nzi/Nx4bFosZypbCak/xh9uo6ns0HPvxGjGxvjw47l6frnYRGaYlj6jheGaU4SZOtlmzuJMdQ/vLU0j3EtFapCWL45XY7TYECDkxXmJPHXwS+r724mSxhCi9WN53wRy3YJ5t8lOjncsc1LCQRzNxv2nSBJ189LcBF7/o4xJszaT5OPB56vtyMRCbCYDzpSVSKRqEEog5z7C/H2p7BtALHLA8ADCij0snD2J0Hk+ZKRNQyAQcGteOE9uK+TlcW68/nsnv11qZHtBOxa7g9/vG8PbS9Jwl9gR7lyJVKakoHWAz453MjvZnyhvFW/sK2fTJCv3eDbhnLCMwuZ+OvVm3t5fjq7zAsuHtzJBKgK3J3FumEXQrO0Mat3xqrjKcGEb2uYGnrs4nbfznISaSiH7TrIS45FLxYiEAsbGeFNQYyU1Zx1nm4w8NiOWGF93DpZ0sHhEMN+eqiPGx43nV09HJBTQpR9mSmoYpqbJmANH4unhzy0hvhwv72JlTghrRoWBvpMCzWQ+21XMtzdnU98zxJKsYJzAqq/PIRcL2Xq3y1nxw0MVLBkRhNVuJ++NI6QGqBgprWft6lTO1nRRYgshVOe6lj5akYHJ4iqmdGIjcpkDm8NGmJeaIJ0Cc38HryY9SWpsPFkhb2C89tiOej0qhDw+M56UII/r194avqW608DOspP8erGNnIhkkgI1zPzwJFvuGEmesBTKGkHkRq2/Gx16E+NjfYn2VnG8qpu7xkdR3mbkqW3neWpmHCNCtQwOW3ljYSr7itp4e0kKP5ypo6HXiLm/nU9O93LmqckopCJCHU38fMbEPTPG8vOFBsra9Dw9K57ndxWzICMIpcKEu7uBGbEB/HyxCR93GbNSA1iSFYxgeICXNv/B8smjcJdLKGkZwGZ38P7BCpZlh3BTbhgdg8PMzwzHQynl+V3FlLQNMivZD5EQcDqJrNkI8gXY7E4CNHLaB4z8XtBGTaeBfYXtXKzrY+3YSEaGexLgoUApFbHxTP01QxQT8QFu5EV4U9qu57PjNYiFQiw2OwdK21HLRPiEuvH65qsoQtTcOjoMtYecqQm+hJgrYd/DcO9FVo0MwmZ3YLU7qOnUs+dqG0MSJ7dNjUItF7P1rlF4u8mZmRJATcIedBpf8upa0Xe30WZT0x66nNDgeLAYoXgbpC7HYIW8N45w36Qo7hgTCcDT24roN1nIifSi22BGp1LTY3BytamfAI0cd4WYS55Wpqtq+fKEmbvHRQGg9ovk8bBfWOrQcvxiI4cvlvNtXBsIRawfH4Xd4cRid9BxzQAEYObU6de/DvdSE+ihZH9JG1O8PdjjHEXBxSYivFRUdhoobR3g01UjmJsWxNw0V+FR2VvJ5vLNvDjqRQQCAfdMiCLUXQC+KWCx0GMYpqx1AMOwlQC5lTHRgWi7LyEXR6OWi6k9u5N0/wDcRj5FTpeei3Uuud6T2wpZlhWHU1VPpLOB/KpB3NVuqCROHshU4hRHUN9vwWw3Y+6rxpp+J/5Js/A/+wmtXX3M/+IiXy2JYGxqLBK1N7r/zr3rhTmJOP47P7/BDW5wg//o/NPF1MaNG3n00UdRKv/rhtob/P/LqhyXte43J2r48XwjK7KCudzUj9Hq4NnZiQCUtA7w1oEKrjb3E+fnRmqQB3a7g3O13ZS36ZmdGoBaLkYtkxDv586Fuj6GLHZSgjTMSfHDXSFlfnogconLFh2rCR9tO2/9qaepb4jsME9i/dy4Y0w4K0eG8tWJGkRCIepru0P7ilr5+WIzG29z9UlVder57Gg1d4wO4+EpMcivGQHMGx9OWesgP55rYHVOKHeODiPAQ4FAICAvNhBif8HWbeBm9SCpwlpaPl/LhGVb0HqXMzDchVd+NPrGeuTyHvISvYjyURPupaKsdYBwLzXDNgdeKgkXGwew2BwsT83BT+WHr8qXpd/sIz0slejECUwxNZHf0McLu0p5eGoM89MDiPfXkNywiS1hnVwdDmD3mUbm5rqkiB+eaqF/KI6B4kusTZ5I9IH7keWuZ2pAGlPD3eGPR2Hup9BRiF/hz5T1jsMRN5v81jLmxMpQFHzPB+teoXFgGIVMQuy1PpwomsApBzc/cDhIkvfzUGAFurAk1HIx392Sxa8Hj/FHnRcb0oVUtOvJCPGgrFVPunccLR63EZOQAT+vwpz7NzZ/u5u5T62jffw6BF355HrI+LZASG17D/bhAXrqe8kaMQcsQ+B00mUwc9uWEnbdM4U/95QwbHXg496NwwGdgyYemRxNQqAHOpUUsUjIn0Vt7Czo4M1FdxGhlFCx9WuOdOVwf9IwAb6JHCrpwE0hImPSct4z27h48SxPHjfz0twExsT4sOve0QwYLXx3qpalmcEcKe7kjgQxCZnBfH1TJkfLOxkfqsHYVsUT25pZlpnG+HGRPLW9kNZ+E7mRnoCAu2Y9SXV/NTO2zWD7vO24Sd14Y++HtA938kHsSCx2O34aOZcaesnMCyAhz2XI8uLuYuq6DahlUl5bkMSjvxaQE6Xk9nHejAuOQyQUcOqJifQazazc1Mf7ISJ8F39B69Fq9hf3Mzbah0UjQhiyWBEIBIR5qZie5I2vu4yFn52he8jCg5Oi+PNKGyvj/PhiTRbvHqggXXCJX6e4o5KJOVnVxdZqCQ9nu3aE4vzcOFrRhdni4NGpcWRH6PBUvwrApfpeeo0WxsV48dPmEmJUCnLn+hOgFqGSy7h/UiALRwRhtjk4WdVDc98wby1JYevFJroMw6wdHYnBYiM1yANr/XlGqh2w62sQirENG3j7QBMfL0vh+R/241T78d7SVE5X91DQ1I8TJ8crOonxc2NsjA9KmZjOQZeD5/gYXzLDdcQHunO1qZ8Ltb0kBnpw97hIKjr0CB1QZbeQ6HDwd1FDnL8bBR1RTF17HJvVTku/iYZeIza7g5u+u8iyzCBenBZHa58Jp9OJSibmrT/LWDsmElTeuCukzDT9wcDec7xiexCrXcdXOcFg6oCy3RA3G7VSy6NxVezt2UDznod4dkYWSzKDiPJx9ZuuHRuJw+FkyGJjaqIf9/10mbHRXsQL60g5/Qz3mt/HbHUQHVnJH2XFpISPQC0TE9R/EblQytuWRTyGy83veGUnr8xPwV0uwel0sreolfGxvqhlYpwVB2lxaME3gc3nGnGMDMaqCuCDaWEkB2v5o+gK/UYL63/M528j7TTb3EmJj0Mj0xCvi+ed/RXI3Ks51HCUDXNew1MXxkDpIbYarKyz70DYvpy28gv4xkpY0/sJgoBUBP7BnDl5BV9TPRx7mvCVv5IQ4I7R4vpbiFKZeMv3ALvclvNiZCHKs+s5wtP4V+1EPus1Dhd+zcnz2xDWj0IoUbBOW4VOKCJADUvi5azbVsev3v4kBLjTNmBCKBDw6t5SpiX6Meua4RFAhM9fvb03uMENbvCfkX+6mHrppZe4++67bxRT/46oaNeTFaaltE1PrI+au8dF8sGhCnxKhxBEq3lsWix/FLXS2GtkSoIvnx2tITHAnW9vyUIqEfLR4Soe2nqFj1dmUNdtQKeSopaJyQjT8fDWq4gEsCAjiNu+v8h8TSVTmz6mxeszPliehlQspKF7iJtzw/mzuI28SE+CPJRUtA3ioZDgoZDgpf7L2t1DISE92INtl1spaR1kUrwvapkEjcTl4NVjcE0kh20O3tpfyd3jIsmJ8OSjw5VE2OuZPWkCNqOUgoA5JEtFSIeH8Kj4nQTf5+iRj8F7VAB+gJ+7gi+O17DxTB1nn5rMC3MS6Wipwz5kQ5i/gdQRq0F8zYRipg5flatVe2DYypDFTpSPGplIyK15rqwhNLPAasT33Leo+tog92vO1nQzPy2QYYudrYfPsKMQwoOeZnXSJJyf52KOX4AxfC5Lvyzm21tGckLcRFmVhIWRdi707mBAGsRvbVNx31pIerAHuwtbCdA7eX5ZKr7nX4WY6TD7Pbi8CbFXFLW2NoovX0WoCSQzXMef7W54e7mklFea+rl5VBjz0gQEaBX8dN6Ty9VCcjOfxBiQw6fuajxq+2geGMZmD+KRqZP4MdN1anf+4ENqTQ9ZYTqGNi6lX+qH0DeB95as5pkdRfxw+0hsdgd2p5Pndxbzzv5KDpR1MCvZj3h/DekhHkjEQtQyEVsvNZLlMcA9teNZmywkMcwToVrGd2fq6Dda+OOBsWhsPSQcvYM58RvIjfIGi5GijQ8yPPJB8huHWZwRwI8xpSgSgxEJBaSHaEkP0fLB5h2U6ZVMTwzh9jERmMx26rqHiPVxu95TRdMFwvxTeGvcWwgQ0D/cz+MzX8LhdGC02Fjy+VlW5QRT1TlE5k06zredJ0ITQbSvGzgdhHm5oZCImBTny+1jwlFK/xoaRUIBHgopS8am4pHikpTlRnvy88UmugeGyD55M2TdCY2+uAsE3FdwM4w4yGsLkxkwmgn1UjNKrqR6aJji1n50KgmmwJncujmfZYYy0oK1vLokg4QADZfqe/n4SDX9RgsCIWSGa3n8t6skStro1RtYMH0GqUEaStr0ILQTHCBn9Pvn+WntdIK8VAwOW9l1pYU7x0bw4Yo0Xtldype7KrhnQTz6YSsv7yllcpwvvUNmlP29ZEfFg2YRxYoReMpkHHkkErlYyFsjDCgTI5BqPEgK9GBbfhMlLQNcbuynvMOAttWC1jDMzbmhPLermIbuIYpqGzGL1dR1DfHi3CQEApddufc104+tsd5UnG/n1GfFLH06i58vNHGloY8Dp7uYqI3jl65eUmM8UUjFfHdLJl+cqOF4ZRdv/llOqE7J20tSMVkdOJxOHv6lgCQfd17NGYn7hW/47M54jtXoXYs5yiBY9SsDRisDPUMsHzsG94EQ2tq1GCx6TvR+SVro/Xx8uArdYBG/NetwUyr44faRfLQ8HYFAQI/Bl5esobyZGEiQpxK5wp2f88toFPay/MA5jqRfQRIaQ6tPOvphK70GM52DluvXzIZTdewvdUmSo3zcuNzYy21nHDw7s5uilkHWCIWcqOzijZkhYBnitjHhXG3qx1+j4GzBH+zsC+Hr+Dh8Vb4sj1vOB72bEErs3J45GU+1DMY+yrcHK9A7Bgl0l6D3iMQtUoDGPwJhYDrI1Jyp7iJ47lN40omtv5TOiitABJaiXUQH5NIzoKelpJKTnj1oR/ixOSSUz6eM4UBxAi3Hqlg6ciF6i4meQCWBHgrGv3uUs5FVqC5v4pVV93PvoAmd2vV38u3JOqRiITePCsdqtzPt/ePsvGc0Cum/Lv+7wQ1ucIP/LAic/9j89N9BKBTS3t6Oj4/P/+lj+nfN4OAgGo2GgYEB3N3d/39//94hMzd9d4HPVo0gRKfEYrPz9fEa5mcE4adRsH5zPokWIZnZART1GKjoGOStxWn0DVk4UdVJQeMA9b1DeChlSIUC8qK8sNgdHCrrYHl2CB8drmL3vaOp6hjkwa1X+WrNCCrb9bjJxWhlTiIDvDCYbdz+/QUae42cfWoyT20rxGS18/rCFMa9fZSHp8SwPPsvuZ/D4UQoFNA/ZOHz4zXcNjqMJ7cV4qmS8Y7hSch7AGJnAC6XtS3nGjhZ3c28tEBGR2iI3DkPYXAWFZkv8rffSwjxVNE9oGe8qAhhzFRCvN356HAVCqmI727JxmZzUNNjIMJThUQscmWetF52yX/mfED1+b0IA9OIiE78b37GV5v68FLL6DaYefPPCr6Y7YWteCdP1aWjR4nBYmdMtBfhniqGhs3E+qjIjPTF4YRfdu4kNS2LpPKPGOppxDT6Sbz9w8BuBjc/3t1fztRg2HKlix67nPeWplHY3E+AQERIkBzhzrtBG8IHglWEeMhYqCgAt0AQCtluaqJvuI+G/l7ClWnI7DEMmKysGx/Fmm/P46EQo1XKUMpE6FRSjGY73YZhDpd1cnNuOKXNnayVHyNp0grQhnGsopNwLxWhnirO/foeFk0oCTHRnOrT8dP5RjaMbMVR/gff+jxJS5+RHoOF+ydFXyu6Jbx/sAKxSMDDU+OQioTUf7OauuAFRGbN4MvjNbyxKIUBo4WW+mqsbv5khOqoae3hoe2lrBoZyrI0Hxp3voRq7D14+gaBwwEn3qIhZAHuvuFoVVJe2FWMVCSg32ghM8yT78/U8/t9o+k3WjlQ0saKkaHoB/pRfZ2D6KZtOLwTeWzHQYadRj5ZNM9VaAHrf8xHP2wlzl/DM7PiefLkk0wPm06KbhSrvznPF2syqeseYt2P+ey7fwztgyaOV3YzK3CIH48UcOvCOfh6adEoJPD7Q5A4H1PQaL4+Xsl6/UeIU5dC5X7wS6JeHMGLF0XcMSaCqk4D1Z0Gnp+TwAs7rhLqrWHd+EicTiffn67jz5I2hswO7pkQyYzkAGo7Dfx4rp4J8d7E+WlQyyR8fbKGKHE77R1dTJ86A5FAiFAI+fV9pIZ40NRrJDNUR9/AAIhlvLW/kvsnRxPooaSsopu6U23MuC2JOzZexO5w0tBjZE5aABfreonyUfPCnERGvnaYBekB13e2/8752h6+PlmDwwG3jwknL8rljtjVpMdqsRMQ6UFrvxFd5S/sKDMSPXY5meE6TCc/oVqVQXJG7r94vd72IaoutDNybiQWm4PdF8roaqmn1uSLxWjnlZszcFe4Fgq2Xmikz2ghWKsgQKukfWCYb07W8sSMOKw1gxgqG0hKU+ATF4RE9w/Oec35DHfXs/y0L4EeCj69Fm4OMGQdYkPxBm5JvIWvjrfg232eQB8PvOPHEu+nYU9hK9NFF6ktzeee5klMTfDhaEU3m27PpktvpvnqMYYl7kwaMxqAJ7YVMSrSk1UjXWqBlj4jOpWM3QUtnK3pZk1uGCNCXSK4ny82MjJMh1gkJFinZNBkxf3PB8ArEsY88i8+p7/3IAP0tjbz5sZ9NHnG4+2mJCvck9U5ofxwpp6WfhPrxkWy4XQtxyq7ifRW8faSNJcsestlZib788jUWA4eOsCHhQLunZzA/lNnCFA6eXTeSIx7n8Yy7W1+KukjMrSVaeFT+OFMHe8cqGT3vXk0d5t4YW8JM5P8MJhtrM9yo7DdSo1ewIJMdxbvXsxPs37CS+GHQAASkZBBk5Wntl/l7nFRJP+DjPbfA/+37983uMEN/nPybwrt/fvgfoP/e2iVUh6cHHPd4vnni00cKOvEYnMgANKDPRgzJYzwEA0X63sJ8FCwt6gNT7UUD6WM4tYBdEoJWaEeGC02Fo4Iorx9gFtzw8iN8GRFdjBWu4NgnYqf1uYQqFWSHOTBB4eq2VrQBcDHh6sI1yl5flYiFpuDO8dEMCc1AIVUxK93jWJ5dghOp5O/7Snlvf3lzPzwBHVdBia+e5wLtT0YzTZywj1p6jPCjLfY2htFa7/RdYJO2HSugfRgD7zVMqL9dQjnfIA9fh4nqrrQKKW0DgwjlMjQh03Gigh3uYRgnYLUIFdQsVgsxFctY/SbR/mjqBWy76Bu/GPcoRFjFEuRNZ9moMUVVPripj+5ePYoAF+fqGH75Wa2XmzidHU3fS0GdCIhMqcFna2b11aMZsBk45HJ0WSF6QjzVDE1OZCihg4+PVLF3/aUsnrmBJJlbbSrYugW+vDrmXI6bApQecOxt/CW2RmSebE6rJ93g08hEgq5a1M+O+u6EUrkEDyS1pZWdl5qprq2Fvv5r+iSB/J7byDRumiSvJMYERDDmMhwIrzUFDT1seV8HZ+uTEcuFiESCsgO1/L1iVp0ailLMoP5fE0md470ZkXQALL+SuqKz3G8opPxsT44HE72XGkgc+gYY5Mi2Fgho6G/B4EQ9hqiOaKahd3u5I2FqbwyL4lHfy2kvF2PViWlsc+EQiJCLRMjFQtxW/YVE6cvQiUVEmappHTrC3x8qBzZkWe464dLDJqsBHp7MC3Rj2GLnUstRsRTnmNQZuW186/hEADjn+S103o2n6+nrd+I0WLjQn0f90+OYW5aIM/PTUAsErKvuJUfzjVgdzg5WDPEHPFn2LwSGDRZ6exzp75Vh8FsY2DIxOmf3+L12eGMjvYizEuB3e5grOZ+xgePR6uU8ujUOPw1csbFePPj7dkuN7P8Zs7X9jAo9sHu5s/GS218d8oVjNrpM4pHDvdj2bGelOYt6Mf/DSIn0D/+WZ7u7WZTowgQUN1pYFqiL1lhOnYXtPB0x0OE9p/jzX1lPLezmJRgDanBWnzd5TT3mlj9zTlOVnfxTGwbHqU/cd+mCyikIpZlBTN65ChuW7qIAA8lD24tYMPpOqYn++OvURDvrWDgm7kM/7iC/vwdaOQS3j3gur7jY72YeXsyRS0DJARoeH1hCnvuH8MdeREuuZ3TiVgk5Ne7cnhyejyt/UY+3lnK7i8LcTqd+GvkjI72YXK8L+9de02AKrMZua+CgqY+ajqHkGfeRETOXAK0cvLr+zipD+D+g0Yu1vWw92oLD/58BYfDic5Pxci5kVys62XnlWZ+vjrA3CnjCQ9y52TnAJfqe2k7/QlHf1tBccsAfxS1U99jpL7HSFKgO7OS/An1VJGVF4y/9wWsVz7E1NULlzf9NUjq2xEP1JMapCEt2ONfjJ8qiYoY6WJqOm08PCWWmBw/Pjd8T2KAB4ZhGxvP1vPHUDxxo2aTHKTBUyll0Yggyit7+fTPKgqMOlqH5TzwUwHLvzpHbZeBKfF/LTA+vfkYew4eYlmiiqdM79De1sSQ2caOKy0sTfYkvOBtgqVG2muq2Pv609gnvMBA0i08+vVeiitr+eJYNZ8eqb5u6nTy5FHUChG+Ceno1EqCdEp0Kgk9BjOjojyZkuDLkMVGU98wHyxPJcRTxfM7iwjWKdlyx0j0w1Z+v9rCRH8L70/zIrb3MPOSfZg2eQoCsRKVuRtnTx3OtgoCTIHY7A5UPSV8vCiKMC81ab5uzNO4uzLAzHbmbKhioOEyEdZKTCYlz2S+yc5LQ0jFwut5ZL1DFvIb+l3j+w1ucIMb/D/Avym0NyYm5n9YUPX29v4vHdAN/vsIBAImx/te//+C9EAmxfkQqFWyr6iNvUVt+GnkNPeZ6DNaqWzXU9yi50BxO419RtaOjcAwbKV1wMTzc12r0AqpmNbKfKJ8xrL1YhNjo73ZeLaepl4Tn692rerGBbhx8yjX6uv68VE4nU4+OFzF5vMN/HhnDgfLOlBIRPQaLfQMmckI1QFOiloH8FLL6Opo5eO8YdQxI/BUyekcNDM90Y+XL5k4XtGMp4cbB0o7iPFxY9MdOZyu7uZ4ZRezUwM4ZwllwGRl++Uq9j0wFnrr6DYIsCDCOzAQo9nO3NRARkd7Y7M7OFDawZQEXx6aEsOYSC9XkKXAhL5xJcNmEcGrPyP42ueX4inAx02BxeZgZ0Ero6M8eW2hqy/qysFG7gjxQxYQAgFv8NovBWSH68gM96SjqZq1W2t5d3EydcVnuX9WNvbhQfh+LflBq/BtP4kzbi7N3Qnc/v1F7p3swU9lGUhF3TgH+6hqNfDSqEBEUhGfr85AK3OAUAi591Dd8AOvT/HndBfcpX+KqY0Oztd183BIDCFKCWHObn75cQcTAwYZHBrNkfIuug029hS18+SMWOwWC5+NMiC2FOJZN0CQrQHq5Iyq3g+37eeX/BbMvUauNvXxyZFq9GYb1szPyJJryYk0cqnzLHKpOx1Wb3wDspnkq2bv+WIEAgFTE33pGDCx+VwD39+aDZ0V8PVkfo79gMoBAdG+bhiGbbhpvCg1J1PZbSLmjg2cELsjajqDwSOB+RzHqLfy/qksJsT5MDpOjt9gJ8LKgxA7jY9XZLD4i9M095m4Z0Ik52r6CNapyC/rxD5ogwiYlx7EiFDXKv/sICOBs5P44s8Kpo4I5KMVGWhVUmx2B+WtHfh1ncGmn09igBaFVMSZ2h4e+bWQpAB33JUC3jt2Frkigdyw6GvXLby5KBVwSfxGxQczOGxFLBSwq6CFk/UxWFQOlImzGF/xO1x4F6a/xh9FnVwujWFGvIQV2f48vbOYqYm+eCglXKrXU5byNKdrbUglzbjpgmjpN/H0zATsDidWu51eo4UQdzHtFedw9vbwvqYSKof5/bKeU8YgBk1WtqwdxewUf6J91HD6I9C38YFtDSKPR1kbZ+WZw4Motb28uCiTivZBNp9r5OX5SQyZbVjtDmZ9dIJf7s4lSKskO0JHbqSSu3bcwr3RdyH2GcXbR47T3eNGWpjLijzEU8UtuSqGrXayI/6yGfjhbD0rskPp1A/TO2RhTIw3O4p7mGQXs+1yM/NTR/FksIN7frrCjARfTNeChM02By/+XoJGISbMU8Vv63J5/LerBCmk7H9gDHdvvsxG8R6SvCP5s3uYm0eFMCXRn/WbLzOxcyO3tZ2G/iiY8SbOkXfQbbMTJq6BxmoAPjhUSaRXGnPGzWJBdQsv/FGFTCzkplxXaC+DbehbK7Hao0kN8iDRoOcpiWs0cFdKuGtsJHF+buAZyxMVHyIMmUSvLITumgGilDIeWpTJhtN1pCtt6FSuoG9vNzkDXU3oOxuYH6uitF8EUiW+8aOZlRFNw6CZ7Zcamd7wNgqxFPY8hG7sM4xcuAyBuw8XL1zG19bKiRIFnUJvpiUH4HQ66TNaeP6UiQ26eu6fPZJndxbhdDj59VIzZqudUZFeRHsqcOo7eE/4Eag/4NbcMDacqafXYKbXaOFcbQ95kV6Iesr5uEBPoo+Ku/KU/FHexxHTMHff9Ae6PfdiNuVypaaVPpEXuR2bkXrPprXfn4d+O8OdkxTI7MG89kcZv63LZdXXZ0nyVRJub2ZuaiJXhpv/xT0qzEvFuadvJEzd4AY3+H+Hf1Mx9dJLL6HRaP5PHcsN/gkq2gfxVsvQXcsl2X65hWCdAneFhI1n65GLhRwp72RFdgh3jAnHRy1n66UmcqO8mJsawKaz9ejNVi43DPDwFDkX63pwGHpZWHE7Tze+j597OG4KCcEeCvqNrh4ALzcZz85KwGF30PHNw3TFzSdp9FgmxXpR0NRHUUsfHgoJCqmIlhYT0msrlM/OTmT+p6eYmxJAnK2Ewarvydelc+v3+Tw+PZb+ITOFzQPkRHpisTn4Lb+ZBWmBLB4RhE96ABqFlGGrncd/K+TxqTF8d0sWWIdxfj2RA37PcNAQTm6ylZQgDV8cr2F0tDdPbLtKt95MeogHy7NDOFvTzaN7hjkyvZ+5KaOo7xniSEUnaUEebDhTz9/mT6W6y8CLu0uI8VG5CtVzX4BSR/qUpQC8f7ASjULMw1NiqO0awuZwEuTvz/PZ9aRGBhF/20JKB2WkRSjB7zvO5esZG6klOSaZ+5OjuNBWyHOX72B1zLcE61vxVQjJd4/FEhXN7e/v4omJYSQee5gyvzls6wnj2ZvX8P6m3xiramGu9SK2wE9ZmhXC0i/OMDJCx6qsICxKDV6DZ3A6c3l1QTIeCilTE3yJ83eH9iLMh1/ma+9n+KMzkK/Gagmq+AEWb4SKveRFTsRkc17fDXx9QTKfH6/m06M1fHdLJgWNCaQFOWjtH+beidEAFB/7DadIwmMrb+FUVRfWa3bRh1uERDs8GRWsIDZMx64rrQxZrby9eAKc+YTx0Z6g1LLxWDWrSl7kSsQ69vcnIXOaaeozUd5mYFpCNCNtiUzdaeOXey14qKR8uXoESpkEjUJCmJcbrR16qk+1Ybc4YGQALb0mmvtMJPjIkW2cSfD4L3m/SEy70UJCiAdBDiGfnKzlw7VZVE37mshgb0YDgz0m3D0VnHh8PN5ucmx2O5nhSkI8XCHPr+0tJb+xj23r8jCYbfxZ1M7cVH/c5S7pWVqwB75uMnIivcAUCeFj4Nr60qKMUKYkBCD5ZQ3Huyeze/0aZn10ivRgDVnhnuzrC2ROmpMvLg5wc4aOny81MinWjy0XGqns0PP2klQ2/nmazMbDfO/9ImsmpkP5tywO92XHeQujI7WIe6pZNTIGBALQzoXmi9wX6I1AHs2J8xd4NqWJ0DGzkEhl1HTosZoG6d/9LKPm/o2cCE+0CjH3bbnCvgdBnAS1AAEAAElEQVTH8vCUWCx2C2erotEotQBMSLERqFYyws9VfCz/8izZ4VpmJPvjpZax92orpW2DfLEm81+MS+dqe7h7XCT9Risvz0nER6OgqkNPsr87Lf0mytr11PUM4amSopQImZrgh5tczJNbLzCpfxudfYvo9ugjK9yDn+1PEeyhI77IwIR5XrgrJPx4x0hokYLajeFhI20VfaQk+tHca2RQOQL3EJfbaUagkgCZq/8ytf473te0sNPwkOsgS3bC+S9Z5BULKR8AoExeRFryIgBsXeWMC1TR1W7GqVXS2VjFgWZfmhUWciO9eGi5Kw9vcrwvO6+00G2wsOLv8r78P1E0HEHsdwuy7j6QjIZR6wHwdreREOBGw7AXj9dm81ZcNYUHD/HLUBrPe/Xz2sl+5vlrqTRrUUicnK/pYXt+M28vSeXoUzM5VNZOfkELF+t7CdEpkYlFVHcaOFvdzdjun9gtm8OHIVEU7fyE5IVP8ODkGDacrqOsbZA9941hyGxnOOYhnvK7yspd/YQNBHOiuYV6s5lDn5/hE39PHo7uhDFrWPdjPq2Wm4kYvEzguUbiAoWopRJy1CbuThZjczi4e1wUpW2DLE4PZOfVVtoGhqntNLD7aisPTon5n7ux3eAGN7jBf2D+TcXU8uXL/5/vmfq/zaO/XiXW1413lqYB4CZ35RZdbe4nRKvkjjHhRPu6seyLM8zPCMBPI8fbTYpQAGaLjXNV3TwyM45VI8MoaOrnkV8KmZ8eyKWFJ5kvdadtYBiVVExqiIeryf0fEAgFWBQ6dpX04huuJyfSm+D8FpQSCcuu9Uilh2ivP76he4jkAA0rRoZitgXR5JbD7HAvTFYboyO9eHJ7ISNCPfB1l6NVSPhgWRpRPm7ctekiA0YbP981in6jhZEROrRqGf4aBX8UtaCd/jv1zULuGetLpI8bWpWUkRGuyfCAycboaG/8NQreP1iJQiokO9wbe+IkVsnEnKzsonPQNZEvbhnAbHWglomJ8FZxxxiX6URHtw/HKixcqijg7SVpTE7wQSYWEahV8vi2QtaOiWBcuIox8QEgEHC02cmT2y7w5ZoMsg3FLK3+nE9DPiTZL4nKyi4++XOYB0Z/hUygYVJuLK19RgKutlLTZ+f2ETp85BZ60+7FIg2jt7efgWEbVoEU74TxuI+aR+5nZ7k9L5S8SC+mJ/nhp1XzyO2r6BhYxJODJnzdFZhtdsw2O7d/f5Gvb8pE9tAV1jucWA9V8IdRws3LfkLWVw1nPmVfWCiHa4b4YnUmW8VNPL+7mKUZQXip5ZS0DaBRikgM0JEe8tdORHD2HLoNVuhvYrTbABx8HkTr6XfG8rbbE3wcHknotd//39sw+5Th3HJEzB3WJhaNCKIveTdjPRTk2p3sK2rjpiA3vFRyVn57jnW5k7gjp5/J7x3nxBMTUErFNHYPkRzsAWYDn//6Kw5pBK+sGwdAU5+R4pZBpib6wT3nKaqzsGiilbnpgWzLb+aZ41U8nB5Cfa8JbzfXwkNLbT97P7rKLW/koZZJeGd/ObflhfPi9CnXz1MkgDCdy2SnS2/mi+PVnKzqpKXfxKrsUCQSAX8Wd7iKqZ9XUBr3AH5x2Tyw+QwvL0hxOUiOf5jpnpHINEreXJTMz+caMZpttPabOCV3JyXMhw1n6jHbHWw7dYWb+7+hbfy74HQyf2w2tjEHeEwm5mxNFwG5D+IhEvKYRyfbLzXwzU+/cveq5aCLxOkRym87fmWq7Qya9HkcqRsiIFiFRCqj12DmoyPVdA5aaB2ZgcNgprWpml/yO4n0+stdTSqScv+Up3n/QAkrBg8xN80VcF1R14C5/AAzkycT5a1m5VfnifBSsm5CFFnhrr/xy3/WI5KISJ0UzP7idtKCPfjsWDUZIR6snxBFfkMvM1L8qe7QY7Y6sNmdnKrqpm3QzOhob17aXUKbwUGXLpPxs+MI8FGz8MguNraHMqnmTZyRU3npiJzHpsYSpFNCYDo/Nuk4V9PBiEO1LHtKx7otl4nxdeO9a+PhWPMpuPATROxCMOYhIpx2HpZrKGkdIFbijlgTBOMfo7FniBBP1b8Y3zr2P0m9NJ3Ky5NY9NgIPOa/QXLrIKsGT+Bo3gXacdg8wln8ZQ0PTIpmRJiWirOnqDx3ijkPPYnFdjtHztZwoKaVxd16wrzc+P50PX4aGWa7AEne/TxreJUrnUH0uieQF+BFrLOWw7dHMu6bWh6a7E2snxs/X2wiSKu4flyv/1FOtI+adWOjkNsG+Sa/jxgaeCFJhCF1CSHycKxWMUb9XhoG64gbaOEW9x4coxYgEgp4YfdVgrUKztWauHNMBBfqerApRYS7KSlq0fORfRHvjXHlkr00TsPFQ99D4Eyu1Bu4Z1w6W851Eawr58di+K60gOdmxTNY9AfKfe8i8HqAIbMNvdmGnX+q/foGN7jBDf7T8U8XUzf6pf7vYbM7eHJ7kcutb1kq7oq/XPIWZgRR3jbIk9uLmJ7oS4yfOxXtA+gtds7W9LHlfDMLMwK52jRArpc78Q1Wfjxdz5OzE0gO1LBlbTYHSzvZXabn1YURmG12Np9vYPGIIF6al8jKr85iMNvYcmcOarmE4FXPsvRSB6d/qWL+Qxl8vDKDtgETXfphvN3kPLz1ChHeKu6dGINSKiJIp+TZHYWUtA0SpFGQGOjB8uwwAD5bPQKlRIRYJORgaTtv/1bI9vV5PDYtDrPVFf7cUNqDu8lJxrWJ/ben6onzc+OJ6bHctekSk+J8uG10BG/8Wc7K7BDen5OEysPVTxbr54ZWIWF3QRtv/VnO1CQ/ztZ28/j0eKx2BwEecuRSESevdhPl89fE6rOWGLrMZiL8zPzt3N94JvtpKs6d5vJQLJPifEgJ8uDzjd+wYvgXPO47xuR4X5ZmBuFEAAnzUASMYt6QguKWAeKUZl5P7uD5U1ampSpY8805wj1VRPioqe0eYu7YMXz0zXe0mCTcOktLrd6AufESj6eYuCgPRqNR89oCEX/bU86ZpyZidhhoM7TRfamEYycu0Jk8B6cT7t1ymRdmx9E9ZEYggLf/LCchwJ1jlV1svHUkCz87TXa4DpPHm2R7aBkR4EBkHUQsEOAhlyIUCVmRHcytGy4R6aPii2O1fLM0ijj5AF3qGBp6h12ZUQeeA7MBxj4GPnEsMvWzyMu1g3lhXy27uvpptVnZcNtItGmzWOtsZkyUN0dPHKN3YIC7Vi7l9+35lOnrWZg0jVaTA5vdQXKYL8bSq7wWWoZcPJm391dwuqqbAw+PA6mKR8b4IQxPRygU8P3pepp6DTw3JwmzyUZLlQOZXIhCKkIuEdE7ZCFIp0Tir+BcTQ+BWgXx/u68cb4WUZoKqVyM0WLjfG0vIoGAO8dGsOLrc9yaF84TMxNcRhhAuJeK1xcmIRUL2X65lRBPJSGeSgJFejD2kZ/2Cg8dHOC3ottZIMridFUApa2DfHbMxtOTTeSZignWhuDlLqeoZZCvFgTx8YUBskI9uSMvHLPdjpu1l/6raTgLNtPVeobS8FtI7D/K6Zh7eGjrVb6+eQRJAR6Mj/UhyltNV48vRrEI5XsJWG49wJ9DsejMgRQdquD926Zev4aVMjGZoVqifdTUiYQ88NU5xocrSfIU8uZK18SZpos07XmdM0kvs+1yO8uDdkDyeBCJsfU3Edi8j5Spt4JIzNa7cq6bgAiFAuoKuxjsM5OQ5w/AC9ckwyPCPPBUyajs0FPapufleUkAHC3vJNJHTaSPmtHafijbw5qc8ZisNn7Nd2OiSkx+Uz/hxiZuFVRwJXAt4wUnaeo+hsgRzZnt1aRMCCIpwA1/jZzcW7xQSEW8szgFL7WMhp4hdl5p4YHxCyDCVXAjcxWNw1Y7N393ge9uySJl0UQu1vVw+8ZTfLoyjVPVvTwwORqlVIx83uf8vr+FvngHt3oqKPqtikAvBZ1Kf3oFQwQV/ow4bjb3TEzms+PVbHAa+aOsl1q3dKZY7cjMPVisDnzcZaikrp1MjVKCViklxlfNG/sr+DJzFKnuocjKd/F53wiGzh9GFprMnvtu4verrZyq7uLleUnUF52mscWNkEB/tq/LpbRtkJOlTbTXlfDzsjws+z9D2hbGIy1zWJXcSkrOKLY1bMF46TW+NUmwmfQ8VhLO87MTeHpWPN2Dwxwo7eBYRReeKgklrYOEeinw95ATEnGa17c3oFDH8+DkaGyB83h/n4hZoy/wU8U5eofGw4jR/JQqp3VgmAuFRdQQQrW3O0cLG1gQ73L7e2RK7P/ine4GN7jBDf5j8k8XU/+k6d8N/g8gEgpIDHBHIxdftxn+R8K9VTw2Lea60xYImZHkx30To9lztZXcKC+XnS4QMT+UCxebMNscfHKihpZ+E7GCJm6yHAPew2S2c7mhl3HRXoR6qVmWFUTbgIXushOUNlaRPe9uokb4EJbidf39vzxei1om4tFpcehUUjoHXTIbb3c5d6XJ6T31I59HLKC2Z5h1m/J5YHIMOpWUKB81Z2u6OVbeSZS3mtcWJDM4bCXK568Q3/pT7Yz2lF+32N18Rw4ysRCHE6K81Wy73MLtYyJQSESIhLDrgwJSFoZTh51oHzVBOiXvLU3FUyXlmZ1FeLvJsDtcAZ33/XSFn9Zms6ugmfQQLeNjXb1oL813TQBb9C2U9eZg6azhqzMNKEPkBPvq0Cgk+I6Yy/e9mSTWHaW9PZzMAAUjwz2prqtHIJJxqLQTUWs+M9VXiO4qYEbcGyweEcyA0YZGIWKGrg2ZsYuhnhxGpKaxOkBD1x8v8WjqTfiIrDDQzOMHCxkX482YKG+WZQejkIr5Nv8Xmgz1PJXwIPJuIZE+KvLre4gJMvB22W28MnMDtV0Ghg1Wyst7qO02ojfbWD8+Cv2wjZ8uNJIUqCGoZgtCfReJgc/yzalaPliRjt5kZWV2MAtHBFHTZcBRtoPWut0YFvzIqZpufGRWppbvRbBsM+8UCFDV9bBOcwHaCzH6ZeIR4UZNQwdh3kr2XG1hdmogY32G2XWuhGGrguQwV8HqJhUSrDCDQOAySLl/LCKhgFKRGINVgMXu4LFpsdx8LU+trE2PV8REvN3kDFvtZIS403X+J67+uoeaxinghHkPpSO6Ji+9b1I0906MoqHXyKzkgOsLQWtyQrC7anSUUjFZ4Tp8NXLUcgljo70I9lBS9eenKJpPE3THFr47VUtGqJbUYC2pwa5i/kRlF8mln8NQIrqIlTwzS0+38zma6u3EqGW4ycUsGNPO8fZdRF/q4yXjbaQEqnkr00zHz+u4allLx4CZq82D3JoXRpvZna2m8aht3bi5+5M7eBWPjjNMmPsy++4fzR9X6nhvfxkBWjXTRJfRdl/kcM4zzElfjdkpYVSICow9SEQaOgZNbL3YxH0To5FLRDwyLY6qDj3BOiVKmYiMEC0KicssBEMnnHgLjXcgE5JCON5opiz9OYJErluCXhPPodDXWD9s5LOz7azIDiHG7y/3s/6OIYb6h/EJcX1v2+VmYn1UhHqpUUjFnK3pIeMfdqmHzDbO1fQwOcEXta0ZGs8RET8bq93Bi3M9uNzYy4YztYwIuYP5o3yxbi7HOnYCM7178du7iquWv3FscwWBsR6EpOo4UNrOvLRA4vxdsnN9lwGjxY5TJEXg7g+GLjj1Poy6B7l5kOOPTUAlc51bVrgn7y9LQywQcryyixUjQwjzFOPp5s9DU7Xoh13hzlEjfJApxXhos6D2K3A4QReGoxeeyRTQc3Y/GaHxhGqUrP8xn3m2/azLS2fd6AkglfNncRvna3t4Y1EKqeJ6JkTH8OJJBZPdGhnbkk+VbAbPWFbxeVwI7k4DDT1DdBssFDf189uJCuTeZiR9e1jnXQxpz+PnqeXxCdM41uKkI/4N3jtYybdjuwmu/hJyvuel3JcYPvwq6Hypi7iZ5j01DJgshHqq2X65hQ+XpVHUOsjBUtcu4m15YQiEQko7K7Day4gMH0F99yApjRt5MedO0tNXIRQIESQpUF/77EJ0SoIqHmdy9kO4pa7kDvcCSgZlyCT/tZdVS3MT1o5ywtInufpBb3CDG9zgPyn/dDHlcNzIMP+/hUAg4Na88H/15zKxiLwob/qGLHQMDhPn706Mr5qKtkEe/a2Qx6bGcMfYSAA81TISAtzRKCSsygnl6e1FuPm6EatxyfTK2gYpbzfw0C9XCfdS886SFAQCAb9uOUowXdeP56vTdbT0m0gP8mB6gh+VnYMABHgo+Pli01/WvnYLOpGJll4TWRGepAVqOVLeQYS3Gk+1lCe2FRLv7073kIWfLjUxPdGPO8dGXJ8AT7wpDpnStcp7sKyBc7XHeW7mGkRCAa8sSMZmdyAQCFiVHYLJZmfBwxlc7Rpky5Fahq0ObhoVxqIRQTT2DCHTXUCkVJD3Ric77snj4ENjMVnsRPqoKW8b5J395VhsDgwWG68tSEHolDCsD0OmVRMb4k6HXEG4l4o3/yxneqIvb+wbZJ3Egq+wn9L3/4bXcx/y55ETKNQaRmXlUmVQc8Z7DD82ziCxX0CP0cLMFD8MfZ3c+6eeb7x30eiU8tRxKRvG1VOa8DAlvQKE/n70useyZuQwS7NDaR8Yxu50Ut1poK4sjiCnGx7jfLhz8UR+udjID+cauH10Gv4+f+N0ySASoYGWoWGCFFK+uzmTC3U9dBsscE2GU9Y2yHO3P0Vzew+KLhGb78hBJBTQO2RhfKwPNeWFhEbGUBI+mx/1qby2ZzF3Jj/Dh5dtdGVuY7VvOF36qwzb7DB+JQbzUp767SrJtlKeyMtgR42d8nY9s1PBfOBlYkQRqCc8QFqwa3I9ZV46Vnsqv54rp6rHwsz0UORiEQm5cwhMm8bqb87z3tI0wrxdOwufHatGLASdWoq7XEpjr5Fgd386hTbEmZ5MzQ7iXF0PuYISBBe+ghVbMFntzP34FKtGhjBstTMtwY+cKNcCwImv3yXKFx6f+whXGvtwOJw8Nj2eHr2Z185lkOkfS1xDHxabA4fDCT21OKoO0hZ7E3f+cIlnZ9zPmswwOuv7+O5UPd/fmsUDSX8NpaH6HDqHItgv0zCv7Qq1vWJM/rO51/EweaFiNlzt4Kc7wthV0MK+4nY23T6SHZebaRvwpyNwJC/Xx7IJ8NcqWVTzDFnSYD4YWkO/fxgWh4PZSb6wu4Pe3m72t8iI18EYbRmni0z8frWHYaudzDAtUT7uzP7kFCceG4/Z6uBMVTdebnKywnXw82oYuRZF3DxUIjGrc0IRCqzsKa5idlI0rX1DnG2qZemx3fTYZl7vkast6MTQayZ9Shjp19SRe6628P6BSuL91UxN9GdJZjBlbYPU9xiZn+6yLLfaHQiFAr46UUOgJpVZ02ZwqqqLF3eX8OysePYWtTFksTMzxY+f81v4zTHIT+c9eHpiPH4BYSguSfFJciMs2YtzDW20dHYCf9mhh3qquGd81D8oKAQgFFN6dAthvadR3baTH8/WUNlp5OV5yUy6ZuLz54OuBaj6q8eQ2IcJzJiOrzt064fxDb/WH2yzgH8a+Cbj/OVWEnI+JevYcoZil9CWMYcLV1sQCgWMn7OGjqvb4ewmfG/fSkqQBje5GBx2ivd8TvTUO8mNDCFMYABjNG/NyMJktcPBx0Dty9OznuKZHUUcruhk4pTZjI7y4kqhFonQk1ERrn/GSz/TWmNDEjGOTbdnE+PjBtmTAGg3tvO1rYWZPqNo6t/Lb+tuw+l08vLvJTT2mZiW4Me0RD/mxbvj/GEBMj6mSxHJ2IilTNpxjFHWTmo663CXrSWvy0njtt+JyZnOa3sLeGpmPKMivej67HOk/vfikTqXD49UkV/Vy7BTwh+eKsJlegTNF8iceSsCgYBfDp9BbxjkecFmyFjzP7jL3eAGN7jBf1z+TT1TN/j3zaGyDg6VdfLlmhHc9O15QMCdY8P4+GgNizKD0SqlFLcMUHGtF0olE+Mmk7CrUcbC2+5DARS29BOiVTA2xhu7w8HeolbGRPsQMmoBIbp/DGx20tZvZGSYFjsObA7XRP220RGszgn7a1KjC4fpb/C60YJSJkYiEhIfJMZd5o5AIGDH+jw81TJa+owYzTYWfXEWd4WYZVkhfHW8hpkp/gTLxeTX9zE4VM4V4y9YBycg0QTTrR9myGwj1EvNg78UMGiysn19Hh/+Uk2Cvzt5UZ5MivcDwO5wYjd74BQpmJ3iz2PbzvHgTDdSvTN4cU4SD269QkOPkVg/NzyvhQ3/VHycHafcib5pFMtmT8dDKeVEZSdHyjtZlhXMntH1+JhqcE57jeSAtzjYZkbon8KDM+JBLOP3q+EkydzJiRxk95VWvNyknKzs5eX5ifz2YCRq2SzaBkw84z5AZLI/kQCXmzlR2YXBbKWh10RDr4mX5iUR5e7kl+I+fN3VLA/0YNFnp3lRtZ2lGXnMSG7hT3soSmc0q3JkbD3fSIyvmtgADVmKdkRD9Xj6ZVHeNkBKkIZJsT58dd4lbewzGgAnb/1ZhlQIdifUVtQw22hnxuiRjOv4AeTxZKaP4INE8fXVfZVMQpinCqvdwfen63CXCVji28OdJ/uJ9FXz6LQ4APxWfIa3UIINMes353NrbhhZ4Z58cKgSQ2sNkzz72HNVikYhJs7fHTe5hFvzwvB2k/LJ8XymxPvywfJ0HvmlgKqOIT5eEYvFbkerTGHb5WYKanvRFoh582Idd+T4Mz/7LsC183TkkXEcLu/k96uteKn7rhdTHt6eyFUmegxmbt1wkZXZQTT1DdPcZ0QpFTMYEMb7hyq5OTeMjFAdFVdK6btSgCZkOSevGVcgEKCWi/FUSRELXdf6TxcauFDbi1wqYnlWBD9cKOTpqSP5/kAd+j0lLMkOp75niG3rUvjpfAMnq7p5dlY8z+8qZlKcDzOS/ZFLRKQEuZzQbHYHfb45JI+cx3e+8dektBPZcqGBvLHvEOap4nZbG7UdgxypbsMuaWHjLWN54fcSnE6YGOfH16tHsPlcI2abg8beIaQiEclBGuRLvgO1L6/sqcBTLeWO0RG8efpbCrovkRbwIT7evRjlR/Ac/SAvOx3g7AJCaSzpxWSwkjLx736YkBLkwYtzEhgZ4UnvkJk135zDQym9bpUNrvBvi83B4fIO/r6JMSJUx+KMIJxAapAH1Z0G5GIRN+eFEeqpwE0uISvWB+twKPazVYQkeqJQS5Fe+YhxA/UwdRsAv5+sxygWsL2wla13jXLJUKVKjvquoSj/PCMCVpLndDIy/zHqPddztTmI1KC/ds0AemquoHAaCcyYzu0bLxI9XMTdfpW4z3mdm7+/zCNT7+RgWTtBIzaz40ovb4x6g28rpMw48TX1w6PotqmR6ILoHNJT6TGDrMFhCpsGmBDvw/6Nr7HJvphJ7d74eYBFpOSSxzQyO0pwO/UOzPkQg01EQVUXz81O4FR1J9nhngiFQkakpdNvTKSxuZ+UoP+Pvb+MjuPa1rbhq7nVarVazMzMtswkM8UMYUPiOOg4zMkOOMwMTmI7hhhjO6aYGWSRxcxMLanV3N+P1pa3d7LPec833vG8Z+9H1xj6oa6uVaurVlWtueac91Rj6GtHbJQwJ8mb1T9dY/kwfyID9LR3tPPKhb/hariNIm0jJV02GfsurZGsmi4enBhKkJs9K368glIupsnyMjtcw3hjZz63OZaTEeCMUC7hmenh/HShmihZHXHCEoTdLtS2MygqRFgEWTox44VC2nsNRImbmODcxptXBKzybyW26Th6093IJSJmJQXwxZk6NEETGaroNMQQQ/wnM2RM/ZvzyNbrLE71Y3SYG4tS/Zg3sBK8fkokno4yXJUy7h8bSo/OyG/ZDaweG8LqAS/VtsvVqO3FTIkNHAyju39cKAykHBhMFlb9fJUQVwdivFQoBxTNNl2sZMfVOk4/NQGAF/bk3hJ+2Kcz0mWx4K6yJVF39xtgoKAjwP1/3M+ahDWM9xuPi1LGobxGhAIBEyLd8VTJ0JksnCpq5becRoLdlUjEQn7NrCU9OJa3Rm3mk8vNrJ8Cb/5eRHFzDwcfGcOb8+KwkwiRioWsGh1EerALDgP9tVisvHukmMsVrmy7fyT59d106Ntxkjvx5K856E2WQQn4/PouggY8ItXVIawYpeLjY0WkOXQgco8gwd+J/Q+PRi4RwZh7YcADt6PcSEVLL5HeKu76KYfPlifx9kKbxPqevOsUW3axZsIGfNUKuvuNtr7tuIcq33s51eSEyWqlrlPL/eNC6ezswE7pSItGR3OPnoorh/E5+xSLH8+BVD805VfxVIFjwnxQCfn5ajenDXXMSfRhfLgbXf0mnpsZZTvfu14iyWJGMHYmYe5Knvg1h5RAJ7QGEwX1GvRmC8/tzqNDa+TrO1II9XCgKMYDtcoO9L309Gk5JZ7CDLGCnRcrSPJXszernvvGhtDeq2fyB6fZ9cBIPjleijb5PjLEDST7q3lxbx7O9jLWTQ6nozof6YlX6DWsHTRU7xoRiFHnwVfnails6kAhldClNVDW2svMeG+MZgt/FDWiULbQ9EcfD4Z7E5bqAc35NNfpuFTpQNxID6qvNKPRaXh+RhQh7kq0BLHrSCn+gY6Mi3An1V/NV6fLCXG3x2CycCS/kRlz7kY0YACl+DtyoaKDUaGuJPqpUclF5Nb38NOKYQgEAn44V8n48AS2qBTU773B7rWjaOzup09vJtrLkS8Gxo3BaObni9UEu9ozN9qHBD8ndtw/AieFFJnMDpGhmxPFDaRGBhLsquRgXjMz4zxo0hhQ20l4/2gx7yxMwGCyUNupZWa8Nzfq2nCsPMNOQTz9LnJ+OF/FtFhPXKsOIpDEgTASZ4WS9acr2bZ6Ep39ZrydFXx7d9rgvejuKMditXKmtJW5CV4czG0kq7qDEU4GOPshq8c8iFQsYtuVGjStidwXMw4PRznlbT54mBchNfTCybdAHYAm+VnMFisT77IZyq1t7XQ3lREaO3xQzKG4uQcHuYTbh/kPGq+dfQZq2np5Yd8NpsZ4crK4lYxoL+ykIrp1RvZmN6CUimjvM6A3W1DIxHx9ugJXlYxYXzV6o5nouYHYq2yhyhHTHqanx+Ylt1qtfHu1hvvTAnhn4H7jj1foNYs5XurF9IkTGZVkU+Hznf0cjiX25NZ2k+BrE0pZtz0bDwcZz863Kf6ZzBZWjg7C1ShHpUxAKBRwz6ggglxkBNnpiA4KQmnpwz7zZxzt7ibKy4V3G3/gnO8atm/5npSwNHw94mhsrOVwQR9T3dqJdTLx1SRvTnfJaerWcahMQ32XgF1tX4PCBezUvLfvBqdLW9m7dhSf/FGOv5MSk8WCVm+mrLWHo/kt/HBHHGrPEBYPHwkFu1mQkEqgXM+ptz+gd1YQn034lpIGE5NjvAavv5O9lL0P2QoMnyorIsK3n0TPcBq7nUAs5bFJoTge2IDYaz5OcSl09BlI8ndm8qThwAI2/LSbTwIzSRS6As60x6ay8WA+5389i1rtznRfHyLzd/Dc9NtJDxkDzB88dlh8Oh/G/8tX1xBDDDHEfwxDxtS/OQuSfYnwvJljJB4wWBL91YOfOYiE1Hf2U9RkC8UratTQqzehkEmYk+BMaqAzP56rwGC24uesoFmj455RQdR2aHlkQhih7vaM2HCCGbGePDAhlEkRHlgHoj6/PFWGutXIhaZehgU6MyLElRU/X8NNKeWbu2yTugc2XafPaGLfg7aX+luj38ZZdlMV0mC2IBSAVCxkz4OjqWjtZe2WTH5aMZxvzlRQ3a5lwwLbW7mipRfFgOH3/Mwo2vps4gf7suuZHW+bREyO9oSC39iUr2PylNl4Otq8UWqFhEM3mnhkUhi3JfsOnL8WKtq0bL9aQ4Czgnt/vMqLk/1ZXvAAj2V8g4O7L0tDhTTt+ZAXWh/Gz0lBt0rGnd9nsWpMEO8cLubz5cncPy6EkiYNj2zN4sEJoZS39PLO0SI2r0xndkw8fm7388reKiZEuDE9VGGTaU65h5Ee0YyUHuRqfi1ftU0g2cuOxF2jODH8e6bal+GvcKUmcCJXjJ8yRiAAqxXV4Yf5fNYHEDAKq9VKnlTCY2MDGBnqBrVXeVl9mpYmOa+c7OLdcY/x6rFa9Nuus3ZcKHqThbQAJ6YEiJnydQFTot2J9lHx6hzbhBNNI1+dqSfSR82aFEe0zeXs6x/B+PJLLK34gMqgb3Cpu865vdksvPt2pkS7sX57NhHeKgytlcwNd+JAqYaiph7mJtrG5fHCZjSaOCYk+XEgtxF7aSt1nVqenBpJv8nCzHgvqtq0vHekiLpOHZ/fnoxMLGTv/bMAqLXvwNF1QN0sbyfCTgEdzbOIcPAncLgnMz0a2XK1iJCMGVRkt3K5qh2tXMC4CHe81HbEejsS6+VIUWM37x4pJtTNgUBXBZsuVvP6vDi2XqvlvjHBHMtv5tfMOt4f58S2V55myctv4Vt/EIvfbbyxII5mjQ6AAzmNXKlqp09vxk0pZXqsF2PCXVHJJSxI8SE9xIUfzlWwMNXmvRkZ6kZ35lm2N5pIykhi3fYsxoS5MDzYlbz6bu5I9+fxKbbk/Zf33aCkuZdp4Y4kBrhjWLuf6oo2FBIxk6M9iHC3p6vZEf/aA9CbT9rodWxeOYwLFZ3MT/YBTRP6G3vZZp5MqrCQmLQJeI8LwdleypRoT0qae9GZrGAxk12Xxbu99zPV5VVivFUsTPHFUSHFarUyIsSRceHDofYKuITBhGeR9JlxclcgGbj/GrIPk11ay+lON1YOKGGmBtqeJ//IEzuzaejU4Wgn4VRJC8/PiCK3rpN4XyfuHxvCkztz0BnMSMVCPv6jlA0L4tn70GgyqzuY9elZ5iZ4IxEJB72drq6BuLoGAraQ498eH3vrQ3HsU2h7dHjq8xmREDv4sV1AKsvd23nlwitM176Mo8yZLq2Bun8oLrv9Wi1nSlqZm+jFpydreXOR0RYSmL2NhVXbaE7cypPXtUTH3sVT7cfh6gku+q7g3SwBs4PDKLlxjo/OiDmt/hsfrT3LobNX6NIGssw/npnmcxCdRoyXA6rDD3Nal0Z5TxB3NjaydJg/CX6OOCqkbL0vHXuZmM2Xq2nT9LPlci1vL4iHrmr441VwCsRkMrKzz5W5yf5ETl3G8OHDEQiFBPyDw+1aVQc9OiMTIm0hjYXd16gxFfN07LjB7wgEQt5UPsfrGbHIxLbrOibcbXB7YsoIQu2jEPnaJM8jvVTIBQZMlioEdh10OQ9n67AlzPGTc6XxCsO8hjHEEEMM8X8bAuuQssT/CI1Gg6OjI93d3ahU/7uDF5oHZLP/mc2Xq2nr0fPA+BDEQiEtGh1j3z3JohRflDIx5a19fH9PGjuu1lDe2sfa8SE8su06SpmY+8eFUtHWh4/ajq1XqhELhcwMduVoZTtTYj0ZF+FOXn0X9hIx9nIRHio7LpW30tFnoqiphwgPJZfafqOys4nNC18hr66bSC8HRAIBa7Zk8vDEUCI8VOQ3dJMkrcescEfo4EZBo4YX995g633pXKvqICXA2eYdAjRaIyPfPs5Hi5PIiLFNHEy117lzTyPxsVaenTiLpd9cxFUp4elpUZT2XkZokWPRhXCmpI2np0Xy1M5clg3zRS4W4eMk576Nl1g7MZxt1xuYE+/NyZJm7GUS1mdEIMDK83tv8NikUHZnNfBYRjiK69+gU/qSK01mWO5LaNLXc63PlQgPByxWKG7q4VBeAzPivZnk1ApHXyBz9IPsKN7KCqeFXMrOI27GfYS62UNjHhKfeOwqjtJhkuEs7oeo2eTkZnG2sJ7750xkz3tv0JM0i2ahI8/NiIbKc+CTTHfFVaQXP0Jq1bMx9FPk2gY8FVYkHpG0C84xzm8cLlYB2795A4fhd9FiVlHY1MPbC+Opbe/D4+yzWCrOsin6O1ZPSxkcM60tzXx1shiNUEWCpIsQVzmFeLAzs5Y5CT7cNzaYbZu+4nBPEKunJNPSY2BMmCvuKjmG3x5nt2UcEcljaerW8ebvhYwJc+WNeXEIBAJe3JtHj85EXaeW9VMi+PFCFQHOCp6bGT14/NqOPuwkIlwd5Lxw9gUC1YGsilvFzsxamhrrKW/q4v6ZI4n0UvHQlkxSAp3/lGdotVq5euRv+AUkUC0dxf2bM9n34Che/q2AGXFezEvyoVtnwFFkob6kiKCYGNi2HCa/Bh42tbrzZa3sy2qgR28keiAkdHK0F64OMvZlNyARgcpOwgt7b/DmNH8C8z/Ha87LCI29oGkEkZStNSqkYgkLUn3Jreti2TeXeHthHLPifbBYrGz6/QSnas08v2Akoe7KW37D5cp2zpa08sSUCFutKaBVo+NvBwt4fHIE7voqina9zjuyhxktK+Oh2WPA5dbzkFfXha+Tgu+uHsbRsYvr+TGYzZZBj9ae0j0cKj/OnSEvk+zvhL1MTGVbL16OdrZ7btd9EDkDYm7jlb3ZeDk5cP+4kMFzXNKiIcLDcfD/lh4dDZ39BLs5UN3RwyfHy7FY4Id7bcfLqunE31mB3mSmoLFnsCi52WKltlOLj9oOATcXiv7O1sKtxDoNx2J0JsFPDUBbuxZXFwXUZ8H2O2DxZvBNGtxHb9az4fzXaNtSeWCCP+fKWgl28mPkgBctp7aT944WMyHYnuzcXF6MkKO5UUvjzAWM9hZgcfBmX3YDcxN9EBYdAIU7+eIQyvKukadVMcOjmzplDHP8zeAcxJenyilo7Ob1mSEovh+HZOnPUH2RVqOUcq9Z6Hf9StC4OOp62xgZ7k2ZPJr5X1zgp3vT8HGyhVUXNHYzKtSNj/8oZVaUIwHaAppOf8cm1Wr2lhgYGerCZ7fb6n5ZrBaEAtt52pNVT1uvjtVjQqjv1OKilA0+M/8Vjd39SIRCXAfKCdxC1i82w9o/jXN15zhbmI+wXER3dCnLou7gidNPsGPWDhzl/3trUf47vb+HGGKIfx+GPFP/QbRVNiJT2uHgprYl3X94hp9WDifBV822g0fJ19jxt2VjuGOg0OTjO7KJ8XZk5eggzj41EU/HW5UCF6f588HRYs6UtPLTinRMZgtN3Tou97SD1YpWbyHBV0mAvyNvJN9MBo/zUbPneh07M+vYsjqd9BDbSqdcKsJLJcco98co7EBnNLPypyt8e3ca3X0GRga74KtWIBULbfWqfl2PIHgCPVFLCXSx58EJoVit8Or+Aj5YnEAsFViPPI8sci4XFwViCFDza2YtrRodayckc9fME5R1FwCwbLg/ZpOZwsYeSnUNtLe7cLW4hEQ/R65UtvHp8iRe3ZvNFOs53Geu5P5JUfxR1EyqvxMOcjFx3k6MCHbBYDbz04VqPl2ejEQk5C47qS1EUunBqWoTJ3obGKapQCWBYFclE947zbgwFzJru3h6WuRA4rsHj0leIrTMQpIxAt/YMSilcfRo+7l3Yz4KqYRNqyTUe05g1qfneCemlclRIBVYsRMYEUqlOKmE9Gq/Jzl5JVjMcPR5WgPncshhPgWyJ9gwIwBZoYmdpRZ2jGxEHD6ad89cY+rJz9kV8gr26feicHLnngibh3DZt5cIFTUxtsdAhftTrGj8G2QugOjZ9LfX0mt1wSx3orNTR1SKGynyZt45Ws/wIGfWjLdNpBcuW0XPhWoaunXsz2nEzUHK5Yp2zhnv4uEJ4fg6Kzhe2My9o4JYMTqIyxXtBLvaU9LUQ1qgEzFqM2KhkDB3JR7/pFr5+I4c1HYSvr07jcdTH0cmtk32wj0c8FSF8NAsNywWK119Bj67PeWWfY9cqGFknAcODjJ+FzYR0evIsrS5XH4uA7lExBtpOhSXH0OY/CtuSttxgxKSqe/Q8kT/E3ybuQNl5EQIHkewm5KJUe7svl7PH8WtSIQCxkV6oOk3keinZvXP10j2V3P6yYlsP5eHVGPgam4jM6R5SGvPQOkxli3dSr19JG8fKmDd5HA+W5ZIwoA4h1AoYPrwBLK6y/noj2LWp8nx8gtBLrf1a3iQC8ODXHh6Zw7d/UZGhbkS5qakR2ci0NUeiMF+8df84u6AyZKOxmhGhS2f0mqFseGuPLI1i9mJ3jw2aTq/Fe7hvcXRWMw3XwWB8jSqSk28XVLEa3NjSQ5w4slfc1k9NoipMV6QugKcAgF45bbEW851YaOGezZeYX6SL8/MiOKjQ7l019zgjpmTaNL0s2ZzFhvvSUNlJxnc5x9r03mrb+ZlioQCAv+pFtS5HSUEJ7rjHa6mvLucU9kKGtqc2f/wGGrb+zjwxjXG3xtNVFwSzP4EvBMAyK7pRNNvZGyEO44SNXK1lhU/FBAVUssdaTeNrUBXJfOT/ejVGZk2IgWlAzT0mLlQ3cfoqEjKmzS8tC+fseFuuJx7H61DEMXqJcyTXafONB7P2HEkqxW8/ssxIr1amRgayB2RAqp6rHzu8h1feMQhtJhxs5jYUtTBSVkED1zZxjb5YkaG6Klp1/L1nSlsOFxMpKcDPToT786PpzqvHUV3GdIeD4SaMkROfjwe0sYMFwOvFiv4+ehlnII6OVy9n8+HvQjfTmDe3E8hyZZ/9+TOXBan+XFbog//EouZL06W4aqU8WhGOLSVg64LfAfupxs7QeUL/mmM9h3NaLcECLoCoasAOLzg8F+WUGnW9PPUzlw+WJw4qCo7xBBDDPGfxJAx9Z/E0Zcwqb1hyavIJSIOPDIGH7XNMzVK3UGU8005c7PFyv1jQ3BVSrlW1UFNh5b5A6Fvbb161m3LJiPanYq2XqbH2cLnHtxynbY+HbseGM2B3HpEIgHNPXryGzQEuiqxWq1cqexg06VqPlicyKhQV06erGKYbyMNl3/FJ+MVIjxVRDGD2yJmAHD6yYkIhbDkq4uIRULuGRXEm78XMC3Gk+QF33I4v4Xvf7rCjytSBhW4jq6zhal01am4olpIW14z/qHe/HQ9j2AXe1p7bdLs00MmAhPRm8zMTfBhb3Y92TWdnCoJYEasJ9vuC+S37AZaegxUtvWQ5ikiIe8Qv5xK4VSTGLPJgrtYwvMXb/D17Sks+voCHyxKpKlbi9lsK0I68+OzJPiq+ez2OYwKg8AOLe/kfsUymT+BTgqOrx9Lv8HE5os1zEnwhppLlGceo6JtPA0aEZ4OGYwyiPjoWClz3Rr5OEpPh+84+GUJ3jM/ZGWYJw52NmMlKiqWqLhkjHodWoMYkTqUZl0JCEdA4Gg+L1Oj8KjijaXjOFXaQqyPPUKTO73B8eSVtLN+1Gs0S3Ygk/swKzmYZo2OD44V0dilZ1asJw0aRxwCU/HqFyBxDUN/4l1+a/ZidOMPlCtHUmAyoRImkyLrhPy9PDTxJS6Xd2C2WPnsRCkhbvbcPz6UXZm1GMxmajr6ae3RIxGJbHLc1Rew61fgZO9Gp9bAq/vzuTM9gBWjg4hxFeDzw2T28j0V7a6IRLZJ2fHCZq5VtvPOwjjkEtvjykmm5qfPvqTcOZYnF45kd2Ydl6s6iPN25MtT5ex5cBTFTRo+OV7KhvlxvHKihHckIsYke/DK+PdAqqC+px4vpW1cix3cKVWPxLWjj0e2ZvP58jgCXdUUNnbTrTVhdAoBpTuXKtqxkwiZFuvF77mNuCulfHv3MJ7dnYuHSs6jk8LYMC+OUE8Hsms6+ex8K2/Oe5lfT1cQN2c6IcmLMWTtZNe1eqKSA8nXbeaj/S085RwOUU9RdG4fV9ulJKSM4Pk5CZS29HLlyCeUOY1lbEoCX5wuZ+vqdNv9HOpKr87EyZJW3JQywgbKCVgsViIGJMy/OJXN7wWl7L1/lk3u22pFJhbx2fIkwj1UVJXnsv36JoZ7p6Pqt9D19d3IFnyGt2MY9w4bzrLh/oPPi433pqGUiWnv0+PkNxyh8K/rDga7KYnxdiTcw+ZRS/B15FizGo0B4ryVfLQkcbB/VquVXr0Jk9mKaft9iIJH4zx+1Z/a1BlMSMUihEIBbv4O2KtteXfPpD3H97pyXp1uMxD8XOwZuTKKiChX6K6HbUvg4eug9mfDoSKMZgtjI9x5bMRddPTqCXRoZGTICJta36GnqItezelWJbenB3AwtwG1QomdFyTsepiENec5XtjMV6fKufTcJOxlYprDlqLt6+NCh4r4yY+Q0qtn2beX+WxZIvldEqK8BOQe/wU/wyE+FL/CMMca3rx0kufSn6C0pYd7R8kZEeqKkzCCWa1WCPJj55ZM1sQJ2aL6DNPIt9Dbe9LTqeP60WoWxNzAXemMQC/Hv3YfBEeSWvkjHw5/jtcudrLCP4q1iWtptKiwJD+BT08TmoMvIcx4iS+WJ+NgJ8HaXED2wa+pGbGEVN8AfBwGjKsz72O9sYv1i3chVw2EYFedgfZyfm1yp6K1l6cDx0LBbmgrA9dQkDlA6KSB94kZkVDEhbI27BQdJHmHD14/RzspM+K8B3NYhxhiiCH+0xgypv6DcF3+NxDdXPnzdbq5yus3ail+//DdLZeruVTezhd3pPDpyRLspbah0Nzdz4O/XGdsmBvjI9xI9FMT5WWb/GREexDrYwvhmBXvw6z4m6ucR/ObOFfWSp/ezKx4L6RiIWqJmPczq3lSJMZR4YOD6NYJ2NWqDjwcZPi72LN77Sj6DLb6Lrm1XfhbGwlRJeEoF+PqfY37Dm9h69wvbH2s0uDm70BpVg5t/Y5cc0phQkoEbw4T2JTW/oHWHh0ffLuRe9xLGDXnLb46WU60twOtPXoe/iWb2QlejAp1Zdm3F/FW2yFK/xYHs5UMJxM6nYmtV+tIdlPy8oEbLE71QW0v4W7NdzQdCyVw5nr2PzKawkYNDnIxAoGA1/YX0KUzEeBix5K0APQmm1Hx5LRIzpW1MdlRhpPamXv8AzGaLWy9Uktdp5a0QCe87Iz4DkvDtyUfAkYhsFOzZqEHAqEAzCb4fBjM/IDNzUGEzl2Ln2Mt24u3A6Cd8CqPuh9B5heOSCjgXGk7Sb6OnMytILRyCw19MhpdHsGvbDs+k20hayXNPRQ29uAgF6OyE3O9Vk9MQCjpcgnggyZkJvvzhIRM/YjGJi3rLMdRNByH8FepckggYdcdtIY8i4Ao4nzUyEQC7v7hCuszwlic4svZ0jbEIhHvL7Z5B/quX+OtrCjeWxaMo1TEcxN9iQ3x4s2DBdR7OeI2bBPf3hCRGihDYLWNlarWXspa+whydbBNentbQeGCQiLETiRC2F3LqOI32ea0lo0XOliSalsQOFvWhtUKvQYz55+bZJv8X/oS6jOxzP+Guw7fxbqUdcwKnoW+4hzu9SfwcXqMuckKlh2ZxZ65u0gKdOb3R8dAfxwY+sgp7EIpF5Pg58Qny5PJOlZDdX47z032x27bYmj9mJSgKAAS/Z34aUUawW4OVHVo2XCkmJfHOSOTuvNHi4lDR0vwcs9AaOrkuksMyUCvxJkqg4jvf8nixBPjcQuRc23CCnadrCNDImRdRhhFjRoivVTMjnOnT2dgeXoAO67WkFvXBcDiry+yIMmbKTFe3JkeTrivHoVEwTyfanCPolvfTYn2JNGi2wgNTeBr5Vc4OflgVurpCpuO3NWP7IZerlV3sGy4PwaThX6DCUeFzYC5d+NVHpoQwpQBkYNfT13lcLGGb1dPRCgUIJeI2HjvzbyZAE9XwsOjSQ6xqWoOC3IBYFdmHe8cKWJJqh+tvXoedU9AVnkCIoajqcmhpTSTwGXv09XYx9O78yjU9rN55XAi0m+KK1yt6uDTE+VMib75WULMgCHg6AOP5oHKtu3VOTE4/oM37NfMOspaelmSFmjz7Kr9OV+t5dfCLm5PD2BmvDdgW3QquO0IsS4hVBZWEuvjSEGDhgQ/NbsLepiRFomL0In6Li2pAc7cnR5AuKeKrWvHA2BstEdiHo3bGT31GjuqNMkUBfawZtNVJkd78uyMKMQiFx7/7RzO9jK+uD3F5hHK1SMWW8hp6qFHZ2LU/THc/l0vP49KxDtgBCQsA4EIBEJC7c38PF4LkWEA7Pj9CE2WOB6x/kF3cyU3SlqZ7msAbT+I5Ti7uLGv4zhI4geNqTyXANyV7rxxuBpf126emhYJqfeiNZg4vi2LWQnekPAY+KQMeiV79SYO5FbSIjxMlaaSDyZ8wOGSfA62P87DyQ+zKHwR9hJ75BIRS9L+8e0zxBBDDPGfxVDO1P+Qf5eY609PlFLa3MMny5IHPzObLWy8UMXCOC9KW/pQqaT06oy8sr+A6bFerB3pxdbsNrZfq2XD/LjBgpj/T3j/SBHnytp5YFwwU2JvTm4au/rZfKkKKwKya7r45b70wW3rtmVxrLCZs09PxGlgsgaAQQvb7+BszGt8m9XHg1PsadW2MitiAnqtkS2vXGLuY0m4eNtWv2s7tGR8eJrTT4zH09GO0yUtCKVtuDkIiXSJ5EbOFbpKLhEzYw0Xy1tp0eg5VtjCo5PCaO3RcyinhnvsztASchvbs7v4acUwXvktn2vVHYwMdiGzppOHJ4RR09GLSCRihmsrDipntpYJ8XdWEOmp4v2jRbw4K4Y+g8kmOT+wCnv3D5fJrevm/UUJfH6yjE2rhqOQijFe/o5m97HYuQfgIJNw78bL+LsoeGt+Ahx+FjzjIXEZHx4rwUEuZtWYYKg4TfGvH3KS0RhS52AvE7NytC3x/5ldOTxXs5Zy3/kkLVoPwLXfKzF0tjJyjAA09bY/J3/wT6db08uBaiEf/VFCuIcDcb4qQtwcaOvRIxYJWD02hL6cfdir3ckVRfHZiTIWxzsT3nWa0uo64uc9QeHxn6lzHUtppwmjGeykIq5XdxLooqDPYCbE3Z7WHgN/uy0W2eXPwMGL95vi6dQaiZV38F2+mQcnhLMrs5YITxWh7vaYLFZuHx74156Pg09AzSV44NzNz3paMGf+iHHEo7z0WxEjQl2Yl+TLhkOFFDZ206uzkB7sTEqAMxMDJKDrBudAPsv6jI6WCKaHp5Dir6aovplofy8MJjMnK6+RETKM0W+f4IWZUUzT7ELUnAsLvr2lO0UXG3B0V+AZ7MinX3zM8IwFDI/w44+CZsDCW4eKWT85nAmRHmj6DQg3TkWbsoavW6I5V9bB0jRfWnoNXChvH/S2AuiM5ltyW86WtJIa6ExDVz9zPz/PmafG0/vHu3Q1lBG/8nPYuQKmvgUuwVyuaOPpXXkk+am5Z1SArdhwzSXYeS/cvosKmZxPLuwl3W0+S9MCOZ9VgMCsY2Rq8uCxT/y+g+FjpuDi7MLmS9XszaojLdCZhu5+6jv7WT4sgPkpNqN139nrHC1q56MVk26RQv87tR19mC0MhCDa2HCokEAXO7r7zSwf7o/FYrUZa0WHIGg0LQ1VXMktYNqsRQiA67ktdElhQqQ7YlMfbL8LZrwHriE0dPbj7XQzL/RsSSsNOW0EKOWkT/9zbb7y1l4ybxTRYrQwLNqOYb5xt2w3lZ1CXHyAn5wewmAyMz3Wi+XfXmJqrCdOdmL6+rWEeLkyP9kXk9kymMd1rbqDMHcHHO0kbD7xNCqpA/Yuqzl2KYsNAZkw8Xkau/upau3j2T15PB7TT5EgkPXuWYhyNlE+ayeNXVq2Xavjk6VJg+N/X3Y9HX0GnO2l7Muu54d7bIbqEzuykUuEvD4vHkqOYOmupypgMZerOlha8gSW2PlsbvJjrK+EIFMFdNeAtgOmb/jzfQW097dzvbEIX7s47KWiQXXGMyUtPLUzl6/vSCFBUGYzPANsId93fHcJvdlITNwp7oxZSrA6GIlQQk13DV/lfEWGfwYTAyf+5fH+v+Lf5f09xBBD/Hsx5Jn6D8VXbYfeaL7ls8q2Pr44VYZbi4HNje2IZEKq2rSszwhjbrQKPoxh8T0HmZOYTn1XP2aLFZFQwG/ZDaQGqvFWK8ip66KypZeZAwpb/QYznVo985N8KWjU0DYQYvd3ZGIhbb0GWnv0pAU5gdU6mDj/wZJECho0KMUimrr7eedwMa/dFotSpoA7dzMGGJPMre0pJNyb8AmCtqXgvQAAP2cFvz8yBk9HO9q0bRyvPkqnoQVHpYFVMQ8TmzCMC8pgaju1FDT2kBrohEe9nJy6bvqNJpylRpLMuQhDbic9yI8Le8q5c7gfK0f48e25apL81AgEVvxd7Dlb2o5jWgptvXpkklakEhFtvTryG3vo1hnwdbKnqEmDwdRHvK+aDfPiEYnAXWVHSqAzoktfQsgouiuuszFXSZrbJaZFezA5OpwRIa40VdYjTVyPs6cr26/U0N6rtxWOBdZnqlk6/EFqcjqIV0pR28tsITdyRx7NCKe8+F0+zjYy+VIVlyo6+GBhArk1Dui8nJDnbqe7qxXl8rXUnt2Ma8FPnFe9zZNTI6ho6eG3nAbSAlxIDVQjEAgxmS3MO6Hm7hFe3J6uZnqsJzKFDLUsEOcePS/svUFVRyRL3WWoFSKswMIkXwobNYwOdWN4iDMioZDPT5RhsQBukWDnTPnFSp6T7YDJf+NQfR0ms5nNq0eQVd3BM7tvMCPOg1+u1DAqxIVNl6tZkORLzIA3lFHrIKby1gHh4I5o/FOIgHcWJQx+/FhGODsza/BWK+g3mPFxsgOFA/UNlxG2F/NQ0kP8cK6CbdmZHCztpy23jTeDb7C+dxnPTI9BJBTwy+p0nty/l4YYX1bPvJv1v2YTqC8lQVpPptN03Bzk3BGiprG7HyJnkhZmMzB+vliFq1LKqlFBpAe5YNffyI6jVxg190dC/f0YuW876SEWOuXBPONZQbmr7T5tvryTJ647s3JCDOMjPcCog+ytjElaDmIRQa72XHhmIio7CXZjV0JnF7SXc0oxjeIcPfdPBJ3RwoMTQthxrZand91g30OjkPkOg6kbwC2SYKEQR+MUdmY2sCQ1gKrKUkz6HvoU3kyO9sRiNjG27msa6oJxcXZhfrIPrkoJ1W1a6jr6mBnjiXNZH32heuwdZcwdk8zcMf/6ObTrej1ag5nnMvxBIKTTKOJUcQvPJFvwlys4mCth6bCBcMLI6QC4B8UxK+imkZOW5HmzQYE9JCwBB1vIr7eT3eBzCiCzphNrWx/CPhPpQL+mgy83bWHl3Gk4+obx9qFCnhJvZ1VtBnm6THwk3mw/k81j8ycgFAkRq/0gcDTlJd2MUrXg6xzC6acmcM/GK4xXt7K242OYeYxHt2WxJM2P37IbCPdw4FhhM8/F9hKXmEqAUzgKqQNeXg6IRiaDZxzVWSf4pdGLpu5+hvur8OzPZc7scdBuhq5RhLgraejqR6s3Y7RYkAlF7LxWy+wEb2QSEY9suc7oEJfB02AwWejV2zz5hE/lru8uEVxfiUwiRrB0M7VdRkqyz6Jr6yJId4Mp8+6hxuqFnUZnK2Nh6INjr8DYJ8DBAxc7FyYHj/rT9Rse7MK3d6US56uG718AsR04B3LNO4YV48YxOjgYhXQcB8sP8tzZ59g2exv+jv7ozHq2FG9hYuBEegw9OEgd/tT2EEMMMcR/CkPG1H8o8wbyn/6RUA8Hrj4/GavZgrRARXWXlow5HuzJqudivZxxd/+GyD0KkdnKZz/mEO7lwKwZoVysaMNLLbcZU7VdbL9Sy9XqDoLdlHRpDVwsa+fLO1P4/p6b4T1/FDSTWd3JHen+dGoNuKlkJDvrMX8YR136ZgJGJiIQCAhzsWfTcxcQTvLAZLFQ0qzBTiwmytu2amixWG/1UvS0IGjMBa9k6Gvn+IlX+d2QzjvzlgGwv2I/Fcbj+BnXoKrpYaemmCdmJ5NV3YWrg5QnpkbS1qPHahUwOsyVpu5+mjWuiAI3A1C1+1PELSb89OOR7riLdfeeR2sVc8d3VxgX7sorc2I5VdTCm4cKb/EmHHzENqPU9XTxzu6LhHq7Ee+biqdazkd/lBLno+K7c5U86dBJSogV+wWfsLCtj76rm6FXSIc2CJ3RxLe/XsFVIeaZh2aTV9/F6ZJWXp4VTWFjN9NiPHB1C+bytWvMdlEyItQV46+rkfgl4zFsDQphAwnOTjR29eOkkCIUC3nj9xxemB1LxOQNTHr3JJuaNTQq08iPj+WLUUm8vecSjZ1aEv2ciPB0IMLDEaHQppy2Zmww12s6WfXTVdICnenRmRgzcgIihyS8s+qYGeeFvVxMs0bP8gFRk5dmxeDrbMfOzFo0/SZemh2Npt+IXfEh8E3lnaUzUeYWYHJxQilvprxVS7fWwL0bL7M0yYXkAGdkAzlWMpGQ27+/zORId+4dHUS0tw+ofbjr+8uoZGI+uyOFX/cfoK4H1i23yah/dryE7Lpuvrs7jTvSg6CzGkRS+qRy8n/7mPPSKgwiCw+FTWWF3Rk22blgcPBh7aIIHJqtTBF44qqU0drXhYfKjuUjHekxtYNcxex4byrKuukVSDCYLYMFjEuaevmjsIn7xgQhl4r54Z40xCIhz+7OZUdmHU+PVpPfKSBd5kJrn5nYlLEEOkvBwZOOE/u5cKMbv6QpOAQm41vRjGKgXc68B7nbIHwy+yoF/J7XyBsZ7hy4WMmsiePwd/KCnG04Si24O9nuF6PZitkC2+4bidZgssldW60QMxcATb+Rth49ny5LQiAQcPv8uZwvbeW3nAYmR3uikMuombOZmV/lct6lEnefIKbFeg+Oc5PRzJUDlfDXKVMAXK20icuEezowPcaTAFcFHHsOpEqcMl5i30OjkV37hrPdnphUnnx4rJjrNV38cE/aX3q3bkEohISl0N8NQGFjN3d+d4WfVqQR46PmsYxwKrzbUfSWklvXRUVLH53qGAxKD7j4OZ/HuSKOeZWTCNmVF0V1TSXa1iqslefAJRB+mgVzPkFTX4y88SykJ2O1c8bL0Y7QqEQ6HT7HCZid4E2Ym5JJke48tzuP3x8bg/y7DNp6lzJm8kOQsx2uvYt3xstQeoxvrnTRL7XQbrLH39WeiIRR7M5p4dearTw1ZhFJ2CTJ/y5LXtfRxxu/F+LqIGVcuDs6s4UANyWdfQaMFgtvLohHZzCx93odtyX7MirEhREhLgiEQk6UdnH4RiMqMdhbNGTJh2Gss+NkSRkhrvY8MCHUFiIoV4FwYKyZDCAQQsN1yNsJM94BQCYW2QwpgNt/heJDcOZdjgg6mJAYgUIaTqeuk+M1xxnnOw6JUMLvuQ20VM3Axe8QDx95gS5zNZtmbPqvr+sQQwwxxL8xQ2F+/0P+rcME9D0glIBEztYr1ZjMVu4cEchvOfVEeKhuqVeVe76ezLoupkwJGpTo/Tt9ehNFjT1cr+kgp66Ljj4jy4b5MTvhZg5VSXMPlW29jA51Y39OA4vcahEZ+2htMqBzTccv+qYYRku1hq8KrmOyWHGT+6KQigbr1jy7K5fHet/HI24yJN9h26HgIIROBJOOn868TIsigSfHrgRs0sBmqxmRVchPTzxAWEIcbnOmcKz6GA8lPQTApgtVnCtv4/YJWjzsPIhwieDr0+WIRQIW2ediRIxL3GRozAa/4QB0a43sy66nrLWHl2fH0tTdj4+TgtKmHnLysqmpreLxFXdiNlv47LdzNJgdeHthEiazhflfXmBqtDuuDjKmxXpjLxUx4b1TfLQkkThfNVasgzVeurt6kIjFKKRWEEl4Ylc+uXXdpAY58eY8W62tjj49VW19WKxWPj58gwczIsms0XA0u5KHkqVIvKJp6tYxPsKNvh334+gXg92Ex3n9YAFrxoUQcGwN7Z6jeCTHj+9dtnCwLYy4JY/j5WTPzsxaRHk7uWvGWPBP52RRM00aHWFuDkjEgkHVufU7silu6mFMmCtPT48i98JhTtQLUbj5c7FSQ0VrL8GuSuYl+7D1ai1bp0tA6U6lUU1ZSy+TY7y4XN7KoRvNvDI3lpydb2FyiyVl3GwA2np0uDrI+fliJTfquhGLRLw53+atOFXcgr1UxKZLNUzx6MFZIWZk+kgAThS2kFffxaMZ4ZwpaaE18wALPJtpTHgA3fZVuMx6CXufGPQmK4q8zaAOgJAJf7pVlux5gAD7ON6ZshaT2cKr+/NZOTqYQEknCITUa8VomquJSkijqq6JbccvcM+IVK4d7WDqfXFIpCLy67v4o6CFRWm+eCslYNTyzdU2ypp7ifdT09KjZ11GGAu/vMBtST7cOSJw8Ph1HVquZucwz70RYufT0aun4cxPGA16vm0M5IsHZt/SX7PFymPbs7h9mD/Dg13QGS02lUltJ3w1CuIWg9mAYcJLbM1sJCPKHbrrUakcQe6Ig0LO0fwmqjv6WD0mhMaiy3iFp0F3rS28csF3cOUb2+R7jK3ArVZvumn4AbQW0374DdYb1yKXiIn2UrEjs5aHJoayNFJu29fe5l158dyLpHmmMSd0DicLmrlc3cFTUyM5X9ZGUoATyn9o12yxYjr+FjJNpS28Dyt8FA8rj2BwCuezU2XcOzKIyrY+9mbX89poBdavRnNm+jFyO6U8PMmWS2T6fgamkAzk4x8H4KvT5cT5qBjl2Amb5tEz5QMO51aT4S/GycmZUvcwrrbmsDx6OQBP/JpDjLeKe1NdMe1cTffY13DxC6eytZd1v1zmsVRHxgxPQCSWwLFXQVOLZfon9Jw4id3kDFqyDtDtOYo9ee08VPcE2/1eQO4mIsozgK5eAVNiPCnc+To6kT1J89b9+fwCHxwrprPPwN9ui+NYQRNP7czl90dHYzBZOZjXiItCQlZtF3emBxDp5YhIKOC7sxWcKGphSZovxVX1OPeWsuqOO25p17ppPjqkyKa/ibD2AiTdun0QbSeUHYPo20BsC83+Pu97ztSd4evJXyMXyyloqeTDS5twc7TSo9fibC/n1VGv/nV7/4f5t35/DzHEEP9rGfJM/V+Azmjmpb15LG9+j5jYRCTjn2DZsIDB7XMS/iyXGzfCG/tsCR5/IWVrLxOTEuhESqATWy5V0tFnvMWQAptktVAAconIFsaTfQ76u3Abu3bwO6dLWvgtq4H3lyTi3HEDi9XCLP80ylv6qO/sx8fJjntHByHpeRDc/aCtFJTuED0TAKNIxt3TPgHgWEEzWTWdPDUtEqFAyLXGaziOi0aAGxKhBIXEZhAeqjxEVpuWMK8APs78mFE+o4hwiWB4sDMCgQCV721UFeRwOHMLw8LGEDbQV6lYyMRId5IDnBAJBYMG5qPbsxnuZsJRavtfJBKSEB3Jj9uzedlgQiEVs2UgT6owp4Wuzn4cvVV8cXsypmsvcvaiO+eV83lpdgzP7MolOcCJxal+8MN0sHPi4Snf0qszsPaXbM6WtjImzA1nexlvHCykoUtLZz8gEGG2WvFydcY3zp4/sjvZl92IVCzgunQpUzWluLdqyKnV0Ke3wNQ3cKi6wvrQRi7qMzhjkCKrrOfVAz1sWZ2O3nMEa/8w8visXoYHuyAUQEOXjmC3mzWP3pgXR1VrL+0DRZNlYjEO3cUkkUWZwzTmJ/kgANRKKe8tjAcnBU3d/Tyw5SpYrYyNcKespY+q9j7MFivBs57AQS5h08Uqwtzseem3Ap6fFcldI/6c92InERHro2bpMCthbkrcVHJ2leyyhRJZ45kaawsL23ypGrk4ngVjE/ESieGBnYCtrtEDm69z5qm7EAsF6A1m9uc04GUPYzIfxZTxN+b6rmV0sDfNmn7clHK6tUZ+uVLFE2xGKrPjD/14SipreCMhjYY+M1V6B0CC2Wylu8/ADyeqeWhiKI9OVtsK3x79ArqqWLXqJCaLlYq2Xvp0ZoxmK/F+jowIthkZnY0N7N7wCpFrXia3S0Zs7GSuXa5m2fAAnFNGg1DMF242pTSLxcqFfV8R6yZGkb4Srd6E3mRhb1Y9P5yr4ulhYkYHKiFmPqiD4Nq3SANPcvfI6Tz400UsmkbeFH5FrudtjFuwFid7KQaTrRq3a1gaX52tZEm8E04xc8FqgYjpILQZ/WUtPTz8SxYu9lI2DygMYudMq2Mi3kY7Tpe2MSnCnXhfRxJ91Zyo1zPCT8bfs5vUprEoLbZn0IRoDyZEe2CxWPn4eCnPzYiitKWHfoOF29P9eWpnLhJdOu+4d9lChOWOsOIQuEUiFQh4fLKt4HGv3kistwqzkx93qTYyy+jAw5NuKhL+zXw//h0BrLBauVHfzZqB2ljgRsXtFzDuWoO3z2SUPaXgNwedwEKXvmtw/w3z48hvy6PdZE+ZcjxHLnfwkp8tH2yJ5ByOpniaeqIRYsRr8stYLFaOHLtG8Y1m0q1bybQfSfnlJj5Ykgjm/dx/YB36Hm++aJ9Jqx4yojxQBA9DPiAi9M+GFMDa8aEcvtHIxrMV3Jbsy+knJ2A0W1jx5RFWOOWgSL+Htl4D3+z6nTfnxSL0CqOxp5l7RgQxJdaTrprP0XoHUdzUw5H8Rh6ZZBtLXSkP8uXpKu6VeOGVdMfg/TQlcMqtHVA4QfziwX/NJhMxLjHEu8YjF9vEf0JcvFmSMJL2/nZyWnOYFjjtT79jiCGGGOI/iSFj6t+c7JpO6jq1zPoLg+jviIQCAlzs6Q56EkmM/5+2P7UzlyR/x1sMLH2/iWtHq+mRC0iMdvuXbR/Nbyba+89CFRUtvazdnMlbC+JJCXCGxOWD2zIPVSGzFxOZ6MpVdQdrNl1jfspCpkR7cqygmWtVHTy6PYtf7x9JtLcKPAbCB7csgph5kLicXp2RSR+c5ucVw4jwVOHvrCAo83VyT08kftw8lEJ7mvZfIGxOMsFqW3L0LwU7ABMLUwIZ6ZNKn+Fn7KW2RGs3pRTPgdpGJZfPc0GVSbOoi8edH+eXS1V8cbqcY+vGD9RKamLHtTq+vjOVT5Ym4uYgG1Q7Axgf4c7FZydR1dbH1vOFvDwvDaFQQPaJWrz8qgmYN4M4XzVfHo3B113F8jR/zBYry4b54fb3YpmeceARS4CLPWDP+HA3smo6UdtJiPNVs2ZcCE/vyiHCU0mivxNJAU70GbuZuWcm30zajJtDMDsz65kc4ktk4cfIpAvYen8S7588zRMTJiJwjiCzth2EZkbKjjIxfjax4TbRDGnwaMZ31tLRq+enC5WUNPfS2qPjwCNjUBTvo7fgKF+p15Hk78Rze/L4YFE8o4ZlEBEWBnIVyXY271VuXRcf7jrJd26/wvyvcFEquX9sEG4OUg7kNHD7iECWDg/g2KUs6spvsOrOu2jq1lHfpUWlkPDC7nwmRbWi0Zl4enokHio5Jdebef5YAW8vTWBkiM272dqjQylR0m/sZ2vFOpK6xhLp+QCfLEtGYAVENgPgmV25LB/uR6yPI9/dnUJJs4avz1RQ067lrhGBWIQSSFhGq9CVXy4W427vwqv7czjz1AR81HY0aPq5r3cmny5Pwb+mh7un20I7Rzp2MvK+yQDMDfeivVeP1mDG8neff8kRW9Hf4fcjFAqQ1lwgsiEHRq6ltl1Ldk03j0y0XXdHdw8mrVwLojaO5DcwMcKV2s5+Wzse0ezJqufK2Tzemh9HfVc/J2tMeLo4EyoWDobZ9uiM9BqMeHcfxlivYA2NPBVyJxH2avC3GT6zUgLxknkjdviKRHvb/W2XvZHEmAzAG6PZQmlzD/0CL5wULrB5AXWLDgwqhPo6KZga4zEY6giA0o1+WQRzTEVo/MIQiQS0SLZzvXEBZ69rGde6gl1J37FgWgaukjBc7G4+N37LqiMj2pOdD9g8jJp+I6dLWunTGdEZjdwxLhH+UcxgoIiypr0fB2c5O6/VYScVsTjN9nx7c9ko6rt0tzyT1k4bj51CQlNTE/Y776V90eeInfx48/dCfstp4OcVn3G2sIW+gBlMCfAkDohzvZm7JRYJ+anwJ6Ls5vBLaQT7H7YVyhUIBLiPvpuAYC/u25SJnUTEplXD0Zss7GsV4BQUg0yQTZJSw+ni/gGBETFNqU9Qf24Lw3W/8k77WC4ermPUjBm39Dn71G5+uGHhk4cWArbFqW1Xa9EZzAS7OxDro+JieRuvZHgSkrsZcaCKmQl+mDe+Cmfs+T3xRfZe72ZmrAgyN6E2dqCOW0GJBQT/EKvpFD2JZ6Osg3WiHKQOWLHS1NtEu1bPL+c1PD8zCoVUjNaoZUfxDsYQz+kfvuH4pB4kQgk/ev1Idks2/aZ+JvpN4Fj1H2T4Z7Dh6gZ+cf8Fe8mtNcOGGGKIIf5TGArz+x/yvy1M4J6NV2jq0nF43dj/v9vIr+/m9YMFPDA+lLEDMfsWi5UFX14gxsuB1+fH/8t9x7x9gkcnhbEw9ab0bbNGx84rNZS39/HBkqQ/7dNQ1olYKqJEr+dg6Xl8FeFkRPrdEmbY2NWPl9oOvcnMqeIWJkd5IjTZpH1/zaxHb7EQ5elAnI+KNl0L3kpvCv/4iXahFM/OI4Qu+JHOkkzUviEIFGryGup4+uJ9zA9ZzKrEe/7Up0nvnSIpQM17ixL/tG31T1cRCuHrO9MAeOrXbLRGC58tT0ZnNCMVCRAK/ynXQ9dNq06I/bb5KPwSYea7GE1mxD9MQRC3AEasJbOqgxB3JfdvyuTOdH9Ghbmx4fdC7hkZRJS3Cq3BxGcnSlk+3B9vRwXvHimipbSbl+5KxFF9UwK+uLmb5d9e4eT68VS3dxDna0vO/+BoMdUdfVwqb+e3h0bTY+zi4W2ZfLpkOCq5A6t+usq96QEMd+mjRexJZc5Z5kUqIXQir/x2g9y6biQiIWNCXZgU5U6Ut5pL+ZWcr6nkfPslPpj2CK09BqK8VYNhWXqTmRaNHmd7KfYyMZa8XQh/ewgezcWicOVQXiNKOxEnC1sRCQVcr+nig/hajAUHUC75BlelnF6dkf05Dfi7KDhR2IzWaOHlOTGo5BK0GgPd7Vq8gtSDY23M2yf5dqUfz19ew2jv0bjbufP4sMfp7DUw/ZMzvDgrmpnx3ry0L4+6Ti0/3DOcK5XtrN1ynbQAJ+Ym+jA5xpP9OfVk13SRFuTMjYZujCYLd6YHYjBbCPNwQGc006rR06E18MbBQn5ZPRxxdzV8MRyWbIOwiVypbCOvTsPKMcF8cbIUg9nCYxk2z0ltRx/FTb1klG+gv6Me0e3bbPW3Bljy9QUeywhnhKQM8/71XE37gPSc52D5Dg5V6DGZraQEOtHQpaOoqQd7mZjbEr0RCAS09epwVcrRGkwczG1kWIATzkcfJMfvDnp8YYymBaXfCPodAtAZzTjZSzFbrAgFDE6gS39ZjzR2Ln5xo8lryyPBzSbocaW0gbCaX/mtoIvpdz+N+z8UVG7o0rLhUDFvzo9DKRNTXVyMXqenRgem4iOccgxjSWosaqkX0tYcamVhg0W8B9vo7GfptxeZEOHOq3NjaezSsm5HDo52YjbEtaEs3ELj1K+4UNbBwhTfQQW9z46X0H2uhZV3xpHVp0UuETIh0jb2D+U1cqa0lbf+6tllNmHI+gVp/EL6kbHxfCUz470IcLHnWEEzQa72hLrbvLAtfS3sKt3F6vjVVLb1466UIxdZKKtrISbE9szT6fTIjz0Nds7UpzwBWPFxshkOpc09vPF7IWNCXfjjSi6LPRuZd7vNO7/9Sg1Xi6p4z/w2reM/QqJQovbw4lpVB5VtPSxM8aetLIsLeYXMnX/7n38H8MP5SnZl1pHoq2akqplhFZ/SOuoLSpsayOsw8NS8ERQ1aoh30sOOe8DOCQJGwsiH/rK9f+S+o/chEUqo72lgjPJNHpoQjkwsoqO/g3euvsMTiY/T29DEKdN17MX2LIpcxO6S3VSU5RJf60zJMCFxrnGM8xv33x7r/xT/297fQwwxxH8G/0227xD/2/n6jhR+WT38f7yfyWjmpwMlHM1vJMbHkbtHBlLT3ktRo4aP/ihGKBSQEuDE8vSb3qrK1j4++qME3T+oBP7+6JhbDCmA0yWtlLT1cryohYtlrTy+PZsurS0c7JPrn1BvX4a7vwo3mYQq826Sw7r5qfQtCtvKKGiwJZZ7DRQbburW8c3pCrZfrUUnkJHf1IPaXoyPoxyrFTKbr7L2+Frqe+qJyribYmsmB+Qijtccp9vNneUnHqCprwmFqJM3079hVeI9tLa289KXezhamkljbyMAD4wP4aHxoQAcrznOhapSqtv7APh0eTKfLksZ/H3Xa7uRCAVcrezgwS3Xuf3Lk3Bj1+B2i8VK17Y1qAt/QTFuHShsIVwSsQjBsNUgsnmxXtufzxsHC4j1dmBYkDMfHi2muLkHgcDm1bFYQWuw8MKefPbnNvDwpDBGC+XU736B+kPvA1Dd3se9P1zjkyWJHC9sYek3WWj6Ddy78QoeKhmvzY5laZo/r+84gycWDj00i9D2Kwhzt2M0WTlwo4m3LvZzsqiVZp3UluAPPDElgufHuxHmrmRfdgOdWiMAGuywd1AQ7N1HW28/wcIGFALbNp3RzMR3T/Potix+z7OdV2HFKQgaC0o3evUmfr5Ujb+zkgBXe+wkQkaHumCXMJew+zbx+I4cDt1oRGUn4VJlB84KKddruojxVqGSS3h53w3O1rbbDKnL3/LtoYs8vj2bTSvTOH2hnefdX2PDuA08PuxxvjxVRqOmn8cnhzNuYIFALhbR0Wvra6Kfmicmh9OuNZDo78iot49zLL8ZvcnCyaJmThW14OlRzytXH+OBzZnUdvRx+EYT3ToDQW727Fgzwjapdw6CO3+DMJvXJLu2m1+u1ALQpzfT3mugsq2XO7+7TGGDhlPFLTDtTR7UPcAfezZC2QnoqqH1xztwEhlRycXglYhozsekJyfBmPVg58TurHr+KGxGZ7RgMFnwdJTj5iClql1LVk0nt397mX6DidoOLb9crmHzlRpyZKnsLDZz4JILyqYb0NPI83vyuO/nqwC8uPcG3521qSM2dPYTtvx9RL6pfHbgBI+deIz2/nYAvjpXh6ijjOUuxRzOqeb7cxUAGM0Wmk5vZLpdIbIBo3BfrZDvS60kOJtwMrdQ2+jMH3lGNl+uwid65J8MKbAp8r08M4YXZ0UDtjHfpTUyJcqDVRdUrOl/iOd33eBoQRNavXnw2HqzFVGSE8+cLiKntosJkbZQwa4+A8KGNrx7Gth0sYrKtt5bDygSI029C6QK7KQi1k4IxZlu8q5fQNi6lcd3XKdq4N43Wo109TVy9GIWz+zM41hBE7L87cScXAkWC1suVbPwmyuYBDJQOOHjpMBOImb3mUwsLcWEOQn5MamCYFc7Hp0Wx6wF9w52Y8kwf967ayzdc39EJdWj9rCVlOjVGzlYepq9ZXtxC0u2GVIVp23hov/E3SMC2TNbzKoUFQZ7L4qcJ1NwqhJ/kSP3TUtBKhYS7C4EqRJiboPRjw8W2v1ntEYtr539jPa+HgAWhy9mZexKVsat4PGMyMG8Tmc7ZzaM3YCryp3AyHginCMo7CgEYH74fFYPe5CGOBkVXRW4ihJp67EpvB6sOMjZurN/eewhhhhiiH9nhoypf3NkEhHOf5HX9N/RVN6N8UILZ0rauFrZgaejnNKWPkQiAVKRiPpOLb16I9+drcBktuVRmKwWcmq7mP/FeVo0thCav6pqvzjVj4+XJnP+6YkIBEK6+w30Dcj4BqgCcJI7UZnTSs7mUt4J+ZB073SiXaMpbzLz0r4bvLAnj6uF1bbvu9jz0dJEXj2Qz6miFh7YnMmFsg681XY8/EsWjoJw7IzRtlwgo46VfSbuDZjBd7nf8dLeKlLVt/H+lfdYdXItsZYWAM5m7SOo9RqXW46T3ZoNwMJUPwIHcoKyW7LZldnEnqx6AMp2vkzVpb2Dv++ne1OJ91VR26nloYmhrIs3gck2YSho6Ob85Qu8abmDr7tSMYRN4wsWsv1qDduv1vBmQzwMW8XhG428MS+OLq2RC+UdLPrqIk4KKS/OiKRZo+edw4WcLGrmlTkxvDI3hsnRHiikYuY9lIg1fjbKaFtY2TuHi1g1JohRYW6EdZ3l8wwpKjspga72BLkqcLSXMjvek2flu7nyy6totEZ0Zrhc2cnt6f5k1XTj4SAjyEXBynlTIHg8AMq2bBJ+m0Z9exe/rhnJt2cr+S2nnikxnqwZnU6C3WrOlXVi3fMAlVcOALYQpJVjAvhoSQIzIgdCuKLnQupqACQiIQ+MCyHI1Z7RYa7E+Khp6NJx4loBh3b9yN3p/hy50YgVm1pajI8j+x8ew/LhARQ1aoj1dbwphiIUkehqJdbXkTB3B0wIMUkc6eizXYe/+9sXp/mjHBijD04M46OlSRhMFia+dwovtR3pwS68tO8G7y5I4IXZ0Xg4yG01nbr1zIlKZU3iKnavHUVjt57LFe08sOU6my9WYzRb+PZMBV19BggYyBnSNDG/bzvhztDQ0s7ESA8eGBeCSi5hfoovG89X4e9sBxI7MhKDifBxtnkKZA5UyaORyOTE+KjZk9fGx0UqfrxcT7f/ZLCY+PqOVD5amkRefTfHi1rIiPJAgM0z7e0op6FLx9uHiwlwseebu1Jo7zXglH4nR2qsTIvxICf2KS5aIlk9Joh1k22ZgAtCBQwzXye7tosZn5xBZzTTozPSXtvIkZFvIxM6crKohWhvFZXpr2Ga9wPnr1whRmwzlL85U4F3+yUmtP8yqMK3ckwQy0fJeaXiSxJvf5kwb2fGh7vz/Izo//J5ZBFY6e63GboKqYixYW5MjfVkcpQ7Xk52aI1m3pwXh0oh4VpVB5M/OM04+2rWNz+Gs1xEj86276G8Ru7bfI3hhl2sNH1JTYd2cBuA5vJm6s78BNjCIX+5XI2pMR/lV6n8mtuBvDOP5SPd8HK0ed98lD48W19DSv1WHpscxoIUX/BNxyJX0f77OtKDXZif5IN41tsw8mEAuvtNuFb/junsh9DbyrfX2mnp6iM9JpTSDiNWqxVai+GnuWDQ8tD2PL4/dHGwj+MjPHhx+igm+tsM9PLWXsynNsClLwa/k9+Wz9o/1iIQWJFe+QJx3RXE7SWMqfuWNucWHrlcS1uPAV3DdYSfpvDzpTw+6J4AfqngHjXYTmVbL+8fLbb1W6flemMhZe2ttGnbyAjMINkzmdmhsynrKuP1i69Tram+edHOfQjtFYyQuvEsTlitVjRtrWx7YT3Ods64Kdz44mQZZ0paqeyqJHv/b2jbOv7LcTDEEEMM8e/IUM7U/6X4Rjpz9yvpfHe+glPFLaQGOdOs0RHgpGDthFDy67tp6zXQ0WvgxwtVrBoTTJi7A2/eFsvp0jac7KW3tJdT20mYhwMK6c0hpZRLSA9xIf0f6qPMDbVJNPcJ9TilubK5qoVno124I8qmHjUqKIi7f7iM6PphziWP5eE5afg523P5Wdtq6obb4smq78TZXkq4pwNqhR0roh8h1NkDrCZwcMcxYAxbYxdQ2tyD2j6RrcXNOBr01NZVEeSXjDoyEPeYcO71GT3YL11fL3knjuI/YjjrU9djTbmZPyB0DsTO6WatmzWbs3g0I4yMKFtIEf5TAVueR0lhLnMvLqRz/GGyOqRYsZLop6bfZOJgTtPfS2xxvLCF+ck+hLjZozdZeHBCNIfympBIxIwLUrP9Wg1fnCxndoIPCgnc8/sanhm2jmSplBiVDgJs4Ut3DA8gfCA80qEjB6GjbdL68mxbTkl5ay8zP7/ApTXrUTTUIRSCKHI6TR1RzI7yxNNRjsAKbx8uJt5Xjc5kpk9vJi0ghfd8PkFgkrInu54npkbgo7bDYrGSXds5qDzXHL+TIBebt8FoNPLp8TLae/XsuFzJDzNVxKVm0N3Wz7XPNqGM9eLdHBnR3g6UtfTSXFPM+/KddHotQnXxBWpi4lGkBXCutIUtl6pxVog5U9pOi6afjj4T9jIhpU09xHo7QtoK0oC0gWvy2h1jWPjlBXaUZLNhiS/DoroIdvXl8ZOPM9Z3LLeF3cYP5yqYra6kXOrEgmRf0kNcsFisVLb1kR7iwrJvL+GlkpPor8ZBLsJqseO7o0JmxreglIt5a0E8+Q3dRHqq+C2njsP5jUyKckf993vBpEPWW49E342lu56NV03YiQTUdusIdrHH19mOPVn1hHuoWD48gCsVDpR3GZji40RrzD2s87CFHUV4OuBkJ+HXrHpmCC/RmbcFwR27UcklzE30wUEuoVtrZFSoG7sfGIGLoI8d98axalsh85J8CHazJ8zDgfCOE/xxTxg+LmZKTn7FJefF3D8uBP54FUQTsOtswbH6ID4TFnLo0bHIJSJ2Z9URlToOia8fe6/V8GtmPXPC7bHP3ohs+qOMS4ggKsY2tm6PsUOb+CVy+5trcnYSEfYSJ6a1NSKtucorc/6slPiPVLb2suVyNcVNPagVUlyUMvZk19PWp2dvVgM/X6ph1ZhAKtv6UcolGM0WYn0cefO2OL47c4PX0x7jg0Rb7lJ/ex0jQlyI91Wj7jaDswvPj4rmYG4DG89X8eGSJCo1UN1hxBebQufJohZmJyTiEDSWl2P6ESV8f0v/2np0uJr0qBMyGBXiypnSNn660MVzI1dSev03fv29gI8W20KZz5S04Otkh0Ag5I3WMUyLdWOdcyBRyaOx6y2hrdePxV9f5Lu7UghUueKZfBdI7HBzdiJWUwpV5yFwFJWtvdjLvHGU2e63785UEBf5KctHDAhmNGSjN3Yz3DkOoaEPlmzCD/Az6aHJn74CJa8kqon0dEBojaNpxjukqiPR6G6tOwiAVWAzgvQaFBIJ8T4uVGuvsfHCCb7K+IryrnICVYFojVpatC109HcQoArAYrWQX3MGR89o/B18eab9IiNLvRnvOx5mx7Cjfi9LIpcywc+ETNSKlzKKSFkQqW7JtGpbKe8sJ80rDZFQ9Oc+DTHEEEP8mzGUM/U/5N815vqFPXl09Bn44g5buNrbh4oYHuwMwLYrNSxK8WXdjhw+X5bEpsvVPDM9imA3JXUdWpRyMWqF9L9qngnvnmRMmBuv3Rb7/7hP5S29ZNd2siBaZat5ArbaOglLqeoWsvlGF/ZSCesmh/N7XiM/nKvkzflx7Llex2OTw/no4k6C3EQsjlx4S7sd/R0UdxQjEohI9EhEKpKSeehH6oXezJl6U53K0l1HW8kh3NNW09PexuntP1F2+TzzXn2DgMAo8uq62Hi+ijfnxyGX2F76Hxwtob1Px0uzY5CJRbzy2w0S/JyYl+TDsm8vIRYIWBZvT3G3hHUDKmM05FBhcGBTXj9PT4scKGSsx81Bzmv7b6CUiVk1OoQjBU2EudsT4uYAAmjo6ifCU4Wm38C757bzYPpMPM+8CbWXYc1ZLle0seFQETvuH4lkIMzKeO4LWgqu4uahRjr3Q8AWCujhKGLGl7sYG+LDUxmjkYtF5Dd28+LefBRSEQqJkER/J1R2Yrq0Rh6ZFM7TO3Pp1Oro6jPx5Z0ptonu9Tpe3JfPhWcm8P25KpL81YyPcAegr62W4oOfohq7lv6+HqJCQxDLFfTrjFz8+TlQeVPtN5cfzlUzP8mHdSOdIGszPzGLmuY2LtQZ2bRyGHf+cBl7qZi8eg0rRweyKMUPB7mEj4+X4qqU8mhG+OA1fHpnLvWdWu4aFUC0pyM3WkrZWvkBSt1kVNZomhWf8dSwp4h0juSxbVks6t6InUco2W6zmRLlwXN78vhsWTIqhYTDeY0EuSqI8HKkrUdHXZeO53bn8lhGKEfyW3h/cSIAO67WcLyoBQECLlW08cfj43B1uJlHBJBd20WCryNlzRo+OVmOo1yCk8K2uJAW4IxUImJ/dj1tfQamRHty749XWJTiy/LhARzIaSCnros358dTVFnLj4fPInCPI9BVwf3jQjjywytEqQyc8LqP4iYNbwm/wuISTP/wx7A3dMCZd9CMfB7TyTfY2BTMiMQYRt54BUY+Cs5BbP/jAn7xYxmZaDOK2tva0B97Be85r/LisQYMJguPTwlnxcarfLAkkfqGBhqrirh93tzBgtsXcwqIPrwEx4dODcqd016BbvcDPCx6kG+XjrEpv/0FL+zNIzXAmduSfChr6eWrU+X4Otvx2MB1bdXoeGV/AZlVbaQHuyIQCHluZiRHbzRxIK+RjfcO470jxdw/NthWgBbAamXDJx9j9EjmxaW35o82a3TUdmhJDXT+y/4A0N8FMtVgmGtfr4HTf1Sz7moZZxcJeaNqH76uE1iVNJfi5h7Sg13o05vYn9MwmMc17aMzBLjImRjhgZvK5uEsbe6hoCCfqbUfwsiHEbcVsfJGDEGOAj64fRRUn8dq70ZrbS9uCQkIxBKe251HgIvtWv9yuZrTJa18dUfK4OKO9b1I2iKn0WXoIkzmCjPf+9e/67/hh7wfCFGH8PqlN8jwuQ2TsJO1iWvRm/RYsbL0wBLmea7jSpWATXfNpkvfxQvnX+DVEa/yfub7zA2ey0iBgjJtAy5+I3j90uu09LUwPXA6FxovoLPoUIqVdOg6WJf4KNHqSO46vgKx0CZk8WzaswzzGfbfd/T/Jf5d399DDDHE/26Gwvz+L2F0mCtjwm/WdorxdsBbbcf4CHc8HeXoTWa8HGW4KWVMivLAxd4WOujrrPhLQ0rTPxCuMsD6KeE8NDHklu9YrVb2ZtVhsfy1vR7irmRBih9snm8rBmm1graN1i4NxT1Cejs6aa0q5YdzFbgpZby/OIFwDweenh6FTCxiVLAPTnaOdGuNTP/4DGdKbGF8JZ0lbCnYxIPHH+R89hEAUqbfw5ypU2jqbeKdq+9gNBtprrtMR+5Wajo6cXBxZdba9Ux8+gn8AyIB2ySsrdcWNtarN7HzWi06o4mGLt1g/sD0OC+S/dUAbJgXxzd3pTIpOYpNl2q4Xt1pOw8XPsGp5jjP1q1l3SebOHKjkXlfXMBqtXJnegAVbX2s2XKN1h4935+rYsk3l3CQS4jwVPHQlmv88tNX/C0mHk8HZ5jxLizbBtpO3JVy9CYLP1+s4lj+QI6SezhitSei9hKwWNiTVU9rjx65WM6rM1M4W17Pc/vOs+t6HXE+an64OxW5REi8ryM7r9Xh76wgxtsRi8XK326LZXaCDw5yEY9tz+ZaVQe3Jfnw65oRqOykhLjZ4+EgZ8OhQvLru3n0YBM3Qh8gNCiQuNg4xHIFhQ2djHznFAG3vcw1x2nUdPTzzPQI5iR6sXJnJZVRqzlX0UWTTsr0GE9cHeRMi/ZgfIQr7y+KJ7+hB5lEiKuDjGQ/R04Vt9I6EGJqNFvo7NMjEgqwE4vxVEs50vgjQoQ8OGIyq4O7+cF7GpHOtuv5yMQwNtvfTYHnXFaMCsJFKSPBz5G3j9jyPZLs+/AQagHYeKGK7Veq2b12FFNivHl/cSLbrlTz7uEior1U3DcmmC9nOLPlrhhcHWzCDwu/vMCHR4to7Orn/p8vc726k+YeA1VtfcxP9mFkqCtpgS5kfHiGnNouBAJw7inCRy3nkznBhHkomfDeSYYFO7F0mB85ez8mO7eWDQ8s5/5xwcxPsil2ysMnclmazpQYd+4ZFUjrqBd4q30sr/x2A0QSWg0SHv/lPL3jXsE5YRqFJj+uxr3CH8cPg1BMqTSa+/bUDYbetvQZaNBK0FsE/O22ON5emICHyg4fZwVZNV2crjVxusfHZkj1tgLwVWYPeVN33DSkAFReWEc+QKHdO+T21Q5+XN3ex+qfrg4er0dnpG8g9C7UXcmcRG9ya7sGnxNFzRoiPO35+s40wjwdCHFTIJeIKG7pQWe0hRxLRAJe/u0GR27Yxj0CAfctWcADM/48MfcYMGz+zvHCZo7mN6E3maloHcinslODUEhWTSfP7c5l5pfnUZlh/5qRuCdOZUbs3SyOGYuTvZT0YBfMJiN5B3cyJ9KRh37JYn9OAztXJCLta6a1PIuJfYdQySWYzWBQ+lDsmoFe7oa6M48J/hLSdRdA2wYlR2H3fVw61kmvxnZ+/nZbLPeNtdXZC3SxZ0yoK/Wd/eiqr9Pc3s6jspfRD3+FsGkfwrhnbP1vK7fVEfwH/naggO/OVvzpfABYD66ns/wPPJWeOMmdeDT2DYq7M8ECbf1tXG66zIvnX+SO+mHEdlsYGylDJBShkqqYEjAFR7kjwzyH4aPy4fyNUl45IeHn/J8JdgzmqbSnqOypxGQxYbVa8bDzQCQU8dzZ53n30MtMcEtgfcp63BRu+Dj8axXaIYYYYoh/F4bC/P4vYVqs1y3//11KffvVGqZEe/DI1mxCPJScq2jHQS7iq6v7uStlBF5Kr79qjtu/u3xLqNtfSbNvu1LDW4eLiPBQ4idoxZi7C6epz/y5sflfg8rXtio8/W2e2ngFg0lDmp+aVDcXsls1dHRaSAsKpbBBw3vHihgXKUeoquKOqDuwWKyMCHIhyNWW85TunU68fRT7f/sGH6NtEpV/eBe+cSkI1QpkIhkIwCtmATsao/ltYw7H1o1DKBQQHz1ysFsZ0Z5kRNvC+6ra+jiY18TdI/24UtXBpQrbqvnwIBdMZgvrd2SRFuDC0uE2aebda0fiP5Dfcz31HR7cnMWn4dN5aOJIzjb2sXJkAL9eq+NYQRPhHg48MSWCQFclebWdNDbWg8kAYikquZTggGFUifw5faGKu1PcsH45kj6naNTDH+T3R2ex7JuL9OrNTI7x4qBQS8yUVXioQ7he08m2K9XcmR4IQKpvGAvjYEKkN94DAh8OdkIS4i6xKHwpHio5zS2tbLzUwOiqs8gSFtKrV5MR44lCKibAWYFAIMDXyY7ndufyxJQInOylPLeng1B3JcvS/PjbwUKSAtXE+TjRWHSZBVva+Wx5CiHuShal+KCQS/BQ2ZHf0E2n1oiDTMzT0yN5eV8+J0taEYkEPDrZZvy09+rZfLlmUHRgXIQ7fxS1oh0QQJGIhHxzd9rg9bJYLUS7RDM7eDaeSg/QZENrIxaLFZPFSrC7ki/vSIGay5B5ElHicuYlu1HeYqSuQ8ve7fsYFh/GsJlzsJeKON+kQWswcbm8jUP5zYwIduFQfiPXazoZGexKSssLxETMhKC7sJOI8HaUIxEJaa7M5bTDi2iNn/D2uU5SA/0wmq3E9JzHvOcQz0x/mShBFd66Ylyvr0cve5zPjouxhKfz473DCFJLQezAtYrhdO3Q0TdOz7YrNeQ3aJiV4M2S0TbPyyNbsxAKrHip5Jyp0hHvIwWFE1dEyfhI+xEIhMT6qDhT0saGfDHjQ1cxXOTAC0tjWNOjI7e+m5ImDW5KGTG3vc6c7zJ5YHwws+K96dAa+GRpElKREKFwIDbVbIKvRsOiH/lpxQgAGru1bPi9iLfmx6OQ2WEXcxs7/Ifd8txwtpcyKcpj0Lv78dLkW25/kcCW+/n3ENhfrhRT0aZn7fgwPjlZyuQoTxzkEp6eGIBi13IEXe/z5NRIHtuehX4gn9NgsmBWuuGmlENXHfQ2g6/NC5/VksVrF1/jy0lf4qn0pE9vwmg28c6lz8ivEbFt+U1lO53JgodKzsMTQxmdclNYZ2rogMiPyQBCERazGU1bG2ajkQfGB+PrpECplPHp7WnQUggG26t1WLAzw4T50F0OoWsh9AseAcB2/pj0IoLiZOZETgCRbR8RFrj6PcQuIrj4GyLbi1h97R6WSy+RMnICG+6ZhuLyRzDqMVAOLJD9/gRFHjM4KBiDv7MCiUjIohRf7KQi2o9/w/lLZcx8+g1bQWGgSeXJq9c/5PMFvyESijhd8yktumaCHAM5XHWY3SW7eXP0mzT7NJDTV8mCqIW8eO5FlkYuZV7YPJr7mhntMxoPew+k6Srk5pc5UV3NyviVnKo7RWNvI3qzbREn3SedAMcA4tUx9DRmoszdRtzwZ/lmyjcMMcQQQ/wnMBTm9z/kPylM4Osz5bRodIS5KWnp0TMi2IU4PzWv7s+nSbKZFUmzGeM75i/3rWjtxcfJbtBD88+cLGqmoaufGG8Vif7OXLh0DmneNhLu+YjME/spcG7mntT7EAoGnKMdlaDtAN8U6jq0ONhJcLSTMOvTs3xp9zXVklDS73iJzj4DL+69QVWnhskjs1iftv6W4+afOUV55kXmrHv2ls+Pv/kgUSlJeE9ddcvn7b16Lld2MCPu5uTv/k3XkAgFvLc4EZlYOBheAzalvuf25OHuIOXuUUG42MuwWKy8uC8Pdwf5LSFoHH8NgsZB8DguV7STW9fF6iR78s8doNc1lhj7HuqchvP9+UpC3OxZMy6UYwVNRJxYRZf/ZGJnPTI4kc2t6+KHc5W8vSAecWcZ352tpMriwnOzE5GLRWj6Dbg4yHnnyvs4Ecv86HG0aHR8fLyMkSEuONlLbymsbDJbqOnQ4uMs4YNrH7AidgVv7G/Aob+eJyW/og5OpdtvIo5+MWjatAiEAhycbQZYVVsvazZf54MlCUR73awVZDZb2Hy5mnlJvqjsJGDQUlqST1hsGpcr23h9fyF3jghkcZptktrVZ+CLU+U8khGGUiZmz/VaUgKc8XexyUpfqWjj1QOFbL9vBEr5zXWfhs5+vJ3s+PR4KTE+KiYOyGH/I5lVnQS5KnBWyli3LYtGjY5t9w1MYCvPQFMe3yqk1PXW4SKOxNvBkd6OKO4YEYBYJMRqtZLfoCHWx5Hl317CTiLiu7tTEQgE/JpZS6S7AxHOAnQmUIn0tmLSwOaLVbx3tJjzdzljb+rmnXNtFBOMg0LKwlABvbW5lLb08XCqPXTXodF0IzR0oxv3GhIHNd1N1azcVsi2VWm4uHnS32PAzkHKe0eKKGjUMC/Jh0h3JTdqmnFQqvj6bAVxPo7cnWCPv7YAS/h0RBYDmHTcvqmQSC8Htl2tZV1GON6abCYVvoRsfR4IBJwubuFqVSetvTremhfP7ut1SERCzpe3UduhZevA+TLqdfR2duDk6Q0dlWR2q4j3d0IiElLY2M2jW7P59q5UAlz/XEcoq6aDcA/VrfWobuwGe3cIGv2n73NjD7kGMR+WNLMkKoOUYJshJRUJWfTledb7FrKnN4a3l40YvDfycjIpM7qxK7uRjcMa6D/7GSK3EOwX2ybrbdo2vsn7hvUp65GJZVB5Bl3BPlbSxKyg22jXNxOqDmVa0M3islarlSf2PsjiyKUMj7oZNti140Fw9EP9T4tC1qoLfHGpleHDx5Aa9E/hhJ3VUHeVKrfxCH5/igAfX5j62q3fMfaDth2j0ptfLpThV38YidIV/6bDIHcgSxOBc3gsD50zcuqhJM4e/ZXZfiC0s4PUFdQ2NvPG0WoQCvFQySlu7mFKtAcTIt3xaDhNWXEdcQvvG3yW1WpqWXt8LRunbaSpr4l+Yz/7yvfR3t/OXdF3UdBRgMFkoKqrHqlEgLfSm/P157kr+i6mBE1h9dHVqCQq3p/wPu397bx37T3ui7+P1y+9jtagZazfWHoMPcS7xvP9je+ZFTyL3LZcrBYrIzySWBB9O0KBkKJGDfYyEX7O/2dqUP0nvb+HGGKI/z0Meab+Q9AatSgkiv/RPs3dOoqbe2jp0aM3mnlkwBCw1WZ557/cN3hA+e5fIZOIkEvEJPrbJhYj00dzrL0R49fjqddPpSdOjMVquWlMXfwMLCbwTcHX+ebveOe2SHz+6OJN0zjyzlYgEgr46q5Ufsj7gVjXPxt6QqseianPpq4nvqlyOOmx12/mZRm00N8Bjr64KGVIRUIKGzVEedm2L071Qwis3ZxJjI8j66dEDNbkEQoFjAlzJa+2i60/fMjicDEVYfdwsqiVMWGuvLj3BkGuCi5VtLPKxZdoiTPfHy8hLdCZMWFuVO57nuD+ckoNvSjbjxK54ncc5WIcZGI+O1FGW48OhzFvsadAy6vabuQ5P8Ow1dyo6+Z4YQtlmX8QkzKOo81tmCx6dl6r497RQbgM5Oz09TlxsVjIjnOXmB7nxVd3prI3qx4XpYyDuQ1cqezg6emRZLx/GoVMxG8PjebZ4TbDM867By/HZN4tD+CF1Cgmv3uKL273Qp/ZjlEspExdj1BWw4r0Ozj8mG2C2dapISf7KpPGjUckEjEn0Ydl31wiNVDNq3PjCIu1eY70RgthHkpmx9uM1ty6Lg5lN9DVb+S9I0U8OimcYUEuXK5ox0Upw14m5mBeE0qZGDvpTYP9wvlTPHHKwBd3phHgosBNKcNssfLi3jx81HY8ONGmVPfN2QrmJ3sT661iXLgrLko5RY0afrxQxYYFYyFoLMFFFYz2FPPKsYMM9+jn/uHpVLb28cOFSjKiPBgb7kZTty3Pzd9ZgeDiZ9DfxaJJL5JT18UX55vJKi7lffn3uK7eDUC0t4o358djH2T7nU+F2Qxws9VKfkMX0WGhTKs+BFFzQCxF8GUGp+wmU5bVzIxEOcG+gbwwsRNnVw/ePFhAo0bHp8uSmRzjyeQYT0qaejh66hQFrf18/shSblTVI+mt5MjpDu4UHmXibhl3jwxgzfhQwj2VtnIGSxIJcVPiKs+gPzkJWdV50NQzLmEJ4yLcMZgsCIUCFqb6sfLHKyT5O6Ezmnlxbx5PTImkMfsqOcd+Z8nLb9Gj8GX1Zyd5eXY0c5N8ifJy5Mi6sQjMRi5dvE7ZIT23rUtC6WQbj+u25zA/yWfw2WIbpC0g/LMKqG1AlRDvFc+iwOFcPH2EaaELQeJCe5+eph49zb7T6M5vwmy1IkSArquFFXsa+XqxgvcXxyORhNPVUIra2Y1TRS2MDnPFVeHKc8Ofu3kMBx/kfulsiV8EwJnaM3jY32qQCwQCRrcHo8+thKix5Nd3s/lyNcLeaajNMp4EclpyeO3Sa+yYtQN9yQlcu0Ao/IvFJ6cAcApAV9eKSaSCAJuRqr30A9dvFJLfG0yoQkOk/hTCRRu5cuUi44I7qW5uYGX3vUzx9eSp+BvQdoEDa+6j5MTP7GwKZngYdGqtRAH7Crup6tTx+fJkQtyVWK1W3vq9kH6DGUX8TOLjwWg2cqDiAL5KX1I9U3l91OtYrVY2F2xmtM9o1iSs4Zvcb0jySOJAxQF6un3IKRpFeNxu2vvaCVYHMyVoCmfrzyIRSLBipaSjhHDncN4a8xYAX2Z8SX57PkaTEY1RQ0ZABi3aFkQmAzODZnK1+SrbyvaS5jOGQHUgO67V4qWWs3pMyJ/P2xBDDDHEvwlDnqn/If8bV7a0Ri3Td03ny8lfEu3yX0sQ/xVlLb209+pp69WT36DhqWmR//K7Vyo66DeaGDcgOtDf00XXT8uxTH8Hn5D/Wnwit6IBbUsF6em3rkhXtfXy+7FjrB3hDoGj/rSfvvAwN8RxCKR2nCltG0xUH+TI8yCSQMYroGmELQthwvMQOeNPbfVptWgu/4JX/RG441cAPj5WzMmSNmbGe7F6TDCVbb0EONvz88Uqajv7eHFWLE9tvo6znYRnFsQNttV84xRuYi3W8OmUt/Tw08UqAl3sCXBRcLG8nZ2Z9fx4byrlbVpiB4rwXi9vxEEm5XpjP+8uShhsa192PY5yMSVNXZSUluDi5MKz00Lh6PMw7R0uNVnQa9oZd/0xmPkBp7ucUcpExPk6IRULef9oMSOCXQjw1CMWiCmohXgfNa29euZ9cZ7Lz2VwILeeLZdq2Hb/CHJqu/BRywm68iq4R2FKvhehQEB7cyVX80oZNXoCLT06gt0cEACVbX0c2pqFo1LPnSszyKvvwt/Jno2nCjhY0M6xBxIG82fePlSISCjgiamRHMhtINBZQayvGoCeTh09rf2cu9bAteZu/IOshEqaOGcIp1troKSll5nx3qweHcSNRg0Xy9p4eFLY4Ir6vd+eZU6giXmTbSpxOqOZV/bdIKeum/vGBTMvyReAPVl1BLooWP1TJv7Odjw3K5oLZW14yC2MCFDi7+vLuu1ZzIr3xkEuIfTsOlZ03gkiGY9NjiDMXUl2rU2AZOcDA6GfrSVg6kfnGsvnJ8qwk4pI8bXDz86Et+/Nemx/5/oP66gVByBLXkJefReXKjq4a0QgcxN9ePNgAZ6OclaE9FFzaiP14Xfy8KE2vr4zhb1Z9fzttjj25zRQ1dbL7ARvAlzsEQgEvH6ggJkBJhKLPkYQOQN03bR0dvN253henhPL8/vyCHO1Z0GyH387WECYpxKdwUq0lwPzcu6H9AdAKIKuWhh+H2/+XsDVyg72PDiatw8V4mQvZdXoYI4WNHGisJknpkXiZi9F369Fbm9bQLle04lQACFuysHSCNai39H88R43En5m5CgfBANeo/KWHvyd7ZGIhdS095Fd28WcxAEP6fVN0NcKYx7HePFrBHaOiBOX2rZZrdB0A7zisFisCIU21bl/9BIDdPcb+T27iNnJwShlA96N8pNo+vUsOGrHxnvS8HVW0Kc3IRSAnfTW9cOa7hq2l2znidQnBtt+ed8NUgKdmBbtikQsRSAQ8EdBEz9frObFyQEcfvERlr/xPgpPVwraCkjxtIUT6rUaGisO4xY+k3NXPuKatoHnp3x+82AHn4SYeRBoG0+m+hxyymqo63fE11WFTmrHyLhwBFXnsVz7gTN1bpTH3sGlqjYme2mZE6bAZO+F7sATSOd+RG/uftprCom752MsIjlGiwWZWERrj46WHj0x3o63/NZrTdfYcHkDw7yG8dSwp9h24xgbCz/hyKL99Bh60Jv0fJ37Ncsjl5PTmgNmKQdKrqChACe5EyaLCbVcjcVqYVrgNAraC5jgP4FE90R+vPEjVquVDl3Hn6IFMBvhg2isizfR7x2PQCDATmz3p/vl/wT/G9/fQwwxxL8/Q56p/wAUEgVfTf6KcKfw//7Lf0Gou5JQdyXFTT1/mmz8M9UdffTpTYwbEKqTKRzoCJ6Ln9u/TiSu7amlR9fDuVIrDVoV6f+0/eXf8nFVBkFg4i2fWyxWDufV0GeIZn9uPT+vHEZygDOtv7wCSk/c5qwZ6IQDMDDJUnnZ1K3cbUpl6Hvg7Psw8lE+3lvLgs6vqBIK8br7Zrz+o5MjMFshxM2eXp2RGR+f46mpEURJGrBrPIvBFE2cq5IQsXRwYgfgETt+sA13BzmdfUbuHenOxgtV3DcmEJ3RwgNbstiyajhtvXqe3Z3HqSdtRsDo9j4ulLaSGOCEQipGVXWECEkzsen309V3gGbvqehkLpwKf40pcjXpwQLotcJ1K8jsyartwl4qIiXQBfY+yL06K81xG/DprAFtG44hkziU10hqkBPrJ4ez/NtLfH1nClk1XWh0Ri6Ut1PToeWt9CU4OLnx2oEC3BxkPOx4nmnaa0z+UsboEBdevc1mPIa4K1Ek+XC8uJlFRjPvHSlhaZofD0+L5+5xBrCXceTrTwhJHsbT029e4Z8vVBHiZs9bA8ZUwflGam60kTY9kMgIF/oMxYzouMCIaXMwmszcqNNwrLCJUe+cZNuEXh6J9B9UkAPYuNq28p9Z3UG8rxrTgMfw7QXxxPupB7+37UotcT4qvrwjBS8HKR06E3YSEdP1BynfcRbpyl/4cEkSVquVq1Ud9Ez9iP5fcnl1VhTpIbY8FJVcgr1MzJ7rdVS09rF+agSXGy+z99wH1NaOZfOq4dCQDbtWw5qznKnQ8PPFar65KxWhUIBd2FjcRK58ebGKVfZnWT5rCT5+PrRodGRVt/NURgCUHMY/KQP/yCR2udUh0pYT72PzkhwtaMJFKuaRn67z2Cg1Exu/5IXxT1NvdAD9OLBaQKKgJnwuXKlFZSfhnfkJzPviPAqZBJVcwsRId4JdHTh6ownL1LcRugQM3C82Fib7Ej9wbRztJPye28j0WE9EQgEvzI5BJZdQ3NRNW6+RUZI88BtGsr8Ty769yL0jg/B1ssNOIiIoYjqOfumMsnfGYDSj1ZlQK2SEuN881pGCZr4+Xc7MeG9EQgF4xIJeA8DWYitShZmlfuXgFGTLn/SKo1tr4LbPz/P09EgiPFW4KKVcrrDJ5ffozEyN9WLTtTIUmj+YO3UdGPvR6E2oYqdx7B/Wdu7fdA2ZWMT396Rh6u9Fb9Bj7+hCRXcFFxouoDPpsJPYJvjTYj051vQ9O47msXnmZgxmA5OiPAbzJy0PPMrWa3WkxSsYHpzCZ8eLWeZUQJODlPty3kdZ9D3vhd9NhurWYub4pYKhD3atgtu+ROyTQIpPAn8vB96jM/Lx8VJWj0nHPlFHguwsw0eF4NZ+kS3lAYwbm45H42mUCjGG3x9lbd9a3po4nKNfPckRt3v/f+z9ZXRcV7a2gT7FqFKJmZnJsszMzDHGkDicdBg7zMzkOOA4ie3EsRPbiZmZZZFlMTOWqFR4f2xFstppOPfec073+fSMoTFUVRvWXnvVrjXXnPOdvL1YONL7+68S23SQmJvvBLmG786UMjTYhUGeg5gZPBOr3cqqPatYHHIrS/yfoq6zjtV7VvO890QmB05m5Z6VeKu9WRq9lEdHLGXtvrW9JS0OlR9CK9NS2V5JaVspNpuQs9Zl6aLD3EFtZy3tpnbqu+q51nSNKUFTeOnH44T7PIGu5Sqbcj7l/bHv/68ZUwMMMMAA/x0MGFP/R4hyifrnG/0TIjwdiPDsm/zQkA8q536KXQsH9Z8giCUSYqbdfsOxui1W6lqNuOmUrL+yHmmNjipjJZGebkB8v23vGRdGuIdwXqvNzqnCBkaEutJdfIox+++kZsVxplyX0yQJSEWsvU52eczfiFr4C2E0dpuVXw8eY7TRgJPdTnCIE+2W2wjx12FV6rk+2+vBSRG9/3+zJpVYb0eq0nNRSqvJq23j8/Ry7pM4UuahJTDOlb9Fr5HzyfIUzmddw8GQj4taCDdL9tdT2dLFsBBXPl+R0rv958eKyKxsZVqEnturnmJ4yhqaOgL5+ZUsLkUN5/4kP6pbjaw/XsywYFd0apmQbL5mNwD3TxCOY7LYKHMcSpuxhNrGDqIK1kNjAU0eI9h2qZKLpc0U17djA1o6TXSabTR1mLh9VAhPbLtClj2IoU6urBnegUImBsdV7JROxK2urLeWFMCJ/HoivLScL23klo9/4+slkcg9vLDa7OzMqGZWgjcRQ0fi4u0LhYcgYDhIFQy9rs6YxWrjx4YmZkTpCYhz7TFKPYHRXMqtpctkZeulCqI8dXy1chAhZRvB5Mz54iae3ZHNN6sG4e6ooqPbwqFfN+I8YThB0YN4ZX6Ph6+rle5Dr9E94iG23D603/3xBcFoMN1GYOwinHqKsj74Yzon8hv5+c6hjAh1xdNRyRfHCqk2GJkU5cHTv2ZhMVt5fZwDGKpwVjoz2CeMRwcLtYVwi4QZ74JMSWZlOaVNnb22X3jCMGrT97BxzVJIzwMPoR6Xi1aBxWpl+4Uy4uOjUOi8Acg49hNJrYfpTPoAgCmxHmw8UkyaRk2KNQsMVdglMuZ+cpk1I4azKMoXJ7Ucx/p2/J2UcPoTpHFLmJ/sw8RId4rq2wlWmeksOc+eHBlR3uH42ZQ4AgdyalFIxYwMdyPITcXB0oPcMWY8Vypaya4SjEIPnZJ4Xz1v7c2jy2RmuOEWWLkTXEP5dk0aMomY13fn4qKVcevIENAIIb1P/5pFTrWBnff2D3lbOTSQ6pZOPjh4jSgvR6bEJvV+5pAwAz+tBPuXw3nR4RmiB42nxtDFPePDWZLmT2qQM49uzWBytCcHc+tQSMR02VpYMFjPW4lGgkrPA9BakYV179NsNLWwOH4Osp5wwkcmRyCVCDdm8297OFEj4bN75jLGfwyirhju+i6Tr1cPZlfhLgZ5DKK9opFUTyFE9cEjDzIxYGKvQWGzWjCZxNjsdq7VtNFlaMQx/wNcFn3Nw7Hfkl5uIDZuUK+na/Ou3aQmxhASfxNkboXWKhCJMdvMrM9Yz4LwBbip3agoaCE7qx7LsECQq3HyCwe1mmEzVjLl2lbkpkooOACuYciTlrHk2FkcfYbgN/ZWph7/lqaL5TinzOGOoZ647T4K7QvAWUNVSxeGHvXEBI8E2k3tRLhEUNRaRGZXOuHN8xnkEkOL3pf0siMsDFvItOBpPHzsYSYHTGZq8FSUUiV2u53pQdNJ80pDLVNzoeYCVru1R+FyNtNThXH8xZUvOFpxFEelI1OCpjA12gXnkrN4yKLYo3TieOVxVBIVoU6hBDr2PWMGGGCAAf5TGQjz+y/y/1KYQPbWl3m4MIGfY86gdg+AwWv/5X3XfnuemlYjt40MJjVUQpuxDUNDG94+Xng5/LlCIEBVRSnrtu+lUpeEsxJeHy7uVeX6M9YfLyTUXcuYCGE1//pQoCvnjhB9YCXlq88T7OXe+/mMD0/g76xmZrwX0+K9qW83crzyIEcrD/L6yDd7VceuVreyO7OGBydFYLHaMBpMqB0VHM+v56PDBYwKcyO/rILbYyBmsOBxenbbJYKtxaycPxfEYk7k1xPlpcNZI+eHPR8R5CzBu1VLly6UdmsnIeU/4xwzka/rw8hrsjHX35XtFQ3sza5l74MpuGlcqGjs4OC1WlYOE+SSOfsFyDXgEUOmPYA7N17kxTmxBLhoCHZRg83cmy/2ym85YAdXnZxB/s5szTpDkk8FC60yGLSanKpWNp4u5dX5fQZup8lCvaFbEBTI/oWrxeU8VZaMQipm3YoUqi/+zkelPgwNdUenknHwai33jQ8n0FVDS0szqo1TUCz6GjyuCzktOQkKB3bUuTAqzA29Ws61mjZMFitF1Y2czK9DrFCT7NAK3e3MmzaFxo5uPHQqLhQ38OWpUt5blIii597U//QAmq5K1Df/2HcOYyvp3z/FVvViXloygo5uCzd/eZZZST6s7DEMj12rpdNsY0qsF2arjZd35XDz0AB+OCfUjrp3XBjZlQayKlt5YW40D2+5wuoRQXgcf5L4mFgcJjyGpa2BjvJ0HKMFi9ZksSGXirHZ7LR3WwTxDaAq+ySGw+/iueYH9Go593x/iQhPFVdqC5gensDmixWsGRHE5Bgv6Gym1SqjqqULJ0ctnjph5b66pZN9ObWUN3fQ3mniuTkJglfOR8+S9We5Z2wIgwOdeXPHBZ4wf4JyxqvUiZzpNNn4y+Z0Pgw6TWjHZVi6mTUf/c7MGGfmjh3CprOlKOVS5ib5cLD0IE+eeJJdc3fhpna74Tv25LYM3LQKHhgfQku3jZ8uVLBqeCAyiZjmDhONHd3461TY7aBQSSltaKemzUhakCu/Z1bT1GFi+ZAAiurbWfnVOUaHu6GUSfjrjL7xUWPoQiYW4yJq41SNnfPFTWRVGvhiZSpWm50usxWxCJRSCWKxCLPVzKtnXmOC9zKGBQX3a+/X6T/zZc67zA2fy0ODHqKpvRuL3Y57T16hobWZ1rZO/HwFb3pLp4m82jYGB7nwzMlnmB82nwR3wUD//MrnNBmbuCvxLhwVjjR1NeGs6hOY+ODEPq607uHL6e/Q3m3hYnETdpGd1i4Lwa4a4nz1bPz0ALHDfEhK6L/gZbKaeOvCW6yOWY2X1gtju5m6MgP+0S79tis99j3W4pM4D16IvmQvtFZCzByIm89fNl8WFjIaD+ETHIXIO/GG+wdQ11HHj3k/siZ2DV2WLk7lN7H+WAlPzHbgjUtvsG7iOix2C19mfElVRxUamYZB7oPYnLeZNK80Hk19lJ+u/cS3Od9yd8LdaOVa6rrqmB8+nyPXavFyVPcuxNV31JNRm8Hp2tMkuScxym8UEpGEu/ffjYfWg9G+o/ki8wtSPVN78zX/p/h/6fd7gAEG+J9jwDM1wN9lo2UCy0MrUMfNAL3vv7bTzvshZi6PTklGIRHh46RBIhbhqnJH6npjWTOzyYrsOoEB7/Zs7tcfZ3fEZAKdNeDryrldxTj6qel0kpPk378QqLNGgVbRl8x+1/eXmBHnyfQEHyKTR1Lje6rXkAI4W9zE2EhXfsuoYVqsELaz6qvzqBVqnFTD2HymlFUjhclZl8lGRXMXAFKJuDepPsRNS6KvHj9nFe31dtSyvjC05+cl88UxPQ9vzeCtRYmMCBMmpzablWZDA5ESC101CkwiN1JTY0E1FOLmMa/TRJfFipNajthNQW1HIzsLdzPGaw5H8+v5/kw5Nw8NEgxFpwBoq8b8w1K+8dnAzntHkF1lQKeUgljMxrPVtHSauXd8GC1dZnJr2vh0WTJbL1VwrcwRZ7sDRtNmlEkrUEjF1Ld3U93ahZejCjJ+Qu0cRIDvIABaJK48XaQhwEPFwhR/HFRyHEbM4e5QA1qFhFXfXGBokDOBPUpuBwva+UX9AV+7RiIF6gxG3HVKKDkBDh7MSlnV21e/pldgNNu4UFSPWAzx/lrcqo8w1r2TvblJfHqkkF/uHkFGhYHqFiNyqZh6g5H2jF9x7yinLmAGgQjhURtPlxDq7oDD6Ofxr2yhu9OMob6LwUEuxHo50GY0Yzn4Kk5VFbTG3w94cam0icvlzawdFcScJF+mx3sR56Nncown6eXNFNR2sHZUCLMSfXi29EHqnVyYA2Qe3YZn+W84Rk/A0Glm/DtH+H7tEIJdNeRUtTIkRKgL5BMzHJeIISgacuDSIUIcRxLddYgy5TGmu3sxZvkgHHsML35eg2PEdMTxK/nx0HlWVb+IZO6nqJXenC5sRCwCs83O1SoDWoUUB5WMT5Ymc8d3F5gQ7UF5m51t1tFcPdzMhdIiDj40ht33j6LkYje4DIfCw3w6pIlvSuBQbi1L0oQcr/amesYHjGen685+hpTBaMZuF0L/npgWhUwiBomEju5OrlYbMFttyCRiPj1SyIGrNTwQ6oOj0c6oxREEuGoJ6ClTIDXbaCs2wBBBtOb4Y+N6z/HliSJqWo08NT2ajw4W4Oqg4P4J4QwLgWR/Z9YfL6K53Yhq41ROOUzHV9VNY9xtHLlWz9Mzo1kSej9zPznFicd8EYtESMSgUchYnTifScFDeqsofn2qmNYuCy/MFuL+dI5O6Bz7niN6tZzBQYIB88Lw/ip7MrEMs9WMwWTAZrcx+efJbJm5hUCHID4/XsjEqCgiOu1k15TyxLY83NU61q9K48OD+TiqhJ/XFXdO6HdMu91Oc95ZnMNSe4Uxnv01i+FeNia17YDov0L5BTj7Ccz5jMqs41icQ/AIHglRY6HgEAXdjlRklXPTID/C3LW4RS2B/P1Cvb6Iqfwtpa2lHCw7yKSASTx+9HnmBS5isCGXrzLy+Xzi5xwoO8CZ6jMYrUasNisPxD/AtaZrBOgCGO49nH3F+9Ar9cwNmcubF95kuM9wKtormBc2D62unEqTgQiEe/vimRfptHTSYe7AS+PFmj1r2DJzC0arkYauBgpbC9kyYwtS8cD0Y4ABBvi/wYBn6r/I/0srWxVNnejUQu7FH1isNh7begWjxcZzs2Jw61nt7SXnV/BOAr1/71unixp5a+817hsXxuiIvglbRXUbv71+iQWPp+Di+ffVAXPPVHOgvoVPz5cyKtyNdxYl9lN4+wNDi5Hl357npsH+LBtyoyAAwJ6saqpbjQwNdiHQVYNSJqGkoZ2mViMt2S2kTfBH8ydFiq02O4VlFYQH+v3JUfuobe3izX3X0MikjAx35aNDBbyxII5wT8d/uN8f/HC2lD2ZNYyOdGF5WgBrv73E3GRf5vYUbMVu780henH7JTJqjcR4OpBVWoWLuIk3b13Ax4fzae2y8Nr8eNLLm9Eqpaz66jwPTgznVEEDIruFp2bEo9fIMVlsfHOqmIUpfjhp5PDNDHCPhmmCmuOyL84wytPMrYaPkCz5AcQSrp35Hf+EsahUKn7PqCSvtoP7Jwr5elvOl2G22vFyVPDViRJyqls5+si4Xm9Nfmklh44c5PaFM0EtTGg7O9rJrGyh2y5HJhUjFomobe0iLcQFD13/3Ir9OTWcyczj6aEK8EqA2kzW50g5Vm7iLxPCSAkQvAZXz1Vz6UgFNYkOFNV3kuivZ6VHMdaz61BNfRlcQ/nhTAk7Mqr7JNN7aGrvptNsYW92LTqllN8za3h7UQLOPYWsrxWVcfz0CZYvugmlTMKl0mYS/PQU1rVx36bL3JTqxyu7cznx2Dg8dEqoviKEZ4VNhl0PYIqZg7ksC0PyA3iFhUNnM3w+EhZ/T5NDJG/vvMAz0o0oJjwBbVVQeRmaimD6W9y+8QJqqZhbR4VgldRz5hqMDHcnykuH3WajvKWLs0WNLBzkT1XeFcZsqOLIlDq8r3wCtx9l7Q+ZJPrpuXtcGG0Nlag/TaFqyQH8QvsSjNqMZh7+KR0XjYJX5vV4LFsrBcXNCc/1U8ksrG1j++UKZsV646dXodL2/+7cv/48AW12/vKXVJo6TSz6/DQfLo3iWttJEp0mYTRbifZ2pMtkpbOxme4z27GPWkyXBdafKMJBISPRmkGYpx7va9/Snnw72VY/Rh+9iZy4v+KVMhk3nZIl606jVUr54uZU/haj2YrNbkddsBvaaiBN8LA3Xt6BzCcBnXv/Z8WBq7Wk+DuhkksoaaxnX9WPnCtp4o64u/F378Lf0R+L1cazO7K5dUQwzZ0mHvl1H/7e1YwJDqOyOpi1o4JufDb2kF1YRunRFbRFLOOm4bcBcORaHaUVlRhKr9CiCeEBj0toi/fCqt8wXtmG0sUP/AbT2imUE4hzl1P08VMMmj+bJOMxVL7xoHQSvNLJKwBoNjbjpOy/+GS327lp05usTZnDIBcdBxtP4KR0oqGzgQZjAxVtFSyPXs6XB80MiWnmQOUmmrqbcJA74KP1QS/XY7fbCdYHo5VpWZ+9nnjXeIL1wSyLWgZAcUsxNruNEKcQui3dTNs+jddHvs7P+T8jFUuZEzKHwpZCFkYsvEFQ5L+b/5d+vwcYYID/OW50FQwwQA++zup+hhSARCzCx0lFR7eVTpP1xp2iZ/czpABcNHLGRbgxKFD4Ya9q6eT9A3nsqf6dunF1aF2ECXPd4Y8xlGb22/dCSROHujq4fVoEW+8YwuQYT5SyPx+2l/eUkOCgZmykYLCZLLYbtpkS68W8JF+2X67EYhPWEQJdtSSHuDJuVigatRxDl5m1G85TUGvo3a8q9xxBPwyjo625972O1m5yTlb1O/6lsmZOFTby+LQoPHVK9GohdOtvqSs1cOn3ghveH+ujZ3CRieW+7Shs3Xy6PIXZCd50W6xsy9tG48+rYM8T0NHETcPCeGBCGGqFlFv9j5OYVIZOJeOJadG8Nj+eZ3/NIqfKQJBew1tzY5mV4M1bixJ50/gi+vL9AMilYqbmPU3V6S1CA1bt6jWkKDrK5z6/sXxkNJL4RSCW0N1pwO/E49QUZgBQWN/JhdImACqaOtiRXkWdoYsj1+qx2Oxsu3t4ryFVUNdOZZuFDqkjSIVJ97cni3lhTyHvHCnnlytVDA1xpaC+nc0Xyrnru0v8nin0757Mapo7TEyM9uTpm0aBfxrsfhSOv8NyfQYfLUkmXKvi0PlKCuvauWzv5kdlF5dLm1mc6kuUp5Yuv1GoVmwRiqqeeJelQwJ7DamrR7dy9KcPAXhmRzb3/pDOLSOCmRzrRUN7N1kVrb33KCLYn1uXLe0NB00OcEIiFhHuqWNYqCtDQ1059shY3B0UPPNLFnniYBj5EHjGwq37kZvayapRYWisEw6odmJP6LNk13Wzs3Qztw4HhU8sZG2Fw69C1AwYtBoOvMBU23GO5DfwybEMbj2wjHHxYJWV8dyhjXSYbfg7a2gzWjiQU4N3SAynbvVDmbiIzFGfgEzJa/PjWTNC8Lw6uPpQvWg3viGCWMuBq7XUGbrILSimwSBcs7GnQDIiiRBaKhJjttqw9Xx3Vn1zHm8nNeG+jr2G1OltH1Ox6b6eS5NjinRg+OuHsFptPDczBpm8DXXhETRNWbx/4BqFdW089UsmDTX5OFb+xHO/ZPD0jgxyaxvQyzuIHTGT8OhEtBXHMZhhdGwgJR7z+Ca9k9o2IwBPTI3i4YlC3qPNbmNb7m5W7XiM+o5GlDIJarkUVPp++Z93HlPwl19LbvgOfn+mlDOFjRzOrePpk69x8EwsKyNuI1JcjMbUAQie6pfnxoG0jiYu8u2KWXwx7x4WxU7GDtCzRLn6u9949eCefsePCfEnI3gFRxoOUZovfDYmwp1polMMlhcjNdbzW40TdLaASISyswoMlQDYDdWYTd2MivVn8TMvE1u1CbvVCoggcXGvIWW0GJn1yyzS69LZmL2R05WnaTO1IRKJ2LLkESZGhPPapZ3UNTqhlWtRyBRoZBpMNhPrM9cT6d/GR1nPIRFJmBwwmVDHUHIachjuO5x6Yz2/Ff/GvtJ9TAmYQohjCNearvHHumyQPogQpxCaqit5//ib3Bp7K5dqL7EqZhW5TbmcrjrNrqJd1HbU3tD3AwwwwAD/iQz42Qf4LyESiXhw0t+XTv8zwj0cegUmAMxWO61GM0VVzTgoXHrzYEoL8jB2+zMyoE9+XC4Vo1VIkUnERHnrifLWA3DiZAUFF2tZdZ+QT7Uvu5psRzvPzkuk0yoYUfM+PsZjbmcYueBeYSII0FiE2GjGYrNjt9tZd6yQwUHOJHprQSKjoK6dJ7dloJZL+xljflGDMep3o3HoW+k9dKUa6bU2ooZ6ITJ3gELL1DhvRoa5oZJLiPXV882aNABa6jq4crCCkYvCsAN5pRsQN5/gr1ueYIxUzZhZIUhlErx8HVn5SCqK7XPJCljORW0sTcZGsksciAg2Ex49ky9OGom/VMDo1CTcNk3Bf9yLaMIfYmhPza682hbCPfTMT/HBSS0n/WApnfVGpOE94Y6TXgCnwN7rsKbcQn6bIx5t3bg69Hkd0Lqj9YkBvQvo52PoNqBT6+ChKwT1rCivGebPnAQhB+7FXVex2u1kVRrw1CsIdFUT5CJ4HH+6UM6ujCpSApx5cNns3lO0dlk4U9TEQxPDidMboamEWQk+qGUSCusMRHgINXN+vFDOL5creWxqJCcKG5gV74PjyIdApkapdUMJ7N9yjcJLDVgWBzMu1oOWDguJ/nrCPLTcvyWdu8aEMDzUTVB8RLi3nSYLarmUwKbj6DTCdaz2c0eeIngVdEoZm9YOQauUcSyvntLGDkGYI28vZP0M8/pUIQGemSkYJsX17YhEInQqKa2dJmoNXby46yovz43D0dxNWrIfXFciIMZ4EUVTIx0aM8fOXqBbUkSksglW7ug7eHs902PcaS1WEuSq5QW/33FRuVBiKOFcljeZAS0MdekiK/0MZT5RDA91Y/nWSh5OqeanCic+jxfEL67HN6JPBGJfVjW/X6ki2ZrOT/45FA5+BqVMgsVqw6JyQznurwAs+uQkib56np0Vwx2jgxkT0T/Pyi16FKLuSGit5JVRamwuoYyJcMddp6Si2cjeQ/ncW3OS/A4Xjuencv8EG3qFlHeuGnGOms6zyYlkVLTQUHiUJQ1fIXP5HtDQvng7a38y8LVvJ+YJc9Bc/YFYnyUA/ZQcm43N/FjwLU7SCKRiKdWtXby2O5eX5gzplXIHuGNiIvrS3VDvDW5hve8vSPHh7f3X+GhpEg/p7uKt32oJcXfgwJl1HJCYWB67BplExnCf4XyQ/gFGi5FPJwqhfFI5PDW9LzdqcrQ3Qa7Xifr08OjoNVw5WIyr3BFOfQQKB9yGLkVcW0Hqd5P4Mfw9mPmesHHEdKjPhd8eQd/RwDMeMaBMROzjQacSNIOXQtCYfsdXSpVsnLIRtUzNh5c+5GjlUVQSFQvCFzDabzTVHdW4aG146SWkeaXxxPEnMFlN6OQ69Ao9syPDcZZ/zcnGTdR2NqCTueOp9qTL0oXRYkQmlqFX6nFWOXO47DAJrgk3eJmaqyqwNXfgESzks0Y4R/DxuI9p6mriTPUZfsz7kdWxq3GQ39g/AwwwwAD/SQwYUwP8jxPgouGZGTEYjOFIrvsBloc8TEiUc79t4331vdLNbUYzrZ1mfJ3VePloaWzs7N0ut7qN3VfrGBnlzv2br/D5imRemRlGWH0pSPvCbSyXv6eksoW1c1/CQSlDKhLx/dFMFI3vEHXbN3g5alk5LJBpcV6IRCJe2JGNRCLiwYkRqLz7QqHyjm7C++ph1LPeQiQC+2cjuJL8IokjZ6KVQX2zgT25Tb2KeBKZBLmDjNzCZvL3lFGbIMPgn0JLbT6W1iiyK1upMBiJ1qnwcNbAiu2cOlVJdUMdVQYjj05OxV2XgF4t53B1PnIXBzRKGfUjn+H7Ai1THSE5wFHwxHxxhpfnxLBgkOAhdGh5E7N7BBZrBOfOn2HY4DQajM28cvhBnh76NH4J43hnSzqRYYIx9caeqwwJdmFUeBS4CxPDvdeyeWlnETvuGo2TWgiPKapvw+niBzTjhTF5IW8siEcll/LU9kxEdhEFtW29/XWioAGZRMR948Po7LbwzK9ZzErw5p7xocxJ9sHPWQ0n3qW9pgDt/I+Ym+xLRVMn9e3diEQiXp0Xx90/XKbdaOF0QSOjwtxwdAngVEEDbro2wtwdGDNJyjjeRTLoe1CouGtcKPdtusSsRB++v7VPrr1CE0W3PALq2pn/6UkOPTwGZ/cQfILHCOOsop0ILw02m50PDuaxKNUPrVLGifwG8urahHvqmQASwRNzPK+eooYOVqpPQ+AIKu2uTH7vOEcfGUNHt5WPDxfyyORwkvz0KGViusc8hUIqobyiksYdT+Ix+2Wkk57lRGEja8L13HzxMq1BY4kMqKJt060opr7E0SoRudU+eDkqOZRbzYY1Qj20uzee5oubB/H7fcI4w9jKE4lm5IkRqOQSnhpkJzXSjwnjIsFqAYmUd385wZUmCUND3Lh9dCgAjac2kJOvwdnDl0b/4djGrSRMIhjnr/x+lSvlrfx8l1AfaeXQACJ7xAaWDQns7deHfkwnwU/PzUN7FkNOvAet5Yinv01asOAR8ndWURORANP2se90PS/N0dBptpFRZWBFuJghUXPxcNLg66SBqPnk14VRU3Gckb4j0QYP4vAjdvJariFGLIhE2O1cOrQVv5RpuOmFBRMXlQubZ2zqbVcrZuJ8HFFI+4cGj4v2gLM/wtVGcOurjeTuoCTBV0+Epw7Qce94J/ydNfhM+xhT7haKmmrIaUlnuM9wHk99HLn0xrBggOrWLi7nmdl0tJQnZzmR5qcSxoxEilgkJkmihY4G8IwDmQpUTuysbMUa9A6rtJch4yz4DxbyoJqLaK8tRtWci6ThGoSMAfdIrrqHkOToK3hb1W6C4mcPgXrh3nw55UsMJgPna84T5BgEwF3772Jt/FrMtlbePPcmSyKXsKd4D81dzQwOHEynuRO9Bqapl9LYYWR/Vjtv3jQfsUhMW1MIRQ2NjAi0sq1gG52WTs7XnecWbuHBIw8yOWAyk4MmE5KSxuMpaf36ZH3Wehq7GvHT+pHZkElVexURzhEMMMAAA/wnMxDmN8B/CzvSK5n4zlFM5j8JBezhjzo+f5Awzg9nL03va7vdjtXWl9L3a3oVL+zKASAkUM/sWX11tVICnVgzIoh4XyfuHBNMtLcj8cE+qNJWCkVKe7COfoLDPrchF4vZfrkSJ60CB50TJ0IeYmt2GxqFlOmxHogqLwPgoJLR2W3F+jephZ/nKil2GkKUtw5EIkomfM4XxW7YbHaMB1+jeNODnC1q6g2HcjDmUWO+wMkvcggf7MHKwbcyxGwhRXGZKWtiaeyysOVyOu/9mkXmyUqQKbltdAjPTh/K50umEe7pgL4nl8titZNdU0eXyUpgymSiArzx0Akeh6QAJ16eE8OMhL66X4ccZ3PZYQTlVTU8frCZlpoSNHINgzwHoZapkYhFfLAkiUgvwUg6U9TE/py6ftc7IjCc+8YH9xpSAHd9d5m32iaxz5LMwZxarlS0IpeKeXNhAi/Pi+8tdtvcaSK9vIVlPaIHOzKqKGnqoKXTjEgkEgwpoDv1bmYUzGLz+TIAThU28PPFCkDIe/HUKQl00/DJ8hR0ShmtnSaO5deTVdFKZXMnnRoPJINWsPvKYZ7dthLsdj5YksyESHf4cjLsfw6Ae76/zFO/ZBHspuGF2TEcy28gO2I8Nk/BWB6zJBKvUCdyawzsz6mjy2QB4MnpUXyzenDP4PWEEEHBUSYVU9XcyZ2ndWw4V0Vzp4lgVzWOahnzUnyQSkRIJWJuGRmMQirhtm8vsD+nhgsVbbTJ3XDSaahpMXI4t47yDjtvprRxh2QHbY4hSBpzaayvwlOnINhNg5+LmluSNPDlZDwl7cyM9+axrVdo77ZwoaSJZ/eW4zZiJRKZHForMTeXkW/xZMUnB8nb/BgA2pZCgjQW/Hv6HcC5+jgfz/Dk5blxZFcb6Lrue7s8zZ81IwJ7X89J8iXS67ocQEs3ABqFpK/ukMlKZ+rdMPUNOrotrPzqLJkVLbjplEyP96Yg14qlzMhXp0pICXBi653DGFn+OZqczWQXlPLSxl0gllLU3UR6fTrF9e38ml6BWCxi3ZUvOFV+hQXhC6Cjnqgrr9BaXwrA6cIGLvWEnWZXtdLcYcJRJeOWOBnyr8ZB+3Xj2myEtDsg9ZZ+Yz01yIW3b0pEJBIhEokYHeGGVCJGIVFwc8zNNNUm4mtZA4CH1uOGvCQQcjNbW1sRdbcz0tmIo0YGO+6F81/0bTT2CYieCcGjwU8YV6uGB7ImVookajpEz4Hv5gvtm/425al/5ezkHbD4e/BJQSvXMnrKu+icQ+Hom5C784Z2ALSZ2shvzme8/3j8dcICy+rY1RwuPcyHlz6kydjEC6df4NHBj6JX6VmfuZ6P0z+mkfMMDXEjLsDGx0uTuPvQ3dx54E7C3B1pkOzmw/QPKWgpYLH7MMJ1QujoyuiVDPIc1Hvubms3Nnufh/+BlAdo6GrAQeHAU2lPkV6X/qdtHmCAAQb4T2LAMzUAAO3dFvZmVVNj6CbYTc3UWO9/ab/fMosRywxMjUzo935qoDPzkn2Qy24UivhXeWFnDvl17UJxVGBxqh8zYj0xdphRaoRwnX05NWRXGnhgYp9hdf1q+R88/UsmMxN8GBzkzH0ThJXQlo5uGjvMPDMrjiPX6ij7w9NVnwffz4f7LvPAxHDe3Z/HqYIGBvnpOVPUyAeHC7h3/GCivXQcvFrLpdJmHpmSxsc9dYLlabcgdq/nvbi43gK/toqLuDfn4rT8PqIShL4doXBghIswCRkX5U5uiwJRtzOpo/q3vyy7kcr8ZobOEbwIq4b7M3fHDKIrXmBi8DD8nJU8+nMGny1PwUEpY8Egf04VNvDtqRLuGhvCkKEjUZsEo+7o41MQSYR7MslvPpPePsnGW4f0m1hvvWNYb7t/u1iITAyTkkJYmBRDR7el1wB+d3ECno5KnNSCXPzmc2WMCu8L+foj7MdJLefTZSlEeQnejPzaNqbGejEryYeMihYO5NTx4KRwFAo5z89NZN2xYsZHerAo1Z9FPXoChi4zFc2dmHtCLz88lI9cIubxaYLn7P7Nl0lzbmfJpAV455/Eo96xr9ivSARJy4WaUMCny5OpNRi5XNbMvuxaag1GqvWPsWHqBvy0QZwsqEchk3Ct2oCPk5IQd8GAtNvt1H/3FOrYcWiThLCuc0WNpAY6E+6u5aXfTBR1SRijkNBltlG/+R7OWBJ4e/EtgIiGtm62Xa6gsL6DIcEuaBUeWFLfQCYRk+QAdbtzeebXLDbN8odaA61aPz4LWMdffMPwUsmI89Wz/nghYpuUiKT7+PJ0Ay5yGBHihFIqxt1BQbyvI1abncnvHuXhNA3m1g6CpFYWpgbi6X4zANqiLJbODCcsru87Lpq/nj9kGNbdPAirzc6jW69w26gQQt0dCL6u+C52O+x6AAavxeQSReXGu7FHzuSFiaOgLpvqVi8+OlSAUibh6RnRgFlIIbouCsw9wIFpSh8mOfd5dTpsMixiDRqxGR8ayamop/ViM/fOv4e3913jeEEDsxN9mej6EN+cLsHZVkq5+STL7zxJqEoYv+nlLeiUUpIDnHl7Xx6zE72ZnejDM4fqmeF3C2nqPs/N7+dy+OYMvDSjhi5NHpuLt/PqyFf5Zzw8+e+EOZu7oD4Pu1c8JZcOkOpRzWttWyF+IXhOh/HPgOJvRA/MRqg4D46+UHEO4m+iKWAyVb+9RpyoEIwt0F4DCh1R2e+Azgdb9GsYzVbUcilV7VV4a71h3ufwN+p4JosJuVROdkM26zLW8dWUrwAhp2xq8FR2Fu4k3CmcmcEzcbDb+Xbf/UyIXsTc0Lm0m9o5UHqAOw7cQbelm6lBU7k99nYkYgkSZS1XDRcZ5DGIMH0ogRe/pz1ACLfObMxELVPjohI8kQ8cfJjWmtE8M2kSIc5KTm76FoW7nLzmPLbnbedc7Tn2l+7nvbHvoZX/fRGiAQYYYIB/ZwY8UwMA0NDWzbG8Br46UUxRXfu/vN+RgjJ+OFvBSz0eoz/w0qu4c0xo72uT0YLJaPm7x3lywyU+2Jbd772ZiV7MThImfC2dJsqbOzl2tIxjm6/1buOrVxHl5UDuuTPUlpXx0eWPuNYofN7aZaamtYusixcJcZTg3VOolbKz0N1OWVMnl8sEQYnR4W7MDhWEMHIL6mleelBIWEcoZlxY386WdRm8f6CAZUMDmBbnTenF0yhMbcT69K3SXyxp5uXjzeR367llw3ma2oVVe3HqanboliK5Xilw+L1sVsCmq0JI0l1Dx3PnmJheQ+YP2luMlGQ2Yrfb6TRZcNIoWT/1M8YGCqvZarkMJ7UcmUTMLd+c41RhLRHuWsaEu/HMrznsyqgm91Q1187WgljML5cqsNvtuGjkvDIvDh+9irrWDvZu2UJXm6Hf+ZOK15GQ/zEABXVtzP3kJHsvF9Cy/RGinCU4qRVcqWjGQSnl42V99cCuFRbx/Ffb2XWxGIBobx1VLV10miw8PSOGW3pEEBRSCXq1MAk0W20MDXFjfoovjuqe3JYer0ecnxO/3DMCkUjEY1uvsGZ4IPeOF/JcsipbUXWUs+TsXDDUkBA2nLumf9B/gCWvwO47iBd3ZvPjxQq+O1vGq7/n0mWx4a1Xsn/hftR4MeeTY2RWtlLdYsSOHcl1fSESibAovUEtjIvyxk7u/P4SmeUtbDhdSqyPI40dJg5fq+WNBXGIIybjERRPU0c392+5zBPbMrhS1sIdo4JxqDiO6Ne7ya02sPNKJTWGLuYmezM81AU8Y8lwmcyDP13hwNVqzl6rhI5GAG4dGcKaRC2H6h24XGagseQKSzUXkUrE+LtomJ/ix9aL5Tw8OYIKqxPlnTKCzPmMiPLllMEdTrzLgoXDCUlJ49YN5zl67ToRgNZKuPyDMF5FEOHhgINSuDfdFivzPz3J6YJ6OPoqIAKlnmP59dzVvJT15e5cvXiE1oPvcCCnFqvNzh2jhXusUcj4dk0acT56GtqMTH73GFmt7YTFuBHl5chbe6/x1Yki/G7+AvexdxEYHMrqFSupqapgXvFzNNWUciyvgTtGB9PSaWKKv52/BO4ko/kEB8v389yuc2w8XQLA3uwaypqERZF1K1IIctXQ2W1hbkogAcMXgLjvJ69V5kxSVCh+B27H/dDnjPTtX2QYAGMrlJ6BbbdDbc4NH393tpTHfxbEWNbvu8RPO7ZzcufXrBH/jotWBTFzQakXnjlOAb0Klr3U58JvD0JzmVDsujqTE8VtfNoxBgJHQ8AokKog+1doLITo2fx4oZy/bL5MTkMO83bMY/P5fJDI+hYPgENlh5i4dSINnQ0M8R7Sa0hlNWQxc/tMod5e0AweH/w4CR4JvHzqOdpNbSgkMsKdwzHZTDSZmhgi1vFX/xlk119hiM8QdhTtoKW7hQ/GfcAo31GYbGZCoxcxJ+luKtsqKTeUU1GUS/q+3wFQSKVYrIIKLCIRYomEaMcIOkztGJsKGOkzkgn+E1BJ+6t2DjDAAAP8JzHgmRoAgEBXDe8vSaK104Tjn0iD/z3enDuassYOTNZ/rLB/4fcSrBYbIxf1eZCMJjNSbEjlCsaGuaF36H/eZH9nkv2dMVttTHznGKMjXNErZdw/vy9ZPNrbkWhvRx5al4GDzkZArAapRBjWHx3M42RBAzc17qYmcDjfdoq4f2IY6r1PwtD7eEZ5AeuqZwCoKcrn55ef4fZPv6G6IBetTotdXI6zjx/T4rzo6LZQHeDCKlcNqj/aee0AqfnHUdx7urc9LloZER4OJPo5kVXZyvzPTrNuRQphHg68viCBHVcqeXBLOsmBTixPCyBUH9qr/NWL1UyH1cTX2V+zPGo50cN9iB7uw9Fr9by+5yq//2WUsB+ClLqvk5qPliYDEB9ezmuXPuSBuLf46EgBUZ46JkZ5EOQmrPpmVDTz5C9ZhLhrifPVC4IMwH2br+DVamao0YjKQVg9bzOaOeq5hliJFQ8g2FXLwhQ/mtvaKW7s4g/pggvFzVhsdhL9hIniy7uyOV3YwAP+TYT46Miqz2b/1Wpyih2ZEO3J1EA5VQX5FKqDGR/tQYRnMBXNncz68AQ77x3JvGShpllhZS0fbtmFb2g8Wkdn7hgdglwqxs9JjV6j6PWQPbktkymx0XTMuoRGJ9QOq2nt4v4t6bwxPx5/FyF0tKXTTHp5K7eNDmJFWgBt3RayKlppN5lRSVV0y0zYJPUE+YiZGTmK7RcrKGnoxGK10d5t4UBOLfMX3N3rcbPZbQS5qVEpJAwPdcVBKeV4fj0Hcxs4W9zMXWPHMtPbkfcP5tNpspAS4ExtaxfLhwZSXSlGEjKfgrp2fk2voqCunQcmRtDRXE9DXS3+zk4sG+zP1osVNBZd4qajKr67ZzKXMzL49VIpL7vs56bbPqDqfD2EC/V9ag1GfrpQjqNSipNKhkvtcQaNHwH+iZSWNnMmt5gpUQHI9L7YECERC+GHvbTVQNlJSFqKSCTilp56a0azFalYxJxEH57ekcMXowaxpaSd+1QejI+UgM3OOwfycNZHkSt5HH1FC8vTAnBzUGKx2tibVcPEaA/kMgl6tZxhoS4Eu2q5UNJEU4eJybEeiAGbWIa4p2+L6tt55VQH11J2UHesggmydLacl1Nc34lPWzrxTUVog8azLnQJeSIfHF2EsZfkqyelZxyeLi7lru9yWTM8kOIGIx8sSRLCIYubGBPpTllTF1NjPekMe50akyMnLluYFnTd9/DaHsjYAlYz2G1CXtPfMD7SnaSenM4wWyGa0Ghy7QGEBfrhET9R2KipGNZPhLtOgdadqpZOTuQ3sCjVH7wT4Z7zwnZHX4Psn5k94TlmJXqDaBIA7T8s4aBmErOnvAKBw5jmaWZIT2mHl1I2UNN84/N6iPcQ7ku+D9frPHGXay/zwaUPWBu3FrlEzqX6SwTqAyluK+a+Ec+T7JHMvtJ9RFjNVHdUk+iaSFHXcRKydjDNJ4b7Dt3H3NC5BDgE8NHlj/Bz8GNm6EwutFVBezEnK05yvuY884LHc1J0GlXTNe5NuhfxIDGBOqGPxi2/lXFAwd61hHV3sSDhDhCLuVLRQqy3Y7/FiwEGGGCA/xQGjKkB+vFfMaT+4I/J6j8ieVIA9r+xGi5seZUAcyHK+Z8xcVhf/aYnt2UgEYt4cY6QyC6TiEn2d6SluYXHp4ei1Qv5QRaTlda6Tlx8HWhTumBDwurY1QDYbHZivHXUt5tYef9rNLQZ+exYkXCCW/aDoRJR7RWk5aehsx6v2PmsfOtjZAolY1Unse96iS31o5n8yGs4eflwrqiBFmM3oUF9K8tj7nwWajIwdJl56KcrPDczmkBXLYE9BUtfnhfP8fx6Aq7rn2gvRxraTLj3qKpdn1+A3c7xiqMcO/seU6JXcKDsAFMCp6BX6gEYHOhEorOWrMJGgty0aHQKJGKhWCl1V6HyMjcnTSdOoWF0uBs/3jYMbycVt39zitUhnQwZOYF4XydOPz4OR7WczefKcNbIGR3hxn3jw4nwTEan7RPruFTazNfnapgiVxOZbEWmkDA93ot39l6jPeJ+khRarla3cqG0iQ+WJPfu5+KgYHFqAOOHjYbOZnK3PkeROoF7xt5NjI8jlVczKcm4zA8iCQ6l5xk8bjyK9loCHMXYOpvAScj3+imjiVaHMBZHBaBRCeNSo5CyekQQDe3dnCtqYlyUO1tuH4pKLuGzo4WI7PncPiYMuw3kEjH95mZ2O4aubtpNLThrvZCKxXx/rozX5sVjsdo4XdTIu4vjCdAJY3FOsg9pwS5IJWLqDN3sya5hZqJ3r5BBgKuWrXcMh9zfwFBFszqAuwK7yFMlUtXcxYr1Z9l213CWpPohl4hZMyKo13nwzaFMLGIZTy/zZUaCd28E3A+/7SOzXcsHd8xmSpwXU+K8aGsNw620BZlEjI9WTJqrCWZ/RFVRHs5HHqNV+zGOMeMxmq0cuFqHXAIRHjpu7T5JSaGa8MhEPHRK7qt4iD3SO/AaFEz6mUKenBbVOzZPFTTg6xSJ/2zBC3kwpwa5TMzIMHf++ksWER4OjA5340xRE07RQ8lOv8z54ibCPLQ0dHTz5sIEoq59Ql7cCL4uVlDW1EmivxPNXSaOnD1P7rkmHohqQzrsHp6dEQ0iERdKmqg2GJkU48mqr88xI84LX2c1uzOreWJaFNPjPEn003Mw9zhr2Yp0zjzu3FnJ0S4fznv8lexsA3f6/crw6KngIyywKOVSPHuKa4tEYqYMy+fmYSMxFZzmyp6v6I6cxou7L+LrHsmpwkYCnTUkDh5Cd0sn0W31/Z5PVF0CbDDnE1D2hedVt1dzpPwIiyMX4+WowstRhd1uR1qQTuCoiQxKSYN1Y0BhhYgp4BxE172ZmLN+QVe8l+eN92Cx2gRjqoeCujaeMj/D16NSUdMXImux2rikGMy6bAcm+tajRsg11SllYDQwwVMDETcWVFdL1cwPn9/vveNXbchb5zHWfxQAzw9/nur2anYU7sBmtxHgEMCuwl2kuKewNW8rfg5+ZHWUUj/jI74/8wKLIhaxLX8bJpuJZlMzNbU1ZDRkcKH2AikeKbw79l1GyZK4WnCBk+oMNu77hanBU0lwS0Av13Oh5gIVbdWMUQ/i5dFvca7mHHktBXiKXajf9iSVg6fgP2TuDdcywAADDPDvzv+pML/AwMDepOE//l577bV+22RkZDBy5EiUSiV+fn688cYb/0ut/b+NyWjBep20uFIru6GgZ/DYlXxrnsNtGy/2Ck1UNXcR6q4lzL1//Py0OG/m6Oo5/eN3ve9ln6hi6xsXMXaaCffUXh/lwtaL5Ty2Las3l8rVQclfp0fze2Y1d/5wmewOHYx7GtrrhZVjwEEtha+ngXcyops2MvvZD3HyEib2Ga2H2Vb5CgBfnyzmQkkjn5+uweo7BJVcwuhwNxzVctqMZk599SjPfrkdQ5eZkWFuyKV9X7NQdy1rRgQxycdMw7H1bL9UwWdHC+DwK7BuDEH6UIaHzyElfAa/zP6FUKdQKDwMP65CLhbT3mnmmwOFHPk+F4CbUv0ZGuzKz8fTaa++xqlLxbgc/RRxtwFvJ2ElfUmQkfCudEAIl/zDYFbKJYTU7eXnn37gjb3XSC9vperMT1AurJSPjnBn130j8Ux1p9Nmo6K5kynvHSW4+QSmlhoAalu7qW4x9vOu3TE6lOXDAoUXYjEBbtFUlQ9CLBbq8wTEJjDl9nv5ZmUSzfnZdLUbcPMLYfuyIPx8+oQzZif68MDUOIaEuRPnq6e4vp0jV2uY8cExsitbOZoniAn8UcB5mLKM4YXvgc2Ki1LG16tS+fRoERdKmjhf3IRILCIh2M5Fwy8AaJRSbkr1w81BQVOHiXXHi3CS+lN/9RpN1ZWIRKLePgz3dGD9ytReQ6rNKNQie+jHy+zKbcOAhtayHNqrcjlZ2EBaiAvHHh1LfVs3FpudvNo2rDZ77/53qPaxVncWEBYLmjtM/HK5gsFDx/D84tG9fWC22ihpEzE+PhCL1YaDTxSzZi8EwDcojK+G3sLPthIAGtq7ifTUsmJIIA9PiUS98FNSJiwi62ohGnsXLZM/wuA5jKzWo/yed4mWTlPveV767SqfHysEBE/UJ0cK+fyosPjwl/GhzE/2IdzTgY+XJaMWWRjpLcW36Gfqcs9y+eIZYnRmqhzccHCx8fqCBGZFOcK6MVxNP0uSzoC73ER7yHTY+yQcexO62xlW8z3pBVW0dpqZNrieLtVxvByVVLd2cfBqLVKxmK7KLKYos3hU+hgWrQcPjA/jgYlhRHjqWDsymPelqzlhE4RDPjqUj5uDnHhfJ2oNRjaequep4XfgotFSXnaU6tJsSupN6JUaXJWefLwkmfkpgiHirVezZLA/2y6WU9XaUxdu7JOwcEM/Qwrg8yuf833u95hsff0nEonwGr0EbWRPqOCcz6BHHRLg18x6fsntotszEZ1KSnKgE5kVLb2fe+tVrBoWiOpvckyvZFzh0dwYNoxoQe3SP4/VkLmZGXtXUtxSzL/CpMgw1qYNx1EhhCXnNOQwf8d80jzTyG/JpzR7Cw8Ez8HHwYfvp3/P05poFqtDQaZhVcwqwvRh2LEzzGsYc0PnMiFgArODZ/PckOeobq+mqKUIT0dvrjbm8tjgx1gavRS5RE6jsZGHjz7MhuwNXK24jP+mcTgaqvg251t+vPYjOq2OMS6t+Lu7/JMrGGCAAQb49+T/nGfqhRdeYO3atb2vHRz6EqcNBgOTJk1iwoQJfPbZZ2RmZrJmzRr0ej233Xbb/0Zz/204eLUGF42CRP8blan+v+HY5jycvTUkTwr4u9t4+wawdKae6Jo2GsvbOLm1gHdMzTzl542Pn3DfbDY7z+/MZlyUO6MWzcdiMffuHzrUgw1V9TR2m5mX5MuF0r6CutPjvXF3ULL1Qjk3DXbjSvMZpgdPJ8bLkfLqWjafLuBWrwJU1zbiYrdw3ncVoQ42XJ2ChCKpUiUqBx0Wq437t1wmxD2SEKkb9WVtlFUY8HNSUVjfjtVmRy4Vs3xIQG97fR3laLtEvH8gjwUpPrx/MJ/nZ8XirlP21WIxd5LX0MXvJTX4OqmwD1mEyDkEXwdffONX9e8oj1hIXo5YKuLWsSF8faqEiERPCi7WEZrijkgEdS6ptCXNYeeObEYkvUCCSs/jP19hfJQHE0ePo7F9OEazlbFvHeH2kUGo5FLmpfjikGHG4KLA165kb1YNEq0Nb7e+nDmz1cbRvHqGh7gS4KrhhVmxDM3YzPZGDbdvVHLnmFC23z0ckUhES6eJc8VNTIrx7Gu70hHV1OfxbL7E/qt1xPn2jK+2GmQOnsx+6EksVhubz5UxMyGAE9nVZFS0MkHjQHicKxrHvrpI+3JqKaxto9tqZ3y0B45qGWu/Pc+zc73IashiSuIIyHgZCg9z8IQnm0xt+Llp0MglzPnkFM/MiObNOWOxVGggZyeS6Jlo5VJu+/YCG29NY/tdwwE4fPkCXmGROHv50HbxJ9ozd6Fb8mU/5cmr1QYaO0zEeOu4YkogOTyAfcZqdhVVseMeIXfsh7OlfH2imG13D+e9xUlcqzFgsdopaexg+ty3QSSG0lNc6PJhR66B0sZOQj0ceHqGD9isUHKSLEksd31/mUMPj2HrhXLKS65xx6wxOGsVIBIxNX4eyh7p/9OFjYK4R5wXjioZIOSd7fl+I51qd5558i6CgQ6zNxfKvmF3bjYJfkKtq01r04TCtoBSJuHnnr4A8HPW8OS2DGK8HVk2JABDwSWO5rZic3didZwLryuqQeXIT6I2pE0ZSIp9sNhs3DfmSQIdoznU5MLc4T7ovJxAtlYoxmQ2ImvKRS1JRSYV0WqtYlfRLhZF3IS/swbfrmtMD9eARxrNAU58EJTEd3uOc7FJQX5DN3eOCcFRLSPSS4eno2Dwjgp3RSoW89T2DHyo56+irajkQu6cIXYkyuBmLJe387q+CkfVaPRqEe0Fp2g99RUuSz4nt6yer06WoFXJ8Hb8+zk8D6c+TG1HLQpJ/5pdwcmpfS/crxOpOP4OC3R+WL2r+SFXxchYCSUlWVTKfYnzFfZRy6VMjfPq26erFXY/TIpzGAfHuaAZeusN7dAlr+av7qH46m70TP0tmRUtiEQiBgX1lZ6Ido1m++zteGg8mBQ0ieqtK/m14gRtGlcGew1G1F6FRu3KkyefRCaW8eigR+m2dNNibMFqs9Jh7kAsErMoYhHRbtH4O/gjchExeOJsvDXemKwmWrtbWRmzks8zPueVYa8wxGcIZ0Mm0tZRzrNDnxUWiwDpss3/9BoGGGCAAf5d+T/lmQLBePL09Oz902j6Qqy+//57TCYTX331FTExMSxevJj77ruPd95553+xxf8evL0vj3f25/3jjepy4dwX/3ibHqTJTtiCb1RnemvvNUoaOnpfB3k7MjfZF727miGR5/g17hgKkYj2ZiMAmy+UcbG0CS+dCrsINlzbSG2HkDQvl0sJ89GhlEkIdtOyMMWX8qYOVn51DqvNzqAgZ1qNFmo6a/it6DfMVjNR3joWN3zIyOatlHj6M0fRRP3MdTz9SzavHKyCOR+DSwj2b6ZhyD2IVCJmRKgb4yMCWRiXSmt9J1M1DkyI9uSNBQnIpWLqWruY9O5Rtl0sQywW4T//JewuEYR5aGnvttHUYWbp+rN8faKYE/k9oURuEQybupwFbhXE+egQuYVCwk29/fLml9/x7WZBDACtG4RO4PNjRbxxrACTTMyluna62oWVcZFIxJ1jQvFyVDEu0p2hMSGAYFDGeOuoaTUy9NVDXC5t5v2F8WRUtvLrlSpyqlohbj4mp3BKGztZNsSfMVNvgpCxZFS08N2ZUjQKKZ+vGESAq/A90mtknE58Gfe4iUyKcuPM59nk5griCKcLGnl3fx5tXUK7zFYz56rPAfD09CjG9BQNNreUwjczqM0QJlCdZiv7r9bQ3GHC30lDY1s369PLMbabaTe1s+L3FRS1FHH76BASKi3cMSiAz3bk8vSvWdw0yI/K9kou1V4ChZamWd9AyDiGzQzG103N4Wv1hHvq2HXvcJYNCUAkEiFrq4UmwQujVUpw1vT3mI5duZbIYYKHodNzEJ8YRnK2qJHShjYaewRFBge5sO2u4dw/MYKnpkfhrVczOsKNiVFCgdKKxg5+z6rm9fkJvYVi9+fU8vOlCh7+8Qqbj1zAdm4dHHieK/nFXKloId5Pz4RoYX/SN8OOu0lysXD4kTFcKGniyxOFPNbwV8Q/r+FcSQMXipsI0YfgU3AcCg9zz7gwfr5rBAHOGh7bmoG5p3j14vseYNrSRb3Xd7LyJP56V4KdPXrfc1TLyakx8EWPd+pvyatpI6OiBaPZimb/fbw+yk6uNILHT1o4V1hN8Y7X+EvyX7g76W5GhbkiE4sxBU/AKlaRWdGKoUtYBLlqdsWo8QatKw5LvuLNm8dS3WLExTKVH6b/gNlqx9jVzoIdXRRVVfD4zkIW/NzCL5cr8ak7xuwAC4/PcmFYuIqaViNuWgWhPV7seF8n1uW+RFVHKRZjB16ybgxdFo7n1fPsFhkB7jOYMXEioamT+HTPRT7ck4FJ48FVeTyb9x0j7ueRLPBqYFyEe+91F7YUktfU/7molWsJcQrpe8PSLSj5/T1qMqgpuILkyhbmhskYWvAu99g3McW7Gz5IgebSG/eRKsAjDobcjmboLTd+DiCRMSRgHDKx7M8/v44PDubz1r5rtBhbet8z28ycqT5Dt7Wb00WNNA79gNDUu/gs/TPqd91PvUzJRYwM8RpCikcKDgoHvpn6DSa7iZ1FO0lwSyDaJRqAAF0AvxT8wteZX5PXlMf8HfMZ4TOCAF0Au4t388G4D0j1TmVT7ibevPoFO4p28PLZl/9puwcYYIAB/hP4P+eZeu2113jxxRfx9/dn6dKlPPDAA0ilwmWePn2aUaNGIZf3TZ4mT57M66+/TnNzM05ON3pluru76e7u7n1tMBj++y/ifwiz1cYPZ0tZOSyIdTcP6lnN/kc7dAhJ6v+ErIoWTpY1E+ymwcNJha+zqtcj095t6Vc7aldGFeeLm3h+diyX5CEYmhxYskxY1TV0mvnwYAHvLU4k3NMBq81KbUctXVZh4iKTiLlzrLCyefBqLd+eLuXT5cnMjPdCIbZjASSqMj4+ZOf5aW8hk8jounoVp3GPM9LRA4VOz0aXjRhLG9m4Ig6Vps/4+yH6czbtN7ErGsaH6zlw9goLxg4j/bvP6EqczPHsWg4W1LN2VDCeOiXBbhqivPS9+z86NYrdmVV8eaKI213S2acMp73bzL6cWkaECfWorl7LI72siZaOeuYl9+WMAbiqxOiU/Sf5sxO9cdHIUUnFDA52wV2nZG92NSfyG5BKxDw7Mwa9Ri6EOxYeYaRXDJ9drMbPScWOe4ezdsNFhgQ5I5VICHHTUN/eDVV5pF19n5SAlzlZ0EBCT/J+dWsXz+/Mxt9FRaSnDo1cikYhRSIWkxbkjFfPyn2BXE1AiLCPXi2lqcPEF8eLMFntXK7Joc3xK7bO/Imihg6e2p7F4YfHItMHUB0yBq2nIKe/M72KNcOD8O2RZ394ciRWu538+nbi7I6sil0lyD8DE+aGoffQsPntC8yJdSPAVUOYexrhuiSuVrcy55NMTjzqipurGrMN7h4bgkQswmC0cPBqDSPD3JFHz+rt07RgV9KChST9KwWlGK4d5zdjHK/NF9rm4RPEI2M8uf1QJk1mGYMCnHh5bvwNY35fdg3+LmruHR9OdmUrCz47zbs3xZMcKPSNzWbH11nNLSOCifLU8dqeHGaNBfUte1GfK2Wusw2VXIKjUsqW82Wo8luZNecz0LqjBIaHuvLKvAR+zXuJMU61rPrqPHeNCRU8DVYjWLuhsxnUTmjkEoLcNHR0W+horsfXx4frfRd1nXXE+ngwMSCs/0X01HW7emADUd56iJ5N3rEfOW6O4plkI+6t55CK4/gs4jNi1P48NV1P/uVjSArKMYSNRywSQ3Umbofeo4K7uf3bC7wwK5oArR2Xi+/zvSGEzekWHh09l5FhbnyX8x0xLjF0d/hzuqSUnIofmeo/npcmeSNSnuXDygiG+euJ9dHxy6Vy3lj0EPdvTse9+0sqjYMxm4ZgtAj1sExWE5+ePMtk7wVc6TIS7+fHXvsgPvrqPFNiPZmf4o1K1QXaaPCIZvyVl8AtHOfjvzFerWd7RRPnh3yMjcBeEQyAo+VH6bJ0Ee4czt/lp5WCSMXyn4XXtTmgce8roLvwG+pKm9jYlsiEwHg21zXzpF6BS+BwmP6mIIv+t8iUMPy+Pz1dYV07D/6Yzne3pvUa6tdT22rki+NFPDY1sldC/b3FSbQaDdyy9xaWRS5l9KXN7AxO40xrAUO9hnKhuBU3BwVzkkeQ05iDSOZGgHscL3nGcrH2Ipn1mTx85GFWRK9gXMA47ky8k7ymPGaHzubxY48zxHsIbaY2vsj6gon+E5kUMImPLn+Eu9qdlu4Wgh2Defzo4xhtRuaEzGF26OxeoSBDexsKqQSFUn3DtQwwwAAD/Cfwf8qYuu+++0hOTsbZ2ZlTp07xxBNPUF1d3et5qqmpISgoqN8+Hh4evZ/9mTH16quv8vzzz//3N/5/gQNXa3j3QD4OSukNk/k/xSdF+PsnvHsgj6oWI9PiPJn9yUlemxfLpBgvGju62ZNVzcphfaF/J/LrMZqFFfSp4ydguc7Q0qllnHhsHN2Wbn5JL2NarA8djU3UFBXgG+ODVNY3kUgJcEKnlJJdaWBBoJHvXn6CUocICkPLcXL2xqMnXKz+ww9xnDqNna4q4n1ExPuH8OOHTzFs4VKaCGTd8SIemRzJ5CFJ+Pi3CP10Np1TV8tYPCKGSJdzXCmPos6updpgpLrViI+Tms+W9wlJ3LHxAhEeDpwqbMRVKyPaeImkwTE4R0eQX9fGczuyWTsyiNv2dLD1ziW9hsnru3MZHe7KkBBXVi9dekO/ejmqWDjIj7LGDnZmVHHLiGD8nTWoZM2klwttPVvYxI/nykmW1uDjJSbYMxx3nYJITx0vzo5BIbLSUlvE1FF/hHH5QMhYnrXb+0IQgckxXjw+pQs/JzXP78hmUIAzCwb58u7+PBYN8mPxYH/ajGZs7gpWbjjPupsHcba4iaEhzoyOcKel08zIsFEMC5mHSCRiaLCKv4wPo7XLjKNKRn7oY1zKaOb+CYKiswgRFU2dKGUS9mbX4KCUsu5YEc/PjmGc/7jeduVJM/jl7C+8/de3eW33VeoN3YS5O/DmvlzkYjHPTA1H25oHqkhuGR6EvqceWW5NGzvTK/nsSBE/9RQTBth2qZzhIa54OKqoKcwmtWQjnaO+BOD7M6W4aOVM0TrweJIJk2csoZ4O7M+ppc5gZNmQvnF8qbSJ47nVTEvwY3CwC49MiuCTw4W4Oajwc1bx/I5sGjtMJHmpWJD3KMOXP4c6aDIASwYHYLXZOVfcwM6LRdze/A61EcvBT6ivhtWMGBFeeiV5juFkuyQT553Pibx6IjwdmJiyCrpa6PhsEpplG3Bzj+KO0SEcPX6EEceWwYM52BQ6bv7qLA9PimR59PLedt/+218Z4TOSFYmTSfBzoqnDzGN7fNkR6Uib0cyrWXpCPLuIj/OBZhNIxFxuEHGyspj3FicxLDWVTEs9vsmTaWvqQqrwQxm7AveDJs5Kuvn8eBFP2z/B1A1nKouJCxza60kqb+ri/LUS3p+bRKR3DHd82UFBVSPfJCawYvBKfs+oITXchlQsZengNCw2OwsH+TEx+mU0cgUikYiShnYyKlrQO7SxJ7ORtGmDubnzHTrT82lJuI2fNR9TFfYFEr2JlbuXk+iRyN0xz2BKu5soL0d2/1BOSoAfHVIv2l3DWRPd560DWBO3pvf/q9UGNpwq4dV5cf2+Kwy9t7eGVkVTB9o9L6OPnQwpN/dukuTvRGnCUMTKJuTeXbiMFFRECekb2/+Mml0vo/OLwSd6JveOD/tTQwqgtLGDEwX13NIWyOPbMon3deShSZFoFM7cFHETElMIxaGzyWq/iF6p53LdZe4dL4zF1XtWE+cSR7PvIOrsZhoqjvHFlS8Y7DmYyvZKwpwEA7zd1E5DVwMWm4VmYzM6mQ5XpSvRTtFo5VrG+Y+jydhEQ1cDEpGEK3VXmBM2hy+zvkTebOXEj68w/ebXhfZufhSlRkvYkjf/5b4YYIABBvh34t/emHr88cd5/fXX/+E2V69eJTIykgcffLD3vfj4eORyObfffjuvvvoqCoXiHxzh7/PEE0/0O67BYMDP718wPP4DmBrrjRQRaaGu/3zj/wIfL0vhamULif5OvLMwnlE94V0uGgUnHhvXT45ZLZfgqRNyPsRiEfK/kcaViEWsv7KZ3097MTTYDUeRloaCAj46V06nczhPLhCMAr1ajk4lY9XX59l/exxjR4TTGjwNN4UVFx9fxGIhsdvvgw8QSaVseOcoYR5aPlmWwqKnhXCT4oZ2JD2TJFcHBb5OPSul+gB8I9wQaZx5S/MgM+O9mRcTxLy/ue5dVyqpNhjxc1YR5KImxEPLqfw6VpWv4DubDTYtRj3lKzwdFfg4qTnx2Nh+k7Lsqla89EqGhPTdjys1+Tx94jk+HP8BAU5CgnanyUpls+Cdi/LSETU9GrvdjsVqY1qcJ9+fK6XDIYEfi1rZMMa9t7/HRLrz49F0arMvYkyKYvWmazw3K4YIT11vOzadK8Vqg+VDAnqlsV+eE4dSLkEmFhHl6YCHg/BdeuaXbCK9tCxJ9UMjl3Dv+HBB1ey6+9vU3s3HRwq5e0wIv1yuItrLEa1MQkt+Kz5uwn1fmhbApnOlbDhVgpeDgroOI04aJbvuE0LtPjiYT3u3mSenRRPtEo1cInjsHp8a1XuexyZH8euVSuozDyA/9wHMfI/InolqS6eJZWkBDA1yYW92NbdvvMD7i5NQyiR8dqSI5g4zt4wMJjRpFMc9EpgdJ4hg6FRStAopBI8mIRg6uy3M+/QUc5N88HDsUz0EuMevmDdPd3Dv5gY23jKYmwb7Yeg2IZWAorOOFHU9IycMI8DdAWJm4u3pIYSH7X0KRj7M5SY5T27LZEa8F4rwMZy/dB5PBxmO0eNgwyxwCSFo9kcE9ShFDgp0prKlEx+9MEZLOmVMb36Br9vdGNwTpTZi2ChMoQdRqRwRA6uHBxHk1l95M0KfRKhT3wKTrvgcSVoRe1qimegtZeaQaKbEeoJcytVdH0ATvLtoAs8ce4uDJZ1cuOrCS3Nvoqm9mzc2ZRKoltPqqmKvuY1b04KRSkQcbBmNxiMYJ6sDHY0KGttNHM9voKo8BZvNTke3BbNZyZe3jOWHc6W0dpkJ99BxzmcH6zNy8dP5cXvC7cjFImK9dTz4YxYfLUlGLhVxPL+BksZOnp4Rze67e57LVX682migsuwCH6WtJtTPC8Ri3hj9BpklFzlx/CwFEg92Z9XSqp3I4JgIVmj/+e9DjcFIQV07hXXthHr05OI2FMLFb2D2RwD8dKmSoy238VnoYDJzaglx0yARiwhw0TA3yQfwIcVLKAx8LK+e7MoWVnd9jTJ1FbiFc+RaHW5aBTHX1a77g0MNjgzydiZcJmFClMcNn//B4GAX9twviJjEeOv46UIFK4YGsrNwO+3WOtLL89CoO7ln0O18lfkVDnIHVu9ZzXODX8eXeVys+5GC1gK6LF08PvhxwpzC6DB34K3xptHYiJ/Oj4LmAnKbcsmuz6bb1s3F2otcqr9EjHMMj6Q+gt1uZ1/pPpqMTQQ4BrAhZwM/zvyRDVM3UJx5FKn4XG97vaY/hlz2z0MVBxhggAH+Xfm3N6YeeughVq1a9Q+3CQ4O/tP309LSsFgslJSUEBERgaenJ7W1tf22+eO1p6fnnx0ChULx/7Uh9p/AxFivf77RfxGlTEJSoDDxHxPpwZmiBt7bn88bU72pOneEDscRjJ8o5Bw8OS26/yrvn7AsdiY3RVvw0Kh4bNrzdLa2ovr1W/IKTgN9ifIRnjqOPzoWiUhE86DleEntbHpwLfOeeBHPECEcUNQT8rntrmG9CfcAje3dBLlqeXBSBABVLV1M/+AE2+8cypK0ACxWG4V17UQ2ptN0Pgdi+kROACwWGx46FcfyGsiqMhDjo+d0YSNL0gJ5bKoGJ9rB4V58nNRM6RFo6MjYibU2F8dJjwLw7S2CJ6Ku1YhCLsZRJSdA78Vwj5m4a/omV5FeOp6ZGYPVZudwbh3DQl1Qy6UU1rdx1w+XOPDgaKpajIiUpTx+/FFm1txKZJoXHkGOLBqdCKMTAVgxNBAfJzWfHinAaLbyl/HhBLpo+oVhfnakAGeNgkWpfrQZzRQ3dnKlooWxUR48NCkMo9lKqIegdtbQZkSEkAMV6KLhuzOlxPs4IhWJOF7QwDdrhCLD3V0WFFVGxg3zFcKjDNVIRGKWp/jy/I4cnp4UwegUH2w2QUzfTSkluadOlovKBReVMLbOFTfhoJQS4KJm/Yki3NQivKIGQ9Qv0FYFl7/HFLeECe8c5fMVKaQEOBNQq+ani5XUtHYR6Krl9/tGIu1RWwxx1xLS4zXJrTYwM6FPWRBAIZMwNMSFyTEevfL32GwgFqONHM/z3tU8ofNH2aPIdv8EYSzt2nUEdUM+925Ssef+0dgTlvDT1R9IMulxL76IY1wZ8d5JzHYpp7BOw9fyUWRpmhjhFk1xZiZRsUtR+CX0nM5Kc3UVLj5+hLr3ieu4OyhI9NNjua7em0QiRuUlhMw2d5oIdtfcEMr74PA++WyrzU77tUMolGl46BRUNneSFuRMe7eFDqMFk3sC32UYeT5FxIKYkegk3hia7MgkYnKr26jGyvh4N+pEJ0k1KxgX5Y7VZuNMpoRr16qQhSXQrP2aLtGdRDsq8EzxI97XkRd2ZdPWZaaxw4yTRsT0eG8cVTIu111Gr9Bze+hCOPE+DL0blUxMVXMXZ4oaGBXuzoqhgdxAVyuPJ92GzSeF67Xx3RXhNJ7PINWrgaVzxlF+aQ9mrzRc/gVDCmBshDtHcutIr2jpM6ZUesFb35O3dFOqH6kBTng6qlh3rIjWThNP/ZrF6cfH4azpfx6VXMKJgkZSFTBIIqOxvZtNZ8uYGuvZa0y9uecqJQ2d3DbSm6mzluDUmA47/gKz3v+X2nzvuHBuGxnMrowadmcrWDUyiT3lXUxILSbSOZI3Rr9Bt7WbczXnaDa2oLCG8NG4z/g0432S3ZOJcI5gVsgsbjtwG3fE30GieyId5g6OVRzDYrNgMBuYEjiFOJc43NXurIhZQXFLMW+cfwOzzcz6yev5Jv0XvDoewUkuPPeC4kZDXJ9ipavXn9zDAQYYYID/IP7tjSk3Nzfc3Nz+v9o3PT0dsViMu7uwVDt06FCeeuopzGYzsp6VsP379xMREfGnIX4D/A0fD4Yh9/QLX/lX8NIpCfPQolPJON/hjqmhDXrqWfYrGvp30Ekcyb9Yi/swOyKxiC1ZTeijZrJ4Ul9OkdlkZeM7F/GPd6Ehs4lj3iKGh7ux5v31KDWaG47poJRhtdnZfK6MQYFOTP/gBIcfHoO3Xgi589armJ/szcHceqJ99JwvaeKxrRkcfGBtr/H39C9ZRHo5sCwtgFs3XkAsFnH36BDiqloZGeLKvqwa3HUKnDRyvjtjYHJ0CtZWI5PePc6hh0fzY6knjW06bm/s4L2D+bwyNw6j2cq9my/hpVfx3k1J6JVaHhu5GIBPT5xmT1Yjv94xA4CMihYe/CmdtxfGMzHaiwhPHSceG4dMIuZYfhmV9Y506A2o3SQotcJ4b+syo1FIEYtFjAxzpdtsZXS4G/t2b6Nk/wF+aRvJghRfThc2MDjIhX3ZtUyK9eztsw1rBvPK5xuo7fyenNC7+PhwAb/eM4INp4r5Jb2KUFcNZc1dbLl9KEX1HWRXtjA5xpOPDhdwKKeGF+bEsTOjmlHzgtA6KeHabjjyGkMW/s6GUyXMTPbmQEUTwxO9mPTuUdKCXDA0GYnoFjMiXjD8OwzdZFYZ2HKlgihPHT5p/jR3mogv/AZ5/BxEznFUVFXga+pALhWz+bYhvR6daXE+TOvxPFmstl5D6lqNgR/Pl/PEtCiqW43M+/Qkhx8eg0Qk5uT5C8wYHIFE68qzM2P6jSPjxyPo8kjGadFH4BJEf3+VwMnuYKaPHsYmbx10NGIxVHIitwa/1n3sty3lLv/BGFrbONvqxL1jtUSHBaBPdcKW9T23HQng1SE6UryEPK2qa7n89sEbrP3oK8SSPhlttVzK92uH9DtvXUcdOoUOpVTJczuyKKzvYNe9I4Xcqv1/5RWdmklRNzHILQEkwvgQ+YWwZnAKnn7OvLgrG4VUjMlqRyISMThkDp2tFahkEjIKPDmZV0eElwMyiZgJMZ5M6FkkqGiaxYJICb9lVOOmVbKpO5xtI0ro7D7MvU2ddBamk3LycdZNvB9z6RLKGzsZEepCiKec1zLuxCz6DLvdnxXBT5Lo7yIINDRcA5sZb72aRD9HThY29Hq8b2DCM4iAP3qnqq2KnKYcUlxHIY0eyqCRwWBsw+/447B4E/APnv0WE5g7BaMJeH52bP/PNS4w5A4AilqKOHGtk6yr1YyQKnlmpuBZHRPhdoMhBZAa6My0eC/0gU+CsxZTaxfNnSbsoj6DOMRNi0YhZs+lx1hkaMFp9sdgm3LDsQ7n1rHpXBnrbh7U732pRMQ9mzK4d0wwb+3T4qOKZXZiGWXGSxi6Dewt2UtdRx1Gi5FdpT8SERKBVOKHUqLksyuf4aZyQ4yYsb5jiXcTxqBaqmZO2BxG+45GLRM8o2crz/JF1hfkNRVS11VNhFMEk3vCWMcGDKeuroJDm9fhEuLPsBEz/n5/DzDAAAP8B/Jvb0z9q5w+fZqzZ88yduxYHBwcOH36NA888ADLly/vNZSWLl3K888/zy233MJjjz1GVlYW77//Pu++++7/cuv/Q0hdC2ET/8u7Bbhqewvwzl/p/Q+3NVlsnC1uZGRYnwFdnd/C2R3FeEfoaRbZOVfUgJdezZxBAdhsdsRiEVKpGPdIPQHRTlxqamd2kjvjIz3+1FjbkV6Jq1ZBnK8j+3NqCHJVs/svI/HWqzh4tRZvRxVR3joivRw5W9hAUVUtCX4ubFqbhuw68ZKFg3xx7qnZdOfoECRiSAl0JiXQmeYOEwqpBLlEgtVm52JpM6mBztS0GnlqeiS+TmoWj4olp8qAg1JGjLcjMomY7CoDErGYRydFCBO50hO9eRVRnm4UNbb0nj/J34lDD43GVauk4PwZOlqaSZg4FYC1I0Ow24MRiYb2bp9Z0cLqr89z19hQ1owIYsOpEkobO7h5WCAVsiD8fVpJ6HCkts3I9ssVlDR0sO1uwfNnNln5+fULVEaosMrUnKgRk26r447RgofRWSNnRIgLt48K4Y/qYg9NCmfFl2eJ9OigqtXIsBBX1AophXXtxPs64g8QNhl8B5FX2s7pwkY+WpKEWCxGKhHz6JRIglw1OJlF1BS2YGw3k3W8ko4OE0/mlTM8zAWJGE4W1tFirmD49FV8c8xGTnc+Hx3t5IHx4ynalskr8+IoqW/nh3NlzE706V31n/vJSR6fGsnwUDcOXa3jfEkzZU0dBLs5cOaJCehUMsqbOphy9XFojuTXkGc5WdDIGwsSevu0MeUv6Kx9kvzf7TuN0S7j1sl9k9pXe8QsOLcecnch62xErHyR3eIY0kZGweFXcLVZ2PTAM32DtKEZceUFfrt/BTJl32KAb1QMN6+agPjyRrDbwTcFvPoLYthtNr557D6uhg5iyFgd88Ln8cyMGJo6BIVMOuqhuZTB/vMIMHbAOzFwzzkkKj2j5t/NritV1JYWIRaJMFvtPDwpgu7MnZwrsfPu7LGILN1YrDaC3NR0mW28vvVr7qxfh27l76B24p5Nlwh115JdaeChSeGsWzGIy4XZVHRV8P2sL8Bm45XWPbhK1YyP8iCvpo1frlSyd9xowty+w0OtJ6+2jSVfnOeTZUmMjQygY+wrZO78ldRZC3jpTwRArsdsNSMSiZCKpZitNn7LvUK1+TwTAiZwd49gDVIHuO8y/BOvOJc2QNFRWPzdP94O2FuyFxTwRowcRH3S6K4Of2ZiCyxL68u783JUEeCiRintM5LnpfhxLL+cDUYpHYn3g95P+PsbEnwdUcv75wN3mazYsTMz3hvPy+9xJiiXa+LBVNbZ6ZaEcN+h+yhqLSLNMw1HhSM2cxdHK4+S6J7InOA5jPAZwcGyg7hJY2htiKPWT4jiEIlETA2aypW6Kzxz8hneHvM2SR6pRChnYrFYGOo9lIauBuLchGd+gJMbj05yY+P2Q3SI6hh2Q+sHGGCAAf6zEdntdvs/3+zfn0uXLnHXXXeRm5tLd3c3QUFBrFixggcffLBfmF5GRgZ3330358+fx9XVlXvvvZfHHnvsXz6PwWDA0dGR1tZWdDrdP99hgH+J7ZcruVLeTFSFhQtWI8+sTemXYG2xCF6EnVcquVzWwjMzY3j19xyOXKvn7YWJxPoKE+TD+eepMxZyU9xidmdW02XsZl5qIKWNHWw6W8ajUyK59dsLuGrkvLEwgT3Z1by06yo77h7O/pxajuTVMyPei+nxgtH31Yb1LG76jJnWN2jpsrJ+5SCS/qYWV6fJgkQsQiGV0NbZjcVixUnXp0z16bHfiXL1ZmcmjI50o8tk5aZUf744VsSnRwv4dvVgYn31N3ZKXS7GTSv4Inwd905N6veRua0RY1UBDhFCaGBJxmW6WluwhySz7lAhf50UQbcUfJzUvRNGk8XG50cKWDEsEL1aTqfJgtliw9Bl5utTJTw2NZKGtm5sdjvNnWZ+PF/OS3OFCRFn11HYHMQzJRpuSvMjt7aDJH898b56KjJPYNO4kZooGA1mqw2pWCia3W2xIpeImfr+ceYmemIw2pib7Etjm5Gihg6WXDeZBHhk6xWivRxI8HUiOUDoZ1OXhfO/F6N1VlCa0UjS5ABcAx3YdqWKXy5XMjquk+9KnuXF8O9o2FvHsDVRKLrLyTy9hzdqBrFhTSr3/HCZlk4zT02LJNhVS5C7lvSyFsI9tb3hnkazFbFIhEgEnxwuYOlgf9x0StorroLKkZ2FZs4UNqISWXgqxYxD2HDsdjvH8xsYFuKCVCLm8PZ1NOPEvLkLb7yfXc3Q2QIOHvDTakr8ZqFKnI+HuQrsNnAN7dvWUA07/wLeKdDVAhIpTH5R+CzrZ+hohPJz0FwGE5+DQGGKuuvkJbCY8NfIOdsmY9WoSGSSf5CPUnUZdj7AlUmbaDCKGR/lwcbTJShkYmIsUsqvNTNlZQztRz/gQKWMCeKLWF0iWGedQYK/E7nVBpxU7YwQnyRo8N0gFnOuqJF1mZ+wyH8KYoUf7+3J4pv5ajItWob6x6CUSfgq8yv8df6Y22L4/kwpTloLCZFZHL3oisVtF59P/oCiOgtRXo4opBIs2+4mp8WV6OVPIZXL//71AC+dfgkXlQt3Jt5JZXMnjx54h2kxESyL/pN78jdsPl9Kp8nGmuE9hkl3OxhbwbF/yGd1TQYtrSVERcz6k6P865gsNmRYOFLQjKNKRnKAM/l1bZzKb2Tl8EAAFn56ikemRDDYQyyMA4XDPz5oD6/vvkq3xcYzM2O4tGcD7h255NjD+LYhjFTXCq5o9zM3dC4Z9RkcrzxOmNoTR407WqUzmQ2ZTPG+hWUJY8mobGF3Zg1PTOvLUcxvzudc1TkMZgO3xt2KTCLj++wf2Fm8g80zNmPvEbSx2CwASMX/Huu2A7/fAwwwwH8H/x5PuP8/kJyczJkzZ/7pdvHx8Rw/fvx/oEUD/FdI9tfjrVdyub2KWTEBNyhV/RGONTPBhzE9dWDmJPnQZrRgw0ZWZSuxPo7ID3xJrA6IW0xM9c/IK8/wYeszGIwmjBY7LXWdfLWqp7hm4WGKazxZluaPs1ZBgp+eQDcNaUEuveeVxKr4sXEa78Uko5BKCOnJ3Xn3xC/k1NXz0aw1vLQrh06TlTcWJHBu+3s0GUUsvOVRfn3zTWTqYIp8DtNWF8WJggh8nJRoFMK13ToyCLPVhlIupaG+g4a6UtJLaxg5dIhgBLlHUrv8CBNN1hv669reLVw7f4H5rwvGVGC8YGxVNncibupmz/4iAlo/xic6CMYIiwVyqZh7J/RJPKvlUpCDxWYnp9pAY3s3P5wrw2K188QYT+Jc9vH1MTmHC1r4dogXIe5KBklcKG8x8uiUvpX37RXt7KyW8YJTEykBzty/5TL+TmoCXNREeumoNRiJ8nJgcVogG06VIhGJ+D2zhqP59dQajCT5ORHto8PNQcnto0Lo6Daz+usL7H1gJG4OSuwItkb4IE9iRvgg7clJWpYWwLwkX5QyMbO1Bhzrt6L76yNCozq9cYkJoCY4AJ1KzkdLk/DQqXhnby7bfy/kxeWJJPrrMRjN/HK5ktmJ3rz8Ww4uWgV3jgmhob0bk9VGQ5uRUetKeNP3JMOi45g2ex7f7j6K7NohCBtOU6eJ53Zm883qwfg7qzktH8m54sY+cZL2Bqg4B5HToDoDDr8Mt+yDJZsIFPd4Tc3eUHAQXEL6PCVmI8g1YOqAwv0QPg3Kz4NfKsT25Dml3iqIU1z4qteY8iv+CXFXA3Frv+AP/016WTPrjxfz/pIkJGIRtqxfqavsxn3sHDaVOvGd8QUW5pbQKRI8RT9fqkCvkjF9fiLuRevg26eoG/MhF5tbyLEm0t1sp9VmZNUwPZ3dVuYkhVNniOa1HRdZ6JjH4LHL2JjhwNXfypHEa/g66hzOjQF8ci6I+rQrXDBsJtZVCJdTySQsTvXHID1OZkMRyzxKMYVMQyPTkOjX56GRho8n3jcV/sSQutZ0jQeOPMCbI98kxi2G1bGrefy3XykrOcmrc4aTGqzlROWhv2tMGYxmXvsth9CwTH4u3clw3R2AYEz9mFlDk6mGO4b1N6aOX/yYnK5anvsnxtSh0kO4qlyJd/9zb9ru799ltPU0eSGv465TkhwgCJ00VBbA7s9g6mu96pOVux9CK9PgOOGFf3jOP1g7Mpg/0h+Tp6yE+nx8G/Pxd4jmwa02bptyEz7KWHa27RQEPros5BjLWB61lMs1OTz6nZEkt2YS/ZyJ8FKyavcqlkctZ0LgBDblbqKgpYBvp37be75FkQsZ4i08j/4Ihb730L1YrVbeH/8+KunfL4Q8wAADDPCfzP8Zz9T/FAMrWzdyuCAXq83GhPDo/9bztHdb+PliGfuza3n7pkTe3Z/PX2dE8/OFCsqaO3h6RgzN1UXoVHIkel/a6itZt+c8pVJ/Cuo6SHVzILrYRNIkf8JjlbB+IgcT38EjOJ7Y69SzukxWVHJhItdl6aK9vRVnrSsSad/aw3untlHS1MDb09ZytaaVN3ak46HX8JjzcbRKOcpR91BwIZ3tpVDSZaa928Yr8+Kpae2i3WRh9HX5Hna7nS83ZdHSeoZmqTM3TR1P/HWeqro2I3KxGL1GTk1RPtU2DVk1RhbE6FA5ufYe49zOYmJGeWOTS9AoJBgqr+LkoKHc7sYPZ8t5ZHIEYrGIL44VsOlcBV+vSiXAVYPdbqeiuQsniZjNVyo5W9TEaC8zN3V+z1eGVIJjBzMxJZpfL1fw1C9ZfLIsCRetgr9uz+bVeXFEeunIrGghwlOHXCrmru8ukF/fwfAQFyZFeyKViOjstjAmsk+BrNts5WxhI3uv1rI7s4ZYbwe+vbUv5+fU/p8pbexiyeI+Ge/WLjO/Xq5gaVoAnWYrXSYLHjphgtaSf5r1Ow+zcOVfCHDpnyN3rqiR53Zm8/XqVDx0Kipym/AO0yOWCF6UDw8X8MXNg2jpNCEVi3F16PNkd1usfHqkkPmqdLy8fZEG/eMgpY5uCw1t3b1Fjik9Dac/gcUbobsNarPB/7rcJpsNGvJhxz1CnSLldc8Uux1+vk0wsHyHQMFe8IoDr0SI6sk7aegpsusqhFt2tjZxbOOXjFyxFo2jMIaOXqvjs6OFfHtLGjKJmO5T35B/oZbgcDEFgbO5eOUKi1QX0ISNgqjpGM1WLlXms7HgfV4KWkzh6UMY4lZxsqSTjMoWVsdKSQ72oN7uyPqj+XzY9SSVEz/jraOVDOIq2oSZxHk7ohDD5ksVLJHspaV4Jxtdn2RumgMbsr/h6SFPk9uci8VqIa85j05rJ4O0N7M7s5rXFwoezpr2GkQiER6avnHz0u8XiPMXMyM6ga+zvmZywGRym3LJb87nlvhbUEiFe3eoIBsXpTsJvm5YbBY6TB04KoXv+JmiSjaeLeTjJaMA4fv+5ck8rnS/g0wi57FBjxPgJITSfXVpF/sqtvBg6r34aH3wcegxqkydgsfQsS9s+dvsb2k0NvJAygO9763ZswYvjRcvjxQUQ48U5iAVKRgRLNyvwtIy3GnCISCx/0BqLGJ/1neEJd1MoC6Q1XtWo7GLGFZXwtJF20F1o9rfP+L5HVlUtrbx0JQAItyENr916ivS6y7z8LDbiHIJ5+nfDpBn2cCPs79DLpHTbjRzoqCB0eHuKGQiHjz8IE4KJ54d/iwmqwmb3YZS+vdDGAF+L/qdjPoMHhj0AArJ/76Q08Dv9wADDPDfwf8Zz9QA/3v8mlGM5b/TmPpxFSSvIE+WzLG8Rs6VtPDJoUJ89CqkYhGzEr2pahWkwp28BGVHa+5eJv0MT88czNIAJywWO64OCs6dqODNc8W8FpmMy73nGf83p+qydLFiyzruG3ITo8I8UUlVHNrwIZ6hEaRMn9273f3D+oTRY731/MUrh29b4qiNuQXXHsMsdFAi7qZiAmQSHJQy9mRXsyDF7wZFtc+OFtKkEXHf7JU4aG5ceX9n3zVMFjv3jAvlwW2F3BtrZ27Bq6givwIEY6q4ykDBpTocfdU8eDyPD5YkEerfI5bQ1IH4utSxA1drifR0wFsvTISuVLTyyE9X8BVLWRzpSbWLiqx2OeHJL3LTwdWcLwdSohkR5sb6mwcxJMSVnKoWxGJ6C5zGXWf8Jfg5ccuIIBJ8nZBKxdz53UXGRAg5cJdKm9idVc1T02MYFenOqEh3bhkRSEd3f+9bsyaYy5X1TO8yoVPJobudti4xZ4ubmZ/ixxu7c8mva+PluXGEujugDxvKmtuTcb78GWjcIGkpW86XkeDlQMbZA8R7BOLek7viG+nce56PDhcwO8EbpUyCZ0+9r5KsrRS15DNuxBN0dVvJqTKwZM4spLr+E8e967NIGO+HZ1DfxFajEIob9xIwVPgDITzLfwhsuRnibxIMpxPvgsUMYZP6G1KmTkE23VAFnfUQPAqWbYGrvwkCEu8nwvyvwbd/+Kes8Spunq7IlUJbL5c2s/l8OZtu68udUwxbRWxsFdisxItExNcUwqhX+WOQVDR3UdbUU6jcO5bK6BC8tArkMiNuOgVbLpYjrbxA/NTViCQy2oc/io+vPwuH6/n5oiO68hZ2XalCLhWB5gpfyQfjEOlN1mUZy1LD+HjCxwB8nP4xIpGIUH0osyU+SF3EnPU+x6tnf+eJtCf47thTyBz9uS3tUcoqzhLhOxSl4zX2VJ4mo82DExUnaDY2c7XpKg6msRyQNvaG5w5x9+Xa+VPgOxWpWNprSAFcajxOnbUOEIwplVzCPWOjeP9iLBqppteQAliTPINZEUNYunspjnJHvpnyDRq5BuRq4e86RvmOotva3e+9BLcE4lzjel//cG0DWrEnI4IFgyskwJ/c/G7aL15gUIqQZ7fpXCnehkwytApCbEIG4h3xdxDkGISzRNl/nPwLFNa3MyXWiw+O57Gn+CoRbkLtrLtSF3Pz9hyuVXeS6K7gqQkTkUunIO8RNhGJRHx5ophQdy2h7g48kfYEXRbhOftHeYJ/xrTgaUwLnvZfau8AAwwwwH8aA8bUAP8/8968qf90m+YOE/f8cIk3FsQLIWz/FVJWUm5zIVpSzucrUtibXYaPs4ZEX2GCvi+nhu2XKvl69WB2pFfS2GFitbOFb8fZCIrxFEQoTn8CIWMJjvfHubkFjbJv6Nd11rF6z2q+nPwlxXVd6FyzifRSUGPoQoSIkUtXIVeryShv4b7Nl3l7UQL+zmpOFDT21I6BLbXezPSpIcZnJHQ0wfrxtM7/gff2l7P97mE0tJvYmVFF16UmhkW68U1jIxarnU+WpzAnyQejyfanhhTAtAgdnx++hkYSyuoJcYwNVSA2BIGsL2zm89OlBI91IzLZk0fUYvyc1ZitNkoaOgjzcMBLp+J4fh2jIzz4/tah/YQ5Ev30TIz2INRJzYQUXyZLxWw+V4pOJcUw9wfCAZvNzvGCBhrbuils6GBZWgA/3zmcy2XNvLU3l3vGhfH8zmxWDg1kXKQ7hk4Lw944xI67h/HK3NjesM0j1+r56WIli1MDemXIg92EHJA7v7uIViHhzYWJDI2PZlv+FT47UsijU6Ng4zx8R9zPx8um8fnRQqpaOnF3UHDLhgtsujUNbye1oJjWWCDkFIWOp7rViL+TkltdsmHwsBvEBo5fzMLV3sTsmP6LAKUVJznfXcc4QK+R9yqk2e12Vnx5jgcmhpMS4ERQgisOzkqaO0w8uT2TRWEeRIfq8XDR/uPxnLgEjrwGwWPB2A4+CWBqFz6rzQanQCFXp60KJj4LZecgbx8kLYfQ8bBxITh4/umkWpbzMyke/qAQjCmtUoJCAsYL36GMnQlKR8HzqrtOCGbM41QXttDZaiIk2Z2ihnaKauQ8m/RX2ipqiPTyY8Fnp/jxtqEgAsv/h72/Do/y3vr24XPcM0km7u5GAsE9OBSKlyI12lJ3393dlV13V+pUsOJQ3B2SQNzdPePy/jFpQkq3Pe+zf/fezz0nB8cBl9vMfD/XWuuzbJFYz37LF/uyCdN5IovNApGQUVFejIrywtpawdc/70SWMAujIhxPiTfttnZSoxuJUMTQvf4umkR+PHftc3QZLby3rxiPqlvoTpjDqvg5dImdnwOJRM3swEnkteYRtOMRHGMeZkVaFqfqNfxY+CN3pN3B1PCpWOwW8msthHgOfK+0tNRx6NAGuqOUjAsdz4GqA+itemZFzOK2jEUsSmi76trdm3HvH94uL5UXW+Zt4XT9aaeQ6uNozQmaWlV8UfoEX07/kjBt2FXrJnolEuw2IM7en/Y8QsFgYYxO0QAA8zxJREFUU5zzF87S0GPrF1PR3hqi83/gWHsmXilBbM+pY0xUOlrlH9e+fXjxQ1K9UxkVeHXUtKKllzWbP2VkfDdrlz82qA2FUqLk89l/RSAyYrVb0SqlbC/bjkqiYkLwBCRiO1kjc/F2c76U8VX5cqj6EFKBlA9zPuS+9PvQKXVX7dOFCxcu/rfxj32pXbj4v4BGLmbh0OB/uqfLICIncuzwbsy/PosD2Nf6Dpe6dgPw+Y6dRDRs59OVQymq76Ku3UiguwLiZ3FZnk6nwULJ6RO0lOby2f58ntl2mZcXpPb3AgLwUnjx5PAn2X6+lxs+L+DZzDfxUXvw2ZFy1h4tx83bh4ouO14qCXPctRj0Fh7bmMum89X925idGU/i0HH05v9KbbcFpjyD1j+aE09MJsxLzdAwT16cn0KstxqNh4xr0gKZ1yfE/LWK/maqJRXZ3LBlJY29A/3QKpo7CZb24KsWMDctEKHaCxZ/RZHRrb8n1PPXJrG6r8nuxDgfcqo7ue2bs9z85RmOFDWxJ6+BYyXOAeQfORx6qaVEBroh6qtNW5oZSpyfG1qFBJlISK/JyrbsOnzdZMT4aOjUW3jj10IkYgG+bnLsDgfeGhmbL9TwzC+X+LWggXBPJUqZhHt/uMiW7DoARkV6khKopUNvBuBEaQvfn6oEYHqSH9MS/WjtMdHaa+bF+SncNKavh9w172AMcVpNR3qrGRnpRVKgltcXpTL1rSP0mqx9N+JNmPAYlUYll2o7kYhFrNetBvdgPj9axl3fn6OgvguAuLBA5kaKkMgHi5/x09/k0bnfXXWNnv7lMkPDPIjqE4Exw/xQaWUopCJGRego3FvN7d9fJOv1Q9S16wev3FwCm+6EnPUQOwMSroHiPThkKpqtKqzjH3cut/4m2PMnp0nFsh8hOBMcVmgvd86XyCHrTzDmgf7UvkFIVSDXcLGqnSlvHMJqg2RfOdKCLdDTxMnSFpZ+coLs6nbaekw8uSmXToMFfZeZrlan29/Upq8YJSnmrh8u0n7sU+L93Tj08AS+OVlJQV0XqcEe7LenUdIJc9ICkIiE7L3UwMnz9c7nyyOYm6YO49rhUeSWepAZGkB7jxi53AB5G6mwevGV0Yfd5bvpNfeQY/qUL4etxL32An6tFcR4Ouv6pN5xSHVhZPhm4LViK4LEa3nlzCt0mDtYk7aGudFzUYgVKMVK2jmDl3rgBUlIRDzdM0KRy5wvHBw4cDgc5OzbRVt1Fd6qAbdQg9VAq6F10GU8UNDEjWudjWVNVhtysZxxweMGLfNpzmesO5/DIxl/6u99BvDIoUf4teJXALJCs4j2iO6fJxFJEAlFg7azLNzAA2mA3Y697hKGtlpqsj7ggiwTo9XGz+dqeGdfESdKW66+30CwJhid/I9FTZiXirfnzmNu9GykIilWq5DqtoFnU6uU8tTRp9hUvAkAm8OG3eGMhn13+TsuNl+kobeBJ448QYexg6/yvqKgvYCCtgK6zF1sOf0Dd264+Q/37cKFCxf/W3BFplz8f4JYJOyP4vxGR6+Z5Z+dYrKbmvtuSPu7zXvHL7qZdw6PQbLnEhWGZlYlOlNn9ph+xOqRSpJIyHenKzlc1Mr2e8fgcDjYc7mBSB8VXp5etEUtRyMMZolSgtFiY+vFOrISfFGJhEjlYkYFjuLImcvcPzmKoL6am5tGhyEXi+g1Wbnlq7M8Oyce92YLkRolAaJe5kda+49vXJqzQev3O/I5YIZPO+6DiIm8uvEMo1LimJwUiFYhYcp1TuOGaENnf93DqfpTJOgSUIrVPLG1mVG+0bjJBqIOCo0njdIQOiwSCopLqC+5SPLwaSz99CSfrxqKrsdOeXYz468bMIUI81LirpAQqlPQbbTyxMx44vz7ttlZC0deg2l/7Y9unSxrI7e2kzeXDKGipZf3DxQzb0gQmy7UkF/XzSPTo8mr6+KO8RH8fK6a2Sn+tPWYCfFUkRTgzmdHyqhq09PQaUQiFJJf38Wt4yOp6zCwOCOIL46XMyzMg2BPFWvGR/bbk9d1GPj6RCWT432Ym+Z8Pr47VUlxRQePXpuERCTAbLVjdotk/KsH+e6W4TR0GthX0Mic1ECGhnny6wPjUMnE1HXo2V/QzPIRM/E0WpgW78uXO46i1fmwMCMYtUxMuE6NR18E0FvnwfiJUwF4ZPfdjG/vZNbSgYL6urJcaC4mYLgzpXNOagABWjlahYS9+Y2oZGJGRuiQS0SsGBWGOSOY1Jo2DhS04P17O+x9f4H6i05xBDD2QZCqMLdW0VlbgbWlFf+AQIiaCm1lznopmxnEMhhzH4x2Rk16Lb2oQjKv/oCc+wqismCq0/EvsNvImGgdl+o6uHlSIvATADUV1QgEsCW7ljXjo9CppEhEAiKH+EDxXqiqA49QQkU+jEFL/Cjn9rzUcvRmK7/1BC7skRPhrSC6r3FwU0Un1ceayUjxpaChmyZTHOPEIpICtahkYh4cP5V39hbzQquBFxbFI2gvJFAdiEQowd8DvDSe/DkogpfiB3oQrUlb0/9vkcZZN/X0iKdRSBRIRVLaDe14KDyoam/l58INDPEZMlBfZe7l0aS7kGo86DR1UtRWxA1JN3Du1I+ozTa8Q8Ox2R2s+nYDSRHtNFsLeH3C61xsusjXeV/zdOZLqOVRdOktTHrjIN+vHkGMr4bChg4KW+q5Jimer2Z+fvV96LtHznbT/yTDnGl31J6jccer/KllJS8t9OYGzRl0R37hy+V/4ceLzVeZ8vzG7Mi/37cp3iec3ww1dl1uYMO5Gr69ZXj//MeGP4ZW5vw8XhN5DRdrTnLNxlk8OvRxUuWxBGgCiHKPQiFR8MW0LwCYFOps1yAMs+Ow23HhwoWL/824xJSL/zHcFBLGBrmjaLbgcFzd8uW13YWkh3owKc6H97Jfp1naRmv5tcxJe5AYdRw1+W28M/Xl/oHAM9c4HcKsdgf59V28tDCFrdl1LB0WhX9UNGFGK29tyeebExV0mWyEqmRkf1rA9X8ZjtJNRlylmYyp/lfsv4gYPw1NnUY0cjGX6ru55wnnIGSZ6AhBZ3fCmKz+5bdm13JcPJzXlwwB0wEuNl6gSvElAc2P09DliV+fWQJNefD5dLgvBxTufJj9IXen3U2GXwa3ZiWTGT4ehXhg4CSQ1lPV1s25qna+Ot5IjEhAQI+BAw+OR6uU0tWiH1QHBGCzQ12HEavNToCHgus+PcWxxyY6HfxEMlD7gUCExWxDv3YVd4fOpTV8PABSsQC7w0FFSw8PTY2lqVPPoUtVRHipOFTcwrmqDjxV0gHbdOCa1AA8VU6hmuSv5WxVO50GCyfL2nDYbVw/PJQDBU38eLaGFSNCGRnlrPUaHeXF5ou17L7UwMpR4RwqaqKhRY//sXbu1Z+jqdeMxe5g+z1j+fyGYUT7augyWgivVRHbN5A3mm1UtPTSa7KSV+eMOq09Vs6YKG+avIRkDfWHwp0s8fWAYU7zh9LmHh7bkMPaGzNRy8RkhUxFq+4ddA0rCrPxaz4Bw+fTa7IQplPg03cPq9t6MNs7GBkxEBGQykSMjPRmZOQfNBmf+54zaiS64itX4YksKoIoxVm48AYEvA5Db4S8zZD9I+RtckanoP/Dcfnn5bgNWUVc/Lz+zfR0mOjIPoxFGkl4X0Nib42cBelBvLA9n5nJAf11XAuHBjM+xpvvT1ehVUp5YGrswPG0FILCg67YRax45whfrEpErtLQmHeYg7XwznUDkZnbx0f219wBLJsdS9EQP2a/e4R5qf5YHKBxr2Rsohb3vl5sq8dFYLHbQSTh24N2IrzbuHV8JG9OfJNWQysBqsE96Cw2O58ey2NecihvHDyDzjePx0fdBcCO0h28eu5Vfpn7C09vLOVmjwx826qgT0x17nuT/MJ8fkwJYFzQKBr0DVhsFnICGjhcfQzP7BraDO34qUeQFTKECG+nQ6LBYqDL1IVKJmBYmPMz9cmKof0unnsq97K/biMy1Soi3CP6o05bS7YiEopI0o6nvvQatHFXNxTeWLSRU/WnWJO25g9TAgnMwH/Z+xwUy+iuPIqidzNWj2GIBEKWDAvhdHkrFpMeiUROZ0szCjc3pPJ/zSHvmtQAJscOPrYA9eDrHnfsPR4We+DTJObwD1+y/hpPYnXO5+S9C+9hd9i5J/0eAEJ9Ign1+YMIqQsXLlz8L8Ilplz8jyEUCnh0QfLfnB/vpyGz8FVwTOCBoQ/QZeyiIbGBJO9ILhZfZsfFvfwl9mGEQgEWmx2D2UZ5ay9dVXm8f7SBJ6cMof1AFSU2O7GjwtF3myms7yIwQIWvA4bFexN+vxylmzP1cO69g4v5/zwnEZlEyC8XapFK9QwPNTstqyVy1Fn3cbp6wSADi5EePQQ3v4Sb4zPwCCNE7sbS9BvYftJMZrdpQEx5x8NNO0DhzuYLNdRcvhFRQij5rflkxTt7uWTXtGO1QUaoByHeAu6a083kGF8mx/vye9y8lMjcBuqt1p2u5OlfLrMwPZDUYHfSgj04/MgElFJxn5OdJ8I+u3QJYI5egH9MOpq+5rAB7kpeXzyETr2Fxzbm8FSaiVuKHyB9+i+crOzg/snRxPoPrtfpMVn56nglwZ5yXtlVxMGHJ2C12dmX30h1aw/Bkk6mj8wgxlfDkL7+Uc9uvcyC9CBuHRuJm8L5VRTiqaQ3xMaQu/3J6DbQKnHg5+6shUkMcEMkFLDxfC1eGhm/5jUwNNyTb09WIpMIaeu1cNs458BOq5DiwE6RPYh5nj6Qtx3UPpy1x/BrXiP3ZcUwJcGX3ZfqWJARwtT4OQMnY+jkQpONYVOXIRE53QSf2nSZ4uZutt3tTDXcmlOL3e0AN9qSkYqkgxwgARrqa/HzvyIS+5v72tb7ICgTwsaCfyp4x4LcHbpqnPN1EWA2wNnPYc47V93rofpuHPrOQdNKzjaSb7iLsX5hVO39iPXmEdw9LYUgd2ePsfZeM9bmFs7ua2PC8ji83eTce4VF/qM/X2TJ0FDSR97pfJ6A1xal9Kee6s68wXBpJDCOlh4TbnIJQ/uEhtVmRygQIBQKaLc1EqAzMis1gBCdmg8ufICfyo9YT2cDaoXDjKIvpVIkBOUVRh06hY4xQWNo7G2kvreeNJ80bHYH+1vfRlo5EYc1inSfgYhcpn8m18ddj5vMjVdmR+Jz6CPILXVaxwPq8XfhCC5H0f4zG0s28s3MbyjvLOfL2o2sTF6JVCil2dDEG/MnDLqWIwNHMjJwwKyjWd/Mxuq3ifZ/FI1Uw+3D5rLUMJ51hetQSpT4q/xRS9VsL9tOuHs4YwOmkBhsp6geGlpq6ZDsJUmXRIZfBuWd5ZR1llHZVfnHYgpY+UMJN44OZ1RgGvu9g5Enz2acWEa30cK9P1xkX/h3ZFuDOLG/mPQlCxk9bQFdBgsKqQjJH6Tv/h6RUIDb36i9+g35lOcZK9fiULgjCdIRZa5jZ8VOTFYTcyLn4MDB6frT1PfWMzdq7t/dlgsXLlz8b8Alplz80xS0FtBh6mBEwIi/u1xrbTVFJ48xcsHS/+N93fHtOWYk+6GOzwJdNDqFDp1CxwunX+DayCVE+gUTkuyBUOh8Y//T2WoOFjaTEerB9NLPeNE/Ap3Ij4kBrXj2OZ65eyv5/N7RNHYZaek2YbU78A5xioI9lxto6DSyclRY/zFU559EXn+GgJgVbC+7wIbvg9g9eg97tQtZPCyEYO8EzDYzhW2FJHsn4xUSy77hL3DwRCv3ZenwVHgyOXQyk0N/d3ICAfg6o2j+WgVLM4NxyOo5sOtB/NIf5bQ9gboOI2arnYxQDzL9M8F/8Caq2vQEaOWIRULqOnuZ++5x7p0UzfJRYST6a7k/K4aJsT54qZ0iSyOXYOg1s/urfBST/ViWFcnXJyqYluiH76Rr+epYOeeqClk2PJT9BU08MTOeth4TLT0mWt3jCbx5C6PVPjy3s4AhwR5EeKudaWGl+2H6Xylq7Obz6zO4XNWOl1qOwWzju5MVlDb38kCqjVKLO18dr6DTYCIzXMexkmZ0UjFquZjEQC0nS1t4fU8hD06NJdxLTdGZBs4UNmPwlZGV6M+l2k5Wf3WWfQ+NZ2GYEd2F13lZcjurvjiFQiKkpcdMapCW/Pouwr1VrBoVRn27nvKWHtp7zQSOcgoFz6YeIr1VyCVCfjxdTZSPGr+2c7xT6sMHy4fSZbAQunkBB40zEV97M8l9zaAfnBJNq94MxfsgbxOvLXwJnXoYUpGUL46W8fO5GnbcMxaBQMC5S/ncur6MEw9Ikbr9LkqVdr3TPGLPn5xOfbfsga5ayPsFcjZA9GTnfJUvlB/iQmUzAXGZ+Ir0oPKkZ/lOSht7GAI0dBrwdZOTOjmYlIlBCI0d1BVuocvTH0jBXSVl3eoRoG/H8dFQQoeuQygQ8MnhUnqMNh6YGkNrjwltj4POog4I93Cap0iVlLf04nDASB8HYquesOHOdMg1355Dp5Ly0QqnWcI96y5gstr5ZOVQjjdvZ0iSiBDdNOdneMgd/ad98ee/4m+uwHfVl7R2m+g0WJjU5/C4JbuW85XtPHNNEmcaznCq4RRSazgvnnyV6xJnMT1iCqq0wXb3m4o3sa9qH6sSVxHg7wt96ZmX6zoJ9lTipnRnaHwqI4Rp9Fp6sdqtHKg6wKa5mwhQB2B32DnXcI579t/DO5MGi9anjz1Nk76JxbGL0Vuc9UUV7RUk+ybTqm+l29LdH5lZuGUh92fcz62pt3Kp5RIauYTnZs5g96UGes1WZCJZf8PaB4c9yD/inqxoor01yJQShBmriPSI6v/8Hn10EvklvZR3GAnwSeBjy2ZGs4AHf85mYqw3y4b//ovmn2Prx2+gz/RmduJ8p8GGZxgVLT2cKtvNgfrNBKgDeGncS4AzJbmqq4poj2iMViPvnHuHzIBMRvj//d8EFy5cuPh/GZeYcvFPU9heSF1P3T8UUw6HA7vN+neX+Xu095qRioUoJSKImTZo3gyvP/H+tkrW3RbJ6pSBGqG5aYFMiPEm0EPJky33MD7Oh3DhBRJDSyFkPocKm9AqJaQFe+DrJufZrZfJDPdk+bBQbDY7GoUEk8WGxWbnbEUbIyO9sJmMCE1dDI/Q4atdinxMJzWGRIqLBtLB1hWs44vcL9i3eB9ioZiU8CCCe03kHa0lJtMPsXRwsfm3Ow5gFUi5YcZoAIZH6BgeocNssBJv1dJckcOGZm2/g9zmC7VUtvZyb1YMp8tbGRrqiVAo4Ia1p/nLnETGxngToFWxYkQIs1Kd6Tpmm51jpS3cMTFq0L4VKin26b7sKm1iiS2cS7UdfHaknO33jGFklI4ZyX70GG3E+qrZfLEWIRDhpeKhn3PYdvdYpMCXN2YO1AN1VOGoOYPNZuen09VssFUxVSDH7ClgzbfnmZ7ki0ggYFOdliR/FeMi3dl0sYbbvz2HyOKgq8PI4iFBAOTWdZFT09F/rDHD/DhnN7H9dBWrRoUR66fh/evTUUrFDI0Jpac7HU2jhI4OM0/PieeTQ2UgEPDATxcZE+WFm1KCv4eSLXeN7d/mkWPVNF9uY/GtqWCz8FzwaYIik1GU/Epb7yI+P1pOfaeRN+d/zJ0qf6QSKfZ9LyDMWImPNhAPtYyPj/gwO/Rap5jse8ZnJvvjrpT01/ylJ8axRfMHQqr0EBhanNGoqCxw64tcNeVBcyGEDIfCHbD4a/AMg5MfUG9to6HDyozL91B903Y+OXWckoow7p0YznM7inlqdjwjI70QiASg8iR/1sPYavYjEV0RMVB6ILh5D1E+8SAQMDHOB7PFWefyys4CKo0GHhzrFPenNn/IF/qRhAb4EeSuALsZJEoIygDg6VkJ1HUa2JvXiMlqY+XIEL46VsaW7DruTb/aDa+ytYe1xyoZEbGYv+y/xFd6CxWtPeQ3dFPc2EWAh5Ikfy3by7bw2bkibsmYz+zI2XQaLPhp3DlSd5AFsfOu2u6IgBHU9dYhEQ2Osvx1ez4rRoYyMd6Tx9bnMTTUjetHRGK0GjnVcIpU71S8FX48e3AtQ4L8GFfgx/qO9zjiXsy4oHEsiFnArPBZtJvaqeqqwl3mTrAmmB5bD99d+pmt5RvxVfowJ2oOWaFZLI5ZzLd535Lmk8bxuuOsTFzJ57mfMz18OoHqIGApBquBvZV7mRwy+Q/rQrsMFtz62iUMDR1I180KzRq0nEgooEosxqGFazIWMNo4AYBn5iSgVf5zVuV/RGusnE/yP8QgsXFD0g0A/JrXSLG+ErFYTH1PPevPVlNpOEduz2bcZe4sS1iGQqxgza9r0Cl0LjHlwoWL/9W4mvb+i7ia/v37eevDc4S7Q4ZvB4ETJw0agDR3m3h7bxEPTInB819wBvz4UCkhSgszUoJApqa2XY9GIaH4YC0djQYmr3Km15U0dbPqi1O8vcKfoYFOS+Auo4XT+zci909izJDE/m32mqzUdXVhpp5E70R25NSRHubBxtM15Ja08drSNFTuMkqbe3hhWz4fLE8n5+AGesQqxo2f1v/Gurqtl0vH6pA2tDN5VaKztqaPy7WdtPSaiPd3Y/Jrh3hlYTIzkgNo7TWhlYm5VNdFWohH//L1nQZMFjthXgPb0Fv0iIVipCIpL+8qQCyEB6fGYbbaOVPRyugob279+gxT4v1YNCyYP2/OZX9RMw9mxZAYqKWlx0i19QDjg8YPaqD64e4LlJYWMm3CBC7VdHCkpIUfbhxBl9XGT2eqWDU6nE8OlWCw2HhiViKv7c6nrLkXoVBIgLuMysZeCpp7OfjwhL9rPvIbZyva0MrE3P3jBd5aMoT395egVoh5cX4Ka4+Vkx7ijp9Wjq+bgtzaDv7ySx5PzorvTyvcu6uU1tx2Fj+UgQDg9KcQkAFl+6hLuRs/rRyr3YFULKS2XY/BaOLmtad5brInX59r4a45I3h2ax43jQ5jdmogfDYFJjzqFEb/CEMn/HCdU5g0XobY6aANg7H3Qk+zs5eUz2CL9vLOcr756C/MGbmMIQkR1MmVbCrexG0pt1Nw9BeOl3ewYOH1vLq7kMdnxF01oD5Z1kJ6iCfSPofG6h8fos1nJKkTF/Qvc6a8hcc35fL9LSPxcZPT3lTLxrxufr7YxA2jQ7HZ4PoRzojHjvIdFDQX80DmvWzLqcNitXOt4DCmvO1YFnzFa7sLuCYtgPQ+QZBd3ca967JJD3HnlUWpNHeb8He/usantl3PI9s2kxUdB1YtMX5ujI7yxu6wY7KZUIj/ubqg5t4OHHYpDYYSnj/5PJOCZvNL+Q98M+Nr1FI1K3euZFroNK6LW8XDO75kZkIkQxx+5BlL2dl+gAlBE5gcOpn63nrCteHM+2Uefxn1F4b4DGF94XoOVh/ERxpLWkAYCoOAifHTKO4s4bOcz1gqn4qlR0++XxuN+kaWxi4lwt3pRFneUc4zJ57hg6wPEOitNJQWEZHuTEes7zTw8NvrWezRzDV3P/BPnef/bRwOB7vzt2E0GZg3ZDE95h7WFaxjecJytp3/mfzeUiRSb6p6L2OymwhQB9Br6eX+jPsJcQv5Hznm/1Ncv98uXLj4d+CKTLn4j2PG2BAkVQfwzH0f08hxyK9ocuutkQ0yPgDngGTrzt1cY9mF34qPrtreutOVnKto43rzX+g9Z0Z841bkBTvJr7eSOm0+VosNh92BvsdMlI+G58dl43fmRwj8FIDC+m7yarsQWjr45fRplk2MYEiMFzty6vn6ZAUvzU4AdyPRp56gVfQAWrUbfoFqVO5OsedwOPB3lyHEQeaUxazcvpqK3G5uSl1Kb3U263acweI/ggeXDQHpwLna7Q58tbJ+57v1a0YS6+ccAOhUMr4/XcnLOws588RkpBIRPSYr605V02kw85e5SVhtdsQiIYf2PorBI4z5Ix7k0elx/ce06KMTPD3bKSKfnZeEe59bWGaEJxKxkJoOPXPSAon21bDz2CVSvVPxVflS164nt7aT6GA/jAIl35+sZPmIUEJ0SlZ8fQaxUMCL81NQycTckxWLw+HAYrPz3clqlo8Ipk1vxV0hJSFNywP+boOE1K5L9by3v4SXFyRRUN9NTscJlgxJJkGXwNcnKsgM9SBQq8BPK+f+qTF9phOdnK9s52xFG+9f74yg+GrkpIZo8bvCJCFreiQto02s+fY8y+KEjIudAe7BEJxBAE6xllPbSainknMV7RhtNm4aH0N78TbONUXScHQXG29dAL9ZW894GbyctUc2q4XjP39P9KQZnG+0MtN+2BmBChwCRbth//Mw4SlQ6ZzNXnc+Cj19Vtdqb9C3QPVpaKuE0x/B6n2Ea8NZfeeL+KudOZ4BwJ1D7uT1Y38h3TeRW4dMp1ssxL8v3fNKDGYrN395lmeuSWDRUOeAt1eXjMgjmKZuI/s2rWVuoifDMpaw94GJ/et5+AQS3dHMSxWfEJDfRm7EbYBTTFUUqDlUnEZt+QWmJfkzOyUAeqcjC0hFKBFS1NTDybI20kM9sTvsFPUeYHZaBGvGJyEWCf9QSAEEeij5bsUyAP6y5TKbL9bywSozgZrAf1pI1fbU8sTBP6O2jmBcnAYEsDBuJjqVguzmbD7N+ZT7Uh9jVEg6AO/Pu6V/3QDiycLpiLfn3IesrTvIjck3s/GajYiFYmq6a1CKlXSbu4nxcDDKaxhvf/skG+u28uG0jxEIBKwv+JkoUShVcgsPDLsfD/nAC45w93C+mvEVeoue0rIcyg8cweQm4vxPP7P4wT/zyOJxKHsaBk5G3w42C2gGG0Vkl/3KrtqDPDr2hX/qmvyzCAQCDGdL6WltgSGLMdlMVHZVcq7xHO+VfY6f0g+NtBa9VU+YWxiFbYUM8x2GUvwv9gt04cKFi/9HcYkpF/9xxCZ5Ux88h2/kKYxo6WHP5UZuHhvubMoK7C8/ytHCNp6cOgeRUIDd7qDRIKNKEIIf0NGk5+i6S0y7PQOJTITBYkcsFtKQ9ADVpUV4Hykg4fyj6NKeQ66SABLKsps5u72CxU8MY2L0HBBrae81c6muk7HR3gxbvRqb1c4LP+Sg6Gv4Oznem1NlLez/5DKB96YRHZ8GoQEkqr1oraki/9gR4kePRdV0geC6kxg+vIsjCY/gZVpGkCQSuuqos2qJkbYzfXr8VXbKh4ua+evOfPbcPx673cFvMeT6TgMtPSaWDg0hLdCdnhYjQiF8drGaHqOVP81OoKC+ixvXnmHvg+OZZBNj0ziNGXaWbmdoYwne6Tfx2IxYIrzVdBkt3P71OSbEenPflFhmpQSSEuTOh4dKsdkdiIQCHhzyFJK+CMcPp6vYW9DMjnvHkpXg77Qyb+6hvdfCdA83Gg1mPFQSChu6iPbR0GRopEnfxI+3jSTSR41IOCCeTFYbN35xijlpAWTF+fHViUpGRer45EgFlS09hAV4IBM57/s716Xz9YkKxGIh7kop7kop605VcrqinefnJdNjsmCx2blc10VasDt/mp3I899mExugYdEkZ5TAjgOREBKqf4AOObbJz7A3v4GhoR68uruAERE69jX2MDnOh7Ex3pwsbeHj7ATevcYPdUsl7HgEPIKd1uaB6U4Lc7sdhwOsZjP1nWa+O1XDpLhW5Oq+xrARE8AzArwG+g2RsgS0ofDFDBj/KOx+EtQ+oPTotzcH+oUUZr3T/ETlSXp7A2FCD4gLQAPcmxVDeXMvssLvsVnMKIetwEMpZctdY5yNkRvzQBdJXNYqAAxmG1HeSiRXZqDufgpCRkD8bMbFeENvBrSWMjklrH8RL3c/Joe2YFcqB+6hypMyvRRFj4nvVw+kehmtRg7XHeShEUNRySQ4HA7e3V/CtUMCCPZU0dFr5uXdBTw6LQ73K5pVPzojjtdPfMZrZzbx5qQ3+6fvrdyLUqxkVOAozlSfxN5txNdnGG5yMZ5qGSfqThCs9WdN8kJ0ajmZfpl4Kb1YFLuIw9WHyQqZxp1ftvD1TR0kB7kD4Fh3HT+56zAFZ2Kym7g+/nqmVFzA4h7OF7lfEKQO4lLrJbaVbmNFwgo+nPIhz558FrPYjj5Bi1dfBNlD7kFbuIN9jXZu9b6FZ44/w0PDHqKko4RjNcd4auRTAGwv286RtiO88+A77M/ZgT5YjlgiJSUulN8EKwAnPwBDG8x6fWBa2WE8z31DtErjbEA+cqAe7R9xsv4kbYY2ZkbM/JvLzFl6B3ablVZDKzvKdiARSoj1iOXhYQ+zp3wP7aZ2AtQBzAucSYW8jIXDll/VfNiFCxcu/rfiSvP7F3GlCfx/S2lTD4eKmnh5dyF77htHqE7FsfIchmxcAfM+QhnpdN5yOJxiQygU0FBRTvP6j0i66S7yjW7UtBuYmugHQGtND9s2FNIUKebh2anUd+jZ9ms5K66JwdJjRaMbeBP+7r4i9hU0s/nO0X94bLXtep7dlsczWbHoGvaRc8pAytKpSN11HN5/hOPHTvPgQ6uRbLgJo/9QWjQJZAvjqO4VoW7L4/r8NQgeLACZ0+FswqsHWDUqlBtHOwf+nXozHxwoIbTQQOy4AG7clsPRRyexPaeOizWdvLwgBYDjm0qRyITETgqiorkHgUBAgIeCjw6Wcm9WDOorXNM2n32fuQfeQjDzNUicy/PbLnO6op3piX5kJfgQI25x9jbSDjjR2ewOHt+YQ4iHkrsmR+NwOOgxWaltN/DkpkuMj/ViZKSOzefruC5AR2+zgaSpoYx6aR/rVo8kt/0oewryiVHO4P4psUjFIkxWGzKxiId/ukiP2cb1w0MYEaFj84VaZqUEIBYJMFvsqPqEq8VmRyISojdb6TZaOV3WisVmZ35GMA6Hoz+6lVvTyeqvz3Lw4QnIJSI+3ZJPlL8bE4cFUtnaw70/ZPPaolSidM6IVYvBxu1fn8VdJcFDKeXp2YloFBL+uj2PE6UtLElxZ7ifiCgPIZ8VylgQbsXTXQsKd9h2P1hNIHODOQMD/39I1Wk49yW0lYNQAKnLnDVJXXXQ0whpy6C7CQ6/AsmLISQTjr0Djbkw/1Ow20E4eCD78M/ZPNz0OG+LV5FjDmDx0GBWjAwDwPzOMAyTX0CbOBV75SkE3tEIlM5UvHWnKslK8MW7/iB4RIJ39KDtOhwO3t5bzNy0AML77MHpqge3AUeUJzflEuKp5LbxkWDocF6b3+FwOHhxRz7XZYYQ7q2mx2TlsyNl3DI2YtDzCZDXksfxuuPcknIL63OPU224RIi7DpVExfTw6by8/zn0jS1ky1q5xv8xbhqZdNX+Np+vZtOFOr66eaCfUmFDFxFe6v6XAo6SvXzUeAqp2o9fyn7hL6P+QoA6AKvdyobiDVwTeQ1bS7fhcNjpNHeS15pHtEc08Z7xtJva6TX1MjJoJDXdNaT7pCO1+5DbcYyd5dt5csST7K/cz4XmC6T7prMwZiEioQiDxYDMJuaTO29g6Z9fwjss4urnw2IAu63/ewGA7kaoOw/aILjwHcx46er1/uCan286z+bizZhsJpK9khEKhFyfcP3fXGdb6TY2l2zGU+5Jhl8GOc052O12Wg2tDA8Yzlh9HMWnjjHrnkf+4f7/E3H9frtw4eLfgUtM/Yu4voz/Zyht7ibSWzMwofo0+KVgE4hpOf4zmoRxGC3g6R/Iis9OMS9Ri7XIwnmxFT8fFfdPGbCC7jJasNscuKuklFW0c/DjPKaEFuOx8BrUXs5BZnFTN1arDasDkgPd6TRYEAsF/f16AN7dV8zoKB3poZ4Y1j/E8dKhjBpnRzFyGc9uvYzFZudPM2KQnv0EoibDV7Ph9qOUl4s58Us5w2e7ETEkjoqWXsK91Wy+UMPoKK9+g4c33z1DjsPCzSmBZGT4Y5UI+ht3WuwWJELnv+12BwKBM13ni2PlNHeZEOBgf0ETM5P9EYiMoD7NbekrkYqkYDWD2BkNqG7rpbiqjkm9O2DkXfy65Xverwjg1blReDYe5eXqRAoau5kc581NYyJp7TEjFECITkVzt5FXdhXgrZaREerJC9vzeHVaPF5CMeowDdXtetKCPVh3qpJdl+pp6jFz69gIrk0PYuFHx5AKhVjtkB6spbJdzzvXpfPT2WrmpQWikonZlVvPmBhv7DYHSz49QYi7go9XOWtN/rLlMrm1HaxfMxqyfwD/NNZXqYj3dyPKV41MPBB2MVpsHDu4G21EOp8eq+H1JWmoZWIe+Tmb5SND2XOpniMlrUR4KYnz13Lb+EiKG7u5eGofGa07iIhOxNrbzGNdCzGY7bx/fToceR3KDkP0dIie5DSTyP4B0lf2X1s6a0CmAbl24LntqIb3MyF5KdSfhaDh4BYAYx9wiqSmy+CXDK1lsOUuEIph1RYw9TgjU2qvwR+M7kZoKcISMhp7Ry0tHZ3UCf2J8tH0Nyd+YeNpZg2LJi3Yg+c/+hI3n3DumT8em93B/T9e5I4Jkf0NndedqmTHpXrSQzy5f0oMDoeD6z48RIa3nYcXTXI+O28kQPoKrAovWvyn4R0WibDv+ePdYTDzFYicyB+R25yLSCgiQeesDztV1sKp0jZCvJSMjfZGd0UdpN1hZ97m+QSronlx3F8RCgT9wstg1rOnaj8zwqYjFQ8WYwerD/JN9la8TSt5aYQDWoshZRFdBgsz3znC2huGoVB28ua5N7k15VZiPWN56fRLzImcw7eXv6Xd1M6YwDEUNNWTV2NiSqIX0R5RHK89TllnGe5yd8K0Yeyt2svUwMlc4xHHobW7GL/0RhQR/ty5/05SvFLoMnfRYerAS+HFo5mPDkr9a6utxiMgiHf3FjHNuIPYsFDwjQef+EHnUthWiKX8GEmRWeD+r9UnNembWLptKTPDZ6ISq7jYfBEHDj6Z+snfXe+3lxMOh4MfCn4g3Sedks4SskKz+qPE/624fr9duHDx78CV5ufiPwaD2caru/OZEu9HcrD7oDfWg4QUQLCz50x3UwPa829yqbqVxhY9M+95mEenxRLtr+FYdQmL433ISHaaJrT3msmr7yQ1yJ3rvzjJNWlB3DAqjBueSqPy/ROMfe8Mj8UEsmRZEvf/eJEgd0W/BfRzWy+jkIpYPDSYI8Ut3D4uAovJirwvV0qx8DUmG7tAomTre9l4RspYd7kBd4WEB/28ofY8xiU/I3cLwCvESPRQH4Ligsiv7+bGtae4c2I0148IpepSCwaJnpB4T+I81Vw73I9Ko4nv3r1AZ7yKC629PJTkzYsVT3NNygw6TB0cP5tBrL+Gp2cnctPocMDpAhjkocDucPDNmXz8I3Ix2UxOMSUeSKsyW20EYKS5rgJvq5HPGqPQqAR8susUj7gdIiJgGDG+GibGeqORS3hvfwlyiYj7p8TgrZHzwNRY5n9wjKRALe8vzyDOT4NAIOCpTTlUtOqp7zTy9tI0JsX5sjOnFnptADwzJ5HLtV1E+Wpo6TYil4kxmGwcLGhiQow3YpGAr05UEOalIs7fjekJfsT5DTwDT89JwFJ9BqpP05r7K20lhdS5X4efVo7F5uDO787wp9kJhOpUGHu7GHv+XgxRP/Bxn0vijpw6qtr0tPea2F/QzJAQd9ZMiOoXy9G+GqJnz4HuYaDyRiyW8lCXgSNFfXVOGTc5I0q/RWi66qDsACQtGLi+e5529j0asWbguXUPhrvPwbcLIf4aKNkHff2daC6AL+fAvdnOflMrt0CfNTeNl5wRC/VEtlysIz3EnSBPJdSeg0sbkADsepzAzloCb9oOKh0fHyrBanPw5PyB/kzXL1hEs7GKgtYC4pT+vLMklS6TjXnvHuHPMxP4/nQV7goJapmIDeeqOVTUzEvDelE5jHx8qJSy5h5evvlX6KqnvrKKI19+zLJnXx04v6XfgWf43/yMn2o4RVFbEeODxzM5eCp3fX+eKYm+FLd0E+mjHiSmhAIhH2V9gJ/aj2e35qOQCHl0hlNsKKRK5kTM4kBJARqVkW5rG5dbLnNz8s2keKWwPMVBhm84VJ6EjkrA2ST89UWphHupqOlpxWQz8XH2x8yNmku7oZ0OYwfD/Yfz3sX3SPBMoNFYSlrAaOp6i0jwiie3NZcwTRhN+iaGeA3h4YyHCe2sx3fTXeSlTmGot5AwtS/3Z9yPRCDBZDdR1F7EqsRV/WYzv+EZGAzAiDANIafPQ12r87n5nZgqaM5l5t7n+SBXxJpVK/8po5bf8FH6sGfhnqv2/Y/4bR8CgYDSjlLSfNJwOBzM3jibnfN3Iha5hg0uXLhwcSWuyNS/iOvN1r+P1h4Tt39zDgQwLy2ARUND+p3IrqS+o4ebvznOp8vHEuihcNasADabjUv1Pdzy1RkWZASxZFgIEb3ZFOVnox55A2XNvXx9opJPVg5le049352q5O3r0vBWOyNBpwubCVXI8A1xo77TgFggwNvNOe+VXfmcr2wnNciDC9XtPDQsnBUbL3Lw5iSsr71EwMsvIdI4B/uVl1qolxr47tJJrotIwevSN4RbcxhXfy977h+HRi7hi6Nl+LsrmJHkT25NB+/sK+atpUPYvrmIbquNm68fMNmoKiuh8vQl2qIy0Kqk9B5sQJsEPokKeiw9nMxzY3i4B0GeKh7bkENWvA9LhoUgFgl5ZstldCopN48N57lNlxiqUTEjKxxlzXE6jq1lZNFiRnopuVm9jdHXrKZRFoxGLsHhoF9YfHG0nJKmbv46P4Xfvi4EAgHHS1sI81JS2tTL6EgvKlv1SMTwwvY8atuNDA31YFSUF8lBWnzdFOzcXkJLdhsrnnAO7vd/W4BIBOOvi+NidTsOB+jNVrKrO1FJhSwaGsLl+i4aOgykBLsTqlPx6eFSZBIRK0eGwckPwW7lkiieqh4BMyc7WyjbWkro/OE2Lo//hLHJzlqx9adL2Hqpja9uymR7Th0ny1opbe5hzfhIuk1WhoV69t/rQex8HCRyyPoz4Dy+b09UIBYJuWnM71K0Tn0CbaVOYwoAYxdIFPA7+24MnbD3zzDkBvhhMdy4E3SRfet0g/x3Lw4Azn8Dpi4YeSdPbcplTloAw8N1A/PNvc7aKM9wUDmjVy9sz6Ox08hz1yajvcLE5evDf6Ze34GiIpa7R3pDykLe/Pg8MzMDueSwUNzQxeOzEmnuMtLQbSQ50J3K1l4+P1rKpdpuntftIkHZzZK6xUTolLy4MG3QoXad+5EzNUbGzlrxh5/fk3Un8VJ40WXu4pmjL/Ht7M9wk/3979L2XjNCgQDtFQ1n39qXw/bCbCYmm9FprPxU9BMTvG/iYtdm1FIlzYZmdszf8Te32dTbRGHhOewNnZgTdDxz/BkCNAGsnbaWy62X0Vv1ZDdlA5DQ7MUm0TEMVgNB6iD2Ve8jQBXAjPAZrIicx/7GM7xw+gXenPAmibpElJIBc4bOjl6+Of4DN0xeysaSDRyrO8ZHWR8NEkZXRpoH4XDQXXGBnUVmFkzOQCSWYDrxAbL6izD/70eY/k84Xnucup467NhZHLu4f3p9Tz0bijawInEFWpn272zhPxvX77cLFy7+HbheMbn4j0GnlvHzmlH0mqw88OMFEAi4vq8Rpd6i50jNEaaGTeVQ7R5ATX5dp1NMCYUY8/Pp3r8f9fAMnnRT0WYUIBQASk/Wt8Qh/bmAJXOi+/s3zUrxZ1aKM6qgN1uRioRk9jURffPXIsK8VFw7ZKBu6LbxUVyobEeiLmX6UDfSfPzYFzgWH7WArvHjESoUTjMCq4nQJC/8bCYudNnZWW5A7uNgpnI8H2dl9Kfp7c1vYni4J2nB7qw9XsFbS4egkok57jAhGNyaCnPpQUaJTyMaOo/vLvyAqcEXfYKeMZ6zABCaOjBaLKjFIqI8lHx7tIIZ8X50WWysGBmKXCxEKBAgtkF1WQc1bQZiAtJwH7eao9dmILbacdv5IZi68fVWYLHa2XmpnmvSnOc/I9mPLoNz4P7W3mJMVhuPzYhnw/lapsT7OE1AHA7WHi/jYnUnacFaMsM9OVfRwadHysiv72bd6hFMmx6BcVwIb+4pZE6qP7oAJU2GJvZW7qWqOgK7w8GwME/MVhubLtY6z00oxGqzMfXNw7x73RAKGrrRyPouUF/EJwlIsprpqi3E4R6C9ugbdEdfS4j/gI17gE5LnJ8ZgNQgdzxUUoaH67jp82NkhUkRhuuoOrubEMNlGPsALd0mihq7GDX6bkBIWVMP547uIl1YzPH2ydw69o9qXfTONLjfkP+NwZpCC3Pecv77gfwBd0AYEFJVJ53Nez3CnP9Pux666wH63Sy/uPQFEwKzsJjcia3f5oySqQbSAB+dHsdNX53h57PVrBwZxmcH83Fry2GluIFybSbr7HZQebPs01PckxVFnLado2eaaO5xCgFvjcwpMEv2Ib90kNbeefhqpDRGLCYsVMVdHWr83BS8vbeIYE8lVcbTCIU27qjaxfDOFsovxUD+VmKv++ug0x8RMIJ1pys53LiPEYFpSEQSnjz6JHel3TVguPE7PK4wqQAo6yxjU/0T/HXG24wOdwrRZM21PLf9Mhlpidw/7G6ym7MHiRS9Rc97F97jttTb0Mq0+Kh8MMkCqTV0Mj5sCiGaELQyLWqpGh8CuGA8w6iAUciFMupOH2D5pMV8VPIFk0ImsSppFcXtxXgqPBHI3UjzSeOJzCc4WnOUj7M/ZlrYNBbFLgKg19jNWcNJ5htmEqAOINYjdpCQ6jJ1ceOuG3li+BNk+GXQZezkhX1/5v5RD+O972kUxm4seX58aJeTGK9nU80O3nLP5N9h/7CxeCM9lh5SvVMHTfdX+5PXlsexumPMDP/bRhYuXLhw8b8Rl5hy8R+HSibmmblJg96mN/Y28kPhD4wJGkO0ZiTeqnI0ioHHVyCRcKIrlRiTJ/5hnWSNDUPtIcdijuGBBVHc8+158hq7CfbTcNfHvzLRQ8+Cxc7Gpo+szyZFJCdzqB9pkTqGhnrg1Zdu9NqufOJqj+Lr7cmEaxbwcfYm/FR+DPFNIsjPWSDusWQx7HyUrQITF/U1XD/pFUI0YWQFz2Rzay33TXoWmwPu+O48N40OY0y0N68vTsVTJcVgthHrq0HSZ20d4yVn++UWWnqMeKnl/Hymih9L4nl27iISALVGgdvKHlK8U8jeV01wgiefHC6ho93ESpkb12Z4k6BS0NBrYvHHJznx+KR+sfPcsrTBFzp0FLtOVnK0pJnJca9ScLGbp4LgaGkzj2/MJT3EgyBPJT4aOT5912NCjBcP/JTD8hGhvL4oleYeI4s+PIFAAH+ek8Qd352jqcvEiHAdZyvLeXxGLDF+bvhq5Kw7U8XEOB8OFDYT5Klg0aQQdpfn02Ho4KYxA72aUoM9WDg0CIlIyDXvHeWl+SkEuMvRKiS8vjgNm91BfacBf62C8pYeTpS2Mla8F89jH/Jx3Dc84JNAaNICcHPeH5PVRnKgO6MinWI5yFNJkKeS2p5aItUGtPZePjx6noLKLr4d62x0/MavhVS09DLqVqfBSUtrG8UEMX9YNO94JjibrDoccGXa1Zj7Bl3ehk4Dvm7yQQNnh8NBU7cRX7c+oxPh75Tzb+RugMAM8Ahjb14jXs3HST3zOOdGv0/GCGfvtWZ9M09vucTZEgEfTvBktNLGlZJDLBLy4rwkrvv0FCGeCoYGqpF3N0DCAsL9U3iibBlYHuCeyUlO+/3sjdwirYMlzzg3sPFW2nyGIwkZim9EMu+nZHB5/V956vhIqnc38NmqYUT7aUjvMqJTS/lxhxs6tQPx0rWoAXn+OUzSq6N9ZqudXy7WoZSNAEUPYqGYKPeofqtth8NBs6EZH+UV1uDFv3LR1IwvUvyTFhKhjWD9/LV4K735IvcLxEIx10Zexx2TAxAqhqGWqnnuxHPEW14kSCfmiRlDaTW0crzuOOODxhPlEYVOoSM4MYXgRKeRyzcF3xDtHs310cvZ+1oZ9undzJ+SyeXGXCokzZwoWcvcqLnsKNuBRqbh4WEPU9tdy3Xbr+OpzKdI1CUyKmAUBW0FBGoGXsQE+Pnx5QJnm4UAd/+rmvFqpBoUjmAuVOrJ8AOsdmjVY+7W02sWYDXrsQ43caq+nplDR3Jn1ut889d3yfI9SWCAbrBL5P+fvDbhtb85L0M3hV9LzrrElAsXLlz8Dlea37+IK03gf57LtZ3sL2ji7smDBxHV+W14h2jI2V9NpdnE/s5uZuplzLlr8FvWj3bnMC5MTUKsM7pQ1dLDRxvyUfsqeGLegDOYw+Fg/KsHWZPpxaw4D9z8gijvKEcqkuJm1dFe10tIYl+qVdkhKhwWSmpPM7roCBujX2NvWS9rb8zkYGETUd4qXt5VSLi7HB1iVs2MGXRMTV1G7vj2HM+JPsN3/E14xo0F4LENOaTbxWjabMy4LdlpkW5zIJIIObWljPA0L3RBGrq7jFi6rez/poDJK+PobDZQfrmVrOXxPL89DyECnpg1uB4D4MmNOUjFQmYm+9PQZWRmcgAOh4NuowUPlYwtX+dRrNfzS1c3by5JIyPUg3OVbSQFaFnz7TluGRuO0erg3PnThAX64VB6U9bcS3W7kefnJfLqnkJ8NHIywz15bXchy4aHsDDDWS/C0TedpgoTH6ekqYc3d+bweIaAoCSngGmt6+Wdw8Vkt/YwJsqLlSNC8dEqeHNvARvP1XHk0UlcqGpnW049UwOOUa1vY/qwB+k1Wek1WYnwVvP18Qp6jRaKmnp5c2kaXUYL7+0r4c6JUbxy7hmiPaK5IekGmntbudxawIQQp3PjxvPV1LYbuHtyzFXXDAC7nd53RtA49iUiMiZdJawcDgdjXznAKwuT+0Vch97Mwz9fpK7DwLZ7xl1V/7LrUj2tPWaWZoYgKtgGpk4Yspw9lxvQm8wkdh3hsbxQbhoTwayUAAByajrwUkm5be0xvpG+hPraNxAHpjoNK46+yaNlqXgGRjM92Y9gD+VAo+vmElh/Myz+EnThNOX/Qk35PtJnvgMnPqS96hJuI1Zwx45WvHyDeWG+U3AY6/N46UgHApkboV5KbhgdASX7QRdJvdAHhVg0yOr873GgoBGZRMSoyMGmGucazvHI4UfYvXD3QL3P3mf5i7WGDJuI2bM+HLR8dlM2n+Z+yl1pdxGni+uffqSoga2XiigwbWbz0tcQCoTYHXamrZ9GhHsEH0/5eNB2yjrKAOix9GDstDEsMg2BQMCaX9dgtZgRisUYrUZCtaFUdVbhq/LFTeZGVXcVblI3DlYfZPeC3biJNdgsZqwSUIqV/1Sd0y1fnWFZZjCT4v0GTW/rbeXJ40+yJHYp7nJ30nzSAKgvKsQt713ErYWUzHuLRF3iH2z1/y5b87K51FjO4xPn/dv39e/C9fvtwoWLfweuyJSL/zoSA7X9jWyvxOEm5uNfi1kxMgQPvQV7o5LRYV5XLXf7tJRB/w/RqRgyoYLzjeeBF9lSsoXjtceZ2baKTauHI3d0ofx0JNyyl/VlG9HKtEwVzKfsQhMXLAYivNQkR4wnDAgLHsm+Rg+Uag3Pzg0DYPflBmzxvry7LJ0NRyo4nNdIzc85pETpmNOXSigTC/HWyFBm3oNn5MDA6KUFKez69BIR6c4B+cVfK+loNjJpeRy6ET40txoQNQjpLCklYlwqnhle7NhQxLzr45H02YqvGR/J74dz5yra+OlsNTeavkWTOJXA4BjI3cn930dhE0l55zpnc9O4oT7Q2sOTngHEeDv76mSEOh0Pu01W3vi1iFWjwpEb23DXm1HX7+OUI4txMd54qGTcOzkGqUiIh0rKxju86NJbWP3VGf4yN5GAmOlOC2hAJRNhMFsoKq/uF1ObD5RhM1p5eGoMu/OasNid730ivdRIxQLe3lvEsuEh/Gl2Apw+ynCLA2Ri1p2qorS5hxfnJ3OxqoNzVe3cNj6CXy7WMjHOB5vDAThYnfgQu3Kb+eF0JalBHv1CCsCqOkG9/iLgTFGzmG0IBCD+rTmTUEhp5jMERzqvE19Mh4jxMPEJwFlT9uOtI/HXDkRmOvUWWnstPDU78Q8H2GqZmL15jVS09LLU04JNbyHG2NVv6w/X83JCN/5aZ1TrbEUbh4ubeWBKLF+uHofol28Qlx+AwFSnSG0p5s5ANcrRU3hq62VmBDgQXzrIzLseRPDrUyASgnsQAPq2cmLKjjvPVeVHVf1exHotI5J8+PFcDeXNPYR7q5H7J3D/bAsfHyrhl4t1TjGVvxWisvCPnzXofHra23A4HGg8dfwRE+N8B0/Qt8Hup0if8gzfzvp2sHFC1tP8+Q+3Ai2GFrrN3RytOTpITG3NbmRiXCT3hT9EY28jNocNBw4eHfZo/3JVXVXkNOcwO3I275x/Bx+VD836Zqq6q9gQtYF2Yzsvj3uZU/Wn+DT3U6aGTqW+ux5/jT9JuiTs2Lk3/V6sditrUtdQ2lmK4FwNjWUlfB9xEblIzvtZ7yMX/0E93hXMTPInOdD9qulH64+hRMgEz0Rnc+c+/GNiebDSjkgVjTH7Y96Z9M7f3f7/DeYkpDInIfUfL+jChQsX/8twiSkX/89Q29DDxap21syOw9NXSHS4By/tzCc91IOpCX78WPgjAgSDCquf++UicTXrqYoIpc3SiUlvIcM3gw3nyjjY0UiGMIKV6w8yP+Y5rvcI56GhDyG4+D0Ed/DD6RJyD0hYOWUIyUFQU9jOgUOVJE1fyuQQD+x2B58eLuOBrBiON+2mqXckC8aGEeSrYv2+MnYZG5iZGkBxYzdx/m582OccCLDuVAXhXhpGROqYvnogWhY7wh+rySlAPj9SBtW93DRUR9Th6RC3j5BoP8wiB25eSty8nGlTvZU9aL0VoIZt679kqOUMheGPUN9pJCBjDOrgaPKqmoi/vIlZUU9yuEFCXYeeAFqJCZIQkxDGn746z46j1bx920DfnpcXpLDrbA3155u5+6aV0JQPR3cycu6DXKzr5sa1pxkVqWPlqLD+dRStuUwMFqORisk+ryZ2uB81+75ka2c4X6weD8DPZ6sZF+NNq6eYoqoexppsPDt34BpckxbE0DBPPj1Uxq7v8hk3NhiFewDuF75B0l7BON8Z3DB3NgKBgIenx3DDF6cRCgRIRELc5BIemR6LVCSktVdCWZMBi83Zm0slExGicwrGicETSfNO69/nNz/nIZGKWLEooX9ayqgr0p2GLIeAgeUBAj0UtPWYUMnFyMQiQr1UbLzj6p5ljb2N1PfWMyY6jXAvFVa7g5d2GjAZeviibiWs3Ny/bJTPgDmFu9DANS2fgeFJdBp3iBnHofxqfqm9yBvzoiib+AGN3UZGahV8sCwDQ3cnZbQjEAph+ksgEsOPK8A7jrApz8BwZyNYScQYwi02OnxCaKqo4tZx4Xx3shKZVMTD0+LQKiVUt+uJ7BPXjLkXepquOq8Lu7ZiNZuZuGr1VfP+EH0bNF5CoG/H3yf2n1vFoueV068wP3o+c6LmDJr3yqKBgf97F97DYDWgU+jILdWgDxUQlAabijdR1lnG7MjZZPplkumfiUgowl3mDsDte28nWB1Msi2MR+Ur+bxpB9G1GlLdIqltLCPFLxW1VE1BawGbSzbTZmzjuYl/Jm7kOKytR5EKpVcJKbPFxtv7i7ltXKQzXRSYnxF01bmZbCbON55HYjVD42VQTwAgpzkHh8PBhPAZeCm9GO4//Kp1/ynqs53RySXfXOUi6MKFCxcu/nlcYsrFfz2tPSZq2vTEx3mxNiNg0Lz0UA/CdCroaSG0YC+ClOsGzZ+RGoJXxAy+3G9jUfAYrPteItBLx8zImSglSlRaGfdmxRDhHgZCIQLgdGUXZ2uaCPbWEhDqzrQkZ+RAoZEi85AhFQspa+4h2FNJcWM3xU09HCxsQCOsYlKED8NjvBke0xdpqu7glq/OcPTRSf026wCX67r54GAp94yNYnqKHxq1jJ52I2qPgYHZ89cmg8OBSCSEpJPgHkqYp43YCGfkqLNJT3lOCxurmoj0d2PVjBhKHP7otKNYNiKUqYl+7C1qpnJPG1trWrg29CZC6gq43BBKt9EK598BhSdMfJzZw4N4/UAxzd1GWnvN3Pnted5YkkpmgDuV7W1YbXZOd3sxar6zNkSnkpIe4kGAVs696y4wM8Wfa1IDee9gA17VSop82mkp7SQ8RUdI2zH83CL4+UwVi4aFcLy0hVg/DQ9Pj2PF56cQ9UVxek1WdubWo5SImZbsx+rxkUx49SB7dFHUCYPYkzSd+V0Wjl8uYWtPMQ9Ni8PfXckHC2MROsyEhzrNDW7/5jzXpPozMdaHxi4DJ8vbqe8yIoB+MeWt9MZb6bxHmy7UUiKzE+OjwuFwsL54PZOCJ6FT6LDbHZhtduTpK/7w2Xx0Qw4T4nz6jVSupNtoobChm0b7WU7UHCHNJ41AD6cAfm9ZOgJzL83Nr+NhsyM2tIGxA7yiMFltNHeZiHJzQMsBaLwWwkaBXxIeXUrGKETwehyVU7YRVPoDmMcijJ+FSutO8qSpYOqGX5+Gqc9RqJuEr60FdxiwdFd745axkLaWXnDA2Ghv8uu62Zlbz8QYb4aG6xgb7YNW2ffzUXEMas/3tyv4jZELlznTH/v46/Z8QnVKrh9x9bUAwCsKbj88aNLFJmd0x0fpg6fC86pVlBIlm+ZtGuSeB84eVUKBEIfDwfG64+CABzIeQCQUsZsG5BJnjaJUJMVD5uz/tDR+KRcaL/DS6ZcZo1uGp3AIL4x6gW3l22gsqyVa7MUDEx7A4t6AVCDFUHYcsdF5fpdbL5PXmodGqkGmVCFTqpjrOfcPT7OmvZeDlyrJilQzJOoKEdVR4+xFN/tNiJyI3WFHLpFz5/gXoe9Z7DFZefXMq/gofXh9wut/fB3/WXRRkDAPPP62lb0LFy5cuPjHuGqm/kVcOdf/WbQ36jlQ3sJP2bXkN3Rz6onJg0QJwLdnLlJ7MI9JXpUMu/5ekCqv2k5ZQxc568toUZaTluTF0MyxVy2jN3VTeO4TzLpFvH+0nr9em0ygu5wTGzdgc0Qzcl4yeY3dPLYhF41MxBfXJVBbrOftCzXI3CRsy2lg34PjCfYcvP8eowV1n8vfbd+c5daxEaSHelDU2M2v3xSQGuVJTJoPW9+9yPJnRyBXX1GT0tME3y+FxV9xrkPFvd9f4MPxcSSPDqQsp5mLv1ZhSnQDdyHkbcMcNYNV45x1QJfrOvngQAljQzyo7DDg2ZXNCFEBJbG3ohCLSAtU4adV0G0BoQCOlbQyOd4Xm83OuwdKuGl0GGfK25kQ50NNu55VX5zmySGhpAzz58PDpfhp5BwrbaXLYOGbmzNRySTsv9xA26V2xk8LJa+pl7MVbTw+M54nN+bS0GXg8xsysdvstF48jVf68EHpcFsv1vLegVIkIgGLMoJYNTqclm4j7kop7x8o4brMEHzc5Fyq7SS3pgMPlZQJsT78+bu9VPdK+f5OZyPZooZufLVy3ORi1h4tRySyU139CSviUglNvR6ALRdrifJRkxCgZV9+IxVH6wmTyRi/PJonjz7J6uTVRHlE8cPpKvYXNPW7RIKzpi/AXYGHSkpjlxE3uQSFVAQ9LXDkNZj0FMjUHC1u5u19xfys+xRTazXPaJ/jjmkpBHuq+re16KPj3DAyjFmGLVDyK8TOpqG2HFn9aTzW7AKb1Rlh+j31OeCfAj9cDwHpMO5BLlzOp6qlk7ljhsK5tZCymCd2VKJVSHh0Wl8k6MT7kLIYNM4UvPcPlJBX18m716XzwYFiFg0LwVst4/vTlYTpVKw7U837y9L5+WwVc9MC6aovQ1DyK7qJd1x1SI+szybEU8ldk/55w4TVu1cjEoiobDXhZZ2BX/BZXhn/8j+sQ7phw1/R2sYTEnYSk82EUqpkWtg0Hj38KK9OeJWW3hZ+Lv6ZEX4jGOo3lPy2fA5UHeCNiW+wNud7tl9sI1Y9rr9WDKC2u5b8tnwmBk/EbDejECsG7bPL3EV1VzWJXlfXL2XnFRIW6IdWq3Xa2G+4Gab+1dlP7Dfsdjj2Fgy7BeRuHKg+QHlnOTcl3dS/yGMbsgnQWblr/BCEgn+Hn9//27h+v124cPHvwBWZcvFfw9tnPuRSjYUXp9yK3QE+Gjm7NxaRkOrN8/OS6TJa0LcYscpFgyI4e5rfIdVtHB12KXktFhL6glfN1d38+vllFj0xjFCdmppANUd7QrDXKcmwOxAIBw/YLjVn83r9fr6Lno9OJaO0pRd/NymdjbWoPEMAiPRW8+j0WIaEeHDgkzeQ+4+gvkvA7eHePPFYPO4yp2gqOddIzoEa5t0/hD15jaQGuRPpo8ZbLeXL4+VY7XaGR3gRsiYNzn9N5VkdAdERVBe0Ez3UOdB94KeLJPlr8PG+hdC8SyRlTuXZybF4+zhd7HL316BQS5g7OZJfjl1EVh+IOkLffz5PbMzl8ZnxjIjQUVFRzpO/htEVEcMorYIN52rwUAXh5+nGnzdcJMZXw+3jnRbU2dVd3Dg6jNf3FLEtu44dwy4QMXIJ98cGsCuvifAEL9p7zIR6KPl0RQYyiYjTu9+jqruDpUuehlTnDdiYXUdJUzcWm50X5g/01dKX5aLbtZAO3UnKC210h8kJFbczOzWWWH8NVW16hoY4oxReGjlmq52GTiPLPzvFo9NjmZzgR6hOyY1rzxDjq+GheSMpb+oG4Itj5QR7KIlpOwSNl7hp4mMANB7yQGcx9R9DaXMvGrmYhAAtk+N9KTFCe50esVDMy+Ne7l9uepIfmeGDIyYP/pxNRqg7L1ybgu+VvasEQmfPqr5B8Jhob8ZEe0NRD+LuZoK7vK96EfDudUPwVMlAeCv16gR8bQ34duWiH3OXcwGRmA69GXtjAZ7bb4HbDjl7W/n3iYCl3/VvK7j0B3x62kA0AjKdqXczk/25UNUBG1dD+FioOIq97CBVnlMJnbGaUE8lx4ubEQoF3DIuko8OlrA0M5gDBU1EeKuYkehLZUsPf9maT0LNz3S0dXJYmMbjXM0rCwfS7ux2B3aHA7FIiNFiw95YgLI5B4YMjhx/kPUBBquB2q423j+xE5VkwNChx2SlpduE0WIjzn/wwHhF6ky6e9z5oeYMDoeDYf7DqO2pRYAAk9nE9wXfo5FqKGgvoFnfjIfcA6lIisPh4MaUZdw4uKwSgB+OfU5vcwtlKWXsrtjNq+NfJcI9gm/ynC6AIwJGDAip3jZoyIVIZ+rq678WsiKpgimTp4FUBdf90L/d3JZc9pTv4cFhD8LYB/qne8o8MalMg47h/imxyMRCl5By4cKFi/8gXN/ILv5rCFMnoe8OZH9+E/f+cBGAPXIzZ+xdBHgKUTUY+emFM1zYUzVovTcnvsq9Ny+lNGwmnUZr/3QPPyXjlsQgkYoQSYSMuzaKeydEs6O0mfLmnqv2nxk0hh/mb0PsHc6ISE981DJEYjEz7ryPcdcNRywR8ePBMmpqunFTSIhfcAPWTndWCtUUduj58Otcvn36JE2VXdhtDjz8VAhFQs5XtbPpQg0Ad02OJiXInYd+ziGvrhOFWord0IO+tYuxS2OISvdhzdozfLe7mF6jCZVcQqyshYD2M8jEIgJC3LBone9Ixi6JYdKKeLZdquNsm4iwcaMYlp7AvvxGTpa2sDQzmOQ+I4+wgs+4JWIb541vMjJCx+wR7Vzu/QWAG0aGEuwh56czzuv67LY8Ltd24aeVs/72EQSIe6htbiNELmf1iDBig7T8eW4iP52rYdOFWsQiIR6yDlTSRm77+iwAJr2FUE8lwZ5KRr64l32l53h252H25DWgjk5F8MAlBEoPOpoNNJfnErpuIgJDB6fK2th0oQ6Hw8G2HGcvKqlYyFOzE0gO0vLdqSre3VeMRi7hm5uH88PpaoRiGZkxTqMPL5UUrVzCfee9eKxuoH7Jd/zjiIfehN5k5a29RfjZ6vFpbmfvl5cBiBriy7BZznSo5m4jd353nvZeM+5KKRHe6kHPyUfL03l8xkBtld3ucDY7VnlC1jP9kdG6njoaexo51nQekaGZOyZG4a2Rs6NsB48ffpyDhU2c2fEl0tqT2BFw7VYzpzWT2JLyHkcFA5Gw+364yH37emDmq04h1XgZyo8MHJCpGzqq8Jr1JwIXO1PDDGZn3d2YaG+nK+aY+9CHTedk+B0YdSmUldVjtZiZkezPu9eng8NBR6+JbTkN5NV1M1bXy8myNuRiIaFaEZtuSmRfrQCLI5y7Fs6+6rPzez44WMKz2/Kc1+tQKS/sKnJGy3Bap/+GRCTBTeZGvHcYI6I1LItfBsDpsjZe213Ik5tyWfbZKYwW5/lcarlEU28Tk6PSGBmtYKj3UII0QdyUeBNZoVl8M/MbijqLsDqszAyfyezw2c6eU0ofMv0y+4VaY5eRP23OxdhY3J+qGB+cSlrsaK6NupYwt7B+9z+VWIVUJKXb3E1V4TZnhKkhG844U17bDe28uTKdKROnAM6+Uku2LqGyqxKjxYbVrCRIc3XNVKpPKtPDpw+a5usmx135z7klunDhwoWL/29wRaZc/NcwN34sc+Odg60JsT6cqj/FygnwVf5rbNrWyN0tL5J1Qxxhqd6D1ttxsZtAdyu3Tx5cZC2WiAiKHxxVCAzUcP2oEIRdZpBWgEcYvZZeXjn9ClEeUaxIcNbGLBkWgt1hx2a3IRKKnFbUpi4w2EDowGqzc/3XuSxO9meknxcauYgebxUjZ3viFaTGJ9SNmExnrdWsZH9yajqx2x1sOl/HtekBJAVqieyLMDmGr8FqrkXlJkMgFDDH3UR3bgcSo5kJc71RRt1KfVE7OuDHM9UEeigYFalDIRHhJhPxyu5Clo8IJXGsMyJ09mALhi4L9TYL12X21a9k/ZkJQFR3F2cr2vBUeWJQGbDYLTy9/2tU1nTSg32w2Oz8dOsIpBIRtR0G3j9Uzsr023hmRwnPL4hEb7Wx8vNTvHtdOimBWrR96Yvxk5/Gt8dEUKszMnbg2wLCot0JTw9CpS1jT83PNDo6WO7t7HOz/fMKwlO9mLQ8DoiDkWdB6cH1w91ZPCyYn85U892pKkZHeXNnX/+u1xalcrmuk1/zGuk2WnA4HNhxYDQaqC49SXDq+P5GxOUtAbT2OhvsVrT0IhEJCPRQcqCwkV259USrDCQnNhE7fMD44jdUMjEZoe7O1D3gUm0Hb+0t5s0laWjkEsK8BourW3Y8hJ80hb9OXTVo+rsX3iVIHcSh1jNkDHmY32JYjr4/sX4abO566HVGhjbfMRpftZQXduRhstFfq/fkrDjsDgf49Tlc1mVDe5mzJib7R2cj4YYLcN06APZcbuCDgyUopGI+XzUUpVQMfslknz3D86dhyz1PM+mKqKynSsaO9V/i23mezSsfRl22CUftdwyb8DaRvXtg4z78532BJHgUoghfNOp/PNhfkBHUL5pWjQzDnBkCbtdyrqKNB3/KZt9DExD9LjK8PH55/797zRZCdUoenBqDAPojet/lf8dI/5GMCRzDo4cexV3mTrOxmfVF6xEKhdySfAsTgiaQ5JXEJzmfYDN6UtVq5b1rJnOq/lT/9iVCAWEaO9LPJ3Bi+jOMTF/NzMSBGqjXJw7UK82PmQ/AyYq9ZGxcg/mGrTxQsYGU+HHcCtx78F4sNgvrZjuvv+bij9xhEhIg9+bjM7+yteYD4uQLMRi2ccOQ2dDdhKn8EKc8fRkXNO4fXksXLly4cPE/i0tMufivQyoW4uMm58Gj7xHmFsb82HkoRAqywpL/cPmjxS34uMmYFO+Lw+H4u/UWQqGAFIGMssMVhDXNgwfyqeqppq6njuvinClIxzeW0Gq1ct5vB2qlgweHPgjZ6+gov8gp0w2kCCRgc7Dt7jF8cqQcoU7OuCB3xGIh5Tkt5B6swcNfRfpUp5AZGelFZaueE2UtlDZ3U9bUw6gob44UNdHcbWJ+RjDaYV7YcIaSZ/IdL3itYER4IF5KGY0VnVQXtBMz3J8/X+NMM/py1wncVFLCx2aw/6EJiIVCPv36K+aPSiI018oJDwfjtBq+OVFBQ4eRIJ2CpcNCOF9p4ofT1Xx3y3BSvJ25Tqsy0xgZkIKfVsvz2/OQiYXclxXDmGgvUoK0dBz/ijHeEaw9XsHUOB/kUhEyiRA/rZyzVe3MGRKIwWqgocvMkFBnsf/w+ZGcqG7j3LlqYhpDGR6exC+9zYR7OeuFbFYHVstAOWe3zAeFzc6bewuZEOPL8hGhTIrz5WBhIz4aKdG+agT6NpJyXiNp0hPc8N0FOg0WXpyfxPG8SjadaeH7mA4aLDL25zeRFe/Lgo+OM0VRxNoKD3y9dNybFUNWgh8bztUyeUgUSWmBNHUZBz0fZqsViRBuGhNBdlM2hQ1d5Ja5o5CIrkrR+43rE5biLb+iAW13I6i8+dOIPyERSbhzyJ2Dlp8VMYtZEbN4a28hw2NWEdTXh8lPq4AznzGzp56ejNv7l4/y0eBwQFlTD+vP1/DIdGf0htwNkLcJVN6w4PP+5cdEe+HrJqOm3YDiimP2O/cGryZMQSQcT2lzDw5xC1/lfc7TI56mVpOCQSjnvk9y2TmiF+WEJ9lQLKO5LZI58RlEdRrxzd9NeMzCqy9AyT7orIGMATH5m707gMcVfamSgrS8dd2Qq4QUAPp2aMqDsNFX26r38eLYFwFn/dJQ/6Esi1uGp8KTdfnrKGgvAKDN2MaGog28MOYFvjhzgDrT9/RaetlftZ/C9kLmR8/HT+nHps4nYfL9nGw5jX/nFMK0YQD0GrpRyFVXpdqNCMti+4J3OFW2kS5TFzq50xL+rtS7sNqdEfETpS0k61KIrj5GVfVRrkvKZIivjlONxahkfRG59nLslzew1kPByICRSIQSXLhw4cLFfy4uMeXiv4q6Dj2v7iri+WuTeGviW3jIPRC2lIJF/zfX+bjPHGDDuWoOFTX391C6kpZuIzq1DIFAQFSmH9u7uigJ3YlPXifzhsTz6bRP+5fVxWh5flMu03zGsyDWl+LGLlo95xAQNAfDjkrCfJUIhAK8NHKemOmMhj3y2T5iA7W454JcI8E/qi+KULwXmvJoNs7ATyvDSynls6PljIrypuD8UY50eDBcq+GtzZe4cX48mRE6uPZDFjV246GQcGJzCSKxiMmrEmiu6sLTX83h/EouFZXwmuAdiF6LSRsLEii0+tMj9mD+gyHoj1eQ6OuGp7iGOpGJb4p7WZgRTEqQlh259ZisdhRSEWfKW7H2RKBVqClq6ObGUWEIhQJe2pHPmYp2bhkbzjUzlpNgdvBDTgfHy1qx2ux8dKgEgAenxnCkqJm3Lz9EV1M6Xy+9lTOlbUT7q1l7vNJZJ6aU0V3STWSsBw4HHP6xkNgRvoQPHRgwr/76LBKhkF6zlTCdimHhnjT3GFl3uhqtXEKoTs272y4ztK6bkQIhf56TQGVFKY/+fBEvNwUfrbkGFBLa2js5X9mBViEm1FNJUV0tlxtlvLBkBAAysYjnr03CUyUjr66LxR+f4OTjk/oNQtbseA65WML7M5+iobeB9ackOKzdfHPLcCSiP86anhwxbPCEb66FrGdQxkz9e48656s6UMskjLyyqW3iAtLD20Ahgl/uginPcf5wJz3tJkKmBCITX3EMyQsgbhbYzHD0Lag6Add+itIjiBBPFSarnRe/3cbYAAFjJ89GvPBzwjROYXPvDxe4cYwOmSMAkVDE6mnpOBxDSMxopGvPl2yyjSLIXUFZSy8nGgSINL3Mf/RvdIISCPvrxH6jta4bsUyMVjfYxEEmFpEW7M7d++4m3TedG5NuHJhZdw5OfQJhfemZDgdc/M55jgqPQdtxk7px15C7aNG38OWlL5kcMplQN+fLiz8d+xNBmiAaehrQFmdzc8Zi1FI1S+OWcqD6AJ2mTmI9Y3ly+JMIBAJ+bT7L8ZxC9p/exI3338s3T92Pz9ThzJ+2mnfOvUNlVyXPjXmOvNY8hgaOIlAbTppvGgBGq5Fkn+R+s4pXdxfy6PQ4gkRifOovkX2mEZvFyAMrrrCPDxmOJPB73rPakQhdP9EuXLhw8Z+O65vaxX8VSqmYhAA3pGIhKlFfM9DSfdDTCAF/v6HkuBhvYv00fzhv0UcnmD1cT2qoDIXAk0sdF1Fa0wi39dBdVszpPQ2MvWEkUrmYnppe7ooKYOYEZ+PP+7dfoLpdz/o1o/n6Fr/+bb6yq4AF6YFE+mgwebbQpdJz8yNzKDt6mer8FiwGGyFuWtplARzLaWJYuAejBQrC9M6i89UhtazO9KKgUc+NiYFkRui48GslVqud1InBSOViFFkhCIQCbBY7m9+6yLBZYcRl+CIcOwxCf8KgDmH8S/v5+qZhvHbTwOD9pklRABi3f4jOIeL9658CINxLzcd9/a7qOw3c8tVZRkZ64aeVcf9P2Rx/bDJSsZCh3hr0RoszFdFuwP3UW9w+6Wl2FLTz0aEyylt60CnltHabePqXSzw4+z7K3MQIEPD63kJmhnqxws2D3jYzcdcEUGc08/DasyQEammr6aG928SbxXXOYzn1MR/5lrM/5C58NDLGRHlT3aanrcfEsFBPxkQ5xYZD4c7nspW4NVtJDNSi6IQZfu2UCrR8dLiUKG818zOCeGFuPLd+fogpcb7MmrSCWcDr286Tqe3E7BXPutNVzE8PZmayPzvuHdsvpADWpK9EInZGTdK9xuM9RU+ElwL5sddhyPWgDexfNv/yRWpbusga/7tUrWU/gsZ/0KT6nnoa9Y2k+aT1T/v6pj/oH6T0AJsF9j4DVhOIJMQM98NqtuHhpeLerJjBy0vkzr+eEU43QFMbEEROTQdfn6xkbrAHgX5qNl2oZUKMN0qpmOzqdnw0MkLai9hekYpwjFMICTqqibOVUx01g825eh6erkBYXcR1oSpk/m7QVAA+zs/EhnPVjIryckagIidedRoHvy1EoZUy87Y/cHoAYj1iidRGDp4YleX8+xtWE+RvwxE4jBarAu+iH0Hl5RRXffRaeynvLMdP7UewWzAANybdiONwKW26Kow93VyuzmZzzQ4+mvYRPkofojycn42Rgc7m0T8E/MC5SyVUuteBQMCom24mMNh5bN5KbzQyDecbz/NJzic8NPShfiEF8EnOJ/Raenl8+ONg7h3oMxbxGQBDK84jW38dGBaBwr1/vRcOf05jbysfzH7sD6+PCxcuXLj4z8Elplz8V+GulLJ63ICd8PXbrycrJIsbR/yNt+JX4K2R460ZcFdr7THw0IE/c0/GzXxz83Autx9BK9VyMK8XpULPe3OcESz7prtI9/ZDIh0DQFpWCLYriuRfWpCC2eb8/9fHK9AqJVyTGsCR4hbi/DRE+mh4a97ifnfA+nPZtFgj6WmzYJ4Wz8YWNZcaKmkozeV8I6yYnEJ2dTupo+5G32UiLkLWvy+5UkzesXoiU72dFukCASqtjMtHahmzMBK7zcHBd3OJmBXIX0928cRMET/cOnxQs9d+TN3k96Txpzp3XqrtIMJThbKviejR4mY0CgmnnsxCLhFR19zEaynhiIXQ2NHCrydrkPorqdpaSeAMLe5iOc09ZqYnBTA0TIfDbqXoUANnD1WzSlVJVsl+njEtwmPXGr4ctoSyGjEj5kWSs7+aPcUtnKhsY+8DY5HaBVhXxSFTSnCramP6m4d4MyuT+GEjmB/gLNK32x0s/PA492RF4++u4HxVByOjvLhlTCQfHy7jVEUb7koJO+s1VIojeHV+CsdLW/BSy8CsR2a3MD+wgwnJAzV0UbJOvFvPUuEei95sp6Kll+LGLqrb9IRcYWU/NGhggH+spJVf8xt5f2kqGFqdtUlXUFFbT16TmSx+h3vwVbfibONZzjacHSSmruTBdafpNlr45MbRgM0ZiQ0bDWWH0OT9wpHgWzl0RsFTs6+25QYgfbnzbx/jY30YH+tMPbTY7Pz123PE+2nwUEkJ8VSxKCOI5/e28+hYBVQeh/LjcP4LCB6OKOVObpAISfRXMUK4A5qioLcG2ithyAo4+QFnHHcQ5aN2iimLwWmMcQVTVycilv7tn5+70u+6atreyr0k6hLxV/cJUYkclv3A0eJmnvrqBHumKTEJxVzp6xcq92ZJjx5xd1O/0J0ePp3z+VuI8ovj/JgiWjrKaGhroFHfyHXbr+OlsS8xJcxpFvFbTWSGuoGM6+eDQIBXaChGLNgddqaETkElVfHIoUfAAfcduI/vZn9HTmM2PhYTy+OXY3fY0Vv05G9YRUbGaoiZ1n988uAUWPzVICHFrifBWI9N4k6roRWdQvc3r5MLFy5cuPifxyWmXPxXkxWaxaiAUf/yersv17P2aDk+Qf4oxEqCPJUEeToHOSneNrra0sk+XktSZBciuZpaYRDSjXej8gqiJvluWnsMbDlfh1AkJMZPw9LMUAy9ZqQiAQ/9nI2newtb7x7Tv78/ff8yw+JSmTt0BqNvnQUKZ5rfYz9nk+Kp4tOVGQTsWo1ZMpSTbeGcOd/OY5kCmjZ9QGpcC4LZbwAQPzqQ+NHOQeGhdQV0txqZfVcavZ0mAqI9kMqEGDUiqvUWgj2UnNhUjNpTgcBbzZGfikmZEIRFIWLt8TKuS9UxRHOCBybfxvGjNZy53M2Nzzrfxl+o6kAmEZIa5E5ddx3zds9jccdznN5zmP3dW5mT8grnK9vRJfgz49siPll5Hw9/nc0j0+JZd6oEWWsxyTFRDE+Npao8mDZDOY9k6ijcMZTI8Yk091owdFsYMTeKppO/Mj9JRoC7iic+O4tUCPctSWFUlDczkruQ+vizt1XPMHczQqGAv+7IZ3iEB/UdBuYOCSSvtpPNu38lueAd7rz9J+7ZdIALlR28c10adge8saeIiXHexPhqMP/6LLklVVTHPspn53sZHd3KiAgdfpHJmCWpdJxpIkgr546JUTy5KYdLtV1MindGG7dm1+KllvWn3c0bEsi8IX2RqBkDdum/MWPqDGZc8X+jxcaD353g4ckRhAUPbi49J3IOcyLnkF/dzAtbLjA/LYD5owfML2b6d9PaWAOMdka1FjojG+z/K4gkVPRKuVzXjc3ucNYbVZ6AsoPOpstCYb8VOoDVZkfcl5J4vrKdYA8Fn60aSEX0qN7LDA8ffOZnEuengfqT4LA6m7vOeAXVtytR+i1Go0mHpd8PnMTZL/nl4seotB68NLkvStxcBF9Mg7vPg9IDY08PYqkUjcdgcUV3Exja+iNbADQVQlctRE0C4HDNYdykbgNiqvosyLWM8tWyZVwD6yr3UOjux4tXiJU2Sze3tBxmnXEFodpAXty3j9YuG69dew213bWcbTjLjUk3crfb3fgofHgi400mBjmjR0arkWXrn0VnnkO68m0iw8bjEzKGLy99SahbKJn+mbx85mV+mfsLI/1H8vnlz/k462P8Vf7s6dzESwXf86VbCKH+QzjbcJZXlHYe71DicXobsoQMTtWfYl70PAgZMfha+MSTWR/Jxi4d7154l2dGPXPVs+XChQsXLv5zcIkpF//VDKqp+B0bizYS7xlPvFf8VfPGRHkT6qkizv9qISYUCNh4uAJ7WS8yWw1xUhU9BhvtbjHYwidwrKSFutpu9hW18OCkaFLDPdh+eRc1H8tZ9PgwtKt0pPiHAVDZVYm/yp/kuBgCfZyDcINITXl2IwqBkOE1VhoOVRP/lwC6feIYHRCDMbIHH99mlD6JaKbMRqCz/OH5RQ/1pa60na3vXqSr1Ujy+CDyj9fhp5Ezra+26diGImoL20kaG4hcKUEoEXK4qJmTZe3MGxIEM19iEhAb0ENvmpHG8k48A9WEeinZfKGOW8dFEqAJYN3sdXhWlyHZ/z7+oz9mets2KiUZxGb48UGomjh/Nz5fNQxfhYOkgDSq81vY1WImLcaL4IAx2L95A0uunHj28mZeCnU9DTzFTABKuopQy9TYbWMYI5ByqqaLVV+c5pe7x3BvVgxPb8olv6Gb46XuPDI9FoVEREF9N9ekBHK2oo3cmk4kFjkjh1zLyQu5XLQ9yzdTtyAQCBAJoEu6nzdyj/NNyNdIMlbR1bmdkUFyTtYY2X2pAT83Gau+OMOyzGCSK+pJDFDy+MZcbhodPigq1d5rwWBqIClQjEbu/rcfysY8sBohcHBtnkQkZIQjF21zEwTP/8NVA3UaMrwFWAWDHfEmT5j8x/tSeYNvPCsSU1hxZTadUEx+VSMhqhpUXiH9k7fn1LHxfA3PX5tMbZuBL/bnMEXbzqSEeLQJfUKmtQSsRiS+UuyCcAgeAf5p0FUHu55ENf0ZxnvHQEeNM9rzm6GL2htZmxKJ94AtPLoouH69Mz0R2PfFh/hGRDF09rWDz6NgC1SdhAWfXXEdL0FDTr+Yenb0s4PX2fMkNqUXVqUfWpmMBTFLMPjFw4XvIGUJNfoGdlfsZu/iAyglzvsY4+1GW1/vJk+FJ3Mi5zDMbxgioYimLiNfZq+nyVBKlC6IdN907h+5CIvej52NYVgFDrrbS0nzSeNC0wXqe+rxU/pxrPYYVoeVDN8MFFIFDT0NrBpyB0neaQT5pvDVub2sLXme72d9T+2+X8Bcysf6RizCCqeY+j3py5kBTLQasdgnDZpltpnpMHbgo/K5ej0XLly4cPE/gsDhcDj+8WIufsPVQf2/h4+zPybNJ43h/n9Qe/J3sNjsvLyrgFlJXnD4Jry94wmyC2DmK8x65wgPTIlhcrwvZqMVqdz5PuKz3M9IEAzByysGmURAXXcdJ0o7OWt4k9Upqwltc2PX+6+T9eAD7Cx0cK7EyCvzkpEoxMiVYr7NrmXfxQpC7E0ME57nuGIkYeGJ5NV38enKYegtet489ya3pdyGl3LAkGDLxVpOnKjl1tFhhKf4YLPYsVpsmEQG3GTO57O+Q09uZQdT+5rl4nBgdzidC3tNVt7eW8SC9EBM+V0Unm5g1PwomuRgsdrJjNCx6XwNoTol6SEe5BQUsKXUwVMBZyFoGPjEw87HwT2YAx4LCTv/MuFxafxojOTL02Y8oj7lsehFdBlieGl/Ld8v9GNjvYVeaxd3jp5AY5cBXzdnlGJrdi2SGgOCFiOSQDWTssIAWPPtOdwVYs5UNvPTbWP54XQVpyvaiPBSEeypJLByMxNjvJEMXUFTZRfNXW0kJof1X6Oc6hNcrj3GdSMeAmMXHH0TRt87KLWqXW/GQymla99+JKEhfFsnZE6aP35ugyMoZZ+NRxg2jrCs5/qnfXiolKVDg/BQ9aVjnvwIDB0w8ep6l11lOzlRdxK5WE6nuZOXxr30Tz2TBrMNs9WGViGhormbF3YVcV9WNIkBWjj3FUiUkLKIkqYeuo0WhoR4sD2njsxwz0GprZ16C0//ksvoKC+KG7tYafiWU+3ubDVm8tU9k6B0P+z5E1z7MQcPP4N/5DRij3+MY8Zr2A+9SpPAk7q4G8kI84Sv5sCQ5ZD1FxD/c72PetpbkcgUyJTKwTMcDrDbQPTPv99rayun5OBxzBYja7VHidFPZK63mtjKH/k1/lkC/Y3sqN3Dw8MeBpwOf2KBuF9Y5TU0Ut6dx6zoARVa1lGGRqrhpdMvsTJhJak+zgjbcyeeo8PUQVlnGU+PeJpjdceo66mj1dDKqsRVBGmCeOvcWwgEAhy9ZrwUXqzKvIVAdSArvzjOyOQG1gxzCuidZbvYcPkoT454jHBvNZVdlWwo2sD9Gff/XadRgE3Fm9hZvpOXx72Mh9zj7y7r4mpcv98uXLj4d+CKTLn4f5bbUm/7P1qvscvA/LRA/HV23grP4PrQqVB1DoAPl2egL+mkoawTvwht/zq3JN+C1WZnzMv7CfdSExfcS0mTiY+WfoRGqqG2N4/EJfO55/JTPCCJY5mvGLdjH2Ob9wVKNylLhgYzNlxLeXUDAcpYoj3DifFVIxYJ6Sk8h1BkQCvTIv6du1eolwr1hDDC451vqkUSIcebjvHWubfYcM0GAE6ca2D75QanmCo7DPv+gnD1PgCauo0cKm6mqq2XF6ck4Bvuhm/u02y2rECo1RLooWBL/mlmJ8aTHuqJ2iuYREMHPYnLqGrtJdxspSpgDjnNdg6UVjM35gbCI9xZ8uFomsbfSqcwHnN5E3eflHHokSye+uUSKUHu3Dk2HaPFxrz3jvPCtUlEWETUlHcRFuXOhAmhSGQiGroM1LcbuWNiJG8fOEdkWDmeqiwMZjvDw3WsmRDFys9PMkGoRqh2CkyfUDd8GDxIisnZRlLFfhjxEMjdYOKTkLeZjtBpuLtpaO81k1Nfit4kZcZkZyRgtdODgLoOPUKhoF9UBc/9BMlvUYHyI5gubeHL3GkoxAJuGN1Xyzfidv4WX1xeS5IuiZkRM6+6l3+PtcfKKW3u4XXD05hs/kikt+Ln1ieSVN4gdgq5k2Wt1HcYGBLiwayUK1IJz30JHuFo3UN4u+MeSNgCIT3w+be4rTpIqqTPOVGihumvgs3KeL/hCDQRNIbO5q0zEqTqpzD3tGG/3E1GRiZMfY79Lbkk6ZtxU/ix/LNTPDc3ifiAvutv1vPgZ9uZPiSSKSOdUTp12Q4wtsPoewafoEDwd4VUTZue4qYeJsYNRGSeOP8y05KmITFMILhZyTy3DsL1ZRgWf4dj7aMoZKXct/JnjtQcYUzgGN48+yY+Sh/WpK2ho7GBT/btpkGzf0BM9bYSoQoAiZzXJ7w+aP9PjXiK8s5y9lfv5+u8r7kz7U4ut1ympKOEYLdgPOWejAoYhUQkobmygkZhB9Ut5Tyx7QFemvkyAX7OCPjx6qPYjSY+mf0s3aZu3t7xHAk9gcij5fwjLHYLB6oOoLfo+TTnUx7JfOQfruPChQsXLv79uMSUCxe/4409xRQ1dbN8Si291l5igkdD8GjMBivVO3JI73me1qEvQ4SW/AvHkKvcCI9JRiwSsue+8ZhsNjxVsv5eOVabnfU1ErISprCmoZkxp75CPvpBmnLz0G1bDXPfpLRZgK+bklmjk5j9zhHS3cp5emk8YoGQskM78fS0ctfCZwYdZ0F9F6lB7lcd/wi/Ebw+/vX+nlpzx4UxOaXPZTBwCP8/9u46vqmrf+D4J56maeruLlChUIo7FJcBG2NsbIwx5sJcmTCBuTFnzBju7u5OjVJ39zRNmuT3R1hZH5g+z57t9+y8Xy9eW+49uffk5DbJ955zvoeUeQAU1+o5XVDH+1O6IJdJcHa3x1CaCR7RhJyRcaSxgQVVjagc02gy+lHZaOBsYS0DIj05eKmSRQfz6BvmxpJjBqK8tCT460jpHm47z4zNjFCqkEvleKj9mNSWSavZzFMjwpG12YZZqRUyxsR7M39rJh8PimGglxNRnb358kAOY+J82JFWzubzZQS4aghzd2JKD1tSAL3RhElvW7dnRp9gtlzQoA+KRlrfSkujEZlcirOXPZlFVdjrC/iwaRwJ4SO5wWSwZYAzm7Ac/5LZW6w8d1MKD/xwmgi/JrAqGdGpYwa5Lw/koVJIeTTFNgRO4R55ZafOF1VQd3YM7oeD5ud7ZnLqcliZtRIPjQeLRyxuT5P9e9zSKwiD0QwFtxGpr+OjpK5XdkaNbP/fnenljIy9kimwxWhGKgWV1QpWC+h8WRU6j+wtaTzqdggmfUlhaQmaAC/IOwgrZsCgZyB2MhL3cNj8OHrXfnTzCyTUzZ6vDuXx/JhO1BtbMRZe5LSzEm1zMd11vtzZP4QA15/0OCk1TFSfJlRtf2VbZRroa373688oa6Th7DoM/jegtrcFay/2fBGdSkdWmQG1YhCRMbaAUAkkTJxFdlUqiqYy3j31LrFusdzX5T7kPwnYJspl9B71JQCnyk4Rt/YhJOFDqelxJ2uz1tLLrxcxrjGsu7SOZO9ktuZtJacuB6vVyitHX2Fo0FDmJM3hdNlpVm9fhMVOisLLmT36A0Q4RbC+YBPxrnE4ObpR1lyGh50HC469QXKFL31DByKVSrFXOuCgduDuLjN+tQ3kEjl9fPuQ6JGIj4PPr5YXBEEQ/jtEMCX8o1itVox5Z1FlLoPhr1xdoPQcL44Mob5NTrPVk8H+trkqRRm1FJ75hgNtYXRNnIxfvO1HtzJtBW1aT4iwLRis0yiAjotsfnc0n4OXqugiz2Fc2T4MYz+nRBOCX9QIOLUYlHasPnOJRH9nsiubsFPK8JHKMOx8jZySMtwmvMnStDIGVTQSfjkrX25hPfcuPs639/XBy/HKXW2r1YpCpmDh/jOc1M9izbjVaFX2OLpf/pGrcsDo052SykZWnS6mqsnEdYm2LHlHcqp4/Idsvh4ZxjdSA45yBYYWM8OCb2ZXegX20gqeXZvK4tvs6BXqRs8QNyxWKzXNRkbH+xDkooH6YlDpwLMTlzt3MJjMWC8PX6o+d5S0A/txmXAXg6I8mN4zkO7GY7infY39lM+pKG1k/dFC3OxVxHg7ciKvhihPB8I9HQhytPU+Ka0S8ksaMLWacVMqqC1pIreyCWmenrLsenLPVHLLq73JP7OD/lmv89DMw1TUG9j38Su4KhroNPsdpDM28VJFI2HuWj69pRs+Tio+3ZvDxnMlHXp0eoW6suFc8bUvJtcQcA3h2sn2bY6VHuPFIy8yLWoaPX17/mwgVZJdh1Ipw83/2kfTquRoVXLoNIFX3v+e/PMHKG628uTwKGL85cikMltyBkc1ng5XArtXNqXhYq/khrhR+JTt4c7vz+GqdWZ0gJGqOgUOa+5na8hHZKZl4OfkgEv0N9xrzYQND9iGKhqbebfldobGSEkIcGamTMrLG1IJd5YRmW/goQGPILW0ws4XGDroOVsPk6HB1gMI9JryaMdMdcNf/YXW+onU1dBUAcm23uUh4TpqdnzHweMh7fPHfpw3FOunJhbbdfb1iYMEOPozPCYSTzdb4Lti7IqrDu/k6UXfG65kN0QCWaF9Wdaaj+zcJ2TVZVFtqCLGNYajZUcJcQphpKwnZ7QupJqziXKJ4oeMH4h1i+VkxUmqDFX4qHw5W3WB68KuI949ns/Of8bk+OtRqtRMWzGe1/u9zkMJD1BWW4yu8Dj4dmXmkAc71MtqtfJ9xvekBKZ0GM4L8M3hfNJKo7g+KgxBEATh70MEU8I/Su65KnJ3FzM4+cp6QA2tDTy+/3Ge7fEs3qvvZL3zXfQaNpF3z7zLiKARhDqF0qS3YLILA42Me2uOMKdiHOEeCkJverv9OE+sf5C+rj0Z1esGTm7Np7nWQL8pkYzv4kdLq4V1ea307z6FNSU6tqVd4Mtbu0PPewB4ebwtGKvXm3gsJYqkYBcaKl25lFpMUWkDa86UkBjgAoCxzcLOjTk87O/dIZDaX7yfxRcW83nK58R6hOJUM5mTq4rof+OV3pSSuhZmfnUce7WcptY2HhvjxMIzC7kr4S66BDjzRGQ5uy45EejmwT0xRhbsLGDo+QU0aiZS2eTCzocH4ONsx0M/nMbHUc0IrY7Hh0Ty2LpzOCsUPF12L3KFDG7bTEFmE401BnwS3UkOdkGlkPFeng7vqPEc25RBi76NJrOZQxUeBMaNIxywUyt4PNKf5HgfJFIJiYHONNe3Ul/e0v4aHhgRxRtbM6lrNaGsMjLKqqHR0EZEvCudevtgmByGyk7OsNFTOJbZgyArTP7kMK927wNle2jaMZ+X6obzwrhOSCQSgt1sPSfR3o54O3YcbnWuqI7SegO1zS2cqzlGP79+7fNafuz5+9F3ad9hwcLNMTcD8OnZT9lduJsHEx9kcMDgX5wPU5hZy46mRmY7ReDqoKaptQ07hay9d/OnRveJo17pxIb0akLctXx38g2SmhpJTpnPK9fZ1m0qPLSck5Yw7hkURb3exH1fbmKpw3skdfqcIE9nehV+hAEZR3t8yBc7Wnn1Om+yyhvZnd3IzABH1JGjQV8NhnqmefsSkPYZqLvy5Wkvgt3sGR0ioflcAU2tVnQKgMv1NLXAe11g6nLQutsCqV+ZB9SuzWibe1WRBhp3QNq+La/BwtjqJ5na4sNgbHMF1UUXCPV2IbRrEk0GE1NXPUWQfQwtBiXDY4JZnbWarp5dCdBdScDRZmkjuy6bSJfIDqdO9EwEz0Sur07H38GfwoZCQpfPBK9+zOtj68ldPP8+/GI64xjvyI6CHSR5JGGvsOf22Ntp69yGQqpgacZSKvQVHC8/znM9n8NZ7czR0qPc2vlWnNRO3Ln9Th7p9ggcfBcGPIEloAdVLVV4aGyBoVlfQ1rWJnq6xl4VTA3t5En3YJff1paCIAjCf40IpoT/efWt9Ty3cR8qix+1HGZg3wDoek/7fo1CQ0pQCk4qJ04PW8Ub359n22g57wx4B4VMwctHXibcKZwbPKpJdvBmVdM9PPTtGW5sUDBuhhqntgqIHEG0LhJfF3+4uB2N1BVn02EoM+Ho1ZmuQc7IjGZWb9Ex8QFfyhsMlNa32Nbh+QlHjYKkYBc4/BE6Rz+uGzAWgEHRnqgVsvZy1QFqRiZfWa9o8aE8qhpdSbK/C4Bp3WNprA3HbDID8Pn+bAZEehLiZs8jKZFEemrxcLQjpz6LUoNt7pdKLmOYayUWJw2KhDjY8QLPhWnQ+dxEQEsk2/Ia8NAFoze2UdNsJMbLgYz0KuZnFBHkrsHPwY6Ldk/jocnHTa4iPzWf8pwGWgM0LD9RyA+H8onUaegc6kqARcnnh3N5LCWSgTcOwlOn5oNdFxmX4EuP0SFsSy0jvbSBB4ZEcPFoGWV59fhEOAFQVdhAQGYzLckmtjQ0cPOMaJ7fkMaEBF8GRnlgZ2/rmWloMfHKlmwWRy1hz723sivfiFeAFouDgqACe+T/EqgMuTxMDODM9gKaFfDxvhy6+Dtyx9cnkfl9RIJ7Ao5qR0qbS7lty218PeLr9h/Cndw6rvE0MGAgvg6+DAm8aqWpq4T29WbGgkz6JXhTdamaZScKGRfvw5TuAXDsc/BLYmOVOwMi3Ynr0hmr1UrfTrYexVnNBlpLc5ix6Bhzx3YmIO1jPHMP4eg5FS9dF7x0drx/20Dk9gOY6WAb7llgPwMPrYJ+rn6sDK3HeHYnnWN7Q8kZpE0NNEntUFecQz7mLbqWZyDJWQbxQ7l1oBRfB1c0+laOeg0mTG4Cl2AM/Z5mb1oZw2K8kExfR5OjH9pvJmKQqHjZ9VXu6uGGr6cXSKWsPVNMVnkjnX0c2XCuhEnSPYRExhGQ8TkED4BdL8EDZ+HoQjj6MQx7iUDfrnw9M5kEf9s10GBow9piwGyyre2lVStI8XOjX9EG3AcNByCrNotgx+AOwVR6dToP7H6ALRO3oJRdPTQz2tWW+dOlQc6Bykj6ukZRXNXM8+tSWTDnOXSuDizeNotAh0DyG/NZcXEFF2svcm/CvcR7xOOodKSoqYjDxYep1FfySNIj5DfkozfpCXUKZeN1G/G094ToKQAcKj7AR6c/or9/f/r69iVQ5UJQ01icT27AEFxIdZuBBRX7WTBgAd6Odld9XgiCIAh/PRFMCf8IUokVpUxKnLsTQ469Q7rcg+j47gDIpXLGh40HIDFMw7Y5LmhVchpb2jCaW3i025OU1xtIK38bJ4kHI+NDibNzpbypjaXblnJnlBEiR+AVEMKqki0kZBwmOqA3rZ4yFm8/xsgJYSQFuxDvpaPIt5ZHlp+lxWTGYPRh26FCGnMbmXhTTMcK63wwq135am82A7xbCfX3BoUte5dSLuXxkR3TvVc3tXKqoJ4A1yvzUxycf5LFraWNVpMZqVTCoGhb0HA8t4ojORLuGzwVALPFSmbEneRX6RncZkE55Hl+XC70yNrzFFS3oDeakcskhHs6oNMoaY2TY5/VRrS3jhuTA1lz2o73Ul34GOh7fUT7+T+9JYmFK9IIUysx5DYxIM6TNIuRGr2RXjo1rW1m1p8rxVOnplZvwtfJjrY2Cy8t3Ud/tSvuoY6M/+AACybH0VrchKNcjoOdipI6A+vPldA/3J2BUR5szUjl24xv+HLsS+jsFKy5KRj91sVUNTWz6GAxukFhvLy7iHnjvdvXWlp7aS1NxiZuirkJi8WKVCrBM0SHRCJh32MDyK1sIrfpLMmBr+GotgWenhpPXuj5Au527u2v8V8X3A13DifcOfw3XZ9uDmoOPzEYnUbBsuOFTEr0ZViny/PcDHW0GJp5cX0lemMkk7v5M2/1cZpKL9IzoRPjku9GVlfG6EwjOqUVHLxRDn6a/Dwndl8oZKBjCQZ1Jw6VG+h1eRThw5tKmdEnmJGuEG7KZOPelfSNTeCRif1593ANKRWLCLQWY6gopOWH2bSMWUpAQBSfbXuA8OqeNGjjOFYTzZBFE3CcsYpSizuf7culV6gbJp03I1elsHT0Qlz0Vjzy7FBuuB/63Q6RI4j0csBZoyDG2xF3BzWas7vRysww+Dlw8IH4KbZU6t3vAKcA0PnRnHmIcM8r1/y0HoHQI7BDG8a4hVBcfwkvmcLW63qNBA0B9pG83/tjDv/wLd3HTabQWEqwUzAKacehuS7+AbQOHMaLZ97miaTnGZlgh5u7Gxk16dwUfRN9/PqgkqmwYuVQySGCnYK5VHsJrUqLu5070wo3c0kTBEBZcxludrZeJk97T6hIh30LYMKn9PTuiUxiu0kyZ+8cpkROodmjJ23SSr7NXErX1lb6d5mCQqqgpqUGFzvRMyUIgvB3I1Kj/04iter/b1aLhf2rP+HdgmBWPjT8Z8t9cySPfReraGptY0ycF5/sP8vw3pfo5B7OghNv0d/uNboH+nOhuI5ZAz1x07hR1lRGUVMR3ZRutgVKZVqWnSjE36cAe6Wa7t7dKatvYdrnRxnR2ZM5KdHsPFxI/aUGrru501V1uLCvmGfO5TJGd4nb41SQcOPP1vf17UfIr5Dj56TBaLYwd2znny37o3u+O0l5Qysr7rJlGjuWW809353G00HJ4yOiuVBcT0ubmYeH2oZE7c2sYNP5Um50dgYLfFtXg9liZVSsD7XNRiYn2XrKLA2lSNfdB+MXgtadz859hovKk+zcMOQSKfKLTUzo54n/uQfYE/kMy7Lgo5ts2d5SS+pZtWwx1wc2c8zrRpYcvsQtUXI8Q6N5cuUFVt3VEx9JLThdGaa5/EgWZdlnGKIrQenrxaL6Yub2u58vDuRRVpzHsRITc0YlUlbfwg/HCkgOdeOhoRFolLZ7SSfLT9LS1sLRvAoyKwt5d/QsKvWVHXo03jjxBp1cOzEi+KfL8EKjwUSd3oi/iy2ItVqt1Bpqr/mjt1pfhbapEpXH1eueAbbhbe7RPzssrk7fipPGlrXvXH4563buJ0WdSlJyfzi/lG/2tdHnltkEJ9iSU6w6VYRXSw4HTp9DEtKfjAoDX9xqW5y3rtmIzk6BVCqBqmwoPkGWJJBSmRfRBctwLtiCVCpHqtaRaxeN39inUSjVNDcbGfrufobEeDImwZsgSnEPsl1rpfUtHMmpYYLyGBc1zjTJQ5jxZQY7Hu5HTXkRK9P1PDkm7pqvbdR7+3l2dAw9Qlyv3ll0gkNfvoXZvxd9b7v32m0HHC09SpOpiZqKCH44XsADgyPoF+Heocw9PxzkUuNxnnDQ0W3EBMZsmcDM2JlMi7HNn0qtTsXX3pcNORs4W3GWFnMLr/R5haErhnJ3/N3k1ueS35jPouGLqG6pxlpXyNKctcxIfowXD71IgmcC+hqQFG/mpv5PUaNxRKPQIJPI+PLCl9wcczOOhia4uBW63UbOmjtQu4bh0+8Jzpdn8Na2QpKja2mVliJFytDAoeTU5XCs/BinK07zat9XiXGNudbLF34D8f0tCMKfQfRMCf8oEqmUvtfNJqqx9RfLTUr0Z3gnL5DAsZwaAvzzSC0Afbk3L3afT5x7PAcuVbLmbAmV8s08Enw7JVktJI3q1n4MNXBLzyCWZhxFZbT9CPZytOO+eH98nW0JITRlrcQPtP1or202UlrfQoyPrfcjsocX38a5odUmwU+GJJ0tqmNfZiX3Dbb1erSaWznZ+BUzut1DtFsgJ/Nq28vqW1rYtv8wYwf1RyqXUVSj59uj+cT5OfH48CiajbZhgJvOlxDspmXrQ32RSiQ4aZTsv1SJ2WK713IgqxJfZzV9wlxZcaKYm5JDeGNoAgATPjxAz9Ar8zvOVkvxDJuCz7nlENiLaJdolGjZmF+GWa6h2mri/s7eYJ1M54BgXHwknM0uQaa2p7OvI+Hjkii4lIpMKmHTw0NYvuMQ3ffdxqH7v0GasxG2PAH3ngSNE7vSy3l5cxbPRVahtLQQ0lDAc33n0Gaxciy3FgeVK32iVQyK9sRisbItrRx/ZztWnSxCKoGpPYLo6mkLPlpbD+JqD7sLdrMqaxWfp1xZQPaRbo9c8zpZe6aEfRcr+fQW2/v+2fnP2JW/ix/G/HBV2Y27n+Sm89tgTiYo/iUVtr4GPhsKs3aDe4Qt+UPqaki8BaS2nosfA6n00npu/focwztF8H2jB0k6L+j/BFNankPhf3ntofUPcp1MiaHvk+zMKGdIiBOxvhJY/yD0eZAPD7Uw3LiNaFMqms6joPQMfnmfIvEcjHv+UrZJ3iCybziBpR8QHD8eVt9BW9yN2KvsWXhTHAFujny0J5t7BlzpfSytM3A4u4r+jZ/i4hHHEYs34/s64eagpqlOTXnROb7dbWXawPir2ubFcZ2I9tbxyqIVRCkrue6mu67s3P8mySlDsHS5kjCiae197HSKZVz/We3bflxPrsmzjYYWE8Y2S/u+wupmzFYrL49JoqY1iFAXWzD+Zv83CXEKaS+38MxCRoeMZmTwSLp4dOH9M++zp3APc3vOZUveFlRSFb19enOs9BhP7X+ST02J9NfUsDN/J6HOoUwIm8Dio4s4KZcxZeeLvO3jR7RrNNeFX0ddUwnGXfOwXtyGZMr3IJFQ6RWDh6vtZkWESyhy5RlqDOUoVSaGBg6l0djIx+c+xlfrS3FjMV4ar2tchYIgCMJfSQRTwj+ORCLBQ/fz67qYzRbslDLslLYfsaXmA7ir/aiu15Crr2bBenseSini+/MHGNY5gu2VyykLGYZGZwso9CY9FosUrcp2jhuibgBsKdItVghEAXUmAOy0SmQK23CzjedK+OF4Ic+NiaF7sCsKpQyFUvav1UMplWKnlLY/VslULLvu4/bHgT8Z6ldeVsLy83UMTqrCwdmTwznV7MqoQKOQMTLWm0aDiaomA+/suMj1XQOQyiQMinTHSaPkqZG2O+DlDS3MXZfKQ4MjWHYogwBFHTL3aBbuucSd/UKxVynoH2577fnVzXx3oownWzaAzh78k+jj3wdOfEUX+80YB71MjdoXZHKaoiez8uQRgpx86bVnJke9pvJRSwIf3dSTYmMYS7dn0tlXRzdFHq96eTC+MRt3aR/U8S/hqXHCZLbw5Kpz3NM/hHePq9n72ECWnShk9w+n+HhaN764NYk5y85wsqCaz06u5vbEcbw3JRE7lYxXN6VRVt/K1B5BAMzfnE5hrR1zhg0jwFVDX7++7e/ZjvRyhkR7tg8LbNyzB4WvL+rwcKRSIx5aVXt7BzsE08OrxzWvq1H9XsAUeQNrj7xBvFMEobHXX9mp0IB3LJhtc4DQ10DmZoi7HpT2sOYeiEiBmLG4alU8MzKazLJGugWFgLsfWCy8bLmFSY32xFZshNYmMBtROzjzbEQBOEaC2gnq/UBuR5CbhLzyYA7X6ripLA+ZSyd0g54lrOAwRHehn1mGKkhHWoEnlaVSYjvfzmd783i05U3ip62gSf4vC8ZmbCLRO47ESfGU7emDXX0jboVmbhobA6vvJSPkEWrkHsSFX+7tyztgWxvLz9ZT1jXQ1pM3OEiFu9wVLBa+WHMv4SGT6TfhY2QWCzLllXYuldtzvOoSI9osKOXSDlXRquTc3jekw7bHV53HCiy5owfO9r7UG+p5eP1y7uk+EaPZyMqLKxkVMor3Br2HVGI7noPKgZTAFJZmLMVT64m/1p+M2gw0cg2JHok84nY7JzUGMixFdJcqKG0q5WLtRVaVrmHtyB9QGht5EC2OdnbYKVU8E3kzJQffpdyzE5/vrWRE6Hnyd9UT99hAZmyZgUwiI8S3K1uL1vPxgB84VLGOiuYKnu35LN08u/1sj6cgCILw1xLBlCD8qKmSEzt2kJvuQs8JSfhFuXBq61ma87OZMLIPJTUW1uR+y3P95iHV5CFz2cYjQ8Zxe+sPBOmCkITbhme9dPhVzuZK+XDkQ2wvWcYNkTfgqHbk473ZlNUbeHlCbPspE4ZeGUo2qZs/p4vqyCxrpHvwNYY7XRbto7uyMOqvCA4O5bs5V9ZOmtzNn8ndriSumLsulTAPBx5LiUSef4BUSwC1ZfZc2ljAoGlRpJc1ct+Sk1zfLYC+ke7kVjlT1pqPFahuMiKVSvh2ZnL78b44kItGLuFgyzhM7j6M8+lq+5BpKEGhtkfhE466rpzWulZqLUo2l36KS9Egst0eJCwkiqL9JTy5dREvDb2ZnqG9+GxfDgGNMtysgfg6+NNWp6JaOwBPQCGTsumBfujsFPSP8UEikTAsxotwD217fd68PoE92Rl8mfkZ17cOwlFj6/Ubl2xGKrH19m3M2Yiniw9ZeRVQrUDqFouD0ja5qLq5lU/25ZAY4NwegNcdP8K289nkZY0n1biY6yNmALb39Hz1eU5WnLwq0x+Aq4MPOPgQm70Vj5ZzNPmPA6xondS2nqpe91Fc5Yir1oQEGaqcXdBcZQum4qeASwgbzxWzYOtFnhsdjZeT2pbW3mIGqwVXrwA+OHqAyfr3GRI9FXpc7rWxmNGnHyI34zjS5DsIqc5Hb3SjW9IAQoxmsg5/jF2ThFilBo58DBIJ6vxDkPIqnoYcLPJWnGP6c9f+V5F6xcLZH9DGXc/TScDlZB+c/YGdmavoNep9vIIHQdZ2Rt47h28WL8RDGUGSNRX3ob2I87schJWctgWQl4OpHyUPHHO5zhYKFUYCpI20HF+CXflxmPRFe7nwUa/xMnA+NZUIfw9UOncyi84T5hODTNrxBkSd3ohGKeX+gWHUNhvZd7GCMP9GTJojBLlOpdxQxsmyk7xx4g1Wj1tNi6mF+cfnE+8ez+yE2TSbmglyCKK3X29u33o7O/J3oJQreavsA74d+S0ZF9bwXfr3JDsksn/vGt7q/xafZ3zH3V3u5smliwjxbuGBHtPQeUSTnjiZCKcI/NLq+bhsIQ728VwnVaOWqXFXu3O6dhdTg5/k6YNP81SfWSR2SqTZ1IxEIhGBlCAIwt+UCKYE4Ud7XqG1SEPcwBvwDLH96LZzcKGn9yi6+8VR51aHg0MzPYI9AA++H/0dZpMFa4sCiaMEg8nMibwaHux6L1m+BjwcFZRcLMFgNuCIIzd088fQZqa+Uo9SLcfOQUllowG90Uygqz1qhYw3Jyd0rJPVChmbIHyI7U5+UyVUX4LAnu1FKpsqcdfa5oZUFjRScqmW+EEBXOXccqgvgL5zANiWWoazvZKp3Xw4uOp9urYcJizwFpz9E8iuaEUilRDiruXmnsFM6xGIQialX7yCZZm5+DjZsSu9nGFte0jTdOXWYbaAanSsN69uTsffPZy1F2vp3asVL0c7GPSUrQ6HP+T8cSsVbREMe3gYS8d/xtvbLrE2s4pPuhh4sVsR86v20Wgcj7PamevDPXDzu/5KYHJ5utGO9HK2pmWhVmXzwoibifSyBZfNZ1ez4Wg5Mffe0579cEBoFP2CF9vmB132Tdo3FDYVsnT0Ug6XHGZi+ESmNx2HQ+9D5Mb2cp46O1bf3RuTxUSdoQ4ntRNec+ZgOv8FkmI1N4c9zoioK/Pd7utyH9+kfcOzB5/l5T4vX/MyC09ZAMCGD79FX3ue65+cB6VnIWQgZz7PQlJrQaM/SRevXjg6B2K2WCl27EqAo4Z+ESYqG40MiPREWnAQNn9sW4i4zUjvtkBKVL2J6PcReMdcvnysnPC/jThPLWsqu5F1opkbgxq5UKPAYGyjtKGVecEuSMwmMDbD5EVgNkFLLbgE49r1ZlwbSqG5Ct30ZVBXBLm7IGMDmIwgk4N/Mpndn2PhmWcIOv45oXINDHmODWdLqPTsR6hzE66pb+J606grjdDrvqvapdXcytLMpUwMn4i9wp65Yz4n8/B+1m7fz5RH53UsXFuAZe09SFrdqdI4UOY5hP0/fE3y3TPp2S0Fq9XKoeJD9PbrjdlipbHFjEwm46UNaeRVN/NBcAyTosbiZK/GRRtNub6c0cGj8bL3osnYhL+DPweKDzA7YTZ+Wj8WnFyATqVjavRU7GR2aBVaBgcMxknlRENVGG7aBk40nKJGWkd/rkMjt+frw3kMivBhS8nXpFZ2oYdPDz45+wlDAocwLm4cu/Y00qmPEbkEnuzxJGqZmqqWKqJdo9HalRB0di3W/pHcvOVmJoVP4qaYm655PQmCIAh/LemvFxGEf4ghc+k962kie8S1D6+L7uVP915a0NfipHZiZPBIfpqzpSCjmt3fZmA2W8itauKF9Wk4KFzpExqIvdKeub3n2jJ4Ae46Nf4u9hxbn8vFY2UArDhZzMI92T9fp5Y6OPAWNFxeOLbomC1l9GVGs5EJa69jyepNAJjbzLS12uaKVOQ3sOOr1CvHcg0Dzyu9YgGuGroHu+AoM5IiO0l1wmtszFFQU3Sak07nmLFkDcY2M2kl9RzKy+SWrzcQ5hjDy31exkGt4JUJnejccpQ+7rahaeeL63hreyZdA5254/rObHigL16OduhNek6Vn7KdtNMEokf1JHh8PFarldyKVg5dqmHVXb1wqMskpjaNzwZ/xIX8Vloajax58zT1FS00VOkxGtrIPlVBUW49n+zJxsuljaDTflw6Ww5AWmk9aTn53NXDFblUwollr1JRlEVJXQt9Xt9Fab1traoGvYnstLGM8p8OwMt9XqaLZxfb/KQ4W8pqq9XKvuxMW/nWBlJWpHDrllvZV7gPhVTB7PjZ9I5uY1B4GGr5lSGjF2svsj1/O8G64F+60gDw7OfBFt+LmOoLYN29UHyKcqOJHXlV1Lh1RTb+IwCO5lYx7bMjWK1Wyur09PSQcOui49RLnaGlAaqyoOYSXZ1bOZHdxpEiW6+a2WIlY98K7vnuJN+fKeHBCdG4+xXyabYzZbV67h0Uzssh6UjUzuyucWbul6v49nQlaJypMUoxrn+SrMPZsP8tOPYp2DmCdycKo27n8ywtxvI0yNlDHVry9fZ0VT1PaGk6lJxhxcUVNJhLiPZ1oVdyMty07KrXv27zHj5/78rw1Na2Vi5UXUBv0rdvC+2azIh7HwGNKzTa/mZoKIE9ryC1d8Gz90xaIibj6unL5Lmv0CNxKGBLKvLQ3ofIqsnCVauiR6grBpOFR4dH8vkt3TBJG9icuxlDmwGAoYFD29cH0yq1PNXjKT4Z9gkA/f3746p25csLX9Lfvz/nzi4i7dxX2BkaeeHIC7jq9hEmaWZoUApPdH+CQxfXcEPueQ5kF6KyemEyqYnziMNsNePr4EtzsxOHLhoZGjSUZGMLdV+Poa06h+z8fUQ7BNBYnobl9FfUyeUsvPAl10dcz/Dgn0+WIwiCIPy1RM+UIPzoctrrq+yaB4HJ0H0WK7dsYE9aA+8/bEsn7hnkiNFgpqm2lWhvR7Y/3P9nD789rZy0knruuTkK6eX5N7P6hbQnebgmjTPcsfPK46hRtn+XKWVK3oz5kGDXIAC8Qpxw8dVSVdSIVtmIT/hP5rb4drGl/sa28K+Pkx1RXjoMJjPqm5YSBqhqNuFTcBC9pYYUr2AU8jG2XjOlFY3KypOrzjE+wZcgo4ziLQUkP/oVS9efZ6JXAy4aJYkBLszqb5uvMvWzI9zZLxSdcxHzDr/ICm0ikiHPIbX3YuqrO5k7phNni2pJDHDi9S2ZNBlCifXrSsje9cy94Ea0l453XuuNUi1n44dn8Al3xmK24iyF5Xf1os3SRnNno22YHFBQ1cQ3Lf3o29U2dMxVn4PU0Ii3r5o3b4jH6/IwPa1azsw+YQy+nCK+vLkcD40HEudA6Gr7QX2yqJhZX2Uzf1ohB0q3cX3E9VQ0V1Chr6C0qZSs2izWZK8h0DEQR6mS8j2Lcep9Cz5aHxI9EhkfPv7n39PLkmKHERXZE4XSgaqpO3Bzsicw9AQj3Gvx7tq1vVzPEDdW3dOLrw7m8c2RPCaZTjFm6PXY+fjC2HfAqIfD74NvNyZIfYn1tg1zPFdUx2vnnOnko+GtbVnIVBWk6tczPfhpjHVSJBIJkuqLoHLkcEMElRpHmgsa6BnayOgP09gT3UKVoYHwCS+CRAonvwaZCmOzI6Mte5HbuYKxgR0VWg7ml/FcHy1nLfOI3ziGPBdHhkaGE+/h/bOvP9DbA7m+rv2xTqVjfr/5HcrIlUocPTxtyTj2vwmBfaDfY+AcBBIlF46dQevuRdKY6zo8r5tXN1aMWdGekdHdQYW9SoabVs7cQ3MZHjScx5Me52LtRRpaG5gSPYX6FhPv7LjIrH4hSFr1pJedx6yVk+ydzPjQ8RwrP4bFamF6QAr7m/K50b8/p+VyHtv/KDqFFh8MtFpbOVCXSWBAXy6kPc51unl0cx1Ifk0uXjof5FI5p4sLsPeMZ1a/mzmemkWW/ARFVefpcWYVNDeg9u7CBJd47Ac9h+zsx/T06Ymr3c8P+xUEQRD+WiI1+u8kUqv+A5kMtmx6UimHMi6y6lQeb0wd9otPyV78IU6JfXCNvZK57NiaDZRWOTJuZt8/rar6RiNLXjxKiG89/Zpn82nSBu4algBAfYuJKZ8c5vkxMaSWNHA8v4Y+YW58sCubb2Z0Z1t6Ocdzq3kt9AIudjKqPPvgExjGkqP5hLtr6Zb1FvOrktF4RzOrVzB15Xo+P/kNey6F8floZ1xDE7FXyTm5JQ83Xy2VDlLCPR1wtFNgrbyEpOAgi1v7o1ZIcVLL+WjPJSZ2DSDI1Y5Llc0sOVZAmLuWE3k1PD7Qj935LYyK9WZknA/5qVWodj8O3UchO/8Dl/pNZ1HuWj4d9mn7az+eW8MXB3L5+OYrgchDS0/joJbz4rjYf20qANosbaSsSGF+v/l09eraYV9RbQ0WWRPvnX4PtUzNy31e5t39C5DZ21FtqGZ2/GyWnT2LtNaJ2y/eQ+PYb3APjfrV96iwqJDmzc8TMuVNlA7OmMwW+ry2k3EJvtzjk4VjyQEYZRsKSFMlVak7mX3Kn14hbkT76hgUokOlse9wzM3nS+kR4krJif1cOn6EcQ8+Che3kHH+BEfVPcnBj/L6Vjr56tA31lOih3enJkHaOlA78fH6PXQbNIluUSGgUPPp0RWMCO2Ev9vlcZUZmyF9PWRuJMPrZvQmGYldgiFiODkbP8az21D2bl7KN6obmepTjCnEQHZDHo/2fOZX2+M3MZug5JxtLIVvV6jLh/eT4P4z4OjToeiKk4X0CnPD5/ICt3XNRiQSCY4aBRarhXdOvkNadRoedh6o5CqCHYOJytbQ2mbhe6Mbbhotk+1r2FK6E/tOQSS6pLBg906+nXo7GqWc9dnreefkO6wbvw57pT3p+18ntaqVLG1/An2L2ZK3hVs63UKYUxivH3mdmFp3jDnl+E8cjIPSgSjHrjgoHHGyl/H85h2k157h/XE34yvTgErLu7sfxSCT8/jAN/4zbSe0E9/fgiD8GUTPlCD8gqa6WorTU4ns2QeAXlER9IqK+JVngYc6FzmR7Y8fXX6WWxxVJDschU3rYaTtDvzZwjqeX3uBd6YkcCSnhindO851slqsZJ0oJzjeHYXq6sx+/0rjoGTk7FhcPDUsO/AdYX5eVDQaeGF9GkqZBCeNgjh/J2J8HZHLJOzOrCDB3xGrBBpaTNyUHIjh0g6W5Huz6XQlP8wK5YcThUR4aPG10zBnRDwPb61mQ2opvZ1b6ZYQySRtNhk1jny29yhf3NYdrYsatVZBtyBH2mpraTx2Hm3fvuAeRsjFSgpr9WxPK6fJaOHVzRncHebFexdL2HBfH5adX8UQCYzsPgh/33oCXW0p5NMOlJIQMZJjrUE06iNoyvehs+LuDq89KdiFpOCOk/QT/J2wV139MddmtrApexfI9Dza7VGq9FUd9huaTMhrpHiFBvBGf9uP2q8ufMXZ3GME2vlzx4hHSC+r4GJpA30ColCPOs7P54fsyN5OQ4vOG7nCVi+FTMqy2b34eE82Fb6DcOwy7sr77lOJtvggnX1m0S/SlUPZtXT20dFYV8/51CpS4ryQ6BSsOlWEt5OaiC7d8AgKgW8nUBIzlnXKfOLr1Ry0uhLv78ys/qEoltxAjUVFzc4kXJRmcA1n9vQZ0FQG73eF+09TL82l5XJSDaxWOP4ZDHgCxrxNlNyWVa+k+Bipe7aSk6tkTLw9I2c8S1iTipL6EIJrl+JRkMUWh1KGd766d0rfUE9pViahXbv/tkaTKcD/J8GuUyA8lApa96uKHsutIdxD2x5MvbcrC7VCymPDo2mztFFjqAFAKpGSW5/LvV3uxaSuQSKV4JD/BRqlH7E97sKxLorXT81HnlFDgGctmqPvgcnAmEFPkeSRxGP7HuO+LvdxX9l2ZoW/RlZaEXYuFXw45EPmHpxLjGsMFizsVJ9jZN/BHCo+RKhTKOczg9EbKxnfw8SR5gVsvnEzpc2lLLz4A3cl3MVUx85YHDx/49UkCIIg/NXEnClB+IlNx7OY9cnu9nlRDeVlZB099LuP43DDG8gj+2K12OYvDYh0xy25D15DJ0KnCe3lwj21zBkWyfHcGl7emMba00UdjmNqNZN2sJTm+o7rYr21LZPFh3KveW7vUCdUWiU3Du/P0Bgv7JVyEv2dCHbREOSqQaOU46CSM71XMB5aFUFu9sgkEtadLSIpyIXquNnMv+TD7X0CeX7dBT6d1pXEAGfSw2YicwlkRp9gItXNrHljHn29ehPY93a6xndBrZRT02TE4qHGYLVQWKPHmJ1NxaKveXX5+8w7Mo+kYBcSA5zoGeaOj6OaT6Z14WJrK9MS/Yn00jFYkslc/SLUcim5lc0opbaPqC4j/ZB07cuY3l1w738nNa0KVhyvwdhm4WxBDd8uPtfeRhfLGqh5fxCG7INM7xXMpK7+V7XRnouVfHe0AKvVilwmR2/W02gw8fn+HAwmM6U5dexfn86Lh19keeZycutykUvk4GRHq7sKdzt3aPPEWR7JDUm2ALjR2MiB4gO/em24uLoSccM8pGqH9m2BqiZebZ1HuMY2h+fwxQpO7C6kSRGK+rr3mTsuFge1kpyKRjLLGsitbOZCaT3V5c0UZ6fiZy4iwd+Zvfl6tpZKWOI/F1PIIOT+8XgMuJOqqgpmt32LQialYvSXvNB2G+U+Q6Hvw5CzG5rLwTsebvgW5Eoe7TqHiKXT0ecd4NVjr1Hd/Q5bALPlSdtwv7R1nDi7mLCKHfTr5oNPztdwdgkRXjoGRHrimziLtcbb+WRvNgaTGbbPhe9uaH+9NSVFnNu5xfagsQwWj4emil9tuw6uEUgBzJ8UT7y/M3qTnvLmclpMZn4cSVtnqCOzJhMP+jDIcwZSqZS3TryFV2g4ntZinrcLZE7yHUikUtwdPOjr2xdvF1/m9XrONrw2Zqzt1EotreZWLBYLr/V5jdVFbzIhWUayVzIOSgdkUhm1hlru73o/JU0lJIQlMyxoGNM7T+e+QeE8mhJJgkcC68avQylTYrFYaDW3Ut1SzX2FW7lYYvp9bSEIgiD8ZUTPlCD8RIPBjNF65R6DT2Q0PpHRv+3JTZWw8wUY9jKYTexd9CnuUYnEDx3BqLgfhyIFg8uV5AQapZy+EbYfhRq1HIVVQnp6Gb4hDljrJTTXGxn/UJerTlXRcCW4qipqorG6heD4a/+4tL/GujvPrD6P2WrlldFRpK/4Er+G7sQ7NHMyp5icxipem+RPnJ8T7+7MJn3bPm4Y3Q+9VEFpfQvxPg5wdA55E25i4fJM3PINhEwJ5YYkf4Lc7Hlj+XE0MjmflFZw+MnB+H6wkDGluShcLKw/W8L6s6V8GboHPxcVfh5xdI+13YVfe6aYcQNf4kj5SWo+u0Cuoo3qQGdctCqOtO5lc8ZmPhn6CSnpT5HiFceTjz5yOcufFerbaGu1LUAc6GZPXreHcXSIxAswmS2sOFnI2HhfkLRir7RnYKQH8X5TcXe40p9U3mDgXFE9rSYLwXHuqEPaWLTtCCaLiaLGIkYEj6CvX18u1V0ivyEfbycn/Jzs2LdkMXZx/ZC71bPowiJ6+/S+Ki36zzm9I58o1/M0n9lJg9vN+Fo1oDeRV17J8QAZUz017WWfW5uKUi5he1oFr0+K54sdp7mYdoQRziU8H6AAJmCnlCFphl1FMKpXIAPcb6NLgDOruqbBJdtNgfJGM+l1cjJbdLbkiD4JmBSOKGQK0PnSuvo+lMNeQDL6bSQeUWhqTiLLWA8SGZaQQfxQWgrZDQwNGYCb9TiSrv3A0gur0p7U3dsJS+qJumg/CxJbodN4W+WD+oDWo/21+EV1wu/HLIh2zhgSpqBU6ZBeWA0BPUH37y9Ouy57HSfLTnF7r8fxdrQN6fKw92D52OV8fzQfZ7WOzq6d2xOIbChUUFfrx/G9jxKmDmKAZx/iPeJ5t/Bd9MU68vM7cVufMDzS1qJtLG1f1Hlx6mKc1c708unF8JXDWTl2JdO9+mBCTSe3TiwbtY7v95cxrWcfPthezINDw7lUUg1SOfGBtrlQwU7BPNj1QdosbVznN4YQj2sPSxUEQRD+fkTPlCD8xJS+UXw1u/9v/jHcgVzJ5rau3Ph1Gux7g3jvOkxBCb/56aNifehsZ8cHxz/m1jXPU5xdR87pa9+tf21SHA8Psw0jrK/UU5HXcM1yS48XsORY/lXbuwU5YwXKGkqYZvmUcpUdnoHVnK+7wPJjjeQ2ZVLTbGLtLbGE7FiNub6erw/ncduiY7SYJRDYhx6xkRhdlZzzlbHsRCFFtS2YKiq4a1oMs2bEse2hfmiUctRaBbHhEUS5RjEuwZe3bohHHpBM9+R+3PjpEcI9tRwp383WtAKQyYkY3BkHXSaj7LYQ7qKAnH0MDxrOi71etFXewQcc/drfo/gAV6bdn4ijhy3wUMllmAL6MOD94zToTbS0mtmbWcnO7FMMWT6EzJpMZFIJ7g5qHtv3GD9k/ACAp07Njb3VTP7kAKllRTx78FnGhIzhhV4v4KBywIoVJPDYvsd49eirnM07Rc3Jdzhqn8vM5RdRW0L4IuULmmqqaNU3X/1mHHofTn/bYVNTTSutKj9kAUm49xrMye2l7PvqKJMz7uce1UYu7Cti/+JjlBRc4qtbu/Hlrd3pE+bGujPFvDfWh2EuFZBwIwx5HoD+Ee4kK+z46MZEmgxmZi4+Ycti2PNuGP0OmE3EupiZPymegZcTcJQY3Vj6zocAmCRyCksrMC+fAaYW7OyceSDxAZzGfQQRw6gK7M7m2nJ2GYJZUp+AZNR8cA0B9wjMdu5cOn6U5rpaW0KMi1tpqqlm7Zuv0OKdDO5RsHImNNmyL36+P4fl6zdCZQaPVx/m4d3zeOFCDuy/MleopLGEyesmk1qZys+xWq2YLCaqW6o5W3G2fXsXpxSOHB3O7G9OsfjQPj44urx939TkQMK85PTz60drWyulTaVUyrzY3+DF1OiplNQUcDLvKFX6KsxWMzl12ZjMVixWqy2zoIPt5kilvpKbom/izQFvcn75ahaHvEJAZQ7eOz5GuupjjNvn4VqVjSX/MLLCQ1isVqxWOLZ/C8eOHkBv0rO/aH97veRSOdFBXbj1+N2crzjPPStncDR1z8++dkEQBOGvJ3qmBOE/5MChA7RlpTPUQwODn2Xz0QKaiprpFv7bj+ET7sRM1W1crGokJtEHekJ1o4FGg5Eg9ysTpk/k1WAwmekT7k5oFw9Cu3hc+3iOdrYfgNhSZR/PraZHqBvDOnnz/Lo0pvcMZuvErXhoPLjbxRUsKrIKczA2+nCuqI6Y7oEEfGJLXz3FoZXjzW+zJa+GCUkzcAW81ScZZr8F72H34+3iSMHMmehGjcJ+wgQ8dXbszazEy0GB3K6GhWc/4pW+r+CsUfJujjfXJ/nz+fQ2XLRKlOpK3NShNLe2IXFQENbZHUVFKVRfhE1zUM/ai/pyinlGvg5AZVED7n46Tm7NRSaVdVgAWSGVEufriFQKWrUCB7WCnFIJdyfcTbjzlTdEK9fiqLJlcbxQnsOpygP0jXUj1NWD/n790bfpkUqkmC1mzFYzgbpANk/cjKPKkY9Of4S1iweBnpHsHNQTN63t/dn33Vd4hobTbdT49vM0t7Yhc41FrbnS0wTQ9/rL8+9iEjC1mtHolESa92Js0OHYdRLuGhd0pStoXrkNx8AwFLFj2X3RjbTiesZ38WX2sLkdjmc0mDm/p5iSpiUYTaUcfuoVVHLbXLs292hyj27ALXs1CTcvoqm2Bpoq8N55OyNmbKOptQ2TRIvD+De4eOALXCwOeAEGkxljm5ktF8qpVmwk1KeNqWEh6NR2Hc4tVyoZ88jTnCuqw/WWtSBToDS0ENg5HoVKDXUFkLYWBtmSUgyO8sDp9ArIucRI39vZm3uenBZP8La0H9NJ7UQn107tywtcy7cnFrEvZytvmIys8I8g3sOW9CXU3ZH5k+NoNZWxefsqagINwGRqzi/DcPIr3g2JJcoligZjAwUNBXjq/Lnd/ANdm0aSPPptXtq1goNnN/PZqM+QgC2j4IGvYOgLVDSW8ua+x9lXtI9VY1dRVqPGLSQCL2sR7PqCEuduZESFEOekRmmv5bGpKVTIZTyTGAjAHWMGgNKB9IZ8Fp5ZSLJ3MkqZbQHkJZlLiHWPJcgpiCiHCNx1Yv6UIAjC35nI5vc7iWxAws+p2rOQ6pIyPAbdibPXTzKMFR23LYgaMuCq57QYzdz57QmeHR1DuIfDVfsB7l9yikPZ1Rx8YhAlqTUcTD1JpbcTWrk7d/QNveZzHlm9EzMG3p5wJY36trRS7vv+DDse7o+/i4asikbC3LW/2gu3M70cixWGxnjyypFX6OrZlZTgFADe3nSWY9nlLLnPlt2wrboamYMDEqXth+E9i0/gU2IkYbQ7euUZJkVMwmyx8vLGdGb0DiLA1Z6tqaUsOphLq8lKlLcDBqOZt6fYhjau27iOwRWLWRT4GimdvdrbqKamgSlbr+dBr6dpOKBk8PRo3AOu/D0eyKrk2TUX2P3oQMCWCl4ulSCVSrBYLUglUracL2XpqSwWTI7Bxc6FBze/T4ulgs9G/8sCsdgW+e3u1Z1Il0isViuZZY2Y5Pk4KB04V3WOLblb+GiIbV2oVr0euVKBTK5of/5zay+QW9nE2HgferleYubpBXw94mvcNVeGZjbVtbLvh0wG3hiCnZ0ElPbsPl5EpLcDSksJVed3sbvSnvETbiC9pIGDl6q4OzEQFx/7q97DLWtmkCvVktp0Kx9MTaSsqIrrFp8l0tue4REOTOgWwid3Tef6Z1/F00MHWnfe25FFSb2e1ybGU/7VdKTxk3HvMpoJ7+4izkuFxNjIXUNDkDg48cC2eXjbBXJft1mcK6onWXYR2emvqUt5j5mLjrLu/n44apTt9ck48QktzZV0ib8FnAIg/zAc+wQmf8XXqV9zpkCPVFnNG3GTQOUAuo4Z+q7lrRNv4aRyYpT/cC6lHaC3ogm63QaX28JoNrLs4jKuC7sOU22jLcU60FR2gYLM9bh0vQ2NUkNFUyXzDr3DO0NeRrJ/EQf2n2f43E8prW+luKmEcmM6ipJzpBz6nJI+r2Iov4iDopIFWhUB+5uYNO0RblpVyB1DpYyN6cbTm2/n5tCJmF0C6e7dnU0Hl1Ikq+JAzVGmB99I+Yq9RNw0lii/OLRK7VWvq9nUjFKmRCFVXLVP+PeI729BEP4MIpj6ncSHsfBTj604S89QNyZ08YXDH4KDN3TuuOYNJ78GQx30vv+q51utVtadLWFQlAcO6mv/eHpvx0UyyxvpnZCPoaEF1wYvQhM96ezeGYCCGj3+znYdflB/e+IYS05m8/VN1+GqVbWfK6eyidDLAYnJbEEhuzLS950Tb+Pj4Mv1kdcDcMd3O3HzyKS72zjMbW1MSgq8qm4/HM3HZLES4+3I2aJa6i25aBwvcWf8nQCYTRYyMqsJj3Llja2ZRHk7cF2i31XHaTG2Udlk5LVNaUxJCqBfpAcWi5Vj6ZcIajrLNxdaiXCyMm7iNACeX3OBCL8GhvpGk3+qjqRRHRfJtVqtnMirQSGTkhDgjNFsJKs2i3NV5zheepy3Br5Fo8HEI3sfoN5Uw4SwCYwNmYREAmrF5Z4cSxty6dWd95llDdz65TFW3d0bbyc7moxNVLVUEeQYdM33D6Cy0UBmeSPB0mrcVoxl3dC3mZQw0vaeNZRBSw14xgDQqjdhkUtZfqKAt3dkMTLUnXk3daHx4Gd8fMkJX99IfDzcya3WY1pbxMRHu+Lq+y8/ys1tVOrbKKxpITHQmS0LF5LdqmP67BuwV8mQSCTUlZfh5HllblJzaxumNgt5NY3sLdnKLXFjcbbTsmLLDuxVCnrXrKBJ7kpbtztpMNTj7+VNWpkJyfHPcHH3ozT7DP2D7Fl5uJmYCC+cevXApaEMVW0hP5QdpF7nxZ19XgCtGzRVcv7Efpp0IXxW+R4JbgkkeCTQfcurNDh24oG6SSyZ1eNn2xNg3pF5WK1Wnrmcfr2mpYYDxQdYeXElj3d/HD8HPx7Z8TARNa506jOAEcEjKKzR45/5FRSfhElfALDw4EGWHGpg36MjaDOZqCrIwys0HOpLOGUoY0POBtQyNbcGjeSZLQYSvaTM7ukHDh7UVZTh6O7Jpwc+4NO8r9g4YSP7ivbR17cvznbOrFswj+OB5fh7hTG9152sSF+Ge76V9cqjzEyYRbJPMsbWBhrXP4CTzh/ZsBd/8TUL/x7x/S0Iwp9BDPMThH/DlKQAvC8vGkvPezrsa6qppCznLGHdbvnZ50skEsYl+P7s/srCi3R2MtJoULM99wBOKjtcKqrYdWY97w59B4PJzNj397P49mTi/ZzanzetW3eS/KLaA6kfz/VjIHUyr4ZHlp9lx5wByKQS1pz4gEvlp3GWqiFrB4QPISnAm07+PnT19WLtZy9iarRHMegx6msqqMpP48EDMkZ29mZYJy9ePfYKEZrBBLtqaZFfGf4lU0jp1NnW+zIo2gOPn9QHoMFg4oklZ3BUKxgY58WDQyIJdbdn+Rsn2SI18O7d3akxqqisXUCkvS2QMlus5FQ1kxAYgIe3Mx6jrixMnFqVypKMJbzc52UWH8qjVm/iuzt6kFqdyutHX6esuYwF/RfQ3NrGxrMlOKq0RDh0IUiTiJ3ySur5Hfk7WJa5rMM6VhuPryDIPYxOQQkMj/Xmi4O5PDMqBq1Si1appb7FhEoubQ/GfsrdQY27g5odaRbKe62jm7fvleA3cwMUnYQJCzG3Wfhu7lHCrwti1eliHhkSTrRSTXVJM05lp7G3JHH8UjMR1VLuuqEzhuQA1BoFVquVsqo6vGX1sPsl6PMw7p6d2hNsDJw+nYGASn3lI789kLKYYf+b2He5GXTe1KamcqRoI+dT7ZjWNZ71GU28NimB9PzxmGrz2bZmK66yFh7o5UpPt0gwHoO2ZiK6dILqLPr1CMZBDfceeZkJXj0Z5RjNFJebofw8bH0SEqZC6EB0jWk8XfkJ4yNvoNJQye7C3Xg2aFFE9OahpF8fG/twt4epbqluf1zaXMrewr20mlvJqMnAzc6NRwLu5ETjPrbnbUcl1XHvFw2svnMKkV2msSejgkhvB/ztfPGwa8Ny4D3kJj3S4OtBXwsfdMV7+moeiHsCe7WcHzK+x8/LhfE9BoKD7Rq3qGWU52XTJmtjYef5fH7+c5pMTZyqPIVGrkEdZWGIJZkm1wBMZhl99D5s9srg7cR323ulmvQ1tDSVow4dgv01X6kgCILwd/b/pmdq3rx5bNy4kTNnzqBUKqmrq7uqTEFBAXfddRe7d+9Gq9Uyffp0Xn31VeTyKz8g9uzZw8MPP0xqair+/v4888wz3Hrrrb+5HuLOlvBbndv2DTEnnkL+SAYof/vPJJOxFdPFPaisBs7uWoJzUAJjTnVh4322npDU07m00EzPrnFIpBIqGw0dstL9FobaUtK+upfEWxaAawhHig7QVptPH9fOsPFhuG0zyG3DtErrW3hyyUHeHh2Is184+7csJSr/e+bIniTS24EITx1m7UH66lvwDegLbmHt5ynPb2DnV2nc8FR3ZApbL1h2ZRMvb0hn4bRETNnZnPhyBZf6TEDnbc/3RwtYe28fLp2uoFojITnSneqWatZnr+fmmJuRSX95ra3y5nIOlRxiQvgEvjuSR051BU0OK7kh8gY8NB5sytnErPhZlNTpeX5tKm9OTuChNRswW2R8NW1s+3EajA0UNxYT7Xolk+Nja+4l1rkTN/e/i3q9EasVnOyvDGWbs+wMoR5a7h4Q1qFOVGbCmrvhlrU8vyWPfhHuDI7+l3kwFgtcTgNfU9qEk6c9ZquVupJm9i3J5LjOgqv1DFHRsXSPj8dNp0b2k17F3RcKOPvmYwy+60Ficz+0BUjBfaHHXb9+MZhNtpTnPe+1JVd4N4H1kml4DhiP0tOLzFNHGRcCakMlh89lII8cQWJ0CDInHzj9DVzcCqGDwSXUtpiusz/W8BT25G6nz+43UAT2gdLTFI56HY/M7ai0nmDvAZUXOe7XGbXcnhJ9Cc1tzfQgBnf/IBTqX76e61vrWZa6iaOVu/g85bP27UazkQ2XNvB95vcE64J5pucz3L71duLd42k0NVLd2IajBpK8kjhwJojrE0NJcLaye81qxoTX0VRZzuJNRdz21kJaTaUM2zadvs6z6Okxkoy2L1HKlDyV/BQAJXV63l/zGBEZErb2rqSXby/c7dyRSCQMCRjC3MNzSfEfRt3Xe/imcypTA+9Fv2U98u6B3DjoXipSMwjp2v2PJbsR/hDx/S0Iwp/h/00w9fzzz+Pk5ERRURFffPHFVcGU2WwmISEBLy8vFixYQGlpKbfccgt33HEHr7zyCgC5ubl07tyZ2bNnM3PmTHbu3MmDDz7Ixo0bSUlJ+U31EB/Gwu9hqslH4XL18LhfcnLjGkqPbWV0RA2lwxfi6aglo8JApJcDJrOF1rpWti9KwzfcmcieXrh4/cH72TW53Hb4HQb4DmN6wqhfLFpbV0fu6V0k9h1Nm0SO0WxBKZMildjmIQGw5WmIGg5Bfduf12Yys+DQWzhXNbI+pwcrHkjBZLGwI62c2mYjCeZaAi4cwf1eW69eU2sbUgl8vCeb2f1D0Vxjwd2fMpjMZFc20cnH8crG1DWg0lHl1Zsj5TvZkrsJc3M0D/eYQrina3uxzJpMZBIZPvZ+1OtNaNXqnx1q+VtUNhpQKWTo/vUYphZyjm7gQmsiw/r6cehsJd1CXNC5XklGsfhQHp461dUL3FosUHiMFRW+ZJ7Zx1PTRpFWI6GTryNsfcY2Dy98CGaLlZ0HTjGgZxzK8nO2jHjOQTD81d//QpptabstKh39F+zm1Qmd+X77YZ7poaSgHpIP3IJkTgZc3AIKLdRmc8lvIub1DxDUbSSqXrOoNdRyy+Zb+LLrE7j7dgdTC/cdfJoB/gOYGDERlk0HuQrCh0HEcIxWGac3r6fLiDEoLye2KGwoxF/XcY2wosYiNuZsRIaMxdv8eWl8FAPCbX9f+fX5VLRUsOD4AqZFT+NMxRkajA309ulNmF0QlU3lrCreQIRzBDqlDoVcwdacrTzZ48kOQXOrXo/qcqKQQ8WHcFeG4+vojEbZ8Vp8eOkZfFzgnr5hbMrfir5Nz/qc9YyT9KFv8ABydbWY9QEoDAaCfB34OH0h56vOs3b8WurKS9n47nwmPv0Sdtprz5UU/vPE97cgCH+G/zfB1I+++uorHnzwwauCqc2bNzN69GhKSkrw9LTd9f344495/PHHqaysRKlU8vjjj7Nx40YuXLjQ/rwpU6ZQV1fHli1brnm+1tZWWluvrOnT0NCAv7+/+DAW/jQmgwFjqwGVVkebxdo+bGzRwVzOFtbxzuXkDEfX5xDezRMX747BlLmpmdRTGTi6BFGe10D30cFXnQPgYG4Op0vS6REQSzf/gA77zhXV4WqvxNfZ9qNy68lMVhzO5LMZfUFzZVjdmPf389SoaHqGuHV4vsVibQ+yNuZsRFFfi7XBmyqPMgYHDMbL3ovbFh0jwFXD1D4KcutzcTwXjr1OiTrakRlfneDDqV2ID7Cd61LtJcxWM5EukR3OcyCrkhc3pLHtof5XNp5cDHZO3HjQkxuS/OkfoeXO7ffyYu+XiHILAuD8zqfZ3JSHc/AA7oi7gydXncNTp+bBIbbsenV6I7mVTXQJdAGgtKmUddnruCPuDqSS37+ixP60MtK+yKRtQANfn1Hz7sAoevS5EihsOFeC0WQho6yR8cEmotT1bNeH41B/kZ77bsI8/jMK3fvQZrQwfuEh1s1IJrRlH7jHgPu/DIlbciMotTDxM65iNsGxTzEED6Hw0HLq1LUkRYyEsMEApJbnY6iooeu6UXDPMfaWKaiormTZiWK+UL6JbuoiDC0G1C5e8PkQCE+BzhMwecRyJquApHA/kF0OOi5uhxW3wX0nQaWlwWKm+PQZ6svK6DH4csC9+k4Y/RZ6hQdrVn1EYHwXLBllRI4bQcrKFDaM34Cvw5VhsHn1eay8uJJ7E++lVm9g8fK5VHtZmD/yXW7fcjsAPe2fZlyCDwqFgbdOvMUjSY+QvWsvywzbqXdoo5NLJ9Zmr0UtU3NH7B0MDhqMRnE5sE3fCK2NkDCl/ZzFjcWcqzrHiOARHZoyrfIiPloflHIp9+68F1e1K3YNVvrqo/DxDqZT/8G8simNUn0WCte9zO8/n7KmMkwWE68df42u7l2ZXFTErjI51d28mZVwx+++roTfRwRTgiD8Gf5n1pk6fPgwsbGx7YEUQEpKCg0NDaSmpraXGTJkSIfnpaSkcPjw4Z897quvvoqjo2P7P39//58tK/yzHV65hONL3qKlpvjfOo5Crcbe0YkvDuTyzJorgf/4BF8eGhKB2WKlttlI8piQqwIpgJzzmVy/tYwWQylOHnZX7QeoaDBw4PAzdLZk2QKp8ytsvQWX/XCsgP1Zle2PU7pG8tm9YzsEUgBzx3bqMFfrR4+tPMs3h/MAGBUyimFdptE9OZEd+TuYuG4iJ0rPkBziyrTkAN46vIhN2TsJiXPDL8qFCC8dB54YRHyAM3qTHpPFxL6ifewq2AXYhnL9qE+4Owtv6srjK85hMLbZNnadDjHjePv6eEZ09sJZ48idEW/ww6Gm9uf5Ro7j5tAJ3BFn+wH7xPAo7vjJwsYf78nm0RXn2h8bLUZqDbX89N7Trq/TyL9Qdc327dDWBQ08tTqV0j6OuBV482H38A6BlN7YBljZcroEF42cKUuLybp0kdL6Fi60+VAQdA+monSC3bSE++hYe2NXggIcIWZceyCVUdrALV8cJfPoFmhthk7XUfntBM4ceB3MbVcq09YKJacx1ldgrklnnj6Veueg9t0fHd3EOyfq4PZt4OSHe9E2RqQ+TlKYNxeSp/DQ4ReQXVoPDSWgdACXMNj+HAqZlKSoIF7dmsXq08XQWAVbnoDOkzDte4PmnS8wce149E4S3PwDoK4IFHYweTHkHmBdzmmKouw50XIerYsLHhoPtk3c1h5INRgb+OjMR3hoPJiTNAeVTIWHvY6YqO508ekGwLy+85jb6yUyyxppMpixWq3sK97H0dKjxA4eRnLMQFKCUqgx1NDTpydJ3kkk+yTTZrG1j6m1lRvTvuRQUw2YTZjMJp49+CzzDs9jW942XjzUMTnE/Xvu5uu0L3nj+BvUtdaRVZtFoNybLr2H0qm/LTjtGeJKd9845vaai9nQSkZFGq3mVtrMbZyrPocqYggOEYkkeMb/6nUkCIIg/D39zySgKCsr6xBIAe2Py8rKfrFMQ0MDLS0t2Nld/cPzySef5OGHH25//GPPlCD8K/fwTqiPrKE5OxA7l4n/9vEmd/VDbzS3P3a2V+Jsr2TjuRK+OpTH8tm9rvm88J6JHJF8j1OwBly9rlnGQ6fmHt+uqFWXs8AF9QOXKz1Yr1wX96v1q2iuoEmazodnj/Fw9AwsSFBobT05M/uE4GTfcbibs9qZcKdwhgUOw6j3YfOFi6w4WURcJ28C1J44emhQ2ysoqW3Bx9n2t/jykZeJcI5gRuwMTpadZO2ltXx05iPm959PvLvtB6hGJaOg5Rxr0+u4Ib5f+/m8HO3YmL2RzNpMklwH4+Odj94UglQixcWvO0aPTjTV5GC0c8JF49Khrg8NjeDG7lf+zgN1gTyR/ET74/TqdLZKVvKY5wO/2k7Onva8NCSSnnYnaYvyQeLbMe13Zmkjy48W4G2QMiHci2k9g9CqFUQC1ORx6WwjloDLdTn5NWE+CSBz73CMNouFNosVmVQOzWWgcade64Gp+hK0GUB2+X1WaWHi5+iA3EJ/Pg8wYTFpsJjNSGUyFoyYblvv6HIWwxhVBXWuvlyyrCdKm4CpSoLEOZDCSlfsBi7CbWV/uHUjWUcOYm5rpX9kHJ4OasjbBiH9oe8c5q0+zfOld7Ew+VaCw5KQHf4INq6G6FHQ+TpMBYeR6JdyI57Ikp/Ex6MYzq/AJXIkNNeAvQs7cw5Q3VJNm7WNkqoG8s6fJsuoxmT0ZmavAQA4qZzIKj7OgyNC0Gec51zBJRxVjnR268x9G+7Cx86LPp2HMjZsLGuy1/Bsz2c5VHyIV4+9ykdDPmJ52lL6WOKItHeFr8fzXbfJXKi8QKx7LDl1OTipnNrb+4VDLzA6ZDTTO09nafpSBnsNwLXAijzKh+cyX0d9Sc3LfV4mXttC5NE30SW8xKfr3qWuuQa30dP5IuIWsHMC73iGRfzqJSQIgiD8jf2lwdQTTzzB66+//otl0tPTiYqK+i/V6GoqlQqVSvXrBYV/PL/IKM4VjyY2dvR/5HguWhUu19jeP8KdisZWjG0WlPJrdy479Zj6q8fX9p9z5YGDh+3fT5wprKOzjw657NrneOHwCyR5JhHnFkfm8udoapPQ/Y4PAIjyvvYQmt6+vfF38CfEyYMVd7pRUKMn1KM/O79O50JjMTGD/Rj+7j6+uyOZWF8n7utyH3aXswPWtdax9tJaZsfNJsYlBovVwjMHnuG2zrfh63+WneX78CgwMzBgYPv5yvXllDWX8XXNBzgoHHjzxFlc7Vy5O+FuUve9gl/WLmb5ePHWgLcIdrwSTKoUMgLdrl4D6EehTqFMHJKCq/svDxU6VX6KYMdgBiT7wZ5vUfmoQafsUKZLoDOL7+jZYVt1UysZR0rRWKV0mf00KC8PQ2ssBUMwZyrOkFOXw3URtjT84Z4ODI32xCuuG3i8C74JhAV88vMVs1hwrN6BtSCVVWebGXz73QTFJVwZ7ga0mS3kOyRzX9lWumm09PeIZWzEKMjYRGvaHpqdokgLeQhrrRPeBeux1uTTq88gMDTAoZ2QMJWa42uY2q0XrcGphNnrwNQCDaUgU4CjP7hFsNRuBv7yNKS1p6grL8PHpZncigyy0lYxzCrlRNyTrL3wNfe5DUCn1PH2/jOU51TwWqdM5KZKYAAAG88vYm3eNhJ9e3G98xg8PP1xbXRlcepibo2fgZe9F4FeYZQ2lZLgnoDZYibJIuMGj2ReP/Y6ifaBDC89QabBHbsuk/HSenFPwj3Eu8ezNX8r56vOt7eNBAmBDoE4qhyZlTCL+opy3st+lYycYwwNGkp+fT4yqYwVucsYYczC1FhLRvMlesQOJsEzAfa8bnv93qJHShAE4f+7vzSYmjNnzq9m0gsJCfnF/T/y8vLi2LFjHbaVl5e37/vxvz9u+2kZnU53zV4pQfg9DHWlRGV9ij6hL3beQX/aedZnHmR7yR7O7GnmqZ6P4Gnv+etP+o0MJjOrThUxMtabu789TlKQK3f2D8VTp+6QZh3gpd4voVVqUcqUHNCHk1nWRPdfOX5//ytzmxRyKaEetoBF2tWZNpUcjVLOlgf6tfdMeWu9oew8ZC0iMn4y9a31ONs5o5DZer3iPeJxUjnxbM9n2Jq7ldY2IxfLG4jwtAU5M2JnAFDcVIyD0gFjm7F9MdSIPo+QHzaEWRZDeyBlMJl5bXMGsweE4KW7+jPBaDayPns9+RWFBFcnYBlzZW7YtXyd9jXjQsahPulPcJfZHF6dw7CZrdjrfvkGzabzpeQX13Jzt4ArgRTAgMdt9Sw9QqOpsX2zSi6j3mBid0Y5Y+J7/uvhrtZcQVDRmxAyiBtfWoCdw9VBYVZFI+v3neCNqG54FF/A6HUSHLyxHHgX/+DBnG9rJcCQToPcRETWmzDybVvCjJW324YgBvREvvlVfOuzMF+UkSqPI6J5GxVDn8W3ehB4xWFO3UjahXKkiYlUnL3EoCEh4OvPNzmBWJT9GDYkksS8g9wp7U5s0Q7YXsoTQ+6H3FxUzS5cMFeQlbWaCeETGBN3O/19+uLiEYNUIsUnPIqb0kpxOruMC/IIag1BBHhacVF74Hi6mI8zn+A6iQWdpYl7Eu/ATuOKwhqGqhq2GjNJz9jNp8M+RSVTMS1mWoe26eTWCZX8ynvo6OHJfdfNpaipiM5undu3nzMWcC4knEmtBciC3ZicdHP7+3i24ix7Tr7LA11/vXdTEARB+Pv6S4Mpd3d33N3df73gb9CzZ0/mzZtHRUUFHh62O+zbt29Hp9MRExPTXmbTpk0dnrd9+3Z69vwNPz4E4Vc4eQbC/fv/9PMkBfghUcZgMOuxU/xnbgK8u/QhQpxDSe45k10ZFaR08mL3o4NYcrSQo7nVLNyTw6YH+uCmvZKy2sXuSr9Zn04h9On088fXm/TsLdpLSlAKUomUmtJm9i29SNLIIHwjnMmta0GrktMD2gOpH9UZGjlTuAf3qGF8mfIlzW3N7ftuiLyh/f+vj7qe1OJ6rv/4MAefGIz9TzIB+movJzH4SaeQvdqZmKCBxGALkt49+S4J7l1xULsi+5l01c2mZnYV7GKyz400XtBgbrMgVV6drn37mRLcFQrG5M/CnGcFFwmqtipiumlR2/96xsBpPQIxJQVc6XlMWw8+3cDJlu2vh3cPenj/ZFHb4lNMvTSXL1tfZUz81euW3bNsGwPCApmceDmBh4MXXPcpKB3QtBSDzhFam+DSDgwRKZRczEAnV/Hg3fdTX1/Kc7se5cHqTNzPNrNMPpqdmaEY1W54OXbi9VA/mLkLFo8GlyBwjYCMLXBxGzqNBEvf2VTnX0LSJOWgS2fe3f8Ynw9fhKudK7LmSl4ZZcEaFceuvMNYrVasVit39otALZeBRoE09jpkbscwy0fBxc2omkvg3PfQdw4qe+f24EUpU+Lm2RmLxcymD94gceR40g0VENCN9OJiWusK2ZVezUuD3AhuOEJssxHj6JdYuf9Rehcf4ERNGl1Lvblx1N3EuozilSOvcGbNTJIdAmHYyx3a83jZcezkdowMGdm+zUnthJPaCYA2k4kdn3/IixOexNnTh6qWKjRyTYc06Fql1nazQBAEQfh/7f/NnKmCggJqamooKCjAbDZz5swZAMLCwtBqtQwbNoyYmBhuvvlm5s+fT1lZGc888wz33HNP+zC92bNn88EHH/DYY48xY8YMdu3axbJly9i4ceNf+MoE4fcJcw4jzDns1wv+DhGuUXi5BePhoObz6Unt22/tHYTe2IaHVoWr/R8b7rojvYzC+kIO1q+kn18/7BX2WMxWHN3VOHnael1m9L4642CbpY3yox/yWu0pZF7BPKDQ8Nrx1ylpLGXxyEXt5d7cls51XQIIdrenk68jex8d2CGQ+jlbc7dyuPQwc3vNRYIEtVzNV2lf8EDiAz+7bpez2pkPh3xoexBzjQIWCxibuHS6kso6EzFx7qg0CqJ7+sCmR3G29wTZI9c89sazxRgtFiZ08UcikaCU23547y/aT/d9r6OKGGELePo/1iERyOn8WvblXWR47A080fvqiLa8uZysli0MV93YcUdwXzj2GRSfggkLoaEYjn/OuzUncD1TQ5vWmdvD5rLoeAOXSqdCuB3kLGV83CDCLT6EhgbTUl/HmW2bSOjXx5aGfc8rIJXTZjKT5nwdnXVZWFbfyUrPF5gdK6GlBJKw47v077g/8X7wSQCfBCSAzt0DldqONWdKWHO6mMUzbP2cbZY2FhxfwLM9niWu94O2uk9bDkA4PShtLuW+nfcxr+88dEpbD1ttWSml2ZnsMezDYrVwX5f7iHZKoK7ZhKoli0YvZ9ZotQTlyfGQTSe9aR8JngnojhRw8dwJ0r1qqGutw8kpGGoKO6wDBvBav9eu+R7+SCqT4uoXiNpOy6LURcS7xdPduzucWwZyO4gZQ6hTKKFOob94HEEQBOHv7/9NavRbb72VxYsXX7V99+7dDBgwAID8/Hzuuusu9uzZg729PdOnT+e11167atHehx56iLS0NPz8/Hj22WfFor2CcA351c3IpZL29Oi/+/nTp1M96gHmleuJ9NPxyoRfT2rxU5fqLrFl8/30jhxPUMxknNXOvLD/DQrKNfSN0nBr51vJqs3ioZU7eX3UdXTy9vj1g/5EaVMpRU1FJHklXbvA5schcTp4XitqutrpwhqaLh4iPPcjvg9/kCX7JKy9t8+VnjaLGdLXQUU6DHzqqufPXHwcnVrBWzcktG9rMjZx+9bbeSX5WUId/OHAmxA1Gjyi+TDtawobC2ktm0yh8h1uCLmbyXE/6WU3m9i45UECAvuh8ev28z/c/yVQ2Ju/i+rys1So7JgZOxO90UpqcT09Q91s6c6dg1jy9Rr2O3bnni721J3aSW/Dcuh2O9i5YtaF0lSexa4dpxg5dQJ70ws52uzJs61vs9vYhbRQDTP73t9hmBzAhZUf4xzVE11oDBWNrejKjvJpritzRsS2Lw/QZrbw1tY0piUH4eNqGyKqN+l5Y9cBbkpMJtTdtt7YmW2bCIpPpCT7O06UneLmCd8iydoGnp3B0Zfculw27n6aNFlnaEjho2ldwWpl9fwXMUa4cNAxi8kRk+nr17dDHU0WU/sw0d9qWeYyol2jiXWLtWXNlNvZkm8I/3Xi+1sQhD/D/5tg6u9CfBgL/xTzNqShUcl5aOiVdGMtRjNfHMjhlp6BtFlNmKxNuNq5Ipde3RPUdPAguQYfVN5aQqPckP3C3KKf02Zpu+rYZ8rPkFVxismVJbT1e5TzNWl08ejy+1/grzn4nm3uj/OvL7pstliZuPAgNc1GxscV0OZSSX+3GXSX5yDJ2gKDn7UVPPQBLWl7ueAzn6SRHXvjGg0mFDJpe+DwsxaNoi15Fs9XHyOzJpNvRn3TnqTjpxoailm+7QEiXGLoO6hjWu82kwmJRIJMfvX7tiF7AzWtNdwScwtYrbZ06lK5bf2omlwoO0erawy7N26irbmN0XfcAbn7yXLoxuItm9C3ruL1zsNptZoYeyKB4bFZ6C8Y6JnUDeWxlTgMHcWWloM80+OZKyc1NnPinXtx6zGRoH6jyS89g3rTy3xodwd3je6Kt862jlmb2cL8z79leicpvn2uzGN6YV0qY+J9SAzsmLrfuO4Bmtv0OF/3Gay9F2LGQ/gQ9uQdJre2BBQN3Nb5NlvhIx+Tk34Jt7GPc+fh+0n27M6DSQ91ON7dO+5mdOhoRgaP7LC93lBPa8kpPHyTbBkThb8l8f0tCMKfQQRTv5P4MBb+KcwWKxLokGChXm/kpY1pxPrL+fJgDuGd1zEtZhoD/Af87uNbrBYKGwoJdLwSrBjNRlKrU389OKrNh6OfUtnnPty1XmzI3sCp8lP0cEtiWMQILhTXY7FaibvGGlg/qm6pxtXOFYB9hftwUDrQxfMn573cY3Oh6gI78nfwYNcHf7FKx4vPIrHa4Wgvod5QTzfvblCRAXkHoPvM9nJVhY2Y2yx4Bjv+8mv8OaYWas0G5uyZw9DAoZyuOE28ezynKk7x5oA3abO0cajwEHP2z2Fr1+dQBfTgwQNPcXPMzfTzt6WO3/H5R2icnOg16VeyPp5bDunroSoLJn8NdXmQtQ00rpiNzbR0fxCtsytk7cC67w0Od7qXfOMlbjz5OcdMSWT5BuLVsxf7jrZxodaRlXf3pqSphL25W5Hk7mfU8HfRGxR8dGwZd3cdgGdrA3h1ZuHRBSTlHqE8/nrevvAZr/d9na5eXW11qs0HtaMttfgf9OK+j8irbOPLiffz2bnPyK3LxUflxL0h48A9kpd3zWVv1QE2TtxkSxV/2cWai3jae+KouvLeZddlM3XjVHzU7ixPfBx5cN9rnVL4GxDf34Ig/Bn+38yZEgThv+taPUmOGiVvTE5AbzTh7Wqmi8+rHRJR/B7nq85z/8772T55e/sP1qzaLJ4+8DSrx61GJbsyDGxT5nG2n9fz9qTL2QCdA2ke/BQzN0xlbPAUyg25xKkjyJj3BdGvhLMtXY9GYU+cnxMVDS04qBTYqeQ0tDZgr7BHIpFw48YbmddnHkleSeQ15uGicukYTK2eBUH90Ib1w0Pz60MIT1QewlHpyFS/nwQoHlG2fz/h5u/wh9rrR7XVtax6dS4fvPwOepmROPc4qluqOVVxCqvVyrMHnyXMMYz7utyHS/QYrFYrwU7B7C3a2x5MdR83CaniV4ar6WvBPwnUTnD4A9j8KIQPgVELAJBl76Etay9F9fX4FS7lhMtI9hY58VjJtzR3vgW/sqMsca9hbOlpXuiejCW4F63Fabg6+zPeNR7Jgc9RWKDGZCZXfxJjjhlOfg2zdnNX8qPou7QRZDTzhU8P/Bz8rtTrX3oKN54twdtJTWLgb78On+t3NxaL7T5iVUsVFquFkzXpkPwYRrORbRW7eL3Hqx0CKYAIl6sXhQpxDOG7kd+hkdkh112d/EMQBEH43yZ6pn4ncWdLEP5zalpqrgrGzBYzMqltqFuzqRl7hT2P7X0Ce2sIdZZ0/Bz8CHEKYXjQcO7a9ghSqYkZcbfQx7cPlXk5fFm+nNTqVJ7v9hFrzhSz/2IlrlolM/oE88Wlx5kcMZkRISMoaizC2967/VxgS42eX91MpJcOU8kZdtUWUCdr4YYoW9bAu787xfBOXoxN6Ljw7n+TxWymIPU8gbHx7dnh2qoLkTv7glTK7oLdXKy9yJ3xd/6u4y48s5AKfQXP93retmHNvVB1EW5ZAxnrKa65hHdjDVKPSBrib0Nz5kssWbtokTnhqL9EZshtqGQSAg8/TXHIXRTK0niJSj5264PW3IbLoOd4+cPPULiH8vj1g66uQHMVKDTtqeA/35/DmcI6Ppia+Iv1/mRvNn5Oas5ezOfmQbH4u/62YPWp/U9xfeT1JHgkXLWvvLn8P7rkgPD3IL6/BUH4M4hg6ncSH8aC8N/x7IFnOVd5jkXDF+Eg03Eq6wJ50kwCHQNxs3Pjk3OfUN1SzRcpX3RIOV2lryKzJhO7tmj2ZlUxLMaDXekVRPs6EuVnws3ODaPFyOqs1UiQ0NmlM2UtZZQ1lxEkH807Oy5y16hGjudXcqG4Gbn9YT4f/AF2Dg6cK6rD10mDm8PfZyHvNpOZx9ZOZJLPWHr1mkFGdQYHSw5ye+ztv+s436V/R2VzJU2mJh7o+gAOjVWw/FaInYSp52xSVqSwoPPddD27indkt2LvHcodl+4DlxBwCeOHNafpFq4hzLkZKxLy+z2EQammydTEC4deYN2EdZTX1CNVqPHQ/Uu2RHMb1ndiYeIXSIJ6AbbAtsVoxtleeXVlgcKaZhxUCpzslVzMK+GelRnM7h3AxB6/LdPlxpyNdPPsJoKmfxDx/S0Iwp9BBFO/k/gwFoQ/z4nSE2TUZjAtZhoHiw5ixUo3z54s3ZfN4vK7eWPAfBJ9bD0VmTWZNLQ24Kh2JFAXyMHigxwuOczTPZ7mRF4Ns789yeEnB3Mku5qD2VU8MiySFSeLGB3vQ72pnNePvo5WpSWzOpPObp3Rt+lZ0H8Bza1tXKpPpbS5nFUn0+l2IBVnN3e6DB9LWLfkv7iFrm3d8R/oGTUId4ffltGwqbWN59Zc4LHhkXg5XklesatgFwuOL2Dl2JVoFBo4sxQihsKl3VR5ROLmGgbfTWKx+mZKdLE86XMW0tZB+FAqNTE4evqTv3sJMkMNAZYM5NNXUVLfQm5lHb3DfnlNpc/W7ETiHMDM/uG/Wv95G9PRVZ5kuvE7tg+YycSIiQBUNbZS3JRLsIsPOpX4fBY6Et/fgiD8GcScKUEQ/hYajY28ffJt6ox1TIyYSG+/3gAU1+rZkVvLFxO+Q6U0c7zsOEleSSilSsr15bx67FUeiX+M0m1Wkvr25ETpCRqkDay9pw8KmRRvJztifHS0mMxsTy+jd5gr/i6+vDf4PcCW7jqtKo28hjxKmkrYVbCLaTHTiAfc7VzpNPYOmiur0bn/vtTr/01jk6b87L7MmkwinCPae+/MZgs7Fp6jS4QWO0XHr4BBAYPo7tXdFkgBJNxA2aWL7PxqAzeOzYMBncErjt7WGhqcy8DQAPUFUHgUd+shsLsZh/Akzhw+TaHal/7A+aI6dqZV/GowNah3MpprLIB8LSM6e+Eo6U9thYkKfQUALU2NfLq/iD3ND5ASlMKs8kJMMeNpbnHBKzwShfLv05soCIIg/O8QPVO/k7izJQh/nh/nSP2cLblbOFRyCBe1C7l1uTSaGknySmJ23GyyTpQTFOfG+rzN6M0NTIm6kdpmIx52kF+fj15iIdo1GoCVGWuJ9+zUvvjxgmMLcFA4MDhwMMsuLuPpHk+3nzO9Kp3mtma6eXX7xbqnrn0LXfQA/CN+eY7Pf8v84/Px1fjy/tn3WTpqaYesidmnKvCLdkFl95NgymIGYzOoO36uGQ0tlKSeIahLMhQchgsrIf8wjFwADUW2dZPOLQWsoK+B7nfS6NcfrODgYsuW+GPGQalEyp/hcFoBx16fw6S5r1NjZyDQKYALm+4lx84X/VkNPUZOIi4u8k85t/D/h/j+FgThz/DnfLMJgiD8AT8GUiaziWcOPEN+Q36H/cODhzOj8wy0Ci0quQqlVGnrdZFKMIZU8WXG52w85kBJUSQfHl3Jw+s/gu+v57l9j/F12tcAlBdkEb73YwqqCtqPe6zsGB5aD8JdwjsEUgCpNamcrjgNwK6MAh5evRGTxXRV3RX6ctr0Df/R9vh31BvqabW0smPSjg6BFEBookfHQApg4xxYPOaq4yjVdgR17Wlb2DdtLfglwY1LILgPxE+xrT/VUguj3qZ42Ke8U94ZjYOOMzu3cKLgKPsL9zP/2Hye2PUoRRmpf8pr9XBzIXTGk/gEBRPrFcPRnCbeLp9FTrozTTpXjtXLWX+2+E85tyAIgvDPJob5CYLwtyOTyoh0iUSruHoB1CDHIEYEj+DDsx/ywaAPeGL/E/Tx64NCqkAp1VCvt9JCKcl+ngRoreDwKM+oVPi4dwLAzkGBb9BA4kL6tR/zjQFv4KO9OkOfvrGBSRGT2h9LpGZarfW0WdpQSDumFo+48fWrX4i+FvL2Q8zYP9oUf9i8vvN+3xN63g+1Ob9cZuR8yN4FDrakDXsK95BWfZq7rRYoPkWVNAZDiwGrxUqJpYKPDn3Ki71fpKtnV/yadKTt3YVfVKc/9oJ+QaiHllCPKz2Cg6I8UculfHdUSWl9C0/7OrLqdDHvbL9IiuMeunqHUxRsZnqn6e3PqarKwk7njb1SLLorCIIg/HZimN/vJIYJCMKfr6G+hrTDe0hOmdAhU99PFTUW4efgR1VLFW52bgCYzBZ+OJbPhC5+aNW2YCe7vInP9mczMNKDlFhvxq28iYE+4+juMYBeoT8/D+rCpeM8cexZPh/xBV7Of3D9oOLTsPc1mLLE1rPz/52pBT4fStPgZzhSfxH/gL7kNxTinw8FF05iMMkINJwi4fq7wL8brSpth/XCfqsl6UtYmbWSFWNX/OGqjnx3H546NQ8NiSDO3wmDyczn+9PJyX2fcIeuVPiV8mzPZ9uHHja/n8iF+Akk93v2D59T+HsT39+CIPwZRDD1O4kPY0H483174FPqtp9m9tPvIldeOzX2T63O3MjGzNMsGPwYThoFx3OrSQp2RSKRcM93J6nTm+gd5srdA8NZfe4chhYNq1PPMnuIM4MCrrHmEWA0G9mRvokRncb9bED37zKZLSjW3QNdb4WAv2emwGvJTF3OS1nf0d0+iPurKkjXdMeUvge7lIcJdVEh3fgQBPZuX+D39ypsKGR/8X6mRk/99cI/43huFa72akI8OvY0LTueT7iHPV0C3Tpsby47j8IlGKXomfqfJb6/BUH4M/wP3CoVBOF/zVlzFj43Df1NgRRAmHMIwdoYVAop5Q0GXj7+DN8d/xSA/hEe3Ds4jLsH2lJuT4iJ4sYgI1O6huMq7cwTaw7x4cHdDHxjJ1+fXdt+TKVMycjO4/+0QKqoppnh7+zhpNt4cPL/U87xZ4nsNJmXBrxNiH9vaG0i2tmC7s4PuWWLiSxVNNzwDQx5/g8f31/n/28FUt9eWMJru7ax6kze1XWvS6N0/eKrttt7xYpAShAEQfjdxJwpQRD+dhb0/309GrEe0cQOsmXq0yjlvBrRG8fcQ0w6eZAuAc64/HTh14JDsOlR8iK+xWxqpaD5HI6uKm7t1wm1qvY/+TJ+0bzVJwl1c6Bzz0SQ/7aU4H8nwU7BBDsFQ+gYinNyaM3M5b0pXQh114Lsr73rH6ZNArtvuTF5+FX7QpN64BMR9RfUShAEQfhfJIb5/U5imIAg/P9gNpmYs/ICs/uFEuXzL3+rrU2gsvVC3P39Ie7oG04Xf/f/Wt3aLG2YF/bF0Pc5HONG/NfO+2fJOnaIuvIyksZc91dXRRB+lvj+FgThzyCG+QmC8D+h2dSMxWppfyxTKHhnSpcOgZTVauX9w+uoNF0Zutc31BdvRy1NxibKm8v/8PnTNn9M2varh49dS1FjES96eWIN/nusSfXvCu/eSwRSgiAIwj+SCKYEQfif8ODuB9mSt+UXy1Tqa9lY+CUZlVfWmLoxORAvnR1rLq3hjRNv/PEKKO2RqjS/qWiQYxAvXbcKp8spxn+LuqpLNL4bR3nxiT9aQ0EQBEEQ/sPEML/fSQwTEIS/p7z6PDw0HmgUvy2g+Vcms4kWcws6pe3v2mq1csvmW5jTdQ4Jngn/wZr+MVaLmewTnxCUOAO5XP1XV0cQ/t8R39+CIPwZRDD1O4kPY0H45zhScoRY91jsFfZ/dVUEQfg3ie9vQRD+DCKbnyAIws/o4dPjr66CIAiCIAh/Y2LOlCAIgiAIgiAIwh8ggilBEP6RChsKqdJX/dXV+Fv5/mgeZwr+e2ttCYIgCML/dyKYEgThH+npA0/z4pEX//0DGZvh8Idgavn3j/UX23CujO1pfzw9vCAIgiD804g5U4Ig/CPN6z0PlVz1bx+nqamRaYeCWeBTS3ig3X+gZn+d7+8Qc8QEQRAE4fcQPVOCIPwj+Tv642Hv8W8fR+vixcSuLVTX7/u3j/XSulSeWHH23z6OIAiCIAj/HSKYEgRB+DcVNa1nR+EvLxj8WzS0mmgwtP0HaiQIgiAIwn+DWGfqdxLrVAiCcC1WqxWJRPJXV0MQhJ8hvr8FQfgziJ4pQRCE/wARSAmCIAjCP48IpgRB+Ef58kAO93x/6q+uhiAIgiAI/wNEMCUIwj+Kh4MKL4d/P4ufIAiCIAiCSI0uCMI/yuh4X0bH+/7V1RAEQRAE4X+A6JkSBEEQBEEQBEH4A0QwJQiCIAiCIAiC8AeIYEoQBEEQBEEQBOEPEMGUIAiCIAiCIAjCHyCCKUEQBEEQBEEQhD9ABFOCIAiCIAiCIAh/gAimBEEQBEEQBEEQ/gARTAmCIAiCIAiCIPwBIpgSBEEQBEEQBEH4A0QwJQiCIAiCIAiC8AeIYEoQBEEQBEEQBOEPEMGUIAiCIAiCIAjCHyCCKUEQBEEQBEEQhD9ABFOCIAiCIAiCIAh/gPyvrsD/N1arFYCGhoa/uCaCIAiCIPxWP35v//g9LgiC8J8ggqnfqbGxEQB/f/+/uCaCIAiCIPxejY2NODo6/tXVEAThf4TEKm7R/C4Wi4WSkhIcHByQSCR/dXV+k4aGBvz9/SksLESn0/3V1fnbEO1yNdEm1yba5dpEu1ybaJer/R3axGq10tjYiI+PD1KpmOUgCMJ/huiZ+p2kUil+fn5/dTX+EJ1OJ77Yr0G0y9VEm1ybaJdrE+1ybaJdrvZXt4nokRIE4T9N3JoRBEEQBEEQBEH4A0QwJQiCIAiCIAiC8AeIYOofQKVS8fzzz6NSqf7qqvytiHa5mmiTaxPtcm2iXa5NtMvVRJsIgvC/SiSgEARBEARBEARB+ANEz5QgCIIgCIIgCMIfIIIpQRAEQRAEQRCEP0AEU4IgCIIgCIIgCH+ACKYEQRAEQRAEQRD+ABFM/Q+ZN28evXr1QqPR4OTkdM0yEonkqn8//PBDhzJ79uwhMTERlUpFWFgYX3311Z9f+T/Rb2mXgoICRo0ahUajwcPDg0cffZS2trYOZf7X2uVfBQUFXXVtvPbaax3KnDt3jr59+6JWq/H392f+/Pl/UW3/uz788EOCgoJQq9UkJydz7Nixv7pK/zVz58696rqIiopq328wGLjnnntwdXVFq9UyceJEysvL/8Ia/zn27dvHmDFj8PHxQSKRsGbNmg77rVYrzz33HN7e3tjZ2TFkyBCysrI6lKmpqeGmm25Cp9Ph5OTE7bffTlNT03/xVfzn/Vq73HrrrVddP8OHD+9Q5n+xXQRB+OcQwdT/EKPRyOTJk7nrrrt+sdyiRYsoLS1t/zd+/Pj2fbm5uYwaNYqBAwdy5swZHnzwQWbOnMnWrVv/5Nr/eX6tXcxmM6NGjcJoNHLo0CEWL17MV199xXPPPdde5n+xXa7lxRdf7HBt3Hfffe37GhoaGDZsGIGBgZw8eZIFCxYwd+5cPv3007+wxn++pUuX8vDDD/P8889z6tQp4uPjSUlJoaKi4q+u2n9Np06dOlwXBw4caN/30EMPsX79epYvX87evXspKSnhuuuu+wtr++dobm4mPj6eDz/88Jr758+fz3vvvcfHH3/M0aNHsbe3JyUlBYPB0F7mpptuIjU1le3bt7Nhwwb27dvHrFmz/lsv4U/xa+0CMHz48A7Xz5IlSzrs/19sF0EQ/kGswv+cRYsWWR0dHa+5D7CuXr36Z5/72GOPWTt16tRh2w033GBNSUn5D9bwr/Fz7bJp0yarVCq1lpWVtW9buHChVafTWVtbW61W6/92u/woMPD/2rv3mKbONw7g34K0WhGKcilgYDAVdwG5LDTN3JYgCCwhZM6EMSNMF53K5h8yMx0DRXFiUMg0012RP7aMqVmy/aFkjMsyR0c2RucFJIJcpgGMbA3ocHJ5fn8snp8dDF0HFMr3k5x4eM973vO8T6HytKcvgVJUVPSPx48ePSoeHh5KTkRE3njjDQkJCZmE6OwnOjpaMjIylK+HhobEz89P9u/fb8eoJs+uXbtk2bJlox6zWCzi4uIiJ0+eVNoaGxsFgJhMpkmKcPL9/Xl0eHhY9Hq9FBQUKG0Wi0U0Go189tlnIiLS0NAgAOTHH39U+pw5c0ZUKpVcu3Zt0mKfSKP9/5Keni7Jycn/eM5MyAsROTa+MzUDZWRkwNPTE9HR0SguLobc86fGTCYTYmNjrfrHx8fDZDJNdpiTxmQyITQ0FD4+PkpbfHw8ent7cfHiRaXPTMhLfn4+FixYgIiICBQUFFjd6mgymfD0009DrVYrbfHx8WhqasLvv/9uj3An3J07d1BXV2f12Ds5OSE2NtbhHvuxXL58GX5+fggODsaaNWvQ0dEBAKirq8PAwIBVfpYuXYqAgIAZlZ/W1lZ0dXVZ5cHd3R0Gg0HJg8lkgk6nwxNPPKH0iY2NhZOTE2prayc95slUXV0Nb29vhISEYPPmzejp6VGOzeS8EJFjmGXvAGhy7dmzBzExMdBqtfj666+xZcsW3Lx5E1u3bgUAdHV1WRUVAODj44Pe3l709/djzpw59gh7Qv3TnO8eG6uPI+Vl69atiIyMxPz581FTU4OdO3eis7MThYWFAP7KQVBQkNU59+bJw8Nj0mOeaDdu3MDQ0NCoj/2lS5fsFNXkMhgMKCkpQUhICDo7O5Gbm4unnnoKFy5cQFdXF9Rq9YjPIvr4+Cg/OzPB3bmO9n1y73OIt7e31fFZs2Zh/vz5Dp2rhIQErFq1CkFBQWhpacGbb76JxMREmEwmODs7z9i8EJHjYDE1xe3YsQMHDhwYs09jY6PVB8LHkp2drexHRETg1q1bKCgoUIqp6WK88+Ko/k2etm3bprSFhYVBrVbjlVdewf79+6HRaCY6VJqiEhMTlf2wsDAYDAYEBgbixIkTDvEiAk2sF154QdkPDQ1FWFgYHn74YVRXV2PFihV2jIyIaHywmJriMjMz8dJLL43ZJzg42ObxDQYD9u7diz///BMajQZ6vX7ESlzd3d1wc3ObUr84jWde9Hr9iNXZ7uZAr9cr/06HvPzdf8mTwWDA4OAg2traEBIS8o85AP6fJ0fj6ekJZ2fnUeftqHO+H51OhyVLlqC5uRlxcXG4c+cOLBaL1btTMy0/d+fa3d0NX19fpb27uxvh4eFKn78vWjI4OIjffvttRuUqODgYnp6eaG5uxooVK5gXIpr2WExNcV5eXvDy8pqw8c1mMzw8PJR3HoxGI06fPm3Vp7y8HEajccJisMV45sVoNGLfvn24fv26crtJeXk53Nzc8Oijjyp9pkNe/u6/5MlsNsPJyUnJidFoRFZWFgYGBuDi4gLgrxyEhIQ45C1+AKBWqxEVFYWKigpl1cvh4WFUVFTg1VdftW9wdnLz5k20tLRg7dq1iIqKgouLCyoqKvD8888DAJqamtDR0THlfzbGU1BQEPR6PSoqKpTiqbe3F7W1tcoqokajERaLBXV1dYiKigIAVFZWYnh4GAaDwV6hT7qrV6+ip6dHKTqZFyKa9uy9AgaNn/b2dqmvr5fc3FxxdXWV+vp6qa+vl76+PhER+eqrr+TDDz+U8+fPy+XLl+Xo0aOi1WolJydHGePKlSui1Wpl+/bt0tjYKO+++644OztLWVmZvab1n90vL4ODg/L444/LypUrxWw2S1lZmXh5ecnOnTuVMRwxL/eqqamRoqIiMZvN0tLSIp988ol4eXlJWlqa0sdisYiPj4+sXbtWLly4IKWlpaLVauX999+3Y+QTr7S0VDQajZSUlEhDQ4Ns3LhRdDqd1eqPjiwzM1Oqq6ultbVVvv/+e4mNjRVPT0+5fv26iIhs2rRJAgICpLKyUn766ScxGo1iNBrtHPX46+vrU547AEhhYaHU19dLe3u7iIjk5+eLTqeTL7/8Us6dOyfJyckSFBQk/f39yhgJCQkSEREhtbW1cvbsWVm8eLGkpqbaa0rjYqy89PX1yeuvvy4mk0laW1vlm2++kcjISFm8eLHcvn1bGcMR80JEMweLKQeSnp4uAEZsVVVVIvLXcrPh4eHi6uoqc+fOlWXLlsl7770nQ0NDVuNUVVVJeHi4qNVqCQ4OluPHj0/+ZMbR/fIiItLW1iaJiYkyZ84c8fT0lMzMTBkYGLAax9Hycq+6ujoxGAzi7u4us2fPlkceeUTefvttq194RER++eUXWb58uWg0GvH395f8/Hw7RTy5jhw5IgEBAaJWqyU6Olp++OEHe4c0aVJSUsTX11fUarX4+/tLSkqKNDc3K8f7+/tly5Yt4uHhIVqtVp577jnp7Oy0Y8QTo6qqatTnkfT0dBH5a3n07Oxs8fHxEY1GIytWrJCmpiarMXp6eiQ1NVVcXV3Fzc1N1q1bp7yoM12NlZc//vhDVq5cKV5eXuLi4iKBgYGyYcOGES9EOGJeiGjmUIncsy42ERERERERPRD+nSkiIiIiIiIbsJgiIiIiIiKyAYspIiIiIiIiG7CYIiIiIiIisgGLKSIiIiIiIhuwmCIiIiIiIrIBiykiIiIiIiIbsJgiIiIiIiKyAYspIiIiIiIiG7CYIqJpQ6VSjbnt3r0bbW1tUKlU8Pb2Rl9fn9X54eHh2L17t/J1a2srXnzxRfj5+WH27NlYuHAhkpOTcenSpftes7S0dNQYT58+DbVajZ9//tmq/dChQ/D09ERXV9f4JYSIiIjsapa9AyAielCdnZ3K/ueff46cnBw0NTUpba6urrhx4wYAoK+vDwcPHkRubu6oYw0MDCAuLg4hISH44osv4Ovri6tXr+LMmTOwWCxWfY8fP46EhASrNp1ON+q4zz77LNLS0pCWloa6ujpoNBo0NDTgrbfeQklJCfR6vQ0zJyIioqmIxRQRTRv3FiLu7u5QqVQjipO7xdRrr72GwsJCZGRkwNvbe8RYFy9eREtLCyoqKhAYGAgACAwMxJNPPjmir06n+1dFUFFREUJDQ7Fr1y7k5eUhPT0dSUlJSElJeeAxiIiIaOrjbX5E5JBSU1OxaNEi7NmzZ9TjXl5ecHJywqlTpzA0NDSu1543bx6Ki4tx6NAhrFmzBr/++iuOHTs2rtcgIiIi+2MxRUQOSaVSIT8/Hx988AFaWlpGHPf398fhw4eRk5MDDw8PxMTEYO/evbhy5cqIvqmpqXB1dbXaOjo6xrx+TEwMVq9ejRMnTuDw4cNYsGDBuM2NiIiIpgYWU0TksOLj47F8+XJkZ2ePejwjIwNdXV349NNPYTQacfLkSTz22GMoLy+36ldUVASz2Wy1+fn5AYBVgbVp0yblnGvXrqGsrAxarRbffffdxE2SiIiI7IafmSIih5afnw+j0Yjt27ePenzevHlISkpCUlIS8vLyEB8fj7y8PMTFxSl99Ho9Fi1aNOr5ZrNZ2Xdzc1P2N2zYgKioKGRlZSEuLg6rV6/GM888Mz6TIiIioimBxRQRObTo6GisWrUKO3bsuG9flUqFpUuXoqam5oHHH63I+uijj3D27FmcP38egYGB2Lx5M9avX49z585h7ty5/yp+IiIimrp4mx8RObx9+/ahsrLSahl1s9mM5ORknDp1Cg0NDWhubsbHH3+M4uJiJCcnW51vsVjQ1dVltd26dWvUa7W3t2Pbtm04ePCgskrggQMHoFKpHqigIyIioumDxRQRObwlS5Zg/fr1uH37ttK2cOFCPPTQQ8jNzYXBYEBkZCTeeecd5ObmIisry+r8devWwdfX12o7cuTIiOuICF5++WUYjUZs3LhRaddqtSgpKcGxY8fw7bffTtxEiYiIaFKpRETsHQQREREREdF0w3emiIiIiIiIbMBiioiIiIiIyAYspoiIiIiIiGzAYoqIiIiIiMgGLKaIiIiIiIhswGKKiIiIiIjIBiymiIiIiIiIbMBiioiIiIiIyAYspoiIiIiIiGzAYoqIiIiIiMgGLKaIiIiIiIhs8D/XjT9XxgxocgAAAABJRU5ErkJggg==\n" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "wos_plot = wos_nlp.merge(tnse_data, on=record_col)\n", + "\n", + "g = sns.scatterplot(wos_plot[wos_plot[\"Domain_English\"] != 'article-level classification'], x=\"TNSE-X\", y=\"TNSE-Y\",\n", + " hue='Domain_English', s=1)\n", + "g.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n", + "wos_plot.head()\n", + "wos_nlp = wos_plot[[record_col, \"Document\", \"keyword_all\", \"TNSE-X\", \"TNSE-Y\"]]\n" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 40, + "outputs": [], + "source": [ + "\n", + "wos_nlp.to_excel(f\"{outdir}/wos_nlp.xlsx\", index=False)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 37, + "outputs": [ + { + "data": { + "text/plain": "Index(['UT (Unique WOS ID)', 'Document', 'keyword_all', 'TNSE-X', 'TNSE-Y'], dtype='object')" + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wos_nlp.columns" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "import spacy\n", + "\n", + "nlp = spacy.load(\"en_core_web_lg\")\n", + "kwd_nlp = pd.DataFrame(kw_df[\"keyword_all\"].drop_duplicates())\n", + "# wos_kwd_test[\"BERT_KWDS\"] = wos_kwd_test[\"Document\"].map(kwd_extract)\n", + "\n", + "vectors = list()\n", + "vector_norms = list()\n", + "\n", + "for doc in nlp.pipe(kwd_nlp['keyword_all'].astype('unicode').values, batch_size=300,\n", + " n_process=4):\n", + " vectors.append(doc.vector)\n", + " vector_norms.append(doc.vector_norm)\n", + "\n", + "kwd_nlp['vector'] = vectors\n", + "kwd_nlp['vector_norm'] = vector_norms\n", + "kwd_nlp['vector_norm'].plot(kind=\"hist\")" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} \ No newline at end of file