Appendix S9: Codes for used statistical analyses - ULTRASOUND

Appendix S10: Codes for used statistical analyses

Title: A workflow to optimize spatial sampling in ecoacoustic studies
Journal: Landscape Ecology

Authors:
Martínez-Arias, V. M.; Paniagua-Villada, C.; Daza, J. M.

Author affiliations at the time the work was conducted:
1. Grupo Herpetológico de Antioquia, Calle 70 No. 52 - 21, Postal Code 050010, Medellín, Colombia.

Corresponding author:
Victor M. Martínez-Arias
📧 Email: vmanuel.martinez@udea.edu.co

0. Exploration graphic

This graphic allow to visually compare the indices in the range 0-24khz, for all hours and localities.

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```r
library(ggplot2)
library(readxl)
library(dplyr)

Statistic <- \mean\  # Set to \mean\ or \median\
# Load data (replace path with your own)
data <- read_excel(\#/DF1-1_Interpolations_variograms_and_error_for_subsamples.xlsx\)
# Filter based on combined conditions
filtered_data <- data %>%
  filter(
    (Index %in% c(\BI\, \NDSI\) & Statistic == Statistic) |
    (Index %in% c(\ACItf\, \ACIft\, \NP\) &
     Frequency_Range == \R0-24\ & Statistic == \mean\)
  ) %>%
  mutate(
    Subsample_Color = case_when(
      Subsample == \SLIC\ ~ \SLIC\,
      Subsample == \halfbasins\ ~ \HalfBasins\,
      Subsample == \basins\ ~ \Basins\,
      Subsample == \Landcover\ ~ \Landcover\,
      TRUE ~ \Random\
    )
  )
# Add synthetic category \ALL\
data_all <- filtered_data %>%
  mutate(Locality = \ALL\)
# Merge both datasets
complete_data <- bind_rows(filtered_data, data_all)
# Create plot
plot <- ggplot(complete_data, aes(x = Sample_percent, y = Pearson_Correlation)) +
  geom_point(data = filter(complete_data, Subsample_Color == \Random\),
             aes(color = Subsample_Color),
             alpha = 0.25, size = 1) +
  geom_smooth(method = \lm\, color = \blue\, se = FALSE, linewidth = 0.8) + 
  geom_point(data = filter(complete_data, Subsample_Color != \Random\),
             aes(color = Subsample_Color), size = 2) +
  scale_color_manual(
    values = c(
      \SLIC\ = \purple\,
      \HalfBasins\ = \skyblue\,
      \Basins\ = \navy\,
      \Random\ = \gray50\,
      \Landcover\ = \green\
    )
  ) +
  facet_grid(Locality ~ Index) +
  theme_minimal(base_size = 12) +
  labs(
    x = \Sample percent\,
    y = \Subsample_performance\,
    color = \Sampling method\
  ) +
  theme(
    legend.position = \right\,
    axis.text = element_text(size = 10),
    strip.text = element_text(size = 12, face = \bold\)
  )
# Save as PDF (set your desired path)
ggsave(
  filename = \#/0_Subsampleperformance_vs_SamplePercent_mean.pdf\,
  plot = plot,
  width = 15.27,
  height = 8.15,
  units = \in\
)

print(plot)

```

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```r
library(ggplot2)
library(readxl)
library(dplyr)

Statistic <- \mean\  # Set to \mean\ or \median\
# Load data (replace path with your own)
data <- read_excel(\#/DF1-1_Interpolations_variograms_and_error_for_subsamples.xlsx\)
# Filter based on combined conditions
filtered_data <- data %>%
  filter(
    (Index %in% c(\BI\, \NDSI\) & Statistic == Statistic) |
    (Index %in% c(\ACItf\, \ACIft\, \NP\) &
     Frequency_Range == \R0-24\ & Statistic == \mean\)
  ) %>%
  mutate(
    Subsample_Color = case_when(
      Subsample == \SLIC\ ~ \SLIC\,
      Subsample == \halfbasins\ ~ \HalfBasins\,
      Subsample == \basins\ ~ \Basins\,
      Subsample == \Landcover\ ~ \Landcover\,
      TRUE ~ \Random\
    )
  )
# Add synthetic category \ALL\
data_all <- filtered_data %>%
  mutate(Locality = \ALL\)
# Merge both datasets
complete_data <- bind_rows(filtered_data, data_all)
# Create plot
plot <- ggplot(complete_data, aes(x = Sample_percent, y = Pearson_Correlation)) +
  geom_point(data = filter(complete_data, Subsample_Color == \Random\),
             aes(color = Subsample_Color),
             alpha = 0.25, size = 1) +
  geom_smooth(method = \lm\, color = \blue\, se = FALSE, linewidth = 0.8) + 
  geom_point(data = filter(complete_data, Subsample_Color != \Random\),
             aes(color = Subsample_Color), size = 2) +
  scale_color_manual(
    values = c(
      \SLIC\ = \purple\,
      \HalfBasins\ = \skyblue\,
      \Basins\ = \navy\,
      \Random\ = \gray50\,
      \Landcover\ = \green\
    )
  ) +
  facet_grid(Locality ~ Index) +
  theme_minimal(base_size = 12) +
  labs(
    x = \Sample percent\,
    y = \Subsample_performance\,
    color = \Sampling method\
  ) +
  theme(
    legend.position = \right\,
    axis.text = element_text(size = 10),
    strip.text = element_text(size = 12, face = \bold\)
  )
# Save as PDF (set your desired path)
ggsave(
  filename = \#/0_Subsampleperformance_vs_SamplePercent_mean.pdf\,
  plot = plot,
  width = 15.27,
  height = 8.15,
  units = \in\
)

print(plot)

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# 0.1 Subsampling percent of the initial sample
This section summarizes the effective sampling effort associated with each spatial subsampling strategy, expressed as the percentage of recorder locations retained relative to the full sampling grid deployed at each locality.

We use a unified database that records, for every combination of locality, acoustic index, frequency band, and time period, the number of recorder locations selected under each sampling strategy (Basins, Halfbasins, Landcover, SLIC, and random subsamples at 20%, 40%, 60%, and 80%). Because recordings affected by rain or corrupted files were removed prior to temporal aggregation, the effective number of recorder locations may vary slightly across frequency bands and time periods. These variations are minimal and reflect quality-control procedures rather than differences in spatial design. To facilitate comparison across strategies and localities, the number of retained recorder locations is normalized by the total number of available locations per locality, yielding a percentage-based measure of sampling effort. This normalization allows direct assessment of how efficiently each strategy represents the landscape relative to the full grid.

The first output of this section is a faceted plot summarizing the distribution of sampling effort across strategies and localities. For each strategy, boxplots represent variation across indices, frequency bands, and time periods; points indicate median values, and vertical lines show the minimum–maximum range. This visualization supports direct comparison of how many recorder locations are required by each strategy to achieve comparable spatial representation.

The second output is a summary table reporting the mean and standard deviation of the percentage of recorder locations retained by each strategy in each locality. This table complements the graphical summary by providing concise numerical descriptors of sampling efficiency and stability, and is referenced in the Supplementary Information.

Graphic:

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# ===============================
# Libraries
# ===============================
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)

# ===============================
# Load data
# ===============================
df <- read_excel(\~/Dropbox/Doctorate project - VMMA/Thesis-associated_papers/P001_Sampling_design/Online_Resoures-SUPPLEMENTARY_INFORMATION/Supplementary_information_S12_Associated_Databases/Sampling_count_database.xlsx\)

# ===============================
# Wide → Long
# ===============================
df_long <- df %>%
  pivot_longer(
    cols = c(
      basins,
      halfbasins,
      Landcover,
      SLIC,
      ssample20,
      ssample40,
      ssample60,
      ssample80
    ),
    names_to = \Strategy\,
    values_to = \Num_Points\
  )

# ===============================
# Normalize to percentage
# ===============================
df_long <- df_long %>%
  mutate(
    Percent_Used = (Num_Points / Num_Points_Total) * 100
  )

# ===============================
# Strategy order
# ===============================
df_long$Strategy <- factor(
  df_long$Strategy,
  levels = c(
    \basins\,
    \halfbasins\,
    \Landcover\,
    \SLIC\,
    \ssample20\,
    \ssample40\,
    \ssample60\,
    \ssample80\
  )
)

# ===============================
# Plot (FACETED)
# ===============================
p <- ggplot(df_long, aes(x = Locality, y = Percent_Used)) +

  # Rango min–max (muy sutil)
  stat_summary(
    fun.min = min,
    fun.max = max,
    geom = \linerange\,
    linewidth = 0.3,
    color = \grey40\
  ) +

  # Boxplot fino
  geom_boxplot(
    width = 0.18,
    color = \black\,
    fill = \white\,
    linewidth = 0.35,
    outlier.shape = NA
  ) +

  # Mediana
  stat_summary(
    fun = median,
    geom = \point\,
    size = 1.6,
    color = \black\
  ) +

  facet_wrap(~ Strategy, ncol = 2) +

  labs(
    x = \Locality\,
    y = \Recorder locations used (% of full grid)\
  ) +

  theme_classic(base_family = \Times New Roman\) +
  theme(
    strip.text = element_text(size = 12, face = \bold\),
    axis.text = element_text(size = 11),
    axis.title = element_text(size = 13),
    panel.spacing = unit(0.8, \lines\)
  )

print(p)

ggsave(
  \Subsampling_efficiency_box_range.png\,
  p,
  width = 8,
  height = 10,
  dpi = 300
)

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-->


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```r
```r
# ===============================
# Libraries
# ===============================
library(readxl)
library(dplyr)
library(tidyr)
library(ggplot2)

# ===============================
# Load data
# ===============================
df <- read_excel(\~/Dropbox/Doctorate project - VMMA/Thesis-associated_papers/P001_Sampling_design/Online_Resoures-SUPPLEMENTARY_INFORMATION/Supplementary_information_S12_Associated_Databases/Sampling_count_database.xlsx\)

# ===============================
# Wide → Long
# ===============================
df_long <- df %>%
  pivot_longer(
    cols = c(
      basins,
      halfbasins,
      Landcover,
      SLIC,
      ssample20,
      ssample40,
      ssample60,
      ssample80
    ),
    names_to = \Strategy\,
    values_to = \Num_Points\
  )

# ===============================
# Normalize to percentage
# ===============================
df_long <- df_long %>%
  mutate(
    Percent_Used = (Num_Points / Num_Points_Total) * 100
  )

# ===============================
# Strategy order
# ===============================
df_long$Strategy <- factor(
  df_long$Strategy,
  levels = c(
    \basins\,
    \halfbasins\,
    \Landcover\,
    \SLIC\,
    \ssample20\,
    \ssample40\,
    \ssample60\,
    \ssample80\
  )
)

# ===============================
# Plot (FACETED)
# ===============================
p <- ggplot(df_long, aes(x = Locality, y = Percent_Used)) +

  # Rango min–max (muy sutil)
  stat_summary(
    fun.min = min,
    fun.max = max,
    geom = \linerange\,
    linewidth = 0.3,
    color = \grey40\
  ) +

  # Boxplot fino
  geom_boxplot(
    width = 0.18,
    color = \black\,
    fill = \white\,
    linewidth = 0.35,
    outlier.shape = NA
  ) +

  # Mediana
  stat_summary(
    fun = median,
    geom = \point\,
    size = 1.6,
    color = \black\
  ) +

  facet_wrap(~ Strategy, ncol = 2) +

  labs(
    x = \Locality\,
    y = \Recorder locations used (% of full grid)\
  ) +

  theme_classic(base_family = \Times New Roman\) +
  theme(
    strip.text = element_text(size = 12, face = \bold\),
    axis.text = element_text(size = 11),
    axis.title = element_text(size = 13),
    panel.spacing = unit(0.8, \lines\)
  )

print(p)

ggsave(
  \Subsampling_efficiency_box_range.png\,
  p,
  width = 8,
  height = 10,
  dpi = 300
)

```
```

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Table:

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<!-- rnb-output-begin 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-->

# ===============================
# Libraries
# ===============================
library(readxl)
library(dplyr)
library(tidyr)
library(openxlsx)

# ===============================
# Load data
# ===============================
df <- read_excel(
  \~/Dropbox/Doctorate project - VMMA/Thesis-associated_papers/P001_Sampling_design/Online_Resoures-SUPPLEMENTARY_INFORMATION/Supplementary_information_S12_Associated_Databases/Sampling_count_database.xlsx\
)

# ===============================
# Wide → Long
# ===============================
df_long <- df %>%
  pivot_longer(
    cols = c(
      basins,
      halfbasins,
      Landcover,
      SLIC,
      ssample20,
      ssample40,
      ssample60,
      ssample80
    ),
    names_to = \Strategy\,
    values_to = \Num_Points\
  )

# ===============================
# Normalize to percentage
# ===============================
df_long <- df_long %>%
  mutate(
    Percent_Used = (Num_Points / Num_Points_Total) * 100
  )

# ===============================
# Summary table: mean & SD
# ===============================
summary_table <- df_long %>%
  group_by(Locality, Strategy) %>%
  summarise(
    Mean_Percent = mean(Percent_Used, na.rm = TRUE),
    SD_Percent   = sd(Percent_Used, na.rm = TRUE),
    .groups = \drop\
  ) %>%
  arrange(Strategy, Locality)

# ===============================
# Export to Excel
# ===============================
output_path <- \Sampling_efficiency_summary_mean_SD.xlsx\

write.xlsx(
  summary_table,
  file = output_path,
  overwrite = TRUE
)

cat(\✅ Summary table exported to:\, output_path, \\n\)

<!-- rnb-output-end -->

<!-- rnb-output-begin 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-->


<!-- rnb-source-begin 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 -->

```r
```r
# ===============================
# Libraries
# ===============================
library(readxl)
library(dplyr)
library(tidyr)
library(openxlsx)

# ===============================
# Load data
# ===============================
df <- read_excel(
  \~/Dropbox/Doctorate project - VMMA/Thesis-associated_papers/P001_Sampling_design/Online_Resoures-SUPPLEMENTARY_INFORMATION/Supplementary_information_S12_Associated_Databases/Sampling_count_database.xlsx\
)

# ===============================
# Wide → Long
# ===============================
df_long <- df %>%
  pivot_longer(
    cols = c(
      basins,
      halfbasins,
      Landcover,
      SLIC,
      ssample20,
      ssample40,
      ssample60,
      ssample80
    ),
    names_to = \Strategy\,
    values_to = \Num_Points\
  )

# ===============================
# Normalize to percentage
# ===============================
df_long <- df_long %>%
  mutate(
    Percent_Used = (Num_Points / Num_Points_Total) * 100
  )

# ===============================
# Summary table: mean & SD
# ===============================
summary_table <- df_long %>%
  group_by(Locality, Strategy) %>%
  summarise(
    Mean_Percent = mean(Percent_Used, na.rm = TRUE),
    SD_Percent   = sd(Percent_Used, na.rm = TRUE),
    .groups = \drop\
  ) %>%
  arrange(Strategy, Locality)

# ===============================
# Export to Excel
# ===============================
output_path <- \Sampling_efficiency_summary_mean_SD.xlsx\

write.xlsx(
  summary_table,
  file = output_path,
  overwrite = TRUE
)

cat(\✅ Summary table exported to:\, output_path, \\n\)
```
```

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```
✅ Summary table exported to: Sampling_efficiency_summary_mean_SD.xlsx 
```



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<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->





# OBJECTIVE 1 SIZE DEPENDANCE

S. objective 1 - Analyze the relationship between sample size and the spatial representativeness of interpolated soundscapes using different sampling strategies in heterogeneous landscapes;

## 1.1. Generate the pearson correlation between Sampling percent and design precision.

To evaluate how sampling size influences the performance of each subsampling strategy, we calculated a second Pearson correlation coefficient, Pcoef2. This coefficient quantifies the correlation between the proportion of sampled data (i.e., Sample_percent) and the subsampling performance, previously calculated as Pcoef1 (Pearson correlation between full-sample and subsampled raster surfaces).

The analysis was performed across all combinations of localities, subsampling designs, frequency ranges, time periods, acoustic indices, and summary statistics (e.g., mean, median). For each unique combination, we filtered the data and applied ggscatterstats() from the ggstatsplot package to compute the Pearson correlation (Pcoef2), along with associated test statistics.

The resulting outputs were consolidated into a dataset named DF2, which was later used for statistical comparisons between strategies and visualized using violin and density plots.

Used filters: \| Locality \| Type \| Frequency_Range \| Time_Period \| Statistic \| Index \| Pearson_Correlation \|

Resulting table will be named "DF2", which includes the calculated Pcoef2.


<!-- rnb-text-end -->


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# -----------------------------------------------------------------------------
# 1. SETUP AND LIBRARIES
# -----------------------------------------------------------------------------
# Ensure pacman is installed to manage libraries easily
if (!require(\pacman\)) install.packages(\pacman\)

<!-- rnb-output-end -->

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```r
```r
# -----------------------------------------------------------------------------
# 1. SETUP AND LIBRARIES
# -----------------------------------------------------------------------------
# Ensure pacman is installed to manage libraries easily
if (!require(\pacman\)) install.packages(\pacman\)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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trying URL ‘https://cran.rstudio.com/src/contrib/pacman_0.5.1.tar.gz’ Content type ‘application/x-gzip’ length 274400 bytes (267 KB) ================================================== downloaded 267 KB

The downloaded source packages are in ‘/tmp/RtmpOZQmBr/downloaded_packages’

# -----------------------------------------------------------------------------
# 1. SETUP AND LIBRARIES
# -----------------------------------------------------------------------------
# Ensure pacman is installed to manage libraries easily
if (!require(\pacman\)) install.packages(\pacman\)
# Load necessary libraries
pacman::p_load(ggstatsplot, dplyr, readxl, openxlsx, future, future.apply, stringr)
# Configure Parallel Processing
# We use (Total Cores - 1) to keep the system responsive
n_cores <- parallel::detectCores(logical = FALSE) - 1
plan(multisession, workers = n_cores)

<!-- rnb-output-end -->

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```r
```r
# -----------------------------------------------------------------------------
# 1. SETUP AND LIBRARIES
# -----------------------------------------------------------------------------
# Ensure pacman is installed to manage libraries easily
if (!require(\pacman\)) install.packages(\pacman\)
# Load necessary libraries
pacman::p_load(ggstatsplot, dplyr, readxl, openxlsx, future, future.apply, stringr)
# Configure Parallel Processing
# We use (Total Cores - 1) to keep the system responsive
n_cores <- parallel::detectCores(logical = FALSE) - 1
plan(multisession, workers = n_cores)
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


## 1.2. Visual inspection of sampling size effects

To explore the relationship between sample size and subsampling performance across different categorical variables (e.g., locality, index, strategy), we created an interactive visualization tool using a Shiny application. This tool allows the dynamic generation of violin plots showing the distribution of Pcoef2 values (correlation between sample size and subsample performance) across user-defined categories.

The application permits filtering by multiple metadata fields (e.g., frequency range, time period, statistic, or acoustic index) and dynamically updates the visualization accordingly. Violin plots were generated using the ggplot2 package, with optional overlay of jittered data points. Users can export the resulting plots as PDFs for reporting or supplementary use.

This tool is based on the table DF2, and facilitates visual comparison of the influence of sample size across grouping levels in the dataset.

This visual inspection is only for ALL dataset.



<!-- rnb-text-end -->


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-->\n\n```r\nlibrary(shiny)\nlibrary(ggplot2)\nlibrary(readxl)\nlibrary(dplyr)\n\n# ==============================================================================\n# UI DEFINITION\n# ==============================================================================\nui <- fluidPage(\n  titlePanel(\\Distribution of Pearson Coefficient (Publication Ready)\\),\n  \n  sidebarLayout(\n    sidebarPanel(\n      # File upload\n      fileInput(\\file\\, \\Upload Excel file\\, accept = c(\\.xlsx\\)),\n      \n      # Main variable\n      selectInput(\\categorical_var\\, \\Main categorical variable (X-axis):\\,\n                  choices = c(\\Locality\\, \\Frequency_Range\\, \\Time_Period\\, \n                              \\Statistic\\, \\Index\\, \\Subsample\\, \\Type\\)),\n      \n      # Checkbox to toggle points\n      checkboxInput(\\show_points\\, \\Show individual points (Jitter)\\, value = TRUE),\n      \n      # Checkbox to split by Audio Type\n      checkboxInput(\\split_audio\\, \\Split by Audio Type (Audible vs Ultrasound)\\, value = FALSE),\n      \n      hr(), \n      \n      h4(\\Dynamic Filters\\),\n      p(\\Select 'All' to include everything or filter by specific groups:\\),\n      uiOutput(\\additional_filters\\),\n      \n      hr(),\n      \n      h4(\\Paper Formatting (PDF)\\),\n      # Controls for font size and dimensions\n      numericInput(\\axis_title_size\\, \\Axis title font size:\\, value = 14, min = 8, max = 30),\n      numericInput(\\axis_text_size\\, \\Axis text font size:\\, value = 12, min = 6, max = 30),\n      \n      splitLayout(\n        numericInput(\\pdf_width\\, \\Width (mm):\\, value = 170, min = 50, max = 300),\n        numericInput(\\pdf_height\\, \\Height (mm):\\, value = 110, min = 50, max = 500)\n      ),\n      helpText(\\Max width usually 170mm. Adjust height as needed.\\),\n      \n      # Download button\n      downloadButton(\\download_pdf\\, \\Download PDF for Paper\\)\n    ),\n    \n    mainPanel(\n      tabsetPanel(\n        tabPanel(\\Plot Preview\\, plotOutput(\\violin_plot_output\\, height = \\600px\\)),\n        tabPanel(\\Data Table\\, tableOutput(\\filtered_table_output\\))\n      )\n    )\n  )\n)\n\n# ==============================================================================\n# SERVER LOGIC\n# ==============================================================================\nserver <- function(input, output, session) {\n  \n# 1. Load Data\n  raw_data <- reactive({\n    req(input$file)\n    \n    # --- MODIFICACIÓN AQUÍ ---\n    # Se especifica que cargue la hoja \\DATA\\\n    df <- read_excel(input$file$datapath, sheet = \\DATA\\)\n    \n    # Check for correct column and filter\n    if(\\Statistical_paradigm\\ %in% names(df)){\n      df <- df %>% filter(Statistical_paradigm == \\Frequentist\\)\n    }\n    \n    # Create Audio_Type column based on Frequency_Range\n    if(\\Frequency_Range\\ %in% names(df)){\n      df <- df %>% mutate(\n        Audio_Type = ifelse(grepl(\\24-96\\, Frequency_Range), \\Ultrasound\\, \\Audible\\)\n      )\n    }\n    \n    df %>% filter(!is.na(estimate))\n  })\n  \n  # 2. Dynamic UI for Filters\n  output$additional_filters <- renderUI({\n    req(raw_data())\n    all_categories <- c(\\Locality\\, \\Frequency_Range\\, \\Time_Period\\, \n                        \\Statistic\\, \\Index\\, \\Subsample\\, \\Type\\)\n    filter_candidates <- setdiff(all_categories, input$categorical_var)\n    \n    lapply(filter_candidates, function(col) {\n      if(col %in% names(raw_data())) {\n        options_list <- c(\\All\\, sort(unique(as.character(raw_data()[[col]]))))\n        selectInput(inputId = paste0(\\filter_\\, col),\n                    label = paste(\\Filter by\\, col),\n                    choices = options_list, selected = \\All\\)\n      }\n    })\n  })\n  \n  # 3. Filter Logic\n  filtered_data <- reactive({\n    req(raw_data())\n    df <- raw_data()\n    all_categories <- c(\\Locality\\, \\Frequency_Range\\, \\Time_Period\\, \n                        \\Statistic\\, \\Index\\, \\Subsample\\, \\Type\\)\n    active_filters <- setdiff(all_categories, input$categorical_var)\n    \n    for (col in active_filters) {\n      input_id <- paste0(\\filter_\\, col)\n      selected_val <- input[[input_id]]\n      if (!is.null(selected_val) && selected_val != \\All\\) {\n        if(col %in% names(df)){\n          df <- df %>% filter(.data[[col]] == selected_val)\n        }\n      }\n    }\n    df\n  })\n  \n  # 4. Create Plot Object\n  create_plot <- reactive({\n    req(filtered_data())\n    df <- filtered_data()\n    validate(need(nrow(df) > 0, \\No data available with the current filters.\\))\n    \n    # Base Plot with Colors\n    p <- ggplot(df, aes(x = .data[[input$categorical_var]], y = estimate, fill = Type)) +\n      \n      # VIOLIN: Added lwd=0.3 to make border thinner\n      geom_violin(alpha = 0.5, color = \\darkblue\\, trim = TRUE, \n                  position = position_dodge(width = 0.9), lwd = 0.3) +\n      \n      # BOXPLOT: \n      # 1. lwd = 0.3 (thinner lines)\n      # 2. outlier.shape = NA (removes the \\fat dots\\ you circled, since jitter shows them anyway)\n      geom_boxplot(width = 0.1, color = \\black\\, alpha = 0.8, \n                   position = position_dodge(width = 0.9), show.legend = FALSE, \n                   lwd = 0.3, outlier.shape = NA) +\n      \n      scale_fill_manual(values = c(\\Random\\ = \\lightpink\\, \\Tessells\\ = \\skyblue\\))\n    \n    # Conditionally add points (Jitter)\n    if (input$show_points) {\n      p <- p + geom_point(position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.9),\n                          alpha = 0.6, size = 1.0, color = \\darkslategray\\, stroke = 0) \n                          # Reduced size slightly and removed stroke for cleaner look\n    }\n    \n    # Split by Audio Type SIDE-BY-SIDE\n    if (input$split_audio) {\n      p <- p + facet_wrap(~ Audio_Type, nrow = 1) \n    }\n    \n    # Styling and Text\n    p <- p + \n      scale_y_continuous(limits = c(-1, 1), breaks = seq(-1, 1, 0.2)) + \n      labs(\n        x = input$categorical_var, \n        y = \\Pearson Correlation Estimate\\,\n        fill = \\Method\\,\n        title = NULL \n      ) +\n      theme_minimal() +\n      theme(\n        text = element_text(family = \\serif\\), \n        axis.title = element_text(size = input$axis_title_size, face = \\bold\\),\n        axis.text = element_text(size = input$axis_text_size, color = \\black\\),\n        axis.text.x = element_text(angle = 45, hjust = 1),\n        panel.grid.minor = element_blank(),\n        legend.position = \\top\\,\n        strip.text = element_text(size = input$axis_text_size, face = \\bold\\, family = \\serif\\)\n      )\n      \n    p\n  })\n  \n  # 5. Output to Screen\n  output$violin_plot_output <- renderPlot({\n    create_plot()\n  })\n  \n  # 6. Output Table\n  output$filtered_table_output <- renderTable({\n    head(filtered_data(), 20) \n  })\n  \n  # 7. PDF Download Handler\n  output$download_pdf <- downloadHandler(\n    filename = function() {\n      paste0(\\Pearson_Plot_\\, Sys.Date(), \\.pdf\\)\n    },\n    content = function(file) {\n      ggsave(file, \n             plot = create_plot(), \n             device = \\pdf\\, \n             width = input$pdf_width,  \n             height = input$pdf_height, \n             units = \\mm\\) \n    }\n  )\n}\n\nshinyApp(ui = ui, server = server)\n```\n\n<!-- rnb-source-end -->\n"} -->

library(shiny)
library(ggplot2)
library(readxl)
library(dplyr)

# ==============================================================================
# UI DEFINITION
# ==============================================================================
ui <- fluidPage(
  titlePanel(\Distribution of Pearson Coefficient (Publication Ready)\),
  
  sidebarLayout(
    sidebarPanel(
      # File upload
      fileInput(\file\, \Upload Excel file\, accept = c(\.xlsx\)),
      
      # Main variable
      selectInput(\categorical_var\, \Main categorical variable (X-axis):\,
                  choices = c(\Locality\, \Frequency_Range\, \Time_Period\, 
                              \Statistic\, \Index\, \Subsample\, \Type\)),
      
      # Checkbox to toggle points
      checkboxInput(\show_points\, \Show individual points (Jitter)\, value = TRUE),
      
      # Checkbox to split by Audio Type
      checkboxInput(\split_audio\, \Split by Audio Type (Audible vs Ultrasound)\, value = FALSE),
      
      hr(), 
      
      h4(\Dynamic Filters\),
      p(\Select 'All' to include everything or filter by specific groups:\),
      uiOutput(\additional_filters\),
      
      hr(),
      
      h4(\Paper Formatting (PDF)\),
      # Controls for font size and dimensions
      numericInput(\axis_title_size\, \Axis title font size:\, value = 14, min = 8, max = 30),
      numericInput(\axis_text_size\, \Axis text font size:\, value = 12, min = 6, max = 30),
      
      splitLayout(
        numericInput(\pdf_width\, \Width (mm):\, value = 170, min = 50, max = 300),
        numericInput(\pdf_height\, \Height (mm):\, value = 110, min = 50, max = 500)
      ),
      helpText(\Max width usually 170mm. Adjust height as needed.\),
      
      # Download button
      downloadButton(\download_pdf\, \Download PDF for Paper\)
    ),
    
    mainPanel(
      tabsetPanel(
        tabPanel(\Plot Preview\, plotOutput(\violin_plot_output\, height = \600px\)),
        tabPanel(\Data Table\, tableOutput(\filtered_table_output\))
      )
    )
  )
)

# ==============================================================================
# SERVER LOGIC
# ==============================================================================
server <- function(input, output, session) {
  
# 1. Load Data
  raw_data <- reactive({
    req(input$file)
    
    # --- MODIFICACIÓN AQUÍ ---
    # Se especifica que cargue la hoja \DATA\
    df <- read_excel(input$file$datapath, sheet = \DATA\)
    
    # Check for correct column and filter
    if(\Statistical_paradigm\ %in% names(df)){
      df <- df %>% filter(Statistical_paradigm == \Frequentist\)
    }
    
    # Create Audio_Type column based on Frequency_Range
    if(\Frequency_Range\ %in% names(df)){
      df <- df %>% mutate(
        Audio_Type = ifelse(grepl(\24-96\, Frequency_Range), \Ultrasound\, \Audible\)
      )
    }
    
    df %>% filter(!is.na(estimate))
  })
  
  # 2. Dynamic UI for Filters
  output$additional_filters <- renderUI({
    req(raw_data())
    all_categories <- c(\Locality\, \Frequency_Range\, \Time_Period\, 
                        \Statistic\, \Index\, \Subsample\, \Type\)
    filter_candidates <- setdiff(all_categories, input$categorical_var)
    
    lapply(filter_candidates, function(col) {
      if(col %in% names(raw_data())) {
        options_list <- c(\All\, sort(unique(as.character(raw_data()[[col]]))))
        selectInput(inputId = paste0(\filter_\, col),
                    label = paste(\Filter by\, col),
                    choices = options_list, selected = \All\)
      }
    })
  })
  
  # 3. Filter Logic
  filtered_data <- reactive({
    req(raw_data())
    df <- raw_data()
    all_categories <- c(\Locality\, \Frequency_Range\, \Time_Period\, 
                        \Statistic\, \Index\, \Subsample\, \Type\)
    active_filters <- setdiff(all_categories, input$categorical_var)
    
    for (col in active_filters) {
      input_id <- paste0(\filter_\, col)
      selected_val <- input[[input_id]]
      if (!is.null(selected_val) && selected_val != \All\) {
        if(col %in% names(df)){
          df <- df %>% filter(.data[[col]] == selected_val)
        }
      }
    }
    df
  })
  
  # 4. Create Plot Object
  create_plot <- reactive({
    req(filtered_data())
    df <- filtered_data()
    validate(need(nrow(df) > 0, \No data available with the current filters.\))
    
    # Base Plot with Colors
    p <- ggplot(df, aes(x = .data[[input$categorical_var]], y = estimate, fill = Type)) +
      
      # VIOLIN: Added lwd=0.3 to make border thinner
      geom_violin(alpha = 0.5, color = \darkblue\, trim = TRUE, 
                  position = position_dodge(width = 0.9), lwd = 0.3) +
      
      # BOXPLOT: 
      # 1. lwd = 0.3 (thinner lines)
      # 2. outlier.shape = NA (removes the \fat dots\ you circled, since jitter shows them anyway)
      geom_boxplot(width = 0.1, color = \black\, alpha = 0.8, 
                   position = position_dodge(width = 0.9), show.legend = FALSE, 
                   lwd = 0.3, outlier.shape = NA) +
      
      scale_fill_manual(values = c(\Random\ = \lightpink\, \Tessells\ = \skyblue\))
    
    # Conditionally add points (Jitter)
    if (input$show_points) {
      p <- p + geom_point(position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.9),
                          alpha = 0.6, size = 1.0, color = \darkslategray\, stroke = 0) 
                          # Reduced size slightly and removed stroke for cleaner look
    }
    
    # Split by Audio Type SIDE-BY-SIDE
    if (input$split_audio) {
      p <- p + facet_wrap(~ Audio_Type, nrow = 1) 
    }
    
    # Styling and Text
    p <- p + 
      scale_y_continuous(limits = c(-1, 1), breaks = seq(-1, 1, 0.2)) + 
      labs(
        x = input$categorical_var, 
        y = \Pearson Correlation Estimate\,
        fill = \Method\,
        title = NULL 
      ) +
      theme_minimal() +
      theme(
        text = element_text(family = \serif\), 
        axis.title = element_text(size = input$axis_title_size, face = \bold\),
        axis.text = element_text(size = input$axis_text_size, color = \black\),
        axis.text.x = element_text(angle = 45, hjust = 1),
        panel.grid.minor = element_blank(),
        legend.position = \top\,
        strip.text = element_text(size = input$axis_text_size, face = \bold\, family = \serif\)
      )
      
    p
  })
  
  # 5. Output to Screen
  output$violin_plot_output <- renderPlot({
    create_plot()
  })
  
  # 6. Output Table
  output$filtered_table_output <- renderTable({
    head(filtered_data(), 20) 
  })
  
  # 7. PDF Download Handler
  output$download_pdf <- downloadHandler(
    filename = function() {
      paste0(\Pearson_Plot_\, Sys.Date(), \.pdf\)
    },
    content = function(file) {
      ggsave(file, 
             plot = create_plot(), 
             device = \pdf\, 
             width = input$pdf_width,  
             height = input$pdf_height, 
             units = \mm\) 
    }
  )
}

shinyApp(ui = ui, server = server)

<!-- rnb-output-end -->

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-->\n\n```r\n```r\nlibrary(shiny)\nlibrary(ggplot2)\nlibrary(readxl)\nlibrary(dplyr)\n\n# ==============================================================================\n# UI DEFINITION\n# ==============================================================================\nui <- fluidPage(\n  titlePanel(\\Distribution of Pearson Coefficient (Publication Ready)\\),\n  \n  sidebarLayout(\n    sidebarPanel(\n      # File upload\n      fileInput(\\file\\, \\Upload Excel file\\, accept = c(\\.xlsx\\)),\n      \n      # Main variable\n      selectInput(\\categorical_var\\, \\Main categorical variable (X-axis):\\,\n                  choices = c(\\Locality\\, \\Frequency_Range\\, \\Time_Period\\, \n                              \\Statistic\\, \\Index\\, \\Subsample\\, \\Type\\)),\n      \n      # Checkbox to toggle points\n      checkboxInput(\\show_points\\, \\Show individual points (Jitter)\\, value = TRUE),\n      \n      # Checkbox to split by Audio Type\n      checkboxInput(\\split_audio\\, \\Split by Audio Type (Audible vs Ultrasound)\\, value = FALSE),\n      \n      hr(), \n      \n      h4(\\Dynamic Filters\\),\n      p(\\Select 'All' to include everything or filter by specific groups:\\),\n      uiOutput(\\additional_filters\\),\n      \n      hr(),\n      \n      h4(\\Paper Formatting (PDF)\\),\n      # Controls for font size and dimensions\n      numericInput(\\axis_title_size\\, \\Axis title font size:\\, value = 14, min = 8, max = 30),\n      numericInput(\\axis_text_size\\, \\Axis text font size:\\, value = 12, min = 6, max = 30),\n      \n      splitLayout(\n        numericInput(\\pdf_width\\, \\Width (mm):\\, value = 170, min = 50, max = 300),\n        numericInput(\\pdf_height\\, \\Height (mm):\\, value = 110, min = 50, max = 500)\n      ),\n      helpText(\\Max width usually 170mm. Adjust height as needed.\\),\n      \n      # Download button\n      downloadButton(\\download_pdf\\, \\Download PDF for Paper\\)\n    ),\n    \n    mainPanel(\n      tabsetPanel(\n        tabPanel(\\Plot Preview\\, plotOutput(\\violin_plot_output\\, height = \\600px\\)),\n        tabPanel(\\Data Table\\, tableOutput(\\filtered_table_output\\))\n      )\n    )\n  )\n)\n\n# ==============================================================================\n# SERVER LOGIC\n# ==============================================================================\nserver <- function(input, output, session) {\n  \n# 1. Load Data\n  raw_data <- reactive({\n    req(input$file)\n    \n    # --- MODIFICACIÓN AQUÍ ---\n    # Se especifica que cargue la hoja \\DATA\\\n    df <- read_excel(input$file$datapath, sheet = \\DATA\\)\n    \n    # Check for correct column and filter\n    if(\\Statistical_paradigm\\ %in% names(df)){\n      df <- df %>% filter(Statistical_paradigm == \\Frequentist\\)\n    }\n    \n    # Create Audio_Type column based on Frequency_Range\n    if(\\Frequency_Range\\ %in% names(df)){\n      df <- df %>% mutate(\n        Audio_Type = ifelse(grepl(\\24-96\\, Frequency_Range), \\Ultrasound\\, \\Audible\\)\n      )\n    }\n    \n    df %>% filter(!is.na(estimate))\n  })\n  \n  # 2. Dynamic UI for Filters\n  output$additional_filters <- renderUI({\n    req(raw_data())\n    all_categories <- c(\\Locality\\, \\Frequency_Range\\, \\Time_Period\\, \n                        \\Statistic\\, \\Index\\, \\Subsample\\, \\Type\\)\n    filter_candidates <- setdiff(all_categories, input$categorical_var)\n    \n    lapply(filter_candidates, function(col) {\n      if(col %in% names(raw_data())) {\n        options_list <- c(\\All\\, sort(unique(as.character(raw_data()[[col]]))))\n        selectInput(inputId = paste0(\\filter_\\, col),\n                    label = paste(\\Filter by\\, col),\n                    choices = options_list, selected = \\All\\)\n      }\n    })\n  })\n  \n  # 3. Filter Logic\n  filtered_data <- reactive({\n    req(raw_data())\n    df <- raw_data()\n    all_categories <- c(\\Locality\\, \\Frequency_Range\\, \\Time_Period\\, \n                        \\Statistic\\, \\Index\\, \\Subsample\\, \\Type\\)\n    active_filters <- setdiff(all_categories, input$categorical_var)\n    \n    for (col in active_filters) {\n      input_id <- paste0(\\filter_\\, col)\n      selected_val <- input[[input_id]]\n      if (!is.null(selected_val) && selected_val != \\All\\) {\n        if(col %in% names(df)){\n          df <- df %>% filter(.data[[col]] == selected_val)\n        }\n      }\n    }\n    df\n  })\n  \n  # 4. Create Plot Object\n  create_plot <- reactive({\n    req(filtered_data())\n    df <- filtered_data()\n    validate(need(nrow(df) > 0, \\No data available with the current filters.\\))\n    \n    # Base Plot with Colors\n    p <- ggplot(df, aes(x = .data[[input$categorical_var]], y = estimate, fill = Type)) +\n      \n      # VIOLIN: Added lwd=0.3 to make border thinner\n      geom_violin(alpha = 0.5, color = \\darkblue\\, trim = TRUE, \n                  position = position_dodge(width = 0.9), lwd = 0.3) +\n      \n      # BOXPLOT: \n      # 1. lwd = 0.3 (thinner lines)\n      # 2. outlier.shape = NA (removes the \\fat dots\\ you circled, since jitter shows them anyway)\n      geom_boxplot(width = 0.1, color = \\black\\, alpha = 0.8, \n                   position = position_dodge(width = 0.9), show.legend = FALSE, \n                   lwd = 0.3, outlier.shape = NA) +\n      \n      scale_fill_manual(values = c(\\Random\\ = \\lightpink\\, \\Tessells\\ = \\skyblue\\))\n    \n    # Conditionally add points (Jitter)\n    if (input$show_points) {\n      p <- p + geom_point(position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.9),\n                          alpha = 0.6, size = 1.0, color = \\darkslategray\\, stroke = 0) \n                          # Reduced size slightly and removed stroke for cleaner look\n    }\n    \n    # Split by Audio Type SIDE-BY-SIDE\n    if (input$split_audio) {\n      p <- p + facet_wrap(~ Audio_Type, nrow = 1) \n    }\n    \n    # Styling and Text\n    p <- p + \n      scale_y_continuous(limits = c(-1, 1), breaks = seq(-1, 1, 0.2)) + \n      labs(\n        x = input$categorical_var, \n        y = \\Pearson Correlation Estimate\\,\n        fill = \\Method\\,\n        title = NULL \n      ) +\n      theme_minimal() +\n      theme(\n        text = element_text(family = \\serif\\), \n        axis.title = element_text(size = input$axis_title_size, face = \\bold\\),\n        axis.text = element_text(size = input$axis_text_size, color = \\black\\),\n        axis.text.x = element_text(angle = 45, hjust = 1),\n        panel.grid.minor = element_blank(),\n        legend.position = \\top\\,\n        strip.text = element_text(size = input$axis_text_size, face = \\bold\\, family = \\serif\\)\n      )\n      \n    p\n  })\n  \n  # 5. Output to Screen\n  output$violin_plot_output <- renderPlot({\n    create_plot()\n  })\n  \n  # 6. Output Table\n  output$filtered_table_output <- renderTable({\n    head(filtered_data(), 20) \n  })\n  \n  # 7. PDF Download Handler\n  output$download_pdf <- downloadHandler(\n    filename = function() {\n      paste0(\\Pearson_Plot_\\, Sys.Date(), \\.pdf\\)\n    },\n    content = function(file) {\n      ggsave(file, \n             plot = create_plot(), \n             device = \\pdf\\, \n             width = input$pdf_width,  \n             height = input$pdf_height, \n             units = \\mm\\) \n    }\n  )\n}\n\nshinyApp(ui = ui, server = server)\n```\n```\n\n<!-- rnb-source-end -->\n"} -->


<!-- rnb-source-begin {"data":"```r\n```r\nlibrary(shiny)\nlibrary(ggplot2)\nlibrary(readxl)\nlibrary(dplyr)\n\n# ==============================================================================\n# UI DEFINITION\n# ==============================================================================\nui <- fluidPage(\n  titlePanel(\\Distribution of Pearson Coefficient (Publication Ready)\\),\n  \n  sidebarLayout(\n    sidebarPanel(\n      # File upload\n      fileInput(\\file\\, \\Upload Excel file\\, accept = c(\\.xlsx\\)),\n      \n      # Main variable\n      selectInput(\\categorical_var\\, \\Main categorical variable (X-axis):\\,\n                  choices = c(\\Locality\\, \\Frequency_Range\\, \\Time_Period\\, \n                              \\Statistic\\, \\Index\\, \\Subsample\\, \\Type\\)),\n      \n      # Checkbox to toggle points\n      checkboxInput(\\show_points\\, \\Show individual points (Jitter)\\, value = TRUE),\n      \n      # Checkbox to split by Audio Type\n      checkboxInput(\\split_audio\\, \\Split by Audio Type (Audible vs Ultrasound)\\, value = FALSE),\n      \n      hr(), \n      \n      h4(\\Dynamic Filters\\),\n      p(\\Select 'All' to include everything or filter by specific groups:\\),\n      uiOutput(\\additional_filters\\),\n      \n      hr(),\n      \n      h4(\\Paper Formatting (PDF)\\),\n      # Controls for font size and dimensions\n      numericInput(\\axis_title_size\\, \\Axis title font size:\\, value = 14, min = 8, max = 30),\n      numericInput(\\axis_text_size\\, \\Axis text font size:\\, value = 12, min = 6, max = 30),\n      \n      splitLayout(\n        numericInput(\\pdf_width\\, \\Width (mm):\\, value = 170, min = 50, max = 300),\n        numericInput(\\pdf_height\\, \\Height (mm):\\, value = 110, min = 50, max = 500)\n      ),\n      helpText(\\Max width usually 170mm. Adjust height as needed.\\),\n      \n      # Download button\n      downloadButton(\\download_pdf\\, \\Download PDF for Paper\\)\n    ),\n    \n    mainPanel(\n      tabsetPanel(\n        tabPanel(\\Plot Preview\\, plotOutput(\\violin_plot_output\\, height = \\600px\\)),\n        tabPanel(\\Data Table\\, tableOutput(\\filtered_table_output\\))\n      )\n    )\n  )\n)\n\n# ==============================================================================\n# SERVER LOGIC\n# ==============================================================================\nserver <- function(input, output, session) {\n  \n# 1. Load Data\n  raw_data <- reactive({\n    req(input$file)\n    \n    # --- MODIFICACIÓN AQUÍ ---\n    # Se especifica que cargue la hoja \\DATA\\\n    df <- read_excel(input$file$datapath, sheet = \\DATA\\)\n    \n    # Check for correct column and filter\n    if(\\Statistical_paradigm\\ %in% names(df)){\n      df <- df %>% filter(Statistical_paradigm == \\Frequentist\\)\n    }\n    \n    # Create Audio_Type column based on Frequency_Range\n    if(\\Frequency_Range\\ %in% names(df)){\n      df <- df %>% mutate(\n        Audio_Type = ifelse(grepl(\\24-96\\, Frequency_Range), \\Ultrasound\\, \\Audible\\)\n      )\n    }\n    \n    df %>% filter(!is.na(estimate))\n  })\n  \n  # 2. Dynamic UI for Filters\n  output$additional_filters <- renderUI({\n    req(raw_data())\n    all_categories <- c(\\Locality\\, \\Frequency_Range\\, \\Time_Period\\, \n                        \\Statistic\\, \\Index\\, \\Subsample\\, \\Type\\)\n    filter_candidates <- setdiff(all_categories, input$categorical_var)\n    \n    lapply(filter_candidates, function(col) {\n      if(col %in% names(raw_data())) {\n        options_list <- c(\\All\\, sort(unique(as.character(raw_data()[[col]]))))\n        selectInput(inputId = paste0(\\filter_\\, col),\n                    label = paste(\\Filter by\\, col),\n                    choices = options_list, selected = \\All\\)\n      }\n    })\n  })\n  \n  # 3. Filter Logic\n  filtered_data <- reactive({\n    req(raw_data())\n    df <- raw_data()\n    all_categories <- c(\\Locality\\, \\Frequency_Range\\, \\Time_Period\\, \n                        \\Statistic\\, \\Index\\, \\Subsample\\, \\Type\\)\n    active_filters <- setdiff(all_categories, input$categorical_var)\n    \n    for (col in active_filters) {\n      input_id <- paste0(\\filter_\\, col)\n      selected_val <- input[[input_id]]\n      if (!is.null(selected_val) && selected_val != \\All\\) {\n        if(col %in% names(df)){\n          df <- df %>% filter(.data[[col]] == selected_val)\n        }\n      }\n    }\n    df\n  })\n  \n  # 4. Create Plot Object\n  create_plot <- reactive({\n    req(filtered_data())\n    df <- filtered_data()\n    validate(need(nrow(df) > 0, \\No data available with the current filters.\\))\n    \n    # Base Plot with Colors\n    p <- ggplot(df, aes(x = .data[[input$categorical_var]], y = estimate, fill = Type)) +\n      \n      # VIOLIN: Added lwd=0.3 to make border thinner\n      geom_violin(alpha = 0.5, color = \\darkblue\\, trim = TRUE, \n                  position = position_dodge(width = 0.9), lwd = 0.3) +\n      \n      # BOXPLOT: \n      # 1. lwd = 0.3 (thinner lines)\n      # 2. outlier.shape = NA (removes the \\fat dots\\ you circled, since jitter shows them anyway)\n      geom_boxplot(width = 0.1, color = \\black\\, alpha = 0.8, \n                   position = position_dodge(width = 0.9), show.legend = FALSE, \n                   lwd = 0.3, outlier.shape = NA) +\n      \n      scale_fill_manual(values = c(\\Random\\ = \\lightpink\\, \\Tessells\\ = \\skyblue\\))\n    \n    # Conditionally add points (Jitter)\n    if (input$show_points) {\n      p <- p + geom_point(position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.9),\n                          alpha = 0.6, size = 1.0, color = \\darkslategray\\, stroke = 0) \n                          # Reduced size slightly and removed stroke for cleaner look\n    }\n    \n    # Split by Audio Type SIDE-BY-SIDE\n    if (input$split_audio) {\n      p <- p + facet_wrap(~ Audio_Type, nrow = 1) \n    }\n    \n    # Styling and Text\n    p <- p + \n      scale_y_continuous(limits = c(-1, 1), breaks = seq(-1, 1, 0.2)) + \n      labs(\n        x = input$categorical_var, \n        y = \\Pearson Correlation Estimate\\,\n        fill = \\Method\\,\n        title = NULL \n      ) +\n      theme_minimal() +\n      theme(\n        text = element_text(family = \\serif\\), \n        axis.title = element_text(size = input$axis_title_size, face = \\bold\\),\n        axis.text = element_text(size = input$axis_text_size, color = \\black\\),\n        axis.text.x = element_text(angle = 45, hjust = 1),\n        panel.grid.minor = element_blank(),\n        legend.position = \\top\\,\n        strip.text = element_text(size = input$axis_text_size, face = \\bold\\, family = \\serif\\)\n      )\n      \n    p\n  })\n  \n  # 5. Output to Screen\n  output$violin_plot_output <- renderPlot({\n    create_plot()\n  })\n  \n  # 6. Output Table\n  output$filtered_table_output <- renderTable({\n    head(filtered_data(), 20) \n  })\n  \n  # 7. PDF Download Handler\n  output$download_pdf <- downloadHandler(\n    filename = function() {\n      paste0(\\Pearson_Plot_\\, Sys.Date(), \\.pdf\\)\n    },\n    content = function(file) {\n      ggsave(file, \n             plot = create_plot(), \n             device = \\pdf\\, \n             width = input$pdf_width,  \n             height = input$pdf_height, \n             units = \\mm\\) \n    }\n  )\n}\n\nshinyApp(ui = ui, server = server)\n```\n```"} -->

```r
```r
library(shiny)
library(ggplot2)
library(readxl)
library(dplyr)

# ==============================================================================
# UI DEFINITION
# ==============================================================================
ui <- fluidPage(
  titlePanel(\Distribution of Pearson Coefficient (Publication Ready)\),
  
  sidebarLayout(
    sidebarPanel(
      # File upload
      fileInput(\file\, \Upload Excel file\, accept = c(\.xlsx\)),
      
      # Main variable
      selectInput(\categorical_var\, \Main categorical variable (X-axis):\,
                  choices = c(\Locality\, \Frequency_Range\, \Time_Period\, 
                              \Statistic\, \Index\, \Subsample\, \Type\)),
      
      # Checkbox to toggle points
      checkboxInput(\show_points\, \Show individual points (Jitter)\, value = TRUE),
      
      # Checkbox to split by Audio Type
      checkboxInput(\split_audio\, \Split by Audio Type (Audible vs Ultrasound)\, value = FALSE),
      
      hr(), 
      
      h4(\Dynamic Filters\),
      p(\Select 'All' to include everything or filter by specific groups:\),
      uiOutput(\additional_filters\),
      
      hr(),
      
      h4(\Paper Formatting (PDF)\),
      # Controls for font size and dimensions
      numericInput(\axis_title_size\, \Axis title font size:\, value = 14, min = 8, max = 30),
      numericInput(\axis_text_size\, \Axis text font size:\, value = 12, min = 6, max = 30),
      
      splitLayout(
        numericInput(\pdf_width\, \Width (mm):\, value = 170, min = 50, max = 300),
        numericInput(\pdf_height\, \Height (mm):\, value = 110, min = 50, max = 500)
      ),
      helpText(\Max width usually 170mm. Adjust height as needed.\),
      
      # Download button
      downloadButton(\download_pdf\, \Download PDF for Paper\)
    ),
    
    mainPanel(
      tabsetPanel(
        tabPanel(\Plot Preview\, plotOutput(\violin_plot_output\, height = \600px\)),
        tabPanel(\Data Table\, tableOutput(\filtered_table_output\))
      )
    )
  )
)

# ==============================================================================
# SERVER LOGIC
# ==============================================================================
server <- function(input, output, session) {
  
# 1. Load Data
  raw_data <- reactive({
    req(input$file)
    
    # --- MODIFICACIÓN AQUÍ ---
    # Se especifica que cargue la hoja \DATA\
    df <- read_excel(input$file$datapath, sheet = \DATA\)
    
    # Check for correct column and filter
    if(\Statistical_paradigm\ %in% names(df)){
      df <- df %>% filter(Statistical_paradigm == \Frequentist\)
    }
    
    # Create Audio_Type column based on Frequency_Range
    if(\Frequency_Range\ %in% names(df)){
      df <- df %>% mutate(
        Audio_Type = ifelse(grepl(\24-96\, Frequency_Range), \Ultrasound\, \Audible\)
      )
    }
    
    df %>% filter(!is.na(estimate))
  })
  
  # 2. Dynamic UI for Filters
  output$additional_filters <- renderUI({
    req(raw_data())
    all_categories <- c(\Locality\, \Frequency_Range\, \Time_Period\, 
                        \Statistic\, \Index\, \Subsample\, \Type\)
    filter_candidates <- setdiff(all_categories, input$categorical_var)
    
    lapply(filter_candidates, function(col) {
      if(col %in% names(raw_data())) {
        options_list <- c(\All\, sort(unique(as.character(raw_data()[[col]]))))
        selectInput(inputId = paste0(\filter_\, col),
                    label = paste(\Filter by\, col),
                    choices = options_list, selected = \All\)
      }
    })
  })
  
  # 3. Filter Logic
  filtered_data <- reactive({
    req(raw_data())
    df <- raw_data()
    all_categories <- c(\Locality\, \Frequency_Range\, \Time_Period\, 
                        \Statistic\, \Index\, \Subsample\, \Type\)
    active_filters <- setdiff(all_categories, input$categorical_var)
    
    for (col in active_filters) {
      input_id <- paste0(\filter_\, col)
      selected_val <- input[[input_id]]
      if (!is.null(selected_val) && selected_val != \All\) {
        if(col %in% names(df)){
          df <- df %>% filter(.data[[col]] == selected_val)
        }
      }
    }
    df
  })
  
  # 4. Create Plot Object
  create_plot <- reactive({
    req(filtered_data())
    df <- filtered_data()
    validate(need(nrow(df) > 0, \No data available with the current filters.\))
    
    # Base Plot with Colors
    p <- ggplot(df, aes(x = .data[[input$categorical_var]], y = estimate, fill = Type)) +
      
      # VIOLIN: Added lwd=0.3 to make border thinner
      geom_violin(alpha = 0.5, color = \darkblue\, trim = TRUE, 
                  position = position_dodge(width = 0.9), lwd = 0.3) +
      
      # BOXPLOT: 
      # 1. lwd = 0.3 (thinner lines)
      # 2. outlier.shape = NA (removes the \fat dots\ you circled, since jitter shows them anyway)
      geom_boxplot(width = 0.1, color = \black\, alpha = 0.8, 
                   position = position_dodge(width = 0.9), show.legend = FALSE, 
                   lwd = 0.3, outlier.shape = NA) +
      
      scale_fill_manual(values = c(\Random\ = \lightpink\, \Tessells\ = \skyblue\))
    
    # Conditionally add points (Jitter)
    if (input$show_points) {
      p <- p + geom_point(position = position_jitterdodge(jitter.width = 0.15, dodge.width = 0.9),
                          alpha = 0.6, size = 1.0, color = \darkslategray\, stroke = 0) 
                          # Reduced size slightly and removed stroke for cleaner look
    }
    
    # Split by Audio Type SIDE-BY-SIDE
    if (input$split_audio) {
      p <- p + facet_wrap(~ Audio_Type, nrow = 1) 
    }
    
    # Styling and Text
    p <- p + 
      scale_y_continuous(limits = c(-1, 1), breaks = seq(-1, 1, 0.2)) + 
      labs(
        x = input$categorical_var, 
        y = \Pearson Correlation Estimate\,
        fill = \Method\,
        title = NULL 
      ) +
      theme_minimal() +
      theme(
        text = element_text(family = \serif\), 
        axis.title = element_text(size = input$axis_title_size, face = \bold\),
        axis.text = element_text(size = input$axis_text_size, color = \black\),
        axis.text.x = element_text(angle = 45, hjust = 1),
        panel.grid.minor = element_blank(),
        legend.position = \top\,
        strip.text = element_text(size = input$axis_text_size, face = \bold\, family = \serif\)
      )
      
    p
  })
  
  # 5. Output to Screen
  output$violin_plot_output <- renderPlot({
    create_plot()
  })
  
  # 6. Output Table
  output$filtered_table_output <- renderTable({
    head(filtered_data(), 20) 
  })
  
  # 7. PDF Download Handler
  output$download_pdf <- downloadHandler(
    filename = function() {
      paste0(\Pearson_Plot_\, Sys.Date(), \.pdf\)
    },
    content = function(file) {
      ggsave(file, 
             plot = create_plot(), 
             device = \pdf\, 
             width = input$pdf_width,  
             height = input$pdf_height, 
             units = \mm\) 
    }
  )
}

shinyApp(ui = ui, server = server)
```
```

<!-- rnb-source-end -->


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<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->



## 1.3. Normality assessment

Before a statistical test to evaluate the size dependance, normality must be checked. To determine whether parametric tests could be applied to compare the sampling strategies, we evaluated the distribution of Pcoef2 values (i.e., correlations between sampling proportion and subsample performance) across all sampling designs. We applied the Shapiro–Wilk test for normality within each subsample category using the rstatix package. Additionally, we visually inspected the distributions by plotting histograms with density curves, faceted by subsample strategy (e.g., basins, SLIC, ssample40).

Results from the Shapiro–Wilk tests and visual assessments revealed widespread deviations from normality, justifying the use of non-parametric statistical tests in subsequent analyses. All results were saved as part of the reproducible workflow, including the test results and distribution plots.

The input is DF2_SampleSize_vs_Subsample_performance_Correlations(Pcoeff2).xlsx. Procedure must be done for mean and median


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```

<!-- rnb-plot-begin eyJoZWlnaHQiOjQyNS4yMTYzLCJ3aWR0aCI6Njg4LCJkcGkiOi0xLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->

<img src=\data:image/png;base64
```



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<!-- rnb-output-begin {"data":"\n<!-- rnb-plot-begin -->\n\n<img 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b7/Fx8ejN2AA8PT0ZLPZBp0F6DSuKb3FajXPgBaCVBjM8q6un6/+RYN7W7QxtwkQpjexJcXMy8sDADc3N+NFAABbW9t+/fr169dv7dq1GzduXLJkyYMHDw4dOqTtHadSCq5PZbUjqlIdW8RyMHqF6Y2Ltq01adJEP0yFZyyYW/8Wjkpi3FRnaoNxo9Fo0KoXZs+LPv7+/ufPn8/Ly7t8+fLNmzdv37798OHDhw8f7tu3Lz4+HvnwNoKRdzWGsrIyg9fFYjEA6Gz81sfcdzt4N1NlMF+JRALvvtZpg3dUE0ZGlQ6SoX///t999115YWxsbGiaHjJkyMWLF21sbD799NMuXbr4+/v7+/v7+vra29sb/HCp37gV9har1TwGFnZ1y8FoAoSJTYx+YBcT7Y7REePu3btffPFF/fr1//rrL/0oHA5n8eLFkZGR165du337dnnGDXbBDVIp7WiwsFWC6Y1bt27d1NRUg4PLuP2EUf8Wjkr0nUt/yyShOlAbjBu01h0MzWTqUL9+/QkTJkyYMAEA0tLSwsPDnz17tmHDhh07dlguRmlpaW5uLtrjwPD27dvi4mIWi4UWvhkBva+YBZoKQq9EOqCpXYNHimJgtYyMgLJQKpX6ExgURalUKrRm8PLlyxcvXnR1dU1ISEC71RhomjYrRyO9pTpUiHHea1c3zpUrV/CawMQm1r6OUUz0mlFcXKx90dnZOS4ujsPhiESi8ix1NNljZAIGu+BGsLAd0fJYgxOrGzduVCqVc+fOxZjPw8D0xvX19Y2JiUlJSdFPJDU11UgW76P+jVNYWAgA2EuXCO+VGr+gOD8/f+XKlQDw0UcflfdEWbhwoaur65dffql9sXnz5mjDoY6POEvYtWuXzpXNmzcDQIcOHXS8KSBPdwz3799nNheYDjrWJDo6Wl8R7NmzBwCYfZsWYrWMjNCmTRsbG5tbt249efJE59bixYttbW3nzZsHAOhut27ddLTbs2fPTFzOaUpvqQ4VYhCrdXUjYDeBiU0MlhUTPYoKCgq0LzZr1szLy0uj0ZQ3r1BUVIRWhhk5VMTyvsdQWe2IdkGfOHFCe2EyANy/f3/hwoWbN2+2wpwrwvTG7dq1KwAcOHBAZ/ImLy/v1KlTRrKoxPo3EWTc6LzQEqoJNdi4USqV169f79ixY1pamp2dHXLnYJDAwEChUIh8fTIXi4qK0CZzNP4Zyvu6ZAqbNm1CG6ERe/fuRS9Ya9asYS6igbdz507mZeL169dTpkwp793CiDytWrWKiIigKGrMmDGZmZnoolqtnj9/fmJioouLi85eHmyslpER6tevP2/ePIqiQkJCmM1BAHDixIlt27axWCzk9QtV74MHD7RfzZOSkpidFxVO0ZvSW6pDhWALz2BJVzeCuU3AiGFiE4OZxdShS5cuAKCdPgDweDy0O3r79u3Dhw9HM8EMiYmJYWFhRUVFrVu3HjdunEHhLSm4PpXVjoMGDerWrVtpaem4ceNKS0uZdNDClyFDhjBH0t68efPChQt3794tLykLMb1xJ06c2KRJk6KioqFDhyLrAQDy8vJGjBiBvvmWh+Vj31xQQZA1Rqh2WHvvufmg8czhcLQde7Rt25ZZvWtvb3/16lWdWNr+D2QyWevWrQGAzWZ36dJl9OjRH330EZpKadWqVWlpKYpy5MgRABAIBNOmTfvhhx/QReQKRd9pqb6fGw8PD7R6w8/PLywsjFmmquOFgpndadu27Zw5c4YNG4amhdGiP20/Nwbl0fHr8PLlS7TIzs7OrmfPnuHh4eg1gsfj6bhqQHMJOu6S6XeOWZGxZQTTM6oUJ34GEQqFqDnYbHabNm1GjhzJbHRas2YNCiMWi9EEXv369adMmTJr1qw+ffqw2ex27dqhPZ+hoaEXLlxAgQ02rom9xWo1b4qfmz59+iDfHpZ0dYMuQ3TERo0bGhqqIyRaAoLimt4E+mKY0sSmF9MgCoXC3t6+SZMm+reQuyyEi4tLhw4dOnTowLyXu7u7a7tr0xfekoLTlaeydNrx/v37aNmsq6vrRx99FB4e7urqCgBeXl7arrTN8lCM0StokxuXpulLly6hfWFocPXq1Quta0HHCJbn58assW/hqES0bt2ax+OJRCJTAhOsTI0xbgwiEAjGjh2bmZmpH0tnhGdnZ0+ePFl76aiDg8OCBQvy8vKYKEVFRYwLL2agmm7cREREJCYm9ujRg7G6vL29Dfp3WrdunfZBJywWa9GiRYsWLYJ/GzcG5dH3rV5aWjp58mTmwzmPx+vSpcu9e/d0MrV8MJuY0fszbmiaVigUCxcu1N7jEBAQoO1olabpp0+fap8i3qBBg2+++UYqla5fvx51AMbcLK9xTektpldIpRg3FcL4jMfu6pVl3JjeBAbFMKWJTW8jg6Dzv968eaN/Kzo6OjQ0FHliZGjUqNHq1atLSkq0QxoU3pKCV5bK0lcRb9++HTJkCPNlnMvljh8/XsctoRWMG9rkxqVpOiUlpXv37owu9fDwYOaxjDjxM33sV4o+ZLPZvXv3rjAkoUpg0e9npVUlUlhYqL+ygc1mu7u7N23atDy3BGgrYOfOnbUXuyiVyuzs7IKCAk9PTy8vL2ZKlkGtVqekpCiVSm9vb7Tw8N69e2KxuH379jrnrmmnL5FIEhMT69Wrh1xtKhSK1NRUT09PnSMztVEqlZmZmRkZGTk5OV27dm3Xrl1mZuarV69at26tvUlVXx6D5UJkZWWVlpYGBgaiI5l0uH//vkgkatu2rY43oIyMjLdv3/r5+VXoIMvEjAoKCp48eeLu7t6yZUvj6ZSUlKATv03Z5KzD27dvX79+3bRp0/IclgiFwufPn3t6emov9ystLU1NTQ0MDEQWSXmNi6iwtzC815ovLi7W+YZikMaNG2tvlMXo6jpdy6DYqHHd3NzQvAJDXFycQqHQ6ZamNIG+GAwVNrEpxTTIH3/8MXr06B9++GHu3LkGA8hksuzs7KKiIldX18aNG5e3HaY84fEKXlkqqzwVodFo0tPT0eYGfacVaCy4uroa9+llea9AmNK4AKBQKFJSUmxsbJo3b87hcJCm9fDwQAulyyusKfVvuT48fPjwpEmTDh06ZPBET0KVUwOMGwKBQKhE1Gp106ZNGzRo8P6WmBBqPf369Xv8+HF2drZZfv8IVqMGLygmEAgEDLhc7n/+85/4+HiD+40JhArJysqKioqaPXs2sWyqLWTmhkAgfHAoFIq2bdu2adPmjz/+qGpZCDWPyZMn37hxIyUlRf+LG6GaQIwbAoHwIXLz5s0+ffokJCToHzBEIBghNTW1devW58+fRyvTCdUTYtwQCIQPlBs3btSrV6/Cle8Egjbp6emvXr0aOHBgVQtCMAYxbggEAoFAINQqyIJiAoFAIBAItQpi3BAIBAKBQKhVEOOGQCAQCARCrYIYNwQCgUAgEGoVxLghEAgEAoFQqyDGDYFAIBAIhFoFMW4IBAKBQCDUKohxQyAQCAQCoVZBjBsCgUAgEAi1CmLcEAgEAoFAqFUQ44ZAIBAIBEKtghg3BAKBQCAQahXEuCEQCAQCgVCrIMYNgUAgEAiEWgUxbggEAoFAINQqiHFDIBAIBAKhVkGMGwKBQCAQCLUKYtwQCAQCgUCoVRDjhkAgEAgEQq2CW9UC1CpOnz6dlZXl4+MzbNiwSk88MjIyKSnJxcVl8uTJlZ44AZujR48WFha2bt164MCBlRL95cuX169fl0qlISEh/v7+O3bsUKvVvXr16ty5c6UKbjh3wvvg2rVrjx49AoAFCxawWCwr5Lhnzx6ZTBYUFNSzZ08rZEcgVDeIcVOZnD59+saNG3379n1Pxs2hQ4d8fHyIcVOtOHLkSFpa2pgxY/DsA53or1+/7t+/v0wmAwAPDw9/f/9t27YpFAobG5v3YdxYKDzBRK5evXrs2DEAmD9/PofDsUKOu3fvLi4unjt3LjFuCB8mxLipMQQEBAwYMKB+/fpVLQjhPXLz5k1k2SxZsuR9WDM6dOnSxcvLKzAw8H1nRCAQCNaEGDfvBYlEEhsb+/r1686dO7dp00b7llqtvnHjRlZWFgB4e3v37duXz+czdymKunnz5tOnT9lsdtOmTXv16sW85zVs2LBt27YuLi7o74ULF9LT0+vXr//ZZ5/Fx8c/evTI3d194MCBAoHAlNQIlY5cLr99+/bTp08DAwN79+6tfUsoFEZHR+fk5AgEAn9//65duxpMIT8/PycnB/0ODw93c3PTvpuXl3fz5k2lUtmzZ09vb2/T0xeLxdHR0a9evXJycmrVqlXbtm2ZW82bN3d3d2/UqBH6u2/fPolE0qlTp44dO965c+f58+e+vr79+vXT/pJiJDWCJRhpRFPaRa1Wx8fHp6SkNG/evEePHgazSEpKSklJUSgUgYGB3bp107n77Nmz5OTkwsJCPz+/Tp06OTo6AsDZs2efPXsmEAimT5+OgmVlZZ0+fRoAhg4d2qRJE5VKlZiY+PjxY4FA0KNHD+2eyYjdrVu3c+fOeXt7d+rUqZJqi0CoAGLcVD4lJSXDhw9Hn9gBYOjQoVu2bEEWzMuXL0ePHp2dnc0Erlev3smTJ5s1awYA+fn5ERER6enpzN0mTZr8+uuvjRs3Br3PUhcuXDhz5kzLli3j4uKQrgEAT0/PCxcu1KtXr8LUCJVLaWnpiBEjHjx4gP6Gh4fv3bsX/b5y5cq8efPEYjETuHPnzsePH7e1tdVJZMaMGfHx8eh39+7dv/766zlz5qC/jx492r17t1AoBAA2m7106dKpU6eakv6dO3emT59eXFzM3O3Xr9+BAwdsbGxA77PU3r178/PzR4wYsWLFitTUVBS+d+/ex44dQ89R46kRsDHeiBW2S1lZ2ZQpU27fvo1uDRkyRKPRaKdfWlq6YMGCK1euMFe6du16+PBhZMHI5fLly5cfPXqUuVuvXr1Nmzb1798/Pz9/8+bNAPDJJ594eHgAwNmzZzdv3szlcqdMmfLy5cuZM2cmJyejWBwOZ86cOYsWLUJ/kdgzZsz46aefrl279vXXXxPjhmA1yG6pyicpKenZs2effvppu3btAODPP//ctm0burV48eLs7Gw3N7eZM2d+/vnnDg4O+fn569evR3eXL1+enp7u6Og4c+bMKVOmuLu7Z2Zmzp8/30heKSkpkZGRQ4YMQS9Mb9682b17N3ZqBGwuXLhQUFAwdOhQZFmeO3cuMTERABQKxfz588VicfPmzefNmxceHg4ACQkJv/32m34iERERffv2Rb8XLFgQFBTE3Dp37pytre3IkSMbNWpEUdTSpUtjY2NNSX/OnDnFxcVNmzZdsGDBiBEjuFxuZGQk0yENcvLkSblcHh4e7uTkBAAxMTFXr17FTo1QISZ2EiPt8t133yHLpl+/fgMHDjxz5kxpaal23FWrVl25coXL5X722WdDhw61sbGJi4tbvnw5urt161Zk2fTt23fatGkNGjTIz8+fNm1adnb2oEGDkP0UFRWFAqOO17NnT2dn59mzZycnJwcEBKxYsWLcuHEURe3YsQOtLmL4888/r127xufzrbOSmkBAkJmb98LBgweDg4MpipowYUJkZOSRI0cWLFjA4XBUKlWHDh3Gjx8/atQoAMjLy7ty5QozuXL//n0A6NKly5IlS7hcbvfu3X/77TcOh6PRaMr7nMTn8y9evNi4cWOKojp06FBQUJCSkoKdGgEbT0/Pv//+WyAQZGRkIAMlLS2tU6dOmZmZfn5+ALBhw4bAwECNRhMbG1tSUqI9o8YwevRokUh048YNAEAdhrnl7Ox8+fLlunXrCoXCvn375ufn79+/v2fPnsbTz8nJycvLA4CIiAg0CdSkSZMHDx7QNG2kLAEBAVeuXOHxeExZnjx5EhISgpcaoUJM7CTltUt2dvaff/4JAPPmzUOzJhs2bNi+fTsTMT8///fffweARYsWzZ49GwB27ty5du3a8+fPb9iwoaysbN++fQAQERGBJmlmzZrVs2dPqVT6ww8/bNiwoUOHDvfu3YuKioqIiFAoFMhqDwsLi9E7g9IAACAASURBVIqKSkpKAoBDhw6h+eDMzMxbt27t379/zJgxTO5lZWUnT57s2rUrMW4I1oQYN5VPkyZNgoODAYDNZk+cODEyMlIoFGZlZfn5+aF3r7i4uF27dj1+/Pj69esAoFarUURvb+9Xr15dv369Q4cOffv27d+//44dO5ydnY3k1axZM6RW2Gx2586dL168KJFIsFMjYNO9e3e02snf35/NZlMUhRqiefPm586dKy4uvn379oULF+Li4kpKSgBA56tBhQwePLhu3boA4OLiMmzYsL179z58+LDC9N3c3AQCgVgsXrNmzfHjx/v379+vX785c+bweDwjefXs2RMF8Pf3d3FxEQqF6HMJXmqECjGxk5TXLsnJySjkpEmTUMhJkyZpGzdPnz5FAUJDQ9GViRMnomVhFEVlZGSoVCoAmDBhArpbv379kJCQP//8E70phYeH37t3LyYmRqPRJCYmKhQKDocTEhKCDCZ7e/tTp06hiGw2GwAyMzPVajWX+8/D5eOPP9Zf30MgvG+IcVP5oC/TiIYNG6Ifb9688fPzO3fu3H//+1+kkurUqdOgQYPXr18zgdevX79o0aJbt24VFRWdPHny5MmTAoFg8eLFjM7SR3sxss4zBiM1AjbaDYGMG+bvqlWr9u3bh640btzYycmprKzM3PS1O5WXlxcAFBYWqlQqHo9nJH07O7s9e/asXLkyIyPjxYsXL168+PHHHxs3brxp06bu3buXl5d2R9IuF15qBFMwpZOU1y6vXr0CAC6Xiz6JAkDdunXt7OzQtjsAePPmDXMd/RAIBMxGB2YNe4MGDZg0PT09AQBpp0GDBi1fvry0tPThw4fom1RQUJC7uzu6K5VK0XwPg1qtzs7ObtKkCfrLSEUgWBOy5qbyefv2LfM7NzcX/WjYsCFa0ycWi0NDQ2/evJmcnDxgwADtiD4+Pr///vu9e/fWrVvXt29fLpcrFou/++67/Px8DDEqNzUCHtHR0Xv27KEoasGCBY8fP759+7b2I8R09DtV3bp1eTxehen369fvxo0bf//998KFC9Hz7OXLl0uWLMErTuWmRkBY2EnQDkq1Wl1YWIiuyGQyuVzOBGDMC2YluEwmS09PT09PV6vVyI4BLWXF/EZieHp6tm/fHgBu3LiBjJtBgwbBO1OpQYMGt/Vg9t8BAPkaRagSiHFT+WRlZSEVQNM0Wqbn5OTk4+Pz7Nkz9C41ZsyYpk2b0jR99+5dJtbbt2/nzZs3b948kUg0fvz4X3755eeff0aJpKWlmStD5aZGwAZtJGGz2TNnznR1dc3Ly3vx4gVGOhcvXiwqKgIAkUiENsch28J4+jExMfPmzVuwYIGvr+/8+fMvXbo0b948AHjx4oVCoTBXhspNjcBgYSdBey0B4MiRI+jH0aNHtRdC+fn5IQvj3Llz6MrBgwf79es3dOhQNpvt7++PPiEhFQEA+fn5ly9fBoAWLVqgK2iN84ULFx4+fMhisT7++GMACAgIAICCggJ7e/vGjRs3btw4JSXl0KFDx48fJ6v6CFUO+Sz1Xpg8eXJYWFhWVlZCQgIAzJkzh8vlMl8Wli1bFh8fn5CQgHZ1ornounXrXr16VSQSJSUljRgxQq1Wo32bdnZ2Op5yTKFyUyNgg959KYqaOHFix44dz507h9Y3mLvmRigUhoWF9e7d+/bt2+grA9r4Zjx9JycntB7i7du3ffr0KSwsRH/btm2LsXm7clP7MJk6dSpamMKwYcMGCztJp06dfHx8srKytmzZkpKSwuVyL168qB3A09Nz6NChp0+f3rBhQ3Z2tlwuR1bO8OHD2Wy2q6vrtGnT9uzZ8+uvv+bn5/v5+Z05c0YikfD5fGZzZVhYGNp9ibJDqiwkJMTX1/fZs2ejRo0aO3bs69evDx8+rFKp5syZQ2ZrCFUOMW4qHw8PD6lUeuLECfR35MiRM2bMAICGDRuOGzful19+efHixc6dO/39/QcPHnz+/PmcnJzU1NTAwMD9+/fPnj37+fPnzOZwBweHzZs3M477TIfL5VZiagRswsPD9+/fn5KScufOnTt37oSGhgoEgpSUlFu3bkmlUnt7exPT8fX1zczMRJtsuVzu8uXLO3bsWGH67dq1W7JkyaZNm27evHnz5k2UlJeXF97m7cpN7cOE2bzNsHz5cgs7CZvNXrNmzRdffJGfn4/eYcLCwmJjY7VX7Sxbtqy4uDgqKuqXX35BV/r16/fNN9+g31999VVpaemxY8euXbt27do1AKhbt+7GjRsZn1heXl7t2rVDe6PQNykA4HK5e/funT59ekZGxtKlS5Ek48ePZ/zcEAhVCIts46xEmIMzg4ODY2NjCwoKOnbsiD5XM8TExKSmpiJ/wc+fP0fKaMCAAWhCRaFQREdHv379Wi6Xe3p6BgcHu7q6oog6B2cyHorHjh2LAuhfMZIaobLQP3ty27ZtFEUFBwcj+0OpVF68eLGgoKBNmzZdunS5fPnykydPAGDSpEmurq460R88eMBsBUevv8zBmW5ubnfv3qUoqmfPnj4+PowAxtMHgIKCglu3buXl5bHZbB8fH7QAy6DwjEtZxsOy/hUjqREIBEI1gRg3BAKBQCAQahVkQTGBQCAQCIRaBTFuCAQCgUAg1CqIcUMgEAgEAqFWQYwbAoFAIBAItQpi3BAIBAKBQKhVEOOGQCAQCARCrYIYNwQCgUAgEGoVxLghEAgEAoFQq6gZxo1SqZTL5egMJnNRq9VqtRojIk3TcrlcqVRixAUAhUKB5yBRpVLJ5XJzzx5CUBSFLbBcLtc+SdgsVCoVnsAajUYul6OTdCxEoVDUOPmxBbZkRGg0GktGBPYZmdgjQq1Wy+VyPJm1sVB+pVKJrYKw5bdkRKMRga2FsFWQJWrTkiGMPSIItZKaYdxIpVKxWIzXcZVKJd5I02g0YrFYKpVixAUAiUSCp1aUSqVYLMZ7XiI1ihERACQSiVgsxosrk8nwVKFarRaLxZVyprRYLBaLxXh1jv3sQfLj1TlN0xKJBCMiAMhkMrFYjFfnSqUSr8IpirJkREilUuwhjD0itKFpWiwWY9e5hfLjaSGKoixRQdgjQqFQYKsg7BEBANgqSC6Xi8Viyy1gQq2hZhg3BAKBQCAQCCZCjBsCgUAgEAi1CnKcL4FAIBBqMJGpy5RKpVqttrGx4XA4/QKXVbVEhKqnWp8Kbsm3W0Ktwc3Njc0ud4qxtLS0UtYjE2ouLBarTp06RgIUFxeTpaa1mLiXG7T/dm28UD8Ml8t1cXGxlkSEqqdaz9wIBAKBQAAAQqFQrVa7uLhwuWYLjJbj2dvbmxtRrVYLhULsIVFcXOzi4mLkqVweEolEJpMJBAJbW1tz46JNNE5OTuZGBICioiKapt3d3THilpWV2dra8vl8cyMqFAqRSGRra4saGgNnZ2f0A8lfp04dFotlbiIikYjP59vY2JgbEclvY2Pj6OhoblyapouLi40/lcsDmXTOzs48Hs/cuDKZjKIoBwcHcyNqNJqSkhIOh+Pq6mpuXAAoKSlxcnLicDjmRpRKpVKp1MHBwc7ODiNfAHBzcwMAiqKKi4vZbDb6ay5CoVAgEOCpIKlUam9vj6eFxGIxtgqiKMr4u0F5iMViLpeLp4LKysr4fD6eFiosLMRQQfYF9tozN3hKjFDLIGtuCAQCgUAg1CqqxrhRKBTffPNNSkpKleROIBAIBAKhFlM1xs2BAwcePXokk8mqJHcCgUAgEAi1mCowbuLj4x89emT9fAkEAoFAIHwIWNu4KSkp2bVr14IFC6ycL4FAIBAIhA8Ea++W2r59e0hISEBAgCmBmfNN0DZOtB7e3BxRFIwt5UzW2M71FQoFxs4dlC+273NsgRF4cdEJOBi7bVHroBOaygtjY2NjpBqZfJFTA7lcjlfnKpUKwy2CKfKXByOwuRFBa0RgnMCA3UmYerZkRGDs3EGVbORoERaLZXynGzrWClU4tvyok2OrILyjUSiKskQFAQC2FsI+CAwsUJuANSJQl4Z3YhtMgc1mY2znJNRcrGrcnD9/XiQSjR492sTwOkchYp+xAgDYB7mh83Tw4mKfYgMACoUC+8QlbIEtiWvJqS4qlcqIMcfn841oZ5lMph0Xu84tkR/t18WLa0ljWbJqDds5kCUjwpIhbGREVGjcSCQSxvJGJ0zhyWChCsLWQpZ0Egu1EF5EK48IpmLRKDaYApfLJcbNB4X1jJucnJzffvttw4YNpju6YJ5qCoWCoigbGxuM1z6kxDHcgVAUhV40MdyfAIBcLjc+5VAeKpVKrVbzeDwMjxrowGdsgWmaxnMlolAouFwuhgsTjUajVCq5XK6RBjJehzY2Nqii0JPe1tYWo86VSiWHw8GWn8Ph4OlNmUyGXeHYI0KtVtM0jT0iWCwWhvsTsGBEqNVqlUplZERUmKatrS2auUETe3jyKxQKHo+Hp4LUarXxTl4eFEWpVCpLRjT2iGCz2XgqyPojgsfjaTQaiqI4HA6bzTaYAsboJtRorGfcvHjxQiQSzZo1i7myYsWKpk2bbtu2rbwofD4fjRCVSkVRlJ2dnZWd+CHjBsPdGQAoFAp7e3s8J37IQMF24ocnMJokw4ur0Wiwnfgh4wYvXwBgagmpcgcHBwxVTlEUthM/bPnRsxav4Gge3tbW1spO/CwZEUql0s7ODs+Jn0ql4vP52E780PBH30pYLBae/CqVClsFqdVqPp+Pp4U0Gg22CqJpGk8L0TSN7cQPGTd4MstkMoyIPB6PpmmKotArFrYyIdQmrGfcdOrUac+ePczfWbNmzZo1q2PHjlYTgEAgEAgEwoeA9YwbOzu7hg0bal+pW7duvXr1rCYAgUAgEAiEDwFy/AKBQCAQCIRaRZUdnHn27NmqyppAIFQVbxOWGQ/QoHMFAQgEAqFCyMwNgUAgEAiEWgULz1OTdZBKpcjRAvJUxmaz8TbCsFgsjIho+T2LxcLYawAAGo0Gb/MhyhevsGi/K57AFEXRNI0n83utZBcXFyMpi0QixpMe4G74rHGdxMIRgeKaGxEVFnArGRVW+Hi98WAurRYZzNdIYVkslouLi5E0hUIhUnSWdBKNRoNd4TRN43USS0b0hzMi7mStRxWFZO7mo9uFAIDL5To6OmKIRKihVNlnKVOwtbVFu3PLyso0Go1AIMDo9zKZDM+zhUajKSsrY7PZTk5O5sYFgNLSUoFAgDHCZTKZXC5nym4WKpVKoVAIBAJzIwKAUCgEALzCisViGxsbjG3JSqVSIpEY3yVrXMM6ODig5xZ6gDk6OmJoZIlEwuPxMLayI/l5PB7eVvDS0lK8CkcmnYODA8bOZGyHRmhEcDgc7BHh4OAg0+rVFEXfSszkcNjdOvgwjaafuFwul8lkeM4RmDTRQ7e0tBR7RJeVlTk4OOCpIOTjB6/OJRIJdoVTFIWnhaRSKYfDwVNBYrGYy+XiaaGSkhKMwtrY2CBv3Twer7z+iaEWCDWaam3cMGMS9Us2m42hWVAiGBHRI5PFYmF7f0IepcyNZUlhNRqNJQID7nseelHDbh1LZNapYQ6Hg6HFqkR+1MGwKxwsGBHI3RlGvghsmbVbR0PRUxf/cSU6HQBCg5vvWzuSxzU8VC0pLIJpJgvlt6ST4MVFsxEWNhaeFsJWQWCx2jQ3CjPJhH4Qf30EsL5xExsbGx8fL5VK/f39w8PDsb1yEQiEGs3PpxOvRKe7OXFogMvRacu2Xl79f2FVLRSBQKglWHVB8blz57Zu3erq6tq0adPLly8vXbq0Oq/4IRAI7wm1htp5JBYAVs3w+HGJlw2fdfhkwvVbT6taLgKBUEuw6szNn3/+OWnSpMGDBwNAy5Ytv/vuu8zMzKZNm1pTBkIVor52UfsvdyB5U/9AuRn/4k1ema8Xv3d7BwD4akzdNYfzv1xx5srP0xtUtWwEAqEWYL2ZG41G4+vr265dO/QX2TTYRxMTCISay9nrKQDwSa9/Fn5+NtAluL1DYYlk+KwjW7ZsGTFihK+vb1hYWFZWVlVKSSAQaizWm7nhcDhff/01AMjl8oKCgj///NPLy8vX19dIFHRoHLzbvIrWw5ubL9onjLaUmwXKi6ZpjLgoolKpxFjcivJFx3aaGxedp4gnMAIvLjq7uOKPjP9uPo1CweziNpIvczi8QdChqsxfdGy1iWJryaXBs7NRLOPylwfavIpd4aBXdhNBp4Jj5IvysnBEaDQaiqL/jn0KAP07CQAt2wdYP9tjxvqch8+Kv/rqKxT++fPnAwcOjI+PRzvCjIwIFotlfKebUqlEtW2J/BRFWaKC8EY0Ouwau8IBwBIthKeC4N0B8ubGReDVEios6qIGU2Cz2RjbOQk1lyrYLfXHH3+cPHmSzWYvXrzY+LJ2uVyOjqpGoPO98VAqlXgRNRqNSCTCiysWi/Eigl7ZzQJbYEviIqVmHP6/lY7yXV4qlcqIbeHm5mZEO6Mjo5m/2HWuVquxK1ytVmPXmyWNVSUjgqIobJklEolCoXickVdYIvH24HnV42jeGWd2tnDwG8/zt0TPX6uaePI6B9ot3JWX+uzZ7t27Z8yYAQAKhcKIcVOnTh0j+YrFYsYKpGkaW34LKxy7zi3pJNgjQqVS1ZQRwXQMpAoMpsDlco07QyLUMqrAuBk/fvzw4cNjY2PXrVu3ceNGPz+/8kJyuVzkaAG9eBl/gy8PbE9WaDaiwpfC8lAqlTweD0NgNF/F5XLxZEbOHsyNCO8qGcOzBQCoVCpTdpxy/u2UhWVjgyqZw+EY8ddivA55PB7KFyk4PPnVajWbzcbYMavRaFBc7DrH7l00TTNlNwv0movhIIeZjLRkRHC53PiHrwGgWyt7NutfwtvwYHif/z1+5o2sM2vjm6NHj86ZM0etVhsZERWOMj6fj2ZuLJFfpVJxuVzsEW28k5cHTdNqtRqvd6ERgac21Wo13p5qNKKxR4RCocAYwlwul6Io5OCAxWIZTIHsD//QsJ5xI5fL8/Pzvb29WSyWg4NDSEjItWvX7t27Z8S4sbW1RW67hEKhWq22t7fH0A7oZcuIj7jyUKvVQqGQw+Hg+bUsLi7Gc58lkUhkMhlTdrNQKpVyuRxP4KKiIgDAi1tWVmZra6v/zNBZQQz/DsB1dFQoFCqVisfj4bn8Aq2WRc97gUCAocpFIhGfz8fQqgqFQiQS8Xg8jHqjabq4uBivwktLS1Uqlb29PcYjRCaTURSF4XVQo9EolUo2m40nc0lJiYODg4TPT0h+DQCdW9iz2MZaqkc7QT1XbkZGxrNnz3x8fPCc4CFQ76Ioqri4mMVi4ckvFAqxVZBUKrWxscHTQmKxGE9g9NUSTwshR3x4KghZgXgyKxQKjIh8Pl+pVFIUhSxg4omYANZcUPz69eu5c+eiJygC2dpWE4BAIFQ5NE0nJmcDQIeACiwVNgv6dHAAgKtXr1pDMgKBUIuw3sxNkyZN3N3df/311zlz5nA4nNjY2OfPn0+fPt1qAhAIhCrnxaviklJZw7q8eq4VK59urR3++Ls0KiqKKIoPmcjUZVUtAqHmYdXdUitWrFi2bNno0aP5fL5arZ45c2bz5s2tJgCBQKhy7qe8BoDWviZ97+jY3I7Fgri4ODLFSyAQzMKqC4q9vLwOHDhQUFAgEom8vLzwlvURCISaS9KTNwDQpplJxo2bE6dxA4esN6KMjIyOHTu+Z9EIBELtwarHLyDq1q3btGlTYtkQCB8gyU/eAEAr04wbAGjj7wIASUlJ71EmAoFQ66jWp4LLZDK0mxH5UBGJRBgbYRgHgOZGRF6hNBqNUCg0Ny7Kt6ysDC8iAEilUgwnEzRNUxSFJzACL65Go9FoNPpeQLhGi6AWChk/Y0bydXZ2NtLuYrEYdQ+UVGlpqVmSI9CObplMZm5Epnfh1RtN09gVDgBisRh7RGA4LWT8pGHLXFxcnPI0l82GAG+eiV+amjdxPBsFjx49YrSBPiwWy9nZ2UgiZWVlFEVZLr8lKkgul+NpIWyBUb54Wgi5tcRQQUzvwtZC+hFNEQM1LvKYZTBrLpeLvSWTUBOp1saNjY0N2ukqEok0Go29vT2GrwI0MDD2NCJdxmaz8YZEWVmZvb09xiZMpMQN7qyuEJVKpVQqMXb5wjuzAK+wEomEz+frb0umjBbBViBQKpVSqZTL5RrZJWv8cWJnZ8eYNTRNOzg4YDx+kAwYFY7k5/F4GLt8aZouKyvDq3Bk0tnZ2WHsTFYoFBRFYWyrZp7u2CPixYsXcoW6WUO+g62pYrf0dQGAlJQUGxsbPCdG8M5fAHrfwB7RIpEIWwXJ5XI+n4+nhaRSKXaFUxSFp4WwR4RKpZJIJFwuF08LCYVC/cIaFOPJw9eJt57b2PB6hwQ28HJVqVTIPVh57YuhFgg1mmpt3DB+1VC/xPOChVLAiIhgsVjYcdFIMzcWisJmszHypSjKEoEBt6KQvy/9uGqjxedyuWgGAq+wCJ2HDZ6btfLkrxAkP16dI5sMu8IBd0SgORuMiChT7A7GYrGSk5MBILCJLZjcRE3rFABAyqN7xclruVxug87LMLJGAjNzRZXbySvEkhENlqkgsEALYasgsFht6guj/Vel1GxZdu7qXw/R38M/3Ji1KCRsZDuUryXKhFCbsHYnSEtLu3nz5tu3b728vIYMGWLcaTqBQKhNoKUzgT5mTMC4OnHcnTmFpfLcApFXA9f3JhqhZkBT9MqvTt76O83Wnt19sIukVBN/tfSH1ZfYHBjwSauqlo5QjbDqguLExMSvv/5aJBK1b98+LS1t/vz5lqwOIRAINYsHDx6AmcYNAPg2sgGAjMyC9yIToUbx6/6bt/5OEzhz5m/3Dp/qHvFV/c/+68Fiwe51V7OekR5C+B9WNW5+/fXXPn36fPnll+Hh4atXr7a1tb106ZI1BSAQCFUFTdPJycksFjRvbJ5x07WlfWhwgIM95oIbQq3heXrez7uj2WyY+J2nR+N/FuJ0HujUa4iLSqnZteYqTVetgIRqhFU/S7169Wr06NHoN4/Ha9q06Zs3b6wpAIFAqCpevHhRVlbWsC7PycG8NblTwl3tvD4hziM+cGia3rHyglpN9Rnh2qzNv5bDD5rsnnJX4t+ygUqp5nLJAZkEACsbN7/++iujoZRKZWpq6uDBg42EZw6fYnZlY2SKUkC7hc0CZYdO5cXIF96dNW1uLCQwRVF4MlsiMGBVFADQNI12U+tcN77dF52WDBUV1vjyQFRk7TQxFhSXJ3+FWNJJkNjYFY5yx9uZjN27wIIR8ejRIwBo3tgGsF6v0abo8rI23klQLKY3WtLJMSJW+YjG00JV0knAUOugCoy58uTx/Wxnd27IuDo6MzQ8G/bC/d4tGvTj8TnliY13yDmh5mJV44bZCSkSidauXcvj8T7++GMj4XV8vYhEIuysMRw2ILD93ACuhwkEOkkYL671/dxAOX5T+EarXfkuL6VSacQFiJubmxHtLBaLtbPG83MD7zZ1Y8fFcGGCsKSxxGIxdtzyfMZUCLbbFbRVKsCbr6FwTAQjLkxYLJbxrQloUzT6bYkjKAtVELYWsqSTYGshS0ZEpfu50Wion7ZHAkDo5248G5rS60IcLsA7f2bl+blxcXHBE4lQE6mCLXNRUVEHDhzw8PBYs2aNk5OTkZAcDge5TlGr1TRN4+3yRW8SGDY7evnA3tOoUqnwBKYoSqPRcDgc7PctPIFRJev7qjExLrNvXxum2qUKxZOXrxRKVUAjL3fnfxqdx+OhSmaz2UYayHgdMoVFJg6e/GgKBK/CNRqNcfmNoFKpsCsce0Qgd3bWHxFo5ibQxwbP4wibzWaxWAarq8IEeTweKjV6p8eucw6Hg6eCKIrC6yRoughbBQGucwQLR4QlalO/dTgcTuT5lJxXJfW9+Z0GOBksDZrLMdJJyP7wDw2rtjdFUdu3b7979+7EiRNDQkIqHHJ2dnbI1ZhQKFSr1QKBAKODopcPDB9rarVaKBRyOBzjzk/Lo7i42MnJCUM7SCQSmUxmZ2eH4fJLqVTK5XLjJmN5FBUVAQBeYcvKygx6HVTb2GTl5q385diJ6BiZQgkAbBZrQMf266ZObt20iU38LY1Go1AokLsw7sAwjKwZX2FFRUU0TTs5OWGocpFIxOfzMXzEKRQKkUjE4/EcHR3NjUvTdHFxMV6Fl5aWqlQqBwcHjOe0TCajKArDx5pGoykpKWGz2XgyP378GAACm9iZOyho6h+HQFwuFy9r1DoURRUXF2PLj/zL4akgqVRqa2uLp4XEYjG2CqIoCk8LicViLpeLp4LKysp4PB6eFiosLNQvLI/L/+OnOAAI/bwOl2u4LBRFAwCPx8PW2IRahlWNm2PHjj1+/Hjbtm0eHh7WzJdQVew9d2HR/oNShYLDZndq1NCOx7v/+s3VxPtRSck7582ZFPpRVQtIsBK5ubl5eXmuTnyPOuQFmmAeNy49znlV7NGY37aX2a8QhA8W6ykalUp1/vz5yZMnE8vmQ0ChUEybNu3nn39msVgTOnX4/qN+Xi7OAFAmV6y4Frkz9s6MrTsUKtW0sNCqlpRgDe7duwcALX3JKzXBPGia/u3HWAAY8JkbOUGBYDrWM25yc3OlUunOnTt37tzJXPzoo4/mzp1rNRkI1qGsrGzo0KGRkZHOtrYHRw8LbxnI3HKytdkU/nHL+vVmnzozf+ceL3f3fm1bV6GoBOtw//59AGjRFOdTBeFD5vz585lP8+t48NoHk2kbghlYz7ipU6fOmjVrdC66uhJ/6rWNoqKijz/+OCEhwcvN9dykcS3q19MPMymoo1AmW3zhyuSNW2K3bvRpQCbzajnIuGnp6wyAv8OL8AGybt06AOg70pXNIfM2BDOwnnFjb2/fqhU5+6OGob52UftvhWt+3759+9FHHz1+/Diggcel/37RUGNgfzhiQXDPhOycU8mPZ+7YdWnNisoRl1BdSUxMBIC2/i7EtJTyigAAIABJREFUuCGYTnR09O3btx1dOV1CaswHzXPnzsXExDx79oyiqIYNG4aFhYWGhpq1Dn3lypWRkZGXLl3CWNBdVVy5cmXdunXff/993759q1qWf6jWi/vkcjnazYi2c0ulUoyNMGjzKob3LeQYQ6PR4Dm3oChKLBbjCQxaZTc3U2yBETpxOf923yIrJ2W1Wi2TyZ48eTJ06NCsrKw2jbzOLZhb19GRKio0ktfOoYPvZL26nfLkbmpa1xaB5SUuEAiMVKNUKmX8hgGu6xeVSkVRFIavGpS1SqXCq3OapvEiok4ilUoxNsIgv3DG/SsaBNUwRVHmypyXl/f69WsXR179OjZqkdku/Gj4x9theVmzWCyBQGAkBbFYTNM0kh+7zjUaDbYKAgCFQoGnhSxRQQCArYXUajWeCkLRsbWQdsQVK1YAQO9PXdncf/ZDlcs7l5jlVReHw8HYrWYWeXl5gwcPTkxMZLFYDRo0UCqVhYWFe/bsCQoKOnXqlJeXl4npPHnyJCoqCs9jZFWRm5sbFRU1c+bMqhbkf1Rr44bx0KBSqZBXDzxHESwWC8N3u0ajUalUbDYbz++7Uqnk8/nY3nK5XC7GLl/k/gRbYADQjfvvCueUk7JGo0lISIiIiCgqKuru53t63mwXezsAAKPFd7W3/ylimIDHbxfYnMPhlJe4cdDmTyQ/8tOD59WDcapkFiqVCvnpwahzmqZRJzE3IrxT4kzZzQJVFEa+yP7DGE0PHz4EgNZ+zmw2lo8bmgUAyE+MwawrTJTP5yPjxnAnNw21Wo2tglAHw9NCFEVhj2jU0HjOkPBGBDKJsNWmQqFgIiYkJERGRjo48np84lxhCZDhg7xtGcwa4zXAXEaPHp2YmDh//vzvv//ezc0NAPLy8latWrVz586IiIiYmBgryEBgqO7GDZrNQ545+Hw+hpMJZP9iuDBRq9XoRQ0jLgBIJBI+n4/Rm9F7HpfLxcgXzVHhCYzmPHTiqv+tyrmGUqZpeuvWrUuXLlUqlUM6tj86fYod/x+dSFekk/o0a0pRFM1icTgcg4lXCKN/GfkxVLlSqeTxeHj1JpfLORwORlyapiUSCXamyLjBePwgT48Y+Wo0GolEgjEi0IKb1n4ubDbbuL1rGJqGd8YNXnWhpx2aVMAe0TKZDFsFKZVKvBGtVquVSiW2CkLGDYYWQj5I8VSQTCZjs9l4MotEIiYiWm3z0cgmdia4ZGKc+GF3Egt5+fJldHR0z549t23bxlysX7/+Dz/88Pz580uXLsXExPTp08f6gn2wVI1xk52d7eTkRFwt1QLS0tLmzJkTGRnJYrEWD/542bAhbLJfk/Bv4uLiAKBdc+L8nmAqcXFxFy5csBfwQkY3lcGLqhanYlJTUwHA19dX/9akSZOYWUMAOHXq1M6dOzdv3tyhQwcmzKZNmy5cuHDmzBlt54fXrl27dOnS8+fP27RpM2LEiLZt2zK3JBLJwYMHExMTi4qKvL29IyIigoODtTONiYm5ePFiWloah8Np1qzZxIkTW7RowdxaunTpli1bbt68+ccff9jZ2XXu3HnChAkBAQEZGRnHjh178OCBq6vr2LFjBw4ciKKsXLny3r17v/7666pVq2JiYlxdXTt16jRq1CgmTYPk5OQcO3bs3r17arW6Q4cOU6dOrVfPwP6S90QVGDc0TW/YsGHChAmdOnWyfu4EbbTXC/NlMkX3YCOBdbh79+7Ob78+fiNardF4ODruHTEkrHev9yAjoWajVqvv3r3LZrHa+pOXGYKpfP311wAQGtHUwZEnqwlr0Js0aQIAp06d+vzzz3UW1Y4cOXLkyJHM39evX0dFRZWUlGiHSU9Pj4qK0l7ktH379m+//RatODx79uyGDRt27tw5bdo0AMjNze3atevLly/5fL6rq+vFixf37t27fv36hQsXorhz587dtWsXj8dr1KiRVCo9ffr0li1bzp49GxYWBgAFBQVRUVFffPHFzZs3AwICFArF9evXDx06tGbNmrlz57q5ubm6up49e/bIkSNnz54NDw8HgCdPnkRHR4eFhcXExDg4OHC53AsXLmzYsOGnn36KiIgwWCFXrlwZO3ZsUVFR/fr1FQrFqVOnNm7c+Ndff+kYYe8Pq34ClMlk8fHx69ate/nypTXzJVQKNE0/e/bst99+mzNnTrNmzbp27frL9Ug2wNye3R58OTe0uX9VC0iojjx48EAikfh6C5wEOCc6ET5Azp8/f+PGDSdXm48jmla1LKYSEBAwadIkkUjUr1+/Nm3aLF68+OrVqxKJBDvB5cuXHzx4sKSkpKSk5M8//7S1tZ0zZ86LFy8AYOXKlS9fvty5c6dQKMzNzU1OTvbw8Pj+++/R5FBcXNyuXbt69Ojx5s2b58+fv337Nioqis1mb9q0STv9O3fuXLx4MTU19fnz53Pnzs3NzZ08efKSJUuys7OTk5MPHjwIAIcPH2bCC4XCO3fu7N+/v7S0tKSk5MyZM3w+f/Lkybm5ufrCFxUVRURE2Nvb3759Ozc3t6Sk5K+//lKr1SNHjtSx6t4fVjVu3rx5c+bMGbzV+4QqQaZQXk28/81Phwf832L3oaP8/PzGjBmze/fuFy9eeLm5LuoXnL74y82fhLnY1ZgtiwQrEx0dDQCdWxs7tZtAYFAqlV999RUADJvib2tfrVeF6nDw4MHDhw8HBwenpqauX78+JCTE1dW1V69ee/fuxThffdasWZMmTUInKH/66adr1qxRqVRbtmwBgCdPngDAkCFD0NmLrVu33r9//+rVq1EuKpVq4sSJ69evd3d3R0kFBwc3a9YsMzNTO/0pU6aEhoYCAIvFmjp1Kkrn22+/RXcnTpxoY2OTnZ2tHWX69OnTpk1Dx8d+8sknq1evlslk2muMGDZt2iQUCn/88cdu3bqhK0OGDPnuu+8KCgqOHz9ublXgYdWu06xZs9WrVwPA0KFDTQmPDvIFrT2oluzoxouIdjqYG5eJiCQ3NyJYUFizBNYXj4mLDm8/99dfErmcuetdx62DT+Nuvk17BwS0b9wIigrg3U5dAFo/tXe3jInASFKe2Mb3pzCdhJEfb4eaJb0Lr5MgsbF7F1jQSfAiMlHMivv3338DQFArNzDU30wHbXeypJMwu9+x69ySTmKdEa0Pthay/ohArF27NiMjo5GvU/An3v9IbpL4/zwpyssa75Bzs2CxWBMmTJgwYYJYLI6Njb1x48a1a9diY2NjY2P37Nlz7do1s1acjBkzRvvvhAkT5s6d++jRIwAICgqKiooaMWLEV1991adPn7p166KPR4hevXr16vXPCgGVSpWVlRUZGZmWlubj46OdYPv27ZnfLi4uAKC9UITNZjs6Our0HIMioe0COsTHx9va2vr6+mp/pQkMDASAhISEWbNmmVYHFlGt7WKpVCrXerKWlZVhJ6Wdjlmgk5Dx4paWluJFhHcnCePFNV1gvkymH/fGjRtr1qxJSkoCADaL1cW7UX+/pl29G3X0auis5apBrVazNf/zlULTQOl5xdAOYATkVENZjthubm5GtJJIJNL+UC0UCk3JUR+lUok9h6xUKjF85CAsmaS1xKGRQqHAi2jWiFAqlTExMWwWq2MLZ5qm0Q52vHxVKpVKpTKYNYvFqlPH2MyQUChkLBuKorDr3EIVhK2FLOkkFmohvIg3n63W/tu18UITIz558mTNmjUsNmvcF4EUpUaNpqFM7TNoGBqsLi6Xix7hVkAgEISGhoaGhq5fvz4tLW3KlCm3b9/+9ttv9+/fb3oiOraIQCCoX79+VlYWACxbtkwul+/fv3/UqFEA4OvrGx4evmDBgkaNGqHASUlJ69ati4uLy87OpijKxcXFwUF3y5n++0CFu8wMivTs2TP9kE+fPpXL5QaXV1vSIc2iWhs3aF8fvHvTYmN5yUC2J/bbPLa9T1EUXkSUL3ZhaZo2PV/tkDRNF5499dW+gxfjEwDA08lpRrfO4zq2a+D0vyNddLd2M/9oAIOVXGEJ3kVksVjlvXwbrwedToLhhgQAUENbv5Mg9yd4EaGKRgSYU8nx8fFSqbRlM6c6zjbI4xTOJ+mKOkmFaTIVVSWdBM2+4HUSc0e0NlU7IrTjmiiDVCqdPn26UqkcONLHr/X/TuZhVaxH/pncMdJJ8OrBdObPnx8fH3/lyhXt7U4A0Lx5899//71Ro0Y3btwwEl3/7UgkEmnP9CDnk8h8sbOz2759+9q1ayMjI2NiYiIjI7du3XrgwIGEhISAgIBz586NHDnS3d198uTJQUFBfn5+vr6+nTp1wn7x0xZJ54pYLDY4HSUQCDw8PAx+gUIegKxAtTZu7O3tkU9JoVCoVqudnJwwnEyglw8M35RqtVooFHI4HDx7v7i42NnZGUMrSSQSmUxmb2+P4XtbqVTK5XKd0WUEtVYWR69c/XLvgVKJxNXO7psBfaZ3C7LRq23Wv72q0Ox/9MU/D3g9nytMgPJAqhD5NxNgHTTm6PiP7VVUVETTtIuLC4ZGFolEfD4fwz2GQqFAcRkxTIem6eLiYrzj1UpLS1UqlaOjI4afG+Q1Sv9NrkLQnA2HwzFdZrTgplcbnkbyApnsOA94igYAHo/H5XLxqgsNYYqiiouL2Ww2XiJCoVAgEOCpIKlUamdnh6eFxGIxtgqiKApPC4nFYi6Xi6eCysrKdPzcmFLhFEVNnTo1PT3d29fpszktebz/yczmVCw/8l/M5/PN6p+ViEwmi4uLu3Xr1scff6xzC71OeHp6al/UcRGekZGhE+vevXvNmjVj/j5+/Fgqlfr5+VEUde3aNVdX16CgoMGDBw8ePBgANm3a9H//939HjhxZs2bN3r17KYpKTEz08PjfmX3YU8s6IgUEBGiLJJFI/Pz89EP6+fldvny5Q4cO2orxzZs3CQkJzPTS+4Y4TCSAXKmcsXXHlM3bSyWSEW1bPfq/+f/p1V3fsiEQzIKm6b/++gsA+nY025AifFBQFDVz5szTp08LnHjz1nTi8Wveg2n8+PEAMH/+fOTwRptly5YBAFq9C+/etJHbbsTt27fv3r2rE2vt2rXMZ0GVSrV48WIA+Pzzz9ls9oQJEwYPHqz9lRMtZ0FvLJmZmXZ2dnXr1mXuxsXF6RtPGKxbt44530atVi9ZsgQAhg0bph9y+PDhSqUS7ednwg8bNuzTTz/FOyEHA/IA+9DJKSwasXxVYnqGgM/fOmTQ5507VByHQDCBuLi4rKyshnV5LZqQzXSEchGJRJMmTTp16pStHfc/azrU96qRpnCvXr3mz5+/ffv29u3bDxkyxM/Pz9XVNS8v78KFC0+ePOndu/d///tfFLJHjx4cDmf58uUsFqtVq1YPHjxYs2YNj8fTnlxhs9lJSUndunWbNGkSi8X67bff7t69GxISgiyk0aNH79ixo2vXrpMnT3Z2dr579+7vv/8uEAhGjBgBAAMHDtyxY8cnn3wSEREhEAhu3Lhx4MABZ2fnnJycS5cuWeIlOTs7G4nE4/GOHz9++/btoKAgnVXGiPHjx+/evXvnzp1Pnz4NDw+3t7f/9ddf7969O3bs2DZt2mALYBbEuPmgSUzPGLZ05dviYl/3Or+PjwisV7fiOASCaRw5cgQAwro7Es8PhPI4f/78/PnzX7x4IXDmL1jf2ae5sQNQqznbtm3r2LHj+vXr//jjD+ZivXr1Vq1aNW/ePOYLcosWLfbt2/fVV18x5k6bNm0GDRq0du1aJpa3t/f27dvnzZu3YMECALC1tZ01axaz6Xr9+vUKheKnn36aP38+uhIUFLR582b0zWjNmjVv3749ceLExYsXAaBdu3bR0dGPHj2aMmVKWFjY48ePsQsYFRU1ffp0tFGfx+ONGzduz549BpcBsFisyMjIL7/88vDhw1euXAEAOzu7hQsXrly5Ejt3c6ka42bVqlXe3t5VkjWB4cSJExP+u0imUA709/1l7GhHPvGxRqg0ysrKjh07xmLBp8GmrgAjVFsiU5fpXOkXqHvFLHJzc//6668DBw7cu3cPALwDbD//uoFbg4ISWS5HyXEXBFSYQvVk/Pjx48ePz83NzcnJAYDGjRszzma0mTJlypgxY54+fZqens7hcAYNGlRQUBASEoKOJPr+++/Lysq6dOnyySefvH37Nj8/v0WLFtqr62xtbffu3bt58+aXL19KJBKf/2fvvOOiOL4APtePa/QugqJUQUkAsYKIEnvv0VhC/FmiiSYae0nUxJJoFKPR2BM12LCjRhErKqhRFBQLoPSD6/12f39MvFwoBzeHIDjfjx8/x+6+mbezM2/fTnnj5WU8CMXlcv/888+SkpKXL1+2bNkSLiQMCwuLjY0tLCz09fV1dnZOTk42nj3j4uKSnJxcYVZQYmJihUlXXl5eqampQqEwNzfX39/f+OxHH32UnJxsvBsDh8PZsmXLpk2bMjMzqVSqt7d3Pe/51TDOTWBgYIPki4GQJPntt98uXbqUJMkpHduv7d+bTqXWZo0uWVJcD+phmgBbt26VSqVdP3TydEHZHRrTxNBoNA8ePEhLS0tLS7t+/XpGRgacZmvrwB4wvnVQjLopbZjt4uJiPJm3SqysrIKDgw1jNM2aNWv2JtAGnEADcXV1dXV1rTIFLpdrYmsnR0dHY48HAODu7u7u7g4AcHBwqLAHAovFqrwrQseOHatM2d7evnLkBWdnZ2dn58oX0+n0Nm3aVKfkW+WdHpZSq9UwhAl87yoUCuRNtitMTa8NhtBbaBOg4J7PCCt3oMJqtRr+MAu9Xl+jwnK5fMqUKUePHqVTqWv7957cIRwYlQ9CQYE3ywEqK0ypXWoEQWg0GlU1anO5XBPFqFQqYfWAOqDFqtHpdARBaCvF6akRmDVc0mKuLFzli1a7YL5KpRIhXA18TGgKg9q1CIlEsnr1agDApMFeJPEm6Aj5z7ons/MF/9Su6rKmUCimF3/B/bENEUHRypwgCEtMkEajQbNClpggAACaFdJqtXq93tCiK6+1qU4lWDNhixaXKx7cyX14N29O1skHDx4YJ8Lm0AND7cO7u37Q2ZnOoJYpsg1lQxBkBUtC1KbavDFBer2+St1oNBqM54t5T3innRsqlQoXXmo0GpIk6XQ6gmWBBgVhAaehJwNBFgCgVqvpdDpaVAxgdO/mAguqurOZmZljxox59OiRHZe773+fRjvZGQkCAADa9AgSAEABVTydmlJ7kymFSqVSke4XxgKv7s9aotPpaDQaWoFrtVoKhYIgCzcKRs4UAECj0RCid8CwKwj5wppZm5tduXJlaWlpx3YOHdo6qsX/OjcA1CL0UWVIAACAy8irzLrGJ06n06FzA31BtDKHDwvZBKG1aIIgdDodsgkCAKBZIb1eb6xw5buuTiWtVnv9+vXfE1PSb7x8llVkcGdpNIqHt8DLz9rLR+AdYNPcx5pG+1cr44BZlErZ1UZ7Q5yb6sr5bYcnxrxrvNPODYPBgAONKpWKIAgmk4lskRECNuh0OqVSSaVSEWQBAAqFgsViIbQo6FQxGAy0IBMEQVQpSJLkli1bvvrqK4VCEezR7M/p/2vp5PjfYSY9AIBCQTEBFJKggCpilJE1p0bC+GZ0Op2OVM5M5j+jHvDrnM1mo32nMhgMtDg3KpWKRqMhPCySJBUKBVrtUqvVer2eyWQixLmBsYUQ8tXr9QqFgkKhmJb966+/fvnlFwadumhyG+MwgySFpFCRfed/nAO04oJPliAI2I2BlohKpUI2QdAxQrNCGo0G2QSRJIlmhaBHZci38l0bq6RSqe7evXv9+vXk5OTLly8bQr2xrKgtgzjeQVYhYb5evtZMVrWO+L8VQw8o1IqWpDbVhiQAeOPuoxUXxgRLly6dPn06j9eYpnvXt3OjVqvT0tLKysqCgoI8PT3rOff3lr///nvGjBkwotqkyC4/jh5hhacPY94C2dnZo0aNIghi1jj/1p5mBzZ8r6g8S9eYGmfsVhA3fX2NM4IrXBDu8ZXxn2qVTiZVyaUqmVQlFSvF5Yq7p9fl5eW9fPkyKysrOzvbMJBEoYDmrQWtPqAFhPG8Atg0OvRLSiTaEmD2qC/mXcHX19d4AnKjoF6dG6lUunDhQrlc7urqumvXrri4uNjY2PpU4G2gO3+6whF6j961v970xZaL37t3b+3atfv37ycIwtXGZvMnY/q0e1thBt6f6cYFt5fCH3q9Ho4/8jutbFCN/oNBPYhr2NKqr6vd9YazJEkqlUq74HnVpfP8+fOYmJiSkpLuYbxxPfQqUVatVX7fIfSkXK6m0agcbv3Nv1Yqla9fvy4qKiopKbn6IF1crpBJVTKJSipW0nVXxWKxRCIRiURisbiqqV3HDL/oDKqXr3WrNra+be39Quw4fKpI9QwPA2Ealnp1bs6cOaPRaDZt2sRms5OSknbt2hUVFVXPy8PeBzQazd27dy9evHj48GG40pLNYEyLjZnXr4/ACnfYoiORSA4dOnTx4sXcpzdYDFrbALc+3fybu71z/RN5BaLj5zNu/51XXCp1duR37ckcOHBghYUVL168OHLkyLVr1169esUCpcF+rjGdfcLbNqcaDQEolcpTp04l/nHyyYsSCgCtWzh2CW0+0L/qD/CrV68OGzassLAw1N9q9TRXtAGo94QnT57cvn37+F/nX2YXF7wqLymUqJT/lqq1LcfWnuvf+paXl5enp6ezs7Ovr6+Hh4eDgwPCgItarS4rK8t6mF9aJCnKFxfliwpfi4ryxeXF8aWlpdXLPTL+g82hsrlUKy7Nikfl8GkcPtXZ2cnGge3gYuXcjOviwaXR/3Fl4CQhc5XEYOqcenVuLl68GBMTA9tn9+7df/vtt9TU1K5du9anDnUISZJFRUX5z1/kC4XFIrFQLBZKpOUymWTHXoVCIRaLdTpd5X2AmUoFh82iUakCDofDYvGOneLz+dbW1nw+38rKCm4LRaVSraysDLNJIGKxmCAI2e2baq0WACBTKrU6PTX9gVKpVKlUcrlcLBYXFhbm5OS8ePHCsPDHgcf7pEvHGT27u9bXjrhNkuzs7I0bN+7YscN4IcaFa0/XbbscEdJ80ojwCRGIW2DWLcnJyRs2bDh+PNF4gUnS5QULFixo06ZNjx49nJ2dS0pKkpOT09PT4WoayNXbLzbvve7mLOjR2Sesm6NarU5NTT1z5ozxVnmp93L3HU1bsvHGtGnTJkyYAJeVAgAKCwu///77+Ph4nU4XHe68eoqAxcSuzX8QCoXXr1+/f/9+amrqzZs3hUJhhQuYLAqdQQUAqBR6cblCXK54mX2mcjpsNpvD4cCtppTaf/e+5nCYNryT8Lder4e7l8tkMrlcbmL9IJNNs3eysrZj8W2YTL6Sw6dy+DToxDjaNeNZM3kCFofH4PLoQkXF4P2NNxQN5j2h/pwbkiSLi4tbtGjxT8Z0uoeHR3Fxgw1kGExABdRqNdzRo6ysrKysTKFQEAQhEolEIlFpaWlRUVFxcXFRUVF+fn5RUVEdfKOc/8vSFCrBoNGCPZp19mkdG9yme4AfE+8SVWsMT1+j0ZSVleXm5qalpZ09e/bq1askSVIooPMHjn26uHnal5dL9dfuy09cldy8m3vzbu6yTV4DBgzo1KlT69atnZyc4HZxdDodYUPN2qBUKmUyGVy+Xlpamp2dnZqampiYCHeQYbMoH7UXRH3Ic3Og55fqbmUxzl4rePjwoXFwUj6X0aODS2Sok6cbt6Tw+e1HiqSbspxCye7Dd3YfvgOvoVBAWBu7bu1oQa3YJAn+zlYdTxE/yStctGjRkiVLAgMDnZ2dxWLx3bt3dTodnU6dMcZ32qjWWkkd7GLzLqPT6SpvjyyRSPR6vVQqlUql0Fa8evUqNzf32bNnjx49KiwsNL7YwcXKO9DW0Uvj2oLl6M6wdWTQjdxBlZwQler0EmdhobI4X16cLxcLNWUlKplIo1KqVCpVWVlZVXoVVaktk03jWzN5dqS1Pd3GkWHnTLdzYdg5Mbxb+vME/068K5X9ZwzRhm2PtsspBvOOQDH+enurSKXSMWPGrF271sfHBx5ZunSpu7t7XFxcdSIymaxyz4eBvLy8Dz5oyI2Q6FSqo4DvwuW68HmOXK4Dl2PP5dhaWfEdHPhsNpNO57/pQ+YwGTqC0Oj0AACqRKTQatU6vVyjUWq1EjpDolQp1GqZWi1TqaUqlY4gpCqV/r8hMfhsNo1KpVGpAgoAAPCYTAaNBgBgCQRsBp1KoQqs2Hw225HPc7e19bS3Z9Jr1YtALav4BdkgaP0DAQCaTlFVnrWzszNhZOH+2NWdvXjx4ogRIyxWEHCt6H0jXcf29WzZjAsA0Eqz4XGlmki8Ij14XvI8vw423bUcV0f2iFiPQZ20Nrx/KwCD30pPkPcyRfezRCKplseht2llHRpoy3yz8bLhdjJz1Hceq/JFHAad2ro5r0M7exd7tuEs5H6u/Z9JeRdvFStU/8RKYDGp3ds7TRnu7e3BM06tDuF6/LM5n3XgnMpnKRRK5ahixsD9sas7++uvvy5YsMBCDU3D4TM8W/O9/Kxb+lt7B9hY27MAAOVKUwVla9Wq8kFCT6oUOp2OVCv1YvVL41PWLC/jPykUwOHRmWwa7BCqnFeF9CtcYPpsdeqZuN4sasy9Snwd/6kkEZ5VVBI6nY62uTqmkVJ/3/RVGhfTUXEpFApcRwo9sApLfKlUap1XVi6XCxc9cjgcJpPJ4XDYbLZAIBAIBDY2Ng4ODvb29g4ODs7Ozk5OTo6OjiZeunCFM5oaFsoSFEq1LqFJQVCLkCHVyVp+s2jypisJg8FAqyQ8Ho/P57u5ufn6+nbo0CEqKqrKCGAkSc4aAGYB8PDhw+Tk5Pv37+fl5ZWWlspkstpEfLYQCoUiEAisra2bN28eEBAQGRkZFhZWXZ107Qp6vdEZVPOg3QHoXn12UNANgN6TKVqtNjMzs6ysjMfjBQQE1CY82ttrETV2MJhbIQTDAAAgAElEQVSuJGw220JLIhAIKBQKg8Hgcrk8Hs/a2tre3t7FxcXNzc3Ly8vb2xsGma3nxmVhi0aTNXwtN6AlqRLkZDGNlPrruSFJctCgQYsXLzZ0t3z11VdhYWG1+bAWiUQ6nc7GxgYhyAQcZYC7zJuFTqcTiUTI/n5ZWZmNjQ1Cv65cLlcqlTweDy3OjUqlghN3zEUoFJIkWeVOKDUikUjYbHaFSUK1Qa1WS6VSNptteQQFqL+9vT2CFZNKpUwmEy3OjVQqZbFYCMNPJEmWlZWZ7nKoDthfZW1tjRDnRqlUEgRhOqRvlej1+vLychqNZmtra64sAKC8vFwgECDMTFIoFAqFgsvlWhhhliCIsrIyKpVqZ2dX89WVEIlEPB4PzQQpFAoOh4NmhWQyGbIJIgjCdMdndchkMrTAPBqNRiKRMJlMNCtUWlqKZoKkUqlarRYIBAhWCNMkqb+eGwqF4urq+vLlS+jc6PX6V69eDRgwoN4UwGAwGAymzsnJyZkwYUKVkzjNwtfXd+fOndg/qxPqdapp165dk5OT+/fvT6fTr1+/TpJkWFhYbQSZTCZaWH0AAPIaFiqVihbcE8JkMpEVZrFYaGpTqVSET3mIJS2KwWCgFRQsZLTo8hVgsVjI3ZBoYfXBm4eFrD9ymcMCR9YZTZBCoTS6FmEM1B95eILBYKDJ0ul0ZP3haBeCIACAyWQij/LQ6XRkE2RJi0CODAJzbKgZ0JcuXbp06ZLl6aSlpS1cuNB440wMMvU3LAUAkMlkixYtIknS3d39zp07kydPjo6OrrfcMRgMBoOpc3bs2DFp0qRBQYFzuqFHNhm3P+FpSWlGRoaJvb4xtadee254PN6qVavu3r1bVlY2fPhwvP0CBoPBYJoGjlzOB83ckMU5qF10mCqp7wgobDa7Q4cO9ZwpBoPBYDCY9wccowmDwWAwGEyTAjs3GAwGg8E0Du7fv//tt9/WVWpFRUXLli0rKqo6vHWjBjs3GAwGg8E0Du7du7d8+fK6Sq2wsHDp0qUVtgdpGmDnBoPBYDCY95HWrVvfvHmzdevWDa1I3YO3VMRgMBgMppFx5cqVtLQ0T0/PmJgY4wjpjx49Sk1NLSkpadGiRd++fQ1xvQmCOHfuXEZGBo/H69GjR8uWLQEASqXy7NmzXl5eHA5HKpX++OOPn3/+eXJyclpampubW+/evQ17XVcp/i6DnRsMBoPBYBoNJElOmzZt586dzZs3f/r0acuWLU+dOgV3pF6+fPmyZctsbGwcHR2fPHni5eV169YtBwcHlUoVExNz/fp1Hx+f/Px8lUp16NCh/v37l5aWLl26dODAgc7OzhKJZOnSpZmZmadOnQoKCkpLS5s9e3ZycnJERER14g1dEqbAw1IYDAaDwTQa9Hr9nTt38vPzMzMzHzx4IJPJZsyYAQAQCoXLli2bNm1aaWlpZmbmnTt3cnJyjh8/DgA4fPjwtWvXrl69mpmZKRQKAwICfvjhhyoTv3nzZmZm5rVr13Jzc2k02vbt280Sf3fAPTcYDAaDwTQmVq9eDbdTDQgI+Oabb7744ouSkhIOh3Px4sWQkBC454aPjw+bzS4vLwcAiMViCoWiVqsBAAwG48qVKyqVqsqUZ82a5ebmBgBwcnLq0aOHUCg0S/zdAffcYDAYDAbTmAgNDTX8hnFxnz17xuVyQ0NDjx49+sUXX/Tv39/Hx0ehUMBrRo0a1bZt2+jo6A8++GD27Nm3b992dHSsMmU/Pz/Db8N8ndqLvztg5waDwWAwmMYK3C6UxWIVFBSEh4cvX76cyWQOGTLk7Nmzzs7O8BpbW9s7d+5cuHAhOjr60qVL3bt3HzBgQJU7S1a5m2/txd8d8LAUBoPBYDCNibS0tK5d/9mk8+bNm1Qq1dvbe9euXa9fv87LyzMsntJoNPBHcnKyQqHo3bt39+7dAQCbNm36/PPPMzIyarnnfHXibdq0qft7qyNwzw0Gg8FgMI2Jb775RiKRAAAyMzNXrlw5cuRIgUAgkUiUSqVUKgUAEATxww8/lJeX63Q6AMDZs2dHjRr1/PlzAABJknD2jKFfp0YsFG8QsHODwWAwGEyjwdHRUSqVuru7BwUFBQUFOTs7r169GgAwduxYPp/v6+sbHR3t7e2dmpravn379evXHz16dMaMGXw+PyAg4MMPP3R2dp4zZ853331X+3kzFoo3CHhYCoPBYDCYxkG7du2WLFkSFxd34cKFzMxMHx+fmJgYNpsNAPD09MzKyjp69KhMJgsLC+vUqdOLFy8SExO9vLzc3NyePHly/PjxnJwcW1vbyMhIX19fAICDg8PSpUtdXFwAAAKBYOnSpV5eXoa8hg4dCjt+qhN/l6G843OCMBgMBoN5l9mxY8ekSZM+iwjbOBg9rl34+s338wsyMjICAgLqULf3FjwshcFgMBgMpkmBnRsMBoPBYDBNCjznpp4oLCzcvHnztWvX8vLy2Gy2h4dHbGzsxIkTeTwevODIkSOff/45AODgwYOdO3eunMLUqVMTExNZLBacsg5RqVTx8fGpqakZGRkEQfj4+IwePXrEiBH1c1OYxsKpU6c+++wzAEBaWhocXzdNYWHhihUrbt++XV5e3rJly+HDh48bN45Go8GzGo1m9erVly5dKioqCg0NnTBhQmRk5Nu9Aczbx9xKYiA+Pj47OxsAMH78+LZt28KD72ElKZEr0l/lI4srtNo6VAaDnZv6ICcn56OPPoIr9wAAcrlcKBTeu3cvISHhzJkzBv/GXLKysiZPnvz06VPDkTt37ty5c+f69es//PADnGKGwZiLSCSKjY0tLS2Ff/79999///33/fv3169fDwDQaDQjRoy4desWPHv+/PlLly5t3ry5T58+DaYxpuFITU1duXIl/B0TEwOdm/etksAwekcfZBx9kFEnSWEsBzs39cHy5cslEgmfz58zZ06HDh20Wu3hw4e3b9/+/Pnzn3/+ef78+QhpEgQxffp06NkMGDAgNjaWTqcfPXr0zJkzhw4d4nA4q1atquv7wLwXbN26tbS0lM1m79mzJzg4+Icffti5c2dCQsKkSZOCgoL2799/69YtGo22bdu2sLCwuXPnnj59evHixb169cJ2+X1Dr9cvWLCAwWBo/9vr8L5Vku7du/fr10+pVFqYjpubm7e3d52ohGmC9ewd5O7duwCAgQMHTpw40d/fPzg4eNmyZXDsKS0tDS3NP//889GjRwCAGTNmbN68ecCAAX369Nm+fTsMW3ngwAG4hA/zVvn777/Hjx8fEhLSunXr6Ojo9evXy+XyWp69cePGyJEj27Vr5+3t3alTp+XLl4vFYnhq69at7u7uLVu2TE5O7tixY6tWrfr375+SkiISiaZOndquXbuQkJB58+YZNo758ccf3d3dfXx8Xrx4MXr0aH9///79++/fv9+E5iKRaM6cOV26dPHx8endu/ehQ4cMpx48eAAA6N27d6dOnfh8/pQpU+Dx+/fvAwDOnTsHAIiJiYmNjbWzs/vmm28AAIWFhYbPdEwFmmQlgezevfvx48fTpk2rcPx9qyQCgcDX17elxbRt25ZOxz0OdQMux/qAxWIBAFJSUjIyMgIDA+HB/fv3W7IO/8KFCwAAPp9fwbIsWrToypUrAAClUmkIwo15Gzx58mTw4MHwc41Op2dlZa1Zs+bOnTt79+6lUCimz6anp48cOdLggL58+XLr1q1Pnz7du3evIX2NRjNu3Di9Xg8ASEtLGz9+vIuLS05ODjy7Z88eKyurxYsXG18/cuTIV69ewevT0tJyc3Pnzp1bWfPy8vKePXvm5+fT6XQOh3P//v2ZM2dmZ2fDl5Cnp2dMTEyXLl3gxa6urnQ6XafTwdfkkydPAAAtWrSAZ1u0aEGhUEiSfPz4cURERJ0WcFOgqVYSAEBpaemaNWtCQ0NHjBgBhyyN7xq8T5Xk+PHja9eurZOk+vXr17p16zpJ6j0H99zUB0OGDAEA5OTk9OzZMzQ0NC4u7scff7x58yZJkoZJmuYCpxX7+flVmLITEBAwefLkyZMnY8/mbZOQkKBUKm1sbG7dupWVlQVfIZcuXYJjhabP7tmzR6fTubq63r1798mTJ59++ikA4PLly8Y92yRJDhgw4PLly1988QUAQK1Wl5eXHzx4MDExsVmzZgCA5ORkY320Wi2Xyz1z5sy5c+fgni/btm0rKyurrPmGDRvy8/M9PT3v3Lnz+PHjGTNmAADi4+OzsrIAACtWrNi9e/fw4cPhxRcuXIDv13bt2gEAYIKGWkelUuHWwUKhsO6KtunQVCsJAGDFihUKheL777+vPNL0vlUSOCoX6me15FNn5H8u9nRDUhjLwT039cGXX37p4OCwc+fO7OzsgoKCgoKC06dPAwBcXFzWr19v+EQ2i9evXwMAnJyc6lhXTK0pLy8HAMhksr179/bu3XvSpEkdOnQAALi6utZ4dty4cQMHDnRxcXFyciouLi4oKAAA6PV6sVgMXwOQL774wtvbe+bMmfDLuFevXnA0s1evXtu2bYNZGLNy5crg4GAAwOrVq3v37q1UKm/cuFF5Fufx48cBAGPGjIEB1OPi4jZv3qzT6a5fv14h8Gh6ejp8q/Xs2TM8PBy8Mb6wMxLCZrMVCgXcbgZTgaZaSdLS0hISEuLi4vz9/WE/kDHvZyVp2Yw5LNoaWfzgeVGhEM8lqDNwz019QKPRxo8ff/ny5fT09B07dnzxxRcffPABAKCwsPB///sf3OfMXOBazSo/uTD1w9ChQxkMhk6n27hxY69evT788MNdu3bZ2NjAPjPTZz/44IPS0tIVK1a0adMmJCTk1KlTVWYBZxcymUz4p4+PD/xh/NowQKVSP/zwQ/g7ODgYSr18+bLCZQqFoqioCACwcuVKd3d3uEMN7Jt59uyZ8ZX79u0bMmSIVCqNioratGkTPCgQCAAAxm8p+LtKlTBNspIQBDF//nwnJ6fZs2dXqRKuJJgGB/fcvHW0Wi00E3Z2ds7OzrGxsbGxsV9//fXPP//8ww8/iESiBw8edOzY0dxkvb29s7Ozs7KyNBqNwa4BAK5cuZKQkAAAWLhwIe7XeatERERcvnz54MGDZ8+ezcrKKi0thcMBp06d8vPzM3129erVGzZsAAB07ty5a9euWq12zZo1NeZIoVBqeQGFQoGDBQwGo8I1hn7v8PBwDw8P41OGSRIajWbevHkHDhwAAMTFxS1atMgwfmpra1teXm7wyPV6PRwlsbOzq1H/95AmWUmEQuHDhw8BABX6+T777LPhw4f/9NNPuJJgGhzcc/PWUalUHTp0aN++/Y8//mh8PCgoCP7gcDgIyXbr1g0AUFZWtnv3buPjmzdvPnz48Llz5xwcHFBVxtQKOG1zypQpFy9evHbt2tdff02hUFQqVVJSUo1n9+3bBwAYO3bswYMHp02bZm9vb7k+BEHAdXkAgIcPH6pUKmDkrxiwtra2tbUFAPTr1+/nN6xcuXLFihUff/wxTGfSpEkHDhxgMpm//PLL0qVLjWeGwQRh0Dbw5jseAODv72/5LTQ9mmolMQ2uJG8PjUZz+fJlQ9S0WqJSqS5fvmw8SiCVStPT0wEARUVFKSkpdaWeXC6/fPmy8Yq/hgI7N28dPp8P+4G3bdu2adOmzMzMvLy8CxcuLFu2DJ41rJ+CvHz58tF/MSz+NGbUqFFwUv2yZcuWLFly6dKlv/76Ky4uDlbT4cOHN8l4Eu8UH3/8cceOHT///HOFQuHp6dmtWzf4Bczlcms8Cz9kc3JyxGLxixcvtm3bVicqzZs37/Hjx5mZmXPmzAEA2NvbV9kp2L17dwDAvn37iouLSZI8fvy4n5+fn58fXGf3xx9/XLx4EQAwatSoVq1aGeohnA3av39/AMClS5f++OOPR48eLViwAADg6enZJFfBWE6TrCSOjo4vjIDVBgAQHx8P+5ZwJXl7lJSUREVFwXgNtef169dRUVEweggAYNOmTU5OTjExMQCAs2fPwmddJ2RnZ0dFRRn82gYED0vVBxs2bOjdu7dIJFq1apVxbD0ajRYfH1+hT7jyssz169cPGzaswkE6nR4fHx8XF5eTk7N9+/bt27cbTrVv337hwoV1fROYikyYMGHRokXnz5/38/NjMBjwI9jJyQladtNno6KiTp8+nZKSEhgYSJKk4aO8pKTErMj3xjAYjPLycmiwAAAUCmXevHnwNVmBuXPnXrhwISsrKzQ0lMfjQe958ODBUNYQzmT37t3G/YKLFy+ePHlyv379EhISrl69+vXXX8PjNBptzpw5NY6GvJ801UpiPBRu+M1gMGCYFlxJ3nF27do1ePBg45gCdYWrq+v3338PZ8Q3LPjjvj7w9PRMSUmZPn16UFCQjY0Nl8v18/MbNWpUcnKyJS5zYGDguXPnpk+fHhERwefzXVxcOnXqtGHDhsOHDxubHsxbYuLEiVu2bOnQoYO9vb1er3d3dx8yZMiRI0fgVCfTZ1etWtW3b1+BQODp6Tl9+vQ9e/bANHfu3ImsD5PJPHDgAIy8FxISsmXLllGjRlV5pZub219//TVo0CA3NzedTgfDEMPpHaDStOIKsFisPXv2TJ482d/fXyAQdO7cec+ePQMHDkRWu2nTVCuJaXAlqR+ys7MzMjIqxGslCOLFixe3bt0qLi6uUurKlStwJcr169eNj2s0mnv37lVepGIiQbFYnJaWlpWVBSMtAQD4fH5ERAR0l+EImkqlys/PP3/+fEZGhuGy6sTrEhKDwTRy1q1b5+bm1rp164ZWBPPugivJ2+O3334DAAyPsX74hw/yPz9PFgAgIyPDdF5w4f3OnTtbt24N5x74+vq+evUKnj19+rS7uzt8uVMolJEjR2q1WpIk4TgRDK7m6+vLYDCsra0DAgJIkty1axedTt+/f79hO8Lhw4dLJBLTCZIkOW/ePOM1eo8ePSJJMjMzEwBw7949g6pr1qxhsVhQVX9//9evX5sQr0Nwzw0Gg8FgMI2J2bNnr1u3TqFQnD59+tWrVzDEkUKhGDFihL+//7179woKCtatW3fgwIFjx45VkM3MzPT19Z0yZUpGxj/bfOp0uq+++urYsWMFBQVbtmxJTEyEsyNMJHj27NlVq1YtW7asoKDgxo0bEonEMARZgRUrViQkJCgUimPHjmVlZcFQzrUXRwbPucFgMBgMpjExZcqUfv36AQB69erVtWtXGKlIoVDMnj171KhRMNbRzJkzlyxZYtiLwzRr1qyJjY0FAEyePDk7O3vDhg2rV69WqVTVJZiVlUWhUD755BMXFxcXF5djx46VlpZWmfL06dOhqgMGDIiMjHzx4oVZ4shg5waDafTExMQ4OTkhb+WBeR/AlaQpYbzAzTAf3MHBYcmSJS9fvjx27Njz589TUlJqHyEWejaQvn37rl279tmzZ23btq0uwdjYWA6H065duzFjxnTr1i06OrrKaekAAOMlcs7OznDWfO3FkcHDUhhMoyc4OPjjjz+ubmYoBgNwJWlaWFtXsc+DSqX65JNPWrVqtXLlygcPHkRHR8NgRbXBeI9CKCWXy00k6Ofn9+jRo88++ywlJWXQoEGOjo7r1q2rMWUDtRdHBjs3GAwGg8E0ehISEg4ePJienn7r1q2dO3fOmDGjcuzp6jCOTPP48WMAQKtWrUwkWFxczGKxvv322zt37hQXF48cOfLrr7+u5RCY5eK1ATs3GAwGg8E0erKysthsdqtWreCfV69ehQEYayO7evVq+EOhUKxduzYiIsLJyclEgsuWLQsNDYXbdNjZ2Q0YMIAkydqv6LZQvDbgOTcYDAaDwTR6+vTps2rVqp49e/bs2fPZs2fXrl3z8PA4fPhwdHR0lcNYBrhcbkJCwosXL4KDg8+fP19QUAA3ADGR4Lhx43799deQkJDY2Ni8vLykpKQ+ffq0bNkyKyurNqpWJ143BQEAwD03GAwGg8E0FlgsVlRUlLGzEhgYCPfw6dChw/nz5728vNLT0318fO7evbtt2zZnZ+fc3FwrK6uoqCi4WzsAICwszOBJuLi4DBky5P79++3bt8/Jyenbt+/t27fhLGATCbZv3/7mzZudO3d+9OgRnU5fu3btkSNHAAAcDicqKgrOs4Gq2tjYGFQNCAho06YNAKA68TqEUss+KwwGg8FgMJXZsWPHpEmThsdYL57ojJzI0Hk5mTnqjIyMgICAOtTtvaVx9NyIxWKhUFghyHQtUSqVcP85c9Hr9UKhsMpNK2uDSCSCG+Gai1wuFwqFcL2cuWi12tqv/atAWVkZ3BkRAalUqtFoEATVarVQKKyTLWTLy8uFQiGasy6TydRqNYKgRqMRCoUymQxBliTJypHOa4lEIhEKhXDE2lxUKpVCoUAQJAhCKBSKRCIEWQCASCRCG1NXKBTILcIYqH95eTmauFgsRjZBQqEQzQrpdDpLTJBQKES2QsgmSCgUWmKF0ASlUqlQKESzQpgmSeOYcwOjKSPLWpIpsjiaTTFkjSzYULJoghbmawxBEI2ukjSuB91QNwvqrpI0RktiiQlqdDfbIGYT0yRpHD03GAwGg8FgMLWkcfTcYDAYDAbzLvPnBfGfFxDHEDF1zjvt3Gi1WjjIDfsq1Wo1wiQDrVZLoVAQBrxhpgRBoA2WkySpUqkoFIq5gvCW4c6r5srq9Xq9Xo+mMAR5fpJGo0GYUQFvVqfTmciXzWabKEa1Wg2fFCwupVKJUOZQf4Qucag/WpkbFDZXEBi1CIRZILBqIbcINFkorlKp4ObAZmFoESausbKyMnFWpVIZxkos0R/ZBMH/0crcEhMEAEC2QmijS9AIWGKFkE0QAMCEFaJSqSwWC02lGvnwww/d3NwsMbwQNze35s2b14lKmHfaucFgMBgM5h0nMDBw7dq1yNOoDfj4+FS5WQEGATOcm+LiYicnp7enSmUYDAYM9gy/zlksFp1utjcGPz5Mf9hVCexOoFKpCLIAAKVSyWazEb5TCYLQarUMBoPNZpsrC7sf0BSGK2jQZLVaLZPJZDKZ5gqq1Wq1Wk2n09HyBQAYvsYUCgVJklZWVmjfqUwmE+HDDupPo9EQ9IddCGg3Dr9QWSxW7cOrG4NWSeDnOIVCQdNZpVKx2WyEjRtJktRoNAwGA7mSwKZEEIRCoUDWX61WI5sg2KLRrJBWq0U2QSRJolkhvV5Pp9PRTJBKpUJrEQAAuVyOJqjT6WArRrBClnP06NHRo0dbng6FQnn58iXuvKkTzGioI0aMEIvFY8aMGTFiRLNmzSzM+MGDB82aNav9tl4YDAaDwbyDwD4b5+bMlm0Q/W8AwN9XZXKJHi2uBKYyZjg3YWFhGzdu/Oqrr+bMmdO1a9cxY8YMGTIEzTt58ODBwoULFy1aFBoaiiCOwWAwGMw7hXew1bAZ6EH8crNUckldbq70nmNGd+Xq1auLi4v37t3bq1eva9euxcXFubi4DBo0KCEhwayJVHK5fMOGDTgmAQaDwWAwmLeBeWOxfD7/448/PnnyZFFR0W+//RYZGXnixInhw4c7OzuPHz/+3LlztVkvs3nz5o4dO6IqjMFgMBgMBmMKxCB+tra2EydOPHfuXHZ2do8ePaRS6e7du2NjY93d3b/44ouXL19WJ3jp0qXc3NyxY8ci6ovBYDAYDAZjEsSl4EKh8OTJk0ePHj137hxcQNG+ffvQ0NDExMQNGzb8+uuvp0+fjoqKqiAF+3u+/fbbWi7xMOxvAsew0PZYQY4mAgV1Oh3ajkskSSLvYgMAkMvlaDsukSSJrDAAAFkWbZ8jCFxzVN1ZW1tbE8s9JBIJzBrqj7Y3DVyPgzCVD2aqVqvRNrWx8GFJJBJkWYSdg6Ag3HMNLV/kfakAAAqForotsahUqunJf+Xl5YYgRnCHKQQFSJK0xAQpFArkcDWWVBI0KwRlEUwQFIR7riHkC1BNEMTEYmw6nW68k3ajhs/nf/rppz/99NNbSn/evHknT5588ODBW0q/fjDPucnLyzt27NjRo0dTUlL0ej2NRuvSpcvgwYMHDx7s7u4OANiwYcOBAwc++eSTefPm3bhxw1iWIIgff/xx8ODBLVq0qGV2FaJIWbg3Tf3LNjqFLZFtQIXrpJJYOAmsMZZb/cu+pUxrjL6IK0kjkn1LmeJZnu8bZjg348eP3717NwCAwWDExMQMGTJk4MCBjo6OxtdQqdTRo0evW7euqKiogvitW7eeP38eHBy8f/9+eOTy5culpaUfffRRdTlyuVwulwve7MdrbW2NEGQCfi2hRZgQi8XI/n55ebm1tTVChAn4hcflctGCTKjVaj6fb64gAKCsrIwkSXt7ewRZqVTKYrHQ4tzIZDI2mw0fdJWYjlsjEAjgD6i/nZ0dQpwbmUzGYDDQ4tzIZDIWi4UQegv27dnZ2ZkrCN70VwkEAoQ4NyqViiAIDodjrqBerxeJRDQazcbGxlxZAIBIJOLz+QhxbpRKpUKh4HA4yHFuYL8OQRDl5eU1dvNUh1gs5nK5CCYItmgrKyuEMtfpdHK5HNkEEQRhuuOzOuRyOZ1OR2gRGo1GKpUymUw0KyQUCtFMkEwmg6avQeLcNC5g1KWG1qJqZDJZXYUxNKPSFxUVDRgwYPfu3cXFxWfPno2Li6vg2Rj4+uuvN23aVOGgjY1NeHh4fn7+q1evXr16BQAoLS2t7AMZQ3lDhT/NogFl6z/ThsrXQoVNi5uulrVPx3QWaIINlS9uEVVe8y5XkvqXxZWkymuaNnl5eaNGjfL09ORyuYGBgVu2bIHHX79+TaFQTp48GRwczGQyHR0dP/vsM8NkgLy8vEGDBjk6Ovr6+n777bfGXaFisfjTTz/19PR0dXUdMWJEQUEBPC6VSqdOnert7W1ra9utWzc4UKPX652cnKZOnWoQl0gkbDb7u+++g2eXL1/epk0bHo/Xvn37kydPwmtycnIoFMrDhw/Hjh3r5uZWV0VhxlfIyZMna/nVNcFFtw4AACAASURBVHLkyMoH/fz8/Pz8DH9euXJlyJAhOM4NBoPBYDCWo9PpIiMjaTTarFmznJ2djxw5MmXKFH9//8jISHjB+PHjP/nkk3Xr1p0+fXr9+vUtW7b85ptvJBJJeHg4k8lcvnw5k8lcvXp1UVGRh4cHAECv10dFRYnF4sWLFysUik2bNoWHh2dmZnI4nJiYmOfPny9atMjZ2XnHjh1du3a9fv16WFjY4MGDExMT4+PjoTd54sQJjUbz8ccfAwAmTJhw4sSJBQsWtGjRIiEhYcCAAUeOHBkwYADUbfLkyUFBQXv37q2r0jDDufn222/t7OxmzJhR+dS9e/dWrFixceNGFxeXutIM0/RY+uLEf/5s0a+hNMFgMJgmRlFRkaen5/z583v06AEAGDRo0JkzZ9LT0w3OTb9+/datWwcA6NGjx8WLF+/cuQMA2LRpk1AozMzMbNmyJQCgd+/ehnmx+/fv//vvvx8/fuzj4wMACAsL69Chw9mzZ6lU6q1bt65evdqpUycAwLBhw9q1a7ds2bKTJ08OGzZs69att2/fDg8PBwAkJCR06dLFy8vr7t27+/btO336NJyIMmTIELFYvGjRIoNz06pVK0M/U51gxrDU5cuXb9++XeWpR48eHTp0KDc3t/apjRo1qg47oDAYDAaDeZ9xd3e/dOlSjx49RCJRenp6fHy8TCYzHmPq27ev4XdAQACMS3fz5s3IyEjo2QAAXF1dY2Nj4e+rV6+GhIRAzwYAEBERUVpa2qdPn9TUVE9PT+jZAACoVOrIkSNv3boFAIiKinJ0dDx27BgAQCqVJiUlwcgvycnJPB4vMDDw9Rs6d+788OFDwxJCY93qhJp7bsaNG3fmzBkAgFgsplKpZ8+erXABXKnIZDIDAgJqn/GoUaPMUhSDwWAwGIwJdu7cuXz58tzc3ObNm4eEhFRYqFHlZO2CgoLAwEDjI15eXs+fPwcA5Obmurq6Vk7h9evXFY67ubmVlJTAzWUHDx587NixlStXwlk1w4YNAwC8fPlSKpVW3hPUEF6hzod9anZumjVrBufKPHjwgMlk+vr6VpEKnT548OA636tdq9VC1xL6nhqNRqfTmZsIFEGI6mHIGkEWAECSpFqtRpjFBvNFCxuj0+mQFYagyer1eq1WW+OK3AqPT6VSwSN6vd5EviwWy0Qxwo3QgVH4FrQy12q1CItFa6N/dSDHmwFGLaI2McErAO8UIV9DOVvSIhBW7sBC1ul01eVLoVBMr+tRq9WG1eDI+hMEYYkJMqG/6UwtMUEAAGQrhLZ2Gt6sJVYI2QQBI2tQGSqV2uQXUiUlJU2aNGnjxo3jxo2Dq9UqeAxV1oRmzZrl5+cbHyktLYU/XFxcHj16ZHzqxo0bbm5ubm5uV69eNT5eWFhoZ2cHmyEcmcrKykpISOjfvz9c6+fo6NiiRQvoM1UgJycHAIBgGUxTs3OzcuVK+KNbt27NmjWrw/k+NUIQBGwqhnh6CPcP6zqCSTI0EgRZgyCCWYH5Gu7dLKBJQlYYoN4sSZK1sYYV7I5OpzN4kCbyNf3e0uv1xi94hJc9eKM/giCUQitz+LpFLnCA+gZCbhEG58CSSlKjB1wZKKLX66trTTU6NzqdzjjUjSUtGtkEobVo5IdlwBIrhGaCAOrNQixpESbyRYhB0Oi4cuWKvb391KlT4RMvLCysTVDTiIiIRYsWvXjxAk61EYlEZ86cgYHr2rdvv3fvXsOpu3fvduzYcf/+/eHh4atXr75x40aHDh0AACRJHjhwAE6yAW9Gpvbt23f27Nk///wTHgwNDV28eHF6evoHH3wAj0ycOFGpVBpCw9Q5Zkwo3rx5cz17viwWC9osnU6n0+k4HA5akAkAAFqECY1GQ6VS0XqkNBoNl8tFizCh0+lYLBZanBuVSoWmMFwWiCYL1/vVWD2YTKa8VHR09k9FmS8jJg7gfT9ErVbDoAvI3X6G2Cfw65zL5SKYchiZAy3OjVarpdPpaHFuNBoN2o2LxWKCIKysrBDiVSiVSoIgTAQWqg69Xg+7XtB01mq1HA4H4R2jUChgi0COcwNvliAI2I2Bpr9IJEI2QTqdjslkolkh5MgfGo0GtggEKySTyeh0OpoJQm4RAABk8yWVSvV6fW2sUNPg6dOnhw8fNj7Stm3b4ODg0tLSr7/+un///s+ePVu1ahWDwUhPTzcd9Hnq1KkbNmyIjo6eP3++lZXV2rVrDcHDJkyYsG7dutjY2IULFyqVyo0bN3p5efXp04fH44WGhg4cOHDZsmVOTk47duzIyMj49ddfoRSNRhs8ePCaNWsEAoEhjt1HH30UGRnZs2fPhQsXenh4JCYm7tu3LyEh4S2UzT+Y0VD9/f0tz48kycLCQqVS6e7ujvAuwTRqSJJMmP5Dzq0MAMCFH3YldhhoIoQjBoPBYKrk1KlTp06dMj6yfv36mTNnPn78eNu2bdu3bw8LC9u/f/+lS5e+++678PDwoUOHVpcUn89PTU2dMWPG/Pnzra2tR48e7eHh8fPPPwMAmEzm9evXv/zyy3nz5qlUqs6dO69btw4OeP31119z5sxZvXp1eXl5u3btrly5Yui5AQAMHz5869atI0eONP4YOHXq1Pz58+Pj4wsKCvz9/Q8fPjxo0KC6L5o31ODcbNmy5dmzZ0OGDImIiIC/TVw8ZcoUw4zrKikoKFi5cmVeXh6dTqfT6f/73/8q7z+FacI8PJGScyuD7iywGRRauuXid999h50bDAaDMQsTW2gtWbJkyZIlhj8//PDDr776Cv6uMIRtPB7k4eFx9OhR47NxcXHwh4ODQ5VzUQQCgYmV29HR0ZVHzDkczvr169evX1/huKen59vYHKMG5+bgwYPJycmtWrWKiIiAv01c3LdvX9POzdatWwUCwR9//EGlUvft27dp06aQkJAms5kZxjQkSV7dfAgA4DClO797QPnB1Dt37jx8+NDT07OhVcNgMBhMk6IG52bPnj0KhcLZ2RkA8Pvvv5ve1RZOQaoOkiQfPny4YMECOPA8aNCg48eP5+bmBgUFma82pvFx6dKl4ic5DFcbQc82gErhdw8QHbp9/Pjxzz//vKFVw2AwGEyTogbnBsZghlgYc48kyTVr1hgSKSkpAQA0a9bMkjQxjQjYh2k9+ENApQAAuJ1biw7d3nk6QTk8gE6nM5lMHLAYg8FgMHWCeTP/SZJMSUmxs7OD3S2///57fHw8l8udMGHC6NGjTctSqVS4nOzmzZspKSn37t2bOnWq6e15FQoFXMIDlxdKJBKEhTCGYA9ognq9vry83FxZKG4IT4SQL9xJGEEW7jWNkC8ETRauwDTxdIRCYWJiIoVO5fcKIgg9AIAV1IzCoOXff6pVqSlWFJVKVV3WNjY2JlKWSqXG8QJEIhGa/lqtFi6sMwuYqUajQa4kaIKwRUilUuQWodFo0ASRWwRBEBKJBEEQ5qtUKk3EuTG9UblIJDIM6sO9wRHUgPojL6tWKpVoVgi5ksB80awQDOqDZoIAAFqtFtkKWXKzMpmsugdEp9PRNiqvPX9fkeVlmf2IDZS8MrtJYkxghnOj1+v79+9/+vTptWvXBgUFXb9+fezYsSRJMhiMCxcuKJXKSZMm1SYdFotla2srEAiuXLnStWtXE8sjCYIwjj6CECHDcpAjoADUmCsQS27WknyRZU3PCDtw4IBGo+FG+lFtufBCCpvB8nFRZbwufPDMIyzARDmTJGnijVKhkrwl/WuUbXSVxBLetZut0eGAofAsV6MxVpL36mYtqSSW4OXlRaFQZGK9TIyuPACAw+E4OjrWlVbvOWY4N3v37j19+nT37t27d+8OAPjll18YDMa1a9e8vb0jIiKWL18+YcKE2gRUCAkJCQkJGTt27GeffZaamtqtW7fqruRwODCyhUQi0ev1AoEAIUgG/PhAiJCh1+slEgmNRjMs+jcLkUgkEAgQIkzAL1QOh4OwVF6r1arVauQwHiRJmu5Lqw6ZTMZisUzEXIEz820GhNBo/xaIVVAzVcbrggfZLToEMxiM6rI2XYZ8Pt/QZ0OSpOlunuqQy+UMBgMhQoZGo5HL5UwmEyFsDOzbM93lUB2wv4rP5yOEXVGpVARBIMRcsbBFiMViHo+H0IRVKpVSqbSyskIIuwKxsbEhSZIgCLiHDNoiBolEwuVy0UyQSqVis9loVkgulyMXOEEQ1tbWCFZIoVDQaDQ0EySTyZAjV5WXl6OZILlcDqNGVWeF3qpzEx0dnZKSUpuIeabx9/fHzk1dYYZlhEul9u3bByM6JyUlRUZGhoaGAgAGDRr0ww8/5Ofnm5hDk5eX98cff8yaNQtWPjab7eHh8fz5cxPOjaFNwnpJpVIRLAtMBEEQvjIpFApaaEsoiGBWLLlZGMXVklicyDdrQuGUlJRHjx5ZudhyI1oB8K+JYfu7AwAKM14AC8q5QgnTaDQEK2Za/xpzR1MeVjDkAgf13iIMWNIiEGQtuVmI4THBP99GJa8xdzRZ2G1p4cNCs0LIJghY0JxBQ7QIyzl//nzPnj0tT4fNZufk5Dg5OVmeFMYM56aoqKhZs2bQs3n27FlJSYkhSg2cgvP69WsTzg2Px7t+/XqPHj1g9GWVSpWbm9u5c2dLtMe8Uyx9caLikRb9AAAbNmwAAHgO60xQ/+N2sPxcAADFj1/Ul4IYDAZT9+Tl5QEAaDYchgt6ZBP1y1KVSlVaWoqdmzrBDOemWbNmSUlJQqHQ3t4+KSkJAGDodIHB/Uw/Eltb2wEDBvz00099+/blcDjJyclcLtdEtw2maZCZmZmYmEhjMz2Hdn4B5ManmB72FDZD9KpII1e+J0HTMRhMU4UX7e88pw+yeM64X9VPCutQn/ccM7ore/bsSZLk7Nmz9+3bt2LFCmdn5/bt25MkmZaWFh8f7+DgYLxuvEomTpw4YcKEoqKiv//+Ozw8/Mcff0QeRMc0FubOnavX65sP6ci0rTQGT6WwWjiSBFn8JLchVMNgMBhM08SMnpuhQ4f27t179+7du3fvBgBs2bKFSqUmJSXBCPo///xzbeY2RkdHR0dHI6uLaVzs2LHj+PHjDAHH57NeVV7AbOWsepxf+jSvZXscyxGDwWAwdYMZzg2FQjl+/PiRI0cePXoUHh7eq1cvAIC1tfWIESNGjBjxNnbA0ul0cHoaXOCn0WgQVgnC+CsIESZgXiRJIsiCN3s+I0xuhfnqdDqEfHU6Hdz62FxBA2iyME6M8dNRimW3dp24seUIAKDNvGF0aw5BEJUXl7K8HRnutoDyz47TVSbOZDJNFKNWqzVe/wm3fTZXf71er9VqzZWCuQNgSnkTIEdgAm9aRIV7ryU6nQ6tVsO8LGwRCJNba2wRFArF9LAm3B8bFriF+qOZIIDaovV6vSUKAwAssUJoJgi82YDdXFmIhS2iuhXsVCrVxHJOTNPDvHWkNBpt2LBhxkciIiIOHDhQpyr9i06ngy8PWF8Nvo5ZwAVECCHLYGuBwazMlQV14dwgxJmA8TzQFIagycIgfv/EWiwovbx+/+Mz1/UaLYVK8ZvZ3yX2g3/ewZXuyHp4uGBYWLBtW71eX13Wpt9bBqfKED0MTX/4FjFX0OB8W1JJEAQNphytRaDla3AOkG9Wq9UitwgTNcQs5wbURSU3ixr1N52pJbULoDo38FMEzQQBC1oEsODpgDdfd1VeQKPRmpJzc/To0fXr12dmZspkshYtWgwbNmzu3LmGaR58Pv/TTz/96aefKgtOmDDh7NmzBQUFxgd//fXXgwcPPnjwgCAIX1/fmTNnDhs27K0unq8HzHNuVCrVhQsXqtsbfOjQoaa3lzIXNpsNn5ZIJNLpdBwOByGqBww7ixDVQ6fTiUQiGo2GFtdSq9XyeDyE71S5XK5UKg33bhYajUalUqEpLBQKAQBoshKJhM1mM5nMgofP9n6yWFkupdCovC6+7Sb3sw1uYbiMUrk0SJIkSSqVymQy0bI2PFn4AuPxeAhtUiqVMplMhKgearVaKpUyGAwE5aGXgHbXYrFYq9VyOBwEe61UKgmCQAjMA1/PVCoVTefy8nK0ODEKhUKn07FYLIQ4MRAYc4UgiLKyMgqFgqa/SCRCNkEKhYLFYqFZIZlMhmyCCIJAs0IymYxOp6OZIK1WixwOWK1WowlKpVK9Xm9lZfU+LE3YuHHjjBkzRo8e/dlnnzEYjLS0tHXr1l2+fPnChQvmPmsYmzcpKenjjz8eOnSoUqk8c+bMiBEjsrKyFi1a9Jb0rx/MaKhSqbRLly7379+v7oJ27drVxrmRSqVFRUUuLi5oUZ4w7yzKcun+uG+V5VJeV1+nr3vTHfm2HOeGVgqDwWCaFMuWLfvkk0927doF/xw+fHiXLl369et34sSJAQMGmJXUpk2bzp8/f+7cOcNc2FmzZs2ZM2fJkiVjx4718vKqU8XrFTOcm02bNt2/f79bt25Tpkyxs7OrfEHbtm1Np6BSqVavXp2WlsbhcFQq1aBBg8aNG2eevph3mAtrdkuLyjhhLVxXDaPQzP5YxGAwGIxpysvLhUKhg4OD8cHY2Njp06eb2+kll8sXL148ceLECqt8vvzyyxs3bty9e/d9cW5u375tZ2d34sQJhA5tyPbt23Nycn755Rc3N7dbt26tWLHCx8cnIiICLTXMO8WzZ8/uJfxFYdCcv+mLPRsM5m2zNMPk2cD60gNTv9ja2vr6+v7666/NmzcfPnw4DKvLYDA2btxoblIZGRkSiWTkyJEVjru6ul65cqVu1G04zHgJFRQUtGzZEtmzAQCkpqYOHTrUzc0NABAeHh4SEpKWloacGuad4ueffyb0eut+IQx3lK1hMBgMBlMbjh071q5du5kzZ7q6ugYGBk6bNu3EiRNwnZpZPHr0CADg7e39FnRseMxwbjw8PB4/fiyXy2u+tCpIkoyOjg4ODjYcUSgUlmw8i3l3kMvlv//+O6AAm1G4Hw6DwWDeIn5+fikpKa9fv963b1+HDh1OnDjRv3//du3awV0gag9cW9rYV0VVhxnDUsuWLTt37tzHH3+8Y8cOhI1bKRTKhAkTDH/eunUrKyurcoeYMUqlEi4LhGspZTIZ8jpShBXC0PHS6/VisdhcWQAAQRASiQRtESYAQKlUokUigVsfmytoAE328OHDUqnUKrQF3d3GeClmhWKvapXmP4WsVqury1ogEJgoRrlcDj9Z4POSSCQI+uv1ep1Op1KpzBU0LMlGKze4MTiCILxluVyOvMoX4TsPljByBdPr9VKpFLlFqFQqE0vBTe+bLZVKDWGWLNHfEhOkUqnQrFB1JkitNrWUSSxWwXJDs0Iw8hNyMCTkFgFQTRAsZIVCoVQqq7yATqdbMuzw7gD3t6fRaG5ubmPGjBkzZgxJkkePHh0zZszcuXP/+OOP2icVGBgIAMjOzq68KeTatWutra3j4uLqUvX6xQzn5tatW3379v39998vXrwYGhrq6upaoc0sWLDAz8+vxnS0Wu2hQ4f+/PPPIUOGwE00q6NCaDUEc2wAIdYZBAbnQJO1RGG9Xo8QUQOCrDCy7KFDhwAA/F5BFbriTP9pfNxEGD24MXJ1WRuCIUEsuXfkAoc+JZqsJQrjFgGp8eVdIdphg+hf55VErzcVBcAgYmGZowlaUkneUotoMqME27Ztmzx5skwmM/hqFApl8ODBMTExDx8+NCupNm3asNns/fv3G/bAhojF4gULFnz11Vd1pXODYIZzs2vXruTkZACARCK5ePFi5Qs+/fTTGp2bFy9erF27VqPRzJ8/PywszPTFHA4HBlqAMQz4fD5CkAz4OY4QsAF+aCLHuZFIJGgRJmCfDYfDQQjYoNVqNRoN2geKWCwmSdLGxsZcweLi4qtXr9JYDEGUf4X7rRCFhaatWBokIAmChPG1qsvadBnyeDxotqD+1tbWCN+pCoWCTqcjFLhGo1EoFEwmEyGECUmSEonE2toaALA895XxqcXN//MhVfmsTCbT6XQ8Hg8h7IparSYIAiFmDGwRVCrVdDdJdSC3CJVKpVKprKysEAIRQaDCsDMVWX+pVMrhcNBMkEqlqjFy1fLH/ymZxf4EAECv1ysUiipNEJttqiRtbJgSiYQgCIFAgFDmyC1Cq9XK5XIGg4FmhUQiEYIJAgAoFApo+qqL/NRkBl/gEpz169cvWLDAcFAikaSnp/fs2dOspAQCwcKFC5cuXTpo0CC4jRIAgCTJWbNm6XS6t7HrQH1ihmVMTEw07VNDM22C58+ff/PNN927d58wYUJtmg2VSoXNEtZLGo2GYMphCgiCEAqFgixLp9MRzAoUoVKpCPkSBGGJwsDMglr64gQAIO2Ps3q9ntelNZVb8d3zRFn8n78r2xcSAEBSKBS0+wUAVHjZ0Ol0BCtGoVDQahf8tEUrc+iTQcEK9aRCapXPWtIiYCtGEISZIlcwWMgIzoElLQICBQ0dJ5boj2yCatS/gqmg06mGfKsUNG1aDOLIVgjZBAGLzSaClCUtonERHBy8ePHiRYsW3bx5s3v37k5OTq9fv961a5darV68eLHhsqdPnx4+fNhYsG3btq1ataqQ2pw5c5KSkvr06TN69OiOHTvKZLIzZ85cunTpm2++CQ0NrY/7eWuYUQ/QPneM+eWXXzp27Dh58mQL08G8UzxOugEA4EXVPCKJqQ2kXn/v3r0XL17odDoHB4c2bdo0tEaYd46ioqLru/ZmXUwszLynUcg4tg6eoZGhwye37NCjoVXDvHWWLVsWGhq6bdu2DRs2FBUVeXh4REZGzp07t0WLf8PBnzp16tSpU8ZS69evnzlzZoWkGAzGpUuXfv7554SEhJMnTzKZTF9f38OHDw8ePLg+7uRtYraTe+XKlU2bNt2+fVssFh87dozFYt28eXP8+PE1uj7l5eVZWVkuLi779+83HPT29g4PDzdba8w7g1qqeHnzAYVB43Zq3dC6NHoUZcJrW7feO3RoeZnQ+Lijj49v95jAPn1dAnH0kvednJycVatWwS/1fw5RaYry0sfnDz8+f9gnsm//5du59jgyeBOnX79+/fr1q+6sVCqt7tTOnTsrHKHRaF9++eWXX35ZZ8q9G5jn3Pz000+zZs0CAHC5XLlcThBEQUHBzJkzt27dmpSUVHnGtTEymSwoKKisrKysrMxwEGEqDOad4mlyml6r44S3pPLwo7SI+0cOn122VCWRAACYnl4s71YUJlNXUqzKyix58qTkyZOrv2x2bN06eOCgnGlTK8xLWPoy9z9/ejWvV9Ux9cKsv/KubFt198hveq2GQqPzuwwTdB9nFRRJteLrhPnSy/vL/vz+yeWTvw4PHb35JAisIV48BtO0McO5efjw4ezZs/39/Xft2uXk5AR7wHr16jVr1qyffvpp9uzZBw8eNCHu4eGxYsUKS/XFvGNkXbgJAOB29WloRRoxOp3u1MIFd37fBwDgdY2yj5vMam1UngShfPhAdvGC9MK5kqdP/1qzusXaNeHh4X379h01alRTDcCFMebmzZubN2/+48BB6NYIekywH7WQ4dLScAHd3s128GxB9NiC1aMl9/7aNT5qQrOkyhMsMJj3BzOcm507d5IkmZCQEBgYmJOTAw8ymcx169ZJpdLt27dv374dbWFRdRgWTxpCzqAlApDWQ8Ls0CKCQHQ6HcJUPqgwQRBoOluiMDCzoHQabfbldEAB3E4+AACAstiSBG8iN1SXtekZgvCW/1VJp0OYUAzXotdzJYFqy+XyUaNG3Tlxgmpl5fzNAn4sXLNgVJJUilVwsFVwsOOMLxS3b0mSziguX05NTU1NTV28eHFoaOjw4cMlnbvynJwMEqaVgc2q/lsELGSEFbm1aRGmKwkUNEwotkR/BMFatmiC+I+t0OmIe/fuHT16NCEh4enTpwAACp0hiBlvP2I+w60VVKhCCjRrR/dlpwt/miBN/uOjjz5KTEz09/dHtkINUkmABU8HvAlYVeUFcD44mkqYxogZzk1WVpa7u3tgVaP+kZGR27Zty87ODgkJqTvdgFqthuPKhgBNyBHALAlZJpPJzJWFggqFAk0QmAxZZgLoJaApDDFLNvvqXZVEzvJ1oTvxSZJAjiNBEIRGo6kua9Oru5VKpXEQP7QI2jCGCkKBG0KWoZW5RqMZM2ZMUlISzc7Obe16lp8/YaoUKeyw9uyw9i0XkSXXrhX+db7kSsrt27dv375NpdFaRXcPnzDRPSQE1PQQoc4I72lYwiRJNkiLUKvV1a3WpFAoppdqKhQK4yB+luiPbII0Go1pK6TR/DOwK85/ef/Yzj1/HTR8Q7Id3Nw/+kQf8z+6QzMAAGHCQaTRnWftIgl9ecrB4cOHnz592niSae2xsEXodDpkK4T8dAAAKpWqusCDdDqdx+OhqVRLlOk5Rd+fRBbXFaEHX8VUxgznxsXFRSKRVBlRDTZCe3v7ulQNACsrKxiNQyQS6XQ6Pp+PsMwP2lOEMCQ6nU4kEtFoNLS4C2VlZWgRJuRyuVKpNMT4MQuNRqNSqdDWtQmFQgCAWTf7PDkdAMDr6gco/2BupiRJkiRBo9GYTCZaORsMllAoRI5zI5VKmUwmQhgVtVoNZRH6LPV6/aeffpqUlMSyt3eN38r0qu1LiMWxcu4W7RTVjUoQwtQbr0+dyv/rwpPz556cP9cqMqrngoU2fqZGCZVKJUEQCGFI9Hp9eXk5lUpFe1Ll5eVooaoUCoVCoTBYAwQMcW7KysqQ9ReJRGiBhaD+bDbbtBVis4GkMO+v9fMfnN5PEnoAgJVzc5cuAx079Xf6oBuFSs2qdpLof6FSXb/aK1KJCm8ljRw58tq1axV2kK4NMpmMTqejmSCJRMJgMNCsUGlpKdrTkUqlarWay+UixOaxHOhba16Wal6WWpIOhUKp29GP9xkzGmpoaOhvv/22Z8+eTz75xPi4VCr9/fff7ezsmjfH0xjfIwiCyDx/E+BF4LWgygm/c+fOn2R9DwAAIABJREFUPXz4MEMgiNi6vaAZStuhMpnOkd2cI7ux8/NFhw+JDv6RfTn5+bWrD6dMjZwxk/rmNYznFzcK7ifuPr3yc41cSmGyrbtNCBo40TYwgoQjrWZ+I1HojNDvDl2b1vXJk7uDBw8+d+5cZTelwqbieBdxSxg4cOCJEyeq2/mh9ri6unp4eNSJShgznJvx48f/8ssvcXFxmZmZMLhwYWHhuXPnlixZ8ujRo/j4+NontXv37vDwcH9/f7P1xbwzXL9+XVpUxmxuz/J2Qo4r/94SHx+/bt06KpMV/nO8wMe3QGGRWaTZ2NpPirMZPkK4bavo0J8pG39+lpIydFO8jckFjJh3BIIgZs+efWz9egAAP2q046TVdHt3W/gBj7ppAN2K9+HKYzc/73rlypWxY8ceOHAAzzh5e9y/f3/06NEmFmDXEl9f39u3b+POmzrBjA8CNpt95MiRiIiI77//fsiQIQCAkSNHxsbGpqenz5kzZ+rUqbVM59mzZydOnEDeXRzzjvDnn38CAHjRAQ2tSOPjzJkzM2fOBBRK0NLldh98WFfJ0vgCp1lfN/91B6OZx+v7937t3/fFtWt1lTjmLUGSZFxc3Pr/s3eecU0lXQOfQEgChC5VCUhXsAAqgoWmoiL2Vey7FixYVhddXRv2XUXF7oqIvcKqWFAUBSyoNBVRQHrvLaSS5L4fZve+eQIEuEEUnf+PD2TulJPJLefOnBIYSKLQ9NZe1F93iazVvUN6pmrp2++5Q1HVCg0NXbp06XeTXOkb5O3bt7JrNgCA9PT09mb2RrRE+/aPTU1NY2NjHz58+ObNm8+fP9PpdAsLCy8vrzb6o+bk5Bw6dCgnJwddZl0dgUAAlRuVkWg5u32UZ6R7e3sLhUKrFav0Rnl0eP80mz5G5y6V7vBviH5y8ee5njt2gvW/d/goiI7Cz8/vzJkzZGVVff97itZDO7ZzZYbloL13X60eGRQURKFQjhw58t2kWPoGUZs8VXfdBsLN8+bO5GWkd6A8PzhE0nB4eHh4eBC5Kevo6CxcuBAAsGnTJgLNEd8OERERZWVlVHNdqqlO67UR/8GqrLyyYH59fb3q2HGiWXOz+Hw5Qbu9ltLZHOj7Iy8QANDMs0pOWdlgz96q4KCq4FN3Nqz/o75u165d6Kn2reGfChJvnLp74ACJQtPfeqdjNZt0JhAJ5TAA5BmD9baGl/iPO3bsGJvNPnXq1HeffQmBAMSUm5qaGjqd3lLyVSkoKyvDRDltvM9yuVzo/CmLKzhsS8DxFfeYJbbeCD1mCQgM/UXx794uRCIRYYEhbWx7/PhxAICqZz8MWtv85yXc3uFgA+gK3tLQdDpdyjSy2Ww8tAYg6kcqEAigDO1tCIdubGyUPm+wZwGXe2XRgtrCQsX+tjrrN2AYjOFEzFwJejVjJFKLc665YKFCjx5lu7bv2bMnMzPz+PHj0B0MBpshYCaFu1ITO8FEIhGLxSJ8CfN4PCkhTKR7+TY0NGAYhruyE5af2C0Iis3j8STuQjmJbyN2rwAkku6vZ2i9h2L/+4sIBCKJHjBR+8yK8YtL0cZ5wJ7wxI2TQkJCCgoKzp49q6qqyuf/j28gk/k/HtQCgUAgEBC7BcHmhO9CxBrCKeJwOC25gsvLyxPwmUV0Xdqq3Hz+/Hnv3r2JiYlZWVn19fXy8vKGhoampqZz5syZPXv2FzJVEwgE4mcqgWePeFfEGmIY1tLV0ioyCkxYZsICt7Ftbm7uo0eP5GkU+ug+/x9yQ4a9RhgxrKWhlZWVpTxRGhsbxW/BhL87sfhsECnCQwQCASYS3Vm3tujtW4XuPXR3/4XJK8DAQLJs0f73vG4R5ZEe+lrdyv9Yd+PGjcLCwrNnz2pqasJDhL/vN3hFtKrc8Pl8XJn7WvLDyDH4x5qamlu/zxI28tWnrlMeNq1p6Bpx7fNftRIjsvYGzxH1vsMGHniUtHHi48ePnZ2dg4ODBUoDxas1OyeEb0GtXhFSkOX2JUUbI5PJSLn5oWhduREKhcuWLQsODoZXpqamZt++fRsaGgoLC3Nzc6Oionbv3h0SEuLk5NThwtFoNLg+BPNYKSkpEdCi4KVCIISJUChks9mE9f2GhgbpT+WW4PF4fD4f/+7tAr5sEQsHAt9u22Kof+rUKaFQyJg0mKz278xgmIhEIjW7RdIamEiEycvLk8nkloaWPodKSkrwMQDll77M0xIcDodMJhOY8MbGRi6XSyaTpc85tY75ZN/e9IcP5FVVDQ4cUtD4V8MQiUQEIiGBf9UaTE6u9TlXHjDQ6eyF+BW+cXFxHh4e165d69WrF4ZhBK4IuPQiJydHIEYOAIDFYikqKhL4vnw+n8fjUanUlkKYtPqL0+l0OGNwMZVYMDcWi0Wj0Yjdgvh8vkQgpQULFtSXFijaDO82d2eznt5kMgn8F5YTDion174TGxOJMADghJPJJM1eA4f+/Tpx09SstPjRo0cPW75z0KyV+NAqKv/zvbhcrry8PLFbELyaiN2FmEwmMV8huNStqKjY0qYbsQsN0SrFxcWzZs06fvx4W9yf79+///Dhw6ysLFNTU19fXwuL/4/I9fr164sXLxYXFw8dOnT58uUEzj0JWlduNm3adOrUKSMjo7Vr186dOxc/8zAMe/v2bUBAwNWrVydPnpyUlGRgYCCjNJLCkcnwTIVhxygUCoHdYqiTEbiVCwQCuApNoC0AgMViUSgUAlcUfFsik8kExiWRSEKhkJjAcEOn1bZZWVnnzp0jycuZ/TKqkPTvmyiGkQAgFsQPAIDByOjExMavAVx+AmLw+XwFBQViAsDHgPS2yVcuv/z7JElBweCvAKqRMV5OIrV1i1YC+CrfRoVSzdxi2KWrCWtW5SYnOTs77969e9GiRcTUfbivRGyi2Gw2hUIhoBzAS5jYFQGBWhFUggnLz+FwCN+C+Hy+uPwnT54MDw+XV9XSX3dJjtz8TRyqMlC5gbeR9p4pcC0InmCwNyVdxpDjzz4eX5cTduRRgF/6k1te24K69bQCAFCp//O7NDY2Er4FcTgcOTk5YpPMZDKJNYSLagoKCl8liN+PDIfDiY6Orq+vb7Xmrl27Nm3a5Ozs3Ldv34cPH548eTIqKmro0KEAgNu3b0+bNm3QoEGmpqbbt28PDw+PioqSUR9tpfGLFy/++usvBoORkJDg6+srrlOTSCRbW9tLly7t3bu3rKxswYIFssiB6CqsWrWKz+czpgxRNmx32NMfk/Dw8Pv+WwGJpLtpq6Jthzl+t510NidXUUnr8HH1ad5cHm/NmjVjx479+PFj50uCAACkpaX99ttvAADdlUEwnUKzpDNBOhNkMEEmWx7+3yHIKVBtVh0aHPCArG2Yn/T85JT+Tw5vauRKpsXY/ZmyM53snwrgHwIhI3l5eVu2bFm9enV0dPThw4ffvXtnY2OzatUqAACGYevXr/f09Hz27NnZs2fv3bsXExNz//59GUdsRbm5du0ahmFHjhyREsD7t99+s7e3j4yMrK6ullEaxDdOUFDQvXv3KOp0q+XjvrYsXYPnz597e3tjQmG3ZctVPcZ8RUlICgo6a9Z2Dwikaes8e/asX79+S5Ysyc/Pb70louPg8XgzZ85ks9kMr0V0p0lfSwztQaOMT3xQ91ouFAiendp11NPy/PnzKBRnV6GwsHD9+vVeXl4zZsy4cOECbnnXUrlQKPz777/nzZs3duxYX1/ft2/fwvLKykpXV9ePHz8uWbJkzJgxy5cvT09P5/F4u3btmjBhwoIFC9LS0mDNhw8fzpgx4/Pnz+PHj3d3d1+zZs2HDx9aEi86Onr58uXjxo37/fff8bA9r169EolEq1evhh+pVKq3t/e7d+94PN6zZ8/S0tJWrlwJDzk5OdnZ2f39998yzlIryk1WVhYAwNHRUXo1JycnkUiUnZ3dxlFtbGxQEMYux/Pnz+H513ezN0X9y6ag+z549+6dl5cXh8NRn+atOefnry0OAAAoDxk6/J/bxjNni0ikv//+29zc/JdffklJSfnacv0obNiwITk5mc6wtFl58OtKIqekqrP0CCPwtWIvp/qywnnz5tnZ2YWHh6MgZN84xcXF9vb2N27c6N69e01Nzbx58/78808p5QAAT0/P3377rb6+nsFgPHr0yNHREWotPB4vOjp69OjRpaWlFhYWV69edXd3d3V1TU5ONjQ0DAsLGzVqFDSTKC0tjYiIGD58eEVFhYWFxZ07dxwcHGJiYpqKd/jwYXd399zc3J49e4aGhtra2qampgIAbGxsTp482b37/weo/Pz5M41Go1AomZmZAIDBgwfjhwYPHpycnCzjRLWyf8xms1VVVbW1taVXMzc3B+1JyLxjx4421kR0JnvLopSY/2M97d/TC/7z8uXLcePGcblcjZmO9UMM6tllX0PArsSnT59GjRpVW1vb3XOc8q+/fW1x/h8ynd577e+ms+dk/H2y6N6ds2fPnjt3buTIkevWrXN3d//a0n3P3LlzJzAwUE6Baud/RZ6mDNrtZ93x0MzsDQOeM2Ov1V/c9O7duwkTJvTv33/Dhg2YqRdAFrjfJOHh4RUVFYmJiT169AAA/PLLL/Hx8VLK8/PzHz58eOTIkeXLlwMAysrKjI2No6KirKz+zQk4evToU6dOAQD69eu3YMECLy+vEydOAACsrKxWrFiRnZ0NzX7r6urGjh176dIlEonEYrGGDx/u5+cHh8ApLi5ev379X3/95efnBwD4888/Bw4cuHr16sjISGtra2vr/4/4Gh0dfeXKlXnz5pFIpOLiYmVlZfH0Z1paWhUVFTJOVOvGcW0x6vlChugwUhkQC7BBLFwNNLMl0BAOTdhpFgYUaW8rGb+sLAKDJp7JsKtr164tWrSIzWarjumrvdz9P1PFZlrLMm5LYks3QcVPElxgYsnJCU84aE74tLS0kSNHlpeX6zq79N++63Njsy61sr4iY1i7jUwBAJ+5XAAASbOb4oZNxvMX1Vy/Unf7ZmRkZGRkZA9bW5fVa0yGDhOvv4Xxr10I/h2JnWCEz8y2XBFtOUnwbRfCYshykmRlZU2bPQ/DMO0F+0r1bEvbZkPTAasoGAZIpOZuRP+dOiSSirO37ahJBffPZF788+3bt9OnT9foYTJw1kr7KQsUFJUBAEJhW3esOuS2SaBVqycJiUT6PhymVFVVMQw7ffr0ypUrNTU1Q0JCpJczGAzxX7+oqEggEHC5XLxk8uTJ8B+4RQNzKwEAoAe0eDbQ33//Hd5dlZWV/fz8Zs6cWVpaKi4bXPmDljSw2oIFC/7444/Gxkbc84PP5wcEBGzfvn3w4MH79u0DADQ0NEgkdqXRaAKBAMMwWUKPftOhKrlcLnTkhucrsZh4ULkhEKACv1raYgfe7LjEAsrBcfHv3t62GIYRExgiMeinT5927tx57do1AABj+jDKimEigAFRkxslBjAMIxKGAwMAAKFQKGWe1dXVpfzuLBbr3xBnGAaIRgATiUQSF3wbgYM2NjaKC//hw4cpU6ZUVFRoDxnW7899wv+NWfK/zVs81JZxMUxEeNJh8EA5bW0t35UaPy+ov32z9sqlwuTki3PnGDs6ua3foP2flyb+1eCgQqGQ8BVBLKMcHBc6VDdbgUQiqaurS+mhvr4ev78TvqKFQiHhWxCTyZw2bRq3voY+bJrqON92/Oj/3YXaOyjAg/hhGMAwQZNA2CLR/6iDIpJcd8+F+h5zSx5fybm2vyYvLfKvX2NPbLOdusje27e+e1utCOA8CwQCwnchwmcXkBroVUqwia7F9OnTX7169ddff+3cubNfv36enp6rVq3S0tJqqRwA8OHDh/379ycnJ2dnZysoKEhMEe60D5U/fJaavjCIe2737t0bAJCdna2rq4sXZmZmCoVCeAjCZDL5fH5dXR00201ISJg7d25hYeH27dt/++03OISOjk5tba24KlNTU6OjoyNjUPXWlRuBQJCYmCi9Tl5enixCtISSkhKMMVNbWysQCFRVVQn4YbLZbNhVexsKBILa2lp5eXnp982WqK6uVlNTI/CuwGKxOByOkpKShDLbFvh8PpfLVVVVbW9DAEBlZWVhwqeylKz60ipMJBIJhNV5JfuT0gUCAVmJar1uKmOyU3oLu1FQgyS2ZIKJRPLy8hQKRUNDg4DY+KVYVVWFYZh0TaglmEymRBiSNsLj8WBbXIwnT55Mnjy5rq5O19l1wP6DchQKAECu+ZUbIBIJib1NwqUIOTk5ArGFMAwDACOR/n9cORUVzdlzNaZ51/4TWn02ODfu5dnJkwbMmuX6mx9NVRX/XYRCYU1Njby8PLFfqqamRlVVlYArOJvNZrPZioqKxEKnAADgJSwSiaqrq+Xk5IjJX1tbS6fTCdyCamtrf/nll7S0NKpJP73VZ9r3i2OY6N8fut2IhEI8zo2CgmQPEl3+W0FBwdhrofG4BYlR92puHuC8fxp3Zm/8pcOCX35eu3atiYlJq4Py+fz6+noFBQXCdyFivw6TyeTxeHQ6/bt3BZeXlz98+PCePXuePn365MmTo0ePXrx48ePHjzQardnyjIwMR0dHT0/P3bt3W1tbMxgMwkFbmEwmfg1CHVQi5BWFQtHR0Tl9+rREQ3h7vHPnzuTJk8ePH//06VNxlUhfX18oFJaUlOCCFRYW6uvrExMSp/ULtaGhYcCAATIOg/jGiY6OXrZs2adPnyTK5agKhmMHW/p6KuprfhXBuhZHjx5ds2ZNY2NjD6/x/bbtlOtSSXxIFIqG90y1ceOrQk7XXr/65vy5jxH3R/6xEVu1EuWlIgyfz589e3ZMTAxNu4fB1jtyNCLxDzsbEkl54FjlQZ68rKSa0H3M56EnT548ffr09OnT161b17dv368t3w/N33//XVpaunXr1nHjxo0bN87V1XX8+PHp6emvXr1qtjw8PFxZWfnatWvwKm41V4wUHj16NGvWLPh/REQEhUIxMzMT35nq3bt3RUVF7969cTvdM2fOFBUVOTs7V1ZWent7L1q0CKbuEcfNzY1MJj98+PCXX34BAIhEoqioKNmDy7Ry8x06dGjb1y2kuIvjREdHh4WFVVdX9+vXb+nSpd/HOmGXRiQSbdmyZc+ePSKRSEFfne7em9JdA5BIJAqZrK1Cs+4up0TJB40AWRBLpaKiYunSpWFhYYBEsliyzHKpLxFzmG8AOTpde8WvauMnlu/7syEh/ubqX11v3Tx8+DB6pBGgpqbmp59+ioqKoqhrD9p3v1Tb8GtL1D5oZvb66692K84Uhe8riDh36dKly5cvjxgxwtfX19PTEyXg/CowmcydO3eam5t7eXkVFBRcu3aNTqebm5s/evSo2XJ1dfWKioo7d+54enrm5+f/+uuvLBartraWwNB+fn56enr9+/cPDw8/ePDgkiVLJFZufvrpp82bN48bN+7vv/82NDS8ffu2j4/Ptm3bAACXLl3icrmzZ8+GLtg4pqamurq6U6ZM2bx5s4ODg5GR0aZNm2pra7+4ctOxbk3v3r07ePCgj49Pz549z507t3v37j179nRg/4j2Ul9fP2vWrLt378qR5S2WjRP81IdMlTXo9Y9GY2PjhQsXNu7YwampkVdR1d28FQx36aKaDQ7FyLjH0ZPMhw8qjhyMiYmxs7P7+eefN27c+J29jUiEp/O3bqEeIV6+fDlnzpzs7GyajqH9n3foxr2BZKi8zqPVGIBSKigYmFmu/dvil63Z1w/mhwc9evTo0aNHurq6kydP9vT0HDZsmKqqKj6TQqE8j6e0yZJgUipEq6xYseLFixf4CgqDwQgNDVVSUmqpfMGCBf/888+ECRNgyOndu3crKyvv2rVLS0vrp59+atfQ27ZtGzt2LDR9mzVr1u7duyUqUKnUe/fuzZo1y9bWFgCgoKCwfPnyDRs2AAA+fPggEomGDBki0QQaaZ0+fXrq1KnW1tby8vIqKirh4eGmpqbtnpr/pVNV77t377q6unp6egIAVq9e7ePjk5OT07Nnz86UAYETHx8/a9asz58/U7VUBuxfSLdhZPIqv7ZQXYnKysozZ84cPXoUBqpSHjJU9/c/yDq6rTbsKqh4jFYeNhw7H5Jz+WJwcPD58+cnT568YMGCkSNHfm3RvmkyMzN37doFw+Jp2Dja+l9T0OjyZwWtm0HvZfss5m0ueHA+/05QWdb7EydOnDhxQk5OzsLCQt6or5axhYahqao+Q6mbgdCsy3/fbxYqlXrz5s2SkpL8/HwNDQ0TExO4hNZSuZKS0tOnTzMzMxsaGnr37k2hUHx9fdetW8dgMOh0enR0dP/+/WHPDAYjOjoaNwc2MzOLjo42MzPDh/bx8Zk7d+6nT5+MjY1x06ju3buLt+rdu3dycnJRUVFJSYmZmRm+8/Prr7/Onj27pS9Fp9MfPHhQUVFRVlbWu3fvDvFr61TlJjU11cfHB/6vp6enr6+fmpqKlJvOJzEx8dixY+fPnxcKheo2RgMOLFLU05Al4/GPQ3l5eUpKyuvXr6OiomJjY6Gjloq5heriZcr/60H9fSCnpGS5xs/Ye0bGyeOFd+9cu3bt2rVrpqamXl5ezs7Otra2RkZGX1tGWUm8cerxwfVUZZV/uqnB+5LRfxgaGhoaGrbFHYHP53/48OHZs2e3b9+OiYkRiURyFJrlnA1mszdgJDlZojN8U5CVVXtOWd5zynJmTmrp89sV8Y9qP71JS0sD/4WyhZxRUDAyMjI1NTU1NTUzMzMxMTE2NmYwGMSMhRFN0dfXb9bktqVycR1FXl4eV2icnZ3xckVFRfGPysrK4h8hNBoNrsqIlzSt1r17d/F4fQAA8SA3LaGtrd1qUL2203nKTWNjY0NDA/RMg2hqatbU1EhpwufzYQp76OYH87HhR2traw8cONDquLAtAU0QhmqAOR3b2xYAIBAI5OXliTmOwlR5BGSGTjRNBa6rq4Mu4qWlpWlpaZWVlQAAOQV5s/keZotGyynI4zdf6CHcbv51tCUQ1OdfsRsbG1vyE1ZSUpIyjVwuFwoPRYCZHfGjmZmZ58+fb1UM+EPDCefz+dDDDvzn9szlcmtra6uqqoqKisSFlKNQdF3dekyaou00JKuxUXzqJB5mLc0qhhGdcIDHfyI45wC0dVyhUEjV1euzdbvZ4qV5N64X37ublZUVGBgYGBgIAFBUVDQ0NOzWrZuGhgaNRlNTU4OtFBQUJPbjZbkiXF1dnZ2dW/KIJpFI0vUPNpsNoyQAADAMw3/ExkYKAIDLYnLra7j1NSkloNlgzSoqKtra2mpqaurq6srKynjEDgzD6urqmExmaWlpSUkJ/qPLK9IZo2abzlynqMvA/rsFiUSE/Paxf10K290Q70Akar9H3b+nF352CYWSp5kSw8pkppXJzN8xoaAhPz0z/RO/KKOxNLuxLLexNFdQVZiZmQkjz4qjqKjYrVs3LS0tdXV16Don7t5IpVIVFBSImfIIhcKpU6f27dsXPjKaIi8vT8D/FNF16TzlBlpoiztzKioqSg9pAB2b8Y8SIVjKy8vbotwgmmJoaOjl5bVw4UJDw2/IwlE8WpQ4ioqKUp6IPB5P/HYmEasmPT29Y0+Sbt26WVpa9unTx9HRcfjw4XT6j5SGooceGGCL/bk7OTk5Ojo6Pj7+06dPRUVFGRkZGRkZX3RkFRUVJyenlp5brSo3XC4XV4wwDMPPtNU9OAAA3q/enMUTeDxeTU1NZWVlUVFRUVFRQUFBYWEh/J/JZLbqYEKhUCwtLW1tbV1dXUeMGPGfYift5e37oa8BGGcAwP+Ht25sbCwqKsrJycnJycnNzc3Ly8vPzy8uLq6uri4oKMDzDXUsdnZ2eNTdppDJZKTcEGbQoEHwZaYL0XnKDXwMiD97uFyuxLudBFQqFWrxbDZbJBIpKSmJL2b06NHj6NGjrY4LX6cIrL7A5QQSiUQscAKfzyfWUCgUCgQCMplMTGahUIi/WYpDJpNhJg0zMzM9Pb2mFVgsFoZhxJ7WXC6XTCYTeOWCofMUFBSkxJiR/q6vqKgI28KQicrKyuL17ezs2nKSNDY2ysvLSyyV4aHh1NTUVFRUNDU1DQwMJB6iUH5i900Mw9hstvRLoCU4HI5QKFRUVCRwkjQ2NopEIgJBfUQiEZvNlpeXHz58+PDhw3FJCgsLKysrmUxmQ0MDj8fD170k4PP5TQOItQWhUNivXz/4Wt9shVb7VFZWhis3cGFPYs5bPedZLFZVVVVxcTGPx4OLeVBVUlJSolKpmpqa2traenp6LS218vl8eDcgcEMQiURcLpdAmC7w3xUtcUW0ER6PJycn19KESwFeEYqKijY2NjY2Nk27raqqqqysrK+vZ7FYUEJxzx0ej0fgzAQANDY29unTh0ajtXQX6oTwxPyc7Lpb/xBuLqwj4sHUOfTq1atXr15fW4r20XnKDYVCUVJSEt+Hqqmp0dSUFj1FQUEBXl3wxYtCoYifuDQazdfXt9VxZQziRyaTCQfxU1dXJxzEj06nd3IQP7hWT+zlhvC9m8fjcblcWVaM8UHhjZJGo4nfys3MzMQ3m1tCliB+hOWHyg2xLw6fshQKhcDjB6YRIDCuUCiEEWDF29JotDYaUsgYxE+WN2/4y8IQyRLytwUajaalpaWjo0MsiJ9IJOLz+cTkFwgEfD6f2BeHm3FUKpXAXQi+XxG+BcnJyTXbFm5cSgkGWFlZ2ZaQIk2BQfyI3YVkBw7KSU7iJCfJ2BWBKxrRLJ1qUNyvX7/3798PGzYMAAAtGFDwDAQCgUB0aSZMmODv79/SxnrbMTAwaMvLGKItNJtQ7Uvx7t277du3r1mzxtjY+PTp01wut41xbvBg88QC/IM2LFw3i7idaXuBRsHEGsryZTGiwdoJ798B2dIvwLayLxp3RfmFQiFhgX+cKwJOMrEvK4GM8styknT+Fd2Se0Eb2/44V4Ts5OXl/fLLL7Ik9YNYWlqGhIQs3VflAAAgAElEQVR89xkkOodOVW6AWITivn37Ll++nJjBAQKBQCAQ3whnz56FqQNk5+PHj13OuuXbpLPjZ7u4uLi4uHTyoAgEAoFAfCGggTl9yBTN6RsId1L610x+UUYnLzd8x6DkIAgEAoFAyIq8mjbNzJ5wcxKViE8coiW+uHccAoFAIBAIRGeClBsEAoFAIBDfFUi5QSAQCAQCQYSamppt27YVFRW1q1V4eHhcXJx4SW1tbVhY2JEjR5KTkztEMKTcIBAIBAKBIEJ1dbW/v39hYWHbm8TFxU2ePPn+/ft4SWpqar9+/ebMmbN//347O7uNGzfKLhhSbhAIBAKBQHQGTCZz9uzZEqmFV61apaamVlpampube+zYsd27d8u+foO8pRAIxHdOTU0Nm83u3r17s0fZbPbNmzdzc3ONjY0nTZpELJGTBImJic+ePcMwzNnZ2c7OrmmFZ8+ePXjwAP+opKQky9tqq8O1sU67wDAsIiLi3bt3mpqaY8eObTYL7/nz59PT0/GPNjY2M2bMkH1oAEBBQYGqqiqehb7p0fv371dXV/fv33/06NFfJbLfl0MkEkVGRqamptLp9JEjR+LpLFoqBwB8/Pjx9evXFRUVPXv2HDduHMxgzWQyDxw4sHz58kePHuXl5Zmamo4ZM0ZZWfnFixdv3rzp1q3bTz/9BNNovHv3Ljo6etGiRSdPnmSxWP369XN3d28pTF1lZWV0dHRubq61tbWHh4dEOMcVK1b07NlTPKHYx48fo6KiQkNDYeKgZcuW7du378iRI2fOnJFllpByg0AgvnOuXr1Ko9GaDbMmFArXrVtHpVKdnJxevHgRFxd36NAhYkFycWJiYg4dOuTl5QUA2Llz56pVq5ydnSXqpKWlsVgsXMkgli2y7cO1pU57OX78eHx8/JgxY3Jzc9euXbt///6m6mNCQoKOjg6DwYAf9fX1ZRwUgmHY3r17582bN2DAgKZHi4qK1q5da2tra2JicvXq1ZycnGXLlnXIuN8CXC53xIgRL1++tLCwKC4u5nK5oaGh48ePb6kcALB9+/Zt27apq6tra2tnZGQYGxtD3aW+vt7f3//ly5cpKSna2trv37+Hiu/169fV1dVTU1NPnToVGxtLIpHevn27Z8+esLCwhIQEIyOjbdu2WVpa3rt3z9jYWEK8mJiYn376SSgUGhgYpKWl2dvb379/H08ieePGjdu3b6ekpNja2uJN4CKNm5sbXuLm5vbs2TMZJwopNwgE4rvl/fv3SUlJDx48mDhxYrMVXr16VV1dHRQUpKys7OHh4ePj8+rVqyFDhsgy6PXr16dNm+bt7Q0AoNFoN27caKpJlJWVOTg4dMgyRluGa0uddlFdXf348eOdO3daW1sDADZu3Hj37t3FixdLVCsvL/fx8WmaHpwwHA4nJSUlKioqLy+vpTp37twxNTX9/fffAQDW1tabN2+eMWNGG9O7fvuEhYW9ePHixYsXTk5OjY2NAwcO/Ouvv8aPH99SeVVV1bZt23x9fQ8dOkQikZKSkgYOHBgeHj5//nzYIZfLzcrKUlRU3LFjx5YtW0gkUmZmJo1G+/333/fu3VtQUAB107KyMkNDw8LCQk1NzU+fPrm7u/v5+YWGhorLxuFwZs2a5e7ufvbsWSqVmpKS4uzsvHbt2uDgYABAYWHh4sWLT5w40aNHD/FWxcXFFApF/AfS09NrlxFPsyCbGwQC8X2CYdi1a9c+f/4sJc1LXFxc//79YQU6nd6/f38JJ472UlRUVFBQgKtHQ4cOzc/Pb+pLUlZWpqurK8tAbR+ujSK1i/j4eBUVld69e+N9vnz5UqIOk8lks9k6OjqyDCRBcXHx7du3GxoapOw0xcXFDR06FP5vbW2toqLy5s2bDpTh61JXV0cikXg8HgBAQUHh2bNnt27dklJOo9GePHmyc+dOOGMWFhY0Gq2mpgbvcNWqVXCXCi7srV69Gm5FwfcB8Zp//vknXIPp1avX77///s8//4jvLgEA/vnnn5KSkmPHjsGVyD59+vj6+oaFhcHMX/PmzRszZgzUsMWprKxUUVERL1FRUeFyuTIGa0YrNwgE4vuERCLt2rULALB58+aW6lRVVcG1B4i+vn5aWposg1ZXVwMADAwM8A4BADU1NRJbNmVlZampqREREQKBAFqiELP1actwbRSpXVRVVenr6+Mahr6+fm1trURu1LKyMgDA/fv3k5OTlZSUnJycxo0bJ6P5i6mpKfxNJ02a1GwFkUhUW1uLf1kSiaSnpwdn4PtgxowZQUFBbm5utra2rq6unp6ecEOnpXJlZeUBAwaEhoYmJydnZ2cnJCSw2WzxDrW0tOA/UMXBde6mJ6T4JqCjoyOGYdnZ2eJmT2lpaYqKinDNDJKRkVFXV1dXVxccHJyRkREWFtb0G2loaDQ0NIiXMJlMDQ0NGU8VtHKDQCB+XGpra8XXdZSVlcVfVYl1SKPRcKsdMplMpVIl+qyvr+dyuUVFRRMmTPDy8kpISNi8eTOx99S2DNeWOgTGlZg3DMMk0mJD5aa+vn7WrFkODg6XLl0KCQmRZdC2UFdXh2GYuGx0Ol1igaFLo6GhkZCQ8PjxYzc3t6dPn7q7u0+YMAHDsJbKS0pKBg0atH37dgqFMmXKlAcPHnTIkiHUYvl8vnghi8VSVFTsIYabm5u/v7+8vPy1a9dEItGYMWMcHR0dHR1ra2tDQkIcHR3fv3+vp6fH4/HET56KigrZzbPQyg0CgfhOCAwMrKioAADY2dlNmTKlLU2oVGpjYyP+sbGxkUKhtGvQlJSUq1evwv/Xrl1LoVDEOwQACAQCiT5pNFpAQICJiQmZTAYA9OrVa8mSJW/fvhW3smwjbRmuLXVkHBf+L9GnjY1NYGAg7rNDp9OPHj06a9YsWaynWwV2LiGbgoLClxuxk4mOjmaz2WPHjnV3dwcAHD16dMWKFampqZWVlc2WP3nyBO5L4ls/EhpJ20lMTMTNfl+/fg0AMDc3F1cczc3N+Xz++vXr8Z84OTm5qKhIVVXVx8enuLgYr/n+/XtTU1M3NzcNDY1BgwYBAGJiYuC+GAAgNjbW0dGRmJA4SLlBIBDfCa6urlwuFwCgra3dxibq6upMJhP/2NDQ0F7LUwMDA+iTAgBQUlJSV1cXCoVsNhuu6rPZbKFQKNEnhUKxsLDAP+rr62tqahYXFxNQbtoyXFvqEBj3w4cP+MeGhgYKhSKxkaGmpia+Z2FtbS0SiUpLS42MjGQZWjpKSkoUCkX8N2Uymbi3znfAgwcPTpw4kZycbGJigmEYNLLR1dW9ePFis+X19fUcDofJZKqoqIhEon379tXU1AgEAgJDb9iwITIyUk1NLSMjY/fu3ePGjdPS0hJXbiZNmrRu3bpff/312LFjcnJyWVlZI0aMmDRp0rhx4xYtWiTe1dGjR4cPH75161b40cHB4c8//xw9erSCgsKNGzfS09OhDbIsoG0pBALxndCvXz8HBwcHBwfxCB/SMTExEQ/EkpaW1va2EC0tLYf/oFAohoaGFAoF7zMtLY1CoUi4h6SkpAQFBYlEIvhRKBQ2NDS0XSETpy3DtaVOezE1Nc3Ly4OqJGhh3iIiIu7cuYN/rKurA+3ROwljYmKCG05xudy8vLz2/qbfMitXroSm3Pb29rq6uuvWrdu5c6e2tnZL5XPmzFFRUbG0tHRzczM1NX39+rWDg0NgYODNmzfbO7SZmVmPHj369OljY2OjoqJy6NAhiQp6enrBwcEXL17U1tbu06ePubm5kZHRvn37Wu357Nmzubm5PXv2dHR0nDFjxpYtW2T0WARo5QaBQPxosNns7OxsY2NjOp3u7u5+8+bN5ORkW1vbpKSkzMzMX3/9VZbOlZSUHB0dQ0ND+/bti2FYWFiYk5MTXNLIzc0ViUQmJiYaGhp3795VVlaeOnWqvLx8SEiIiopK3759v9BwUuoQpn///hoaGqGhobNnz66srHz8+PHs2bPhodTU1G7duunq6srJyQUHB+vr69vb2zc0NJw/f97R0bFDYiQ2paKioqyszNramkQiubu7X7p0ydPTU0tLKzQ0VFNTs1+/fl9i0K+CgYFBRkZGeHh4Xl6ehoaGs7OzpaWllHIjI6P09PSbN282NDQMHDhwyJAhOTk5t2/fNjY2VlVV9ff3x2PVdOvWzd/fH9d69fT0/P399fT08KEvXbr05MmTd+/e9ezZc8SIEXQ6HQCgqakp3mratGlOTk4xMTElJSW9evUaM2aMRBA/yLp168TNk62srN6/f//06dPS0tLjx48TWMJsCklGbysEAoH4xtm8ebOJiQkexO/Tp0+///67v78/jKH3+PHjEydOqKmp1dXV+fr6igcTI0ZDQ4O/v39RURGGYYaGhlu3boWPgc2bN3O5XPgi+/r160OHDkHrh27duvn5+ZmZmX254VqqIwvZ2dn+/v4kEonJZI4YMWLp0qXQvWXSpEmTJk2aO3cuhmFXr169fv06lUrlcrn9+/dfvXp1SzGF28ukSZM2btyIPyCvX79+8eLFsLAwBQUFDMOOHz/+5MkTOp2OYZi/v/+XXrk5c+bMggUL1MYu0V1+gnAnecttedlvU1NTcQf7b4dz5879/PPPXUtbQMpNl4fL5R4/fhwAMHbsWCsrq7Y0wTAsMzPz9evXVCrV3t5e4spns9mJiYlpaWm9e/e2s7OD/oGILs3nz5/hBoGPj08bn2p5eXlv3ryprq42MTFxcXGRMMmsqKh48+ZNeXn5gAEDevfuLWNI3y9Nbm4ujUbD30HFV25gCZ/PLyws7NGjh4xmtuJAXyFxzxR8KQV+FIlERUVF8vLy4j7VX264ZuvICIZh+fn5mpqa4nFK8JUb+JHP5xcUFGhqanZsGL3U1FQGg4GPK75yA0uYTGZ1dTWDweiE3AtIufkGQdtSXR4Oh7N//34AgLGxcVuUm8bGxmXLluEZWUkkkp+fH74Un52dPWfOnNzcXPjRwMDg2rVr39OO9Y9JRkYGPElmzpzZFuVm3759R48exa0OjYyMgoKC8Hgwjx49WrZsGR4tY8iQIWfPnv1C2w0dgkSQeCUlJYmYuRQKpcNP8qY6hIQYcnJyzeZj+kLDNVtHRkgkUlPrYPG4QQAACoViamraseM2HUVbW1vCmkdFRUUiNByCMAwGo6UY398sSLn54Vi9evX9+/d1dHTGjRtXVlZ27969ffv2OTg4ODo6crncadOmlZSUGBoaenh4PH78ODc3d8qUKa9evfqi3puIb4qHDx8GBgYCAAYNGmRlZfXgwYO8vLxFixY9f/5cTk7u48eP8+fPF4lEdnZ2/fr1Cw0NffHixYoVK2T3bkAgujTCugpuZiLh5hiP3Xqlr4Srq6urq+vXlqJ9IOVGGkVFRXFxcWVlZXp6eoMGDZJ4zZJ+NCcnJz4+vry8vFu3bvb29ubm5rC8vLz84sWLAICZM2empaWlpaX16NFj6NCh6urq5eXlz58/r6qqGjhwYP/+/WH9wsLC69evAwBmz57NYrGeP3+uoaHh6OiIh5Vslrq6uvj4+MzMTF1d3REjRuBvMBkZGbdu3ZKTk7ty5Qpc5rl//35dXR187Y6MjCwpKZGTk7t8+bKJicnixYsdHR3Ly8ujoqLGjh3bIVP6/ZGSkvLu3buGhgZDQ8Phw4dLvCxKPxofH5+WlsZkMvX09JydnfHfNDExMSYmhkwmL1u27Pbt2+Xl5b1793ZyclJQUMjIyHj9+jWJRHJxccGN+OLi4uLi4hQUFFasWJGYmJicnGxubu7g4ADDqLdEQUFBYmJiWVmZmZmZi4sLvrX0+PFjAMCUKVMOHz4MAPDz8xswYEBeXl5GRoaVldX58+dFIpGRkVFYWBiFQhk0aNDSpUsfPXpUWVnZrVu3DppUBKIrAW1mG16ENbxoJgIvga4QsoOUmxY5derU7t278WBQZDLZ19d33bp1bTn6119/HT16FHf1JJFICxcu9Pf3BwCUlpbCDYLExMTo6GhYgcFgbNy4cf369XjY0I0bN8JMtgUFBbA+AODQoUNwp4BOpx84cMDT07NZyWNjY1esWFFZWQk/qqioBAYGjh49GgBw69YtDMPMzMwYDMbjx49ra2ttbW3xRePExEQAgLGxMVyiNzAwsLS0TE1Nffr0KVJumiISiXx9fcPDw/ESNTW1gwcPenh4tHqUz+fPnTtXPPMtlUoNDAyEEVMSEhL2799PJpOfPXuGp+xxc3NzcXHx9/eH5xWNRrt48SIMdRUXF7d//35lZWUWi3XkyBFY38TEJCgoqKWdypMnT+7ZswffeDI2Nr5w4QL83aGmi1uoaGhoKCoq8vl8aHYDT5Jhw4ZB8xT4PicUCmNjYydPnizbjCIQXRJ3d3cvLy8OhyNjPwYGBl9iC+/HBCk3zZOZmbljxw6RSDRkyJDBgwcnJCTExMQcOnTI0dFx2LBh0o8mJCTAV143NzcbG5sXL14kJiYGBQV5e3uLP2liY2M9PDyKioo+fPiQn5+/ePHinj17Ojk5PX36lM1mBwQE+Pj4wACmkP379/ft29fMzCwyMrKhoWHFihWDBg1qGjSisrJy4cKFLBZrxIgRLi4ujx49iomJWbx48ePHj83NzWEqXR6PN3r06KysLAAAiURasWIFzAYCE7GKv3/DtQTZE7R+l1y+fBnqLtOnT9fV1Y2MjExLS1u+fHlSUpKKior0oydPnnz27JmcnBw0grl//35+fv4ff/yBh4MDAAgEgpSUlPHjx8fHx5eUlDx58uTJkyeDBg2i0WjPnz/ncrmBgYHicTyhZjNixAh5efnIyMjs7OxVq1Y9fPiwqeRPnjzZsWOHnJzc/PnzjYyMzp07l52dPXv27JiYGAUFhfHjx1+8ePHChQs6Ojq9e/e+fPlyXV3dgAEDoOoDTwZ8kUlFRYVMJgsEAnSSIH5YVFVVLS0tJbJPEMDS0lL8no+QBTSPzfP27Vv4frxhwwZbW1uhULhixQoWi1VVVdXq0dzcXOhkFBwcTKFQUlNTR40aBQCAq/r4ED///DPUkBwcHIqLixUUFCIiIlRUVIKDg7ds2cLj8YqKisSN9YYMGXLlyhV5efmkpCQvLy8ej3f16tUVK1ZISB4UFMRisYyMjIKDg8lksre3d79+/Vgs1pkzZ/bs2QPfLfLz8/X19RcuXJiQkPD27dvDhw87OTkNGzaMxWKB/82XBlO0wHKEBElJSQAAHR0df39/VVVVb2/vLVu2AACKioqsrKykH62qqrKzs+vfv/+OHTvAfyElampqKioqxBXWAwcOjB07NicnB2Y5trW1haG3Fi5cGBERAdVTcVavXu3n5wcAOHbs2O7duz98+JCYmGhvby9R7eDBgwCAyZMnw9H79OkzefLkvLy8Bw8eeHl52dvbb9q0adOmTXj8UE1NzZMnT0KvE2hHLHGS1NXVSWTjQyB+HMLDwwMCAjqkKy8vL9yGASELSLlpHgaDAf8ZP368ra2tu7v7kiVL8Chb0o9OnTp16tSpubm5N2/eTE9Pf/ToESyXiHgN1/Pl5OSMjIyKi4sHDBgADTLwZUmJ+rNnz4ZWEXZ2dn369ElJSWk2fXFqaioAgEajwdUjAIC2tjaLxfr8+TP4b0NXQUHhn3/+YTAYAoFgxIgRnz9/Dg0NHTZsWNMMLJ3gRdl1gapneXm5ra3t0KFD3dzcdu7ciZteST+6bds2kUj0/v37kJCQtLQ0PJCrUCgUHwLGXOnZs6f4R/DfSSJRGQAwb948+M/cuXN3794NAPj06ZOEcoNh2MePHwEANTU1Bw4cAACIRCK4+pKRkQEACA8Ph2qNh4cHg8F48OBBQUHB7Nmzb926ha/TiHeIThLEDw60T7A3HT7WfhbhToIid5bWFkhkAUMQBik3zTNo0KBNmzadPHmysrIyMTExMTFx7969Tk5OBw4cMDQ0lH6UyWQuXrw4JiYGAEAmk2GYyKZIxI8RT2PbLOJZUg0MDFJSUsrLy5tWg7sD6enp4kHlAQDZ2dngvxduGxsbqJ+RyWQXF5fPnz/n5OQAAGAGFvF1Gvg63qpsPyaLFi0qKCi4efMml8t9/Pjx48eP5eTkZs+evXXrVhqNJv1oWlraggULoMs9zKMLg9NLIGERLP2HIJPJ+KoP9INlMpkwkaQ4VVVVMGp+VFRUVFSU+KGcnByhULh582ahUDhnzpw///wTALBixYrBgwenpaWFhISsXLkSZkFqepJ8y67gCEQnYKLbe4qjD+Hm11+cKK0t6EB5fnCQYXaLLF26NCkpKTQ0dNGiRdAt5eXLl8eOHWv16LFjx2JiYmg02smTJzMyMvCMwTJSWlqK/w+DcTWbHQZazHh6er78X27fvo03EY/FBGOkwrChULnBLZEBAFB/wleqEOIoKSkFBAS8f//+1KlTU6ZMgXnpzp8/HxkZ2erRdevW5ebmmpub37x5MyMjY+3atbLLIxAI8N+OzWY3NDSA5k4SdXV1uK+/adMmiZNk69atNTU1sBM89quWlhZcOoLrOtDaBu7AAgCYTCY8hdBJgkAgvh2QctM8wcHBK1euPHbsmKOjo7+/f1xcnIODAwDg06dPrR59//49AMDS0tLLy4tKpcbFxXWISJcuXYKGPm/fvn337h0AoE+fPk2rwYWi3NxcBoNhZGRkZGR0//79kJCQ58+fAwDc3d0BAB8+fIC7VPX19REREQAAmKN42LBhsC3c8Pr8+TN8ng0fPrxDvsJ3xvr161euXBkbG+vp6Xn48OH4+Hi40AJPA+lHU1JSAADu7u6DBg2Sk5PrqJMERhkAAJw/fx6qsBLR6gAAZDIZ7mqVlJTAM0RLS+vcuXMhISGZmZlaWlpwexSeMLAaXPaDKg60/omNjYWLN9BomkwmOzk5dchXQCAQCNlB21LNw+fzw8LCSCRSQUGBmZlZdnZ2QkICAAAqMdKPwv2j9+/fr1q1Sl1d/cqVK7BP3DOcGM+ePRs/fry5uXlERASGYd26dfP29m5azcfH59KlS6mpqXPnznV3d3/58uW9e/dIJNL58+cBAPb29paWlunp6RMnTnR2dk5MTCwvL6dSqTDtzvDhw7t3715UVPTTTz85OTm9evVKIBDo6upC72WEBEVFRU+ePHn06NHbt29VVVXfvHkDt3sGDRrU6lF9ff28vLzLly8LBIKqqqpbt27BPpua0bSLgICADx8+yMnJwQjUI0aM6NWrV9Nqvr6+K1euPHPmjEAg6Nmz561bt96+fauiouLr60sikby9vYOCgm7cuJGbm2toaBgdHc3hcBQVFSdMmAAAmDp1alBQUEFBwZgxY8zNzZ8+fQoAGDNmjI6OjiySIxCIrgiXy339+rWdnV0b40ELBIIPHz4YGBg0vWNUVFSUlpZ2VDoXtHLTPIsWLRo7diyGYZcvX96+ffvFixeFQqGjo+OaNWtaPbp06VJVVVUMw0JDQ8+cObNw4ULYJ9yPIMzKlSvfv39//fp1JpOppaV1+vTpZq0cjIyMjhw5oqKi8uTJk40bN967d09ZWXnv3r24LerVq1d79epVW1t7+/btwsJCFRWVI0eOwM0LMpl848YNQ0PD6urqu3fvVlZWGhgYXLx4EXknNsvevXstLCzq6+uPHj26e/duaFWzZMkSaCou/aifn5+8vHx9ff3p06ejoqIWLFgA+8TNzwmgpKTk4+MTERFx7949DMP69u27d+/eZmtOnjwZ6jHnzp3z9/d/+/Zt9+7dr169Ck12Nm7cOHXqVBKJFB8f/88//1RXV+vo6Bw/fhxmdrSysgoJCaFSqVlZWQ8ePODxeI6OjtA6B4FA/GgUFRW5uLhAH4VW2blzp4aGhq2trZ6e3vLly3EDifr6+okTJ+ro6PTt21dbWxvPDiQL6KHVPGQyOSgoKCMj4+3bt+Xl5WpqalZWVgMHDmzLUTMzs5iYmMjISJFI5OjoaG5urqGhwWQyoTWonp7eb7/9BgDAHWemT5/u5OSEp7bp2bMnrAAtYHDGjBkzZcqUFy9eaGhoDB06FD+qqKgI6+Pp1ry8vAYPHvzmzRvo8u3i4qKuro73o6Oj8+DBg3fv3iUnJ+vq6jo4OIhr0EZGRk+fPk1KSvr48aOpqenAgQNRfpaW0NfXf/LkyatXr7KysmpqanR0dAYOHIj/jtKPTp48uXfv3s+fP9fU1Bw2bBiVSoVmT1C9GDBgAPxNceBH3A4GNpGwLyaRSFu2bPHw8Hj//r2ZmZmTkxOeNMPCwgL2ABNLkUikP/74Y9q0acnJydXV1aampi4uLrgKq6CgcOjQIV9f33fv3lVVVZmamjo6OopnpHJzc0tISHj9+nVRUVHfvn3t7OyQ+otAIKQTEBCwbdu2kydPjho16sKFCxs3buzfvz98+V+1alVsbOyjR4/MzMw2bdo0ceLE3NxcAwMDWYZDWcG/deLi4qZOnQoAiIiIwL3NEQhxDhw4ACMUQxspBALRmcCs4D85Ldn4E/Gs4NMDbNOL2poVvK6uLjMzk06nm5mZiW/itFQuEony8vIqKiqMjY3xt1k+nw8NRquqqgoLC01MTODLFY/H+/jxo5aWFu4lUFZWlpub6+DgkJSUxGKx+vTpg78wZ2VlmZmZvXr1ClplQCoqKvLy8szMzPBqQqGQwWDgPpgAAHd3dzqdfvv27bKyMgaDERgYuHTpUiiVnp7esmXLdu7cSXQuAUDbUggEAoFAdCH++OMPHR2dAQMGWFlZ9e7dG/ooSCmPiIhgMBgmJiYODg56enozZsyAoaoqKipcXFx27dplZGTk6Oioo6Ozdu3a27dva2lp2dvbGxkZwYigAIAHDx5Mnz7dz8/P3t5++PDhmpqa8+bNazbdRElJiZubG1yo1tPTW7duHTQ2jY6OLi4unjlzJl4zKioK+vDGxsby+Xw8mxCFQhk5cuTdu3dlnCWk3HzrUCgUHR0dHR0dtPKPaAllZWUdHR2Ut3+ZC6oAACAASURBVBKB+O558ODBnj17tm3bVlJSEhcXV19fDwNJtFTOZrOnT5/eq1evt2/flpSU7N+//+rVq7gHAwDgwoULCQkJFRUV3t7eAQEBa9asefXqVWlp6YQJE/bv349Hl8jLy7tw4cL9+/dLS0tPnjx57dq1zZs3S8iGYdiECROqq6vj4+MrKysDAwMPHjz4119/AQBgOHVzc/P79+/v3bv3+vXrMFYFAKC4uFheXl4887SRkRH00JQF9Lz81rG3t09OTv7aUiC+aRYvXrx48eKvLQUCgfjipKenk0ikefPm6enp6enp3bp1C+ofLZWz2ezffvttxowZMN7HqlWrtm7dCpMMQrZs2dK/f38AgK+v79WrV7ds2QLjR/j6+t6+fbuoqAh/awoICBgzZgwAwMfHJzMz89ixYxI7Rw8ePIiPj//48SN00lyyZElSUtKxY8c2bNhQVlampKQ0adKkpKQkBoPx6dMnaP1paWlZUlKipqYmHuhcQ0MDV30Ig5QbBAKBQCC6Bh4eHkpKSv379581a5arq6ubmxt0LGipvFu3blu3bs3Nzb1161Z2dnZsbCyTyRTvEPdygAY3UAcCADR11RaPCeLp6blv377s7GzcawEAkJycrKqqmpSUBDPrAQCEQmFRUVF9fX1DQwObzdbV1S0sLKRQKCUlJcOHD1+8eHF0dLSSkhIMBIrD5XJlD4uPtqUQCAQCgegaWFlZffz40cfHJzY2dtKkSdra2vv375dSzuVy582bZ2Zmtnv37pSUFDc3Nw0NDfEOJXLDwfyDzSLuOQs7wSOVQ0pLS4VC4WkxsrOzXVxcGhsboaq0Z88eCoUCANDX11+6dOmzZ8/YbLaenl5DQ4O4flNTUyOebogYaOUGgUAgEIiuAQy7umPHjh07dlRXV/v5+a1du3bq1KmKiorNlsfGxl67di0pKQl3tt21axexoT9//ox3AqPY9+zZk8fj4RWMjIzodDoM7AlhsVgsFktLSwsmEhbPzYwv+VhZWQEAEhIS8CjnCQkJLeVkbDto5QaBQCAQiK7Btm3bBgwYAJOHa2pqTpgwAcMwoVDYUnl6ejqNRoMROAEAz58/Ly8vJxYCZt++ffAfDocTEBDQp0+f7t27i1cYMWJERUXF2bNn4Uculzt48GAY/n7cuHE6Ojp4ckY+n3/u3DkHBwclJaVhw4YZGxsHBQXBQx8+fHjz5s28efMISCgOWrlBIBAIBKJrMHfu3FOnTtna2np4eBQUFDx8+NDT09PExKSlck9Pzz179owaNWrUqFFZWVkvXrwwNDQMCwtzc3PT1dVt19AJCQnDhg3r37//48eP8/Ly7t69K7Gl1a9fv9WrV8+fP//WrVuGhoaRkZElJSWXLl0CANBotMOHD8+ePfvVq1eWlpaRkZGlpaVwjYdEIh08eHDatGksFsvU1PT8+fPu7u4w2YssdI2VGx6Px+FwiOVmamxshMpsexGJRBwOR3zNrV1wuVxi2nFjYyOHw4FxCNqLUCgkLDCHw2k2bkFb4PP5xJIiCYVCDocjYU1GjK4oP2GBZbkiBAIBsSsCwzAOhwMzZBHgq1wRCMR3hoODw6tXr4YOHfrx40cymRwQEPDPP/9IKXd0dHz06JGxsXFSUpKFhUVycnJQUJCurm5+fj6VShWPX6+kpOTi4qKqqgo/0ul0FxcX8dDkr169cnV1zc/P9/DweP36NUzpo6ioKN4KjqutrZ2TkzN+/PgPHz7gO1nTp09/+vSphYVFTk7OxIkTU1NToZcWAGDixIlRUVE6OjqfP3/28/MLDw8X38AiRteIUFxbWysQCNTV1QnEemGz2QCAZtMwSUcgENTW1pLJZPHcBW2nurpaXV1dinFWS7BYLA6HQ6fTYRLpdsHn87lcLn6etYuqqiqYj5NA2/r6ehqNBi3F2gWPx2MymTQaTfwSIgaUX0tLS+Jloi0wmUwKhSJu9t9GoPxUKpVAkgoMw6qrq7W0tNrbEABQV1fX2NiopqZG4BYAtSICzghCobCmpkZeXl7CILGN1NTUqKqqEkiJx2az2Wy2srKyoqIigXERiC9N50co7mTOnTv3888/dwltAadrrNwgEAgEAoFAtBGk3CAQCAQCgWgRCoVCbAfjK4KUGwQCgUAgEC0yY8aMmpqary1F+/imbW4aGhoIWy8ivhs0NTWlmC5B65POlAfxrUEikYiZLiEQHQK0uemQrr5Nm5uuyDftCk6n06GdKTIobiM/oEGxmpoa/AcZFLcRZFCMQHQs9vb2BgYGhJ0fcQwMDBgMRoeIhPimlRsEAoFAIL5xrK2tAwICJHI2EcDCwkJ2v1EE5OsoNzweb/v27TNnzrS2tv4qAiAQCAQC0SHcvHlz5syZsvdDIpFyc3PR4k2H8HWUm9OnT6ekpMi+iIdAIBAIxNcFrtn00jIZZmhHuJOw9MdVnNqGhoaOk+uH5isoN2/evElJSen8cREIBAKB+EI4Mwac8NhMuPmbkg9VnNoOlOcHp7NdwWtqao4dO7Z69epOHheBQCAQCMQPQmev3Bw6dMjDw6ON2cwbGxthQhmYQ4fH4xFw+oU9ENgCg4PCDFPtbQsAwDCMy+US8NyBAjc2NhLw0hcKhYQFhhBrKxKJiKVngl9WIBBIGZdGo0mZRh6PB38pOF0cDofAnAuFQj6fTyBVE5QfZphqb1tc4PY2BGJXBIGMS/DUInxFEGsL/rsiCPgP4leElDrIkQqBQIjTqcrN3bt3mUzm9OnT21ifx+OJx7mR5ZlNOLWhSCRisVjE2kIvdGLweDzCKTBlSTFI+MvKMqhAIJDSnEqlStFXuFyu+GOP8JwLBAJZJpzw1yc84QAAWaJAEQ4OJMsVIeMl3NJVTCKRkHKDQCDE6Tzlpqio6MqVK3v37m17oAsKhQKfavDtnEqlEnjtgzdxAuFARCIRj8eTk5MjEP4EAMDlcqU/lVsCrlcpKCgQCOojFAoFAgFhgTEMI/aQ4PF4ZDKZQAgTuGRCJpOl/EDS55BKpcKJgg9O6cs8LcHn8+Xl5QnLLy8vTyDGDwCAw+EQnnDCV4RAIMAwjPAVQSKRCERgAjJcETCNuZQrgkCfCASiQ8jKyjIzM3v16pWDg4P0mnV1dVu2bLl9+3Z1dXW/fv22b9/u6uqKHw0MDAwODi4uLh46dOiRI0dkdxnrPOUmOzubyWQuXboUL9m+fbuJiUlgYGBLTSgUCnxmNDY2ikQiRUXFTg7iB5UbAuHOAAA8Hk9JSYlYED+ooBAO4kdMYLgMQKytUCgkHMQPKjfExgUA4LMElTNlZWUCjzqRSEQ4iB9h+eE2DbEvLhAIRCIRjUbr5CB+slwRfD5fUVGRWBC/xsZGCoWClmcQiK7LtGnTkpKSNm/erK+vf/78eQ8Pj7i4OHt7ewDA7t27N2/evGHDBjMzs8DAwMGDB3/+/JnwQwHSecrNgAEDTpz4/3TwS5cuXbp0KfxiCAQCgUAgvnGg4R2Bl/a8vLzIyMizZ8/OmzcPADBx4kQGg3Hu3Dl7e3sOh3Pw4MG1a9fu3LkTADBq1CgTE5MLFy4sWbJEFlE7z1tKUVGxuxgAAG1tbR0dnU4TAIFAIBCIrs69e/cGDx6soqKir6+/YMGCuro66eUFBQUzZswwMjJSVla2trY+efIkLC8qKiKRSGFhYVZWVoqKijY2Njdv3szOzh45cqSampqpqemNGzdgzXPnzvXs2fPWrVtaWlpUKtXOzi4kJKRZ2YRC4fbt221sbOh0uoODw927d2E53BnQ1taGHxUUFDQ1NaEtQVRUVGVlJR4F0cDAwNnZ+eLFizLOEsoKjkAgEAhE1+Ddu3cTJ040MDAICgpatWrVlStX/Pz8pJQLBAJnZ+eEhIQ1a9YEBwdbW1svXbo0JiYG73DhwoVLliy5fPkyjUbz9vZ2dXWdMGFCcHCwsrLyzz//jHsPlJSUeHt7+/j4XLx40crKav78+adOnWoq3i+//HLw4MGff/4Z6kMTJky4ffs2AMDS0rJPnz7bt2+PiYkpKirauXPnp0+fpkyZAgAoLCwEAIinC7W2tk5LS5Nxor5abqnw8PCvNTQCgUAgEF2R6OhooVAYFBQEc+5qaGiUlZVJKS8rKzMyMvrjjz9GjhwJAJg0aVJERERSUpKzszPscO3atb/++isAgEwmjx8/ftmyZcuXLwcACIVCb2/vnJwcGxsb8F/SpM2bNwMApk+fzufzt2/fLpELPTk5+eLFi/fv3x89ejQAYMqUKXV1dZs3b54wYQIAIDIysm/fvi4uLrDy1q1bYbXi4mI1NTVxg1pNTc3aWlnjGaLEmQgEAoFAdA369u2LYdjMmTN9fX2dnZ0XL14svbx79+5Pnz4FANTW1mZnZ0dHRzc0NIjH9MK9nCwsLAAATk5O8KOVlRUAQDx62Zw5c/D/FyxYEBYWlp+fLy5bdHQ0nU63trYuKiqCJUOHDt28eTOHw2lsbJw5c6aurm5gYGC3bt3u37+/a9cuc3PzWbNmNQ0whmEYAc8DCb5p5YbFYsGNOhjuDN9BbBcYhpFIJMIx1gQCQVVVFbFxa2pqCDSEsFgsAtFEoMyEBZalLeG4KaC1oD4aGhpS7Nfq6+vh0FD+6upqAgJgGMbn8wlkdYGDQp8pYuPK8mPV19cTa0sikQjEyIGDCoVCwjLL8jbGZrNbCmIkJyenoaFBuGcEogvh6uoaGhoaGBg4adIkAMCQIUN27do1bNiwlsoBACEhIdu3b8/Pz2cwGLa2thJeSBI+yFKcXvX19fH/DQ0NAQAFBQXQghaSm5vLZDKbenHX1dVFRES8ePEiLy9PT08PADBq1KiGhoYdO3bMmjVLT0+vvr5eKBTiCk1NTY34WMT4ppUbJSUl6MJdV1cnEAhUVVU72RW8rq6OTCarqam1ty0AoKamRk1NjYBVOZvN5nA4SkpKxFzBeTyeiopKexsCAKqrqzEM09TUJNCWyWRSqVRiruANDQ1UKlWK15901278y0L5NTQ0CLiCNzQ0KCgoEHMFh/LT6fT2toXqL7EJhyqdiooKMVdwDMMIXBFCobC2tlZeXl5dXb29bQEAtbW1KioqBF7IOBwOm81WVFREruAIBABgypQpcMcnJiZm586dY8aMyc/P19TUbLY8Pj5+wYIFR44cmTt3LrxbQvWCACUlJcbGxvD/4uJi8L/qDgBAW1u7Z8+e2dnZTdu+evXK0NBQfOjBgwcHBwfX1tYyGAwMwzIyMnr16gUPZWRkQOVJFr5pg2LSf0h87DRkGVeWhrJ82a8yrowCS2/eCSfJF5X/S4z7Q50kbRmU4C0GgehqrF27FlrPqKmpjR8/fteuXSwWq7i4uKXyZ8+eaWlpLVu2DGo2paWlxJa3AQCXL1/G/z9z5gxUZcQrDBgwIDc3NykpCS+ZP3/+jBkzAABWVlZ5eXl5eXn4odjYWD09PXV1deicFRYWBstramqePn06bdo0YkLidOrKDZfLDQkJefPmDYfDMTc3nz9/vsTUIBAIBAKBaAl7e/uAgAAfHx8vr/9r787jY7r6BoCfO3e2zEz20CQkERGEIBFiLWmjllJ97aGlKK0qWq1W7TyUVvWhtbVStLUL+lBK7FVFlFgqiSVIEElkmUlmn7nL+8ep6cgymTkTk4jf9498MnfOuefcbeY3957ltfv3769bty48PDwiIqKy5a1bty4sLPzkk0/69+9/+/btJUuWiESi1NRUgofLS5cu1ev1UVFR+/bt27Fjx6pVq8o8S+ndu3f37t179uw5e/bsoKCgvXv3bt68Gfcnx3eP4uPjp0+fjtvcbNq0af369QghNze3qVOnLlq0yMfHJywsbPHixX5+fm+++aaTO8qlwc3GjRsvXLjw/vvvSySSnTt3/uc///nuu+/I5goAAAAAnjcJCQn5+flr167dtGmTt7f3Sy+99Pnnn9M0XdnyoUOHZmRkJCYm/vDDD+3bt9+2bduJEycWLVoUGxs7ePBgh4o+efLklClTvv3229DQ0MTExHHjxpVPc+DAgZkzZ65evTo3NzciImL37t24DZCHh8eff/45a9asRYsWKZXKFi1a7NmzB7+FEJo3b55EIlmzZk1eXl6XLl1OnTrlfCs6imDqaWLDhw+fPHkyboydnZ09efLkL7/80vKYzQaVSsUwjJeXl4vb3KhUKqFQSNbCoLi42MvLi2z6Bb1er1AoiKdf8PDwcDQjQqioqIjneT8/P4K8paWlxNMvqNVqqVRK0GalDFx/X19fgocUarWaePoF3N6IoJ0Tz/PFxcW436ajSkpKzGazp6eni6dfUCqVNE2Tfe4olUoPDw+y6Rd0Op1cLoc2N6B22rBhw9tvvz0heujaXnOIVxK9ccjl/OtpaWnWI77UEj/99NPo0aNdGS04z3VtbliWHTFihCWUwdEGWdNXAAAAAIDKuO6xFE3Tr732GkIoKysrIyMjOTm5e/fuDRs2tJHFbDbjTva4H7zJZGIYxtFycRaCjq+WognyIoR4nsdTKOedemIn+3erYhNwuWQ9q/F8imQVxsjychxnMpnKD1dQJXx0WJa1Ua7tqaQt5eJfFQaDgeDODcuyZrOZ4HeJPfWvjKXCjmYU7n8kYRgRxyGRlqEohBDTz4FpTIhPEst+duaKIJvGHP+trFyKouDpNgDAWg10Bc/IyDh06FBOTk6HDh3wkBuVpTQajdYfZ5WNcmEPsmFIEEIcxxEMf4LhgWpMpieeiGk0dm2F7aFfbCOusDN5CeJOC7PZbCOYE4vFNk4SPDyU5SXB4ECYM/VnGIZ4vxFklD0+mS0brnN8JcTjEjlzRThzCdu4IiC4AeCp6t+//+XLl2u6Fo6pgeCmT58+uP/9xx9/HBYWFhsbW1lKS3sCo9HI87xYLCb+2UfQWAffjRAIBARNSRBCRqMRfyuXKbrKljQMwzAMIxKJCFoncBzHMAxxhXmeJ2jogxAymUxCoZDg6OBbJjRN22g7YvtOjFgsxjsKx8Fk9TebzQKBgGCH21N/GwwGA0GFhUIhy7J4EE+8cxxaCfEVYbkZSRZJWK4IRzPiK0IoFFZWZ+gKDmqD3TeO/JV7jTj7zeKs6qtLNfP29n7mxsl0XXBTWFiYkpLy6quv4k+i4ODg8PDw9PR0G8GNRCLBH6P4000mk7m4QTEObsjauppMJrlcLhAIVE9GGgpFFZGHVqtlGEYikRA3KCarMP5ZTJbXmQbFZrNZJBIRNyi2NDLFwZlcLndxg2Kz2SwUCskG8TMajSQbLi41Go0sy4pEIhxQih1ZiTMNivFzJbKDZTabZTIZWYNifEVAg2JQOzVq1IiiqAKdskBHPio9Qkgmk1nmzQZOcl1wYzQav//++5YtW+IhDvHA82T9egAAAIBa4uWXX37w4IEzjR0xLy8vsiHLQXmuC24aNGjQqlWrVatWjRkzRi6XJycnq1Sqrl27uqwCtdOD42WXeHeoiXoAAAAgFRgYWNNVAE9waZubOXPmfP/99ytWrFCr1U2aNFm0aFH9+g708gAAAAAAqJJLgxupVPrBBx+4skQAAAAAPG9q9cSZAAAAAACOcun0C47S6/W4Cw/uvGrp+OoQPPIYQS9lnudZlqUoiqB/B0IId15FCBWeeaLTk1/nJxqdlXkXIeTTUcdxnEAgIKszx3FkFca9iwn6o+G8AoGA4OjgnWx7Yz09PW2sWaPRWAZ5Q0Q9nJET9ec4juM4508Sh0gPFfM8z/O8ZacZev/TCFGtVrMsKz2m9JR5VLY5up5eqEavCEfhnWzjJKEoytPTk2DNAIC6qgbGubGfRCLBvYtLS0tZlpXL5QSfqnq9nqIogm7VLMuWlpYKBAKyOSJKSkpwV3C15IlPZHf3JwZEKfMuQkgsZvDwJwQ9k81mM2HvYoRUKhUinRBDo9FIJBKCsV5MJpNWqxWJRDb66tuOOWQyGQ7QVSoVz/MKhYIgRsF1IOjKbqk/Qc9qnudLSkoIdrhAosX93nFXcJ7nfzl4MDEx8fz589ZjGAZ4148OjRzUue8b3QaKhf8eGlYi4XmeoFu181cEWVdwg8Gg1+vJBkcAADyfanVwY/mhhr+uyIZZwyshyIi/Mol/p+JCBQJBma/aMmsr/0XszMY688O6wurZiaIoZ46OM3Uu82ue7PZejdQfn2AkG05ReBspitKbDMP/O3Hf+cP4HS83D4qiEMdrTLpc5aNc5fHfUo//d9+63Z8mNmsQZqkz8e09jPgkoWmaIK8zVwQA4Pnk6uDm+vXrf/zxR25ubsOGDV9//XWyKZEBAAghjueGLZuw/8LRegqfL1/7dFCbXh5SBUII5RkQQg81j05k/7X4bGLavRvdZg0888W+MP+QGq4xAAC4hEsbFF+4cGHmzJlqtTo6Ovr69esffPABfhQCACDwzf71+y8cfUHue/bDpDEdBv0T2TwWqKj/Rsu+F0bv6BvW7VFJ4eCl400M4XxSAADwbHFpcLNly5a4uLiPPvrotdde+/zzz6VS6cGDB11ZgRpnMOo3bdo0bty4oUOHTpgwYdeuXQx83wAiucpH87YtoxC16bUlYX7BlSVzE0p2/N+yCN/Gl++mffW/ta6sIQAA1BSXPpa6d+/esGHD8P8ikahx48YPHz50ZQVq1m+nds9dNSW/6N9N/v7774P8QxdNWfVyh1drsGLgWbR07xqtUZcQ0eeVRp1sp5SL3BL7zH9x81tf7ln9Ts83FOgZmwAPAAAc5dLgZsuWLZYOKSaTKSMjo1+/fjbSsyyLO3LjppcMwxB0XMdrMJsdvkHCsiwumiAvxjAMRVEcRyOEvtowe9W2JQihNmEdX+86soFfo6y8m3v++PHGvSujZ/WbNHzGx6P/gxtO4grj6aYJ6uxMhRHRjkKPOwkT72SO42zktd0Jq8xZYTabyXp0P736VwZXmyAjzXH5qoKfT+6iKcGCF9/necRznHUCqtxV0rlB9ICm8XtuHl2xL3HGwMVkJ4nlYnTmiuCerKr95do+QGSzsgMA6qqaGedGrVYvWbIkLy9vxYoVNubO1Gg0zk9FVuOKz8mW/TQncdd/xULJpwO+Gdp7jOUtjue27P9uxa/Tzaxp+Kvj501YTlGUT0ddDda2FvLx8bExKEtJSYkzwdyzSHZYtXjPys93fzukWc9t/ZcihNh6T3y10wUV7JDU/PTYn0d4KzyvpP9dx6bXpigKuiYAAKzVQFfwkydP/vDDD/7+/osXL7Y9KzhN0/gHGf51LhQKCX6U49/WZD118a0XspHHzGYzrvCeo5twZLN87N6OzV6hKNaShqbohBenNPaP/GjD/237LdFT4TVt9EKaplmWxd3IHS0UD3dGVmG8k8l+ATMMQzzqIM5r4wDZPuiWjcUhDln9cRd6sh2OBwAk66VsNpsJKswjtPHEDoTQ5Jg38M4RFjJPpKhoj8X4t+zaMPr0g0v79u0bPnx4DV4RjmbEO9nGFUGwTgBA3ebSOzccx33zzTcpKSmjR4/u1auX/R9JKpWKYRgvLy+CT1WdTocQsjFGXGUYhlGpVEKh0MvLy9G8CKHi4mIvL69r167Ftu9gNBmWjNzSp+0IhJDU74lkhkKEEEq5eWxyYl8TY/z6kw3vzhuq1+sVCgXBkGUmk8lgMNgOGStTVFTE87yfn1/VScspLS2VSqUEg+AZjUa1Wi2VSskGHrSG6+/r60vwVadWq8ViMcGoibj+EomEYFw7nueLi4sJbjkcmLOp36JRLf2a/P32Lw5t69b0397YN71z586HDx8mGHWQZVmlUknTtLc3SasdpVLp4eFBEFTpdDqdTieXy+vYDScAwNPj0t5SW7duvXbt2ooVK3r37v08/NgymUwjR440mgwJL07CkU1lOjSNnzN0HUJo5jcTL1686KoKgmfSj8d3IITGtPo/RzMOaBrvKVGcPXv27t27T6FeAABQW7guuDGbzfv37x82bJi/v7/LCq1Zy5Ytu3r1amj95lNfW1pl4tfaj3qz+1SjyTBq1CgY/gdURqVS7f/rqFBAD4/o42heN6FkcPOePM8nJSU9jboBAEAt4brgJi8vT6fTrVq1qr+VVatWuawCLpadnb148WIBJZg7LFEisut2+tT+S6Mbd83Ozv7www+fdvXAM2r37t0Gs7FHo04vyEma0CZE9MErqe56AQBALeK6BsW+vr6LFy8us5Ds4f0zYd68eXq9flD3sdGNu1ovx41sKkQLhEtGbh22LOrAgQPr1q2bMmXKU68leNZs374dITQignBgpLjg9vUUPmlpaTdv3mzatGm1Vg0AAGoL1925kclkkeU0aNDAZRVwpVOnTv36668KN4+PhpSN52zz9wpa+HYiQuizzz5LS0t7OrUDz6q8vLwTJ07IRNL+4XFkaxAK6P4t4xHcvAEA1Gm1elZwvV5vMpnQ4+7cGo2GrCs4RVHEY6yxLFtSUuJoxo8//hghNK7vpx4yH5PSsVHLXop6bWjcOztPrhs2bNixY8cc6jPFcRzP845W2BpZXjw4m16vdzQj3skmk8lGuR4eHjaOu1arZRjGsqrS0lJH64AQYlmWYRiCQZUsQ0SS7TdHD9ZPP/3Esmzf8HiFSIYQ4jmOd7xdfv/I+PUpSXv27Jk4caJDGfEe5jiObGNZllWr1WRdwRFCBoMBfxqUR1EUWQ9BAEBdVauDG7FYjPt+azQalmWlUilBP1KDwUBRFEEvX5ZlNRqNQCBwtBv5//73vwsXLvj7NBzV60OapgUOfppTFDVt2JcXb55OS0tbtGjRf//7X/vzms1ms9lM0O8dPQ4LyPJqtVqJRELQUd9sNjMMIxQKbfTytf11KJFIcBf00tJSnufd3NwIvj51Op1IJCIYcsZsNut0OpqmCfYbHjbGoYx79uxBCCVEvEohikc8JaAQcnhj48M7uUvkFy9eLCkpCQgIsD8jx3E4OiE7SdRqtZubG8FgQkajkWVZkUhEcBUDAJ5PtTq4j+nCHgAAIABJREFUoWkaRzP/jFQmFJJ9fSKisd1woRRFOZSXZdl58+YhhN57fY6bREZRDn/VUhQlFcuWvbc54T+dV69eHRPQq0fHfyepaPiyrbz4+9KZoejJ8uJR7Ajy4h/lAoGAuM5lTgmRSEQQ3NRI/fGNEPsz3rp16/z5894yz75NXkQUQjwiG75OKpL0av7iriuHDh48+O6779qfEd9AdfSKsMCj/xH8PsGXMNkBAgA8n1w6zo3F/fv3nXl0Uptt2bIlIyMjNKDZ611GOrOeZsFtpiV8yfP8x0vH5BXmVFf1wLNr06ZNCKEhUX0ktMODJZbRPzIeIbRv375qqBYAANQ+NRDc8Dy/dOnSW7duub7op81sNi9YsAAhNHngAlrg7F2xN1+Z/HLb/sUlhe8vGs6wTNUZQN3FcdxPP/2EEBrV3uGx+8rr3bybUEAfP35co9E4vzYAAKhtXBrc6PX68+fPf/HFF9nZ2a4s12U2bNhw586dZkGte3cY4vzaKIpaMn5jgHfI+b//WLp+lvMrBM+uw4cP37t3r/kLjTs3auv82nxknl0btzMYDMnJyc6vDQAAahuXBjcPHz7cu3cvWaen2k+v1y9cuBAh9MHghQKqenasp8Jn6Vs7RLT4u51fHTi1q1rWCZ5FeLjLdzolVNe183qrHgieTAEA6iiXNigOCwv7/PPPEUIDBgywJz2e4xpZ9comKBSvAfcWdgguDjfRtSf96tWrc3JyWod1iIvqhyv8+K9j5RqLKJ6nGQGiEI8QigyO/XTAis93Tfx46ZhGgWH+3VrZrrP9Fa4QWV6e53Fvakcz4p3McZyNvLZbkeNNtrzE01Y7Wg0n60+2z3G17cmYkZFx8OBBhUT2VvsBPM8jHrcmxutweGOpfBNCfP8XXpyK0P49+0z9sgQDGtqT0ZmNRY93MsFMvfgSduYkAQA8b2r1J4JOp7MeekStVhOvimAIE4xlWXtmelKr1UuWLEEITR6wwPIRjP/hOIe7hyCEeA5/iSGE0MCO76TfT/0l5Ycxs/of7nywysm5nJmaijgvwUhCFiaTqbIhTBBCPj4+NvoPazQa66KJG6qbTCY8gTxZXhv1t82eHT537lyO48bEDlaIZGazmeb+ifLxtz6ZEHf/NvWbXXl04+jlU7EvOTDeMcdxxCcJ2ShEmF6vr2wgJYqiCCZXBwDUYbU6uKFpGv8gw1ECTdPEI4ARjK6Bf2hSFGVP59XVq1cXFxd3atmjY4uXLdn/qa2jdbb8tLXKOH3QyvtFmRcyTyYkJOzfv9/Ly6uyOnMcR9DbFj2+BUL2C5hlWYFAQHzLRCAQ2DhAtldL07T1LRAX1x/fXLTzJCkPj/FjO01KSsrevXvlYrdpcW//MzwBonDgSzk+yA1CyJJ3QHj8lUc39v51uLOwv10ZHbkiynNyJ9s4SerkY24AgDNqdXDj5uaGx3ZTqVQMw7i7uxN8deGf4wTDjjEMo1KpaJquLJKwePDgwdq1awWU4JOELy1DcZjNZqFQSFEU62BYxfEcz/GUQGA9+h8tkC4f+8vYld3S0v5OSEg4dOhQhbUymUwGg4FstNaioiKEUJUbW6HS0lKpVIoH03OI0WhUq9VisVihUBCUixCyZCwqKuJ53tPTk+CrDteBYIw4S/3d3d0dzcvzfHFxse0dXlJSMmXKFJ7nP+sxIdg38J+lNIuHoqYEjo4Q+U+5PI8EAmpIRM/5f67Z+9fhNe7u9sQrLMsqlUqBQEB2kiiVSg8PD4LASKfT6XQ6y6cBAABUqVYHN8+EB8fRh1/M1Ol0fdu92VjR1jIvJssKGLo6f1G6u3mtnZA8bm1cSkpKfHz8gQMHqnw+BZ4Z/3tYflmxRvV/S8bevHmzXVCrT+PHV3uZLfzCIus1uVaQeeLEiR49elT7+gEAoKbUzCB+dcnF9LN7jm6WimWT+zo2RyYBP4+An2eeaNKgRWpqaocOHS5cuPC0SwQ14npO5ue7vm0xOe6P9JRGnoG/vL1GTD+VwXmHt3gVPR4eEAAA6gwIbpzCsuycbyfxPD/m5en+XkEuKLG+d+CW2X+0b9793r17Xbp0Wbx4sTMteUGtciUrfdK6WcHj20dM6j57y5f5qoJXGnX6c+Smhl5P6xbdmy37CSjB7t27nWmtDwAAtU3NBDeLFi1q1qxZjRRdvZYvX/73rdQgvyaj4z91WaGeCp+Nnx0Z++o0xszMmjWrVatW27dvJ+snD2qJfFXB8K8nRn/Uc/XBH+8XPmzgXv/tNgOPD19/OGFdoKL+0ys32CMgPqSDVqvdunXr0ysFAABcrGaCm5YtWxI0wKxtbty4MXfuXIqiZg/5TiKUurJoIS36dPhXP886Gd4w8saNG8OHD2/cuPG8efPS0tJcWQ1QLc6cORM1tef203vlIrdpHUZfGbv7wfvHfuiz4KWQWBeU/m70EITQ6tWrCUagAQCA2omqzZ9oBoMBP3MxmUw8z4vFYoKOMHhUN4I+GhzHmc1miqIq7ARkMpl69Ohx+fLlgR3Hzx7yXfkEPM+TdVH9Z+A/+/IKvcy/nt2y8eDXmTnpeElg/eBObeLaR3aJahbbOKiZ/4tPjHtW8OcTHYLqdTFa/jeZTEVn3cr0R7NOYIPZbKZpmqC/Pd7Jlj7/FVIoFDZ2hk6nw3etjEYjQoigxxNyov546D/iWc2NRuOxY8dGjx5tMBh6hnZe3+c/gYp69mTEly1hF2h8xT/OynBs0/X9spUPk5KSevXqVT655GCRpVCWZfW9vAm6xSGETCYT2ZzteCfbmFGcoiji3nYAgDqpVgc3DMPg7y2tVstxnEwmI4hRjEZjZQGKbSzL6nQ6mqYr7EY+ZcqUdevWNfJvuuWDCzJJBR+sHMdRAgHBlw/H84jnKYqy52tA4vvP4Uu9eXr/2W3HL/76qOTfKcTlboo20a3btm0bExMTGxsbFhaWd+qJGMK/27+hj0ajKTrrViY4sE5gg16vF4lEBB31GYbBeaXSSm992Q5qzWYzHspIo9HwPG87EqqMXq8XCoUEAYrZbDYYDEKhkKCXMs/zO3bsGD9+vNlsntp+5FcvfWz/rB14jEeKIuwKjp4MjFbe3PnBL4tiYmJOnz5dfu8J9z+yZDSZTIbePnK53OFSEdJqtW5ubgQRpMlkMhqNEomksquY7AIHANRhtTq4scDj3Hh5ebl+nBuhUFh+VI9vvvnmww8/lIplO+afDXFrXWF2lmUFNE0U3FQwzk1lpH5PvNQX8Ldyr126c+pq9rlr2efvFd6yPr7169ePCu8UG9m1bYtOrcLbSiVuDV/+N29RUVHRWbcyO8o6gQ1OjnMjlUqd/+WNx7nx9fV1/Tg3EomE4DHrL7/8MmzYMLPZPK/35PlR7ziUF49zIxCQDDZgGefGssTgSzVf0iu7OOeHH354++23y2b438NSnfqnE0mHL/+eU5TnHuTTs2fPt99+29GRCJwc50Yul8M4NwAAO7k6uDEajRcvXiwuLm7VqlVISIiduWpVcJOYmDhhwgTEo+WTdvSKHWwZ2KaMmgpuOJ6nH/841hhKbpemXrt74e87f125fS636L4lpVAoCg+OiOrQsmnTpiEhIf7+/nK5XJgdFBTYyDo4gODGNuLgZtu2bW+99dY/kU3vKSjPselBqje4Qf7S3VeSB2+c5OHhkZKS0rx5c8s7JpPp23cWLd79rVLzxLwWMpnss88+mz59uv0HHYIbAIDLuDS4UavVs2fP1mq1AQEBGRkZ48ePr/AZf3lPNbh5cLzsEvyNXj64YRhm3rx5eA6pGYNWDe0y0Ua5rgluyuD5J4KbMvJVDy7dPX3l7pmr2edu5Fxm2Ar6kItFkkYNmkQ0bt02omPXtvEvj21pT7kQ3LgfU2c8uPXziV3H/z59Oy+b47lgvwZdItoP6NDnpVad6YH/DhPAMMzChQsXLlzI8/zcnpMWvPoBQqiGgxuEEEJv7Z/187V9DXz9t/8vqWvXrkajMSkpacGCBZmZmQihnqGdx7UZ1Ny70X113k/X9iVdP8wjPiq05aZ92yMjI+0pF4IbAIDLuDS42blz54kTJ5YvXy6VSpOTk3/88ccff/zRnm+U2hDcHD169LPPPrt48aKQFs0e8t3/dRhru9xaGNxYM7OmzNxrObrr2fmZecX3C1S5Baq8R8W5hepc61MiNDR04MCBQ4YMiY2NtRE0POfBTWpq6ooZXx66dKLCqynQ54WBI4Z0797dy8srPT39+++/T09PFwroFQNnj48d+s9OqwXBjYExvr57yuG7ZxBCgYGBKpUKXzut6zddET8dd91iWZZClIAWnM25Mva3OdeL7kql0gULFnz00UdVXpsQ3AAAXMalwc2ECRN69OgxePBghBDDMCNGjJg0aVK3bt2qzFjtwY3RaLx582ZmZmZ2dnbGmQdavbpErUQIebp7u0lkwW18vL29hUKhTqczGo3Z2dlHjx69ffs2Qii4ftjChJ/bhHaustxaHtxg1k+1TCaTWUmzPJNdcPP6g9TUO3+cuZ6cr3qA3w0JCRk4cGDfvn27dOlSvvHv8xncGAyGXbt2rVy58vz58wghD4liVORrQ5r3in6huVAgTCvMPHTn9Na03zKK7pTJ2Kx+6PrhS7qExphMptoT3CCEGI798tz6lZe25asLBZQgNqT1+13fHNHgFUtLZ0twgxDSM8ZPT3y9OnU7z/MtWrSYN2/egAEDbDTKhuAGAOAyrgtueJ4fNGjQrFmzYmJi8JKPP/64U6dOONapLAv+p6SkhGEYT09P6+CG47iSkpLyuRiGsQy3qlQqVSpVSUmJWq1++PDhgwcPbt++fevWrezsbNzFxn6BfiEjX5k84pX3+RK7hrR5RoMb6+8enucz1ReSz+86mLIzpzDrnyxSaXR0dOvWrcPDwwMDA1944QX8jeXm5iYSiRyd3NRkMplMJh8fWx1wbAcrlpOkuLiY53kfHx/r9GazWaPRVFkNrVYrEolsBGdqtZphGIPBoFQqc3Nzb968+ddffx0/fhyfafUUPh+0fWNSzAjPivrNXc6/fuDRmUsP0ksNmmDvwD4R3V9v1UMooHmeN5vNzgU3hDOxVxbc/JPgBUmeutDLzd1NJLWuG88jjvs3uMFOaq5MTJqXkX8bIVSvXr0+ffrExMSEhYXVr1/f29uboiiZTIajxpKSEoVCQRDcGAwGnue9vb1tBDcwMTgAwJrrghu1Wv3GG28sW7asadOmeMn8+fMbNGgwfnylMwJqNBqDodIP/fv377dt25asMmKhJPiFJo1eCA/0C6nnFuzh5iMVuSGEjIxBrVdp+WK1XqU36hBCMok8qH7jVo1jI4Kj8QeoWenwp3OtJfJ+Ylzj8ptmSZCWdfHk5f1/XjuSnp3KcdU5GvLw4cO//fZbGwl8fHxs9B8uKSmxMQHF8ePHhw0b5lT9KkdRVIfgNm+1HzA8up9CZeusYOtV0cmcLqhFc2iUqa3turH1RAzHbkndt/r05isPrz+lKs2ZM2fKlCmVvUtRlK+v71MqGgDwLHLdrOAV3imxPWmAQCDAv/NwsjK/U8VicaNGjWxkVygUQqFQoVC4u7v7+PgEBAQEBAQ0atQoNDQ0ODi4yvE2eJ7nOI6iqMcpdbbTl8FxHMGQHpZynfhRzpOVi28GVPbDuiFq3gs1R2iaVqu9cuXK9evXs7Ky8vPzCwoKcHhhzw2SCvn5+Vnt5ArY3g9lTpIy9ZfL5bZPEozneXv2tqenp1wur1+/fkhISOvWrbt06VKvXj2O4xiKKiXa5yzLEtzJQJVcEXYiHgAQn5mo3E5GCA1GEwejibdv3/7zzz+vXbt2//79wsJCrVZr/ePEzp1cIU9PTxsbC7dtAABluC648fDwEAgE1l+BGo3G29vbRhaZTIbbyuA2Nx4eHtaPPLy9ve/evVtluU52Badpuvw4N/YoLi7Gn8iOZtRqtXq9XiaT2RjXrjImk8lgMHh4eDiaESFUVFSEELJ9RHCChg0b9u3b13qhk21uJBIJcZsbSx9s3ObGy8vL+quub9++ZapaIScbFIvFYoJxbnieLy4urnKHVwgHlO7u7gQDD+r1eo7jCAbiY1lWqVTSNF1Zndu1a9euXbvKsjvZ5sbNzQ3a3AAA7OS6uaUoigoICMjKysIvWZZ98OBBYGCgyyoAAAAAgOeBSyfO7Nat28mTJxmGQQidOXOG5/n27dvbk1EikUilUrKnLUKhkKCPFUJIIBCQ3Y3AJBIJ2d1yoVAolUrJnlbQNE02yRF6vJPJ8orFYrKjQ9O0VColrrM1qVRKXH+RSES8w4nrT1GUMzu8pq4Istm7kNNXBFmdAQDPJ5d2BddoNHPmzOF5vkGDBhcuXHj33Xdfftm+EXABAAAAAOzj6ukXDAbDpUuXiouLIyMj7Z9+AQAAAADATs/GxJkAAAAAAHZyaZsbAAAAAICnDYIbAAAAANQpENwAAAAAoE6B4AYAAAAAdQoENwAAAACoU57tcbE4jrt//763tzfZhAMVKi0tVSqVQUFBFY6QZjAYMjMzrZeEh4cTD2uGEDIajTk5OQ0bNrQxWqA9aRyVn5/P87y/v3+F7xYVFeXm5lpeCoXC5s2bV1fRtlX7xj6Nvefik8R2cfancUiVF9e9e/dKS0stLz09PYOCgqql6Co9jWMKAKhLanVwk5ubu3Pnzg8++KDCd2/cuLF48WKtVms2m3v27DlhwgSyQWYtWJZdu3btkSNHRCKRXC6fOXNms2bNyqS5devWrFmzrJesWrUqODiYrMTk5OTvv/+epmmWZd99991evXqRpXGIUqlcvHgx/vYNDw+fMWNG+amCkpOTt2/fbnnp4eGxefNmJ8vFTp8+rVKp+vXrV+G7BBtb7Su0zcUniT3F2ZPGUfZcXGvXrk1LS7O87NKly/Tp050sF3PxMQUA1EF8bcUwzNdffz116tQK3+U4bvz48WvXrmUYJisrKyEh4dixY06WePTo0YSEhKysLIZh1qxZM378eDxRtrUjR45MnjzZZKV8Gjvl5ub279//xIkTHMedOHHi9ddfz8vLI0jjqOXLl0+dOlWj0Wg0mqlTp37zzTcVpvnuu++sN9PJQrHS0tKJEydu2LChwncJNrbaV1glF58k9hRnTxqH2HlxjR079vTp05ZtZBjGmUItXH9MAQB1Ty1tc5OYmPjWW2+dPHmysgSXLl169OjRyJEjaZoOCQl55ZVXDh065GShhw4d6tmzZ0hICE3TI0eOfPTo0eXLl8ukyc/P9/f3F1khmy4HIXT48OGwsLC4uDiKouLi4kJDQ5OTkwnSOESr1Z4+fXrEiBFyuVwulyckJJw6dQpPnG4tPz8/MDDQejOdKRQhxPP8jBkzxowZc//+/crSOLSx1b5CO7n4JLGnOHvSOMSei4tl2aKiIuuTxMn7pqjmjikAoO6ppcFNfHz8Z5999sorr1SWIDs7OyQkRC6X45cRERGW+caJZWdnR0RE4P8VCkVwcHB2dnaZNPn5+fXq1bt58+aZM2dycnKcKS4rK8tSHEKoRYsW5YuzJ41DcnNzTSaTZZ0tWrQwmUwPHz4skyw/P9/b2zs1NfX8+fMqlcqZEjGKot5444358+c3adKksjQObWy1r9BOLj5J7CnOnjSOFlrlxVVQUMBxnFQqPXfu3NWrVw0GgzMlYjV1TAEAdU8tbXPTuHFjhFB6evrdu3crTKBUKq3bOXp4eBgMBoPBQDzNsl6vNxgMZdapVCrLJMvPzz9//vzZs2cVCsW9e/d69er13nvvkZWoVCqt20Z4eHhkZGQQpHFIcXExTdOW7y2FQiEQCMqELwzDFBUVrVq1ytfX12QyKZXK9957Lz4+3plyEUKRkZEIIUvR5Tm6sdW+wiq5+CSxpzg7q+QQey6u/Px8hNAnn3zi5+dXWFgoFotnzpxpIyixk+uPKQCgTqoVwc3Dhw+Li4sRQmKxuGnTpvZkMRgM1s9KcKcJvV5vf3DDsqzlY7Fhw4Y8z1vWY1mnXq8vkysgIKB9+/aDBw9GCKWlpc2aNSsyMvLFF1+0s9Aym2BdnEgkKl+cPWkcYjQayzxjKr9OjUYTHR3dt2/f9u3bI4SSkpLWrFnTqlWr+vXrO1N0lWxv7K1bt4xGI0LI29u7QYMGzq/QHjV7kuDbIbaLsycNQblVXlw8z8fGxo4dOzYwMNBkMi1dunT58uWrV692plw761a9VwQAoE6qFcHN6dOnz5w5gxDy8fGZO3euPVk8PT3v3btneanT6SiKcqhDuE6n++GHH/D/I0aMiImJoShKq9VaEmi12rCwsDK5PvzwQ8v/LVu2jIqKunTpEllw4+npad3YRafTeXl5EaRxCP4VzrIsbiHBsqzRaPT09LRO4+XlNX/+fMvLQYMGJSUlXbt27eWXX3am6CrZ3tikpKRHjx4hhGJjY0eMGOH8Cu1RsyeJh4dHlcXZk8ZR9lxcUVFRUVFR+H+xWDx06NBp06YVFBTUq1fPmaLtqVv1XhEAgDqpVgQ3Q4cOHTp0qENZfH198/LyLC/z8vK8vLwcatLo7u6+YsUK6yVeXl55eXlt2rTBLx89euTr62udgGGYkpISHx8fS/tQd3d3hmEcqnllm5Cfn+/j40OQxiF+fn7ocXth9PjhQpnN1Ov1JpPJEvEIBAKZTEa8mfazvbEzZ86s3hXao2ZPEpqmqyzOnjSOsufiKi0tFQqFMpkMv1QoFAihGj9JAAAAq6UNiqsUGxurUqlu3bqFX6akpHTq1MnJdXbs2DElJQX/f+vWLaVS2aFDB+sEGo1m7Nixx44dwy9NJlNaWhrxmCKdOnW6fPkyftRiNBovX77cuXNngjQOadCgQVBQ0Llz5/DLc+fOBQUFlXnKk5qaOnr0aEtL2Ozs7DINHZ6Sat/Yal8hcvlJUmVxdqZxiD0X19atW6dOnWqJZi5duuTp6VnZmJDV6GkcUwBA3UNbP4CobdLS0h48eGA9SNecOXPMZnOTJk3c3NxUKtWePXvEYvGRI0cuXbo0adIkJ8cpDgwM3L59e0FBQWFh4fr167t169a1a1eE0MmTJxMTE+Pi4mQymV6vT0pKKioqysnJWb9+vUgkmjhxItmYsAEBAX/++WdKSgrLsps2bRKJRKNHj6ZpOjs7+6uvvgoODvb19a0sjTOb6eHhsWHDBpqm09PTd+zYMW7cODy+3IYNG1JSUtq1axcYGHj58uXk5GSNRpOenr5u3bq4uLgePXo4U6jFiRMnvL29o6Oj8UvnN7baV2ibi0+SKosTCASVpSFm4+KyXICBgYHJyckpKSlarfaPP/7YtWvXxIkTGzVq5Ey5Fi4+pgCAuqdWBzcFBQUsy+JmrdiJEyeCg4Nxp4yYmBiBQHDlyhW5XP7ee+81bNjQyeI8PDzatWt3586du3fvdu/ePSEhAT9ZyMrKunPnzksvvSQQCKKjowMCAu7fv5+TkxMdHT1p0iTiMWBomu7atWthYWFaWlqTJk0mTpyIG2yWlJRcvHgxKirK19e3sjTOCAkJCQ0N/fvvv0tLS0eMGNGxY0e8/NKlSyzLtmvXTiAQvPTSSwih27dvazSaV199dciQIU4WapGVlRUQEBAeHo5fOr+x1b5C21x8kthTXGVpnFHZxWW5AN3d3ePi4kpKSm7duiUWi8eNGxcTE+NkoRYuPqYAgLqHwh1AAAAAAADqhme1zQ0AAAAAQIUguAEAAABAnQLBDQAAAADqFAhuAAAAAFCnQHADAAAAgDoFghsAAAAA1CkQ3AAAAACgToHgBgAAAAB1CgQ3oM4aPnw4VZGQkJCkpKSarl31GzJkSIXbi40cOdKelWRmZl67ds3y8saNGxRFffLJJ0+t1hUUCgAATqoVs4ID8PRMmjQJz9eBEFKr1SkpKfv37x86dOhvv/3Wp0+fmq3b0zBlypTGjRuXX968eXN7so8ZM+b69esFBQX4JU3T/v7+7u7u1VnFqgoFAAAnQXAD6rjBgwd3797desnKlSunTJkye/bsOhncDB06tEuXLtW1tiZNmuTm5lbX2gAAwDXgsRR47kyYMEEkEqWlpbEsa708Jyfn4sWLKpWqwlwMw9y8efPChQv5+fnWy81m8++//37//n2EUHFx8eHDh81ms+Xd3NzclJSU7OxsjuPKr5Pn+czMzPT09DI1YRjm999/z87ORghlZGQcOXLk6tWrBoOBdIsrUL5iarX6999/LykpwVuUnp6OEDIYDL///vudO3fKVIzjuDNnzhw7duzRo0fWm3P9+vX09HTrPWC9UeV3YIWFWtg+IgAAUCkegDoqISEBIXTy5Mkyy1mWlcvlFEVptVq8ZNu2bQEBAfiKoGl66NChjx49sqRXKpXvvvuu9czeXbp0SU9Px+/m5eUhhL744outW7fKZDKEkEaj4Xn+2LFjkZGRlixhYWHJycnWdVi0aJHlcY9UKn3vvfd0Oh1+t7CwECE0d+7cd955x7IGLy+vjRs32tjewYMHI4ROnz5te7dUVrFz585ZfzIMHjyY5/nMzEyE0LRp06wrNm3atG7dullSdu/eXaPRrF692tPTEy8JDg4+f/68PTuwwkKrPCIAAGAbBDegzqosuPn1118RQqGhofjl119/jRBq1qzZV199tW7dukGDBiGEwsPDcYzC83xcXBxCaNCgQevXr//xxx/Hjx9PUVRERAR+Fwc3gwcPVigU06ZN27p1K8MwN27ckMvlvr6+kyZNSkxMnDBhglwud3d3z8nJwbnGjh2LEIqKilq2bNnKlStxEV27dsXv4hgiKChIKpXOmzfv119//fzzz2UymVAozMjIqGx77QlubFTMYDBkZmbGxMR4e3tnZmbm5ubylQQ3EokkKCjou+++++WXX/AjvyZNmshkskWLFu3bt2/UqFEIoZiYGEuhNnZghYVWeUQAAMA2CG5AnYWDmxUrVpx8bP/+/XPmzMH3S1atWsXzfF5enrvSvqKTAAAGEklEQVS7e1RUlF6vt2ScM2cOQmjBggU8z+PHLvHx8dZrHjZsGEIoPz+ffxzcIIQOHjxoSbBs2TKE0LZt2yxLvvjiC4VCsWvXLp7nU1NTKYrq2LGjpVCWZQcOHIgQ2r17N/84hkAIHThwwLKGxYsXI4QSExMr214c3Hh6evpVZN++fVVWjOf5rl27+vn5Wd6tMLiRyWR3797FS4qKimiaRgjt2bMHL+E4Ljw8nKZps9lszw4sU2iVRwQAAKoEbW5AHffhhx/GPdavX7+FCxfqdLpPPvnk/fffRwjt2bNHrVbPmjVLKpVaskyfPl0gEBw4cAAh5OXllZWVVabruEajQQjp9XrLkpYtW/bu3btM0UeOHLE0ppk+fbparcY3IXAEM2fOHEuhAoFg/vz5CKFdu3ZZr/PVV1+1vGzbti1CSKvV2t7exo0bR1bE8szIRsXs1L1790aNGuH/fXx8AgMDPT09BwwYgJdQFNWyZUuWZY1GI7J7B1pUeUQAAKBK0FsK1HHWXcERQv7+/rGxsaGhofjljRs3EEKbN28u88UpFotxG2GRSBQSEpKfn5+UlHTz5s2srKzLly9fuHChTCnNmjWzfjlq1KjVq1dv2LDh4MGD8fHxnTp16t27t6WHNr4dgoMVi8jISIlEgt/CynTeFgrtulpXrlxpo7eU7YrZydfX1/ol7i5eWVXt3IEWVR4RAACoEgQ3oI4r3xXcGh5bpaCgoKSkxHp5x44d3dzcEEI8z3/55Zdz5sxhWbZp06bNmzfv3bt3QEAAbrhj4e3tbf2yXr16165d27Bhw759+3bv3r1582aEUK9evX7++ef69evj7j/Wt1IQQhRFeXp6FhUVWZbgClQv2xUjXi1FUZW9ZecOtKjyiAAAQJUguAHPNXzTIjExsUWLFhUm2Lt374wZMwYOHLhmzZoXXngBL5w6dWqZZOW/3WUy2aRJkyZNmmQ2m//6669Vq1Zt27Zt2rRpP//8My40KysrIiLCkl6v1xcUFLRp06a6Nq0yNir2NIqzcwdaVHlEAACgStDmBjzXcDCxd+9e64V//fVXdHT0kiVLEEJHjx5FCM2fP9/yxYwQwiPQ2DB58uROnTqp1WqEkEgk6ty584YNG8RiMZ5koHXr1gihHTt2WGfZsWMHz/P4rafHdsWeBkd3YJVHBAAAqgTBDXiuDRgwIDIyct68eRs2bGAYBiF07969ESNGXLlyBT/MqlevHkLo0KFDeLA7g8Ewf/78X375BT1uFVshiURy7ty5uXPn4ka1Go1m48aNJpMpJiYGITR69OigoKBly5bt2bMHpz916tSnn34ql8s/+uijp7q9tiuGabVavCuqhZ070FJolUcEAACqVrOdtQB4eiob56aM1NRU3L5YJpM1adJEKBQKBIJly5bhd3Nzc/EtB19f3/bt27u7u7do0eLTTz9FCDVp0uTAgQO4K/i4ceOs11lQUBAUFIQQcnNzw+vE6S2dn0+ePIlX6+Pjg1vjuru779y5E7+Le1y/+eab1uvEt0BWrFhR2YbY7gqO+1pXWTHcbSoiImLGjBl8JV3By1SsUaNGzZs3L18TPCxNlTuwfKG2jwgAAFQJ2tyAOqtFixZxcXFeXl62k0VHR1++fHnjxo2XLl0qLi7u37//6NGjW7Vqhd/19/f/+++/v/3226tXr/r4+IwdO3bMmDEURdE0nZqaKpFIxGJxXFxcmd5Sfn5+6enpiYmJqampBQUF3bp169ix46hRoyQSCU7QvXv3q1ev/vjjj1euXDGZTFFRUcOHD7f0WhKJRHFxcdYtchBC3t7ecXFxDRs2rGxDWrZsaRkgpzJVVuzrr78ODw+/cuUKDrnc3Nzi4uLCwsJsVKxjx45isbh8TfD4N1XuwPKF2j4iAABQJYrn+ZquAwAAAABAtYE2NwAAAACoUyC4AQAAAECdAsENAAAAAOoUCG4AAAAAUKdAcAMAAACAOgWCGwAAAADUKRDcAAAAAKBOgeAGAAAAAHUKBDcAAAAAqFMguAEAAABAnQLBDQAAAADqFAhuAAAAAFCnQHADAAAAgDoFghsAAAAA1Cn/D/UaliP///CEAAAAAElFTkSuQmCC\" />\n\n<!-- rnb-plot-end -->\n"} -->


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#VOY: ACABO DE HACER EL DE ARRIBA. DEBO REVISAR RESULTADOS ÁNIMO voy


## 1.4. Dependence of subsampling strategies on sample size

To evaluate whether different subsampling strategies exhibit distinct levels of sensitivity to sample size, we compared the strength of the correlation (Pcoef2) between sample size (`Sample_percent`) and subsampling performance across strategies.

We first filtered DF2 to include only frequentist estimates where the Pearson correlation was computed between sample size and design performance (`parameter1 == Sample_percent` and `parameter2 == Pearson_Correlation`) using the mean statistic.

Sampling strategies were then grouped into two categories: `"random"` (e.g., `ssample*`) and `"tessellation"` (e.g., grid-based designs such as `SLIC` or `basins`). For each group, we summarized the distribution of Pcoef2 values through descriptive statistics (mean, median, SD, IQR, CV).

Given the lack of normality (as previously assessed), we employed a non-parametric Wilcoxon rank-sum test to compare Pcoef2 distributions between the two strategy types.

Due to the lack of normality, a **Wilcoxon rank-sum test** was used to compare the distributions of Pcoef2 between groups. We also applied a
Fligner–Killeen test to evaluate differences in dispersion between strategies.

All results were visualized using violin and boxplots. Summary tables and test outputs were exported for reproducibility.


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\" />\n\n<!-- rnb-plot-end -->\n"} -->


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" />

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# 1.5 Sample size and subsample performance

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```

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<img src=\data:image/png;base64
```



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/>\n\n<!-- rnb-plot-end -->\n"} -->


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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->

# Display plot in RStudio
print(p)
# Display plot in RStudio
print(p)

# 5. Save with EXACT dimensions
ggsave(
  filename = output_file,
  plot = p,
  width = 164.747,
  height = 107.244,
  units = \mm\,
  device = cairo_pdf 
)
# 5. Save with EXACT dimensions
ggsave(
  filename = output_file,
  plot = p,
  width = 164.747,
  height = 107.244,
  units = \mm\,
  device = cairo_pdf 
)

<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuIyBEaXNwbGF5IHBsb3QgaW4gUlN0dWRpb1xucHJpbnQocClcbiMgRGlzcGxheSBwbG90IGluIFJTdHVkaW9cbnByaW50KHApXG5cbiMgNS4gU2F2ZSB3aXRoIEVYQUNUIGRpbWVuc2lvbnNcbmdnc2F2ZShcbiAgZmlsZW5hbWUgPSBvdXRwdXRfZmlsZSxcbiAgcGxvdCA9IHAsXG4gIHdpZHRoID0gMTY0Ljc0NyxcbiAgaGVpZ2h0ID0gMTA3LjI0NCxcbiAgdW5pdHMgPSBcXG1tXFwsXG4gIGRldmljZSA9IGNhaXJvX3BkZiBcbilcbiMgNS4gU2F2ZSB3aXRoIEVYQUNUIGRpbWVuc2lvbnNcbmdnc2F2ZShcbiAgZmlsZW5hbWUgPSBvdXRwdXRfZmlsZSxcbiAgcGxvdCA9IHAsXG4gIHdpZHRoID0gMTY0Ljc0NyxcbiAgaGVpZ2h0ID0gMTA3LjI0NCxcbiAgdW5pdHMgPSBcXG1tXFwsXG4gIGRldmljZSA9IGNhaXJvX3BkZiBcbilcbmBgYFxuYGBgIn0= -->

```r
```r
# Display plot in RStudio
print(p)
# Display plot in RStudio
print(p)

# 5. Save with EXACT dimensions
ggsave(
  filename = output_file,
  plot = p,
  width = 164.747,
  height = 107.244,
  units = \mm\,
  device = cairo_pdf 
)
# 5. Save with EXACT dimensions
ggsave(
  filename = output_file,
  plot = p,
  width = 164.747,
  height = 107.244,
  units = \mm\,
  device = cairo_pdf 
)
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1wbG90LWJlZ2luIGV5Sm9aV2xuYUhRaU9qUXlOUzR5TVRZekxDSjNhV1IwYUNJNk5qZzRMQ0prY0draU9pMHhMQ0p6YVhwbFgySmxhR0YyYVc5eUlqb3dMQ0pqYjI1a2FYUnBiMjV6SWpwYlhYMD0gLS0+XG5cbjxpbWcgc3JjPVxcZGF0YTppbWFnZS9wbmc7YmFzZTY0XG4ifQ== -->

```

<!-- rnb-plot-begin eyJoZWlnaHQiOjQyNS4yMTYzLCJ3aWR0aCI6Njg4LCJkcGkiOi0xLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->

<img src=\data:image/png;base64
```



<!-- rnb-output-end -->

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/>\n\n<!-- rnb-plot-end -->\n"} -->


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" />

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message(paste(\Figure 4 saved to:\, output_file))

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```r
```r
message(paste(\Figure 4 saved to:\, output_file))
```
```

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# OBJECTIVE 2: EXPLORE EFFECTIVENESS OF STRATIFIED SAMPLING DESIGNS

Compare the performance of different sampling strategies, including random and stratified approaches, genarated by segmentation methods.

To assess the effectiveness of tessellation-based sampling strategies, we conducted a multi-step statistical analysis using R. First, we calculated two metrics for each subsample: (1) subsample performance in the form of Pearson correlation between the subsample and its full reference dataset; and (2) sampling efficiency, computed as the ratio between Pearson correlation and the sampling effort (Subsample performance/Sample_percent).

We then grouped data by summary statistic type (mean or median) and computed descriptive statistics for each tessellation strategy, including sample size, central tendency (mean, median), dispersion (standard deviation), and coefficient of variation.

Normality of Pearson correlations was assessed using the Shapiro–Wilk test within each subsampling group, using shapiro_test() from the rstatix package.

Given widespread non-normality, we applied Kruskal–Wallis rank-sum tests to compare both raw performance and efficiency across subsampling strategies. Where differences were detected, we used Dunn’s post-hoc tests with Benjamini–Hochberg correction to evaluate pairwise contrasts.

Violin and boxplots were generated to visually compare the distribution of both performance and efficiency across subsampling methods. All procedures were run separately for mean and median statistics, and results (summary tables and plots) were exported to PDF and Excel formats.

Packages we used: dplyr, ggplot2, ggpubr, rstatix, FSA, writexl.

The entry database is: DF1-1_Interpolations_variograms_and error_for_subsamples.xlsx


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-->\n\n```r\n# ==============================================================================\n# SETUP AND LIBRARIES\n# ==============================================================================\nlibrary(readxl)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(ggpubr)\nlibrary(rstatix)\nlibrary(writexl)\nlibrary(scales) \nlibrary(patchwork) # INDISPENSABLE para unir gráficas (arriba/abajo)\n\n# 1. Define Paths\ninput_path <- \"/home/vmartinezarias/Dropbox/Doctorado_Personal_Folder/ThesisAnalyses/PROVISIONALES_PAPERS/PAPER01 SAMPLING DESIGN/CORRECTIONS/DB_for_corrections/DF1-1_Interpolations_variograms_error_and_performance_of_subsamples_US.xlsx\"\noutput_dir <- \"~/Dropbox/Doctorado_Personal_Folder/ThesisAnalyses/PROVISIONALES_PAPERS/PAPER01 SAMPLING DESIGN/CORRECTIONS/DB_for_corrections/\"\n\n# ==============================================================================\n# DATA LOADING & PREP\n# ==============================================================================\n\ndf <- read_excel(input_path) %>%\n  rename(Pearson_Correlation = Subsample_performance) %>% \n  mutate(across(c(Subsample, Type, Index, Statistic), as.character)) %>%\n  mutate(\n    Pearson_rel = Pearson_Correlation / Sample_percent,\n    Audio_Type = ifelse(grepl(\"24-96\", Frequency_Range), \"Ultrasound\", \"Audible\")\n  )\n\n# ==============================================================================\n# LOOP: MEAN & MEDIAN\n# ==============================================================================\n\nfor (stat in c(\"mean\", \"median\")) {\n  \n  # Filter: Only Tessellations, current statistic\n  df_tess <- df %>%\n    filter(Type == \"Tessells\", Statistic == stat, !is.na(Pearson_Correlation))\n  \n  # --------------------------------------------------------------------------\n  # 1. STATISTICS (Keep your existing calculation logic)\n  # --------------------------------------------------------------------------\n  \n  # Summary Stats\n  summary_precision <- df_tess %>%\n    group_by(Audio_Type, Subsample) %>%\n    summarise(\n      n = n(),\n      mean_Pearson = mean(Pearson_Correlation, na.rm = TRUE),\n      median_Pearson = median(Pearson_Correlation, na.rm = TRUE),\n      sd_Pearson = sd(Pearson_Correlation, na.rm = TRUE),\n      mean_eff = mean(Pearson_rel, na.rm = TRUE),\n      median_eff = median(Pearson_rel, na.rm = TRUE),\n      sd_eff = sd(Pearson_rel, na.rm = TRUE),\n      cv_eff = sd_eff / abs(mean_eff),\n      .groups = \"drop\"\n    ) %>%\n    arrange(Audio_Type, desc(median_eff))\n  \n  # Statistical Tests Function\n  run_stat_tests <- function(sub_data, label) {\n    kw_p <- kruskal_test(Pearson_Correlation ~ Subsample, data = sub_data) %>% mutate(Metric=\"Precision\", Audio=label)\n    ph_p <- dunn_test(Pearson_Correlation ~ Subsample, data = sub_data, p.adjust.method = \"BH\") %>% mutate(Metric=\"Precision\", Audio=label)\n    kw_e <- kruskal_test(Pearson_rel ~ Subsample, data = sub_data) %>% mutate(Metric=\"Efficiency\", Audio=label)\n    ph_e <- dunn_test(Pearson_rel ~ Subsample, data = sub_data, p.adjust.method = \"BH\") %>% mutate(Metric=\"Efficiency\", Audio=label)\n    return(list(kw = bind_rows(kw_p, kw_e), posthoc = bind_rows(ph_p, ph_e)))\n  }\n  \n  res_aud <- run_stat_tests(df_tess %>% filter(Audio_Type == \"Audible\"), \"Audible\")\n  res_us <- run_stat_tests(df_tess %>% filter(Audio_Type == \"Ultrasound\"), \"Ultrasound\")\n  \n  # Export Stats to Excel\n  write_xlsx(\n    list(\n      Summary_Stats = summary_precision,\n      KW_Tests = bind_rows(res_aud$kw, res_us$kw),\n      PostHoc = bind_rows(res_aud$posthoc, res_us$posthoc)\n    ),\n    path = paste0(output_dir, \"KW_Tessellation_Stats_\", stat, \"_Split.xlsx\")\n  )\n  \n  # --------------------------------------------------------------------------\n  # 2. PLOTTING (The Combined Figure 5)\n  # --------------------------------------------------------------------------\n  \n  # Define a consistent theme for the paper\n  paper_theme <- theme_bw(base_family = \"Times New Roman\", base_size = 11) +\n    theme(\n      legend.position = \"none\",\n      axis.text = element_text(color = \"black\"),\n      axis.title = element_text(face = \"bold\"),\n      strip.background = element_blank(),\n      strip.text = element_text(face = \"bold\", size = 12),\n      panel.grid.major.x = element_blank(),\n      panel.grid.minor = element_blank()\n    )\n  \n  # --- PLOT A: EFFICIENCY (TOP) ---\n  p_eff <- ggplot(df_tess, aes(x = Subsample, y = Pearson_rel, fill = Subsample)) +\n    geom_violin(trim = FALSE, alpha = 0.8, size = 0.2) +\n    geom_boxplot(width = 0.1, outlier.shape = NA, alpha = 0.5, size = 0.2) +\n    facet_wrap(~ Audio_Type) + # Split columns\n    labs(y = \"Sampling efficiency\\n(Pearson / % sample)\", x = NULL) +\n    paper_theme +\n    theme(\n      axis.text.x = element_blank(),  # Hide X labels (redundant with bottom plot)\n      axis.ticks.x = element_blank(),\n      axis.title.x = element_blank()\n    )\n  \n  # --- PLOT B: PERFORMANCE/PRECISION (BOTTOM) ---\n  p_acc <- ggplot(df_tess, aes(x = Subsample, y = Pearson_Correlation, fill = Subsample)) +\n    geom_violin(trim = FALSE, alpha = 0.8, size = 0.2) +\n    geom_boxplot(width = 0.1, outlier.shape = NA, alpha = 0.5, size = 0.2) +\n    facet_wrap(~ Audio_Type) + # Split columns\n    scale_y_continuous(limits = c(-1, 1), oob = scales::squish) +\n    labs(y = \"Subsample performance\\n(Pearson's correlation)\", x = NULL) +\n    paper_theme +\n    theme(\n      strip.text = element_blank(), # Hide facet titles (already on top plot)\n      axis.text.x = element_text(angle = 0, hjust = 0.5, size = 10, face = \"bold\") \n    )\n  \n  # --- COMBINE PLOTS WITH PATCHWORK ---\n  final_plot <- p_eff / p_acc + \n    plot_layout(heights = c(1, 1)) # Equal height\n  \n  # --------------------------------------------------------------------------\n  # 3. EXPORT FIGURE (Exact dimensions)\n  # --------------------------------------------------------------------------\n  ggsave(\n    filename = paste0(output_dir, \"Figure5_Tessellation_Composite_\", stat, \".pdf\"),\n    plot = final_plot,\n    width = 170,   # Max width for paper\n    height = 160,  # Adjusted height for two stacked panels\n    units = \"mm\",\n    device = cairo_pdf\n  )\n  \n  message(paste(\"Generated stats and Figure 5 for:\", stat))\n}\n\nmessage(\"Process complete. Check output folder.\")\n```\n\n<!-- rnb-source-end -->\n"} -->


<!-- rnb-source-begin {"data":"```r\n# ==============================================================================\n# SETUP AND LIBRARIES\n# ==============================================================================\nlibrary(readxl)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(ggpubr)\nlibrary(rstatix)\nlibrary(writexl)\nlibrary(scales) \nlibrary(patchwork) # INDISPENSABLE para unir gráficas (arriba/abajo)\n\n# 1. Define Paths\ninput_path <- \"/home/vmartinezarias/Dropbox/Doctorado_Personal_Folder/ThesisAnalyses/PROVISIONALES_PAPERS/PAPER01 SAMPLING DESIGN/CORRECTIONS/DB_for_corrections/DF1-1_Interpolations_variograms_error_and_performance_of_subsamples_US.xlsx\"\noutput_dir <- \"~/Dropbox/Doctorado_Personal_Folder/ThesisAnalyses/PROVISIONALES_PAPERS/PAPER01 SAMPLING DESIGN/CORRECTIONS/DB_for_corrections/\"\n\n# ==============================================================================\n# DATA LOADING & PREP\n# ==============================================================================\n\ndf <- read_excel(input_path) %>%\n  rename(Pearson_Correlation = Subsample_performance) %>% \n  mutate(across(c(Subsample, Type, Index, Statistic), as.character)) %>%\n  mutate(\n    Pearson_rel = Pearson_Correlation / Sample_percent,\n    Audio_Type = ifelse(grepl(\"24-96\", Frequency_Range), \"Ultrasound\", \"Audible\")\n  )\n\n# ==============================================================================\n# LOOP: MEAN & MEDIAN\n# ==============================================================================\n\nfor (stat in c(\"mean\", \"median\")) {\n  \n  # Filter: Only Tessellations, current statistic\n  df_tess <- df %>%\n    filter(Type == \"Tessells\", Statistic == stat, !is.na(Pearson_Correlation))\n  \n  # --------------------------------------------------------------------------\n  # 1. STATISTICS (Keep your existing calculation logic)\n  # --------------------------------------------------------------------------\n  \n  # Summary Stats\n  summary_precision <- df_tess %>%\n    group_by(Audio_Type, Subsample) %>%\n    summarise(\n      n = n(),\n      mean_Pearson = mean(Pearson_Correlation, na.rm = TRUE),\n      median_Pearson = median(Pearson_Correlation, na.rm = TRUE),\n      sd_Pearson = sd(Pearson_Correlation, na.rm = TRUE),\n      mean_eff = mean(Pearson_rel, na.rm = TRUE),\n      median_eff = median(Pearson_rel, na.rm = TRUE),\n      sd_eff = sd(Pearson_rel, na.rm = TRUE),\n      cv_eff = sd_eff / abs(mean_eff),\n      .groups = \"drop\"\n    ) %>%\n    arrange(Audio_Type, desc(median_eff))\n  \n  # Statistical Tests Function\n  run_stat_tests <- function(sub_data, label) {\n    kw_p <- kruskal_test(Pearson_Correlation ~ Subsample, data = sub_data) %>% mutate(Metric=\"Precision\", Audio=label)\n    ph_p <- dunn_test(Pearson_Correlation ~ Subsample, data = sub_data, p.adjust.method = \"BH\") %>% mutate(Metric=\"Precision\", Audio=label)\n    kw_e <- kruskal_test(Pearson_rel ~ Subsample, data = sub_data) %>% mutate(Metric=\"Efficiency\", Audio=label)\n    ph_e <- dunn_test(Pearson_rel ~ Subsample, data = sub_data, p.adjust.method = \"BH\") %>% mutate(Metric=\"Efficiency\", Audio=label)\n    return(list(kw = bind_rows(kw_p, kw_e), posthoc = bind_rows(ph_p, ph_e)))\n  }\n  \n  res_aud <- run_stat_tests(df_tess %>% filter(Audio_Type == \"Audible\"), \"Audible\")\n  res_us <- run_stat_tests(df_tess %>% filter(Audio_Type == \"Ultrasound\"), \"Ultrasound\")\n  \n  # Export Stats to Excel\n  write_xlsx(\n    list(\n      Summary_Stats = summary_precision,\n      KW_Tests = bind_rows(res_aud$kw, res_us$kw),\n      PostHoc = bind_rows(res_aud$posthoc, res_us$posthoc)\n    ),\n    path = paste0(output_dir, \"KW_Tessellation_Stats_\", stat, \"_Split.xlsx\")\n  )\n  \n  # --------------------------------------------------------------------------\n  # 2. PLOTTING (The Combined Figure 5)\n  # --------------------------------------------------------------------------\n  \n  # Define a consistent theme for the paper\n  paper_theme <- theme_bw(base_family = \"Times New Roman\", base_size = 11) +\n    theme(\n      legend.position = \"none\",\n      axis.text = element_text(color = \"black\"),\n      axis.title = element_text(face = \"bold\"),\n      strip.background = element_blank(),\n      strip.text = element_text(face = \"bold\", size = 12),\n      panel.grid.major.x = element_blank(),\n      panel.grid.minor = element_blank()\n    )\n  \n  # --- PLOT A: EFFICIENCY (TOP) ---\n  p_eff <- ggplot(df_tess, aes(x = Subsample, y = Pearson_rel, fill = Subsample)) +\n    geom_violin(trim = FALSE, alpha = 0.8, size = 0.2) +\n    geom_boxplot(width = 0.1, outlier.shape = NA, alpha = 0.5, size = 0.2) +\n    facet_wrap(~ Audio_Type) + # Split columns\n    labs(y = \"Sampling efficiency\\n(Pearson / % sample)\", x = NULL) +\n    paper_theme +\n    theme(\n      axis.text.x = element_blank(),  # Hide X labels (redundant with bottom plot)\n      axis.ticks.x = element_blank(),\n      axis.title.x = element_blank()\n    )\n  \n  # --- PLOT B: PERFORMANCE/PRECISION (BOTTOM) ---\n  p_acc <- ggplot(df_tess, aes(x = Subsample, y = Pearson_Correlation, fill = Subsample)) +\n    geom_violin(trim = FALSE, alpha = 0.8, size = 0.2) +\n    geom_boxplot(width = 0.1, outlier.shape = NA, alpha = 0.5, size = 0.2) +\n    facet_wrap(~ Audio_Type) + # Split columns\n    scale_y_continuous(limits = c(-1, 1), oob = scales::squish) +\n    labs(y = \"Subsample performance\\n(Pearson's correlation)\", x = NULL) +\n    paper_theme +\n    theme(\n      strip.text = element_blank(), # Hide facet titles (already on top plot)\n      axis.text.x = element_text(angle = 0, hjust = 0.5, size = 10, face = \"bold\") \n    )\n  \n  # --- COMBINE PLOTS WITH PATCHWORK ---\n  final_plot <- p_eff / p_acc + \n    plot_layout(heights = c(1, 1)) # Equal height\n  \n  # --------------------------------------------------------------------------\n  # 3. EXPORT FIGURE (Exact dimensions)\n  # --------------------------------------------------------------------------\n  ggsave(\n    filename = paste0(output_dir, \"Figure5_Tessellation_Composite_\", stat, \".pdf\"),\n    plot = final_plot,\n    width = 170,   # Max width for paper\n    height = 160,  # Adjusted height for two stacked panels\n    units = \"mm\",\n    device = cairo_pdf\n  )\n  \n  message(paste(\"Generated stats and Figure 5 for:\", stat))\n}\n\nmessage(\"Process complete. Check output folder.\")\n```"} -->

```r
# ==============================================================================
# SETUP AND LIBRARIES
# ==============================================================================
library(readxl)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(rstatix)
library(writexl)
library(scales) 
library(patchwork) # INDISPENSABLE para unir gráficas (arriba/abajo)

# 1. Define Paths
input_path <- "/home/vmartinezarias/Dropbox/Doctorado_Personal_Folder/ThesisAnalyses/PROVISIONALES_PAPERS/PAPER01 SAMPLING DESIGN/CORRECTIONS/DB_for_corrections/DF1-1_Interpolations_variograms_error_and_performance_of_subsamples_US.xlsx"
output_dir <- "~/Dropbox/Doctorado_Personal_Folder/ThesisAnalyses/PROVISIONALES_PAPERS/PAPER01 SAMPLING DESIGN/CORRECTIONS/DB_for_corrections/"

# ==============================================================================
# DATA LOADING & PREP
# ==============================================================================

df <- read_excel(input_path) %>%
  rename(Pearson_Correlation = Subsample_performance) %>% 
  mutate(across(c(Subsample, Type, Index, Statistic), as.character)) %>%
  mutate(
    Pearson_rel = Pearson_Correlation / Sample_percent,
    Audio_Type = ifelse(grepl("24-96", Frequency_Range), "Ultrasound", "Audible")
  )

# ==============================================================================
# LOOP: MEAN & MEDIAN
# ==============================================================================

for (stat in c("mean", "median")) {
  
  # Filter: Only Tessellations, current statistic
  df_tess <- df %>%
    filter(Type == "Tessells", Statistic == stat, !is.na(Pearson_Correlation))
  
  # --------------------------------------------------------------------------
  # 1. STATISTICS (Keep your existing calculation logic)
  # --------------------------------------------------------------------------
  
  # Summary Stats
  summary_precision <- df_tess %>%
    group_by(Audio_Type, Subsample) %>%
    summarise(
      n = n(),
      mean_Pearson = mean(Pearson_Correlation, na.rm = TRUE),
      median_Pearson = median(Pearson_Correlation, na.rm = TRUE),
      sd_Pearson = sd(Pearson_Correlation, na.rm = TRUE),
      mean_eff = mean(Pearson_rel, na.rm = TRUE),
      median_eff = median(Pearson_rel, na.rm = TRUE),
      sd_eff = sd(Pearson_rel, na.rm = TRUE),
      cv_eff = sd_eff / abs(mean_eff),
      .groups = "drop"
    ) %>%
    arrange(Audio_Type, desc(median_eff))
  
  # Statistical Tests Function
  run_stat_tests <- function(sub_data, label) {
    kw_p <- kruskal_test(Pearson_Correlation ~ Subsample, data = sub_data) %>% mutate(Metric="Precision", Audio=label)
    ph_p <- dunn_test(Pearson_Correlation ~ Subsample, data = sub_data, p.adjust.method = "BH") %>% mutate(Metric="Precision", Audio=label)
    kw_e <- kruskal_test(Pearson_rel ~ Subsample, data = sub_data) %>% mutate(Metric="Efficiency", Audio=label)
    ph_e <- dunn_test(Pearson_rel ~ Subsample, data = sub_data, p.adjust.method = "BH") %>% mutate(Metric="Efficiency", Audio=label)
    return(list(kw = bind_rows(kw_p, kw_e), posthoc = bind_rows(ph_p, ph_e)))
  }
  
  res_aud <- run_stat_tests(df_tess %>% filter(Audio_Type == "Audible"), "Audible")
  res_us <- run_stat_tests(df_tess %>% filter(Audio_Type == "Ultrasound"), "Ultrasound")
  
  # Export Stats to Excel
  write_xlsx(
    list(
      Summary_Stats = summary_precision,
      KW_Tests = bind_rows(res_aud$kw, res_us$kw),
      PostHoc = bind_rows(res_aud$posthoc, res_us$posthoc)
    ),
    path = paste0(output_dir, "KW_Tessellation_Stats_", stat, "_Split.xlsx")
  )
  
  # --------------------------------------------------------------------------
  # 2. PLOTTING (The Combined Figure 5)
  # --------------------------------------------------------------------------
  
  # Define a consistent theme for the paper
  paper_theme <- theme_bw(base_family = "Times New Roman", base_size = 11) +
    theme(
      legend.position = "none",
      axis.text = element_text(color = "black"),
      axis.title = element_text(face = "bold"),
      strip.background = element_blank(),
      strip.text = element_text(face = "bold", size = 12),
      panel.grid.major.x = element_blank(),
      panel.grid.minor = element_blank()
    )
  
  # --- PLOT A: EFFICIENCY (TOP) ---
  p_eff <- ggplot(df_tess, aes(x = Subsample, y = Pearson_rel, fill = Subsample)) +
    geom_violin(trim = FALSE, alpha = 0.8, size = 0.2) +
    geom_boxplot(width = 0.1, outlier.shape = NA, alpha = 0.5, size = 0.2) +
    facet_wrap(~ Audio_Type) + # Split columns
    labs(y = "Sampling efficiency\n(Pearson / % sample)", x = NULL) +
    paper_theme +
    theme(
      axis.text.x = element_blank(),  # Hide X labels (redundant with bottom plot)
      axis.ticks.x = element_blank(),
      axis.title.x = element_blank()
    )
  
  # --- PLOT B: PERFORMANCE/PRECISION (BOTTOM) ---
  p_acc <- ggplot(df_tess, aes(x = Subsample, y = Pearson_Correlation, fill = Subsample)) +
    geom_violin(trim = FALSE, alpha = 0.8, size = 0.2) +
    geom_boxplot(width = 0.1, outlier.shape = NA, alpha = 0.5, size = 0.2) +
    facet_wrap(~ Audio_Type) + # Split columns
    scale_y_continuous(limits = c(-1, 1), oob = scales::squish) +
    labs(y = "Subsample performance\n(Pearson's correlation)", x = NULL) +
    paper_theme +
    theme(
      strip.text = element_blank(), # Hide facet titles (already on top plot)
      axis.text.x = element_text(angle = 0, hjust = 0.5, size = 10, face = "bold") 
    )
  
  # --- COMBINE PLOTS WITH PATCHWORK ---
  final_plot <- p_eff / p_acc + 
    plot_layout(heights = c(1, 1)) # Equal height
  
  # --------------------------------------------------------------------------
  # 3. EXPORT FIGURE (Exact dimensions)
  # --------------------------------------------------------------------------
  ggsave(
    filename = paste0(output_dir, "Figure5_Tessellation_Composite_", stat, ".pdf"),
    plot = final_plot,
    width = 170,   # Max width for paper
    height = 160,  # Adjusted height for two stacked panels
    units = "mm",
    device = cairo_pdf
  )
  
  message(paste("Generated stats and Figure 5 for:", stat))
}

message("Process complete. Check output folder.")
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

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<!-- rnb-text-begin -->





# OBJECTIVE 3:

Specific objective 3: To explore how the interplay of categorical (e.g., landscape, time, index) and continuous variables (e.g., sample size, performance) explains the multivariate structure that differentiates stratified subsampling strategies.

## 3.1 FAMD analyses

A Factorial Analysis of Mixed Data (FAMD) is conducted to explore the multivariate structure of stratified subsampling strategies by integrating both categorical and continuous variables. The dataset was filtered to retain only relevant observations (e.g., median statistics from tessellation-based designs) and included key landscape, temporal, acoustic, and performance variables. The variable Subsample, which defines the sampling strategy under evaluation, was treated as a supplementary qualitative variable to avoid influencing the dimensional construction.

FAMD was performed using the FactoMineR package, extracting the coordinates of individuals, the contributions of quantitative and qualitative variables, and the proportion of variance explained by each dimension. Results were organized into separate Excel sheets for subsequent visualization and statistical analysis.


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data_ultra   <- data_clean %>% filter(Audio_Category == \Ultrasound\)
data_ultra   <- data_clean %>% filter(Audio_Category == \Ultrasound\)
# Run analysis for each group
results_audible <- run_famd_analysis(data_audible, \Audible\)

<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZGF0YV91bHRyYSAgIDwtIGRhdGFfY2xlYW4gJT4lIGZpbHRlcihBdWRpb19DYXRlZ29yeSA9PSBcXFVsdHJhc291bmRcXClcbmRhdGFfdWx0cmEgICA8LSBkYXRhX2NsZWFuICU+JSBmaWx0ZXIoQXVkaW9fQ2F0ZWdvcnkgPT0gXFxVbHRyYXNvdW5kXFwpXG4jIFJ1biBhbmFseXNpcyBmb3IgZWFjaCBncm91cFxucmVzdWx0c19hdWRpYmxlIDwtIHJ1bl9mYW1kX2FuYWx5c2lzKGRhdGFfYXVkaWJsZSwgXFxBdWRpYmxlXFwpXG5cbmBgYFxuYGBgIn0= -->

```r
```r
data_ultra   <- data_clean %>% filter(Audio_Category == \Ultrasound\)
data_ultra   <- data_clean %>% filter(Audio_Category == \Ultrasound\)
# Run analysis for each group
results_audible <- run_famd_analysis(data_audible, \Audible\)

```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiRXJyb3I6IG9iamVjdCAnZGF0YV9hdWRpYmxlJyBub3QgZm91bmRcbiJ9 -->

```
Error: object 'data_audible' not found
```



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<!-- rnb-text-begin -->


## 3.2 Graphical abstract plots for FAMD

This script generates a visual summary of the Factorial Analysis of Mixed Data (FAMD) results. It includes a scree plot showing the variance explained by each dimension, along with bar plots displaying the top contributing variables to Dimensions 1 through 4. All plots are styled consistently using a minimal theme and arranged using the patchwork package into a single combined figure. The final output is saved as a high-resolution PDF for publication or reporting purposes.


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-->\n\n```r\n# ==============================================================================\n# 3.2 GRAPHICAL ABSTRACT PLOTS (AUDIBLE & ULTRASOUND)\n# ==============================================================================\n\n# Load required libraries\nlibrary(FactoMineR)\nlibrary(factoextra)\nlibrary(patchwork) # For combining ggplot-based charts\nlibrary(ggplot2)\nlibrary(dplyr)\n\n# ------------------------------------------------------------------------------\n# FUNCTION: GENERATE AND SAVE FAMD PLOTS\n# ------------------------------------------------------------------------------\n# This function re-runs the FAMD calculation (needed for plotting) \n# and generates the PDF with your specific layout.\n\ncreate_famd_abstract_plot <- function(input_data, category_name) {\n  \n  message(paste(\"Generating Graphical Abstract for:\", category_name))\n  \n  # --- 1. DATA PREPARATION (Must match analysis logic) ---\n  # Filter valid values and select columns\n  df_plot <- input_data %>%\n    filter(!is.na(Subsample_performance)) %>%\n    select(Locality, Frequency_Range, Time_Period, Index, Subsample, Sample_percent, Subsample_performance) %>%\n    na.omit()\n  \n  # Remove constant variables (FAMD crashes if variance is 0)\n  df_plot <- df_plot %>% select(where(~ is.numeric(.) || n_distinct(.) > 1))\n  \n  # Safety check\n  if(nrow(df_plot) < 5) {\n    warning(paste(\"Skipping\", category_name, \"- Not enough data.\"))\n    return(NULL)\n  }\n\n  # Identify supplementary variable\n  sup_var_idx <- if (\"Subsample\" %in% names(df_plot)) which(colnames(df_plot) == \"Subsample\") else NULL\n\n  # --- 2. RUN FAMD (Required for fviz functions) ---\n  famd_result <- FAMD(df_plot, ncp = 5, sup.var = sup_var_idx, graph = FALSE)\n\n  # --- 3. CREATE INDIVIDUAL PLOTS ---\n  \n  # Scree plot\n  plot_scree <- fviz_screeplot(famd_result, addlabels = TRUE, \n                               ggtheme = theme_minimal()) +\n    ggtitle(paste(\"Scree Plot -\", category_name)) +\n    coord_cartesian(ylim = c(0, 50)) # Adjusted Y-limit to see variance clearly\n\n  # Contribution plots (Dims 1 to 4)\n  plot_contrib_dim1 <- fviz_contrib(famd_result, choice = \"var\", axes = 1, top = 10,\n                                    ggtheme = theme_minimal()) +\n    ggtitle(\"Contribution to Dim.1 (Top 10)\") +\n    theme(axis.text.x = element_text(angle = 45, hjust = 1))\n\n  plot_contrib_dim2 <- fviz_contrib(famd_result, choice = \"var\", axes = 2, top = 10,\n                                    ggtheme = theme_minimal()) +\n    ggtitle(\"Contribution to Dim.2 (Top 10)\") +\n    theme(axis.text.x = element_text(angle = 45, hjust = 1))\n\n  plot_contrib_dim3 <- fviz_contrib(famd_result, choice = \"var\", axes = 3, top = 10,\n                                    ggtheme = theme_minimal()) +\n    ggtitle(\"Contribution to Dim.3 (Top 10)\") +\n    theme(axis.text.x = element_text(angle = 45, hjust = 1))\n\n  plot_contrib_dim4 <- fviz_contrib(famd_result, choice = \"var\", axes = 4, top = 10,\n                                    ggtheme = theme_minimal()) +\n    ggtitle(\"Contribution to Dim.4 (Top 10)\") +\n    theme(axis.text.x = element_text(angle = 45, hjust = 1))\n\n  # --- 4. COMBINE PLOTS (PATCHWORK) ---\n  combined_plot <- plot_scree /\n    (plot_contrib_dim1 + plot_contrib_dim2) /\n    (plot_contrib_dim3 + plot_contrib_dim4)\n\n  # Add global title\n  combined_plot <- combined_plot + \n    plot_annotation(\n      title = paste(\"      FAMD -\", category_name),\n      theme = theme(plot.title = element_text(size = 16, face = \"bold\"))\n    )\n\n  # --- 5. SAVE PDF ---\n  # Using the directory of the original file path defined in previous chunk\n  # If 'file_path' is lost, hardcode your path here.\n  out_path <- paste0(dirname(file_path), \"/FAMD_summary_plot_\", category_name, \".pdf\")\n  \n  ggsave(\n    filename = out_path,\n    plot = combined_plot,\n    device = \"pdf\",\n    width = 8,\n    height = 10,\n    units = \"in\"\n  )\n  \n  message(paste(\"PDF saved:\", out_path))\n}\n\n# ------------------------------------------------------------------------------\n# EXECUTE FOR BOTH GROUPS\n# ------------------------------------------------------------------------------\n# Assumes 'data_audible' and 'data_ultra' exist from the previous script.\n# If not, reload your data before running this.\n\nif(exists(\"data_audible\")) {\n  create_famd_abstract_plot(data_audible, \"AUDIBLE\")\n} else {\n  warning(\"data_audible object not found.\")\n}\n\nif(exists(\"data_ultra\")) {\n  create_famd_abstract_plot(data_ultra, \"ULTRASOUND\")\n} else {\n  warning(\"data_ultra object not found.\")\n}\n```\n\n<!-- rnb-source-end -->\n"} -->


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-->

```r
# ==============================================================================
# 3.2 GRAPHICAL ABSTRACT PLOTS (AUDIBLE & ULTRASOUND)
# ==============================================================================

# Load required libraries
library(FactoMineR)
library(factoextra)
library(patchwork) # For combining ggplot-based charts
library(ggplot2)
library(dplyr)

# ------------------------------------------------------------------------------
# FUNCTION: GENERATE AND SAVE FAMD PLOTS
# ------------------------------------------------------------------------------
# This function re-runs the FAMD calculation (needed for plotting) 
# and generates the PDF with your specific layout.

create_famd_abstract_plot <- function(input_data, category_name) {
  
  message(paste("Generating Graphical Abstract for:", category_name))
  
  # --- 1. DATA PREPARATION (Must match analysis logic) ---
  # Filter valid values and select columns
  df_plot <- input_data %>%
    filter(!is.na(Subsample_performance)) %>%
    select(Locality, Frequency_Range, Time_Period, Index, Subsample, Sample_percent, Subsample_performance) %>%
    na.omit()
  
  # Remove constant variables (FAMD crashes if variance is 0)
  df_plot <- df_plot %>% select(where(~ is.numeric(.) || n_distinct(.) > 1))
  
  # Safety check
  if(nrow(df_plot) < 5) {
    warning(paste("Skipping", category_name, "- Not enough data."))
    return(NULL)
  }

  # Identify supplementary variable
  sup_var_idx <- if ("Subsample" %in% names(df_plot)) which(colnames(df_plot) == "Subsample") else NULL

  # --- 2. RUN FAMD (Required for fviz functions) ---
  famd_result <- FAMD(df_plot, ncp = 5, sup.var = sup_var_idx, graph = FALSE)

  # --- 3. CREATE INDIVIDUAL PLOTS ---
  
  # Scree plot
  plot_scree <- fviz_screeplot(famd_result, addlabels = TRUE, 
                               ggtheme = theme_minimal()) +
    ggtitle(paste("Scree Plot -", category_name)) +
    coord_cartesian(ylim = c(0, 50)) # Adjusted Y-limit to see variance clearly

  # Contribution plots (Dims 1 to 4)
  plot_contrib_dim1 <- fviz_contrib(famd_result, choice = "var", axes = 1, top = 10,
                                    ggtheme = theme_minimal()) +
    ggtitle("Contribution to Dim.1 (Top 10)") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

  plot_contrib_dim2 <- fviz_contrib(famd_result, choice = "var", axes = 2, top = 10,
                                    ggtheme = theme_minimal()) +
    ggtitle("Contribution to Dim.2 (Top 10)") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

  plot_contrib_dim3 <- fviz_contrib(famd_result, choice = "var", axes = 3, top = 10,
                                    ggtheme = theme_minimal()) +
    ggtitle("Contribution to Dim.3 (Top 10)") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

  plot_contrib_dim4 <- fviz_contrib(famd_result, choice = "var", axes = 4, top = 10,
                                    ggtheme = theme_minimal()) +
    ggtitle("Contribution to Dim.4 (Top 10)") +
    theme(axis.text.x = element_text(angle = 45, hjust = 1))

  # --- 4. COMBINE PLOTS (PATCHWORK) ---
  combined_plot <- plot_scree /
    (plot_contrib_dim1 + plot_contrib_dim2) /
    (plot_contrib_dim3 + plot_contrib_dim4)

  # Add global title
  combined_plot <- combined_plot + 
    plot_annotation(
      title = paste("      FAMD -", category_name),
      theme = theme(plot.title = element_text(size = 16, face = "bold"))
    )

  # --- 5. SAVE PDF ---
  # Using the directory of the original file path defined in previous chunk
  # If 'file_path' is lost, hardcode your path here.
  out_path <- paste0(dirname(file_path), "/FAMD_summary_plot_", category_name, ".pdf")
  
  ggsave(
    filename = out_path,
    plot = combined_plot,
    device = "pdf",
    width = 8,
    height = 10,
    units = "in"
  )
  
  message(paste("PDF saved:", out_path))
}

# ------------------------------------------------------------------------------
# EXECUTE FOR BOTH GROUPS
# ------------------------------------------------------------------------------
# Assumes 'data_audible' and 'data_ultra' exist from the previous script.
# If not, reload your data before running this.

if(exists("data_audible")) {
  create_famd_abstract_plot(data_audible, "AUDIBLE")
} else {
  warning("data_audible object not found.")
}

if(exists("data_ultra")) {
  create_famd_abstract_plot(data_ultra, "ULTRASOUND")
} else {
  warning("data_ultra object not found.")
}
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->




## 3.3 Variables projection and individuals plot


<!-- rnb-text-end -->


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-->\n\n```r\n# ==============================================================================\n# 3.3 VARIABLES PROJECTION AND INDIVIDUALS PLOT (AUDIBLE & ULTRASOUND)\n# ==============================================================================\n\n# 1. Load required libraries\nlibrary(readxl)\nlibrary(FactoMineR)\nlibrary(factoextra)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(patchwork)\n\n# ------------------------------------------------------------------------------\n# FUNCTION: GENERATE PROJECTION PLOTS\n# ------------------------------------------------------------------------------\ncreate_projection_plots <- function(input_data, category_name, file_path_ref) {\n  \n  message(paste(\"Generating Projection Plots for:\", category_name))\n  \n  # --- A. Data Preparation ---\n  # Filter based on your specific criteria (Median, Tessells, etc.)\n  # Note: I replaced 'Pearson_Correlation' with 'Subsample_performance' based on previous context.\n  df_famd <- input_data %>%\n    filter(\n      !is.na(Subsample_performance),\n      # Uncomment and adjust these lines if you need strict filtering:\n      # Statistic == \"median\", \n      # Type == \"Tessells\"\n    ) %>%\n    select(Locality, Frequency_Range, Time_Period, Index, Subsample, Sample_percent, Subsample_performance) %>%\n    na.omit()\n  \n  # Remove constant variables to prevent FAMD errors\n  df_famd <- df_famd %>% select(where(~ is.numeric(.) || n_distinct(.) > 1))\n  \n  # Ensure factors are correct\n  df_famd <- df_famd %>% \n    mutate(across(where(is.character), as.factor)) %>%\n    mutate(across(where(is.factor), droplevels))\n  \n  # Check data sufficiency\n  if(nrow(df_famd) < 5) {\n    warning(paste(\"Skipping\", category_name, \"- Not enough data points.\"))\n    return(NULL)\n  }\n\n  # --- B. Run FAMD ---\n  # Identify supplementary variable index if it exists\n  sup_idx <- if (\"Subsample\" %in% names(df_famd)) which(colnames(df_famd) == \"Subsample\") else NULL\n  \n  famd_result <- FAMD(df_famd, ncp = 5, sup.var = sup_idx, graph = FALSE)\n  \n  # --- C. Variable Projection Plots ---\n  # Dim 1 vs 2\n  plot_var_12 <- fviz_famd_var(famd_result, axes = c(1, 2), repel = TRUE) + \n    ggtitle(\"Variables: Dim 1 vs Dim 2\") +\n    theme_minimal()\n  \n  # Dim 2 vs 3\n  plot_var_23 <- fviz_famd_var(famd_result, axes = c(2, 3), repel = TRUE) + \n    ggtitle(\"Variables: Dim 2 vs Dim 3\") +\n    theme_minimal()\n  \n  # Dim 1 vs 3\n  plot_var_13 <- fviz_famd_var(famd_result, axes = c(1, 3), repel = TRUE) + \n    ggtitle(\"Variables: Dim 1 vs Dim 3\") +\n    theme_minimal()\n\n  # --- D. Individual Projection Plots ---\n  # Note: habillage colors points by 'Subsample'. addEllipses draws confidence ovals.\n  \n  # Dim 1 vs 2\n  plot_ind_12 <- fviz_famd_ind(famd_result, axes = c(1, 2), habillage = \"Subsample\", \n                               addEllipses = TRUE, label = \"none\") + \n    ggtitle(\"Individuals: Dim 1 vs Dim 2\") +\n    theme_minimal()\n  \n  # Dim 2 vs 3\n  plot_ind_23 <- fviz_famd_ind(famd_result, axes = c(2, 3), habillage = \"Subsample\", \n                               addEllipses = TRUE, label = \"none\") + \n    ggtitle(\"Individuals: Dim 2 vs Dim 3\") +\n    theme_minimal()\n  \n  # Dim 1 vs 3\n  plot_ind_13 <- fviz_famd_ind(famd_result, axes = c(1, 3), habillage = \"Subsample\", \n                               addEllipses = TRUE, label = \"none\") + \n    ggtitle(\"Individuals: Dim 1 vs Dim 3\") +\n    theme_minimal()\n\n  # --- E. Combine Plots (Patchwork) ---\n  # Layout: Left column = Variables, Right column = Individuals\n  combined_plot <- (plot_var_12 | plot_ind_12) /\n                   (plot_var_23 | plot_ind_23) /\n                   (plot_var_13 | plot_ind_13) +\n                   plot_annotation(\n                     title = paste(\"FAMD Projections:\", category_name),\n                     subtitle = \"Left: Variables | Right: Individuals (colored by Subsample)\",\n                     theme = theme(plot.title = element_text(size = 16, face = \"bold\"))\n                   )\n\n  # --- F. Save to PDF ---\n  # Construct output path based on the input file location\n  out_path <- paste0(dirname(file_path_ref), \"/FAMD_variables_individuals_clean_\", category_name, \".pdf\")\n  \n  ggsave(\n    filename = out_path,\n    plot = combined_plot,\n    device = \"pdf\",\n    width = 12,\n    height = 14,\n    units = \"in\"\n  )\n  \n  message(paste(\"Saved plot to:\", out_path))\n}\n\n# ------------------------------------------------------------------------------\n# 2. EXECUTION\n# ------------------------------------------------------------------------------\n# Define the file path (used for saving outputs)\n# Update this path if necessary\ninput_path_ref <- \"~/Dropbox/Doctorado_Personal_Folder/ThesisAnalyses/PROVISIONALES_PAPERS/PAPER01 SAMPLING DESIGN/CORRECTIONS/DB_for_corrections/DF1-1_Interpolations_variograms_error_and_performance_of_subsamples_US.xlsx\"\n\n# NOTE: Assumes 'data_audible' and 'data_ultra' exist from the previous chunks.\n# If they don't, please run the \"Split Data\" chunk first.\n\nif(exists(\"data_audible\")) {\n  create_projection_plots(data_audible, \"AUDIBLE\", input_path_ref)\n} else {\n  warning(\"data_audible object not found. Run previous split logic.\")\n}\n\nif(exists(\"data_ultra\")) {\n  create_projection_plots(data_ultra, \"ULTRASOUND\", input_path_ref)\n} else {\n  warning(\"data_ultra object not found. Run previous split logic.\")\n}\n```\n\n<!-- rnb-source-end -->\n"} -->


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-->

```r
# ==============================================================================
# 3.3 VARIABLES PROJECTION AND INDIVIDUALS PLOT (AUDIBLE & ULTRASOUND)
# ==============================================================================

# 1. Load required libraries
library(readxl)
library(FactoMineR)
library(factoextra)
library(dplyr)
library(ggplot2)
library(patchwork)

# ------------------------------------------------------------------------------
# FUNCTION: GENERATE PROJECTION PLOTS
# ------------------------------------------------------------------------------
create_projection_plots <- function(input_data, category_name, file_path_ref) {
  
  message(paste("Generating Projection Plots for:", category_name))
  
  # --- A. Data Preparation ---
  # Filter based on your specific criteria (Median, Tessells, etc.)
  # Note: I replaced 'Pearson_Correlation' with 'Subsample_performance' based on previous context.
  df_famd <- input_data %>%
    filter(
      !is.na(Subsample_performance),
      # Uncomment and adjust these lines if you need strict filtering:
      # Statistic == "median", 
      # Type == "Tessells"
    ) %>%
    select(Locality, Frequency_Range, Time_Period, Index, Subsample, Sample_percent, Subsample_performance) %>%
    na.omit()
  
  # Remove constant variables to prevent FAMD errors
  df_famd <- df_famd %>% select(where(~ is.numeric(.) || n_distinct(.) > 1))
  
  # Ensure factors are correct
  df_famd <- df_famd %>% 
    mutate(across(where(is.character), as.factor)) %>%
    mutate(across(where(is.factor), droplevels))
  
  # Check data sufficiency
  if(nrow(df_famd) < 5) {
    warning(paste("Skipping", category_name, "- Not enough data points."))
    return(NULL)
  }

  # --- B. Run FAMD ---
  # Identify supplementary variable index if it exists
  sup_idx <- if ("Subsample" %in% names(df_famd)) which(colnames(df_famd) == "Subsample") else NULL
  
  famd_result <- FAMD(df_famd, ncp = 5, sup.var = sup_idx, graph = FALSE)
  
  # --- C. Variable Projection Plots ---
  # Dim 1 vs 2
  plot_var_12 <- fviz_famd_var(famd_result, axes = c(1, 2), repel = TRUE) + 
    ggtitle("Variables: Dim 1 vs Dim 2") +
    theme_minimal()
  
  # Dim 2 vs 3
  plot_var_23 <- fviz_famd_var(famd_result, axes = c(2, 3), repel = TRUE) + 
    ggtitle("Variables: Dim 2 vs Dim 3") +
    theme_minimal()
  
  # Dim 1 vs 3
  plot_var_13 <- fviz_famd_var(famd_result, axes = c(1, 3), repel = TRUE) + 
    ggtitle("Variables: Dim 1 vs Dim 3") +
    theme_minimal()

  # --- D. Individual Projection Plots ---
  # Note: habillage colors points by 'Subsample'. addEllipses draws confidence ovals.
  
  # Dim 1 vs 2
  plot_ind_12 <- fviz_famd_ind(famd_result, axes = c(1, 2), habillage = "Subsample", 
                               addEllipses = TRUE, label = "none") + 
    ggtitle("Individuals: Dim 1 vs Dim 2") +
    theme_minimal()
  
  # Dim 2 vs 3
  plot_ind_23 <- fviz_famd_ind(famd_result, axes = c(2, 3), habillage = "Subsample", 
                               addEllipses = TRUE, label = "none") + 
    ggtitle("Individuals: Dim 2 vs Dim 3") +
    theme_minimal()
  
  # Dim 1 vs 3
  plot_ind_13 <- fviz_famd_ind(famd_result, axes = c(1, 3), habillage = "Subsample", 
                               addEllipses = TRUE, label = "none") + 
    ggtitle("Individuals: Dim 1 vs Dim 3") +
    theme_minimal()

  # --- E. Combine Plots (Patchwork) ---
  # Layout: Left column = Variables, Right column = Individuals
  combined_plot <- (plot_var_12 | plot_ind_12) /
                   (plot_var_23 | plot_ind_23) /
                   (plot_var_13 | plot_ind_13) +
                   plot_annotation(
                     title = paste("FAMD Projections:", category_name),
                     subtitle = "Left: Variables | Right: Individuals (colored by Subsample)",
                     theme = theme(plot.title = element_text(size = 16, face = "bold"))
                   )

  # --- F. Save to PDF ---
  # Construct output path based on the input file location
  out_path <- paste0(dirname(file_path_ref), "/FAMD_variables_individuals_clean_", category_name, ".pdf")
  
  ggsave(
    filename = out_path,
    plot = combined_plot,
    device = "pdf",
    width = 12,
    height = 14,
    units = "in"
  )
  
  message(paste("Saved plot to:", out_path))
}

# ------------------------------------------------------------------------------
# 2. EXECUTION
# ------------------------------------------------------------------------------
# Define the file path (used for saving outputs)
# Update this path if necessary
input_path_ref <- "~/Dropbox/Doctorado_Personal_Folder/ThesisAnalyses/PROVISIONALES_PAPERS/PAPER01 SAMPLING DESIGN/CORRECTIONS/DB_for_corrections/DF1-1_Interpolations_variograms_error_and_performance_of_subsamples_US.xlsx"

# NOTE: Assumes 'data_audible' and 'data_ultra' exist from the previous chunks.
# If they don't, please run the "Split Data" chunk first.

if(exists("data_audible")) {
  create_projection_plots(data_audible, "AUDIBLE", input_path_ref)
} else {
  warning("data_audible object not found. Run previous split logic.")
}

if(exists("data_ultra")) {
  create_projection_plots(data_ultra, "ULTRASOUND", input_path_ref)
} else {
  warning("data_ultra object not found. Run previous split logic.")
}
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


## 3.4 KRUSKALL TEST

This graph presents the results of a statistical analysis (Kruskal-Wallis + post-hoc Dunn) applied to the coordinates of dimension 3 (Dim.3) of the FAMD analysis, with the aim of comparing the stratified sampling methods (Subsample: basins, halfbasins, SLIC).


<!-- rnb-text-end -->


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<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\n# ==============================================================================\n# SCRIPT: FAMD & KRUSKAL-WALLIS ANALYSIS (AUDIBLE VS ULTRASOUND)\n# ==============================================================================\n# Objective: Compare subsampling strategies using FAMD coordinates.\n# Logic: 'Subsample' is treated as an ACTIVE variable in FAMD.\n# Outputs: \n#   1. Excel: Detailed Statistics (Chi2, p, df, n, letters) AND Pairwise comparisons.\n#   2. PDF 1: Heatmap Comparison (Summary View).\n#   3. PDF 2: Detailed Boxplots (BIGGER FONT, 240x140mm).\n# ==============================================================================\n\n# 1. Load Required Libraries\nlibrary(readxl)\nlibrary(FactoMineR)\nlibrary(dplyr)\nlibrary(rstatix)\nlibrary(multcompView)\nlibrary(ggplot2)\nlibrary(ggpubr)    \nlibrary(patchwork) \nlibrary(tidyr)\nlibrary(stringr)\nlibrary(viridis)\nlibrary(openxlsx) \n\n# 2. Define Data Path\n# ------------------------------------------------------------------------------\nfile_path <- \\/home/vmartinezarias/Dropbox/Doctorado_Personal_Folder/ThesisAnalyses/PROVISIONALES_PAPERS/PAPER01 SAMPLING DESIGN/CORRECTIONS/DB_for_corrections/DF1-1_Interpolations_variograms_error_and_performance_of_subsamples_US.xlsx\\\ndata_raw <- read_excel(file_path, sheet = \\DATA\\)\n\n# 3. Master Analysis Function\n# ------------------------------------------------------------------------------\nanalyze_active_subsample <- function(data_input, group_name) {\n  \n  # --- A. Data Cleaning and Filtering ---\n  df_work <- data_input %>%\n    filter(\n      !is.na(Subsample_performance), \n      Statistic == \\median\\, \n      Type == \\Tessells\\\n    ) %>%\n    select(Locality, Frequency_Range, Time_Period, Index, Subsample, Sample_percent, Subsample_performance) %>%\n    na.omit() %>%\n    mutate(across(where(is.character), as.factor)) %>%\n    mutate(across(where(is.factor), droplevels))\n  \n  n_samples <- nrow(df_work)\n  cat(paste(\\\\n>>> Processing Group:\\, group_name, \\| Sample Size (n):\\, n_samples, \\\\n\\))\n  \n  if(n_samples < 10) {\n    warning(paste(\\Insufficient data for group:\\, group_name))\n    return(NULL)\n  }\n  \n  # --- B. FAMD Calculation ---\n  famd_res <- FAMD(df_work, ncp = 5, graph = FALSE)\n  coords <- as.data.frame(famd_res$ind$coord[, 1:4])\n  coords$Subsample <- df_work$Subsample\n  \n  # --- C. Statistical Loop ---\n  plot_data <- data.frame() \n  stats_data <- data.frame() \n  pairwise_data <- data.frame() \n  \n  for (dim in c(\\Dim.1\\, \\Dim.2\\, \\Dim.3\\, \\Dim.4\\)) {\n    \n    # 1. Kruskal-Wallis Test\n    kw <- kruskal_test(as.formula(paste(dim, \\~ Subsample\\)), data = coords)\n    \n    final_letters <- rep(\\a\\, length(levels(coords$Subsample)))\n    names(final_letters) <- levels(coords$Subsample)\n    \n    # 2. Post-Hoc (Dunn)\n    if (kw$p < 0.05) {\n      dunn <- dunn_test(as.formula(paste(dim, \\~ Subsample\\)), data = coords, p.adjust.method = \\BH\\)\n      \n      # Save Pairwise\n      dunn_clean <- dunn %>%\n        mutate(Dimension = dim, Category = group_name) %>%\n        select(Category, Dimension, group1, group2, n1, n2, statistic, p, p.adj, p.adj.signif)\n      pairwise_data <- bind_rows(pairwise_data, dunn_clean)\n      \n      grps <- levels(coords$Subsample)\n      pmat <- matrix(1, nrow=length(grps), ncol=length(grps), dimnames=list(grps, grps))\n      diag(pmat) <- 0\n      \n      for(i in 1:nrow(dunn)){\n        pmat[dunn$group1[i], dunn$group2[i]] <- dunn$p.adj[i]\n        pmat[dunn$group2[i], dunn$group1[i]] <- dunn$p.adj[i]\n      }\n      \n      try({\n        lets <- multcompLetters(pmat < 0.05)$Letters\n        final_letters <- lets[grps]\n      }, silent = TRUE)\n    }\n    \n    # Data for Heatmap\n    tmp_plot <- data.frame(\n      Subsample = names(final_letters),\n      Significance_Letter = as.character(final_letters),\n      Dimension = dim,\n      Category = group_name\n    )\n    plot_data <- rbind(plot_data, tmp_plot)\n    \n    # Data for Excel\n    tmp_stats <- data.frame(\n      Dimension = dim,\n      KW_Statistic = round(kw$statistic, 3),\n      KW_df = kw$df,\n      KW_n = kw$n,\n      KW_p_value = format.pval(kw$p, digits = 3, eps = 0.001)\n    )\n    for(g in names(final_letters)) {\n      tmp_stats[[paste0(\\Letter_\\, g)]] <- final_letters[g]\n    }\n    stats_data <- bind_rows(stats_data, tmp_stats)\n  }\n  \n  return(list(\n    coords = coords, \n    heatmap_data = plot_data, \n    stats = stats_data, \n    pairwise = pairwise_data \n  ))\n}\n\n# 4. Helper Function: Boxplots (FONT SIZE INCREASED + BOLD AXIS)\n# ------------------------------------------------------------------------------\ncreate_detailed_boxplots <- function(res_obj, group_name) {\n  coords <- res_obj$coords\n  stats_df <- res_obj$stats\n  plot_list <- list()\n  font_family <- \\serif\\ # Times New Roman\n  \n  for(dim in c(\\Dim.1\\, \\Dim.2\\, \\Dim.3\\, \\Dim.4\\)) {\n    row_stats <- stats_df %>% filter(Dimension == dim)\n    letters_named <- row_stats %>% select(starts_with(\\Letter_\\))\n    \n    anno_df <- data.frame(\n      Subsample = names(letters_named) %>% str_remove(\\Letter_\\),\n      Letter = as.character(letters_named[1,])\n    )\n    \n    y_max_vals <- coords %>% \n      group_by(Subsample) %>% \n      summarise(max_val = max(.data[[dim]], na.rm = TRUE))\n    anno_df <- left_join(anno_df, y_max_vals, by = \\Subsample\\)\n    \n    # Format p-value string properly\n    p_val_str <- row_stats$KW_p_value\n    if(grepl(\\<\\, p_val_str)) {\n      p_label <- paste(\\p\\, p_val_str)\n    } else {\n      p_label <- paste(\\p =\\, p_val_str)\n    }\n    \n    subtitle_text <- paste0(\\KW Chi2 = \\, row_stats$KW_Statistic, \\\n"} -->

# ==============================================================================
# SCRIPT: FAMD & KRUSKAL-WALLIS ANALYSIS (AUDIBLE VS ULTRASOUND)
# ==============================================================================
# Objective: Compare subsampling strategies using FAMD coordinates.
# Logic: 'Subsample' is treated as an ACTIVE variable in FAMD.
# Outputs: 
#   1. Excel: Detailed Statistics (Chi2, p, df, n, letters) AND Pairwise comparisons.
#   2. PDF 1: Heatmap Comparison (Summary View).
#   3. PDF 2: Detailed Boxplots (BIGGER FONT, 240x140mm).
# ==============================================================================

# 1. Load Required Libraries
library(readxl)
library(FactoMineR)
library(dplyr)
library(rstatix)
library(multcompView)
library(ggplot2)
library(ggpubr)    
library(patchwork) 
library(tidyr)
library(stringr)
library(viridis)
library(openxlsx) 

# 2. Define Data Path
# ------------------------------------------------------------------------------
file_path <- \/home/vmartinezarias/Dropbox/Doctorado_Personal_Folder/ThesisAnalyses/PROVISIONALES_PAPERS/PAPER01 SAMPLING DESIGN/CORRECTIONS/DB_for_corrections/DF1-1_Interpolations_variograms_error_and_performance_of_subsamples_US.xlsx\
data_raw <- read_excel(file_path, sheet = \DATA\)

# 3. Master Analysis Function
# ------------------------------------------------------------------------------
analyze_active_subsample <- function(data_input, group_name) {
  
  # --- A. Data Cleaning and Filtering ---
  df_work <- data_input %>%
    filter(
      !is.na(Subsample_performance), 
      Statistic == \median\, 
      Type == \Tessells\
    ) %>%
    select(Locality, Frequency_Range, Time_Period, Index, Subsample, Sample_percent, Subsample_performance) %>%
    na.omit() %>%
    mutate(across(where(is.character), as.factor)) %>%
    mutate(across(where(is.factor), droplevels))
  
  n_samples <- nrow(df_work)
  cat(paste(\\n>>> Processing Group:\, group_name, \| Sample Size (n):\, n_samples, \\n\))
  
  if(n_samples < 10) {
    warning(paste(\Insufficient data for group:\, group_name))
    return(NULL)
  }
  
  # --- B. FAMD Calculation ---
  famd_res <- FAMD(df_work, ncp = 5, graph = FALSE)
  coords <- as.data.frame(famd_res$ind$coord[, 1:4])
  coords$Subsample <- df_work$Subsample
  
  # --- C. Statistical Loop ---
  plot_data <- data.frame() 
  stats_data <- data.frame() 
  pairwise_data <- data.frame() 
  
  for (dim in c(\Dim.1\, \Dim.2\, \Dim.3\, \Dim.4\)) {
    
    # 1. Kruskal-Wallis Test
    kw <- kruskal_test(as.formula(paste(dim, \~ Subsample\)), data = coords)
    
    final_letters <- rep(\a\, length(levels(coords$Subsample)))
    names(final_letters) <- levels(coords$Subsample)
    
    # 2. Post-Hoc (Dunn)
    if (kw$p < 0.05) {
      dunn <- dunn_test(as.formula(paste(dim, \~ Subsample\)), data = coords, p.adjust.method = \BH\)
      
      # Save Pairwise
      dunn_clean <- dunn %>%
        mutate(Dimension = dim, Category = group_name) %>%
        select(Category, Dimension, group1, group2, n1, n2, statistic, p, p.adj, p.adj.signif)
      pairwise_data <- bind_rows(pairwise_data, dunn_clean)
      
      grps <- levels(coords$Subsample)
      pmat <- matrix(1, nrow=length(grps), ncol=length(grps), dimnames=list(grps, grps))
      diag(pmat) <- 0
      
      for(i in 1:nrow(dunn)){
        pmat[dunn$group1[i], dunn$group2[i]] <- dunn$p.adj[i]
        pmat[dunn$group2[i], dunn$group1[i]] <- dunn$p.adj[i]
      }
      
      try({
        lets <- multcompLetters(pmat < 0.05)$Letters
        final_letters <- lets[grps]
      }, silent = TRUE)
    }
    
    # Data for Heatmap
    tmp_plot <- data.frame(
      Subsample = names(final_letters),
      Significance_Letter = as.character(final_letters),
      Dimension = dim,
      Category = group_name
    )
    plot_data <- rbind(plot_data, tmp_plot)
    
    # Data for Excel
    tmp_stats <- data.frame(
      Dimension = dim,
      KW_Statistic = round(kw$statistic, 3),
      KW_df = kw$df,
      KW_n = kw$n,
      KW_p_value = format.pval(kw$p, digits = 3, eps = 0.001)
    )
    for(g in names(final_letters)) {
      tmp_stats[[paste0(\Letter_\, g)]] <- final_letters[g]
    }
    stats_data <- bind_rows(stats_data, tmp_stats)
  }
  
  return(list(
    coords = coords, 
    heatmap_data = plot_data, 
    stats = stats_data, 
    pairwise = pairwise_data 
  ))
}

# 4. Helper Function: Boxplots (FONT SIZE INCREASED + BOLD AXIS)
# ------------------------------------------------------------------------------
create_detailed_boxplots <- function(res_obj, group_name) {
  coords <- res_obj$coords
  stats_df <- res_obj$stats
  plot_list <- list()
  font_family <- \serif\ # Times New Roman
  
  for(dim in c(\Dim.1\, \Dim.2\, \Dim.3\, \Dim.4\)) {
    row_stats <- stats_df %>% filter(Dimension == dim)
    letters_named <- row_stats %>% select(starts_with(\Letter_\))
    
    anno_df <- data.frame(
      Subsample = names(letters_named) %>% str_remove(\Letter_\),
      Letter = as.character(letters_named[1,])
    )
    
    y_max_vals <- coords %>% 
      group_by(Subsample) %>% 
      summarise(max_val = max(.data[[dim]], na.rm = TRUE))
    anno_df <- left_join(anno_df, y_max_vals, by = \Subsample\)
    
    # Format p-value string properly
    p_val_str <- row_stats$KW_p_value
    if(grepl(\<\, p_val_str)) {
      p_label <- paste(\p\, p_val_str)
    } else {
      p_label <- paste(\p =\, p_val_str)
    }
    
    subtitle_text <- paste0(\KW Chi2 = \, row_stats$KW_Statistic, \

```r
# ==============================================================================
# SCRIPT: FAMD & KRUSKAL-WALLIS ANALYSIS (AUDIBLE VS ULTRASOUND)
# ==============================================================================
# Objective: Compare subsampling strategies using FAMD coordinates.
# Logic: 'Subsample' is treated as an ACTIVE variable in FAMD.
# Outputs: 
#   1. Excel: Detailed Statistics (Chi2, p, df, n, letters) AND Pairwise comparisons.
#   2. PDF 1: Heatmap Comparison (Summary View).
#   3. PDF 2: Detailed Boxplots (BIGGER FONT, 240x140mm).
# ==============================================================================

# 1. Load Required Libraries
library(readxl)
library(FactoMineR)
library(dplyr)
library(rstatix)
library(multcompView)
library(ggplot2)
library(ggpubr)    
library(patchwork) 
library(tidyr)
library(stringr)
library(viridis)
library(openxlsx) 

# 2. Define Data Path
# ------------------------------------------------------------------------------
file_path <- \/home/vmartinezarias/Dropbox/Doctorado_Personal_Folder/ThesisAnalyses/PROVISIONALES_PAPERS/PAPER01 SAMPLING DESIGN/CORRECTIONS/DB_for_corrections/DF1-1_Interpolations_variograms_error_and_performance_of_subsamples_US.xlsx\
data_raw <- read_excel(file_path, sheet = \DATA\)

# 3. Master Analysis Function
# ------------------------------------------------------------------------------
analyze_active_subsample <- function(data_input, group_name) {
  
  # --- A. Data Cleaning and Filtering ---
  df_work <- data_input %>%
    filter(
      !is.na(Subsample_performance), 
      Statistic == \median\, 
      Type == \Tessells\
    ) %>%
    select(Locality, Frequency_Range, Time_Period, Index, Subsample, Sample_percent, Subsample_performance) %>%
    na.omit() %>%
    mutate(across(where(is.character), as.factor)) %>%
    mutate(across(where(is.factor), droplevels))
  
  n_samples <- nrow(df_work)
  cat(paste(\\n>>> Processing Group:\, group_name, \| Sample Size (n):\, n_samples, \\n\))
  
  if(n_samples < 10) {
    warning(paste(\Insufficient data for group:\, group_name))
    return(NULL)
  }
  
  # --- B. FAMD Calculation ---
  famd_res <- FAMD(df_work, ncp = 5, graph = FALSE)
  coords <- as.data.frame(famd_res$ind$coord[, 1:4])
  coords$Subsample <- df_work$Subsample
  
  # --- C. Statistical Loop ---
  plot_data <- data.frame() 
  stats_data <- data.frame() 
  pairwise_data <- data.frame() 
  
  for (dim in c(\Dim.1\, \Dim.2\, \Dim.3\, \Dim.4\)) {
    
    # 1. Kruskal-Wallis Test
    kw <- kruskal_test(as.formula(paste(dim, \~ Subsample\)), data = coords)
    
    final_letters <- rep(\a\, length(levels(coords$Subsample)))
    names(final_letters) <- levels(coords$Subsample)
    
    # 2. Post-Hoc (Dunn)
    if (kw$p < 0.05) {
      dunn <- dunn_test(as.formula(paste(dim, \~ Subsample\)), data = coords, p.adjust.method = \BH\)
      
      # Save Pairwise
      dunn_clean <- dunn %>%
        mutate(Dimension = dim, Category = group_name) %>%
        select(Category, Dimension, group1, group2, n1, n2, statistic, p, p.adj, p.adj.signif)
      pairwise_data <- bind_rows(pairwise_data, dunn_clean)
      
      grps <- levels(coords$Subsample)
      pmat <- matrix(1, nrow=length(grps), ncol=length(grps), dimnames=list(grps, grps))
      diag(pmat) <- 0
      
      for(i in 1:nrow(dunn)){
        pmat[dunn$group1[i], dunn$group2[i]] <- dunn$p.adj[i]
        pmat[dunn$group2[i], dunn$group1[i]] <- dunn$p.adj[i]
      }
      
      try({
        lets <- multcompLetters(pmat < 0.05)$Letters
        final_letters <- lets[grps]
      }, silent = TRUE)
    }
    
    # Data for Heatmap
    tmp_plot <- data.frame(
      Subsample = names(final_letters),
      Significance_Letter = as.character(final_letters),
      Dimension = dim,
      Category = group_name
    )
    plot_data <- rbind(plot_data, tmp_plot)
    
    # Data for Excel
    tmp_stats <- data.frame(
      Dimension = dim,
      KW_Statistic = round(kw$statistic, 3),
      KW_df = kw$df,
      KW_n = kw$n,
      KW_p_value = format.pval(kw$p, digits = 3, eps = 0.001)
    )
    for(g in names(final_letters)) {
      tmp_stats[[paste0(\Letter_\, g)]] <- final_letters[g]
    }
    stats_data <- bind_rows(stats_data, tmp_stats)
  }
  
  return(list(
    coords = coords, 
    heatmap_data = plot_data, 
    stats = stats_data, 
    pairwise = pairwise_data 
  ))
}

# 4. Helper Function: Boxplots (FONT SIZE INCREASED + BOLD AXIS)
# ------------------------------------------------------------------------------
create_detailed_boxplots <- function(res_obj, group_name) {
  coords <- res_obj$coords
  stats_df <- res_obj$stats
  plot_list <- list()
  font_family <- \serif\ # Times New Roman
  
  for(dim in c(\Dim.1\, \Dim.2\, \Dim.3\, \Dim.4\)) {
    row_stats <- stats_df %>% filter(Dimension == dim)
    letters_named <- row_stats %>% select(starts_with(\Letter_\))
    
    anno_df <- data.frame(
      Subsample = names(letters_named) %>% str_remove(\Letter_\),
      Letter = as.character(letters_named[1,])
    )
    
    y_max_vals <- coords %>% 
      group_by(Subsample) %>% 
      summarise(max_val = max(.data[[dim]], na.rm = TRUE))
    anno_df <- left_join(anno_df, y_max_vals, by = \Subsample\)
    
    # Format p-value string properly
    p_val_str <- row_stats$KW_p_value
    if(grepl(\<\, p_val_str)) {
      p_label <- paste(\p\, p_val_str)
    } else {
      p_label <- paste(\p =\, p_val_str)
    }
    
    subtitle_text <- paste0(\KW Chi2 = \, row_stats$KW_Statistic, \

>>> Processing Group: AUDIBLE | Sample Size (n): 489 

<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucmVzX3VzICA8LSBhbmFseXplX2FjdGl2ZV9zdWJzYW1wbGUoZGF0YV91bHRyYSwgXCJVTFRSQVNPVU5EXCIpXG5gYGAifQ== -->

```r
res_us  <- analyze_active_subsample(data_ultra, \ULTRASOUND\)
```

<!-- rnb-source-end -->

```r
res_us  <- analyze_active_subsample(data_ultra, \ULTRASOUND\)

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin {"data":"\n>>> Processing Group: ULTRASOUND | Sample Size (n): 107 \n\n<!-- rnb-source-begin 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 -->\n\n```r\n# 6. Output 1: Excel Table (Detailed Stats + Pairwise)\n# ------------------------------------------------------------------------------\nwb <- createWorkbook()\n\nif(!is.null(res_aud)) {\n  addWorksheet(wb, \\Audible_Summary\\)\n  writeData(wb, \\Audible_Summary\\, res_aud$stats)\n  addStyle(wb, \\Audible_Summary\\, createStyle(textDecoration = \\bold\\), rows = 1, cols = 1:ncol(res_aud$stats))\n  \n  addWorksheet(wb, \\Audible_Pairwise\\)\n  writeData(wb, \\Audible_Pairwise\\, res_aud$pairwise)\n  addStyle(wb, \\Audible_Pairwise\\, createStyle(textDecoration = \\bold\\), rows = 1, cols = 1:ncol(res_aud$pairwise))\n}\n\nif(!is.null(res_us)) {\n  addWorksheet(wb, \\Ultrasound_Summary\\)\n  writeData(wb, \\Ultrasound_Summary\\, res_us$stats)\n  addStyle(wb, \\Ultrasound_Summary\\, createStyle(textDecoration = \\bold\\), rows = 1, cols = 1:ncol(res_us$stats))\n  \n  addWorksheet(wb, \\Ultrasound_Pairwise\\)\n  writeData(wb, \\Ultrasound_Pairwise\\, res_us$pairwise)\n  addStyle(wb, \\Ultrasound_Pairwise\\, createStyle(textDecoration = \\bold\\), rows = 1, cols = 1:ncol(res_us$pairwise))\n}\n\nexcel_path <- paste0(dirname(file_path), \\/Statistical_Results_Detailed_With_Pairwise.xlsx\\)\nsaveWorkbook(wb, excel_path, overwrite = TRUE)\nmessage(paste(\\1. Excel saved with Pairwise comparisons to:\\, excel_path))\n\n# 7. Output 2: Heatmap Comparison (PDF 170mm)\n# ------------------------------------------------------------------------------\ndf_plot_combined <- bind_rows(res_aud$heatmap_data, res_us$heatmap_data)\n\nif(nrow(df_plot_combined) > 0) {\n  df_plot_combined$Dimension <- factor(df_plot_combined$Dimension, levels = c(\\Dim.1\\, \\Dim.2\\, \\Dim.3\\, \\Dim.4\\))\n  # Note: Adjust labels here if you want full text\n  df_plot_combined$Category <- factor(df_plot_combined$Category, levels = c(\\AUDIBLE\\, \\ULTRASOUND\\))\n  \n  font_family <- \\serif\\\n  \n  p_heat <- ggplot(df_plot_combined, aes(x = Dimension, y = Subsample, fill = Significance_Letter)) +\n    geom_tile(color = \\white\\, lwd = 0.5) +\n    geom_text(aes(label = Significance_Letter), \n              family = font_family, fontface = \\bold\\, size = 5, color = \\black\\) +\n    facet_wrap(~Category, ncol = 2) +\n    scale_fill_viridis_d(option = \\plasma\\, direction = -1, begin = 0.1, end = 0.9, name = \\Sig. Group\\) +\n    labs(x = \\FAMD Dimension\\, y = \\Subsampling Strategy\\) +\n    theme_minimal(base_family = font_family, base_size = 12) +\n    theme(\n      strip.text = element_text(face = \\bold\\, size = 11),\n      axis.text = element_text(color = \\black\\),\n      axis.title = element_text(face = \\bold\\),\n      panel.grid = element_blank(),\n      panel.border = element_rect(color = \\black\\, fill = NA, linewidth = 0.5),\n      legend.position = \\right\\\n    )\n  \n  heatmap_path <- paste0(dirname(file_path), \\/Heatmap_Comparison_170mm.pdf\\)\n  ggsave(heatmap_path, p_heat, device = cairo_pdf, width = 170, height = 90, units = \\mm\\)\n  message(paste(\\2. Heatmap PDF saved to:\\, heatmap_path))\n}\n\n# 8. Output 3: Detailed Boxplots (UPDATED SIZE & FONT)\n# ------------------------------------------------------------------------------\n# Width = 240mm, Height = 140mm\n# Font: Times New Roman, Size 12+ (handled in function via base_size=14)\n\nif(!is.null(res_aud)) {\n  box_aud <- create_detailed_boxplots(res_aud, \\AUDIBLE\\)\n  box_aud_path <- paste0(dirname(file_path), \\/Detailed_Boxplots_Audible_240mm.pdf\\)\n  ggsave(box_aud_path, box_aud, device = cairo_pdf, width = 240, height = 140, units = \\mm\\)\n  message(paste(\\3a. Boxplots Audible saved to:\\, box_aud_path))\n}\n\nif(!is.null(res_us)) {\n  box_us <- create_detailed_boxplots(res_us, \\ULTRASOUND\\)\n  box_us_path <- paste0(dirname(file_path), \\/Detailed_Boxplots_Ultrasound_240mm.pdf\\)\n  ggsave(box_us_path, box_us, device = cairo_pdf, width = 240, height = 140, units = \\mm\\)\n  message(paste(\\3b. Boxplots Ultrasound saved to:\\, box_us_path))\n}\n```\n\n<!-- rnb-source-end -->\n"} -->

Processing Group: ULTRASOUND | Sample Size (n): 107

# 6. Output 1: Excel Table (Detailed Stats + Pairwise)
# ------------------------------------------------------------------------------
wb <- createWorkbook()

if(!is.null(res_aud)) {
  addWorksheet(wb, \Audible_Summary\)
  writeData(wb, \Audible_Summary\, res_aud$stats)
  addStyle(wb, \Audible_Summary\, createStyle(textDecoration = \bold\), rows = 1, cols = 1:ncol(res_aud$stats))
  
  addWorksheet(wb, \Audible_Pairwise\)
  writeData(wb, \Audible_Pairwise\, res_aud$pairwise)
  addStyle(wb, \Audible_Pairwise\, createStyle(textDecoration = \bold\), rows = 1, cols = 1:ncol(res_aud$pairwise))
}

if(!is.null(res_us)) {
  addWorksheet(wb, \Ultrasound_Summary\)
  writeData(wb, \Ultrasound_Summary\, res_us$stats)
  addStyle(wb, \Ultrasound_Summary\, createStyle(textDecoration = \bold\), rows = 1, cols = 1:ncol(res_us$stats))
  
  addWorksheet(wb, \Ultrasound_Pairwise\)
  writeData(wb, \Ultrasound_Pairwise\, res_us$pairwise)
  addStyle(wb, \Ultrasound_Pairwise\, createStyle(textDecoration = \bold\), rows = 1, cols = 1:ncol(res_us$pairwise))
}

excel_path <- paste0(dirname(file_path), \/Statistical_Results_Detailed_With_Pairwise.xlsx\)
saveWorkbook(wb, excel_path, overwrite = TRUE)
message(paste(\1. Excel saved with Pairwise comparisons to:\, excel_path))

# 7. Output 2: Heatmap Comparison (PDF 170mm)
# ------------------------------------------------------------------------------
df_plot_combined <- bind_rows(res_aud$heatmap_data, res_us$heatmap_data)

if(nrow(df_plot_combined) > 0) {
  df_plot_combined$Dimension <- factor(df_plot_combined$Dimension, levels = c(\Dim.1\, \Dim.2\, \Dim.3\, \Dim.4\))
  # Note: Adjust labels here if you want full text
  df_plot_combined$Category <- factor(df_plot_combined$Category, levels = c(\AUDIBLE\, \ULTRASOUND\))
  
  font_family <- \serif\
  
  p_heat <- ggplot(df_plot_combined, aes(x = Dimension, y = Subsample, fill = Significance_Letter)) +
    geom_tile(color = \white\, lwd = 0.5) +
    geom_text(aes(label = Significance_Letter), 
              family = font_family, fontface = \bold\, size = 5, color = \black\) +
    facet_wrap(~Category, ncol = 2) +
    scale_fill_viridis_d(option = \plasma\, direction = -1, begin = 0.1, end = 0.9, name = \Sig. Group\) +
    labs(x = \FAMD Dimension\, y = \Subsampling Strategy\) +
    theme_minimal(base_family = font_family, base_size = 12) +
    theme(
      strip.text = element_text(face = \bold\, size = 11),
      axis.text = element_text(color = \black\),
      axis.title = element_text(face = \bold\),
      panel.grid = element_blank(),
      panel.border = element_rect(color = \black\, fill = NA, linewidth = 0.5),
      legend.position = \right\
    )
  
  heatmap_path <- paste0(dirname(file_path), \/Heatmap_Comparison_170mm.pdf\)
  ggsave(heatmap_path, p_heat, device = cairo_pdf, width = 170, height = 90, units = \mm\)
  message(paste(\2. Heatmap PDF saved to:\, heatmap_path))
}

# 8. Output 3: Detailed Boxplots (UPDATED SIZE & FONT)
# ------------------------------------------------------------------------------
# Width = 240mm, Height = 140mm
# Font: Times New Roman, Size 12+ (handled in function via base_size=14)

if(!is.null(res_aud)) {
  box_aud <- create_detailed_boxplots(res_aud, \AUDIBLE\)
  box_aud_path <- paste0(dirname(file_path), \/Detailed_Boxplots_Audible_240mm.pdf\)
  ggsave(box_aud_path, box_aud, device = cairo_pdf, width = 240, height = 140, units = \mm\)
  message(paste(\3a. Boxplots Audible saved to:\, box_aud_path))
}

if(!is.null(res_us)) {
  box_us <- create_detailed_boxplots(res_us, \ULTRASOUND\)
  box_us_path <- paste0(dirname(file_path), \/Detailed_Boxplots_Ultrasound_240mm.pdf\)
  ggsave(box_us_path, box_us, device = cairo_pdf, width = 240, height = 140, units = \mm\)
  message(paste(\3b. Boxplots Ultrasound saved to:\, box_us_path))
}

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-->\n\n```r\n```r\n# 6. Output 1: Excel Table (Detailed Stats + Pairwise)\n# ------------------------------------------------------------------------------\nwb <- createWorkbook()\n\nif(!is.null(res_aud)) {\n  addWorksheet(wb, \\Audible_Summary\\)\n  writeData(wb, \\Audible_Summary\\, res_aud$stats)\n  addStyle(wb, \\Audible_Summary\\, createStyle(textDecoration = \\bold\\), rows = 1, cols = 1:ncol(res_aud$stats))\n  \n  addWorksheet(wb, \\Audible_Pairwise\\)\n  writeData(wb, \\Audible_Pairwise\\, res_aud$pairwise)\n  addStyle(wb, \\Audible_Pairwise\\, createStyle(textDecoration = \\bold\\), rows = 1, cols = 1:ncol(res_aud$pairwise))\n}\n\nif(!is.null(res_us)) {\n  addWorksheet(wb, \\Ultrasound_Summary\\)\n  writeData(wb, \\Ultrasound_Summary\\, res_us$stats)\n  addStyle(wb, \\Ultrasound_Summary\\, createStyle(textDecoration = \\bold\\), rows = 1, cols = 1:ncol(res_us$stats))\n  \n  addWorksheet(wb, \\Ultrasound_Pairwise\\)\n  writeData(wb, \\Ultrasound_Pairwise\\, res_us$pairwise)\n  addStyle(wb, \\Ultrasound_Pairwise\\, createStyle(textDecoration = \\bold\\), rows = 1, cols = 1:ncol(res_us$pairwise))\n}\n\nexcel_path <- paste0(dirname(file_path), \\/Statistical_Results_Detailed_With_Pairwise.xlsx\\)\nsaveWorkbook(wb, excel_path, overwrite = TRUE)\nmessage(paste(\\1. Excel saved with Pairwise comparisons to:\\, excel_path))\n\n# 7. Output 2: Heatmap Comparison (PDF 170mm)\n# ------------------------------------------------------------------------------\ndf_plot_combined <- bind_rows(res_aud$heatmap_data, res_us$heatmap_data)\n\nif(nrow(df_plot_combined) > 0) {\n  df_plot_combined$Dimension <- factor(df_plot_combined$Dimension, levels = c(\\Dim.1\\, \\Dim.2\\, \\Dim.3\\, \\Dim.4\\))\n  # Note: Adjust labels here if you want full text\n  df_plot_combined$Category <- factor(df_plot_combined$Category, levels = c(\\AUDIBLE\\, \\ULTRASOUND\\))\n  \n  font_family <- \\serif\\\n  \n  p_heat <- ggplot(df_plot_combined, aes(x = Dimension, y = Subsample, fill = Significance_Letter)) +\n    geom_tile(color = \\white\\, lwd = 0.5) +\n    geom_text(aes(label = Significance_Letter), \n              family = font_family, fontface = \\bold\\, size = 5, color = \\black\\) +\n    facet_wrap(~Category, ncol = 2) +\n    scale_fill_viridis_d(option = \\plasma\\, direction = -1, begin = 0.1, end = 0.9, name = \\Sig. Group\\) +\n    labs(x = \\FAMD Dimension\\, y = \\Subsampling Strategy\\) +\n    theme_minimal(base_family = font_family, base_size = 12) +\n    theme(\n      strip.text = element_text(face = \\bold\\, size = 11),\n      axis.text = element_text(color = \\black\\),\n      axis.title = element_text(face = \\bold\\),\n      panel.grid = element_blank(),\n      panel.border = element_rect(color = \\black\\, fill = NA, linewidth = 0.5),\n      legend.position = \\right\\\n    )\n  \n  heatmap_path <- paste0(dirname(file_path), \\/Heatmap_Comparison_170mm.pdf\\)\n  ggsave(heatmap_path, p_heat, device = cairo_pdf, width = 170, height = 90, units = \\mm\\)\n  message(paste(\\2. Heatmap PDF saved to:\\, heatmap_path))\n}\n\n# 8. Output 3: Detailed Boxplots (UPDATED SIZE & FONT)\n# ------------------------------------------------------------------------------\n# Width = 240mm, Height = 140mm\n# Font: Times New Roman, Size 12+ (handled in function via base_size=14)\n\nif(!is.null(res_aud)) {\n  box_aud <- create_detailed_boxplots(res_aud, \\AUDIBLE\\)\n  box_aud_path <- paste0(dirname(file_path), \\/Detailed_Boxplots_Audible_240mm.pdf\\)\n  ggsave(box_aud_path, box_aud, device = cairo_pdf, width = 240, height = 140, units = \\mm\\)\n  message(paste(\\3a. Boxplots Audible saved to:\\, box_aud_path))\n}\n\nif(!is.null(res_us)) {\n  box_us <- create_detailed_boxplots(res_us, \\ULTRASOUND\\)\n  box_us_path <- paste0(dirname(file_path), \\/Detailed_Boxplots_Ultrasound_240mm.pdf\\)\n  ggsave(box_us_path, box_us, device = cairo_pdf, width = 240, height = 140, units = \\mm\\)\n  message(paste(\\3b. Boxplots Ultrasound saved to:\\, box_us_path))\n}\n```\n```\n\n<!-- rnb-source-end -->\n"} -->


<!-- rnb-source-begin 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-->

```r
```r
# 6. Output 1: Excel Table (Detailed Stats + Pairwise)
# ------------------------------------------------------------------------------
wb <- createWorkbook()

if(!is.null(res_aud)) {
  addWorksheet(wb, \Audible_Summary\)
  writeData(wb, \Audible_Summary\, res_aud$stats)
  addStyle(wb, \Audible_Summary\, createStyle(textDecoration = \bold\), rows = 1, cols = 1:ncol(res_aud$stats))
  
  addWorksheet(wb, \Audible_Pairwise\)
  writeData(wb, \Audible_Pairwise\, res_aud$pairwise)
  addStyle(wb, \Audible_Pairwise\, createStyle(textDecoration = \bold\), rows = 1, cols = 1:ncol(res_aud$pairwise))
}

if(!is.null(res_us)) {
  addWorksheet(wb, \Ultrasound_Summary\)
  writeData(wb, \Ultrasound_Summary\, res_us$stats)
  addStyle(wb, \Ultrasound_Summary\, createStyle(textDecoration = \bold\), rows = 1, cols = 1:ncol(res_us$stats))
  
  addWorksheet(wb, \Ultrasound_Pairwise\)
  writeData(wb, \Ultrasound_Pairwise\, res_us$pairwise)
  addStyle(wb, \Ultrasound_Pairwise\, createStyle(textDecoration = \bold\), rows = 1, cols = 1:ncol(res_us$pairwise))
}

excel_path <- paste0(dirname(file_path), \/Statistical_Results_Detailed_With_Pairwise.xlsx\)
saveWorkbook(wb, excel_path, overwrite = TRUE)
message(paste(\1. Excel saved with Pairwise comparisons to:\, excel_path))

# 7. Output 2: Heatmap Comparison (PDF 170mm)
# ------------------------------------------------------------------------------
df_plot_combined <- bind_rows(res_aud$heatmap_data, res_us$heatmap_data)

if(nrow(df_plot_combined) > 0) {
  df_plot_combined$Dimension <- factor(df_plot_combined$Dimension, levels = c(\Dim.1\, \Dim.2\, \Dim.3\, \Dim.4\))
  # Note: Adjust labels here if you want full text
  df_plot_combined$Category <- factor(df_plot_combined$Category, levels = c(\AUDIBLE\, \ULTRASOUND\))
  
  font_family <- \serif\
  
  p_heat <- ggplot(df_plot_combined, aes(x = Dimension, y = Subsample, fill = Significance_Letter)) +
    geom_tile(color = \white\, lwd = 0.5) +
    geom_text(aes(label = Significance_Letter), 
              family = font_family, fontface = \bold\, size = 5, color = \black\) +
    facet_wrap(~Category, ncol = 2) +
    scale_fill_viridis_d(option = \plasma\, direction = -1, begin = 0.1, end = 0.9, name = \Sig. Group\) +
    labs(x = \FAMD Dimension\, y = \Subsampling Strategy\) +
    theme_minimal(base_family = font_family, base_size = 12) +
    theme(
      strip.text = element_text(face = \bold\, size = 11),
      axis.text = element_text(color = \black\),
      axis.title = element_text(face = \bold\),
      panel.grid = element_blank(),
      panel.border = element_rect(color = \black\, fill = NA, linewidth = 0.5),
      legend.position = \right\
    )
  
  heatmap_path <- paste0(dirname(file_path), \/Heatmap_Comparison_170mm.pdf\)
  ggsave(heatmap_path, p_heat, device = cairo_pdf, width = 170, height = 90, units = \mm\)
  message(paste(\2. Heatmap PDF saved to:\, heatmap_path))
}

# 8. Output 3: Detailed Boxplots (UPDATED SIZE & FONT)
# ------------------------------------------------------------------------------
# Width = 240mm, Height = 140mm
# Font: Times New Roman, Size 12+ (handled in function via base_size=14)

if(!is.null(res_aud)) {
  box_aud <- create_detailed_boxplots(res_aud, \AUDIBLE\)
  box_aud_path <- paste0(dirname(file_path), \/Detailed_Boxplots_Audible_240mm.pdf\)
  ggsave(box_aud_path, box_aud, device = cairo_pdf, width = 240, height = 140, units = \mm\)
  message(paste(\3a. Boxplots Audible saved to:\, box_aud_path))
}

if(!is.null(res_us)) {
  box_us <- create_detailed_boxplots(res_us, \ULTRASOUND\)
  box_us_path <- paste0(dirname(file_path), \/Detailed_Boxplots_Ultrasound_240mm.pdf\)
  ggsave(box_us_path, box_us, device = cairo_pdf, width = 240, height = 140, units = \mm\)
  message(paste(\3b. Boxplots Ultrasound saved to:\, box_us_path))
}
```
```

<!-- rnb-source-end -->


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<!-- rnb-text-begin -->


# 4 OBJECTIVE 4: LANDSCAPE REPRESENTATION

Specific objective 4: Determine the influence of landscape heterogeneity on the performance and representativeness of soundscape information obtained using tessellation-based sampling strategies


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<!-- rnb-chunk-begin -->


<!-- rnb-output-begin {"data":"\n<!-- rnb-source-begin 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-->\n\n```r\nlibrary(tidyverse)\nlibrary(readxl)\nlibrary(ggpubr)\nlibrary(openxlsx)  # For exporting to Excel\n\n# Load Excel file\ndf <- read_excel(\\#/DF3_Subsampling_Heterogeneity_Comparison.xlsx\\)\n\n# Filter valid values\ndf_filtered <- df %>% filter(!is.na(L_proxy_mean))\n\n# Extract unique localities\nlocalities <- unique(df_filtered$Locality)\n\n# Set PDF output path\noutput_pdf <- \\#/Comparative_Plots_Lproxy_mean_POLYPOLES_ALLTOGETHER.pdf\\\npdf_opened <- FALSE\n\n# Initialize results table\nstatistical_results <- data.frame(\n  Locality = character(),\n  Method = character(),\n  Procedence = character(),\n  KS_statistic = numeric(),\n  KS_p_value = numeric(),\n  Wasserstein = numeric(),\n  stringsAsFactors = FALSE\n)\n\nfor (loc in localities) {\n  cat(\\\\n\\n📍 Locality:\\, loc, \\\\n\\)\n\n  df_loc <- df_filtered %>% filter(Locality == loc)\n  full_vals <- df_loc %>% filter(Tessell == \\complete\\) %>% pull(L_proxy_mean)\n\n  for (method in unique(df_loc$Tessell)) {\n    if (method == \\complete\\) next\n\n    df_sub_all <- df_loc %>% filter(Tessell == method & Procedence %in% c(\\Examinated\\, \\Poles\\))\n    if (nrow(df_sub_all) == 0) next\n\n    # Compute statistics by procedence\n    for (proc in c(\\Examinated\\, \\Poles\\)) {\n      df_sub <- df_loc %>% filter(Tessell == method, Procedence == proc)\n      if (nrow(df_sub) == 0) next\n\n      sub_vals <- df_sub$L_proxy_mean\n      n <- min(length(full_vals), length(sub_vals))\n      ks <- ks.test(full_vals, sub_vals)\n      wass <- mean(abs(sort(full_vals)[1:n] - sort(sub_vals)[1:n]))\n\n      cat(\\🔎 Method:\\, method, \\| Procedence:\\, proc, \\\\n\\)\n      cat(\\   KS p-value:\\, ks$p.value, \\\\n\\)\n      cat(\\   KS statistic:\\, ks$statistic, \\\\n\\)\n      cat(\\   Approx. Wasserstein distance:\\, round(wass, 4), \\\\n\\)\n\n      statistical_results <- rbind(statistical_results, data.frame(\n        Locality = loc,\n        Method = method,\n        Procedence = proc,\n        KS_statistic = as.numeric(ks$statistic),\n        KS_p_value = as.numeric(ks$p.value),\n        Wasserstein = round(wass, 4),\n        stringsAsFactors = FALSE\n      ))\n    }\n\n    # Generate density and Q–Q plots\n    if (!pdf_opened) {\n      pdf(output_pdf, width = 14, height = 6)\n      pdf_opened <- TRUE\n    }\n\n    # Density plot\n    p1 <- ggplot(\n      df_loc %>% filter((Tessell == method & Procedence %in% c(\\Examinated\\, \\Poles\\)) | Tessell == \\complete\\),\n      aes(x = L_proxy_mean, fill = interaction(Tessell, Procedence, drop = TRUE))\n    ) +\n      geom_density(alpha = 0.4) +\n      theme_minimal() +\n      labs(title = \\Density Plot\\,\n           x = \\L_proxy_mean\\, y = \\Density\\, fill = \\Tessellation / Procedence\\)\n\n    # Q–Q plot\n    qq_dfs <- list()\n    for (proc in c(\\Examinated\\, \\Poles\\)) {\n      df_sub <- df_loc %>% filter(Tessell == method, Procedence == proc)\n      if (nrow(df_sub) == 0) next\n\n      sub_vals <- df_sub$L_proxy_mean\n      n <- min(length(full_vals), length(sub_vals))\n\n      qq_dfs[[proc]] <- data.frame(\n        complete = sort(full_vals)[1:n],\n        sub = sort(sub_vals)[1:n],\n        Procedence = proc\n      )\n    }\n    qq_df_all <- bind_rows(qq_dfs)\n\n    p2 <- ggplot(qq_df_all, aes(x = complete, y = sub, color = Procedence)) +\n      geom_point(alpha = 0.6) +\n      geom_abline(slope = 1, intercept = 0, color = \\black\\, linetype = \\dashed\\) +\n      theme_minimal() +\n      labs(title = \\Q–Q Plot\\, x = \\Complete Distribution\\, y = method)\n\n    combined <- ggarrange(p2, p1, ncol = 2, widths = c(1, 1),\n                          labels = c(\\A\\, \\B\\),\n                          common.legend = TRUE, legend = \\right\\)\n\n    combined <- annotate_figure(combined,\n                                 top = text_grob(paste0(loc, \\ / \\, method, \\ / Comparison with Examinated & Poles\\),\n                                                 face = \\bold\\, size = 14))\n\n    print(combined)\n  }\n}\n\nif (pdf_opened) {\n  dev.off()\n  cat(\\\\n✅ PDF successfully generated with all comparisons and statistics.\\n\\)\n} else {\n  cat(\\\\n⚠️ No plots were generated. Please check available data.\\n\\)\n}\n\n# Save statistical results to Excel\noutput_excel <- \\#/Comparative_Stats_Lproxy_mean_POLYPOLES_ALLTOGETHER.xlsx\\\nwrite.xlsx(statistical_results, output_excel)\n\n```\n\n<!-- rnb-source-end -->\n"} -->

library(tidyverse)
library(readxl)
library(ggpubr)
library(openxlsx)  # For exporting to Excel

# Load Excel file
df <- read_excel(\#/DF3_Subsampling_Heterogeneity_Comparison.xlsx\)

# Filter valid values
df_filtered <- df %>% filter(!is.na(L_proxy_mean))

# Extract unique localities
localities <- unique(df_filtered$Locality)

# Set PDF output path
output_pdf <- \#/Comparative_Plots_Lproxy_mean_POLYPOLES_ALLTOGETHER.pdf\
pdf_opened <- FALSE

# Initialize results table
statistical_results <- data.frame(
  Locality = character(),
  Method = character(),
  Procedence = character(),
  KS_statistic = numeric(),
  KS_p_value = numeric(),
  Wasserstein = numeric(),
  stringsAsFactors = FALSE
)

for (loc in localities) {
  cat(\\n\n📍 Locality:\, loc, \\n\)

  df_loc <- df_filtered %>% filter(Locality == loc)
  full_vals <- df_loc %>% filter(Tessell == \complete\) %>% pull(L_proxy_mean)

  for (method in unique(df_loc$Tessell)) {
    if (method == \complete\) next

    df_sub_all <- df_loc %>% filter(Tessell == method & Procedence %in% c(\Examinated\, \Poles\))
    if (nrow(df_sub_all) == 0) next

    # Compute statistics by procedence
    for (proc in c(\Examinated\, \Poles\)) {
      df_sub <- df_loc %>% filter(Tessell == method, Procedence == proc)
      if (nrow(df_sub) == 0) next

      sub_vals <- df_sub$L_proxy_mean
      n <- min(length(full_vals), length(sub_vals))
      ks <- ks.test(full_vals, sub_vals)
      wass <- mean(abs(sort(full_vals)[1:n] - sort(sub_vals)[1:n]))

      cat(\🔎 Method:\, method, \| Procedence:\, proc, \\n\)
      cat(\   KS p-value:\, ks$p.value, \\n\)
      cat(\   KS statistic:\, ks$statistic, \\n\)
      cat(\   Approx. Wasserstein distance:\, round(wass, 4), \\n\)

      statistical_results <- rbind(statistical_results, data.frame(
        Locality = loc,
        Method = method,
        Procedence = proc,
        KS_statistic = as.numeric(ks$statistic),
        KS_p_value = as.numeric(ks$p.value),
        Wasserstein = round(wass, 4),
        stringsAsFactors = FALSE
      ))
    }

    # Generate density and Q–Q plots
    if (!pdf_opened) {
      pdf(output_pdf, width = 14, height = 6)
      pdf_opened <- TRUE
    }

    # Density plot
    p1 <- ggplot(
      df_loc %>% filter((Tessell == method & Procedence %in% c(\Examinated\, \Poles\)) | Tessell == \complete\),
      aes(x = L_proxy_mean, fill = interaction(Tessell, Procedence, drop = TRUE))
    ) +
      geom_density(alpha = 0.4) +
      theme_minimal() +
      labs(title = \Density Plot\,
           x = \L_proxy_mean\, y = \Density\, fill = \Tessellation / Procedence\)

    # Q–Q plot
    qq_dfs <- list()
    for (proc in c(\Examinated\, \Poles\)) {
      df_sub <- df_loc %>% filter(Tessell == method, Procedence == proc)
      if (nrow(df_sub) == 0) next

      sub_vals <- df_sub$L_proxy_mean
      n <- min(length(full_vals), length(sub_vals))

      qq_dfs[[proc]] <- data.frame(
        complete = sort(full_vals)[1:n],
        sub = sort(sub_vals)[1:n],
        Procedence = proc
      )
    }
    qq_df_all <- bind_rows(qq_dfs)

    p2 <- ggplot(qq_df_all, aes(x = complete, y = sub, color = Procedence)) +
      geom_point(alpha = 0.6) +
      geom_abline(slope = 1, intercept = 0, color = \black\, linetype = \dashed\) +
      theme_minimal() +
      labs(title = \Q–Q Plot\, x = \Complete Distribution\, y = method)

    combined <- ggarrange(p2, p1, ncol = 2, widths = c(1, 1),
                          labels = c(\A\, \B\),
                          common.legend = TRUE, legend = \right\)

    combined <- annotate_figure(combined,
                                 top = text_grob(paste0(loc, \ / \, method, \ / Comparison with Examinated & Poles\),
                                                 face = \bold\, size = 14))

    print(combined)
  }
}

if (pdf_opened) {
  dev.off()
  cat(\\n✅ PDF successfully generated with all comparisons and statistics.\n\)
} else {
  cat(\\n⚠️ No plots were generated. Please check available data.\n\)
}

# Save statistical results to Excel
output_excel <- \#/Comparative_Stats_Lproxy_mean_POLYPOLES_ALLTOGETHER.xlsx\
write.xlsx(statistical_results, output_excel)

````

```r
library(tidyverse)
library(readxl)
library(ggpubr)
library(openxlsx)  # For exporting to Excel

# Load Excel file
df <- read_excel(\#/DF3_Subsampling_Heterogeneity_Comparison.xlsx\)

# Filter valid values
df_filtered <- df %>% filter(!is.na(L_proxy_mean))

# Extract unique localities
localities <- unique(df_filtered$Locality)

# Set PDF output path
output_pdf <- \#/Comparative_Plots_Lproxy_mean_POLYPOLES_ALLTOGETHER.pdf\
pdf_opened <- FALSE

# Initialize results table
statistical_results <- data.frame(
  Locality = character(),
  Method = character(),
  Procedence = character(),
  KS_statistic = numeric(),
  KS_p_value = numeric(),
  Wasserstein = numeric(),
  stringsAsFactors = FALSE
)

for (loc in localities) {
  cat(\\n\n📍 Locality:\, loc, \\n\)

  df_loc <- df_filtered %>% filter(Locality == loc)
  full_vals <- df_loc %>% filter(Tessell == \complete\) %>% pull(L_proxy_mean)

  for (method in unique(df_loc$Tessell)) {
    if (method == \complete\) next

    df_sub_all <- df_loc %>% filter(Tessell == method & Procedence %in% c(\Examinated\, \Poles\))
    if (nrow(df_sub_all) == 0) next

    # Compute statistics by procedence
    for (proc in c(\Examinated\, \Poles\)) {
      df_sub <- df_loc %>% filter(Tessell == method, Procedence == proc)
      if (nrow(df_sub) == 0) next

      sub_vals <- df_sub$L_proxy_mean
      n <- min(length(full_vals), length(sub_vals))
      ks <- ks.test(full_vals, sub_vals)
      wass <- mean(abs(sort(full_vals)[1:n] - sort(sub_vals)[1:n]))

      cat(\🔎 Method:\, method, \| Procedence:\, proc, \\n\)
      cat(\   KS p-value:\, ks$p.value, \\n\)
      cat(\   KS statistic:\, ks$statistic, \\n\)
      cat(\   Approx. Wasserstein distance:\, round(wass, 4), \\n\)

      statistical_results <- rbind(statistical_results, data.frame(
        Locality = loc,
        Method = method,
        Procedence = proc,
        KS_statistic = as.numeric(ks$statistic),
        KS_p_value = as.numeric(ks$p.value),
        Wasserstein = round(wass, 4),
        stringsAsFactors = FALSE
      ))
    }

    # Generate density and Q–Q plots
    if (!pdf_opened) {
      pdf(output_pdf, width = 14, height = 6)
      pdf_opened <- TRUE
    }

    # Density plot
    p1 <- ggplot(
      df_loc %>% filter((Tessell == method & Procedence %in% c(\Examinated\, \Poles\)) | Tessell == \complete\),
      aes(x = L_proxy_mean, fill = interaction(Tessell, Procedence, drop = TRUE))
    ) +
      geom_density(alpha = 0.4) +
      theme_minimal() +
      labs(title = \Density Plot\,
           x = \L_proxy_mean\, y = \Density\, fill = \Tessellation / Procedence\)

    # Q–Q plot
    qq_dfs <- list()
    for (proc in c(\Examinated\, \Poles\)) {
      df_sub <- df_loc %>% filter(Tessell == method, Procedence == proc)
      if (nrow(df_sub) == 0) next

      sub_vals <- df_sub$L_proxy_mean
      n <- min(length(full_vals), length(sub_vals))

      qq_dfs[[proc]] <- data.frame(
        complete = sort(full_vals)[1:n],
        sub = sort(sub_vals)[1:n],
        Procedence = proc
      )
    }
    qq_df_all <- bind_rows(qq_dfs)

    p2 <- ggplot(qq_df_all, aes(x = complete, y = sub, color = Procedence)) +
      geom_point(alpha = 0.6) +
      geom_abline(slope = 1, intercept = 0, color = \black\, linetype = \dashed\) +
      theme_minimal() +
      labs(title = \Q–Q Plot\, x = \Complete Distribution\, y = method)

    combined <- ggarrange(p2, p1, ncol = 2, widths = c(1, 1),
                          labels = c(\A\, \B\),
                          common.legend = TRUE, legend = \right\)

    combined <- annotate_figure(combined,
                                 top = text_grob(paste0(loc, \ / \, method, \ / Comparison with Examinated & Poles\),
                                                 face = \bold\, size = 14))

    print(combined)
  }
}

if (pdf_opened) {
  dev.off()
  cat(\\n✅ PDF successfully generated with all comparisons and statistics.\n\)
} else {
  cat(\\n⚠️ No plots were generated. Please check available data.\n\)
}

# Save statistical results to Excel
output_excel <- \#/Comparative_Stats_Lproxy_mean_POLYPOLES_ALLTOGETHER.xlsx\
write.xlsx(statistical_results, output_excel)

```