BERT-FRIDE: An Efficient Approach for Front-End Issue Detection and Extraction from User Reviews

BERT-FRIDE: An Efficient Approach for Front-End Issue Detection and Extraction from User Reviews | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article BERT-FRIDE: An Efficient Approach for Front-End Issue Detection and Extraction from User Reviews Muhammad Sohaib This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9141663/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract According to recent research, app Stores like Google Play, Apple AppStore, and Windows Phone Store contain more than 5 million applications. The reviews provided by users on these platforms contain valuable information that can assist developers in enhancing their apps. The large volume of daily app reviews and their noisy nature make it difficult to extract front-end design issues. Accurately classification of reviews is the only viable option for achieving this goal. We proposed the BERT-FRIDE approach to effectively classify user reviews and extract valuable interface design insights. To achieve this objective, we developed a comprehensive dataset by utilizing our groundbreaking zero-shot annotation technique. The dataset contains nine specific categories of interface design issues. Furthermore, we proposed a tailored loss function to address challenges like underperforming classes, assigning higher weights as needed, resulting in an outstanding reported accuracy of 98%. Additionally, we compare our approach with state-of-the-art deep learning models such as XLNet, RoBERTa, and GPT-2, demonstrating superior performance. Our proposed approach has the potential to greatly benefit front-end developers and improve the overall quality of their products. Front-End Design Reviews Deep Learning Zero-shot annotation User Review Classification Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION As of January 2024, the top app stores had over 2 million distinct apps that were made specifically for mobile devices like smartphones and tablets [1]. On a monthly basis, the Apple app Store experiences approximately 1 billion downloads. Once users have downloaded and used an app, they have the option to rate and review it. These user reviews offer a collective knowledge to app developers, enabling them to better understand the requirements and concerns of their user base during software updates and evolution [2,3]. Incorporating user feedback is crucial for enhancing applications and retaining existing users, as well as attracting new ones. However, it can be difficult to accurately extract relevant data from these reviews. As a result, a range of techniques and tools have been developed to automate the process of extracting valuable insights from reviews while minimizing the need for human involvement. In a study conducted by Maalej and Nabil [4], user reviews were categorized into four distinct groups, namely bug reports, feature requests, user experiences, and ratings. The classification was achieved through a combination of various techniques, such as review metadata, text classification, natural language processing (NLP), and sentiment analysis. Dekhtyar and Fong [5], identify the functional requirements (FR) and non-functional requirements (NFR), allowing software specialists to quickly locate which requirements interest them without having to read the entire software requirements specification (SRS). Previous research studies [6,7] discovered phrases in user reviews that were not related to interface design. To address this issue, these studies followed a two-step approach: first, they extracted the features, and then they estimated the polarity for each feature using either manual rules or supervised machine learning methods. However, due to the potentially ambiguous nature of sentences, it is crucial to carefully consider the context of the reviews to ensure the effectiveness of the identification process. The primary objective of our research is to provide developers with a reliable approach to automatically classify user reviews based on front-end design problems. We tackled a major challenge, which is the subjectivity present in user review content, The use of different labels by various reviewers for an item can result in dissimilar opinions or understandings, leading to uncertainty in classification. Another challenge we tackled is underperforming classes, where certain classes have more data samples than others, leading to biased model predictions. We proposed loss function to assign the higher weights to the underperforming classes. We developed a classification system that can accurately identify issues related to Accessibility, Uniformity, Interactivity, Readability, Content, Color Scheme, Navigation, Screen Size, and Loading Speed. Overall, our model led to significant improvements in the accuracy and efficiency of interface issue classification. Our system has the potential to revolutionize the way developers approach interface design issues, and help them create more accessible and efficient applications. The contribution of this paper as follows: We proposed a BERT-FRIDE a modified BERT model, for accurately classification of nine interface design factors. We also proposed a loss function the assign the higher weights to the underperforming classes leading to accurate classification. We prepared a dataset of 2398 front-end user reviews sourced from both an open-source project and the Google-Play-Scraper API. We proposed a zero-shot annotation algorithm to obtain precise labels for each user review. This approach effectively addresses subjectivity and facilitates precise classification of user reviews while reducing the number of required annotations. We ensure the effectiveness of BERT-FRIDE model by comparing with the state of art XLNet, RoBERTa, and GPT-2 models. We conducted experiments for each front-end review category to compare accuracy, recall, and f1 score. There are several sections in the paper. The summary of relevant studies in the area is provided in Section 2. The user reviews classification techniques used in this study are described in Section 3. Section 4 provides an explanation of the experimental findings; Section 5 discusses the limitations of our work. Section 6 brings the paper to a close by outlining conclusion and potential directions for further investigation. 2. RELATED WORK Many researchers have used various techniques to classify their reviews [8], [9], [10], [11], and [12]. Reviews classified in studies as complaints, bug reports, or feature requests. However, a lot of these studies don't focus on problems with app interface design. According to [13], the suggested method divided user reviews into four categories of non-functional requirements, including dependability, usability, portability, and performance. The authors used four classification methods—BoW, TF-IDF, CHI2, and AUR-BoW—along with three machine learning algorithms—Naive Bayes, J48, and Bagging—to categorize user reviews and assess the functional measures of the classification outcomes across all potential method-algorithm combinations. The study found that the AUR-BoW method coupled with Bagging yielded the most favorable results among all the possible method-algorithm combinations, with a precision rate of 71.4%, recall rate of 72.3%, and an F-measure of 71.8%. The drawback of this study is that it does not account for ambiguity in precisely classifying Non-Functional Requirements (NFRs) and Functional Requirements (FRs). Using information retrieval (TF-IDF) and natural language processing (NLP) methods, the authors of [14] developed a method for automatically classifying user reviews into functional and non-functional requirements. They also investigated the impact of user review size on classification accuracy. Hu and Liu [15], used data mining and natural language processing strategies to perform feature-based summarization of customer reviews for a product as part of their research. In order to determine whether a positive or negative sentiment was expressed, they extracted the opinion sentences from each review. C. Iacob et al. [16], design a prototype of framework that extract feature requests and bug reports from online reviews and make it available to developer for the maintenance of app. Many tools have been developed that can retrieve information from online reviews for the developers, but there is no tool available that can directly available to the developers. To reduce this gap, they presented a framework that can integrate tool and make it directly available to the developers. Fu et al. [17], developed the Wis-Com system, which examined 13 million Google Play reviews and provided summaries at the per-review, per-app, or per-market level. This system is useful for gathering large amounts of data. The system, however, has limitations because a large sample of reviews is required. The Wis-Com system can summarize reviews as either "complaints" or "praise," and can provide accurate results within a fixed time period. It also helps to identify why users may dislike an app. The authors found that there was a significant difference in the types of complaints between paid and free apps, with paid apps receiving complaints about pricing while free apps did not [17]. Kent Blake et al. [18] used different methods for distinguishing between truthful and deceptive reviews. They used the Naive Bayes, SVM, Multi-layer Perceptron algorithms with multiple combination of features (Unigrams, Bigrams, and Deep Syntax). They found best results using unigrams, bigrams, and PCFG lexicalized production rules with the Naive Bayes algorithm. They found that model performed the best when it is trained and tested on positive and negative combined features (88% on Naive Bayes). Zhou et al.'s study [19] looked into the use of social media to solicit feedback from a large number of users. They investigated the use of machine learning techniques to more accurately detect rumours. The authors considered the challenges presented by rumour classification, rumour dissemination, and deception when creating these systems. They also considered how these methods might be applied to develop useful frameworks for assisting individuals in determining how to assess the accuracy of information they obtain from various social media platforms. Vosoughi et al. [20] investigated rumour characteristics by examining three aspects of online information dissemination: linguistic style, characteristics of individuals involved in spreading information, and network propagation subtleties. They tested their proposed algorithm on 209 rumours and 938,806 tweets gathered from real-world events. They aimed to introduce a new approach to evaluating style-similarity between different textual contents, utilizing various machine learning models and achieving a 51% accuracy in fake news detection. Kaur et al. [21] used a deep learning technique to detect extremism in online content automatically. Using specialized annotators, they labelled the data as radical, non-radical, or irrelevant based on specific characteristics such as endorsing terrorism, anti-national discourse, and inciting others. Word2Vec was used to generate word embeddings, and LSTM was used to classify the content. In addition, to detect radical content, the authors used machine learning algorithms such as SVM, random forest, and Max Entropy. The proposed method had an 85.9% precision and could be improved with a CNN layer for more accurate feature identification. 3. USER REVIEWS CLASSIFICATION TECHNIQUES This section investigates various aspects of classifying user reviews. It begins by exploring how the subjectivity of reviews influences their classification (Section 3.1). Then, it introduces the Zero-Short Annotation algorithm (Section 3.2). The paper further details the architecture of the BERT-FRIDE model (Section 3.3) and discusses a novel loss function designed to enhance model performance (Section 3.4). Additionally, the paper provides insights into the dataset used (Section 3.5) and outlines the preprocessing steps undertaken (Section 3.6). Section 3.7 describes the evaluation methodology employed to ensure robust analysis. Finally, Section 3.8 explain the classification process. 3.1. Subjectivity of reviews In the context of classification, subjectivity refers to the variation in labels that can be assigned to an item by different reviewers due to differing opinions or interpretations. For example, the sentence "This is a light app" may be labeled as referring to either loading speed (LS) or color scheme (CS) depending on how the word "light" is interpreted (Table 1 ). Table 1 Subjectivity in Multi-Class Classification: Example of Label Variation Sentence Possible Label Possible Sense This is a light app. Loading Speed (LS) app not weighing a lot. It’s too light Color Scheme (CS) app that uses colors that is too light This subjectivity presents a challenge for developing accurate classification models, as it can make it difficult to predict the correct label for a given item. To address this challenge, it is important to consider factors that may influence subjectivity, such as the reviewer's background and preferences, the context of the item being reviewed, and the language used in the review. To address the subjectivity of reviews, we establish clear criteria for each category and provide training to the annotators to ensure consistent labeling (Table 2 ). This helps the model learn more effectively. Table 2 Description of each Category Categories Representation Description Color Scheme CS The colors used in the app, important for visual appeal. Uniformity U Consistency in design elements like icons and fonts throughout the app. Loading Speed LS How fast does content load on a single page. Content C Quality and relevance of the information presented in the app. Interactivity I Features that engage users, like animations or clickable elements. Screen Size SS App's ability to adapt to different device screens. Readability R How easily users can read and understand app content. Accessibility A Ensuring the app is usable to users that have vision problems and blindness? Navigation N Ease of finding features or information within the app. 3.2. Zero-shot annotation algorithm We proposed a zero-shot annotation algorithm. This algorithm helps to obtain more robust and accurate labels for each user review. Our methodology can help to overcome the challenge of annotation and ensuring the dataset is comprehensive and accurate. The Fig. 1 present the zero-shot- annotation algorithm that is used to assign labels to a collection of text data. The input to the algorithm is a dataset, which is a collection of text data and candidate_labels, which is a list of possible labels for the text data. The output of the algorithm is labeled_data, which is a collection of the text data with assigned labels. In step 1, the Hugging-Face library was used to load the Bart-Large MNLI model as a zero-shot classification pipeline. In step 2, a loop is used to iterate through the dataset's text_data. The zero-shot classification pipeline is used for each text_data to predict the probabilities for each candidate_label. The label with the highest probability is then chosen as the top_label. The labelled data is then appended to the labeled_data collection as a tuple that includes the text_data and its top_label. Finally, the labeled_data collection is returned as the output of the algorithm. In general, the algorithm employs zero-shot classification as a means of assigning labels to a set of textual data, by utilizing a predefined list of potential labels. It selects the label with the highest probability for each text_data and returns the labeled_data as the output. 3.3. BERT-FRIDE Model Architecture Our approach utilizing a pre-trained BERT-base model that incorporates 110 million parameters and has already undergone training on a large corpus of text to achieve this goal. To make it task-specific, a dense layer is added to the pre-trained BERT-base model for text classification, which contains nine output neurons, one for each category. Finally, the softmax activation function is applied in the last layer to generate the probabilities of each class (Fig. 3 ). In order to perform text classification on nine categories, we use a labeled dataset in which each sample is assigned to one of the nine categories. During the initial training phase, we freeze the pre-trained BERT-base model's parameters and only update the task-specific layer's parameters. This process allows the model to adapt to the specific classification task while still retaining the knowledge gained from pre-training. This approach is a powerful and efficient method for addressing front-end issues across nine categories, enabling the model to utilize knowledge from a large text corpus while adapting to the specific classification task. Figure 2 illustrates the BERT-FRIDE model pipeline, to address front-end issue in text classification across nine distinct categories. Additionally, positional encoding and self-attention mechanisms in the context of the BERT-FRIDE architecture, allowing the model to understand the sequential order of tokens and capture both local and global dependencies within the text effectively. 3.3.1. Positional Encoding Positional encoding is typically implemented using sine and cosine functions of different frequencies. The positional encoding matrix PE for an input sequence of length N and embedding dimension 𝑑 model ​ is calculated as follows: $$\:\text{P}\text{E}(pos,2i)=\text{s}\text{i}\text{n}\left(\frac{pos}{{1000}^{2i/{d}_{model}}}\text{}\right)$$ $$\:\text{P}\text{E}(pos,2i+1)=\text{c}\text{o}\text{s}\left(\frac{pos}{{1000}^{2i/{d}_{model}}}\text{}\right)$$ Where “pos” is the position of the token in the sequence. “i” is the index of the embedding dimension. 3.3.2. Self-Attention Mechanism The self-attention mechanism computes attention scores for each token pair in the input sequence. Let Q, K, and V be the query, key, and value matrices respectively, obtained from the input embeddings. The scaled dot-product attention score Attention(Q,K,V) is calculated as follows: $$\:Attention(Q,K,V)=\left(\frac{\left(Q{K}^{T}\right)}{\sqrt{{d}_{k}}}\right)V$$ Where d k ​ is the dimension of the key vectors. QK T represents the dot product between the query and key matrices. softmax applies the softmax function along the rows to compute attention weights. These attention scores are then used to compute the weighted sum of the value vectors, yielding the output of the self-attention mechanism. 3.4. Proposed Loss function In this thesis, we proposed a novel loss function to improve the performance of underperforming classes in the classification task. The loss function incorporates class weights, which are assigned based on the inverse class frequencies. By assigning higher weights to the underperforming classes, the importance of correctly predicting instances from these classes is emphasized during the training process. This approach aims to mitigate the imbalance in the dataset and improve the overall accuracy of the model. The proposed loss function adjusts the standard cross-entropy loss by multiplying it with the respective class weights. By incorporating these class weights, the model is encouraged to pay more attention to the underrepresented classes and optimize its predictions accordingly. Experimental results demonstrate the effectiveness of the proposed loss function in improving the performance of the underperforming classes, leading to enhanced precision, recall, and F1-score values for these classes. The proposed approach provides a valuable contribution to addressing the challenges associated with imbalanced datasets and can be applied in various domains where the accurate classification of underperforming classes is crucial. The proposed loss function can be defined as follows: $$\:Loss=-{\sum\:}_{i}\left[\frac{{Weight}_{i}*{Y}_{i}*\text{log}\left(\widehat{{Y}_{i}}\right)}{{\sum\:}_{i}\left({Weight}_{i}*{Y}_{i}\right)+\in\:}\right]$$ Where \(\:{Weight}_{i}\) is the class weight for class i, \(\:{Y}_{i}\) is the ground truth label for class i, \(\:\widehat{{Y}_{i}}\) is the predicted probability for class i, \(\:\in\:\:\) is a small constant (e.g., 1e-5) added to avoid division by zero. The loss function takes into account the class weights \(\:{Weight}_{i}\) assigned to each class, the ground truth labels \(\:{Y}_{i}\) , and the predicted probabilities \(\:\widehat{{Y}_{i}}\) . It penalizes the model for misclassifying instances from the underperforming classes, as the class weights amplify the loss contribution from these classes. By adjusting the loss function in this manner, the model is encouraged to prioritize accurate predictions for the underrepresented classes, leading to improved performance. 3.5. Description of Experimental Materials We collected user reviews from the popular open-source project [22] and some using Google-Play-Scraper API. These comments were gathered from thousands of users all over the world. We prepared a dataset containing 2398 reviews that were manually examined for the presence of front-end reviews. The data is distributed among the classes as follows: Content (622), Interactivity (542), Readability (376), Color Scheme (252), Uniformity (189), Accessibility (176), Screen Size (166), Navigation (44), and Loading Speed (31), as shown in Fig. 4 . 3.6. Pre-processing Table 3. Improved Performance of BERT-FRIDE Model with Random Oversampling Technique and Custom Loss Function We pre-process the data to make sure that it is in a format that the model can understand. We make use of the natural language toolkit (NLTK) to pre-process the data and remove noise and irrelevant information. This pre-processing phase includes several important steps. First, case normalization is carried out to transform the entire text to lowercase, which aids in avoiding feature duplication. Stop-word removal is the next step, where words that do not aid in classification such as "am," "are," and "the" are removed. The noise removal stage removes any data that impair classification performance or confuse the model during learning, including numeric data, email IDs, and special characters. Then tokenization is performed split the natural text into representative sets of words. Encoding is then used to map each token to a unique integer value using a custom vocabulary. Padding and truncation are then employed to ensure that the input sequences are of a fixed length. To indicate the beginning and end of input sequences and to identify unknown words and padding, special tokens such as [CLS] (starting position of every sentence) and [SEP] (ending position of every sentence) are included in the sequences. 3.7. Evaluation Procedure We evaluate the efficacy of our methods using common metrics such as Accuracy, Precision, Recall, and F1 score. We evaluate the model's performance using metrics such as true negatives, true positives, false positives, and false negatives. The true negative rate is defined as the number of times the model correctly identified the absence of a specific condition or characteristic in a sample. True positives correspond to number of instances where the model correctly identified the presence of a particular condition or characteristic in a sample. False positives arise when the model incorrectly predicts the presence of a condition or characteristic in a sample when it is not present, potentially leading to false alarms. False negatives arise when the model incorrectly predicts the absence of a condition or characteristic in a sample when it is present, resulting in missed opportunities for intervention or diagnosis. Accuracy = \(\:\frac{\text{T}\:\text{P}\:+\text{T}\:\text{N}}{\text{T}\:\text{P}\:+\text{T}\:\text{N}+\text{F}\:\text{P}\:+\text{F}\:\text{N}}\) (1) Recall = \(\:\frac{\text{T}\:\text{P}}{\text{T}\:\text{P}\:+\text{F}\:\text{N}}\) (2) Precision = \(\:\frac{\text{T}\:\text{P}\:}{\text{T}\:\text{P}+\text{F}\:\text{P}\:}\) (3) F1-Score = \(\:\frac{2.\text{P}.\text{R}}{\text{P}+\text{R}}\) (4) 3.8. User Reviews Classification Process Figure 5 depicts the entire process for classifying the user reviews automatically. This process is broken down into six distinct phases, each with its own set of guidelines and techniques. Phase 1: Input User reviews : The initial step in the process involved collecting user reviews from a well-known open-source project, as explained in section 3.5. Phase 2: Pre-process User Reviews : In this phase, we manually analyzed each sentence in the user reviews and categorized them into front-end types (Accessibility, Uniformity, Interactivity, Readability, Content, Color Scheme, Navigation, Screen Size, and Loading Speed). We then preprocessed the user review sentences by converting them to lowercase, eliminating stop words, and removing noise. Phase 3: Token Embedding Generation : The third phase involved generating token embeddings for every token in the input sequence to prepare the input for the BERT-FRIDE model. The token embeddings capture the meaning and context of each token in a high-dimensional vector format. The BERT-FRIDE model uses self-attention mechanisms to learn contextualized representations of each token based on their relationships to other tokens in the sequence. Phase 4: User Reviews Augmentation : To address underrepresented classes in the dataset, a machine learning random oversampling technique and loss function is used. Phase 5: Task-Specific Training : In this phase, the BERT-FRIDE model trained on our dataset specific to the downstream task. Tuning of hyperparameters, such as learning rate and batch size, was carried out to achieve optimal performance. The objective was to optimize the BERT-FRIDE model for the downstream task and enabling it to make accurate predictions on unseen and new data. Phase 6: Model Evaluation : In the following phase, the performance of the model was evaluated using a variety of metrics, including accuracy, precision, recall, and F1 score. These metrics highlight areas for improvement and show how well the model can predictions n new, unforeseen data. 4. EXPERIMENTAL RESULTS In this section we present a comprehensive discussion of our experimental results and highlight their significance and relevance. Section 4.1. showed the evaluation of model on our dataset. Section 4.2, explain the model result with random oversampling technique. The Section 4.3 express the model results by addressed the underrepresented classes using our loss function. This helped to reduce biased model predictions, resulting in more accurate classification results. In section 4.4, we also compare the performance of our model with the other state of art deep learning models. 4.1. Evaluation of Model We evaluate the performance of our model using various performance factors on our dataset. We used the scikit-learn "classification report" function in python to calculate the weighted averaged precision, recall, and F1-score. By leveraging these metrics, our purpose is to provide a comprehensive understanding of our proposed approach in classifying user reviews. The number of false positives, true positives, and false negatives is used to calculate the recall, precision, and F1 score for each class. The support column displays the number of instances of each class in the test set. The Table 3, showed the BERT-FRIDE model results with our dataset. For example, the precision for the class "Content" is 0.99, which means that 99% of the instances classified as "Content" were actually related to content. The recall is 0.96, indicating that the model correctly identified 96% of the instances related to Content. The F1-score is 0.97, which is a harmonic mean of precision and recall and a measure of the model's overall performance in that class. The support for "Content" is 631, indicating that there were 631 Content instances in the test set. Overall, our model achieved 94% of accuracy. 4.2. Model results with random oversampling To enhance the performance of our BERT-FRIDE model, we implemented the machine learning random oversampling technique to balance the samples, we evaluated its performance using the classification report, as presented in Table 3. The accuracy of our model was also increased to 96%. This successful outcome demonstrates the effectiveness of the Random Oversampling technique in improving the accuracy of our model, as it overcame the challenge of the imbalanced dataset and enhanced the performance of model. Our evaluation of the BERT-FRIDE model has shown substantial improvements in its performance. Initially, we evaluated the BERT-FRIDE Model on an imbalanced dataset, achieving an overall accuracy of 94%. However, after implementing oversampling technique to address the challenges for underperforming classes, we observed a significant increase in accuracy to 96%. This cumulative improvement in accuracy is a testament to the effectiveness of the technique employed to overcome class imbalance and refine the performance of the model. Our efforts to address the underperforming classes by analyzing misclassified samples and implementing targeted measures have further contributed to model accuracy. 4.3. Model results with loss function To enhance the performance of our BERT-FRIDE model, by incorporating the loss function. We evaluated its performance using the classification report, as presented in Table 3. The accuracy of BERT-FRIDE model increase to 98%. This successful outcome demonstrates the effectiveness of the loss function in improving the accuracy of our model, as it overcame the challenge of the underperforming classes. Our evaluation of the BERT-FRIDE model has shown substantial improvements in its performance. 4.4. Comparison with state of art We conducted a comprehensive evaluation of our BERT-FRIDE model in comparison to other cutting-edge deep learning models such as XLNet, RoBERTa, and GPT-2, using our dataset. Our findings revealed a consistent outperformance of our BERT-FRIDE model over the other deep learning models as showed in Table 4 . Table 4 Results of different deep learning models for text classification in the context of front-end design Issue Model Accuracy Recall F1-Score XLNet 0.77 0.70 0.77 RoBERTa 0.86 0.86 0.87 GPT-2 0.89 0.89 0.89 BERT-FRIDE 0.94 0.92 0.93 BERT-FRIDE+ Random Oversampling 0.96 0.96 0.96 BERT-FRIDE + Loss Function 0.98 0.97 0.98 These models are evaluated based on three crucial metrics: accuracy, recall, and F1 score. XLNet demonstrates a respectable accuracy of 0.77, with a corresponding recall score of 0.70, and an F1 score of 0.77. RoBERTa improves upon XLNet's performance with an accuracy of 0.86, a recall score of 0.86, and an F1 score of 0.87, indicating enhanced precision and the ability to capture relevant instances. GPT-2 further elevates the metrics with an accuracy and recall score of 0.89, along with an F1 score of 0.89, underscoring its effectiveness in accurately categorizing instances. However, BERT-FRIDE surpasses all others, boasting an accuracy of 0.94, a recall score of 0.92, and an F1 score of 0.93, indicating superior precision and the ability to identify a higher proportion of relevant instances. By incorporating random oversampling, achieves even better performance, with an accuracy of 0.96, a recall score of 0.96, and an F1 score of 0.96. Notably, the enhanced BERT-FRIDE model, using a proposed loss function, achieves remarkable performance improvements, with an accuracy of 0.98, a recall score of 0.97, and an F1 score of 0.98. These findings highlight BERT-FRIDE effectiveness in text classification tasks related to front-end design issues and underscore the significance of tailored optimization strategies, such as the specialized loss function, in achieving state-of-the-art results. 5. THREATS TO VALIDITY This section outlines potential risks associated with the study, provides a discussion of each, and proposed strategies to mitigate them. Internal Validity Internal validity of our study could have been threatened by the labelling the app review set by the authors because it might not accurately reflect how the development teams interpreted user feedback. Future research might involve closer author and development team collaboration to ensure a more accurate representation of the issues addressed in the dataset in order to lessen this. External Validity The external validity in our study was the limited dataset consisting of design-related reviews for open-source Android applications. While the dataset provides valuable insights into front-end design issues, it may not cover the full spectrum of problems and considerations that mobile app users have. To enhance external validity, future research could expand the dataset to include reviews from a more diverse range of mobile applications, thereby increasing the generalizability of the findings. Construct Validity The construct validity in our study was the potential for misinterpreting the construct of front-end design issues or failing to account for other factors that contribute to user comments. To address this, future research could refine the definition of front-end design issues and consider other factors that may impact user comments, such as app back-end functionality or user demographics. Additionally, we acknowledge that our investigation focused solely on English-language evaluations of open-source mobile apps, which may limit the transferability of the conclusions to commercially produced projects or reviews composed in different languages. Therefore, future research should consider the language and commercial context of mobile applications in their evaluation to increase the generalizability of the findings. 6. CONCLUSION AND FUTURE WORK In conclusion, our study proposed a novel automated approach for classifying front-end design issues in mobile applications using a dataset of 2398 samples across nine categories. Our model achieved an impressive 98% accuracy by incorporating loss function. This breakthrough represents a significant advancement in the field and has practical implications for product development, paving the way for more efficient and effective identification and resolution of wfront-end design issues in mobile applications. Despite the success of our approach, we acknowledge that our study has limitations, including the restricted dataset and language bias. Nevertheless, our findings have significant implications for software developers as they can use this approach to gauge user satisfaction with app design and make necessary adjustments to enhance user experience. In the future, we intend to improve the front-end review selection and analysis process by increasing the number of keywords and the sample size to address the limitation of not identifying all design problems. Additionally, we aim to develop an algorithm that can take these design issues and directly send them as an issue or improvement to the development Git site, facilitating the immediate integration of user feedback into the development environment. These findings are valuable to software developers as they provide insights into whether users are satisfied or dissatisfied with their app's design, enabling them to make necessary adjustments for better user experience. Declarations Ethical Approval: This study did not involve human participants, data, or tissue; therefore, ethical approval was not required. Competing Interests: The author declares no competing interests. Funding: No funding was received for this study. Author Contribution The author solely contributed to the conceptualization, methodology, analysis, and manuscript preparation. Data Availability The datasets that support the conclusions of this article are provided within the article itself. References Number of apps available in leading app stores as of January 2024. https://www.statista.com/statistics/266210/number-of-available-applications-in-the-google-play-store/, last accessed 2024/05/13. D. P. a. W. Maalej, "User feedback in the Appstore: an empirical study," presented at the In Proceedings of the 21st IEEE International Requirements Engineering Conference (RE’13), 2013. P. A. P. Liang, K. He, and L. Xu, "From collective knowledge to intelligence: pre-requirements analysis of large and complex systems," presented at the In Proceedings of the 1st Workshop on Web 2.0 for Software Engineering (Web2SE’10), ACM, 2010. W. M. a. H. Nabil, " Bug report feature request or simply praise? On automatically classifying app reviews," presented at the In Proceedings of the 23rd IEEE International Requirements Engineering Conference (RE’15), 2015. D. A. a. Fong, "RE Data Challenge: Requirements Identification with Word2Vec and TensorFlow," presented at the IEEE 25th International Requirements Engineering Conference (RE) Lisbon, 2017. L. B. Zhai Z, Xu H, Jia P, "Clustering product features for opinion mining. ," presented at the Fourth ACM International Conference on Web Search and Data Mining, Hong Kong, China, New York, NY, USA:, February 9–12, 2011. G. Z. Liu Q, Liu B, Zhang Y, "Automated rule selection for aspect extraction in opinion mining," in The 24th International Conference on Artificial Intelligence, July 25–31, 2015: AAAI Press, pp. 1292–1297. A. S. A. Ciurumelea, S. Panichella, and H. C. Gall, "Analyzing reviews and code of mobile apps for better release planning," presented at the In IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER), 2017. S. P. A. Di Sorbo, C. V. Alexandru, C. A. Visaggio, and G. Canfora, "Surf: summarizer of user reviews feedback," presented at the In IEEE/ACM 39th International Confer-ence on Software Engineering Companion (ICSE-C), 2017. N. A. S. McIlroy, H. Khalid, and A. E. Hassan, "Analyzing and automatically labelling the types of user issues that are raised in mobile app reviews," presented at the Empiri-cal Software Engineering, 2016. A. D. S. S. Panichella, E. Guzman, C. A. Visaggio, G. Canfora, and H. C. Gall, "How can i improve my app? classifying user reviews for software maintenance and evolu-tion," presented at the IEEE international conference on software maintenance and evo-lution (ICSME), 2015. G. G. L. Pelloni, A. Ciurumelea, S. Panichella, F. Palomba, and H. C. Gall. Becloma, "Augmenting stack traces with user review information," presented at the IEEE 25th In-ternational Conference on Software Analysis, Evolution and Reengineering (SANER), 2018. H. Y. a. P. Liang, "Automatic Classification of Non-Functional Requirements from Augmented App User Reviews," in In Proceedings of the 21st International Conference on Evaluation and Assessment in Software Engineering (EASE'17), 2017, pp. 344–353. H. Y. a. P. Liang, "Identification and Classification of Requirements from App User Reviews," presented at the In Proceedings of the 27th International Conference on Software Engineering and Knowledge Engineering (SEKE'15), 2015. M. H. a. B. Liu, "Mining and summarizing customer reviews," presented at the In Pro-ceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '04. ACM, 2004. H. R. Iacob C., Faily S, "Online Reviews as First Class Artifacts in Mobile App Devel-opment," presented at the Mobile Computing, Applications, and Services. MobiCASE 2013. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Tel-ecommunications Engineering, 2014. j. L. B. Fu, L. Li, C. Faloutsos, J. Hong, and N. Sadeh, "Why people hate your app: Making sense of user feedback in a mobile app store," in 19th ACM SIGKDD Interna-tional Conference on Knowledge Discovery and Data Mining KDD ’13, pp. 1276–1284. A. D. Kent Blake, A. Glassford, "Detecting Deceptive Opinion Spam," presented at the Stanford University. CS 224U, https://alecglassford.com/assets/detecting-deceptive-opinion-spam.pdf, last accessed 2023/02/21. Zhou X, Zafarani R (2018) Fake news: a survey of research, detection methods, and opportunities. arXiv:arXiv-1812 Vosoughi S, ’Neo Mohsenvand M, Roy D (2017) Rumor gauge: Predicting the veracity of rumors on Twitter. ACM Trans Knowl Discov Data (TKDD) 11(4):1–36 Kaur A, Saini JK, Bansal D (2019) Detecting radical text over online media using deep learning. CoRR. arXiv:1907.12368 L. O. Marcelo Medeiros Eler, and Alberto Dumont Alves Oliveira, " Do Android app users care about accessibility? an analysis of user reviews on the Google play store," in 18th Brazilian Symposium on Human Factors in Computing Systems (IHC '19), 2019, vol. 23. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9141663","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":608622076,"identity":"93185de8-a113-4d4f-aa25-96f4ff98f5e2","order_by":0,"name":"Muhammad Sohaib","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAq0lEQVRIiWNgGAWjYHACNgYGgwNycDbRWoxJ1cJwILGBaC26/YePPfhRcCd9w+3eBwwfyg4T1mJ2Iy3dsMfgWe6GO8cNGGecI0oLj5kEj8Hh3A030hiYeduI0XL+jJnkH4PD6QYgLX+J0nIgx0waaEsCWAsjUVpupKVJyxg8M5wJ1HKw51w6MQ47fEzyzZ878nw30hgf/CizJqwFBRwgUf0oGAWjYBSMAlwAAKXyPZZ202FOAAAAAElFTkSuQmCC","orcid":"","institution":"University of Nevada Reno","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Sohaib","suffix":""}],"badges":[],"createdAt":"2026-03-16 20:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9141663/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9141663/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105563344,"identity":"f49a4baf-3b19-42d6-96ec-0bd96dc86a34","added_by":"auto","created_at":"2026-03-27 12:46:45","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":239807,"visible":true,"origin":"","legend":"\u003cp\u003eZero-short annotation algorithm\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9141663/v1/d980faa53c71f864cc0144d7.jpeg"},{"id":105151648,"identity":"71d842df-7a78-4df0-ba6b-a4e88f943bcf","added_by":"auto","created_at":"2026-03-22 15:33:34","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":434064,"visible":true,"origin":"","legend":"\u003cp\u003eThe BERT-FRIDE pipeline predicts user reviews by converting each comment into a 256-token embedding, using the CLS token as input to nine softmax classifiers, and determining the highest probability for the predicted class.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9141663/v1/cba88a2e7f6d42bef5627ee2.jpeg"},{"id":105563681,"identity":"ea62b986-69f3-420d-8d11-d9cfe5b89961","added_by":"auto","created_at":"2026-03-27 12:47:26","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":202025,"visible":true,"origin":"","legend":"\u003cp\u003eBERT Model Layer Modification: FRIDE Tuning\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9141663/v1/8652267c50ac8d9473a5f41f.jpeg"},{"id":105563714,"identity":"566477c2-b415-40dd-9eba-b5e4c26f2a69","added_by":"auto","created_at":"2026-03-27 12:47:33","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":123036,"visible":true,"origin":"","legend":"\u003cp\u003eDataset Overview\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9141663/v1/ebc81289d3412726c7548ed7.jpeg"},{"id":105151650,"identity":"5c8e4025-5952-4e20-9e2c-b11567d3be97","added_by":"auto","created_at":"2026-03-22 15:33:34","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":64329,"visible":true,"origin":"","legend":"\u003cp\u003eUser Reviews Classification Process Phases\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9141663/v1/e47d344a16c56dc56003de9e.jpg"},{"id":106728653,"identity":"b1b5481d-dc4d-48ca-af75-9212edf007a0","added_by":"auto","created_at":"2026-04-12 18:43:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2008728,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9141663/v1/f029c5fe-bb9c-4517-8e3a-0cf3b716ba7e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"BERT-FRIDE: An Efficient Approach for Front-End Issue Detection and Extraction from User Reviews","fulltext":[{"header":"1.\tINTRODUCTION","content":"\u003cp\u003eAs of January 2024, the top app stores had over 2\u0026nbsp;million distinct apps that were made specifically for mobile devices like smartphones and tablets [1]. On a monthly basis, the Apple app Store experiences approximately 1\u0026nbsp;billion downloads. Once users have downloaded and used an app, they have the option to rate and review it. These user reviews offer a collective knowledge to app developers, enabling them to better understand the requirements and concerns of their user base during software updates and evolution [2,3]. Incorporating user feedback is crucial for enhancing applications and retaining existing users, as well as attracting new ones. However, it can be difficult to accurately extract relevant data from these reviews. As a result, a range of techniques and tools have been developed to automate the process of extracting valuable insights from reviews while minimizing the need for human involvement. In a study conducted by Maalej and Nabil [4], user reviews were categorized into four distinct groups, namely bug reports, feature requests, user experiences, and ratings. The classification was achieved through a combination of various techniques, such as review metadata, text classification, natural language processing (NLP), and sentiment analysis. Dekhtyar and Fong [5], identify the functional requirements (FR) and non-functional requirements (NFR), allowing software specialists to quickly locate which requirements interest them without having to read the entire software requirements specification (SRS). Previous research studies [6,7] discovered phrases in user reviews that were not related to interface design. To address this issue, these studies followed a two-step approach: first, they extracted the features, and then they estimated the polarity for each feature using either manual rules or supervised machine learning methods. However, due to the potentially ambiguous nature of sentences, it is crucial to carefully consider the context of the reviews to ensure the effectiveness of the identification process.\u003c/p\u003e \u003cp\u003eThe primary objective of our research is to provide developers with a reliable approach to automatically classify user reviews based on front-end design problems. We tackled a major challenge, which is the subjectivity present in user review content, The use of different labels by various reviewers for an item can result in dissimilar opinions or understandings, leading to uncertainty in classification. Another challenge we tackled is underperforming classes, where certain classes have more data samples than others, leading to biased model predictions. We proposed loss function to assign the higher weights to the underperforming classes. We developed a classification system that can accurately identify issues related to Accessibility, Uniformity, Interactivity, Readability, Content, Color Scheme, Navigation, Screen Size, and Loading Speed. Overall, our model led to significant improvements in the accuracy and efficiency of interface issue classification. Our system has the potential to revolutionize the way developers approach interface design issues, and help them create more accessible and efficient applications. The contribution of this paper as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eWe proposed a BERT-FRIDE a modified BERT model, for accurately classification of nine interface design factors.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWe also proposed a loss function the assign the higher weights to the underperforming classes leading to accurate classification.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWe prepared a dataset of 2398 front-end user reviews sourced from both an open-source project and the Google-Play-Scraper API.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWe proposed a zero-shot annotation algorithm to obtain precise labels for each user review. This approach effectively addresses subjectivity and facilitates precise classification of user reviews while reducing the number of required annotations.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWe ensure the effectiveness of BERT-FRIDE model by comparing with the state of art XLNet, RoBERTa, and GPT-2 models. We conducted experiments for each front-end review category to compare accuracy, recall, and f1 score.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThere are several sections in the paper. The summary of relevant studies in the area is provided in Section 2. The user reviews classification techniques used in this study are described in Section 3. Section 4 provides an explanation of the experimental findings; Section 5 discusses the limitations of our work. Section 6 brings the paper to a close by outlining conclusion and potential directions for further investigation.\u003c/p\u003e"},{"header":"2.\tRELATED WORK","content":"\u003cp\u003eMany researchers have used various techniques to classify their reviews [8], [9], [10], [11], and [12]. Reviews classified in studies as complaints, bug reports, or feature requests. However, a lot of these studies don't focus on problems with app interface design. According to [13], the suggested method divided user reviews into four categories of non-functional requirements, including dependability, usability, portability, and performance. The authors used four classification methods\u0026mdash;BoW, TF-IDF, CHI2, and AUR-BoW\u0026mdash;along with three machine learning algorithms\u0026mdash;Naive Bayes, J48, and Bagging\u0026mdash;to categorize user reviews and assess the functional measures of the classification outcomes across all potential method-algorithm combinations. The study found that the AUR-BoW method coupled with Bagging yielded the most favorable results among all the possible method-algorithm combinations, with a precision rate of 71.4%, recall rate of 72.3%, and an F-measure of 71.8%. The drawback of this study is that it does not account for ambiguity in precisely classifying Non-Functional Requirements (NFRs) and Functional Requirements (FRs).\u003c/p\u003e \u003cp\u003eUsing information retrieval (TF-IDF) and natural language processing (NLP) methods, the authors of [14] developed a method for automatically classifying user reviews into functional and non-functional requirements. They also investigated the impact of user review size on classification accuracy. Hu and Liu [15], used data mining and natural language processing strategies to perform feature-based summarization of customer reviews for a product as part of their research. In order to determine whether a positive or negative sentiment was expressed, they extracted the opinion sentences from each review. C. Iacob et al. [16], design a prototype of framework that extract feature requests and bug reports from online reviews and make it available to developer for the maintenance of app. Many tools have been developed that can retrieve information from online reviews for the developers, but there is no tool available that can directly available to the developers. To reduce this gap, they presented a framework that can integrate tool and make it directly available to the developers.\u003c/p\u003e \u003cp\u003eFu et al. [17], developed the Wis-Com system, which examined 13\u0026nbsp;million Google Play reviews and provided summaries at the per-review, per-app, or per-market level. This system is useful for gathering large amounts of data. The system, however, has limitations because a large sample of reviews is required. The Wis-Com system can summarize reviews as either \"complaints\" or \"praise,\" and can provide accurate results within a fixed time period. It also helps to identify why users may dislike an app. The authors found that there was a significant difference in the types of complaints between paid and free apps, with paid apps receiving complaints about pricing while free apps did not [17].\u003c/p\u003e \u003cp\u003eKent Blake et al. [18] used different methods for distinguishing between truthful and deceptive reviews. They used the Naive Bayes, SVM, Multi-layer Perceptron algorithms with multiple combination of features (Unigrams, Bigrams, and Deep Syntax). They found best results using unigrams, bigrams, and PCFG lexicalized production rules with the Naive Bayes algorithm. They found that model performed the best when it is trained and tested on positive and negative combined features (88% on Naive Bayes). Zhou et al.'s study [19] looked into the use of social media to solicit feedback from a large number of users. They investigated the use of machine learning techniques to more accurately detect rumours. The authors considered the challenges presented by rumour classification, rumour dissemination, and deception when creating these systems. They also considered how these methods might be applied to develop useful frameworks for assisting individuals in determining how to assess the accuracy of information they obtain from various social media platforms. Vosoughi et al. [20] investigated rumour characteristics by examining three aspects of online information dissemination: linguistic style, characteristics of individuals involved in spreading information, and network propagation subtleties. They tested their proposed algorithm on 209 rumours and 938,806 tweets gathered from real-world events. They aimed to introduce a new approach to evaluating style-similarity between different textual contents, utilizing various machine learning models and achieving a 51% accuracy in fake news detection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKaur et al. [21] used a deep learning technique to detect extremism in online content automatically. Using specialized annotators, they labelled the data as radical, non-radical, or irrelevant based on specific characteristics such as endorsing terrorism, anti-national discourse, and inciting others. Word2Vec was used to generate word embeddings, and LSTM was used to classify the content. In addition, to detect radical content, the authors used machine learning algorithms such as SVM, random forest, and Max Entropy. The proposed method had an 85.9% precision and could be improved with a CNN layer for more accurate feature identification.\u003c/p\u003e"},{"header":"3.\tUSER REVIEWS CLASSIFICATION TECHNIQUES","content":"\u003cp\u003eThis section investigates various aspects of classifying user reviews. It begins by exploring how the subjectivity of reviews influences their classification (Section 3.1). Then, it introduces the Zero-Short Annotation algorithm (Section 3.2). The paper further details the architecture of the BERT-FRIDE model (Section 3.3) and discusses a novel loss function designed to enhance model performance (Section 3.4). Additionally, the paper provides insights into the dataset used (Section 3.5) and outlines the preprocessing steps undertaken (Section 3.6). Section 3.7 describes the evaluation methodology employed to ensure robust analysis. Finally, Section 3.8 explain the classification process.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Subjectivity of reviews\u003c/h2\u003e \u003cp\u003eIn the context of classification, subjectivity refers to the variation in labels that can be assigned to an item by different reviewers due to differing opinions or interpretations. For example, the sentence \"This is a light app\" may be labeled as referring to either loading speed (LS) or color scheme (CS) depending on how the word \"light\" is interpreted (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubjectivity in Multi-Class Classification: Example of Label Variation \u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSentence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePossible Label\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePossible Sense\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThis is a light app.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLoading Speed (LS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eapp not weighing a lot. It\u0026rsquo;s too light\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColor Scheme\u003c/p\u003e \u003cp\u003e(CS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eapp that uses colors that is too light\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis subjectivity presents a challenge for developing accurate classification models, as it can make it difficult to predict the correct label for a given item. To address this challenge, it is important to consider factors that may influence subjectivity, such as the reviewer's background and preferences, the context of the item being reviewed, and the language used in the review. To address the subjectivity of reviews, we establish clear criteria for each category and provide training to the annotators to ensure consistent labeling (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This helps the model learn more effectively.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of each Category\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eRepresentation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eColor Scheme\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eThe colors used in the app, important for visual appeal.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eUniformity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eConsistency in design elements like icons and fonts throughout the app.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLoading Speed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHow fast does content load on a single page.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContent\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eQuality and relevance of the information presented in the app.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInteractivity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFeatures that engage users, like animations or clickable elements.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScreen Size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eApp's ability to adapt to different device screens.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReadability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHow easily users can read and understand app content.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccessibility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEnsuring the app is usable to users that have vision problems and blindness?\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNavigation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eEase of finding features or information within the app.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Zero-shot annotation algorithm\u003c/h2\u003e \u003cp\u003eWe proposed a zero-shot annotation algorithm. This algorithm helps to obtain more robust and accurate labels for each user review. Our methodology can help to overcome the challenge of annotation and ensuring the dataset is comprehensive and accurate. The Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e present the zero-shot- annotation algorithm that is used to assign labels to a collection of text data. The input to the algorithm is a dataset, which is a collection of text data and candidate_labels, which is a list of possible labels for the text data. The output of the algorithm is labeled_data, which is a collection of the text data with assigned labels. In step 1, the Hugging-Face library was used to load the Bart-Large MNLI model as a zero-shot classification pipeline. In step 2, a loop is used to iterate through the dataset's text_data. The zero-shot classification pipeline is used for each text_data to predict the probabilities for each candidate_label. The label with the highest probability is then chosen as the top_label. The labelled data is then appended to the labeled_data collection as a tuple that includes the text_data and its top_label. Finally, the labeled_data collection is returned as the output of the algorithm. In general, the algorithm employs zero-shot classification as a means of assigning labels to a set of textual data, by utilizing a predefined list of potential labels. It selects\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ethe label with the highest probability for each text_data and returns the labeled_data as the output.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. BERT-FRIDE Model Architecture\u003c/h2\u003e \u003cp\u003eOur approach utilizing a pre-trained BERT-base model that incorporates 110\u0026nbsp;million parameters and has already undergone training on a large corpus of text to achieve this goal. To make it task-specific, a dense layer is added to the pre-trained BERT-base model for text classification, which contains nine output neurons, one for each category. Finally, the softmax activation function is applied in the last layer to generate the probabilities of each class (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In order to perform text classification on nine categories, we use a labeled dataset in which each sample is assigned to one of the nine categories. During the initial training phase, we freeze the pre-trained BERT-base model's parameters and only update the task-specific layer's parameters. This process allows the model to adapt to the specific classification task while still retaining the knowledge gained from pre-training. This approach is a powerful and efficient method for addressing front-end issues across nine categories, enabling the model to utilize knowledge from a large text corpus while adapting to the specific classification task. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the BERT-FRIDE model pipeline, to address front-end issue in text classification across nine distinct categories.\u003c/p\u003e \u003cp\u003eAdditionally, positional encoding and self-attention mechanisms in the context of the BERT-FRIDE architecture, allowing the model to understand the sequential order of tokens and capture both local and global dependencies within the text effectively.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Positional Encoding\u003c/h2\u003e \u003cp\u003ePositional encoding is typically implemented using sine and cosine functions of different frequencies. The positional encoding matrix PE for an input sequence of length N and embedding dimension \u0026#119889;\u003csub\u003emodel\u003c/sub\u003e​ is calculated as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{P}\\text{E}(pos,2i)=\\text{s}\\text{i}\\text{n}\\left(\\frac{pos}{{1000}^{2i/{d}_{model}}}\\text{}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{P}\\text{E}(pos,2i+1)=\\text{c}\\text{o}\\text{s}\\left(\\frac{pos}{{1000}^{2i/{d}_{model}}}\\text{}\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u0026ldquo;pos\u0026rdquo; is the position of the token in the sequence. \u0026ldquo;i\u0026rdquo; is the index of the embedding dimension.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Self-Attention Mechanism\u003c/h2\u003e \u003cp\u003eThe self-attention mechanism computes attention scores for each token pair in the input sequence. Let Q, K, and V be the query, key, and value matrices respectively, obtained from the input embeddings. The scaled dot-product attention score Attention(Q,K,V) is calculated as follows:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:Attention(Q,K,V)=\\left(\\frac{\\left(Q{K}^{T}\\right)}{\\sqrt{{d}_{k}}}\\right)V$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere d\u003csub\u003ek\u003c/sub\u003e ​ is the dimension of the key vectors. QK\u003csup\u003eT\u003c/sup\u003e represents the dot product between the query and key matrices. softmax applies the softmax function along the rows to compute attention weights. These attention scores are then used to compute the weighted sum of the value vectors, yielding the output of the self-attention mechanism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Proposed Loss function\u003c/h2\u003e \u003cp\u003eIn this thesis, we proposed a novel loss function to improve the performance of underperforming classes in the classification task. The loss function incorporates class weights, which are assigned based on the inverse class frequencies. By assigning higher weights to the underperforming classes, the importance of correctly predicting instances from these classes is emphasized during the training process. This approach aims to mitigate the imbalance in the dataset and improve the overall accuracy of the model. The proposed loss function adjusts the standard cross-entropy loss by multiplying it with the respective class weights. By incorporating these class weights, the model is encouraged to pay more attention to the underrepresented classes and optimize its predictions accordingly. Experimental results demonstrate the effectiveness of the proposed loss function in improving the performance of the underperforming classes, leading to enhanced precision, recall, and F1-score values for these classes. The proposed approach provides a valuable contribution to addressing the challenges associated with imbalanced datasets and can be applied in various domains where the accurate classification of underperforming classes is crucial. The proposed loss function can be defined as follows:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:Loss=-{\\sum\\:}_{i}\\left[\\frac{{Weight}_{i}*{Y}_{i}*\\text{log}\\left(\\widehat{{Y}_{i}}\\right)}{{\\sum\\:}_{i}\\left({Weight}_{i}*{Y}_{i}\\right)+\\in\\:}\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Weight}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the class weight for class i, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the ground truth label for class i, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{{Y}_{i}}\\)\u003c/span\u003e\u003c/span\u003e is the predicted probability for class i, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\in\\:\\:\\)\u003c/span\u003e\u003c/span\u003eis a small constant (e.g., 1e-5) added to avoid division by zero. The loss function takes into account the class weights \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Weight}_{i}\\)\u003c/span\u003e\u003c/span\u003e assigned to each class, the ground truth labels \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{i}\\)\u003c/span\u003e\u003c/span\u003e, and the predicted probabilities \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{{Y}_{i}}\\)\u003c/span\u003e\u003c/span\u003e. It penalizes the model for misclassifying instances from the underperforming classes, as the class weights amplify the loss contribution from these classes. By adjusting the loss function in this manner, the model is encouraged to prioritize accurate predictions for the underrepresented classes, leading to improved performance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Description of Experimental Materials\u003c/h2\u003e \u003cp\u003eWe collected user reviews from the popular open-source project [22] and some using Google-Play-Scraper API. These comments were gathered from thousands of users all over the world. We prepared a dataset containing 2398 reviews that were manually examined for the presence of front-end reviews. The data is distributed among the classes as follows: Content (622), Interactivity (542), Readability (376), Color Scheme (252), Uniformity (189), Accessibility (176), Screen Size (166), Navigation (44), and Loading Speed (31), as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Pre-processing\u003c/h2\u003e \u003cp\u003eTable 3. Improved Performance of BERT-FRIDE Model with Random Oversampling Technique and Custom Loss Function\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg 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ZD5Uet4byRWqe34MTPrYjWEv8+qrrzpvOBYX7Jaw9eDHi00NCwQLCMbd7BghrCz+7HxYJCAbEBVUtD179jTsPXCKoH1sYB966CHDIBqSy0BkUYKEQC5828L0xED3SjsE2AkzeUIuGSskJgUmCaQKmHkwqbMIQGBQRTLeGF9Ic5jIOacO59RnkmdSZxJi0cCGicTiijSfccSigeSVdpGyomZjvGH/hRTV/4ZI+bCHhBj7efpMGQL85rEvBVewRioUWBO7SOzXkRwhCaG/CWnFAosTCP2EzRk2ZGhiIL04kqBuY4NMf1OOzTGLNIs5dn3UYaFCMshCgxkBEUZYrCFcZcuWdfZkOHGy8fEXejZElSpVCnzEDHt85513umgakEiIC/P6vffe65xRwJk5nL5gc9exY0cDIySuYMdCTR59gZACgstvk3kdQcOQIUNc5ADmdTYhqGghtdj80T/8BjMasEjtwZHvyJzDhivwO4Kt/1tgrWMdC7zOMeseBBfHwebNmxsY0wcIaegPhDnMYw0aNLCYOIkf45r5WdFr2QAAEABJREFUEBMDfh/MYWgfaB8SRRsQKNpGG0RfMu/59cmP1MRmiDHJfN+6dWtjHkeazHoC/vz+IaMQSfrF/54XXHCBMa9AXuEqrBNcY54Ha0gpONGfbLrYMIM52jk2yQgu6Cd+N2gZWBuoj10sfcecIhtVEDnJiR0cCzs/FBKicTqZyYedjN/BTHwcUwbzABYhBhLniOj58fFVuEYeA4j6kA4kK7RLPrt4fmycMwi5F5JYJk2u04ZSZCMASaHf6U8SO1o2MXwrJhLy/LFDOcYWEwP5jAnK+efkUQeSwiQFSSJhOO8vDpicMH4Yb4xb6jPOqEs+18ljAcZIn7HrlyNfKWUIgCOY+onzwJp+Pn3o9zdjgbmAa3ny5Ikvzu+fPBYK+oM5hHM+WZwZF/QnY4e5hX7kOm3TCPaunFOeOYTEOYk2qUuiLcpn9OTPu3x/EvN6IG7MwZyDK9d9HMHex5JPcAIzsANz8ihPf/JJ4vfot8PvmDaol5ESaxTf1U+MqcDvB37+NT4hQYHXOWbsMw/54x8sGadcI4EddfnknD7z12LwJ4/yPtaMdcqQT3/47fr1yY/UxHdgTgYPUuDcAvbkMWfAQfhEYoqGBS0MawV4gTXfH60OpgKUATMS+STKMq4Dy/PboH3a9vsRfPkdkM/vhronM0W9RPVkgq97C4GUIoA0jxBXSHJI7JRRy6S0PuUgqZidsAix2JKnJASEgBAQApGFAKY/EFGEFWgEAp8eoQYmYJgsYqoSeC1Sj49OVCP1W+m5hUAGRACzAVS9fkrtV8R2FYKLSi21dVVeCAgBISAEwgMBpK/+OoD9aeBToTnjGmZEic2TAstF0rGIaiT1lp5VCAiBiEdAX0AICAEhIARSjoCIasqxUkkhIASEgBAQAkJACAiBdEQgBUQ1HZ9GtxICQkAICAEhIASEgBAQAv8hIKL6HxD6EAJCQAikGwK6kRAQAkJACKQIARHVFMGkQkJACAgBISAEhIAQEALpjcAxier69ett/fr1SsJBY0BjQGNAY0BjQGNAY0BjIKRjIDERPiZRJWajUgsTBsJAY0BjIG3HgPAUnhoDGgMaA4nHQKqJKvG4lD41YSAMNAY0BjQGNAY0BjQGNAZCOwZSTVQDK+hYCAgBISAEhIAQEAJCQAikFwLHVP2n14PoPkJACAiBKERAXzmECBw+fNh+/fVXZ0/3559/JrnT33//bVu2bHHX169fb5s3b7YDBw64crt27XL5mzZtsiNHjrg8/UkeAbD+/fffHWZ//fVX0IL0wcaNG23Dhg2WuMw///xjO3bsMPokaGVlxiOwZ88ehzNjG9z8C2AKvoxZP49PxjXjm3qcR1oSUY20HtPzCgEhIASEQIoQWLJkib366qv21ltvWefOnW379u0J6q1du9Y6duzofBAoV6NGDVu2bJlBqBo2bGiNGzc28qZOnZqgnk6SIvDTTz/Za6+9Zm+++aa9/fbbjnQGlgJTsH755Zddf7Ru3ToBWV25cqXxOtA1a9YEVtNxIgQgoz169LDXX3/dnn32WZs0aZIrQX7v3r0d/g0aNLDZs2e7/BkzZhivVa1Xr561bds2AeauQAT8ST1RjYAvpUcUAkJACAiB6EaAhXv8+PH20EMPWZ8+fWz//v02efLkBKCcc845jshic/j+++/bzTffbOeff75BlubPn+/qPfXUU9alS5cE9XSSEAEkzhMmTLAbbrjB+XMgWZ04cWKCQr/88outW7fOmjdvbj179rQpU6Y4aTaFkAoOGjTI9u3bx6nSURBAGn3rrbe6sXn//ffbZ5995kovXrzYaQ86dOhg7733nn388ceGVoDNWZs2bRxJRaK6e/duVz6S/oioRlJv6VmFgBDIkAjoS6U9AqiRd+7caZdeeql5nmdXXXWV/fDDDwluBCmFrJK5evVqO+WUUyxv3rxWtGhRJxFcunSpISl88sknKaKUDAKo/efNm2fXXnutw/DRRx+1b775JkHpM8880yBJq1atMlTTF110kcXExLgy48aNs+LFi1uePHncuf4kj8All1zicM6cObNdc801bgMG0V+0aJEbu9mzZ4//xAygYsWKli1bNvvxxx/t4osvtty5cyffeJheEVEN047RYwkBISAEhMDxI4AE9Y8//nDEiVZY2JH07d27l1OXMmXK5EgsJytWrLArrrjCyDvttNOsZcuWduONNxok7N5776WI0lEQgBRB9CkCfuvXr+cwPl1wwQVOVY16/84773RS1VNPPdWZY0Byr7/++viyOkgegSxZssRf/O2335zGgDG6detWN3Y9z3NjGltrNmuM+/bt2zuzCmxVDx48GF8/Ug4yHd+DqpYQEAJCQAgIgfBFANIEYQp8QhZ5Fu7API6xn5w1a5ZTXXNOQm0aGxtrqLSnT59OltJREDjjjDMSXD399NMTnPtq/cqVKxvSVMwpIFJ169Y1JKtDhw41pK0jRoxIUE8nwRFgEwbxxAzA8zxLPNYZ52wEqI29NaYY2K2CMXmRlDJF0sPqWYVAOCEwc+ZM++STT9L9kbZt22avvPKKu+/y5cudjRL2eC4j4E/NmjWdDRiLME4OSJO4PGDAAJs2bRqHSumAALZjderUMRYLbMSC3RLniGrVqtlHH33kVHmUQfLXtWtXqxaXj80keUopRwBV89lnn20+5tjqFSpUyCCriVuZM2eOUzvnypXLXeJ8zJgxzt4SZyqcsdwF/QmKgOd5hkoajCmAk9rVV1/NYXzCtvK7776zpk2bGuN94cKFzhb48ccfjy+jg5QhgFT022+/dRJ/zFfYlEH2GetcO3TokDF/cM1vsXDhwsb455qfFymfIqqR0lN6zrBDAAN1nDBwEkjPh8uRI4chhUC1+eKLLzqJD2qexM+AF/N5551n7dq1M54VwkoZbJZKlCjBoUF0IdzuRH/SHAE2Bzg1IJm76aabnCNJ4psgSWIMNWnSxBYsWGBjx451RSZPnmyoT+lrHIJcpv6kGAFIJ/Z62O6hGv3iiy+sTJkyQev36tXLqVD9i/y26BMkVrQRjNz6ZfVpTtVcrFgxQyptcf8go/fdd1/c0f//0wdEVIDMxsTEOEcfpK6U4/fBGL/sssusUqVK/6+ko6AIYNOLxBTMsaNmjGPji002UmqcCJGwFihQIL4+GwXWDkww4jMj5OBEiGqEfEU9phBIewSQGJQtW9bZBOEdHHgHFjkWOCYGdrhcw9gd0kFCpUgenrI4FZDnl6Mutl6UQUrK5I4ElUWTCZ565LGDzpo1q/NYRmqEfVLi+3me554PooNDicX9455+feJHdu/e3ZEh2uc5sHlix/3zzz87aWxcFf0/AQSQzF144YXOUQSPcrBl7PhN0heoOgkzw6Jz1113ubAy9D0EF2kTDhM4qQwbNsyvps8UIICtaa1atZxTD04k1atXd6p9fk9g7ePJOQl7VL9ZCO2DDz7oiC2hfugj/5o+kyLgeZ4j+swfBQsWdJJoxizzCBtjpNNgDqZVqlSxIkWK2MMPP2yXX355fGMQr5w5c5qvro6/oIMECBBm7aWXXrJGjRoZxP7555+3EiVKOCxLly5t2P8inEAbwzpxxx13GH3y3HPP2SOPPOIcrRI0GAEnmSLgGfWIQiDsECB2HRNu+fLljd2sH6QaOywkZM2bN3fG6zhkoIoZOHCgi8mII0GrVq1cGBbs3t544w2rX7++CycCiUQ1hiSUdx9/9dVXBmnFEB6J6NNPP+2CkSMZuvvuu+Mxgfh06tTJqZZRUUJyP//8c0eOWIDjC8YdUBYbMZ4fyR077759+xrx91g4iLfHjhyzAdRzcVX0/zgRgIRC/pFsIJFjY4EdH5sCv0kIqZ/Iy5Mnj4vhSR8wrvLly0e28/IlHqI70Z8UI3DWWWcZvwU895HYUREtAx7QkCb/PLGHOvn8hqlHQvJHnlLyCLBhJk4nePnOZ2zSmGP8czYLOK0xDz3zzDMJGmOsE1YJE4IEF3SSAAG0aGDsp8B5Aek0JhVz586Nr4ONNWXR0ESqw5qIanx36kAIpAwB1LkQD+x/UOcSqNonhEwIqGIgmtivQmSRmg4ZMsT69OnjJA1IzbgTO2FIJcdcQxpBW4R2YcJBIkEetnZMTkjXKBssEcy5W7du7s06qPODlUmcR3zI2267zQjAjQ0rQboJXcJCzs6chTpxHZ2nHAGIqi8p92shMSf555BXMEf1TB7XkChBVDkPTL5EPTBPx0JACAiBjI7ACRPVjA6Qvp8QSIwA6lzeqoI6BSknRu0+UcUTk/JIcojHiNoLFTu2QsSyy5Mnj91yyy0uniCkFunphx9+aNiJovbCAQGpKKrgmJgYwwAeqSqenQTLRjpHCB3a4j4k1JqQHeJBUh4JHQSZa4kT9km+GUDia5BTJMKjR4+2kiVLJr6s81QigNqNPg2sxqaD5OdBSnn7ERsZxtMTTzxhSKGCSZUYT349fQoBISAEogUBEdVo6Wl9zzRBABtDHJcwZodoQh6RgKJK5wYQSMgqRu2cQ/zIQx0DgSQPb0wIDKST9iCkOH5wDSkqQcmRnuJUgMqR1w6Sh3qft7lQLjBhdoDZACYGBNRGIhp4/WjHkCn/Os8JieW7iaj6qBz/p+d5zsuWfkdSyphAUoqKM7BVSOmGDRvcm3owvahUqZLR72xOUJPSv4SWSW7zEdiWjoWAEBACGQ0BEdWM1qP6PiFDAFUur6vDng0Cyo0gmhDD/v37GwkCipSMcqjz+YQA8lYcVOzkESkACSwhpjARIA+pKgSX1wjiRANRwZ4IooKNKnZemANgB4bzFiYBkGOkbBBn7k1oI8KPIFXlGXm2qVOnGvZKkKSRI0catmG8gQfiA0lFEvv111+7MDGc4/CAxM/zPL6e0gkigLMIZiLYSeLcAL7EkMQcZPDgwU6yTh8xBuhD3oiEgwS3rVq1qmGnjGMEYwozDfKVhIAQEALRhEDaENVoQkzfNaoRQPIJgYRI+kAgfcRGlHNIK6FZUMd7nmcQE6SUENLrrruOIu61d7SDyhcVP5mo3bF5veGGGwz1PFI32oR4ImWlDDalkBjMCCC55EFsiM8JQaYOjlnkn3vuuYbNKuYAEGUkddwTKS7enxBnyA9SW9qgDgkijLkCYUw4VzoxBBgPEE5wpf/BPrkWMSNhHPjXkahWqFDBMBdBmu7n61MICAEhEE0IiKhGU2/ru54QAoS7wXs1NjbWIIs0BgkkHAh5EBIIbP78+Y2IAHi14mxFOYgjZUilSpVyIViQYGI2QF65cuVcLEIkcHgnk0fb2LpCHDlHoobXOCpg/5y2sVnl3tg3cg4ZfeCBB4wyEB2egWPeVw7R5ZjkeZ57ZSTHqJ+RGCPVhdwSKoa2lE4cASSomHIQGiZr1qyuQTYV9D0EllBI9AHl3MX//iCJLVu2rOtHJN//ZetDCAgBIRBVCIioRlV368sKgeAIfPnll4bkF1Uzkr/gpZQrBISAELASUi0AABAASURBVBACQiB9EUhDopq+D667CQEhkHYIEEMV+9V33nkn7RpVS0JACAgBISAEThABEdUTBFDVhYAQEAIhQ0ANCwEhIASiHAER1SgfAPr6QkAICAEhIASEgBAIVwTSmqiG6/fUcwkBISAEhIAQEAJCQAhEGAIiqhHWYXpcISAEog0BfV8hIASEQPQiIKIavX2vby4EhIAQEAJCQAgIgbBGICRENay/sR5OCAgBISAEhIAQEAJCICIQEFGNiG7SQwoBIRDlCOjrCwEhIASiEgER1ajsdn1pISAEhEDGR+DgwYP29ddfW58+fYw4wcG+8YYNG6x///42dOhQ2759e3yR2bNnu3rz5s2zI0eOxOfrIDgCYD1t2jSH2fr164MW2rRpk33xxRfWr18/27lzpyuzY8cOV6dPXB+RhgwZ4vL1J3kEVq1a5TDjBS379u1zBXmr4MiRI10+OI4aNSp+PC9YsMDlL1q0yHgDoasQQX9CR1QjCAQ9qhAQAkJACGQ8BKZMmWLffvutW5y7d+9uGzduTPAlf/nlF/v000/t77//tvnz59vHH39sf/31l82ZM8dmzJhhkChegvHzzz8nqKeTpAj8+OOPNmjQIIdls2bNbPPmzQkK/f7778Yb8LZt22YrVqyw9957z/bu3WvLli2LLwfZ6t27d/y5DpIiwGZq8ODBduDAAevZs6cNHDjQFSL/t99+c8f79++34cOHG+fr16+3t99+25Xv0aOHMeZdoQj6I6IaQZ2lRxUCQiC6EdC3TzkCu3btsunTp9u9995rzz77rOXJk8e+++67BA1ASMl/5plnLDY21hFZyOzZZ59tNWrUsHr16hmLPucJKuokAQJI6ZBcly9f3qpXr26XXnqpjRs3LkGZNWvW2J9//mnVqlWzV1991SBQy5cvt4suushhD/7FihWzu+++O0E9nSREAKl0nTp17Pnnn7enn37aaQz++ecfY4yCLTg+9NBDbsNwySWXWKdOneyBBx5w5QsWLGgTJ05M2GAEnGWKgGfUIwoBISAEhIAQSBUCEFWkoxdeeKGrx6KN6jNQjb9161ZDZU2BnDlzWubMmZ0UqnDhwpYpUyYnrbrzzjvtjDPOoIhSMgiAKZLRyy67zDzPs9tvv92+//77BKX37NnjJNSnnHKKnX766VaoUCFbvXq1xcTEuHKQXUwHrr/+eneuP8ERuOqqqyxHjhwOZ8Y2YxNMzz//fDv11FNdJUwAKlSo4MYw5gFly5Z15a+88kr74YcfXJlI+hNiohpJUOhZhYAQEAJCIKMgAAH17SD977R7927zbfrIy5Ytm1PzI5EikceizyfqaexWJ0+ebL/++itZSkdBADVz4GU2AYHnp512mpNYo94nH3OLLFmycOgSGJ955pmWO3du07+UIYCtapUqVZIUxj71xhtvdBsDcA4sINV/IBo6FgJCQAgIgbRHQC2mCAGkoxCfwMJInwLJ0a233uocTp577jmrW7euQaLy5s3rqpQsWdJwSkF6hVTKZepPsgiAU+DF7NmzB55a8eLF7ayzzrIXXnjBmWKg9sdEwC+0bt06Y5NwwQUX+Fn6PAoCOAEyxkuXLp2g1OLFi51kFWzZiFEmsMC5554beBoRx5KoRkQ36SHDBQEcAbANwtYHNRd2WDhsJPd82Le99tprzo4ouTLB8nHq4B4PP/ywWyyTc+ZAOoTjQiTaHQX73hk1D6lH+/btk/16bdu2Nfq7adOm8WWQ/tWuXdvl43kef0EHKUIAUgRRxcmECkiSihQp4tT7nJMgpXig42xSqVIlQwpFP3CNdM4559jVV19tSAM5VwqOgOd5zi51y5YtrgBe5olV+JhW8BvAZvKee+6xMmXKWNGiRV15TAcgXtirCmsHyVH/sK588803hhkAtqmBhdlUPfLIIy4ra9asRj9glkEGm4ESJUpwGFEpPYhqRAGihxUCySGAvRtOF/ny5XNeq3i5dunSxZgwmDiC1Zs5c6Z17tzZebcGux4sb+7cuS5Uzk8//WSvv/66q59YjebXw1mka9euqWrfr6vP9EGgcePGbqOCB3mwO06aNMmWLFnixlSuXLmM8Dws3NiZIV1iPLDwsCkJVl95wRGAGOXPn9957/NbQh2KrR4qfkgRjj14nZOmTp1qqPhxnkKqB7nF2QfpFP0D/sHvolwQwJ63RIkSznkN3D744AO7//777dChQwZ5Ze7ESx07VfDE8eqVV16J3zTQJwsXLrQbbriB5pSOggDmLESwoAhEdPz48QZ2nGPuwvzx2GOPceoSDlZEBiCqBc6EFStWdPmR9EdENZJ6S896UhEgJAgTLx6X7PpRqSCNue+++9xz4QwwZswYQ12I0waZTLxPPPEEhy4xUTOxUMY3amdRJI9wOKNHjzYmeiRBfDL5E1KEBZcGIDssuNSHKKP2QVrHNfKojxMJ55Qlj8Q9WAwgPUg7CBMzYMAAp/akPSTD1CEheeK7ElsSGyjylI4fAaSlqJWDtYD9GFjjYc6YwusZBxM2PoTpwXsXwoW0j/iT/29DRylB4KmnnnJheVq2bOk8zcEXgkpoJH6j/IbatGnjCJW/+NMuv8cWLVpYhw4djLo4/5CvlDwCd911l/PgBzc2WTikMRd169bNhaAimgIEFoefTz75JElDRF/A4S3JBWUkQAANHsSU9QKsJ0yYYKwTFGJT0KRJEw7jU7ly5ezBBx90Ao8nn3zSaWjiL0bIgYhqhHSUHvPkI0C8ReysAm18kIBBRtnZotYi3A3xApGEchz41BBFdrt+PnZxEEsWSHa9fv4111xjkBPyWrdu7dRjnFOfBYDFdenSpc7Wy2//ww8/dIstpMdXE7/55pvuMuQUQo3UiDZZpCGxqJmpR9gYzBMIiM4izsTHdaQg77//vrGDdw3pT5ojgHqfeJMFChRwbWPnx/hhs4CajvHFBezN/H7lXCllCGCT+vLLLxu/3ZtuuslVQp3Pb47za6+91oXv4XfhLv73h80ldUh4Sv+XrY+jIADWhKYCM9TNFGXewqyCc8Yw2p+qVatyKUFi09+oUaMEeToJjgCSajD2U7t27eILxsTEmC+4iM+MO6hcubL7DVAXjUFcVkT9TzeiGlGo6GGFQBAEIA/JSVbweCUweLVq1axBgwaGIwbENbAZpLEQTWLaxcbGuniCTDYsmBAVJnmkswULFjTqohp76623DHsuVJeozlCZodZh8kfi6bf/+OOPG2YJhHxBcgGZRRLLjhvyilSDBRcJLHEleU6kS0gwkBCjasb+FikuizjfBZKKuog8/z76TFsEGBNsQAJbRZqK5D0wj2M2DnwqCQEhIASiCQER1WjqbX3XE0IAordhwwYnufQbgmRAIAitApEkrh3SAaQHnPvl+MQ0ADKIRIdzDOEhKpwjkUXdTz5EBUJMHDzIMcQVNb1fH+N5rvvmANTxQ7p4nufewoMKiGeA4GLfunLlSqN97kV9nhEVHF6hqJw9z6MZgyBBspHe8V1Rj/pqJVdAf9IUAfDH6QfbMhpmPNGXbDjoF8YH+YwlNhUcByQdCgEhIAQyPAIiqhm+i/UF0wqBhg0bugDVSDWxLUQKiVqdc8wBIBi8to77IdXEq5VjP6FyQZWLbSqEhDJ4FPvX/c9Zs2a5V+NxDpGBcEIsqQ/ZRO3PNSSdyanliUjAdQgqZXHW4fNYCdUzdmWQVL4fkmJC9hyrnq4fHwL0J6YjmFswpuhbbPXYiGDzx4YB8wDsiJGsH99dVEsICAEhELkIpC9RjVyc9ORCwIWuIVxQz549ndoeG1OIKkSDSACo13HMQK2OnSGG65BSvInx/Ids4tzRt29fVx+nJuyJcGSCUGLXGggz7ZC4hnSV+I+YDWCTRD5hXlDxY1yPrSnG9ZgWoOqH1PKaQsqRxo4da0QgIHQJjlQ46iAtHTZsmLPRQwKL0w/PiN0eJgPUI6pB4DPpOPUI0B/0MQ46bGpogb5/6aWX3FuQGAPYo4I3GwpeIUk/cE5/xcbG2m233eZC0VBXSQgIASEQTQiIqIawtyEYOLEkd4tjXU+unvJPDgKEYIFoEuMUG06clXAMQN3PEyEBg/hhV8o1JKyQPggKxJD6GLVjO4oncfPmzY24jtimQiIhwbRTqlQp955x2sEWFQKJmQDkE/KLQwLXiJ+KzSnhsfCixQkLiS5EmnsTFgmTAcoSZJtnwXkLBy2M6nGuatWqlWHzCqHGLhZbWeLEQrp5vhdffNE5dvFcSseHwB133GH0CxsE4nTSCh7RSOiRxBM7EntkxhTjwjfpwNSEPqBfEnvy0oaf9CkEhIAQyMgIiKiGsHfxnCYcx+eff24sUokTJAGCEMJHUNNpjACSrjx58hjelSTsPf1beJ5nSFbJz549u8tGdc85iQzsQomNyTkkBfJ63nnnGefkUwZ7VaSn5JEwF6Ac17BbhNiQDxnF05Zjkn8vnoH7BLaBbap/HWJNm9ThmHYgqBAk6nF/2uA617iv0vEjgDkF+JL88UK/gTF40zJlwJt+4ZxEn/tjg34nT0kICAEhEG0InASiGl0Q45zCW4NQveLh/cYbbxjHpOTeNhRdCOnbCgEhIASEgBAQAkIgOAIiqsFxSbNcbMsgqCRIauB5YpvENLupGhICQiC6ENC3FQJCQAhkUAREVEPcsXj1+rcgmLp/zKcfkoZjJSEgBISAEBACQkAICIGECJwsoprwKTLoGXZleHwT4odYm3j/Tps2zQj9g1c4TjWKjZhBO19fSwgIASEgBISAEDhhBERUTxjC5BsgRiaOKXj38no4vLMJZYSnOF7XhKThHbzJt6ArQkAICIGUIqByQkAICIGMh4CIaoj7FM9+VP4EdycsEPEpCflDCCBCCuFpHeJHUPNCQAgIASEgBISAEIhIBE4qUY1IxFL50OXLlzcCvBOi6qGHHrK8efPaI488YgTxJjZmKptTcSEgBISAEBACQkAIRA0CIqoh7OrFixcbUlTU/7wSkVck7t+/3/xEkPeOHTuG8AnUtBAQAlGGgL6uEBACQiBDISCiGsLu3Lt3r/GGH95SxBuGEifeGLRmzZoQPoGaFgJCQAgIASEgBIRA5CJw8olq5GKXoifnzT/FihWzSy+91HjTzKxZs9wx5zhWpagRFRICQkAICIFUI4CwAK1WtWrVbPr06UHrz50716pXr24vv/yy4UsQWOiXX34xBArUD8zXcVIEwLpXr14GVsm9cXHBggVWv359e+6558x/4c2mTZtcHeqRcD5O2rpyEiNw4MABI3IQmOGYzfX+/fs7LFu2bMlpfMJP5t1337VVq1bF50XSgYhqiHsLO1TsUUn33HOPFSlSxNmncn733XeH+O5qXggIgWhEQN/5XwRGjRrlDni5Ci9d+fHHH925/2ft2rXWr18/R1KLFi1q77//vu3atctd/umnn+zRRx+1woULO7LqMvUnWQTmzZtny5Yts2bNmhkOwytXrkxQdvPmzTaYi+x1AAAQAElEQVRy5EiH6R133GF16tSx3bt3G1pFNgMkCCzhHBNU1EkSBDAfbNq0qTGe2WAhEPvuu+8M80JwnDFjhn3xxRfx9RCWQWJ//fXX+LxIOhBRDXFvIUU9cuSIEdyfXQ27IAbZ4cOHQ3xnNS8EhIAQiF4E/vjjD1u6dKmVK1fOLr74YrvuuuuMuNaBiMyfP99KlCjhrt9+++3meZ6tX7/eDh06ZB9++KEjVaVLlzaIQGA9HSdEgPWM8IsIXyD2jz/+uPmbBPvvH7iecsopds0119jDDz9sZ599tuHHAYmKiYmxmLi0ZcsWg8T+V0UfySAwZMgQQ9rfoEEDu+CCC4yY7Who2VhddNFFVqpUKTv33HPN/8cGgrdi+ueR9hkmRDXSYEv5827fvt3tGtnhfPvtt7Zjxw7jB80nQf9T3pJKCgEhIASEQEoR2LNnj/GiFX/BJuLKihUrHAn120B6ylzMOW8RRLDA+cKFC238+PGGFLBSpUpG1BbKKAVHAGHM6tWrHWmiBDHEE6v/9+3bZ6x5kFrP81xZ8D3//POp4oQ5S5YssSuuuMKd609wBBjTaAjy5ctnNWvWdKYUlDzjjDNs27Zt1r17d4PIZiTCn4kvqBQaBM455xzbuXOnDRs2zO1unnzySSOOKjtOJKyoPNjNh+bualUICIGoRiDKvzxSURb1QBiwo2Tu9fNy5cplX331lVOZUp78LFmy2IQJE4zQgi+99JK988471rx5cydk4LpScAQg+IFXWPsCz7Nly2aYAyA1JR/iCrnimEQ+mwWfuJKnlBQByDxjlFjsmLPwtks0A35JNmaM8zFjxvhZEf8pohrCLsRhqnfv3oadKqqjyy+/3EgQ1dy5czuDcshrCB9BTQsBISAEohIBFvPs2bMn+O6omwkV6GdWrFjRxbbGPvXaa691Dj5IqrCT5GUsefLksZIlS9rWrVuNxd+vp8+kCCQmmKxxgaVQ+RcvXtxuvfVWZ2oBuWI99MtgL8wxqmw+lYIjwIYgR44cdtlllzmpdMGCBeOdADnG7AKBWKCNavCWIic3Uxg9aoZ7FAyd+/bta9jgsFPENtVPGD1jN4KYPsN9cX2heARQc6GOueWWW4xJBNshbN8GDBgQXyY1B0ghGDupqUNZVJx8KqUvAv/8849Tw7EgQ3iCed1iu/7ss89agQIFjEUGUsRT4sxz3333GTZneFP7Ej+uKR0bATRakFIkdZRG7Y/QwPM8Tl0ixjVOJtiydu7c2R577DGDqFaoUMGmTp3q1NHM2RBehA2ukv4kQcDzPCtSpIhzjOIiZhMQUo79xMahXbt2zvMcrIm0UKhQIXeZeRIbVoQ4mF+4TP0JigBaWEwmwAvtALhi3xtYmHNMXQLzIvlYRDWEvccOXHFUQwhwBDTNZFK7dm174oknDOLBBI5DHWFaUvv4ENTPPvvMfMlDSutzv0aNGqW0uMqlIQLY7U2ZMsXwyEW70rZtW4OYBt6iS5cudueddzq1KIs9khBIKWUJ1UP4pK5duxqfgfVSdhy9pSCqSEn5zaEiZZOAYxWbPf93tGHDBhs0aJCxESA0VZUqVRxgOAWhqv7ggw8MUiWBgoMl2T+QS5zO8MXo06ePzZ4928CQsY6nP/MgG7CBAwe6kErMYc8880x8e2zo5syZYzgExWfqICgCENB69epZmzZt7L333rObb77ZaW2XL19uYE/CD4ZNr98AfYDZBeExE5vD+GXC+VNENcS9w078kksucbFT+TEzUDAJIOlHGWLwT3LzOBhAUpDa4OXK4zAeiCP41FNPcepsmJGwEguvU6dOLo/J/Y033nBqSPKJlcfkQjgSf3IiBiG7aq6TWAggN4MHD3Y20U2aNLEXXnjBkaKPPvrIhg4datg0EULG3UR/0gWBRYsWOYkoKmdUyajsWKT9myOtY9PCIs84uemmm5xUik3u5MmTnalQnjj1c6VKlWz48OF+NX2mEAE2AGgzPM+zV155xeiDYFWJCEBIpcBrxF9Fuoq3NKQr8JqOkyLAGEYDwBU2ZWgCOE6cbrzxRsNbPTCftZFXjEvtH4hK8sfYTjOukZwyzycuyfqCQ5ufz9xStWpVZy7g50XSZ9gR1UgC71jPet555xneeTVq1LDY2FgX4gSJCcckTX7HQjCyr6POYmPCDhjpjP9tCCWCChIpAhM6+cS+Q2rTvn17FxaHRbJs2bJu3Hz99dfOBokJickfNTH2X4wh6rHhgYzitAcZRfpGmBKiTECUUcFBkF577TXDFo/7KYUeATYObCYIyeMn8gib5N8dKTnJV0/jXMI5YwNzDeJMUpb+xoSEY6WUI4BaFLLKb4UQVdTEHICFnHPMLVD3Q7K4FpggtdSDfNF/gdd0nBQBsEa6B2YxMTGuAPPeAw88YJwzhpHyYf7kLgb8Ad+M5KUe8NVCcsgagvQfrBF+eJ7n5nbOSfCMwBsjKCGfDS9miIHXIuFYRDWEvcREyADBMJ8gyLwZArtUpGFITGbOnOnin4XwEdT0SUQAiSpkNblHgLSg7mKRZCJHBYynJpM5Ew3qGyTvZ511lgupg40ciywSNuIPYkOHKhP1JMdMQhBVAmkjIcLYHokdE1nmzJmdDSREKLnnUX76I0DfIvkgaDdScSKBsJCceuqpxrhg40I+NpTY7x3nE6qaEBACQiBiERBRDXHXEToCNSyvOCMMSpkyZaxWrVpODQXBYLcf4kdQ8ycJAdRZONJhB4c6P/AxiDEIkSWfclxDAo9kAckE50dLmAIgXcXulcSm52jldS39EWBzQB8hHfUTeTlz5kzwMKhBIatoX5Cq4x3NhoKNCxta7FS///57Q4KSoKJOhIAQEAJRgEB4EtUMAjy2aI0bN3ZepNgf4vmL+B3ywqvMsDlEVZJBvq6+RiIEIKCos4gniLOGfxk7UjYukFJs4IjZiOQTe0YkoZAUv2zgp+d5lj17dpfFWEJdPGnSJHc+duxY9xnsD+1xr2DXlBdaBJCME+ScjQVSUULLoGkJvCtS1Y4dOxq2kEjECZnE2EFV2rJlS8NuFYcepO6B9XQsBISAEIgGBERUQ9jL2JQhMUMigrqWWxGOo3nz5s7zEeN9SAT5ShkTAYgKJANnJsJTkSCplStXNtS7vFkEW1LUurxyEIcPIkUQSodXOmLDSt1q1aoZZBPbJPIhqSNGjHCveKRNTAE4x/EKhys2QZBYVMdsiiiDKYDesJO+44y4kdjkYSfM752NK3Z7EydOdK85XL9+vRGRgf6BsOJQ4m9GIKiMD0wAcJw40SdXfSEgBIRAJCIgohrCXsOBBcLA20/823ieZ9gZQlJQ6xEqxb+mz4yHAOQSgogtKip6EvbKEE6+LU5VSFQxDyAsEWQFpydCSiGRRx3MqyAJTYRJAMQVBx0kqpiNIK2jTQguDlRIbzEDwKyE8YXpCXH3xo0b5+IX6gUToJ5+CVU/5JO3ySAxZyxwdzYbONohJSW2JH34yCOPGCSW66Tp06cb/YaXNOOIPCUhIASEQLQhEMZENfK7AinZ008/HfSLIAGDTEjCFRQeZQoBISAEhIAQEAJCwERUQzgIsEtD7RrsFjhMQGQXLlwY7LLyhIAQEAJpj4BaFAJCQAhEGAIiqiHsMOwM33nnnaB3QPXvqwGDFlCmEBACQkAICAEhIASiHIFwJ6oR3T0E7u7bt69VqFDBiHGZOBE7MaK/oB5eCAgBISAEhIAQEAIhREBENYTg0jShqAjATtiZxAnPb8ooCQEhIATSDwHdSQgIASEQOQiIqIawr4iX+OGHHxqvkON1qYkTQb5DeHs1LQSEgBAQAkJACAiBiEYgIohqpCLMm4aIoRjs+VevXm2EpQl2TXlCQAgIASEgBISAEBACJq//9BgExLYcPny4HTx4MMHt3nrrLRfsO0GmToSAEBACoUdAdxACQkAIRAQCkqimQzcReP27774znKv821166aXG6zJ5I42fp08hIASEgBAQAkJACAiB/yMQOUT1/8+sIyEgBISAEBACQkAICIEoQEBENR06mZipOFbpNYjpALZuIQSEQIoRyOgF9+zZY08++aShuRo6dGjQr/vVV18ZMa1vvfVW+/HHH+PLNG/e3NXjDYL//PNPfL4OgiOwe/dua9y4scNszJgxQQtNnDjRrr/+ekOjuGrVqgRlNm3aZE888YTdeOONCfJ1khSB/fv320svveSwLl26tCuwdOlSu/POO10e4/2uu+5KMJ5nz55tlK1Xr54rH0l/RFRD2Fu8JrVVq1Z21llnWc2aNd2dGGB+mjlzpnXs2NHl648QEAJCQAikHQKHDx+2AQMGGAR0zZo1NnbsWGOxDrzDkiVLXD6fTZs2tffee8+2bdtmo0aNsiuvvNKYo0ePHm3UD6yn46QITJ061bJmzWq86Ab/i7lz5yYotHbtWsMEbsiQIdavXz+3JoI1hXiDI9Fx7rnnHoc5eUrJI8AG7Nprr7WffvrJZsyY4Qqec8451r17d5e3aNEiI6rQGWec4a6xGWvQoIHBNzp16uTyIulPhBHVSILWDNvUbt262Ztvvmlvv/12ksSPWRNgZPWpnlYICIHIQOD333+3jRs3Ogke2qxbbrnFIFOBT48U6qqrrrLMmTNbiRIljMV+w4YNTvL04IMPGmS3ePHiVqBAgcBqOk6EADhNnjzZSfROO+00I3Y4BD+w2ObNm41+OP/8851/xiWXXGIQqh07dlitWrVcXfw4fPIaWFfHCRHo0aOHIcFet26duwD+jN2YmBh3zqZg3759xjkRhtq2bWv33nuvbdmyxf766y9XJpL+iKiGuLeyZ89u/CBRdWTKlMlmzZrl1B6c33DDDSG+u5oXAkJACBwDgQx6mQWZxRyNFl/x3HPPNRb2wOgrlPnjjz+47KSBnufZn3/+ably5bL58+dbtWrVHHmFyLpC+hMUgSNHjjipMxhTAFO3BQsWcBifwB2sMaPwPM9y5sxpbCaQWi9fvtwuuugiGz9+vH388cfxdXQQHAEIKFLoRo0aGZstuMXZZ58dXxjTizvuuMOdf/rpp26DAJFF4vr111+7/Ej6kymSHjbSnpU4qq+//rrVqFHDYmNjDbVGkSJF3DHnvAAg0r6TnlcICAEhEAkIQIgOHDiQ4FEhS4cOHYrPgxx99tlnRjmuceH000/nw5irec31999/7wQMLlN/kkUADWLgRSR6gec5cuSwhQsXus0C+WwIIFdTpkxxtqnPPfecs7scPHgwl5WOgkCVKlXslVdeMT4///zzJCUhp+XLl3f5qP2xZ60VJ7V+5JFHbODAgS4/kv5EIlGNGHzZVTIwEMtv377dsNnBpgSjcVQcEfNF9KBJEPjll19s/fr1hpoQe6EkBdI5Y9euXcYYO9ptKbNz586jFdG1ECDAgo0KGtUn5CnYLZAsrY8bT6g9mS8oQ1mkJuQnJgFcVzo6AthLIrUDR0pCUJGU+nZ75FWsWNHuu+8+u/zyy51NbfUl+AAAEABJREFUH/jHxMRwySjHYo9TClI/l6k/ySKQP39+87GG+Ps4+hWwqaxcubLdf//9TnqK4xp2lFxHYk1/YRaAeQB5Sskj4ON1++23G74wgSVHjBhhTz31lGGC4edny5bNmbewMfM8z8+OmE8R1XToKhYZxO38eJn4UG9AdBDZp8PtdYsQIPDJJ5/YzTffbM8//7zbgFgy/yCG2AUlc/mEsufMmRNff9q0acYYi88IcoB9HnZkXPr555+d0wjHSqFDAJLau3dvwx4dZ51vvvkmyc2wU2/evLmhfWnYsKHhjEIhzISwbyeP66ipyQ9NynitIsGDbKLuRzWN0wke54m/6fvvv2+TJk2yN954w9lWQpb8MkhZmb/lie4jEvwT1XPRokXjSRPzY4UKFZIUJirAt99+67B+9dVXLW/evIaKetiwYcZcycYMQpukojKCIoDgi41W4EUcAStVqhSf9dBDD1n//v3dORvmYL8BdzGM/2QK42fLMI+GjdTjjz/u3kKFTQmmAEyi7HjY+WSYLxpFXwRSgVdw9erVrVy5ckG/OYsjC6BPDoMWOs5MJhwWVr86ZiWMMf882CfG9P4E1rFjR8PgPlg55aUdAsuWLXNS9/bt2zuyOnLkSGeXF3iHXr162cMPP2yo63gJCAsNBKlLly6OOPXp08dthlBBB9bT8dEROPPMM61inMT0yy+/dLamSOzKli1raBaIwjJ9+nSHK+F6wBiihcMVrRItoFq1aq4eUthSpUqRrZQMAp7nOcJJVAVwAzOkfdikMk/98MMPhlMPTlaQpsKFCxvXaY5NAGGVYmNjDefjp59+mmylZBBAcwbGJGxRmdf9ouvjtDJsjkuWLOlnGbhiq015+gEnwfiLEXIQsUQ1EvBduXKlC8MR7FmZRFF7JI4lF6ys8sIfAbwwkX4hNaNfUWsNHz7cSVxZCCEdmHtgh0WMOyRsfKvJkye7xZD4dkhnkaLhhUyZli1bUsQlFlbyIDSQ30cffdSFemE3DeGkPSR3xCmkHO2h6iTcDnWQ4LNgYM9Eux999JHRBpMc5dl1I/H3N1IsMO7G+nNCCOAkgq06qrfcuXM7Jx0WE79RpKRIVDETIt4yYwcTAaR4LCoXXHCBYceHujSxF7Xfhj6TR+Caa66xd955x5o3b27NmjUznH3oC6R5/M6KFStmTZo0sRdffDHBhhOCy2/q3XffdddQtSZ/F10BAaIjgBmpdevWhjAGB54XXnjBuIZpAPNj3bp1Dc0idUhIvZk72TwTzpG5i3yl4AgwH4AxqX79+nbhhRfGF2S+YG6Pz4g7QN2PgxrlWQ9iYmLiciPrv4hqCPuLsBsEi4YcsKsPTKg6kLKwGIXwEdR0OiBAH3bu3Nkgo8RsxEmO/iUuICSVyYHJuU+fPsbCCYElliChWVgg8Y4lriDSBOyNUEUiVWPShmB27drVUIex8WGSQdoG2SxSpIhBXpBUUJ9dM1IKJKs4gbC4QkZZoFF70j72tCwKBDjHaYEdee3ate3ZZ5+1LFmyOKeGZ555xnnkpgN0GfoWbBToP8/zzPP+TRDTnTt3JvjeqE3pdzI9zzMk8SRsVSGt5GO359v/cR6ilOGaBds8efIYvxsIKl8QLAk3xTkkKV++fEnGO4SWOiTfuYq6SskjANZIUsEMQQwlmYMgT5yjQYSsYjfMtcCEtJt6mAJQJ/CajhMiAD5gRWKcep4XX4A5nA1CfMZ/B9mzZzfK0z/0k0XYPxHVEHcY5AGbVIJNo9pDqsUxaevWrSG+u5pPDwSw+UFdVbVqVYOkMikwKSe+d584ogqx/PDDD43QZBBONjJIELCLYwFFqoraxnfewDOWYOQQVSYh4uEh7Qlsm3Mko34etmFIdCE6jD924EhMIa1+mcBP6kJYeR5IL44lgdd1fHwIeJ7nQh4F1kZqSvLzWMAx20DawfjAEYKFhvGDWRCbCvKxbWWc+fX0KQSEgBCIFgQim6iGeS+h9sARAoJK4vVwt912m7NF4xw7xzD/Cnq8NEQAIoqNFn1Pwh4xsHmIJaFEOnTo4OzoAq8hnQ88P9oxpBYpLY4JSOTYSR+tPLZ5SOtwzkKSB9E+WnldSxkC9DcSJEw+kK6yEQBnTAACWyDEDBJwTC+wP8NOEikeeWww6Efq4zEdWE/HQkAICIFoQEBENYS9TFB/YpexOKG+hbRis4axM7fFtpBPpYyJgOd5zibO/3Zly5Z1r7hDysk4GDdunH/JfUJGIKpIziAvZKKexAZp0KBBnBrqf5xAgqnPXIG4P9RhQ4T5AW0iiY3LTvAfFVtgBgb3SISxJQvM1/GJIYANJEQTkwvsj1H7My8EtoraE2k3DnLlypVztpKe57lXL+PEg/kAZiVH6/PA9k70WPWFgBAQAuGEgIhqCHsDWxKcUjDcxz4Qu0S8HRs0aGA9e/Z0RvxlypQJ4ROo6VAhgKQLsolqFoN2vLkx5yD8Cup9DNohnajRsTlFtYvZB9I0nDiwCYVMtmjRwihHXcYL5WkDooo5AM5NjBfIDk5PSGSJIoGtK4SX0C4Efp48ebIjwdjL8p2xlWXM+ePLfx7IK6YEqJuffPJJFxKJ8pgicE8iGXCulDYIYAtcqVIl5+HMOGjTpo2LZ4jTGyQUxyr6mL7FwYTNgm8acNNNNxn2xdg342yVNk+kVoSAEBACkYVABiCq4Qs4waNxVCGOJiQV0hAbG2vEsORVZixM2BOG7zfQkyWHwNChQ40YdpBVbE6JY4r3NjakvGmFa5DBsnFSVI4hpBAQ4gtyjg0qkjTCuXD+2GOPGUbu2KhyznU+CZMDMcW5inMIjk8mOZ8wYYJBgCCfPAvSWJ4ZSR6hkfA455xxSHlsHa+44goX9giJPh7RXOd1kZBVjpXSFgGkpDjO4dCG8wOt4/SGhBWzjHbt2rmxxMaFa35Cco7NamIJrH9dn0JACAiBaEBARDWEvQwRgDxAUrAD9DzPsEuEpOIggZQLx4kQPoKaFgJHRQAyC4lFqot09qiFdTF6ENA3FQJCQAiECQIiqiHsCEKfEDczUCKC1AybQ1S8eGOH8PZqWggcE4FChQrFq5exbT1mBRUQAkJACAgBIZCOCGQUopqOkKX8VoSegaR63v/jnAXWxjEG1V5gno6FQHoigHc5num+Sjo97617CQEhIASEgBA4FgIiqsdC6ASuY6OK/Rlq/mCJIPHYHJ7ALVRVCAgBIRAiBNSsEBACQuDkIyCiGuI+wKsX+79gad68eSG+u5oXAkJACAgBISAEhEDkIpChiGo4dsNdd91lPXr0CJpwsgrHZ9YzCQEhIASEgBAQAkIgHBAQUQ1hL/AqzI4dOxpvqAqWCF1EaKEQPoKaFgJCQAicCAKqKwSEgBA4qQiIqIYQfkL/8JpU3izz22+/WeI0ZswYw041hI+gpoWAEBACQkAICAEhELEIZDyiGkZdceDAAfvss8+MIP/VqlWzxAkSy8sAwuiR9ShCQAgIASEgBISAEAgbBERUQ9wVV199tXu1JcH9E6cXX3wxxHdX80JACAiBE0cgklsYP368FS5cONmv8MMPP7jrvC1s06ZN8eV4i1vBggXt5ZdftkOHDsXn6yA4Art377bGjRsbmKEtDFYKrG+++WYjbOPWrVsTFAH7J554wm688cYE+TpJHQK8uKVfv36pqxTmpUVUQ9xBTJAxMTEWEySVKFEixHdX80JACAiB6EWA19C+9dZbtnr16qAgQJaaN29uCxYssNatW9u7775re/bssb/++svwL5g6daplz57dePVx0AaUGY8AWPFa6BUrVhiYz507N/4aB7///rsNGjTIENiweUDT+Pfff3PJfv31V3vggQeM107z+miXqT+pQmDv3r320ksvGa/lZtOQqsphXjiDEtXwQJ1g6tilJrcbR+3P26vC42n1FEJACAiBjIXATTfdZJ9++qkxFwf7ZjNmzLBbbrnFsmTJYnny5DFefIFkr2/fvlaqVCm76KKL7O6773ZRW4LVV96/CBw+fNgmT55sd955p5122mlWp04dGz169L8X//vLZgGcCxQoYKRLLrnEFi5caDt27LBatWq5uhBX4o//V0UfqUAALtGhQwd77LHHUlErMoqKqIawny6++GJbs2aNJd5Zckt2l+wsixYtyqmSEBACQiC8EciAT7dx40ZHUj3Ps6xZs9r+/fvtjz/+MAhsTJwWjK8MgV2+fLkjVJwrJUXgyJEjbq2D6HOVtQ8pNcd++vnnn50JBUTW8zw766yzjE0BElTwZVOApPXjjz/2q+hTCDgERFQdDKH5ky1bNrvvvvusQYMGNmnSJGPXyZ3WrVvndpDsJomzSp6SEBACQkAIpC8CBw8edOTJvyvn//zzj/35559+VvwnqtX4Ex0kQSAxPvv27YsvA5FNfB2cwXvKlCmGbepzzz3nVNeDBw+Or6cDIQACGZmo8v1Oeqpdu7blzZvXHnzwQbdzz5Qpk11xxRXOJmrChAnGbv2kP6QeQAgIASEQhQjkypXLSVL56ggSsEdF0lekSBGDRJGPOjprnLT1wgsv5FQpGQTy589vkE8uE/Em5j+JNOee5zkzCs/zDNJK3qmnnmrgz3HmzJldP5x//vl2yimnmP4JgUAERFQD0QjBMSS1d+/e1qtXL4O01qhRwxntjx071ogIEIJbqsk0RAA14NChQ61Pnz6GJ2WglOBEbrN06VLDA5Y2cCSYPHkyh6lOxOgdMGCAez4WiZUrV6a6jWNVWLRokf3444/HKqbrySCwefNmGzhwoA0ZMsSploMVmz9/vutDnH/86zj04HzC2EssjfLLpP9nxrpj8eLFDQ0XfgSoocH5ggsusIceesjw+odwIVBA4pexvnnafhsEMJixLV682DWM81mFChXcsf8HXCGhzKnMedisFitWzPBSHzZsmO3cudPIv/baa/0q+hQCDgERVQdDaP6wQH377beWI0cOe/jhh61Tp04uVBWG5uzYsdnhemjurlbTCgEWMDyC06o9vGIbNmxoEMwTaRNJD+TZJ5GozkaMGHEiTSapy2JCyJlx48YluaaMYyOA9y2bVBxG2ESwgCeutXbtWuvSpYvRn4T1gbRShlcv44E+bdo0w0mCPKXUIfDTTz9Z165dnXSUuNWo9Hft2mU1a9Y0NgUQVQjWCy+8YO3atbOyZcvaeeedZ9ddd51Booh9Tbl69eql7sZRVtrzPEc48TgHMySlhPuClL7xxhtuU44D1ZVXXmnNmzd3+LMZgLwSjgonrNjYWOvWrZs9/fTTUYZe2n3d/v372/fff2+YT2BSkXYtn9yWMjxRPZnwQkRbtGjhJsQ5c+ZY4oTROAvTyXxG3fvoCOTMmdNuu+02w0SDCTQ57+Gjt5LwKiqx66+/Pj6TtsuWLRt/ntIDVJM4fTz++OPGJP/KK68YdtEprZ+ScqjzkPwjCUlJeZVJiIBPOqtWrWrPPPOMk0yzgQ0sxWaDRb169erOLPIMKbQAABAASURBVIiFhr4tX768Pfvss64ekvPAOjpOGQIQoVdffdWFp0KjhWc0vxHyrrrqKuOcfiGc0gcffOA8/FFD0zrxU9977z0jn7if5CkljwCkHxxJbOwR0JxzzjnGJoBrzJ2YwEFU2TxwTGv0wZtvvmmEA2vVqpWVLl2abKXjQIAIFV9//bVBWEuWLHkcLYRnFRHVEPYLtjgsVE899ZQ9+uijLk4ciw/HJEwCkKKE8BHUdIgRwBSgadOmhrQAwrlkyRLnNEdEh3z58jn75HfeecfZbqHKbdSokd1www22atUq92SoHPv27eskDKjumeBZUH1y/NVXX7lyqNRy585t2Mmx0N5///3Ggsokz4YH9SUxe5HW47nMQgyxhsTigYtnLePx+++/d/enHSR2NM4zE4SbQN2EiYEkIXlCMsJzzJs3j2JKqUQAvJGW0l8s0meffbbTriCh95tC4krflilTxjzPs5iYGOd1Th9efvnltn37dmvfvr0LpO7XCYPPiHkEPMwZ6+AKaUVFzaaL3yv9whfJmjWr+/1Sjt8UeSR+W9RDOuh5HllKR0EAbMEKzM4880xXEjzB3T8Ha7z7wZ9+cIXi/pBPPUzlqBOXpf/HgQCCFXAEX398H0czYVdFRDXEXQIhRf1Ewg4HyQrHJGwfQ3x7NR9CBCAi9CkTLmrd7t27G28bg4ig8mKT8uWXXxpkhcDiqH0pixTU36CgEkZdBomFtI4aNcpQ9UIimzVrZrTPV0D93qdPH0Odg8qsTZs2Ll4hkgjiDpYrV87tojds2GCMLexfIaAt4iT6lIEAQ354pW/lypUdMUZiRCzfnj17ukDb3A9V/6xZs6xly5aGeQqxEFnAIdE8h1LKEQAzxkFgDfqZ5Oex0aFf/HM+IafUJR8VHuMJ+1auKQkBISAEog2B6CCqJ7FXkaScxNvr1iFEAC9h4v9h44ZEgFBjqLtwCMAuCzKKKgsHDR4DksibV5AuYJdFHvZwOBMgbSMYdrVq1RxpRBrrxySkHFJRCAy7ZO5BHol7I71F6gbJhcCyo0ZyXy6OvF522WUucPnOnTudDSTmJzwrKn3Ub7yQAjKFFAPp0/PPP+88cbFJrVKliguAjtqT69xPKeUIIGHCgzywBtiT/Dz6nOSf80kd6iLRQ0KObTs2rPQ/15WEgBAQAtGEgIhqOvQ2nqMQGogEUjakXThJpMOtdYsQIYDEk6aRfEH2OCZBIiGTqO/xFCbKQ/bs2blkqHkhhe4klX8aNGhg9evXd04eqNIKFSpkjCvIL4QWKSsON3joQ0oDm0cyh9mA53nOdACyjE3rk08+6UgpJJR4v+RBTgkRAwlHUhvYjo5ThwBkk00D/YQ5Bf3ApgX1pt8SNnyYXCxbtsxlsclhDLFpcBlxfzAVYZyxmYk7DZv/ehAhIASEQHogIKIaQpRR8xKGCFUsntnYjUAGsE+EqKLyRWoSwkdQ0yeIAAQDtT0kY/369bY+LqGaRzJJ/1asWNG9KnDq1KlGQooK+cAmtE+cqt7zPEcoIY9169Y1HAawScSTGJI5cuRI4xrjgU+ILF7JkFreXoaaGHvR1157zbAVRfWPOQmEBzKJ5HP48OHuub744gtD+ukTIexReV4iS/CcxC2E7BAqiXxeLck9IbqQXPJ4MQWbKogv0l0iFECi3n77bRf79wThjLrq2JmCMaYhI0aMcHbFSMfpP1T6ENAHHnjAsEVmjmBc4ASB1JX+INGvOFWxoYg6APWFhYAQiHoEooiopn9fIynBsxECg1c3IVFIOFeh/kVti8NL+j+Z7phSBPC2Jp4ijkrYe5KwNb3llltcE6jwCQuFYxwxL1HR4iyABzdliepAX0MycVTCOQlSSPgbVLrEDISMQEIgwJBE7BaxE8XGFDUwUniiBNCenyDBEE8IDHap5GNfStvuweL+IHUln/YgQ5AfHLsgweRDgnn5BI5XSH3JY/MEuYKY8owQa8wEILd4/8c1q/+pQAATDuzSCVEFWcXjmeocY66BOh8TDcYTZht4R/vxJ+kPEtFDsB+mnpIQEAJCINoQEFENYY/jAUn8PdSpqIMDb4XUjdhxkILAfB2HFwJIwXFEQvoYmCAfPCn2hWw2uIZTEv1KPraF5OFghS0omxPIKESFfGI6YhqAoxJkhDzU73yS/GPqo7ZHjUw+iefJkyePk84R1qhBgwZGPt7//v15BkgS+UhjkZqShzSvbdu2rjwSXvKwkyUED2UJcQVpJZ9z2uR+PnkiXyl1CBA7kjEAMWXjQW1IP5sKzCw4R4IN3mw8OCdxTmrSpIkLo0Re2CU9kBAQAkIgxAiIqIYYYM/zDHVroM1ZiG+p5jMYAtg5IulEQk/i5RFIQpP7mpgdEJuT9PnnnydXTPlCQAgIASEgBMIegWgjqmHfIXpAIZAYAVT2hCfC1pmE7SlvzUlczj/HHAETAmxccZjy8/UpBISAEBACQiDSEBBRDWGPESMTZ5TkbnGs68nVU74QEAJCIHwQ0JMIASEgBEKHgIhq6LB1gd55nRzevHj6J04DBgxwQdpD+AhqWggIASEgBISAEBACEYtAVBLV9Oot3jSE5zZe44SfCUw4reDkQiii9Hoe3UcICAEhIASEgBAQApGEgIhqCHsLByocX/DK5tWaxKUsUaKEe0UmcTgJvE5g+BA+gpoWAkJACKQHArqHEBACQiAkCIiohgTWfxslBA3hiIiTiZqfOJrEQyS0EN7Y/fv3t6M5xfzbiv4KASEgBISAEBACQiA6EYheopoO/U1AdeIn1qhRw3r06GGEFOrWrZsRx7JMmTJGbMx0eAzdQggIASEgBISAEBACEYmAiGoIu+3AgQO2Zs0a4+1GqPgbNmxoJeJU/5gEhPC2aloICAEhcFIQ0E2FgBAQAmmNgIhqWiMa0F727Nmd9HTUqFE2ffp04w1GxMAkvuXff/8dUFKHQkAICAEhIASEgBAQAokRiHKimhiOtD3fuHGjjRgxwgoVKmSdO3e29957zwhV1alTJxeWau7cubZ37960valaEwJCQAgIASEgBIRABkFARDWEHfnrr7/am2++6Qhqu3btHGn9888/bffu3da7d2977rnnHGEN4SOoaSEgBIRA+iIQRndDENC6dWsj4gparWCPhsCgevXq9vLLLxvCBb8M/gXU461whw8f9rP1mQwCYN2rVy+H9Q8//JBMqX+z8dNo1KiREb5x06ZNrg5YkzCR+7eU/qYEgT59+jj8GOc7d+6Mr7Jy5UoDYzBlDMdfiMADEdUQd9r27dttz549hmMVad++fXbuuefa7bffbpkzZ3Y/1BA/gpoXAkJACEQlAphd8cVff/11+/TTT51Gi3M/rV271vr16+dIatGiRe399983YluPGzfOiM7y1FNPWYcOHWzGjBl+FX0mg8C8efNs2bJlRmQbwjFClBIXBdv77rvPvQznpZdesksuucT5cfBiHBLCm99++y1xNZ0ng8C0adMME0M2WQsWLHBjHLNCEhuAypUrG7jCN5JpIiKyRVTNQtZROE09//zz1qJFC2OXyA8YCSuJsFU9e/Z0pDVkD6CGhYAQEAJRisAff/xhS5cutXLlytnFF19s1113nU2ePDkBGvPnzzccXLnOYu55nq1fv96R1IceesjKly9vFSpUcLGvE1TUSQIEkDjzYpu7777bChcubI8//rj5mwT77x8vwCH6TcmSJa1p06Z25pln2umnn+5CNMbExFhMXNqyZYvdcccd/9XQx7EQyJcvn915553GJuuZZ56xzZs3G8IwHLkZx2wEKJMjR45jNRXW10VUQ9g9xFGtVq2atWnTxurXr+926ytWrIi/Iz/Yli1bxp/rQAgIASGQMRA4+d/C12ShweJp8ubNa8y/ECbOSUj4duzYwaEjToQM5Jy52WXG/cmWLZsVKFAg7kj/k0PgyJEjtnr1arvgggtcEda+xOr/devWOYnrzz//7FTSrImoqs8//3xX5+DBg07DSBhHl6E/x0SAcQnZp2CWLFnswgsvtFNPPdVpcTF1qVmzppOycj2Sk4hqiHuvcePGtnjxYrv22mvdRIgaacOGDSG+q5oXAkJACEQ3AhBSzK0CUcCOEkLk5+XKlct4tTWqUsqTz4LPJwnSShtIVTlXSh4BsAq8CgkNPMdnAx8NpKnYsnJ99uzZ8UWQpiJl9Ylr/AUdHBMBxi8mE2wQIK5nnXWWI6i8qn348OGG5uCYjYRxARHV/zonFB/YjDAxou5gZ9OgQQO78cYbLbH6KRT3VptCQAgIgWhGAMKZPXv2BBCcffbZTt3sZ1asWNGQtKI6RZiAtA9VqX990aJFhjrbl8r6+fpMikBigpk7d+4EhVBHgyPl6AdMBALJLfbCVPClshwrpQwBsIVr3HTTTa4CWgAOSpUq5RytRo4cyWnEJhHVEHdd/vz5nSQVlRK7HnaVqElCfFs1nw4I/PPPP85xgIn3nnvucQ5z3BZbrauuusrZuRHtgbyUJryO2QX/9NNPKa0SXw7HPf+Et6EhKfLP9XlyEGCM4HF7+eWXG+rkVatWBX0Q4isXLFjQSEibqIcUhHMWdxxVglYM78yT+nTnnHOOI6VI6ngQ1P78Lj3P49SlrFmzusgr2LISQvCxxx4zn6iC//Lly42+w4zAVdCfoAh4nmdFihRxjlEUGD9+vN16660cxifmSc/zzCenOBOTRwFsXLGphLyyVpKnlDIEsMVm3OKIxrwBloE10RT4BDYwP5KORVRD2FuI4fkxMgH26dPHhaqaOHGi3XzzzSG8q5pOLwROOeUUa9WqlXs9LupBCCaG7KgJ8SSm3wkPkprnYWOD5zEEJTX1WIybNWsWXwVHvbvuuiv+XAcnBwHs9qZMmWLfffedC0lHWB7Un4FPs3//foOIsjkhESqJsTVhwgTjHOL6yCOPxG+EAuvqOHkEIKpISSFNePyzSShXrpxzNvnss8+c5zlmWIMGDTIwZpNYpUoV1yASqObNmzvMv/jiC0vthtM1EkV/IJelS5d20RH6xK11qPSRRDPWwRISij3leeedZwMHDrS+ffs6bG+44QaHEgRrzpw55p+7TP05JgKzZs2yhx9+2DkMgjvjmI0AY51zEpEYWJOO2VgYFxBRDeycEBxjo4pIHoLKzuadd95xITlCcCs1eRIRIPzN999/b5988kmSpyBMC4QVx7r27du7CZrID88++6wNGzbMkPRw/euvvzZUj5BcdsnsjAcPHuxUN9RFSkrMR1K9evWM+kxESH0I9UJ9XirBRMUi66vS/OsQIAgTD8ii/MILLzgzFNomzAn5SmmLAKrjiy66yFB1sgnB+9bvF/9OlJk0aZIxVwRK7ugvyrDgo9oLlJiTr3RsBPCIvuWWW8zzPGMjSR8Eq0VEAEIqBV578MEHDRtW8miHT6XkEYCoEnqKEsQJZ9xz7Cc2DmCMJzoCnFdffdXOOOMMdxmiS5QFqf0dHKn6g7d/zpw5XZ2MVLpnAAAQAElEQVTixYubr/Z3GXF/mFfiPiL6v4hqiLuPHysSVBILDzYjIb6lmj8JCBDeZvTo0e4NZEjQAh8BiU2xYsUMieeYMWMM4lqpUiUj/7bbbjPCsiBtQPpDhAgmeSQRSOOQBBHaDIkpkziSVqQRFStWdJ6z2D9DaFkksLODvPbv399JgLZu3WpIMrp37+523dhIQ2iJEVm1alUnRcLQnkWa4OaBz6zjE0eAjSnhYpCO+ok8NiGBreOpi/QdySpzBHW47i8+SOuvv/56Z0tJfqSlk/m82KlCMmNjY12IKp4FZxPio/Kb5XeH2pTfD9f89MADDxh1/JT4ul9On/9HAKxZ58AsJibGXYA0gaV/jqofCeDTTz9tefLkcWX4w+9DYalAInUJPgHefmI9ISzmZZddZn4eG4TUtRp+pUVUQ9gnLEpItvhhoiKGXEBEAr1OQ3h7NZ3OCLApIeQKpANi6d+egMtMGp7nubi5jAvsFe+//36nfsT7tUiRIgYxgahAaqkL0STUCOcspkjmKUP7fsxHJiEmeSR1SCeY/JG2MlHRBmQXIstOG/svFm3UQh07dnQ77w8//NC4N/bTlFdKfwQYN2xAUC+zWUFt6j8FKtGxY8caGwykUH6+PoWAEBAC0YKAiGqSnk67DEjCiBEjDBudNWvWGFEA1q9fb6iI0+4uailcEEB9haQUZzmkov5z4aiBih6zj19++cXPtrp169qXX35pqP6DxQ5EigYx7dSpk6Hu9yUO2HLRPmr+QFVxfMMBB2yKSGR5nmfYiCH54FwptAhALCGhkE0/kUefBrszkpBLL73U6C82FyRC26F+RnISrI7yhIAQEAIZHQER1RD2MCom7KNQG7FAseBASAgyTUqsIg7ho6jpdEIA9XyTJk2ccwy3RG2L6h37QgzdY2JiyHYJlfu2bduMSBCMD5cZ8Of33393AZyRlhJVAIN4pK+0Aymm3ezZs7saSFbdQaI/qJU3bNjgyDBkCWLrS1sTFdVpCBBAWk0/Mg7YsGA/jMo52K2QmNM/eJnTv5gIEEECp0w2FxG9wQ32hZUnBISAEEgBAiKqKQDpeIuw2BB4t3nz5gapwIEGpxVs0JCG4WH6448/Hm/zqhcGCLDZ4DW5bEhQ0fJIEFBMPThms4Jd1kcffeReLThz5kxD3Y5aH5U90jXGA59I0qiHRzJe+9h3YTvaokULq1OnjnvVIHaMqPjJg9BAfrp06WLY2uFMhTMDdqrkY4fKvfEYx2GK8khU2Sxhr7p7927Dfox7Ecqqdu3aPLJSGiKAyQXSUEL14EiCYwP9inMl+WhYmAtwMCHGMhsPbI1xsMI8hAgQbHQxDcAMJA0fTU0JASEgBCICARHV4N2UJrl4mCIRwclmxowZhmQLcoIJAFI0JGR4ZKfJzdTISUEAgsobQTZt2mRIPXkINiiPPvqoQRA5JiIAYYZIvEbw888/N4JhI0X94IMPnLc35ZCaQVIhkJgJIIXDbIR6pKlTpzoTAN5uRnQA8iA6ENUSJUq4aAKMNcwC8BLH3IRg5hBXxhmmKJgbQF6RziFhpX3CxXAvyDTPr5R2CNDHeDMvWbLEIJ++NBsbY0LLxMTEGN7P9BXX2WhQh3ifjCkk6PQzCcKadk+mloSAEBACkYGAiGqI+wk1LZ7beGoHSyxiIX4ENR+hCCCFJwYk9q0kwlaVKVMmQr+NHlsICAEhIASEQOoREFFNPWapqnHNNdcYEiw8rYldmarKKhzVCODp7UvgAAKP/eRsUbmuJASEgBAQAkIgoyEgonqUHk2LS6h5iW3G680I3C31alqgGh1tYK9IWCs/EaYqOr65vqUQEAJCQAgIgX8REFH9F4eQ/MV28d133zVem0hgdrx9iVeJfWBIbqhGhYAQEALhjYCeTggIASGQKgREVFMFV+oK4wBBaJm77rorvuKZZ55pOE7EZ+hACAgBISAEhIAQEAJCICgCIqpBYQnIPIFDJKjnnntufAuEoiLAOyFr4jN1IASEgBAQAkJACAgBIRAUARHVoLCkTSYxK3lT0fz58423zhBLsWHDhsZrEtPmDmpFCAgBIRB5COiJhYAQEAIpRUBENaVIHUe5tWvXGm+iwQmGNxYR75K4m4cOHXJvIyJAO7Esj6NpVRECQkAICAEhIASEQIZHQEQ1RV18fIW2bdtmvI2K9Pbbb1tgIsg3wb2nT59+fI2rlhAQAkJACAgBISAEMjgCIqoh7mBCDPF6xEsvvdQCE5JVIgHkyJEjxE+g5oWAEBACYYiAHkkICAEhkAIERFRTANLxFilYsKB16tTJatSoYX4szMSfV1xxxfE2r3pCQAgIASFwFAT27NljTz75pDEXDx06NGjJr776ynixBj4EOLxSaPPmza4O9fxEvlLyCPDq58aNGzvcxowZE7TgxIkT7frrr3dCG16CE1iIVwY/8cQTduONNwZm6zgIApMmTXI486rurVu3uhJLly41Xgrjj1eiDfnj2c/jFd2ucIT9EVFNeYeluiRvpOJH6XlequuqghAQAkJACBw/AocPH7YBAwYYBJSQgGPHjrXZs2cnaHDJkiVGPp9Nmza19957zzDZwgGW8IKk7777znLnzp2gnk6SIjB16lTLmjWrrVixwt566y2bO3dugkL4bIDlkCFDrF+/flazZk2HNYV+/fVXe+CBB+yee+6xmTNnkqWUDAJsooYNG2aMzQsvvNCNcYry1sLu3bu7/EWLFhmhMc844wzDgZuyAwcONPxkVq5cSfGISiKqEdVdelghIASEQEZCIHTf5ffff7eNGzc6Cd4pp5ximFtBpgLviBTqqquussyZM1uJEiWMxX7Dhg1WunTp+GIjR460Bg0axJ/rICkCbAomT57sJHpEuKlTp46NHj06QUEIFv2AAOe6664zTOIgVDgc16pVy9XlhThsFBJU1EkCBCCfEM6//vrL8ufP76IIgT9jNyYmxpVlU7Bv3z7jvF69ei4P4lq0aFH3m3AZEfRHRDWEncUP89tvv032Dse6nmxFXRACQkAICIGjIsBCjjr6rLPOcuWIab1u3To7ePCgO+cPZf744w8OnTTQ8zz7888/LVeuXC5v7969TjJ4++23u3P9CY7AkSNH3ItswJgSxBBfsGABh/EJ3MGaSDee51nOnDmNzQQS1OXLl9tFF11k48ePt48//ji+jg6SIgDGjNv69esb/KJAgQKWKVMmO/vss+MLY3pxxx13uHPIqjuI+0P0obx588YdRdZ/EdVU9ldqiv/888/WokULw7N/zpw5ljjxg2RApaZNlRUCQkAICIFjIwAhOnDgQIKCkCXCA/qZkCPs9ijHNfJPP/10PlxCjV24cGHLnj27O9ef5BGA1AdeRaIXeI7j8MKFC43NAvlsCCBXU6ZMMWxTn3vuOXvppZds8ODBXFY6CgKM0SZNmhhvuuzdu3eSkp9++qmVL18+QT4qf2xVA4lrggJhfCKiGsLOYZeJnQ7G/Bg9Y4PD4OGYxABD1RHCR1DTIURg//79tn79+gQJWyvUMMd7W+qi+mLhDNYGiy9SiGDjZteuXbZz505XjePt27e7Y56TNt2J/qQ7AizYqKDRoNB/wR6APl0fN5boJ8ZAYBn6EnW037eB1zLIcUi+BvaSSO18zCGoSEpRnfo3rFixohEm8PLLL3c2feDvL+TM3z/88INBVDEN8OvoMzgCqKF9rJm/fBz90rzopnLlynb//fc76SmOPqijuQ6+9BdmAZgHkKeUPAJIUBmXzz//vIFjYMkRI0bYU0895V4y5OcjgWUOQjOQLVs2PztiPkVUQ9hV7Hpq165t7GQwZsbQuWrVqsYxKTkv1BA+kppOQwToQ/qXxe6VV15xMXMfeughJzk/ntuwMC5evNhN5HwmboPrM2bMsCpVqrgxlfg69nfYif3yyy+GXdI777zjivCcvpQCqQeSfhZtd1F/QooAJJUNKc4lOOt88803Se6Ho0/z5s3t9ddfd44PSPEoBGHFyYe6xGLGEYV8pZQhgAQPUooEz//t4NyauPb7779veFG/8cYbhm0lZIkyRAxgkwDh8jw5xIJJcgnihP2jP2998sknVqFChSTFiQqAuhqsiSWOGhoVNc5BbMTY6ENok1RURlAEGKM+2fcLjBo1yipVquSfuk+c1zhgLK9evZrDiEoiqsfTXSmsg50Oqgx+xKj4mRAJwcFukyYgFHwqRSYCTMwQ1Ntuu806d+5sEBIcBOjrYBLPUH/Le++9N8kExT15ThZgjpctW2bDhw83fwySpxQ6BMAbaWj79u2dJzSOOUhPA+/Yq1cve/jhhw11HU4mLDSoobHn69atm3MG6tmzpyGNCqyn46MjgFqUTeSXX35p1apVczaoZcuWNcgnHueYZKHxYlPXp08f43eCw5XfKqSJjUahQoX8LH0mg4DneQbhJKoCWCO5RnrHGIaUIpmGIDEP9e/f30mpuU5zhKMirFJsbKwx3p9++mmylZJBgM0AGJNmzZqVYF5AK8OYLVmypKtN6Co2B2x2EYxRB5tgdzGC/oiohrCzELGzqyeWKoT1hRdeMAz727Zta0hW+AFfeeWVIXwCNZ2eCKD2wt6KmIyosrj35MmTXbw7bIPYpKCGR5LAOaobJnbKkVgQCSWCFIhzpJ7YIVGW5HmesfnBrg7pKXmYlbCrpjzj6fPPPzekFEWKFCHLJeJEco1yqDk5RrJPfaQXjEUWcxZqXuvrKulPmiCAk8h5551nzAWEOGIBZzHxG0clh0SVfj311FOd+hkTASTfhFbasmWLtWzZ0qJh8fYxScvPa665xtAsNI+TWDdr1sxwRKEv+A3i7V+sWDHjN/biiy9auXLlEtwa6RMOK9RJcEEnQREoXry424xBilq3bm2sfXiis+5xDdMAtAp169ZNYD/JfIfGoGPHjtaqVasEEReC3ijKM3lxEBiTXn75ZRcD2IcEZ6mPPvrIP3XjvUGDBsZGgfIktH7xBSLkQEQ1hB2F1Irduh83DrsRiAXSkccee8xQR2GvE8JHUNPpgABhWCCdTMrTpk0zSKDneQbZgDgiRWMc4CwAWWXhg6ByzlhAeoZErWvXrtauXTuDnPDY5KOmZwfNgstETj7hXIjziIoYey4IDUSTCQoyShk/rV+/3sXOQ5rBJgnzE0gPUg3aLVGihNE28QsfeeQRFyLGr6vPE0OAjQZaE8/zzPP+TRBTVJyBLaNxoS/I8zzPzQvMDUhAWODZSMybN09OJgCUygS2efLksZiYGLdZoDq/GTylIayQpHz58jkPdK4FpixZsjiy5XleYLaOk0EArNmIgTXSbIqxYYc8cU7YKsgqdsNcC0zYp1KPTTZ1Aq/pOCECmBSCFYm1xPP+Pz79MevXAEsEG5T1E33hX4+UTxHV4+6pY1ckPAeLDAQD9Ybnec5uB2KDpyl2UUyix25JJcIZAaSUqA6xbUOd6D8rRBJpZocOHQwiwkTBbhhVDASSWHeUHxtGEQAAEABJREFUxROWCebuu+82FlDf5ohFFNsi7BRRV1KWhKQCSQ91sAPDSJ5nwHaV64GJe6LuYUILzOeYxQGJKqQZc4BnnnmGbKU0QsDzPKduDmwOqSnJz2PRYJOApIMxhCME/cuijm0l6lAWdz4J3ePX06cQEAJCIFoQEFENYU+z2GDUDBnwb8MOh9emQkpEUn1UIv+TjQgqWmxVfQLKt0IFj6QU+8MePXq4N7Gg3sIkgOskJK/YEnEcmJCooirDnCAwP/ExZIdxlTj/WOcQJsYihBopLurnY9XR9ZQjgOQOkom9MtJV+hPzEEwAAlthg4EKDw0MkRqwk0RqgmmGPy7YaJAC62XIY30pISAEhEAiBERUEwGSlqeo7Yijis0ZMVQTh5FIy3uprZOPAIQDiWibNm0M9TzmADjO4OXK0zEWVq1a5eIIPv7443bzzTeTbZBZnA7wlCVA+W+//Wa1atVyr3vEGQebLsq4wnF/aBspKsSH62XKlDFIUdylY/5HSosJgF8QCS+Sf6R4fp4+0w4BbCBxysEkA8cH1P5gHngHJNuEq6NPsZMkeZ5nOMf50RrY7Dz77LOB1XQsBISAEIgKBERUT6ybj1obKRc2gXhDYrDPJ57/SE6OWlEXIwIBvCex90S9T2ghCAcSUDYkeF0iqcTsA/tUpGMXXnihe+UdZBSpGfaphJsi7BAOdzh9YOgOAUUCiwc4EjaknngnY4PKKweRyJJwxLv11lsNooq3LM+BXeMHH3xg2KyOHTvWuD8EmGu0ib0S9o6MRUDG0Yf4vnhHc66UtgjgWIdWBQ9nfvtsYpgXJk6caKVKlTJMQBo1auQc7oixjH0zkm6eAicUpLGMHcYVpJd8JSEgBIRANCEgohrC3kZCgiPVwIEDXdgNTABQ3/kSthDeWk2nAwKo+4lRSuItY9wSUomqnjyclMqWLRsfN7d69eqG48agQYNcHuUgtUhXIZvUgUTi0Q8JhfjirUk+pBhp7F133WUQFuoSn9cnmJAayk2YMMEgNbTD9QYNGhgklWvYquLZzzkEmedFkouJCuYDnCulPQJISNlggDm/f+4AcUXCig0xDnT0D2HOuOYn3trD5oRrEFg/P+N/6hsKASEgBP6PgIjq/7FI8yNUfhAXCKrfOOGJUO365/oUAicLAaT82NVCniBFJ+s5dF8hIASEgBAQAskhIKKaHDKpyE+uKN7bgZIqJCp9+/Z1Kr/k6ihfCKQXAkQOQPqKiQGhZdLrvrqPEBACQkAICIGUIiCimlKkjqMctmbETUV9iwML7+Ul3BDq1+NoTlWEQJoigL0qdrOEuUrThtWYEDhxBNSCEBACQsAhIKLqYAjNn23bthnOEpBUAn9nz57dCLz+559/huaGalUICAEhIASEgBAQAhkIARHVtOrMIO2g9if0D3aphCZCcsX55MmTg5RWlhAQAkJACAgBISAEhEAgAiKqgWik8THSU0gqr8EkzBBv/sETnJBAaXwrNScEhIAQyHAI6AsJASEgBERUQzgGiH/ox7+88847jVBAhBXirTMhvK2aFgJCQAgIASEgBIRAhkBARDVNuzFhY9ikEtQdhyreNJQnTx6rXLmyycM6IU46EwJCQAgIASEgBIRAMAREVIOhkoZ5u3btMuKpfvbZZ0ZQeN4ORPDvNLyFmhICQkAIZFwE9M2EgBCIagREVEPY/bwZiLcOjRo1ynhjEGnr1q2m4OohBF1NCwEhIAT+Q2DPnj2GRgszLF4v/F92gg+ECJdeeqnxYpavv/46wTWEDGjD8DVIcEEnSRDg1dCNGzd2rwMeM2ZMkutk8Grnq666yi655BLjFc/kLV++3NWhj0i88Y98peQROHTokHXu3Nl47fa+fftcwaVLlxomhmBI4i2GvPnwiy++SIAvr/R2FSLoj4hq2ndWghbLly9vePmPHz/eSEyWDKYEhXQiBISAEBACaYrA4cOHbcCAAXbrrbfamjVrHDGaPXt2gnuwkHfp0sUWLlxoaLsGDRpkOL9SCDIwbNgwN38fOXKELKWjIDB16lTLmjWrrVixwt566y3DPyOw+E8//WQQ1b59+xq4Q7I2btxoCG+4RmKjwItyAuvpOCkCQ4YMsbfffttw0vbH5jnnnGPdu3c3cERrC45nnHGG5cyZ0+WR36lTJ7v44ouTNhjmOSKqIewggqnnz5/fZs6caex2SIMHD7bvv/8+hHdV00JACAiBjIZA6r/P77//bhCh66+/3k455RS75ZZbDDIV2BJk9vzzzzfKep5nhBTkRS2UgXAR8zpfvnycKh0FAXBEIIMQ5rTTTrM6derY6NGjE9SAUOFI/Pfffxu44rOROXNmQ8LqF8TZuFy5cv6pPpNBAE1t165d46+CP0Q1JibG5a1du9aQtHJesmRJl4dWgJcPlSlTxp1H0h8R1RD31ldffWW1atWy2NhYl9599123uwnxbdW8EBACQiCqEfjrr78MdTQvXAGIc88919atW2cHDx7k1KVChQoZIQNr165tPXr0cHN07ty53TWkrzfffLP5xNVl6k9QBCChSK3BmAJI7YgZzrGf8ubNa1dccYWxBr7xxhvWqFEju+CCCwynY8rs3bvXNm3aZJdddhmnSqlAAAftQJNCTC/uuOMO14LfJ7x0iE0EbyR0FyLoj4hqiDqLZrNly2ZMdLFxJBWVBwnVBrshrisJASEgBIRAaBD4559/7MCBAwkah6Si0vczUY0+8MADNmnSJMOXwL/WsmVLQ/KUI0cOv6g+j4EARDOwCBK9wHOkqWXLlrVly5ZZ7969DWlq4PXNmzc7iTaENjBfx6lHADMWzA4DaxLXnU0bktfA/Eg4FlENYS8hfi9VqpSxW0fNQWK3zg4dI31IK5NpCB9BTacDAix+2LVt2LDBSXCYoFHFpMOt3S2wU+JVvVu2bHHSIlQ8XOA5sP/iWOnkIUA/oIJmIU7u947qmT7ktcv+2OGTc/ITE66T921O6p1TdXPsJbHP8zGHhObKlcsgp35DYEsIwR07djhJX5s2bYx+wlYVNTZSKVTaRYsWtd9++82vps8gCGDm5mPNeI2JiUlQijly3Lhx9s033zjb4ZdfftnA3y+ERBbJIGukn6fP1CMwYsQIe+qppwzpqV+bNQptQuHChRPk+9fD/VNENYQ9xCLz5ptvGgnD58D06quv2n333WfTp08P4ROo6VAjwOI3cuRIw9u1RYsW1qFDB+vfv7+hdgz1vWmfMca4atasmb3//vv20UcfxY8pNkosuJRTOjkIQFKRHuFc0rRpU7dIJ34SFujmzZvb66+/bg0bNnTOKJQhagj9Smrbti1ZSqlAAGkopJQFGtX0jBkzDHvVwCbYXLLBIw8JKuQK+0mcfXA+IVILUkA807FlpZxSUgQgmJD5xYsXu4uffPKJVahQwR37f3jhDWOaDRhrH5EWyOM6BJf56sorr+RU6QQQQDNQqVKlBC0gvMCsAswTXIiQExHVUHZUXNvY3xCKgwESmDDsxyaKyTSumP5HKALEyUVC8MILLxiT83XXXZeu3wRvZZwTiNGLujLw5pdffrlFYiiSwO8Q6ceoOSFD7du3d57QbGqQngZ+r169etnDDz9sqOsYPyw0SEA4J8RMnz59nGoaT97Aejo+OgI4RlWsWNF5mlerVs15pEM6+c3WrFnTbeiKFy9uqEOff/55Q3hw3nnnGU6wR29ZVxMj4HmeIX3GrheskVzffvvt7m2M2KPixFOgQAFDWooktXr16kYZbFlpC6I6a9asJBsJriklRYB5H6EEaw9mK34JJNRsjn0HKj9/1apVxgYBqbefF0mfIqoh7C1imREOokaNGhYbG+sSxvuI32P/O8e4PISPoKZDjADqRWytIBOe57k4dlWrVnW2VkwMqBUZB35sQCQOSNhLly5txYoVMxbNBg0auDh3LKr+4yJho969995rSBowFaG8HxOPSYqyLMZMVKiW2fRgZnLTTTdxyQiF9tprr7lj2qE9P1EHmzI/H6LrCupPmiKAJA7yg706izSLM4uJfxMk70hUWbAxCSKkDKpn+gYVHn3JGEMC5ced9OtG62dqvjeOUoRB4veEZBrHEvoCUoq3Of3x4YcfulA/7733ntWtW9cRV/8eCBnYMNAHfp4+gyMA6UdzQGrdurUxH2EPySaeaxxzjU0b/cHclD17dtcYY598/9xl6k+yCOBwxrjEaQpNgF8Q5zR/bfDz+AR/xj84cx5pSUQ1hD3Gjw6iglQEEkLiR9ukSRMjDAdEJoS3V9PpgABEEaklakLUjDjMIQ1DFYbXK7FzuYZ9MtI0JvCOHTsasRtRL6KORKX43XffOfvSgQMHGrbLTOrffvut85IlZh5SCsLlEEUCUox5AV8PwktAcwznGV9I6wjFQ9uQVl+1Rn1UoCzad999twtu/sEHH9gzzzxj/fr1M46xK6NNpbRBALMQPG09zzPP+zdBTHfu3JngBowVNjBkep5nqKk3bNjgHIHYBJGPvRmElmOllCMAtvz2YmJiDIJKTX4fSPf8c+ZpzpE2+XmUI7Gw8/v0PI9TpaMgANYQf7BmXqQo4xfy5J/jmQ7WlOGYMiTP8wycOVY6NgKsNWBIYq3wa2TJksVtEPxz/xNHtsByfn6kfIqohrCnkIQtXLjQypUr59QiqEbY4TNxshCh7gjh7dV0OiGAbRZEE2kM6n+kNdi5QUwhhTzG8OHDDe9ipKdPP/204dkKkZk8ebKzW8S+FWknkhtUYNTF9hRJKqpIiC2TTatWrYwJhwWUdklIJpC+IbF79NFH3SYI6SukmOukhx56yObMmePUoDwniwSbKAKiY0NZtmxZ50RCWaW0QcDzPKduDmyNfiP5eSzg99xzjzML6NOnj9GPSKKQ/Pll/E/K+sf6FAJCQAhECwIiqiHsadR5SLAIR0XMOD8hUUU9HLhghfAx1HQIEUBFi+0gxBRiSMLBCUkakjFU+0e7PZKy2NhYZ58Iya1cubIr/uCDD7o81DuMFQisu5DoD2pipKeoiH3CiZQWEhxYFK/m+vXrG4QVYs017g2p5h4kxiv5SmmDAJI7pHTYENMfSNrZnLKhCLxDlSpVDLs9JNpEcMB+nViHmAkhIacsoWXY5HKsZGYCQQgIgahBQEQ1hF0NEUAli/TMvw0OLtdee61/qs8IRwBy0a1bt/i3jUFcUb8g/UQtz9tZkIryNZGa8uknVGW89QY7I8JIQWRQ6UMkO3fu7IoREmdynNQVlbHLSPQHdTB20JBjTEkohwoNkhRYFG9ywu1AiggRA5Emxi+SWsrxtjRMFThWSjsEkGzT/3v27DEk5Tt37jScKgPvwCYESTh2xmhfSJ7nGZsW+h6TDeyN6bvAejoWAkJACEQDAiKqIe5lbHROPfXU+LtAIEjxGTqIaAQgm88995wNHjzYOUQhLceDGIkYxBC7URypUOuj8ofU4hSD8xJ1ISio9m+44QYjNAtxd8nD7pQ6mFk0IZIAABAASURBVA7gxUn4KyRxeMtCYgk1gu0zZXDAuf/++w0nPSS73J9XRTZv3tzmzp1rmBsg9e3bt6976wuvN0TC2r17d+eVSxuQXdqJ6M4Iw4fH6YFQMXhAEz6MOJ3MCRMnTjT6ev369e4NPfQBm1qk576mBUn7kiVLjPxhw4YZm58w/Ip6JCEgBIRASBEQUQ0pvGo8oyOAvSghhSCBOE0RFxfVLd8bIkrsTPJJkBHU+xz70lXq41xFHs5SvrQNkknevHnzDEJKyCLOkb76beBYhWMCoXZmzpxpXMfulHvzDJxDVGl//vz57jp5JCR9GOTTHudI7KinlPYIICFlo4BJhm97CnFFwoozRLt27Vzf3HbbbQlujh0xHrz0j9T+CaD570QfQkAIRAMCIqrR0Mv6jkJACAgBISAEhIAQiEAERFTTsdN0KyEgBISAEBACQkAICIGUIyCimnKsVFIICAEhIATCCwE9jRAQAhkcARHVDN7B+npCQAgIASEgBISAEIhUBERU07vndD8hIASEgBAQAkJACAiBFCEgopoimFRICAgBISAEwhUBPZcQEAIZFwER1Yzbt/pmQkAICAEhIASEgBCIaAREVE9K9+mmQkAICAEhIASEgBAQAsdCQET1WAjpuhAQAkJACIQ/AnpCISAEMiQCIqoZslv1pYSAEBACQkAICAEhEPkIiKievD7UnYWAEBACQiCECGzZssXuv/9+i4mJse7du9vBgwcT3O3vv/+2zz77zC699FK77rrr7Pvvv7fDhw+7Mv3797d8+fLZ888/bzt37nR5+pM8Alu3brVnn302Waw3bNhgjzzyiBUsWNCl4sWL2zfffOP65OWXX7b8+fPb9ddfb3Pnzk3+JrriENixY4c1atTIGjZsaHv37nV5/p99+/ZZxYoVbcaMGS7rjz/+sBtvvNEuvPBC69Chgx06dMjlR9IfEdVI6i09qxAQAkJACBwFgf9fgpQOGTLEnnjiCVu3bp2tWLHCpkyZ8v8CcUfz5s2z5cuX27Jly6x9+/b26aef2i+//GLr16+3kSNH2oIFC+zqq6+2zp07x5XW/+QQOHLkiE2cONEgn6tXr7bRo0c70h9Y/qyzzrLWrVvbTz/9ZJS599573UYAjHv37m1LliyxBg0a2BdffBFYTcdBEFi0aJHNnz8/yZUDBw7Y4MGDHZb+xUGDBtmYMWPcpoDfwG+//eZfiphPEdWI6So9qBAQAkJACKQUASRJLMpXXHGFZcqUyUnrfCmT38aqVaucJO+UU06xyy67zM4880zbuHGjDRw40G666SbLlSuX3XbbbY7A+nX0mRQBiOrMmTOtTJkyduqpp1psbKx99dVXCQqec845TqpHJkQVSeDFF19sEFjqs0H4888/7ZJLLqGI0lEQKFu2rNWuXTtJidmzZ1uBAgXcuPUv0hc5c+Y0JN5Fixa1c889178UMZ+ZIuZJM+iD6msJASEgBIRA2iMAEdq+fbtlzZrVNQ4J3bx5s6EadRlxf5C6soBDlCBYnucZUikkrSzucUUMgvXrr78apJdzpaQIgN/atWsd0efqeeedZz/++COH8YnNQJYsWdw5Kv8bbrjBOC9cuLC98cYbVq5cOSf5rlKliiujP6lDADMXiP61116boCLjHm0BuPKbyJw5c4LrkXAiohoJvaRnFAJCQAgIgZQikOJySJgGDBjgVNEQLez3zj777BTXV8HUI8DmoFevXlahQoX4yiVLlrTTTjvNkMpidhF/QQcpQoBxi7YAovr7778bttfbtm2z/fv3u/rYso4aNcpImMG4zAj6I6IaQZ2lRw0vBJAiTJ8+3fr06eOSP8EijekTl9evXz9DEpPcU7MwUt+/zo6YesOHD7fdu3f72Sf0iXQIBxEkSyfUkCofNwJI8VAlYy/pLxzBGvvrr78MezL6i4UGyR92koyJYPZowdpQ3v8ROOOMM5ya08ccfC+66CI7/fTT4wuh3seRp02bNk6qh9SvUKFCds011ximAxTctWuX5cmTx84//3xOlYIg4HmeocYHYy5DkooVK8ZhkgQZRZqaO3dud23hwoXWpEkTwwzjwQcftI8++sjl60/KEcABkPGKuUWLFi3cujNixAhnxuK3gq01GzPK+XmR8imiGg49pWeIWARQpfTs2dMgnYFfggkj8Dwlx7Vq1XIesKEiJkxmOI0cjTyn5DlVJuUIsOFAeoSX7sqVK+2TTz4JWhkHk7p16yZwghg6dKh9+eWXrjwSEX8j5DL055gIoLrH/hFnKTaV48ePt9KlSyepV61aNXvhhRccOcWZJ3v27Pb4448b5SFew4YNczaXSSoqIx4Bz/MMYupvqHCauuuuu+KvBx4g1Xvqqafis9igY37Bxuzyyy83+ir+og5ShAAmFDVr1nS21DgEEq2iRo0azu7abwBhRY4cOYzNmp8XKZ8iqpHSU3rOsEPA8zzDHggJzN13320xMTHuGZHGkP/00087SYzLDPIHCQQSHS6xmE6aNMmYXPCAJUwL+SeaUKfdeuut8ZIlSDUSohNtV/VThoC/cFetWtWeeeYZZ7eHhDVx7YcffthIr7/+utFf2EvOmjXLOfLExsY6FR5jJHE9nSePAIs3uKPZ4DfKb+qWW24xX9oHAWVDye+VUFT169d3Yapokd8yZBXytWbNGqtXrx7ZSskg4HmePfDAAy60VMGCBd1YZxz//PPPLlQSXudU5ZxN280338ypS7fffrvbQOD0hoMQfeYu6E+yCLBW1KlTx218GbvJFoy7cMcdd7hwYMwvd955Z7xDW9yliPkvohoxXaUHjUQE2MU++eSTLoYji1+pUqUMVRfSg6ZNmzp1I4SEyRnpLF6zSD0JI8KumNh3qHKw60KiBrGFFBMX7+uvv3aeyUiDcF4gTmSrVq2cx6cf4gUpHrELcWzAJqxHjx6OELGIIG1C7cn9WCTefffdSIQ4bJ8ZyRCS9mzZsjl1M7aPSDQ2bdqU4JmRuLKAsPCwgE+ePNlJlUqUKGHjxo0zzENQkyYnoUrQmE4SIID0CAkeEmvwhbzyW+H3gJoZTDHV6dSpk4FxYOXYuA0CsT+RgiNlDbym46QIXHDBBcZYDsSa+QvJNKGoqME5G/FAhx5MMT7//HOnpma+YjNBWaXkESASBVJohA5IUhOXJHyVrz2YMGGC0SfMI8wzmLckLh/u5yKqYdNDepCMiAALHQshxBHJDER0zpw5zruVXTHfGfLKYohN3bRp04wJH+kO8QUJN0JissfOEbU9xBQ7r27durkJCOKKXdeHH35oRYoUsXbt2tm3337rApcvXrzYOYpwH6RH5cuXd+pk7CAJzk1dAm3XqlXLXnvtNYoppREC//zzT7ydo98kqmSSf44tKgHOUX8SsgcpPHapbFoeeughZwpCH6lvfMT0KQSEQLQhIKIabT2u75umCKCizfpf+JtgDRN6pWzZsvbiiy+a53kuZiDlCIyNRIfjxAmpAm/KIYYjEiEcD8iDvEJEuUadt99+2zCORwJBCB2kRZUqVTIkeEgsiB3JrvrKK6+keJLEG3dQyREEnXJJCijjhBAAf6TWgY0wVkh+Ho4+SEYw+WAsIUXFcxcJOpJ3+ha1M0QVibpfT58pREDFhIAQiHgERFQjvgv1BU4mAqhRIIWJYywmPk/NM6IKQ2Xs18HONLFa0r92Ip/YyKKKxvYRKe6JtKW6SRGAqMbExLi4nBBPSCmS0rx588YXJsYhEm08nsmErOJdzrhCjcpGBLtVbCWRglNGSQgIASEQTQiIqIZXb+tpIgwBSCX2Qth3IvEidenSxbCD46tATvbs2ePeCoLKd+fOnYYzAYljpGccQ2yxaUQFjL0o5Jcg2Ngaoe7HPADJG7at2CXhwU9Z2ifcCAkyRFk8zbkn9rEcc19i60F+8uTJYzg0UBYJLDZj2C/xPXhepbRFAC9m+hmJOOFi6Fc2CBBWbFUxD8AJBdthTEOwJ0Oqij0rmxPMNXhjDyYc2Cyn7dOpNSEgBIRA+CMgohr+faQnDHMEcFYifBBOTyRIoG/gDimEEOIwxbusIaW8opE3s0A8IY/YquIY9eijjzqnK4I3E2IE7/COHTtaxYoVDRMC4nDyejzIK4SVmJu89pF3bONdXqpUKcPDGbIKIcIRB4ID6UG9z3NgRoCXKGWQ+BFbD1tIyoQ5zBH5eJBLHOWQjkJWmzdv7r4Hx7wths1EuXLlrHHjxsZmBxMOnN4ohCTV8zxjTOGQx4aIfKXUIqDyQkAIRDICIqqR3Ht69rBBgFA2kEsSpNX3EkY6Sh4Jj30+kbjyOjuO27ZtaxBFHKA4h5SgGkYdzDkJosoXJSg259iWojLmmMQ5AcvxpsWL/5VXXjHyiVXo3wfSA3mtXr26u0b7SPOQtHI/2lcKDQLYCHft2tUgpr7NKhuEDh06xAeRZwzQZ8Tx9DcNxAHFG518bFhD83RqVQgIASEQ3giIqIZh/+iRhECoEcCsAPtH1P5IZUN9P7UvBISAEBACQuB4EBBRPR7UVEcIRDgCRAzAgQcJK+YHEf519PhC4FgI6LoQEAIRioCIaoR2nB5bCAgBISAEhIAQEAIZHQER1XDtYT2XEBACQkAICAEhIASiHAER1SgfAPr6QkAICIFoQUDfUwgIgchDQEQ18vpMTywEhIAQEAJCQAgIgahAQEQ1rLtZDycEhIAQEAJCQAgIgehFQEQ1evte31wICAEhEH0I6BsLASEQUQiIqEZUd+lhhYAQEAJCQAgIASEQPQiIqIZ/X+sJhYAQEAJC4DgQOHz4sP3666+2fv16+/PPP4O2sHfvXtuwYYNt3rzZDh48GF9m27Ztrt6OHTvi83SQPAJgzZvuwPqvv/4KWhCsN23a5PD++++/48v88ccfDms+aSf+gg7iEQA7sCXFZ8Yd7Ny502HHOOdtg3FZduTIEfPzGcfkkbjOOKeNrVu3WqRgLaJK7ykJASEgBIRAhkNgyZIl9uqrr9pbb71lnTt3tu3bt//3Hf/92LVrl/Xq1cuaN29ujRs3tsGDB9uBAwfcwt+hQwd76aWXrF69eo7E/ltDf5NDgLfcvfbaa/bmm28aLxRJTPDZKAwcONBhXbt2bevbt6/bGKxbt871TdOmTa1hw4bC2pL+g9SDXYsWLezOO++0MWPGuEJbtmyxTz/91GFet25dmzt3rsuHhLZs2dIoX7NmTVuxYoXLnzFjhsXGxrryjHc/310M4z8iqmHcOXo0ISAEhIAQOD4EkOqNHz/eHnroIevTp4/t37/fJk+enKAxFvZ9+/ZZjx49DKLE9Z9//tnWrl1rLPzUy5w5s3311VcJ6ukkIQJI8CZMmGA33HCDI05IVidOnJig0OrVqw1paps2bRzeo0ePdjhDaGNjY11etmzZDJKVoKJODEnohRde6LBt3769G5u7d+92WoJ77rnHPvnkEytdurT9+OOPDq2vv/7a8ufP78rfdNNNbuPAhWHDhln+805aAAAQAElEQVTlypWtZ8+eVrBgQZs1axbZYZ9EVMO+i/59QP0VAkJACAiBlCMAAdq5c6ddeuml5nmeXXXVVfbDDz8kaACV/1lnnWWZMmWyfPny2WmnneYkejfffLNdcMEFds4559jVV1+dRBKboBGdOBXyvHnz7Nprr7VTTjnFHn30Ufvmm28SIINaH/X1GWecYXny5HH9smzZMitRooTFxMQ4cwD6oFChQgnq6cTcuCxfvryDAonqnj17jA3WJZdc4nDk+LfffrOyZcu6MvPnzzcfR4jppEmTjM0D+H733XeO+NIPxYsXd+XD/Y+Iarj3kJ5PCAgBISAEUo0AElTIEcSJykhGWawhS5ybmSOoqKyx1fM8zxFapIMQVsogyaIOUlnOlZJHYOPGjY6kUgL8sIPk2E9sBpCW+vhzDu70z8yZMw3JIATLv+7X06e5cXnqqaca/9asWWMPPvignX/++W78QvavvPJKQ62PKQvjl3KMa8Yv+B46dIgsZ1pB/dy5czuJKxJwdyHM/4iohnkH6fGEgBAQAkIg9QiwQEOYAmtmyZLFIKx+3vXXX2/Tp093KtLPPvvMqVJz5crlX3a2fUitkPjFZ+ogKAJI6AIvnH766YGndvHFF9vOOAk3tsLYpyLNLlCggCtz44032oIFCyxr1qyW2GTAFdAfhwCSVEwsXnnlFXfOn8svv9xWrVpl5cqVsy+//NKZuED6Ma3o0qWL9enTxwoXLmyM6+XLlzvV//3332/YvGIfTBvhnkRUw72HAp9Px2mKADvPoUOHWrVq1Vxi94nzBOdMptxs6dKl7hrOAZwHS1OnTjXaCXaNPGzlcDAI9L4kP7UJ5w5s6lJbT+VPLgKLFy+2OnXqOGkGC02wp8FGknH30UcfuYUmsMy0adPinScC83V8dATOPPNMO/vss83HHEcq1KGQVb9msWLFrG3btk61+ssvvxjEqWDBgu4yXtSzZ8+2ihUrJiC37qL+JEDA8zyD0IMxF7DxxWSCYz+BLfMgn0j18ubNa5As/zpECjtL5mE/T5//RwAnv7FjxzpbVMbx/6+YMaZR+7M5AD+OsbnGdAXy+uGHHzonQeYZNmeQ15IlSxpklvKBbYXjsYhqOPaKnildEPA8z1CZsEBhW4Uq6pZbbnE7e37EPARSAOx4+OFzHiwxId92223BLrk8JokPPvjASWtcxn9/mCCw42JB/C8r2Y9u3bo5D84NGzYkW0YXwg8B1MYff/yxxcbGGk4NzZs3T/KQbHIYg02aNHFjj8XIL8TmBueJSHF68J87HD4hPtmzZ7dFixY5G8ovvvjCypQpk+TRIEfnnXee+X3gSwZZzFGNcg1nlSQVlRGPgOd5Bun3xykk6b777ou/7h8wr9InzGN4pbOZ8K+htkbCd8UVV/hZ+gxAANU+5izYWvOJZNW/TFQApKXYoIIpZgLMNwsXLnQRL6655hpHZrFlpR3P89w55Vj3/HbC9TNTuD6YnksIpAcCOFqgduKH7nmecwYoUqSIs/3h/thLMQH7O39+6NhekXy7Hwin/2OnHUKGcJ3E4le9enUrWrSoIcElD/KBbRZSBUgIXsbUI3GdxO6Z+3MPiGylSpUc0SEvMHFv7L6og40Y50g1OOc5OMfInntSj8WAa8TS45y4kdjxUZb6fD+OKcMnZUh+PfJJhJrh+0DEOPfbp6zS/xGYM2eO4a3LZgcHHfoTaZNfgnEwYsQIe/bZZ91Cf9dddxlSPKTw9B31KZNYhe3X12fyCPCbrFWrlnPqYcPJ7xDiyVjlN40HNN78LOJoRVBH43VOi0hRu3fvbvfee69zSuG3Sr5ScAQ8z3PRFZhrkEgTMonNP3MbWBJOCW1QiRIlDDvUzz//3Em7aQ1tFXVKlSrlHN6Q+JGv9H8EcIZijiDcFGsW4xipar9+/Yy5hfWFcf300087e1bWleuuu86ZAzz88MOuIc/zDJMBnKkuu+wyFyHgkUceceVdgTD+I6Iaxp0T/NGUm9YIsFCxy6RdVIWoT/gxcw6Jw24qR44cTiozaNAgI1YgEwIqFa43atTIkHhC3LCv4hyJAosjakXCiNAW5YldR4J8vvPOO27SZoGkHUKGMBExIbGI0h4TFHEgCekC6aWdwIRtEu29/vrrhlQXz1uktCwS/r3btWtnTGiQX9rhHnjlYtbAM3FM/ffff9/GjRvn4kZSBukyJJhna9asmSENfPzxx53TA/ch1EyrVq2cpLdq1arGRBn4bNF+DMGExEMyUc0xjpDWBfYjhNRP4IU3NJsANhBIlyCrSPy4ppR6BPDohxThWOI7RCEhJYwPDilsDBjLjP3A1glrRR0/Jb4eWFbH/yLA3MmcA2YQfHLZpIEl58xJSPiQpHLNT2+88YZRB4ngk08+6WfrMwAB5nYw8hNaAtYX1iGcqdhIMT/7VYhHS/4DDzzgZ7lPzC4wJaIdbLL5LbgLYf5HRDXMO0iPF3oESsTt8pEwEucPUgaZwAsVoojxf86cOd1DQC6ZiN1J3B8IZNxH/H+ICY4Zd999tyFRQEUDCTz33HNdGdQsHTt2dPZuSD2R2DDZvPzyyy7wNWTRFYz7g40sZIUA5EiFAq/FXY7/jxoNu1rUlBAapBWPPfaYsYvGYB6CjUTjqaeeck4jEFMqE7oHQ3uOSQ0aNDDsI5EcI+Hg+SFJEGEWdaSt5EHSmfwwd4D8ghP1CfsDSedY6V8EGA++feS/OebsT1Hb+eeQVzxwCTpPHtcYJ0gD6Xuk/eQrhRgBNS8EhEDYIiCiGrZdowdLLwSIlwjxYuePVBC1CtJCyBmfqAl5FlS2kNZ3333XSSiRunKOqQAexpALdqxIISG7qPL5pC4J9T3lIDAk8vwESYaw8Aad3r17uzfkoCaGKCKVgOzifOCX9z/J49mJlRdo2/jMM8+4INCQayR6SDtQIyNB4h6oPJFsoDbCI5T2Pc9z3qHYOJUtW9Ygs9wHJwlw8DzPOZ0gHeR7oMrjPrQHQcbTlPJK/yIARoyPf8/+/Qu2pH/PzCClSMSHDBniAnA/8cQTzlQACT/SatR3fKLKY0Pj19OnEBACQiBaEMgULV80g31PfZ00RACCWaVKFcOrHjJ6xx13GAb9OMFggwmZ4HYQWqSMEL+YmBhDrUi+nzzvXxtXCC82V/Xr13ft+NeDfUJmyKdtyCDExm/b8zwngYP4Ugb7UWxFIcCck7CtQwr63nvvGd+BPBKSOL4L6h+eF3MGDO0hn9yLewSG6aEOEmQcTpACIy2lPPk8E1JavhMSZaS1kC3aQG1NuYsuuoiiSgEIeJ7n7BvBCEkp0nnwB6+AYs5bGueSKVOmGJsgNjRsKJDGoqLD7AJVXuvWrQOr6VgICAEhEBUIiKhGRTfrSx4LAbz/IYAQO6SUqO8hZYGG/RAzpKeoxlG1cx3nAcIPIV1EeokKHJKIlBGCiynAkiVLnHRy+PDhzoAdeyLU5BBkPJNHjRpl2CaioieMEW1j84rkEskmBJo8pK6QGcgz3wdiyf0h1Tjg4LjAvXwbSGxdCT+CNJXyeIFicgCppT3IKN935cqVhrQVEs53oD1sY5Hoks93g0jxnbBRxbYVwoxpAd8DMwUS3537KP0fATYR9CN2kl27dnUOU5B6NiWo9rFfZrNBf/Tv39853eHo8P8WdJR+COhOQkAIhCMCIqrh2Ct6pnRHAFtPVO41atRw94Y0EpIGYuoy4v4g/YSk+uTVN/xH+kWKK+LUtpBOjiGSkEtsTXFOwnCd+zz33HPudY0QmLp16xp2pJTnfhjNcwxRRuKJJI1nIQ8bUdqBSHPueZ7deuutztuWshjWlytXjksu8Uy0A8EmI3/+/E5i558jJSUPRyiegTYwIYDQUh5nLzxMMV+AyJNHQsLL9+LeSPv4TtS//fbbuawUgAB9Dr5sFlDj0/cBlxMc0n/YGSfIjDuhD9kUxB3qvxAQAkIg6hAQUY3gLtejpy0CqPx9EgfxgjigMg+8C2rz2NhYi41L2HfyGjukjJxTB7KJnSHnODAhpYV8cg5hIcIAx4QFQaIKuaM+dSF8XCP5xBDS67dHWWxgA58HIoQqnjp4NXPsE1lsWytUqODi5VHH8zxnikBZEm0TMonjsmXLUsS9Vo9zEu3hrYsDFlI+8kiQW+4LcScuJXkk14D+JEEACSp9TJ9nzZrVXUf9j4kFBNbHkHLuYqI/SGVxzEuUrVMhIASEQFQgIKIaFd2sL5keCKDqR+2PcxP2nDhhIXWE0KXH/UN1DySvhD7hO5EwL8DxK1T3U7tC4CQioFsLASEQZgiIqIZZh+hxIhcBJKR4ZuMAQ8JjH0lk5H6jf58ciR62tnwnEnEPE0ua/y2pv0JACAgBISAE0hYBEdW0xTP9W9MdhYAQEAJCQAgIASGQQREQUc2gHauvJQSEgBAQAseHgGoJASEQPgiIqIZPX+hJhIAQEAJCQAgIASEgBAIQEFENACNyD/XkQkAICAEhIASEgBDIeAiIqGa8PtU3EgJCQAgIgRNFQPWFgBAICwSOSVQJR6NU0ISBMNAY0BjQGNAY0BjQGNAYCO0YSMyOj0lUE1fQedgioAcTAkJACAgBISAEhECGQuCYRJW4iUo/mTAQBhoDGgMaA9E2BvR9NeY1BtJ7DCRm2cckqokr6FwICAEhIASEgBAQAkJACKQHAiKq6YFyOt5DtxICQkAICAEhIASEQEZBQEQ1o/SkvocQEAJCQAiEAgG1KQSEwElEQET1JIKvWwsBISAEhIAQEAJCQAgkj4CIavLYRO4VPbkQEAJCQAgIASEgBDIAAiKqGaAT9RWEgBAQAkIgtAiodSEgBE4OAiKqJwd33VUICAEhIASEgBAQAkLgGAiIqB4DoMi9rCcXAkJACAgBISAEhEBkIyCiGtn9p6cXAkJACAiB9EJA9xECQiDdERBRTXfIdUMhIASEgBAQAkJACAiBlCAgopoSlCK3jJ5cCAgBISAEhIAQEAIRi4CIasR2nR5cCAgBISAE0h8B3VEICIH0REBENT3R1r2EgBAQAkJACAgBISAEUoyAiGqKoYrcgnpyISAEhIAQEAJCQAhEIgIiqpHYa3pmISAEhIAQOJkI6N5CQAikEwIiqukEtG4jBISAEBACQkAICAEhkDoERFRTh1fkltaTCwEhIASEgBAQAkIgwhAQUY2wDtPjCgEhIASEQHggoKcQAkIg9AiIqIYeY91BCAgBISAEhIAQEAIhQWDjxo324osvWsGCBW316tX2559/WpMmTSx//vz20EMP2Z49e9x9N2zYYDfddJMrV6tWLdu3b5/LD/c/Iqrh3kNp+nxqTAgIASEgBISAEMgoCEBMIZ1Fixa1n376yS699FJbs2aN3XfffbZs2TI7cuSItWnTxg4dOmTz58+3kSNH2sKFC+2XX36x0aNHRwQMIqoR0U16SCEgBISAEAhLBPRQQuAkIYDktFmzZnbZZZfZnDlzbNSoUfbPP//Y74nG2wAAEABJREFUhRdeaLfccoudddZZ9uSTTzopK494++2327nnnmtnnnmmlSlTxrZv30522CcR1bDvIj2gEBACQkAICAEhIAQSIrB27Vr7+eefnSr/rrvusk8++cR+/fVXO++88+ILova/9957LXPmzI64ZsqUyUlZzz//fEdw4wuG8UGmMH42PVpoEFCrx4kA9j1///33cdY+/mo7duxwNkeocDjeu3dvksbYGe/evdvl79q1y/zjgwcP2h9//OHUPu6i/hw3Akz469evN/qAvkjcEJKMTZs2GWV8/FG3sXCQR+L6/v37E1fVeQoQwJ4ODH///Xe30CauAv5bt251+P/222/xl8nHho9EH8Zf0EFIEWCc01/0SbAb0S9cowxkyy9DPnmk5H5rftlo/1yxYoWVLFnS6tevb+XLl3dkFHW/j8tff/1lEyZMcFJVP4/Pbdu22ebNm+3666/nNOyTiGrYd5EeMBwQ2LJli11yySX2/fffp/vjfPPNN862aNWqVfbcc8/ZV199leQZxo0bZ7NmzTImICatHj16uDIs2GPHjnXElQVg5cqVLl9/UocAEz7OCS1atLCWLVs6KUbiFsaPH2+xsbH2yiuvOEcG6kBMO3fubNR7/fXXrUaNGjZ79uzEVXWeAgTAsXHjxta8eXPjt5C4CnZ3derUsTfeeMP1A3Z6lMEmj7579dVXrX379m6jQX7aJrUWiAAbuX79+tlLL71kb775prOVDLzO8aJFixzB4ndRtWpVW758OdlOff3oo49a06ZNXV9DZt0F/UmCAKp9hBRcyJo1q51xxhkucY7AYtiwYfbhhx9yGp8QXGAiwFyVLVu2+PxwPhBRDefe0bOFDQKQkMqVK9vAgQPT/ZmqVKni7I2OduOnnnrKKlSokKRIvnz57Omnn3Z2SUiUOnXqlKSMMo6NwPDhww2V2aeffmp58+Y1xkPiWv3797fq1asbZbiOYwMLAYs1eSwYN954o9Enievq/OgIzJ071yA24IjtXZcuXZJUmDlzpttMUgaM2aDhMPLtt98aJLdDhw7GxoF2klRWRpoiwIZ4xowZ7rdQqVIlt3kLvAFElo11oUKFXBk80YcOHeqKdO3a1dhU8IlElXnLXdCfJAjccMMNBtY4STHWc+XKZTExMYbmb+rUqW6uOuecc9zGjutoFHr16mXXXXedu7Z06VKnrUvScJhlZAqz59HjpBMCuk3KEdi5c6czUEdShlqFH7xfG/UWEgHCghQpUsTPdlI38u655x7ngckFJl7yHnzwQad2YTJ59tlnrUGDBtaqVSuK2HfffefsjSjL5IO6s1mzZk59Q12kuizIEJ7LL7/c1eHP888/7ySt2CZhWA+pIp823nrrLZsyZYrdfPPN1qdPH8Og/oorrjCe9+uvv7bPP//cOF+wYAFVlIIgAEY4JXCJRZV+4jgwXXTRRTZt2jTnTYs6Du/bnDlzuk0C5bAnw2yjQIECnCqlAgE2ChBUJEilS5e2L774IkntCy64wC3ISIwWL15sFStWNDQhSJnOPvtsYxGnTxjnhw8fTlJfGWmHABu14sWLW/bs2e22226ziRMnWqBk1PM843fAfIpE8Mcff7Q77rjDPQDz15dffmloJDjGltJd0J8kCDC/QDyxTyUMFXM8vwPWqmrVqtkzzzzj7FDRsmEGVrNmTXvnnXfs4YcfdusM2rlTTz01SbvhliGiGm49oucJOwRQKUI8IHPEpUO64z8kEzDH2AoxQaCCZxHNkiWLI6jXXnut88ZEekA9CE7u3LldiJB169YZIUXuvPNONylDXFF/0SbqftqdPn26ffTRRwbB4ZxJHXsuVDfFihVzkgqkvKg3/TKUIx04cMCFJWHRLly4sPXu3dstBrSP3RKLPovINddcY++//75dffXVVFMKggCqZvqUSzglcM5xYHr55Zed+QWLRseOHe20005zUlh/0zBv3jwD61NOOSWwmo5TgAC/L/CkKPizQeR3wLmfGMsQ0jx58ljbtm2NMe95npOi4h1NOfoCExgkepyncVJz/yHA78PvL343zG2BRJVieJ3jgU5/oYlAOkg+8x0q63LlyjnPdOZc8pWCI8CcgokXmgLIKmOc8Y8ZGL8RNDsQUjYGAwYMcDFVySNhEuP3U/DWwyNXRDU8+kFPEaYIsAuF6E2aNMlQ7SLhxMaQiZcFD4kAEwU/dsqgfv/hhx+sRIkSBiHBNvHxxx93qmLKcg6BRB1DiBAknevXrzdstJhgcL5BmooqH3KM9AjVvw8PEzu7YSSnBHimTdpHwuqX8T95pnr16hmSPj/P/2RxwPge0or6h+f1r+kzKQL0VWAuKv3Ac443bNhgbDpQO6PmRyJEPoljpEy33norp0qpRCAY3khKA5vBJg9Sw+8FO1ZsVAsWLGiQ19dee815RKNhII/fWmBdHactAsH6i/ko8C5sHvitxMbGGv3DJp3rzJ9169Y1JLKDBg2KmBBKPLtSaBAQUQ0NrpHRqp7ymAigOkQ1AjH0PM+QkGLnxm6VyhBZSAjHgQlboMBzjlFtYT9HgogigUWtjw1Wo0aNHLHFiYB7YFPH7pd6ySXP8wzCm9z1Y+VjLjB48GAn+Uu86B+rbrRdZyPAhoLvjRc/mwiOA1Pr1q2dupn+RQpP8q+jbkaFSfLz9JlyBMAfCRA1kB5htpIjRw5O4xPBy1GFgj/aj88++8woAwlCJYpJAOO8VKlS5nlefD0dpD0CeJP7vxfmS7RIgRtmJNpohdh4019s8AitxFzKJgOVNWZKaInQRKX9E6rFSEJARDWSekvPmq4IMJkiJcPgn10/qXbt2oZ9G2pcpDKQTZw1MPpHGgq5ROWIOp6HZZKePHmyXXnllYYnPiqxnTt3GpICJnLU/9iQYvdKfcqiwsSmCMkCbQQmVP+EFSGvW7du9sQTT3CYogTZDizIs3NPiDILeuA1HSdEAPtUTCdYSHH6YGFNWMIMiR7SdxZk+hOJul8GG1c2J/65PlOHAA6BEFF+Z3iT81tM3AIhwTARIP/00093oXo4RuWJxgLnqoYNGxrjnvxQJbVrhvkLcxu/g549e7poF/RJIDb8lpB6I1mFyGLSQRn6GE0T557nueD0gfV0HH0IiKhGX5/rG6cQAew+WRAhnUhxIK5MoDjMoFJHAoAaHpKHhA2bUaQ1SCpRraNifOyxx5ykFAeq5s2bO9UwtqDYptIeEk2OuQ+PBXl95JFHjDA8eCmzOKOex+EKWy9C7+BNS9sQHxYEbLpQm3Gtb9++ztEEiSzl3n33XRszZoxh8wWBRkJM+ywgqOJoFxLteZIwgX9yCUceTCxwYENahwofMxAw5/WEmHNAZNu1a+ecFHBkYCzQHpI87PNog3Ol1COAJBS7O35nnue56Aq0Aub8dsCfT3Dmd8hvFCdHHNh4lSS/F367qJM9T2Md7EKZMJXB/hHTJfqEuY/74fjDnOh5nmGiwW+D39PHH39sOJSy+adP0U5gjsSmj0/qKkUvAiKq0dv3/31zfSSHQKVKlQx1Y58+fQyVred5hrE6eSQcAFC9E5uRc6SleKnSHuSRPJyhcBogjxiP5JGuuuoqu/jiiw0iyjk2WUgQmjVrZoQVIc/z/p3MOSbRBtIhnLU4h6SSh5SXECV4zqIyw6ie6ywSkFSOX3jhBSeZQEo7ZMgQF5oEs4XVq1c7j3/aUTo6Akj1wJIFGJKP9Oftt992MVM5Z1HlOgnHKr811NFIYf1zfR4fAmgawBbCyqaNVoiawe8T/JGU4i1OGUgp1wN/Y5wrpR8CRMegLyCh/l2ZM9Emcc7vBQ0UZZi/yCOxcSePhIc6pJd8pehFQEQ1evte3zxKEUDVht0e5BjJb/bs2aMUCX1tIRAiBNSsEBACaYaAiGqaQamGhEBkIIAaFSksamukHpHx1HpKISAEhIAQiEYERFSjsdeTfmflRBEC2IFdeOGFRigfIhpE0VfXVxUCQkAICIEIQ0BENcI6TI8rBISAEBACkYCAnlEICIG0QEBENS1QVBtCQAgIASEgBISAEBACaY6AiGqaQxq5DerJhYAQEAJCQAgIASEQTgiIqIZTb+hZhIAQEAJCICMhoO8iBITACSIgonqCAKq6EBACQkAICAEhIASEQGgQEFENDa6R26qeXAgIASEgBISAEBACYYKAiGqYdIQeQwgIASEgBDImAvpWQiCUCBw4cMBatmxpBQsWNN42GHher169+Ftv377dbr31Vrvkkktc+fgLYX4gohrmHaTHEwJCQAgIASEgBIRAMAR40+C9995rW7duda/8vvTSS43XeRcuXNjmzZtnvMa7Xbt2duTIERs+fLgNHDjQZs+ebePHj7dRo0YFazLs8kRUw65LwuGB9AxCQAgIASEgBIRAOCOA5JQ3DCJJvfbaa23RokV2+PBhO/PMM61KlSp27rnnWqNGjWzu3Ll28OBBu//+++3888+3s88+2+677z7bsmVLOH+9+GcTUY2HQgdCQAgIASEgBEKEgJoVAmmMwPr1623KlCl22WWXOSkqktM//vjDChUqlOBO119/vZ122ml23nnn2SmnnOKuQVh5Q6E7CfM/Iqph3kF6PCEgBISAEBACQkAIJEZg6dKldvXVV1vNmjXtxRdftD179hh5geW+/PJLdz0w76+//rJNmzbZjTfeGJgdtseZwvbJ9GAnG4Govz+2P507d7aLLrrIGam3bt3a+vTpYxs3bgyKDTZAEydOdJNCalQqS5YssVKlSjkD97ffftsGDx5sGL0nvgntf/PNN1arVi379ddfE1/WeYgRQK122223GX0Q7FZM/NiKMV7mzJnjiqCGo/xVV11l11xzjf3www9ONecu6k+qEBgwYIDly5fPqlevHvT3wW/uueeec7/Vxo0b2759++yff/6xCRMmGPZ69AE2efRJqm6swseFwMiRI93c+fjjj9vu3buTtBHYXzVq1DDIk19o586d1qVLF9ff9Lufr8/gCJx11llOnZ8tW7b4+eXvv/82sOvWrZudccYZ8RUxARgzZoxVrVrVcuXKFZ8fzgciquHcO3q2k4oAix3EgvTTTz9ZsWLF7Pvvv7cdO3YEfS4WwMmTJyd7PVglbIy6d+9uTZo0caqbggULGsQ1WFkWXdpnEg92XXmhRaB3797Oziu5u7Cpefrpp50DQ4MGDez333+3n3/+2b744gvDcYGFm0UDp4fk2lB+cAQ2bNhgQ4YMsfnz5xtqzK5duyYpOHToULvlllucQwk2eZ9//rnrAzacSJk6duzonEeQOiWpnG4Z0XGjX375xTnuMHdiF8n8FvjNmStHjx5tN998s+uvXbt2WY8ePVwR5lckhGzW2Rw+8cQTLl9/kiLA5heMEJ6AIUQVtT9rxcyZM+300093BPa3334zru/fv98JQjADoBybBdagpC2HV46Ianj1h54mTBD48ccf7ZNPPnGG6BdccIF7qnvuuceQePrn7EqrVavm8jaX9HAAABAASURBVLZt2+ZsfypVquTsgKiABBSCQpmGDRuS5RIS088++8yeffZZN5mTiZ1RpkyZ7JFHHnESWYzhmczHjRtnfv3MmTPbAw884HbBY8eONaRHI0aMoLpLbdq0cWV79uzppBNI+MiDYNEGiwb1OGZyoxKTFN+TvDp16pCllAwC9D0kKdhl7MIYDxUrVnTOCsWLFzc2FRAkFoRzzjnHLRj58+e3devWBWtCeUdBYNCgQXbTTTe531bZsmWtf//+SUovXrzYrrzySpePAwkqzxw5cjinkVNPPdUgT9Tlt+UK6U/IEPjqq6+sQIECxlzJb4JNRuAGm7kRclW0aFH3DPXr1ze0UXv37jV/E4JwgLmSjYYrpD9JEIiJibHatWtbbGysNW3a1G3iwJw5/dVXX7VPP/3UXWfuYo5q27atNW/e3Px1BQ0DpDZJw2GWIaIaZh0Sbo8Trc/z7bffmud5dsUVV8RD4HmeU2VhkA5B7NOnj7311ltODfnKK6/El/MP2NEiJaAMUpwKFSq4sCBIanv16mVvvvmmMYkjPeB+GMRPmjTJ3YMd75o1awySSf3Jkycbkz9tU4ZF99FHHzVIKW0jTeK5mPC5vnbtWmdOQF0ke5dffrmzYSJcCeFLmMhoC/KKiohyLAioishXSh0CSPxQNeNNS00cFSCpSE+RYuDAQGJjwKaGMkopRwBJqq+mBGNMX0iBLSAx8lXMlAXnLFmyGBs8xj+/NyROnAfW03HaI7Bw4ULLmTOna5jNAnMUc5bLiPsDUYW4+v1FWcrwe0HSCtliI89mY+XKlXE19D85BFDhs0689957ztOftePBBx80hCGdOnVya9Rrr71mbJIxsWBDwHxPwiwDqauF+T8R1TDvID3eyUHgWLvMfv36GSqpmLgdLRPq1KlTkzwoRBZySpl33nnHeWeyUDI5PPnkk0Z+9uzZHRmG1CCto62XXnrJSUSRQtxxxx1GORbqu+66y90DO0ns9EqXLu3UOUz27JzZQVeuXNlmzZplK1ascMQWKRREmIUa2yQWawjxoUOHjMRzE96kXLlyNm3aNEOS7G6iP0IgwhDADpWN2/Llyw2ixDj3vwK/LwKi85vcvHmzn32yPqP+vmiP8Dgnpid9s2DBAkdsmcuwp+zQoYOVKVPGbrjhBjePRT1gRwGAjRdrBPbbYEdRNmrk+Slv3rxuw8YG2s/jE02P53lUCeskohrW3aOHO1kIIEll15/YXhSpAHaHTKhMtjwfhuwQTo4DExIDvwwTBNfY7fLpJ8giKjCkceyAUc1DFslDQoQUzi97tE+eB3Uo6jIS0tajleca34/7v/vuu0YdpLAQaq4ppQ4B1JxIJugHamITxuYgd+7cljVrVufUw+aH/kfyTRmllCNQsmRJZ29KDTDOkyePkTj3E17PLNY4Pb7++utWt25d/5L75I08kFckdy5Df0KGQIkSJQxVMzfA5pQ5ErLEOYl5EXU1vxtCKmF2RIJooeFBu0M55lXIFMdK0YuAiGr09n3Kv3kUliRsB9LJVq1aGQQOCFA1orLnuHz58vbBBx84iSa2oEhDyQ9MFStWNNRYGKwvW7bMIC5IEQLLIOXs27evodonHyIDucGmjsUZG1cmeq4hfeAzWLruuuuMCZ9rqKGRKnF8tMROnIUC+1gcF3gW/zmOVu9/7N0FnB7F+QfwZ6lRWmiAQqFACVKkSFPcCU7xAi0UDVpciksheHF3C1DcXQvB3d2DWynSorV/vtPu/d/cXXIXuDe5yz35ZO7dHdvd3+zOPD5Z1hYBqkuEkF1g2KBicPr37x/MLIy/hXfIkCElYgSb1bY9ZM6IEFh99dWLXR0cmd3QSLSu77vZcccdC+bHHXdcsWltrIPAxZip15ifx12PgLnPnGdeMW+xvUd0Nl6Jep+TlTnIXLr44osXL3/MBqYdo27MEL2N7fK49yGQhGrvG/N84k4ggLNnX8rTnwqKN76dPjbddNMSJNn+yQgOkldSyI033jhIAtgCkWxyAlBfHUSshZUBO4cPEzebVup8t8Jzk41pfY1a3S/UEcKWg4gyRA+bI2YHFmKmALybHbN7JSlSb6211ir2tcoRTgMHDgz3xRzAfTATsBCwaXKNqqpC6B6JlNU9ZWqLAHMLtr1MM0jWScGZTXBYY3vKmeGEE04onszGifrNeHCwM6Zsybw/SSi1xbajHMTMeuutVyJvYMJIT7Vhm0cyB38SuWWWWSYQNggj5b432BsHTieS3XqUZWoeAohQc405BfPrG3E1jD6mg6PozjvvHMyXmCcZW+UYdc5UQlOZW2kp5plnHkVjesrnGwECSaiOAJxRUcTLEdHAyYXTReuEI2VXZXIeFfeT1/h/BPr06VMcnkjEqMZvvfXW4nGvBs9h4W+o6K+//vpiZ0oK6pgUQHgidY488shgE8dmDoHC5kobDk4kpghii6sJ3DXYawlxRNrJnlV7EltltshzTf2zt2NT6n2huqdWY2+qnt8ZZpihhEbyfrH3QtAirLRnXqAPxCtJIMLaNbxjiyyyiMfL1A4CmA9SHpIieFtEedOSCllg+/btG0JQeV9IuHXBdAOhJMqChGmh9lSWaeQQQOj7djBaNbHJptv8CH8EDgdG31ONMSZPHFXfxS233FJiFVdV97fJGzlkumdtGinziu+GA5y7xOyJ4GB8aKt8E74P851yydiaB401u3vzqvxMvReBJFRH89jjLH2svBtJ3Fon4SQQFwiL0XyreflE4P8RyKNEIBFIBBKBRGAUIJCE6igAuaNLMBanYmboL5HMsEskOaB+5jRQO+N01FeWJwKJQCKQCCQCiUDPQyDvuH0EklBtH5dRlks9zN6RlzZnG6pgIVbYvnEcYBZgxxsx0EbZTeWFEoFEIBFIBBKBRCAR6AYIJKE6mgehqqoS30xIIvY7HGvYU7HrEQSe9yRbSTY9o/lW8/KJQCsE8jQRSAQSgUQgEWguAkmoNhffDnsXLkV4IMHj77rrrrJ9HHvVBRZYIJI47RC+rJAIJAKJQCKQCIw5COSTtEEgCdU2kIzaDF7BwqkI3yFEh/iLtjj75JNPRu2N5NUSgUQgEUgEEoFEIBHoZggkodrFAyJouniV999/f0gddc/rn1RVsHBxAKn/BZYXg05sTbExO+ojyxOB0YhAXjoRSAQSgUQgEWgaAkmodhG0tkdkU7rQQguFAMVLLbVUSBykxK0UDL69S4ltKYg4KSqiVOxM8ecEkRf7UlzL448/PmzH2V77zEsEEoFEIBFIBBKBMQmBfJZGBJJQbUTjaxzbAcjONIhUXvu89aVll102TjzxxBCcfXi7/gj+Lvix9nbrkA499NAgWbW7jUDWzz777Ne4u2yaCCQCiUAikAgkAolAz0MgCdUuGDNSUdtaIjAPOuigsH2f3VEkuwbZUeXee+8tW2y2dznEqC04t9tuu2id9thjj1h00UXba5Z5iUC3QSBvJBFIBBKBRGD0IEBje8455wQ/FzvjNZ6fcMIJbW5q0003DbsltinophlJqHbBwFDLjz322LHkkkvGt7/97WF6tDWcbTNJR9miDlM49EQc1W233TZWWmmlsN3cCy+8EBNNNFEwGZBIaG3T2K9fv6G1838ikAgkAolAIpAI9AIEOv2I66yzTlx11VWx5557FvrhpZdeio8++ijEaBeHnSBNZ3/961+DP8zZZ59d6A15PSElodoFoySM1KeffhocqdrrjsSVDWt7ZVVVhZeH1HWjjTaKbbbZJoSqeuihh4KpgL7tXPWd73ynveaZlwgkAolAIpAIJAK9EAHO2IcffniJxb7//vuHmOuEY2gOUtNf/OIXscMOO8S1115b0Jlgggni1ltvjf79+5fznvInCdUuGCnbm9o56sgjj4w33nhjmB6J4QXvn2aaadpIW1UkZeXd37dv37j99tuDRBUXdMkllwzXVEC7TIlAt0MgbygRSAQSgURglCHw6quvxumnnx4zzjhjIEztaPnxxx9HowZ23HHHDfTFKLupJlwoCdUuApWN6ZNPPllsRNiJ1Gn99dePxx57rJgF4HRaX+7zzz8vklhS1KmnnrpwRlT+HKyGJ4Vt3UeeJwKJQCKQCCQCicCYh8CInuipp56Kn/3sZ7HyyisHFf8rr7wSwmM2trnhhhsKEduY19OOk1DtohH78Y9/HKecckqsuuqq8eijjwYbEHFUOUIddthhMcMMM7R7JTaqbFK9cAhTIaqEq6LqtxlAu40ys1sjYGKYc845Y6qppiqMi/PXX3+9W99z3lzHCLDzMqY25/jiiy/aNMBcLr300mXc77vvvlLO7ny11VYredqyORchpBTmn5FC4Oabby44br/99oW5b92YTd7vf//7Uueoo45qKRabGvZ1Ov/884tZVUuFPGgKAt5zmG+yySbt9k/yV4/XwIEDW+psttlmZQy1lTgJtRTmwTAIoBeo89EXNLukp76DutKVV14ZW265ZdD41nk98TcJ1S4cNS8MySr7UjarQlLtuOOOMcUUUwx3O9Sqqgo3dPfddwdil4jehEwi29oxqwtvNbtqEgLXXHNNCDN2xhlnxMsvvxzrrrtu2MiBHfLIXvKDDz6IY445ZmSbBaaHuclIN+ySBmNmJzA95JBDypga11tvvbXNg4r6YewvvfTSEAvZHMCGDGGrDc2KiCAcL9s0zowRIgDLAw88sNjXYexPO+20NvWZS5lrYS2832WXXRbPP/98iEkt78UXX4wtttgi2PxXVdWmfWZ0HQLGi+AGkz7LLLNEIyHqKvwvmLzV42XexAhSZSNe6/GizhYVR5tMbREgTa3pDYIuNMOUU05ZGDECM2sImoLmViSAtj30jJwkVJswTpyqSFR23333TvWOwCV1ZRR9wAEHxHnnnTeMjUmnOslKox0BEwFpOImACcQN9e/fP+RZXJ2zYR40aFAMGpr+/ve/yyqL6cMPPxx33XVXGfshQ4aERRXhYyOIe+65p9grm/S1Y8tMomcSuv7660s7+dQ+b731VhxxxBHFA/SKK64o/eefr48A+3P2X3qykQfcHdfJlsc2/LBBR79+/WL++ecPkjtSjoknnrhUE/nj3XffjZ/+9KflPP90HgFEDGk0yRCpdXuE6oMPPhi+N71yLDn33HNjsskmC/OrvHfeeadIYhN/aDQ3ITzHH3/88q6vsMIKIbY4B5/6qhg4ROm8885bsvbee+8wX5EIIrRk+l7MqdNNN53T3p2G8/SYABhaK0466aRCN3jnMdK0CkOGriUXXXRR2DSIxkc3GGmaHhpf34S87p6SUO3CEbJYkYYKEYHg5BjVme59kKQFAvszGcCJIjg60zbrdB8EfPQkONQwjXdlt7JJJ520LJJCjSEwEaWk7yR13pcBAwaEieO2226Ls846K3DAjVJY9ZV7x0w6iFJSd+m6664LajYxd10XAWyCd5ypaxCg8ejbt2/pbJJJJglSjHLyvz/vv/9+kGg3czJVAAAQAElEQVRQxcmafvrpQ4gY0lNJ3uOPPx6TTz55jDfeeE4zjQQC8KcG1gTxzw7Pd+S8Tu+9916Q5Dln72/+HWeccVqcWJVTixoDdTI1DwGCmvp7ITXFoDWaP5GoIpzq8VJXnaqq4lvf+la5MfWZv6VEtcAx3D80BrPNNlsg8Nmq2t2SP8yCCy7YovJH7FuD6k623nrrQtTW5939NwnVLhghixOpmRdDwH92qThEtmkddU/6ymuPRI1EFefJbpU0RkSAjtpnefdBwHtgPKlf2rsrce4QmqI6kKCbzC247FkRs2zpSIswLiYdk8/Pf/7zsiUvbplUz/tF8mADCWoxizeC1wRF1WMy4oynT5KM9u5jFOSNcZcwbo0PhRloPGe2893vfjdqezrRPhC0jXXYi5G4NublcecQaI23VjWR41iitWDyQnKHaWyNvwDns846a7D/Vz9T8xBob7xogeorCrtonfNNqItI9f3UTBxCFqOByFWvbpe/bRGAD0EHegNeaiywwAIhr052yJQvMUuSb83A9Mnr7ikJ1S4YIdIr6lkvw4UXXhgC6poQf/Ob33TYu7YS0f1cc80VOB/tfKTyET6NH3iHHWaF0YYAohFzQXLTeBMWVGNInY+QJCXA9ZK6VVVVJGzs5kw4yhrb1sfasukiSTWxi7uLOCKtoy4bXru6ff5+PQR8l75FvRhj547rRIJBEm4rZOPCPtKCUJf7nmlJMBF1Xv52HgFaCt+QFvD33lNxOq+TeJFPPPFE8YK2aIu4Upf5Za5BmOA4U3MR8H3U4+W7ISWtTWBcGaGKMfddUF8zpxH5Rr5y2gmMN4a7qtKeGCYRvfdvEqpdMPZsoEhCV1999WAn9fTTTweJFxuRjronfZtpppmCBIZq0MeJ6J1wwgnj2WefDbZZzAHqj76j/rJ89CFgMkacGLOaWP3www+DjRCTADuUcbAz1ohXktPalrW9u0a41vkYH4SqcyYBiB7H7SUSoyRc20Pmq+eRPlx++eXFVpj9V3uSUYwmKTnnKVoSBGt9RQG3V1lllfo0f0cSAd8VQtM8yBYYIdq6C1oIErrjjjsuOOQ04o3BY05TS5xat83zrkWAZshaSBNBk7T44otHI6HqatNOO22ZG62TNrvheCpfMjea59JMAxqZklDtwncAwUmaasIkDeU401H3CAoTKJtWCxxvVeqr5557Lqh6SWg5z1CNdNRXlo9eBIzlNttsEyScHKrYj7IbJUXlwDHffPMFCYF8iVSUZNXCa4E1oSNw2DljdDjlII6o/UmHBg8eXMJd7bLLLkG1ybkHU0SSJ8oAm65BgwaFBYDdKtOA0YnImHRthBIHEOOG0bAQI5owJRgR37syElXqSxKi+vmZhFx99dUlEkCdl78jh8Acc8wRzGCoLNlxi6ShBw6GzGjgz8Zf6CraB6ZXyuskxqRvqD7P3+YiQFvEodA3QbtgfXNFzIZvxLdkrhOFARPB+U15nTgUYwbZGNd5+dt7EUhCtQvHnvTsgQceiCFDhgQJGCPnjrqn6mCLaCHcb7/9ArGKMOG57YO2EAqLg9jpqK8sH/0IUEdiMDAcnKSYdPTv37/YxVEPG+MTTzwxOM+ZpEkZjC+ClFqSnTLpG+kcb06TPGmec5O8PhG/mCLvCCcrknz9efeWX375QCR5hxC0ox+RMeMOOHTA3jfNIc4404Yw91lsscXKRh3KqZ9/+ctflvGun9xia1HWR52XvyOPgFB/CB7vOkJIDyIBrLnmmgV/JlO77rprkM7Rbiivk7ZLLLFEfZq/TUagqqoyDmzqzW/mK5c0p2E2rHts6DHbbOwx7MrrhMA179Xn+duCQK88SEK1C4ad4fctt9xSpGWkWBtssEGwv8Hld6Z725/ttddeRTXSp0+fsKAxcq4TMwBETmf6yjqjHwGTrjA6ffv2LbFxGxdNZfIlY6qMitg5O1WqScekssocex9M7N4H54ikusy5cioyx94V9nv6cQ+jH40x5w6MCYyF3amqKqqqCnjX58o48BjXxqcmaU9GsxGRr3bM2QbG3veqqkonpNe+i6qqgjOV74DpSyls+IMhxFg0ZOVhkxEwDxkv2NeXMl6+Eee+HXNUe+OiTHv1MiUCSah2wTtgh40TTjghSFPYr+Hwf/GLXwTbm850zzmG6hf3KcA7tUfr0Cud6SfrJALdDoG8oUQgEUgEEoFE4GsgkITq1wCvbspgnDSM12Kd55edqd8RJbY6VL1UtWxbEajUvewWeT6OqG2WJQKJQCKQCCQCiUDvQqC3PW0Sql0w4lRP1Ld1V5yo2OXYLrHOG96vmH9CqjAV4FzDYYD9FdtDYT2G1y7zE4FEIBFIBBKBRCARGNMRSEK1i0ZYuBRe2+zReHfzMl1xxRU77L2qqrLFHxu2qqrKHr28VnkUV9V/7bA67CQrJALdGoG8uUQgEUgEEoFE4KshkITqV8NtmFa2TzzttNOCo4xQUtT5dg/qjOqfR7DQK8wHtOOY9fLLL4e4c+0ZmQ9z4TxJBBKBRCARSAQSgd6HQC964iRUu2Cw7ZQiOH8dloi96d133x2XXnpph71ry77VpgEqL7TQQiGUzTLLLBM8vT/66KMQ9kpZpkQgEUgEEoFEIBFIBHoTAkmodsFoCxUkduKpp54aZ5xxRvzpT3+KU045JTojEaXiJ31lk2onD0HbRQF47LHH4v777w+2rmJvsmXtglvNLhKB0YVAXjcRSAQSgUQgERhpBJJQHWnI2jYQ10/sVPHhOFY5Fx/umWeeaVu5VQ5Vv12n7KQiYHXrhOh96qmn4tVXX23VMk8TgUQgEUgEEoFEoPci0DuePAnVLhjnTz/9NG6++ea48cYbQ0xVxOVNN90UW221VYe9c76ae+65wxaLL730UrBPbZ2UiQTQYWdZIRFIBBKBRCARSAR6FQJCWd53330xaNCgYirYeH7rrbcOgwWTRDteqj9MQTc+SUK1CwaH6p/UkzQVUXnxxRcH56hzzz23w97tvmHbS2Gpqiq9/DsELCv0aATy5hOBRCARSAS6FoEDDzwwjjvuuJZOhci87bbbghnhTjvtFOKyK0Skvvfee4U+sblQZxy+tRvdKQnVLhgBQfq9GLZCPeecc0J4qiWWWCI6IwWtqqpEC0DsdsGtZBeJQCKQCCQCiUAi0EsQuOCCC+Lhhx+Ogw8+OAYMGBB9+vSJd955JzbZZJPYdNNNgznhWWedVdAgEBOvXfrpT39aCNlS0M3/JKHaBQPkxSBB/cMf/hCvvfZaLLzwwjF48OD43ve+12HvbFS1/cc//tFu3Y7K222UmYlAIpAIJAKJQCIwRiPw9ttvx5577hkzzzxzzDrrrLHbbrsFx+s555yzCMCYFiJI/cbQf1NPPXVw4L7llluCb8xss802NLf7/09CtQvG6Dvf+U55QRCVnKLERbUBwLzzztth714qkQKI5nFBrRO71eOPPz4645jV4cWyQiLQHRDIe0gEEoFEIBH42gg8/fTTMc0008QCCywQ119/fTz66KPxwgsvDNMvW9TNN9+8JY/NKs2vjYVEGWop6MYHSah2weAgNs8777wQYkqoqeeff76EqBIDtaPuBflnJ7LrrrvGeuut1yZtvPHGceGFFwaHrY76yvLmIHDHHXeED9347L///qN9LESAOOCAA0b4sMxRqH468w6OsKMsbEEAM+kduOiii6I9DQhHyt133718w+Iq1w2FmvP+cK7EeNb5+TtyCFiE4S/0n/jTrVvTTJ188skFf/NwXS6+tXZ1YrtHqFCX529zEOC3AXO7NLZ3BWtaPV5nn312SxVzl3bSBRdc0O631lK5lx+QioowtNhii8WUU05ZpKhvvfVWCyr33ntvUPc3Sk5XXHHFEj5z0kknDZLVlsrd+CAJ1S4YHAH5xTw97LDDYv755y8e/D/+8Y+DsXJnumcigKg49thjS9zUX/ziF4FAZRx91FFHxVJLLdWZbrJOkxDo169fsTdGIK6//vrBAa69S2E6brjhhmicKNqr91Xy7Hj20EMPlabCoJnEy8lw/ow33niB+bFbmom/M459w+kqs4ci8Oabb8a2225b1GwkF2IeD80e5j8bMXbqFoKVV165lDEFYh+24YYbhs08mAfZya4U5p+RQoCK0/f3yiuvxOWXX96mrXExTuphDBCkpEaLLrpoGTf5k002Wfgeqqpq0z4zug4BXuc0gWussUaJhIMgbewdo8DxuB4v345zdXbeeecwTptttlmYTwly5GdqiwDiFEOg5Jvf/GbwdUGAOvfuP/HEE8FfxnljsoaJNmRTocb87no8Vne9sZ50X14MxCT7j3nmmSc+//zzYtCM2+noOWwKYFGzuHnpEDscsyaaaKLo27dv4JYQsh31k+XNQwCxN+GEE8Z3v/vdQCQaIxMsqdqQIUMC4SGPyuXQQw8N46fMe6Acgfvll1+GyVld5h0WW3X+8pe/hDpS/QTaUcmo87e//a0srPvuu2+phylyLTZH+nMf2pJOuIY2PD0tFPojeSIBRFgpq+urSxOAkNJW3VGbetbVjjjiiNhmm23KN0l6cd111w3zAHAWlm7NNdeMlVZaKSaffPKAu0XEd/2zn/0sEEzs2RulrcN0kifDRYCqcqqppooFF1wwll122TjxxBPb1L3zzjtjhRVWKGOEScMgTDzxxDHttNOWPN+xXQCnm266Nm0zo2sRoH0wjyGSVlllleCVXs9JrmQOM19aN61zbCtpJeUjTAl8rKfOzVPaZGqLAHrDWmA3TIwZB25zj3le9CG0CcaMhgHePP6HDF2zmBKa+wnF2vba/XKSUO2CMbHQexl+97vfFSkWVT3Col+/fh32jlCdffbZg50rqdcWW2wRXiTOWF62xx9/PJgSjD322B32lRWaj4DJFpEy11xzFfUJKdsOO+xQDNONOe9LMeoQoIcffnjstddesdFGG8W1114bjzzySPHA3HrrrYMtM0aG1MAk3b9//0DUeJe8PwMHDiwem/q86qqrAqFpa11SB6rPAQMGFJWYXcswOd4TktxaknTCCSfELLPMEgjeK6+8MsT1dc0//vGP8etf/7psIGGycm9+I/+NEAHfIzxVQjBRqTmuk2/Vompxluf9oKrGNPz1r38NZb5xTCdGRZ1MnUeAXR0VphZ9hzLw99xzTxsTHGOAAVPH3EsDMe644xYGU57vDaOH+XeeqXkIMJeqv5cZZpghMMkIo/qKmGyOQPV4qasO8xnaRIwGQqv/0HkRo1G3y9+2CFgvbNlOo7DccsvF+OOPX9Yb9qvKrEHmoimmmCIuueSSsibR9mIgMNBte+x+OUmodtGYkHyJh4po+P3vf1+kL0JDdKZ7Af4RK4MGDSq2kKRyPnRqLlIc0hpcUmf6yjqjDgHcKpWVK2JUcLWkADvuuGMgTg466CBFJWYdh7lyMvTPMsssE+qKu2uMEaCLL754MYb3LiBYEZUIUlI4jnmII5P3r371X+HZhgAAEABJREFUq6E9/Pc/Jsf7YcKx+Lofkzyp039rRDG0J4Hybp566qmBqPaOYnxI7dddd91otF+q2+XvsAiQhDfmYEQaz0ncqd0wMfLVt2D06dMnfMsWYEyOBHt1MnUegdZ4a+mb81snElOSPMQPghT+dZlfDJnFGrPgPFPzEGhvvDBt9RWrqipqat8G5s54mc/GGWec+O1vf1uYcHMVgtY3VLfL37YITDDBBGENofXBoKlhp0x5dWJyhFEmTJPHLKOuq353T0modsEI+dColNZaa60g6aJ+IjXBzXfUPQkaooXU1AuE6PACHXLIISHEBLOAPfbYI7yMHfWV5c1HgJpFKBCT50orrRTOESitr8yBhqScXRyVMPtj7ahmEJTasSkSyoz0FOerDxM8uyHvDjtnqjETuDLJRD7TTDOFX+cIYwuA943JAdtUbdlDKm+d2H5ZwEl+SWrF/G1dZxSe95hLGYvGm4Vj4zniB9OC8cBUUDtjEHjjUjVT0ZEakXynhKgRuc4dt8eoM8NpbE0wwPkG3hgwc2ljOXvHRRZZpDErj5uEQHvjZd6pL2f+w4Cbg8yJxmrJJZcMc90+++wTmHzz5mmnnVY0TXW7/O2dCCSh2gXjjlBl3E9qtfTSS4ePjlc2FXBH3eMYSUypYC1+iBQfMWLEB8uwHDFSVVVHXWV5N0DA2LkNhA3bVFLLvkNVlTVhqaxOpAikmQiYAQMGlGxSIXZEtbc++yy2rHW/pVLDH/mIUpIJ6mjvTUNxOVSnqv77/ni/+g9Vp1m0xdNjP1kq5Z8RIoDoZANGhc+Eo1FqXTck7WZ+QRJOK2IBJsWgZnvyySeLFF3wbXEN6zb52zkEfvnLX0Yj/rQS3uvG1r4lJjLMAhwjXOtyAgGaCmNS5+Vv8xBgxy0skrVNNJx+/foVB6nGKxLEmLeorTEXNFHmxMsuuyzMS745JjSEOI3t8rgjBMa88iRUu2BMLf6kKD5OxuPLLbdcMeq3qHXUPWmcxYsHHgKFHZVJtaN2WT7qEGCEbvFj70kCQBppQsWIMPyXTLjegx/+8IfBcJ1EAUFi+zomHaTlPDA50mhPCspmiyQewcg+yyLrnbEAkyZo45eUVTDna665ppgHIJTUF2kCCt4dNqjMTxDG+h48eHBRn1kEqDvVFwIG4YuYZZrAXED7TB0jQPrDzov3sgUY4WQR9l6whaTSN86IVGPM21mv7CZJ+ZhdGHfe//IzjRwCBADwFgmF7SLbbj0gPr378PfO+2Y47tgqUnmdMAuEAfV5/jYXAUSoOVDUGt8FoYsrYtiYxxDQIGBJTAl1fCPKaaow0mzz2dmb+5K5gEzvTkmodsH4k5ogTtmmbrfddiHh5mup2IguQdXRd6jEDUHCkYotiY8UUTSidlk2ahGweQN7UFdlj2iMHCMMqRlJUKnyxcqk4pIQLTZ/UK+WANm6jgmAPIbs3hnHvMWp+Xkpk6JjfBC+iEqmAltuuWXxXlaXc0K9UDt3D2ySqM6cSyQS7JAcWzRI+x3XSR6mqj4fnb894drGg42wb53D2vAWT8G311lnnRLPsPG5SJSMa2NeHo8cAmIY0zj43niFt9caoWOcROhoLPcdktA15uVx8xAwd3EaZbLG5hSj0d7VmMwMHDgwzGF1uVjE5kJaKDb3vqm6LH97JwJJqHbBuFP9C0WDAxQ2RWJjQ9LSUfckAewTSWZMxAgIUhhcJ5VvR+2zvPkIsCkkIaOex5BQtTtGkFAxOrYImpxJ1U3MJmgEpjKJaotU1DGi1YJLckqaLo8nPiIWgSmR4FmQaxtS5gHOTeCkedp4V+qnR4hq51zf1NDqI7Ddy6qrrhqIYQs4kwT2feqpn6lzCGAQ4G7MjR3GhO1pfa7MgtyIKwbGGLdnKtC5q2atGgESOhiz+4W/fMweR0TnJHHe80aiRx2JU6LvwHGmUYNAPf812gVjvH0jVVWFuc1cifFvvCNMvnGWzF+0jo3ledwpBMaoSkmodsFwUmNQ2wv4L/wPxxm7pLBT66h7qliOMKR1CCKqYBMyNSNVV0ftszwR6CwCGCqTP4cfi0Rn22W9RCARSAQSgURgdCGQhGoXIE8dSNIm/InwQjy+qf/FMeuoe9wiqQs1h7qkqOwd9UlKIC9TItAVCAiLxF6MTV+3s0/tigfMPhKBRCARSATGOASSUO2iIeUQw+mGY41Efc9QvKPuEaTsrcRdZAbAZodElQoLEdtR+yxPBBKBRCARSAQSgUSgNQJjynkSql0wkohU4X44p4iLKXGcYIvTUfeIU3aDvMXVZX/FwcbWZlX135BC8jMlAolAIpAIJAKJQCLQ2xBIQvVrjDg1vR1nONEIRSTEhtBEiFYhbDizdNQ9QlXYGlsECi/EMYBXsTAsaaPaEXpZPmYhkE+TCCQCiUAikAgMi0ASqsPiMVJnYsIJwcG7nySUY5T4mmxV5Yvx11GHiFxb/vGMtEOVnYX8IlqTUO0IvSxPBBKBRCARSAQSgeEiMAYUJKH6NQaRF7Xg04Iak4DuvffewdMfoSnQeuug0+1dikSVPev1118fwlqxTXUutIfwN+21ybxEIBFIBBKBRCARSAR6AwJJqH6NUeb4tMMOO5StEcXI5FF9xhlnhOD94qgKMt1R95ypbLkqnBUiVfggUQDEB6yqtFHtCL8sH+MQyAdKBBKBRCARSARaEEhCtQWKkT8QhN1OUnaX4qEvBuqPfvSjssuG3TTOOeecTnVqi8vtt98+OFHZPrWqqrjzzjsjVf+dgi8rJQKJQCKQCCQCvRYBsdxtiz1kyJCydXbjuRjvEf+Fhgb39ddfj1deeSXU/29u9/+bhGoXjJGdNXjt77PPPkGqSoW/xx57xNRTT91h716cG2+8MTbYYIPgfCVtttlmhVD1snXYQVZIBBKBRCARSAQSgV6LwNVXXx0iDe21116BMP3LX/4SRx99dOyyyy5hV0V+M8DhT3PAAQeEbYbRGYhW+d09JaHaBSNEmrrpppuGrRTvuuuusB0q7315HXXPkeqBBx6IueeeO2wSIJHSesH021H7LE8ExkQE8pkSgUQgEUgEOkbg9ttvj1NOOaUQpKeffnowHXz11VdbzmeaaaY4/vjjQxhNDt8IVX40TBdvueWWji/QDWokodoFg8BD/9lnny0vhm1UjznmmLAlKjMAEtM33ngj1GnvUnYLYsu60EILBeJWLNZ55pmnvFQpUW0PscxLBBKBRCARSAQSgQ8++KCYDM4444yx3nrrBcduYTNnnnnmEDITfbHYYovFu+++G9/+9rdjvvnmi3HHHTfkM13kG9MTUExCtQtGCRHqBbET1XXXXRc8+C+//PK46KKL4uyzzy4q/VNPPTUQra0v56XC5SgnuidJZat64IEHFluT1vXzPBFIBBKBRCARSAQSAQIyklGEKp+Ya6+9NkhTOWnX6Lz88suFiK2qKkQSqqqq+L/06dMnbCxU1+vOv0modtHoCFV18803B1uRxkS0zlnq3//+d0itL8cRi9PUvffeG8JSvf322+UlmmuuueIb3/hG6+p53kQESLDZ8LAzlsTJ7YrL/f3vf4/LLrusdGWshS4zmZSMkfjj/bnjjjuKHfTjjz8e7g+TMxJddFjVBhbMVzBfHVZudoVu1r9vnD2577S9W6Naw6R6d4x5XYfN2HnnnRc2BIFvnZ+/I4cAezrY+gbaY/qZUVGDqvPoo4+2dP7nP/+5fDPypeeffz586y0V8qApCLz11lsFd2tgexdoHC/rX2Md4yR6DkKsMT+Ph0WAU5QIQeuss07MMMMMRVr64osvtlRCpJpzllxyyZY8By+88EKhR0Qrct7dUxKqXTBCCEoB/08++eRgI8L+gx0IKalziZS0PZtThMtPfvKTOPjgg8tHra40cODAIqr/6KOPwiYCXXCb2UUnEbjkkktieJNrJ7toqWYyPuOMMwpH25L5FQ8QpkcccURpfdVVV8Umm2wS3o+S0QV/vIsIKlJ9xFUXdDlGdSEyx7bbbhtPPPFEu8916623Bo3KO++8ExwrVULcMgV6//33i5PDscceG/KUZRo5BGiZvJeYPsxU69YYQN+FfP4Bxgmx1Dh/YiQeeeQRVTI1EQFMtfUPU+ebkFpfTmSberzMOffff3+p4htBSGnLttIYloL80waBiSeeuEhQFRB6kaROMMEETgNu5iROUyXjf3+GDBkS11xzTQiL+d3vfvd/ud37JwnVLhgfHyWvOpOnj81k2Ohp5xImWPUcNyZSGJJYnA9pnvaN6ZBDDon99tsvulpy1ngPefxfBKqqKg5xuEx2wozQ/1vy1f9iTjAxE000UemE6mXeeecNzEnJGIk/N9xwQ0w77bQxYMCAMPksssgiI9G646oYLvbSX+XeOu6959fYYostYv755x/ug4idbGwwpRwqH3vssSL1xqysueaa8Zvf/KYQq6Qgw+0kC9pFAKGDwMcoLLXUUoGYaV2RtHvFFVcs3wcJ0plnnhkWbvWNy69+9auYffbZi+1eVWWM6tb4deW5NYzkWpzxlVdeOTgIN/ZPIm69++Uvf1nGC9FEQENq/uCDD5b6mA3rHmfjxrZ5/P8ImI9o56wNJKmTTz55WVtquoJJQJ8+fcJ4KBeSyqZEG264YQinSQPRyMj9f8/d6ygJ1S4YD2qk1157LX73u9+VxWjLLbcMhCdJmu5JWISKMNE6b0zaMnS2q5WFrHWi/rAVq5exsV0ejx4EqKgYpyPmdt9992CMjklBfPbt2zcWXHDBMCG4u5deeql4YO67774tjAbHupVWWinuu+++Yurh2GT+85//PCyobJbF0v31r38d+jPJ4JCp+pmDYIKuuOKKYhbincFR45pFjRAWbbLJJgv9uS+T/I477hhTTTVVuS/9IppI+xna69/zYKAs8p7J1r/uvfuk7nMnvGnZg7V3R753424cMSecIy0C3oHxxhsvSC7GGWecMJ75LbeH4Ijz2N5ZlDF6nEBIhBA7ja1gjTCV51ui8vftfP/735cV5mFqUO99ycg/TUMA9pgC34K56ZFHHglzX31B655zUkB55lQScGNonTSOnH4w5uZXdTK1RQB+1gS0B+KTQ7Y1wdokrb766iGm+/7771/mIPM7KbYIRdYFJjLDm9PaXm305Yw1+i49Zl3ZB4Xzs2A9/fTTQX1MEuopef8LO2Wxct6YSLF8yOxafaTaU/F6+Sx8zpVNP/30jc3yeDQgQDUuqoOP3geOi2XmQVpO1UtdxduSRIDayiQxePDgova3qJqcTbqITrdv8XWMszXGt912W5BCIERxxt4H8Xi9O+yQeGyS6omBZ8JRDzHKZo+UDkFEnfbcc8+Ffl1bf3vuuWewmcQ4eUflUWOb1DBQnuXII4+Mhx9+uKisk5AyOiOXxC1s3YK9OdwxDXWZc6k+z9/OIUDS1romfBvzEEXeYd8ppgxj0Fj+yiuvhEUZs9CYn8ddj0B748VDvb5SVVXFRhIVj/sAABAASURBVBJxWjPnmBBjY/6xBqpLMmhcHWdqHwHbraMTCD8Q/GoxJZRXJ9u5Y7TPOuusIkR7+eWXy6/1pCfgm4SqUe2CRCrKJodY/Y9//GNsvvnmQaJSd40Yqaq26iZhIhhBIzAQQY3ta0Kk7iN/Ry8CJDImYBs5jD/++GWTBupdUlREn6gNiD53yV5ukkkmCQwGIlP9qqqKaQGzAnXWWmutcExNiTCVJzETQIBWVRWYHHkSpsbGEFRkCOKNNtooMEXLLrtsTDfddPHb3/429EMyini+6aabinpHW4QtCQfmh72kvpgkMMKnFqLyt2kFiay+tMnUeQR8361rG3PMqW+8Lmt9Xufn74gR8G62rgHfxrxVV101LMTrrbde0GII9ddY7nuoF/LG/DzuegTaG69asu1qY401VgnHePHFFxdGnvRvzjnnLPPlcsstV/KMIwa+K0ywXDNTz0UgCdUuGju2H2xNqVARKZxcTIwddY8gOf/880NoCW0lqlzqfoQvT+GO+sjy5iJARUXCbWEkqaklAyZehKQxM8lS49fhPowrCc5XuTOEL0ITQczOjhkAwpLKhjTUtbbaaqtiKsDOq/EapBNUZ4jkPn36tNi0DhgwIJgOkCjpF1ErD5FM9c/wvrGfbnbc7W8HphZnEgw3S7pBU4JxMCZMLkiHJMyAOpk6j8DCCy8cNRPIGQSD11piyt7RPEq7galkK9x4BRqLfv36NWblcZMQwATT6uieFmnKKacM0W+cS1VVlfkL042pMK/ttttuxaYYU23dY9e90korFbtibTL1XgTG6r2P3nVPzk6EdAwhwegfUSOuamuOvr0rssXxUfNopQrWnuf1gKGExYUXXhi2VG2vXeY1BwFEKONyUkcLosT2EzFnrEgyqdDlM+8wkZKGk+ZQwVRVFfro379/CbJ83HHHBWKSbRyikNqXKhgh6hqOXQ+hyDSACvnKK68MUgWRB1ybrRZ1pvokD67NJhojVC+81KAceEhHEamIIYu7dxEDpc3AgQOjb9++wTRBXSYCJFD64AhIPUTlxmSAY1BzEO65vdKaGEdjZDyMlzEx3hgJ0mnfMK9052wqaUswsRZr46k+m7Gei8LouXPOqd5tqn3EDW2COzEeiFJjYRx8B5g4jAJGUh3JN8sUh3rZeabmIkByjVkntDEH7rTTTsWu3ngZI1d37HvYeOONwxzFdMl3ZRz9Dho0qHimszNWP1NXI9Bz+ktCtQvGqqqq4lRjkWdzIyE8hKvqqHsLGsN/kheTrfokMxyyEEbOM40aBOBvwkQ8UpHvtddeISE0qO+NFe6fhEy+xZJkh6cxm1RlFkJSNXWptSyuFs3ll1++bAThHSGZ5WzAxtWxPNJzUk5SH6pixKRrSGydTe7ugUmJPMlETkoKHcSp69lgAkHMHgmhxAYJMaq+Y0b0nK0wQ+yYSDk8AwcrpiaIqU022aTYquo30/8jYAMPtsaIfIssKSnVpLG0sFJVYkRs9kESrqVveZtttgnnTIG8I6TayjKNHAI8/QkAaBR48GttLLzz8CeFO/fcc+Pwww+PRiJVPeNVt3GeqbkImP/Y0rPh57hprnRFhKt5C+NvvsGIWyfNQ8ox7Qhb5nPGm++H/Ey9G4EkVLtg/KnzSMF8jCShQgeZMHGVnemeMbMPkt2O+s4RHlXV1qZVeabmIFBVVeHgSWwakziYVFeu6pe0UfnSSy8tq+zuwRlJnjinJlnSVc5P8pybfKuqCm3ksUf2rjgWcoektD5GJMtzLlFpupAFmje5PAlBKr9OCE8TP9MBeRaL1VZbLdSVEE3yEc0WB4nkVx4Jrjqeg/MWO1n53SmN7nthgwwjxBIpD0aSFJU03TeLOBJlQZ0aa/dMgsppzbvBVEReppFHQEQF2PJuxhDqwTdAxQ9/TJxwfvBW1pgw/my3G/PyuLkIYKyNF+zrKxHK+I6sdRi7xrlVHd8NZu6UU05psa+Xn6l3I5CEaheNP1UryRWpi5BStlNlY9NF3Wc3vQQBzjYma9If4UMk0QQwLu1BQAIhKDbJ7TLLLNNelcxLBBKBRCARSATaQ6BH5CWh+jWHiQ0gZxve+tStEkkWNbLfr9l9Nu9lCJA0sMcTTYAJgcS5oJYgtYajT58+QYrKHkxsydbleZ4IJAKJQCKQCPRkBJJQ/RqjR5plxw2OEtQb1BWIhvXXXz84V4lv+VW651kuEDu7q6/SPtskAmMsAvlgiUAikAgkAr0KgSRUv8ZwsyVkD7XIIosEpxhxKnntCwjP/pAJwMh2z6NYO31w2hnZ9lk/EUgEEoFEIBFIBBKBziLQ3eslofo1RgghiUClrhWiSlw/KloEK5WssCmd6Z4zlhBHHGgEXieVFQuzM22zTiKQCCQCiUAikAgkAmMqAkmofo2RnWeeeUqAYmFqEKv2oEZgyueB6rij7tURoLoO6s4ZSxgbnuWI347aZ3ki0PsQyCdOBBKBRCAR6C0IJKH6NUdaqB8xL23zJkQKz3/hNYQkEp6jo+4Ro8LbCC0kHqeQNwKxCyZPYttR+yxPBBKBRCARSAQSgUTgayHQjRsnofo1B8euUiSpwgkhOieeeOJYfPHFw45FVPkddU8S269fv/jNb34T7FMFORYX0E5BIgd01D7LE4FEIBFIBBKBRCARGFMRSEL1a44s4nS66aaLCSeccJiexMPs27fvMHntndjphqpflADmArbLtAXgBhtsEOxd22uTeYlAIhAJQSKQCCQCicBQBPi57L333iHu9vPPPx+N5+iJoVXKf7ssLrzwwmGDIfVLZg/4k4TqaB4k6n2hqP72t7+F7TvtUGQnImYASaiO5sHJyycCiUAikAgkAt0YAds221nw3XffDXG3bVnLfHD66acPZokctcV5p6G99NJLw66ZaA4RimxjO+yjdc+zJFRH87iMPfbYsf3228dxxx0XTz/9dCFWb7zxxjj55JMDETuaby8vnwgkAolAIpAIJALdEAGSU7HcSVJFDHr00Ufj3//+d3zve9+L1VZbLWz1vN1228UDDzwQnL5XWGGFsI3zeOONF/xr3nzzzW74VG1vKQnVtpiMlhxmAuxcl1122bC71Wi5ibxoItDDEMjbTQQSgUSgtyIwZMiQuO2224L5ISkqyen7778fU0899TCQzDXXXCVC0UQTTRT1jpkI1skmm2yYet31JAnV0TwyuBzi+Trmqpfohz/8Yfz2t78NUQRG8+3l5ROBRCARSAQSgUSgGyLwxBNPxC9+8Yv43e9+F3bHZEIor/FWhbxU3pj3ySefxGuvvRbzzjtvY3Z93O1+x+p2dzSG3RBP/sMOOyx48yNKWz+ePKGoDj/88Ljwwgvj7LPPjvXWWy/OOeecYFNCjP/666/Hhx9+2Lppno9iBF566aWYcsopg8Pb1VdfXa7+0UcfhSgNVCxsgt57772S35k/xveCCy6IAw88sKhlOtOmrsMu6Z///Gc5vfvuu4MjXjnJP01DgFpt0UUXjRtuuKHda5j42YpNPvnkcf/995c6vl/1f/7zn4eNQO67776imiuF+WekEDAnTjHFFLHhhhsGp5DWjakxOaFONdVUxZzqs88+K1WefPLJYLc344wzxlFHHTXS31rpJP+MNALPPPNMLLXUUmVda6+xNW2LLbYoc+p+++0XYoqrZ07t169fzDrrrGGXxnqeU5apfQSsSdT53//+91vmF6aDvpnjjz8+bEZUt0RzXHXVVbH22msHoVid351/k1DtgtHhuf/xxx8XwrJ1dwgYC9Qdd9wxXJtTLxQbEsTPLbfcEj/5yU/Ky6ZfL9VDDz0UDKVb953noxYB6hTECqJw1113DQvgD37wg8CI7LLLLvHss88G1Upn76qqqhKWbKeddgrRIzrbTj2xejEwjnHFGCHHvTKNooc+7bTTRkjkHH300bHOOusUB4ZtttmmhJt744034vzzzw+OC5dffnlYNPJbHvkBe+WVVwrBYy6kxmzvfb/44otjoYUWKg4l5lNMP6LH5ik33XRTSLRX+hj5O8gWI4MAJtya9+qrr7bbTPngwYPLWmfefPvtt4PAhs2luVQccgzeww8/HLzY2+0kMwvza02Cs3cdoWqdQjsQYIg+hIClsVWOGSAcEa9dPcwdzLs7lEmodsEI4QxPPfXUFo6wsUtxUnHzwyNgqPeXXnrpsoANGjQoTj/99EL4zDbbbFFVVXC2Uu6lauw3j0cfAnPPPXcsueSSRbKDEGm8E4zFGWecUaTiJD8mYTZEm222WWy00UZB4mqcSWFN1tdee21I+jBhkKbXSQQIKpoTTjih9LfxxhuXRdg7csopp8TOO+9cnO8symeeeaYuCiGFoNLHpptuWvL8OfLII4P355577hn7779/uxIp9TINHwE7yCGS2qvBLoyUwrfK9ssGIIOHLsTUcL5dDI0FAxNKMt9eH5k3fATOO++8mH/++Qsj2L9//7CxSuvajz32WJHCyedAQuXJC3qSSSYpDiTGxVioR9KtXqbmIFBVVay55ppBA9HeFeCPabDOWeNWX331IKihlZB8R6R9nILMoe31kXkRffv2DWvLgAEDgsDD/MTHBT1CmGKtUG7uMkcdcsghMXDgwLjmmmvKmsLrH1HbHpbdKW+s7nQzPfVeEBgWqVVWWSXWXXfdNkkcM4bO7T0fzoZY/ogjjmhph6BBaIgEwOaEtC1DVbWH3ujJMx5bb711MVgXsaHxLoYMGRJUXohEsXWNockYAet3/PHHj2mmmSaWWWaZIikwmT/++OPhHzsiEw11GS7ZBGOS1qfrUWWSzi244IJFZcMmyQYT9cRj8rc4k0CQSuCgEbfUP4jTNdZYo3DgFgjErWtm6hoEXhkq8TM+iFE9IooQqaSnpBhszyVzBWZFnUydR4AUFOGiBYxJ4CTndfK+02w5VxfOtFXqYQrNoSROBAvO1cs0ehCAv3nNeLgDcyUNkXUScSrPeFFpJ2MHjeEnKnzCCXM8T380xcorr1zMJpi6EE7stttuRXqNtqBZkCfxhSF1jW7+LwnVLhogktP55psv2ktzzjlnmFzbuxSJGZUVgqSxnKRVrLPLLrusMTuPuwkCpAAkowgU3GvNlfK+POCAA0pA5UknnTTYkpqMcbyIQ8SKujPPPHOpg+utH+mpp54qtnR2NpOHM0bcUoM5lhBDiF3vB49N/WBqmJhYlC3oJL7ugySW9IjpiTwmCvom3VPXNcaclE/S2xFgIy6snzB/jzzySPGEZtOKYMUwIoKoSElYzde9Ha98/jEDAQQ9yap3ndDLU2HU5NXJWqQeBrrO82stqKpKk26dklDtguHxciAWdthhh+J9RzLWmEiyvCDtXQo3g/vZY489itqf2hiBQYKGS8Ittdcu80Y/Aj58KnxSTtIzd4QwZY+IkxVcWZ5kq12EqsXSIumdkd+YcL1U+Ahgak5lH3zwQVDPHHzwwUXNL294iZTi008/Df2r49eijKh1nqkuRBADAAAQAElEQVR5CHCy8y3XEj02YZgIEm9MDeZEIu0YnhlQ8+6u5/eMYeOY6klg7L2WnNeJhsFizSaV2QxHHXXswEPLQcvg+5tnnnmKWVXdLn9HPQJVVRW1tfnS1amlOSFisAlv5HGiolFkruE802hAoJtcMgnVLhiIPn36BGLUItS6O6o+RuXUGq3LnPsYq6qKRs6GxGyttdYqDjbUwoyi1c3UvRCoqiqWXnrpoK5nlO7uSMbZzwmmTM0ir07bbrttYUaYDtTqrbrM7wsvvFBU87hhTI88JgD6o8LH0MgjoUUkO25M8hBM119/fVjMX3zxxfAuWawb6+Vx1yNAdWlrQnbAVJW+2/79+xcJuXfDgkzVSaKXC+/I48+GkV0dHGmZmMy07gVTsOOOOxbMSVBrZk8gdJJW3wE7vsS/NXKj/hwTPcsss5RA9LQ7GHsxxDEa5jfRMXw3pOEk5aP+DvOK3QmBJFS7cDTYPglLQ+2EE9Q1QpQEhad4e4Ssj5Tq3wRLPczGdf311y8x0ZyzdeQIQ0Kjv0yjDwESMRJUIXAkd2JS5dT0s5/9zGkx8TC52rxByLErr7yybFmncKWVVipSUWYgTAAwIOyKTjzxxLIrGdW/gM177bVXIHqYFGCCOBdQ2XMoEa5FZAjmJAihTTbZJHibyyeNxTC5Pmkem2nG8xZ2ZiTu3T0Jg+YdGx7z5F57aGrqbXOOMyYYjrfeeiuYYdgVhrQbQ4oIog0hPbfwYjiESjLuSyyxRAkH43tGUDX1RsfAzjFgtFa+M6p90lOPSes0YMCAgL85lu230Ea//vWvFQdmDdNoHJjZGBsMXSnMP01DgHbnmGOOKU5vxoVdvItxAsV0VFUVvglzEGdj44oRt0aK6MA+H+EqoolybTP1XgSSUO2CsfdR1sSDhQgRY/JEMJCcITKkzk6QVFxUvm6N4w1bRJIE55lGHwKISwsj5wyhhuo7QayalJ2bVIVVITkzOXOmYbCuDNFJUmBSdk7iIx6kxZS9ECcp7Xgqk66Silp077nnnmALS7qqLinuSSedFCR0CCPe5eoiPkkkhB/Rjz5I+hCvGB19MC/ARDmnanMfmTqHAFw57AhLxuaLqp83Lem3BdYYei9IgjASevXOWJCNnUT6TpqkLNPIIcAMynvNk5lNttbeeaYy8EfgYP4QqzXG5k/Mom/KPNrZOVjfmb46AlVVhe/CvGQOZCevN8wehtsxbY950zzGsYeJjHxjy0yKVkK4txwzqIzONPqvnYRqF4yBjxHB4MO89dZbS1gOu0WQqnTUvYWs/1AVIVUV+1SJ0wvvPdync1I2E25HfWV5z0WAupjHvkVXIrmrCdqe+1R554lAIpAIJAKJwNdDIAnVr4dfac34m40pyVXJ+N8fhvv/Oxzuj5BCiBQOOGywpBtvvDHYGTIbGG7DLBijEBB3Uxiz+qFIG4Spqs/zt/MIZM1EIBFIBBKBMQeBJFS7YCx59AsdVHclCDzbwNpOqs5v7xehWse95EDFdODmm2+OxRZbrDhTtdcm88ZMBGwiwGRE6sy7M2aikE+VCCQCiUAi0M0QGK23k4RqF8BPfc/2hn2a4N5CqVDZc6jpqHu2VEJyCMLLXpEdoa3kOFXpt6P2WZ4IJAKJQCKQCCQCicCYikASql00smJVcrTgXMErFZHJyaqj7sXTFO+Pk0VHdbM8EUgEOolAVksEEoFEIBEYIxBIQrULhlGIqeOPPz7uuuuu4MnNkYr6XuqC7rOLRCARSAQSgUQgEUgERisCo+viSah2AfJ21aCyP/DAA4NtoXhwAlILZdQF3WcXiUAikAgkAolAIpAI9EoEklDtgmGvqioE9xbIXXccpOotNZ2PKNnyUrB2MTfZt7ZO7FaFK2JOMKJ+siwRSARaI5DniUAikAgkAj0dgSRUu2AEef0vuuiiIcC67hCfju0E5HxECVH70EMPhR2K9t5772Dj2pj22muvOPvss8uWmCPqJ8sSgUQgEUgEEoFEIBFoKgKjofMkVLsAdDv9IDjtUKS7cccdN2ynaHeaL7/8MmwCYPcNZe0ljlh2TeH9/5Of/KRs+8cZyy5Hgr7bPrC9dpmXCCQCiUAikAgkAr0bAX4y55xzTthm2M54jec2I2qNjjCYjzzySOvsbnuehGoXDI3tTW3fZxs4YaUk+xuLh+nFsS+1bf+8PK0vh0gVP9P+4eoLaTXJJJOEfY6ds3WdYYYZWjfL80QgEegcAlkrEUgEEoExGoF11lknrrrqqkCDTDTRRGETIVGIbCJz9NFHx5lnnlmenynhwgsvXLS0r732WsnrCX+SUG3SKHGuYm/6zW9+M0QBEKqK1LX15b7zne+U4P6TTz55PPXUU2X71QsvvDCefPLJ+Oyzz+K9994LLxQJa+u2eZ4IJAKJQCKQCCQCvRMBNMXhhx8e6Azmg3369CnHaAdSU7SHrd2vvfbaAtAEE0xQNLy2bS8ZX/nPqG2YhGoX4I3Y/O1vfxukpgL9S/vuu28sv/zy4Vhif6pee5f7y1/+EieffHJstNFGMffcc4doAeeff34MHDgwdtlll3j77bejceer9vrIvEQgEUgEEoFEIBHoPQi8+uqrhe6YccYZA2H6hz/8ofiz9OvXrwUEpoh9+/ZtOe+JB0modsGo/fOf/4xHH300rrnmmpYkhurVV1/dYe9ffPFFEcOfd955hTDdY489gmh+4403LpLU733ve/G73/0uJp544g77ygqJQCLQPgKZmwgkAonAmIYALSw/FiExqfhpcp955plhHvOGG24oROwwmT3sJAnVLhgwhOoDDzwQ66+/fqy11lrFkQrh6ryj7tmtihCg3WKLLRbf/va3Y+yxx46VVlopGEEfccQRQUxPtN9RX1neXASmmmqqaEyc3e65556vdVHtMScffvhhu/1geJR/8sknw5STss8000wtpiHeHRWofDjy2ZLXeaauQ4Cdl/E/+OCDA4PZumeakaWXXrq8I/fdd1/r4mCHPs8888Sdd97ZpiwzOkbAtwD/7bffPjiptm7BJk+kFXWOOuqo1sXhm9hvv/3a5GdGcxDwnhuLTTbZpN0LfPzxx1GPF+1hYyU+H9pSXSPGGsvy+P8R4MhNnc+PRfQh0lPfQV3jyiuvDD4ynLTrvC78HWVdjTXKrjQGX6iqqiB6Z0vKWJk33amnnhqLL754h0+NAOVMReXfOPmySRWX1Ys31lg5TB0COQoqPP/88/HLX/6yMBAvv/xycJhjrE798lUub7ypavT1r3/9q00XbJy33nrr8E6xcW6swOGOHbNIE0ceeWTcfvvtpfi73/1u2HjCu6dP3DQmqhTmn6+MgMXykEMOCWMlieTRurOdd965MKmXXnppcIoUpk4dC/LMM88cvmmMid3r5GfqPAKw9F7DnbMI86jWrS+55JKYYoopyhgdeuihcdlll7VUMQbMsT744IOWvDxoHgLG65RTTgnzzyyzzBKtCVHz2cUXX9wyXrSRGEF3hNl+6623yjg+/PDDQWIoP1NbBGAjvCW8zfcEXaIEwZewzPtO7W8tIRRr20PPyEkKqAvGyQKEYBg8eHDceOONYcJkb2px66h7L5aJl/RUzNQ33ngj7r333rBhgJeto/ZZPvoQGH/88QMTgdlwFwzb//znP8egQYPKGMojZTMhy1MmTzK+zD1WWWWVwJDI+/DDD0vbQYMGxWOPPVYk6wcccICi4tF51llnxSuvvFLOEa/i6yJMEcxTTz11ySfps5jbLY0BPUN73qAWAv2SSuHCvaO33HJLadNr/nyNB8UMYCp0sdRSS8X111/vsCWReBvfRRZZJPr16xeIUXbmsD7ssMOKVmTWWWcNG3u0NMqDTiOAiCGNJhkitW6PUIUt7ZNOOZace+65DotTKsFBXVYy809TETDfmB9pnVZYYYXig4EArS9qrsTgzzvvvCWLD8cVV1xRJOW+mz59+sQdd9xRyvLP8BHABMAQk3zSSSeFuUeoS2sArcKQIUPioosuiuOPPz6sRXrCSBOq3X///fHOO+/I6vYpCdUuGCKqf6o+anqcvBcGJ9jI0Q/vMjgdBAVHK/aoXi4SG0RFT+aAhve8PT0fgWiMhR17/fXXywYNtaMbBsPHj2ihrn/33XcDYTh4KAODCMW8YETYEFFtmawff/zxwA3DBSFK+qmc9EeehAAiYSBBpbo0yey+++5t7I4wNiYo94agdd6oBkLYPvfcc7oshG85yD+dQuDuu++Ovn37lrqk2aQY5eR/fzAGJBoIU1nTTz99CRHz4osvhrmApM9CUTOj6mTqPALwpwrWgorTN0Ja5LxOIqTU3xLGjUmVMgyZDVmMgfNMzUfAelh/L3A3F5ov6yubm3wP9Xipq45z8yl1tm/lggsuiFwHa9Ta/7VWzDbbbEGSylaV0IzwZMEFFwyMnVZitE866aQOS6KpQ9SWk6/5Z1Q0T0K1C1AWC3W55ZYLXDsP/z/96U/h5VlooYU67J1EbK655gpx0Hbcccew2HGcIhlz3GEHWWGUIkD6STJz1113hQlhiSWWaLk+20XSs4MOOigQpxZXpgKkO+yEBGJGsJKkkrgNGDAgvDdsknWyxhprhLa/+c1vhuF0bRyhLq6ZChMXbELSpjFV1X9NUNiuyme3asF2jWWWWSaYKSBcMVb6JP1TL1PHCGA+GmuJndx4bnH1LQu6Ld9YI2hJNBBYG264YQnG3adPn0K4qpOp8wi0xltLRI3fOtFMIWxI7nwj8KeZMJfW30RdN3+bi0B742Uc6qvSRBHMsKFUF5Hq+zG/mrcw25hyTB4mvW6Xv20RgKP1YbXVViumFGossMACIa9O4rLLl5glybeGYPrkdfc0Vne/wZ5wfwjVvn37xjjjjFPUtQgPnIyQU525fyoSCxwpHAmboL0kM1VVdaZ51hmFCOBUZ5999mIHuuuuu0YtoXQLjhGhpJkm5RVXXDGqqgpSVpEc1LG4mnhNys79mrQd6/u2224LER+oxeRJJm/vmLoIoqqq4oc//KGiNsmkJbUpGJphAbAgUP0glIdm9cL/X+2RSSTYFGuN0HfuuE7GjkT8j3/8YwklR5tiQVBu7GhMmPkYv2RAoTJyibOIb0or+JtjqTid10m8SCY17PYs2pxZSV532223oILG6JmTTzzxxKDNqNvlb9cj4Puox8t34xsggKmvZM7DvJF6U18zp8Goy2dKV1VVyPfdYO7rdvnbOxFIQrULxh3xYQIURgqRwWaQbeE000zTYe8+Yg4WVISkABYyk7LdrXykHXaQFUYLAqIyrLrqqoG5oM53E3PMMUfx6DamTATsDsKhABOCcFEHQYPIrO2vSBMspohbXuFCmpHMYnTUl9Sh/uLpj4HxjsjvKFVVFYjbup73yeTPlo+0qc7P344RIH24/PLLixqSeUV70miaESpP4yemsnGadtppi3Qcg2DhRiBRhXZ8xazRiACin10wDJnCIEQbyx1TfZLQIQgrmQAAEABJREFUHXfcccWbnP037QRfAeZUmAjmN+ZpBJE2mZqDADti8wxNBBt5zp2NhKqr+jbYTzJ3My7WPPl1QqD6VnqK1K++71752+SHTkK1iwD+wQ9+ELz3qXprNSuCtaPuq6oK6tl99tknLG64faGqqAurKiWqHeE3Kss32GCDYnNqjBCWVPpU8dRUpGnsfhCgzoVdqe8NE0LaQ3LOLISkleRUvdZOISQMiFtELjtUdkSkEYI5e0e0Qewyjrdos3U12SNi1UdEIZZM/iQT3ku2XiJROOd9TtpB+l/fX/52jABCCZEJf2p9CzH8OfnAHyOhDDFEAk5CpFfMKuk1ad52220XNC8kfsoydR4BTODPf/7zGDBgQPh26rmVVzlzG/j7FoSu8r1xzul871mzqxFgo+29901g0gluXAOz4RvxLVnvtthii+Igai5VjrClUdROPd8Kxl5Zpt6LQBKqXTD2Fn07U1E1mUhx8rhFhElH3ZtgETcIGxylD9THy9aViquj9lk+6hBA8F133XXBaY5hOtUjQoXzHOYEUYlgZVvFRhkDQr1lQjb5MgGwkDIdIGWQh6BkV6ctD30EqEndO7HZZpuFrXXZrZq0Lb7zzTdfeK9Icp999tkgMfLOIUTV5zTCgcv1EbjUngJBI04hVUtVh2ceoE4vSCP9iBZL4wVXDIOxp8pn+8WkAvOgHEOCWYWzi5BoGx8OeN4f3ze1tbJMI4cAG37fhjBVCCGtRQJYc801A/6+BeY4pHPefeWNiRRWeWNeHjcHgaqqyk6L5i5h3WobYR7qvgcSbdEARNKghWTv7058LyTeviXMHVt6DLayTL0XgSRUu2Dsqf6pdzlTka6QiJGocLjRPZs0RIVf561TVVXFllG+CZfal0ckrlNepu6BQN++fYtEzPggUtwVAqbv0PwJJ5ywjCH1lnMEpnITsHNJHgLXxOu3ztPWxE36KU//1JhUx/pAmMqvz70jCCV5HEgkx8rrPlyrqqpC1CrDEJHSkgQ7r6pK15lGAgExjWFnPKqqKuNt7OpzZUwqjE9jt4hWKkzjaiFuLMvjziMAOxj75qqqKg1Jr6mGq6oK34H3Ht6lsNUf3yJv8lbZedokBDBkxsucWF/CePlGnPt2fBP1XCrPPGge084c2fpbUidTd0WgefeVhGoXYSvMFEkWG0Mhhqg9BIjXPfWwgOztSUgRLf379w92VaSoElUWSUFV/Xcy1kemRODrIOD9I+n1DpL0fp2+sm0ikAgkAolAIjCqEEhCtQuQpkal0qOapb7nFCOAP1MA3ZO4UE81co7yJVJTki4qZUGSJZsGsOVBVKiTKRH4uggIUUXizwns6/Y1prTP50gEEoFEIBHo/ggkodoFY0SdQc3BXo0nOKeqbbbZJjhb1N1TW1VVWwkpQpXkdZdddglOVIhb5gPs3tojbOv+8jcRSAQSgUQgEUgEEoFuhEBTbiUJ1S6AVRB1HqcM9dmWIjzZs1144YUd9s4mh6MLCarYmTxWmRCQzDIL6LCDrJAIJAKJQCKQCCQCicAYikASql9jYMV5e/rpp0sP4lPy/HeCSOVhKnSK8xElEQPWXnvtst2inYN4hSNwEb8japdliUAi0AUIZBeJQCKQCCQC3RqBJFS/xvCQfgqhIXYlO9Wbbrop2JheccUVce6550Z7QcFbX44d6iWXXFK2X5111llD6CJbbwpcPbwoAa37yPNEIBFIBBKBRCARSAS6AwJdfQ9JqH4NRIX8EXxa0Hae/uJliqnJaeXxxx8Pdqsddc+OlTPVlltuGZKg8uIA3nvvvWGHo47aZ3kikAgkAolAIpAIJAJjKgJJqH6NkRWvT3B+WyuSotqBQ3IuTJXA3x11rw87rogMYKtMKn/bb4oJ6PyWW24JJgScrjrqK8sTgUTgqyCQbRKBRCARSAS6KwJJqH6NkfnFL34RdqnRxfvvv1+21xRaSrJjEecqZR0lRKjQVnbpkI488sh47bXXwrZydiSy7ea7777bUTdZnggkAolAIpAIJAK9DAFmgrbOHjRoUPCdaTxnmtgIx6WXXhroE/Ub87v8uAs7TEK1C8D829/+FraKs+2brRIlgfuZBnTUvZ037FpjNw67WdlWzhZytgNcZpllgnPVJptsEmxgO+oryxOBRCARSAQSgUSgdyFgW2GbBtVP/cYbbwSzRFrZnXbaKf785z+XIkTqe++9F5988kkcc8wx8dxzz5X87v4nCdUuGCF2pgafrSoCVbrgggsK8dqZ7u1n7CVDmC6xxBLROs0333whkkBn+so6iUAi8JUQyEaJQCKQCPQ4BNAaDz/8cBx88MExYMCA6NOnT7zzzjtBwLXpppsGoddZZ51VnouZoShD0k9/+tNAyJaCbv4nCdUuGKAJJpggbHlKgsqBSvrggw+CvWpnuq+qKsRfPf744+OII46IRx55JPTVmbZZJxFIBBKBRCARSAR6HwJvv/120OTOPPPMIWrQbrvtFp999lkQfn3/+98PsdgRpH5j6D/bZxOs8X1hTjjbbLMNzW3m/67pOwnVLsDRwONgxEHdeOONQ8LJnHfeeZ3q/aGHHiptrr322nDMjEDQf7arneogKyUCiUAikAgkAolAr0JAHPdpppkmFlhggbBp0KOPPhovvPDCMBiwRd18881b8tisnnPOOSG85uuvv96S350PklDtgtGxPaptVD/99NPg6S/ZWer3v/99h70jcjlfsU8V3koSSYBNSYan6hC+0VLBFrfrrbdecJZ7880344knnhhl92Fi2WOPPcL1n3nmmeLAV18cN91TbI7qe+5uvyO6H98k3I1/exqPjz/+OHbfffcyNs8++2ybrq666qq466672uRnRucQsAjD/5RTTgnxp1u3Ynd38sknF/xFYWldTj3aWeFB67Z5PvIIPPXUU2Usjj766HYbWy/r8Tr77LPb1DGvUmu39621qdxLM0hFf/KTn8Riiy0WU045ZZCivvXWWy1oCHNJ3d8oOV1xxRXDNzTppJMGyWpL5W58kIRqFw2O2Kc86Waaaabo169frLLKKoH47Kh7H+G3vvWtwhH98Ic/LKJ6vzz3/vOf/3TUPMtHMQImTxwp+5+ll146fPQM10fFbVicbSTB1IS6B1F04okntlx6s802C5NWS0YedBkCGJJtt922qNlILh588ME2fbMRm3HGGcs7sfLKKw9Trj0tCynGMAV50mkEvPPrr79+vPLKKyEEYOuGxgXO6pEgcSZprIOxFO+6MS+Pm4MAr3OmbGussUZg4BCkjVeytgnhWI+Xb8d5XcdcJ+qN8UrNYo1K21/EKYZACcdstAQC1Lm5xnrF58V5Y2KeOPfcc4ct3Bvzm3T8tbtNQvVrQxjho8O5kKRajATtZ9TMwaqj7nFAvP5vv/32EPjfRgE8+MRR9eJ11D7LRy0CTDxMvNNOO20stNBCZQcyEnV34T0YMmRIDBmaSAvkYTi0EW6MxOfzzz+PV199tdQhTVdHHkmpBVgECXli6Yqpqy1CGENj8ldnqqmmKgQpKcTyyy+vekkmd7ZIru167kPCdWvLcN65/tQtjfJPpxBgO77NNttE3759i/TiuuuuG6YdPIWRW3PNNWOllVaKySefPEheVWIzdsABB0TjWMnP1HkEqCq99wsuuGAsu+yy0cig1b3ceeedIWqKMUKU1g4k3n3jx46vrpu/zUWA9sG8hkgitLGmGYf6qohPKmrCHONFG0TaLV8d3xKG3HGm4SPAN8acb6MhjNn0009f5h4MwMUXXxyIVv4yNAzwRpNYA2jjrBFCbA6/9+5TkoRqF4wFwuWEE06IHXbYIUjbLFZegKOOOqpTvZt4q6oKBC7JLC6H5AB31KkOstIoQwCBiqi08JmMnSNY3cCFF14YiJm11lorSBAQhMZRuLH99tuvmAiYMKju5QkPglhF9IjHK3+vvfYKdkdMR9QhaSCVuOeeewLjMssss5S+SSsQr67l2myaSfOff/75EopEXF79Lb744iEWrwnqsMMOC/2zob7jjjsi/w0HgXayBw8eHLBXhGDCmDquk4XBImtxljfXXHMFVbUxsmFHZ8yAtMvUPgLmVSpMpX2HMgu+BwyZ8zoZg9pcql+/fsXeXxnp93TTTRczzDCD00yjAAHzS/29wB2DjTCqL42p5whUj5e66lhLSQHNdRyC6vr5O3wErDt33313sVFdbrnlwuZB/F2sI8rM+eYiArFLLrmkrAHWHgzEz372s+F33I1KklDtgsEgKfvBD34QCIrG7jpjL+hDxd2sttpqwT6VpMDLhktq7CuPuwcCVC2IR1JK9nKIQQukSXjgwIEh4LJxnGOOOVpumH2QTRwstIjGeoHlNEdtQ1pEgorQuemmm4pkXWOTNXVzv6GLLptH74RQI94z/WBs2oss4dqYJESyayOeH3vssRA+Tb9smGppn/NMHSNgQ4/GWqJ0NJ5POOGEgbHEdMhX34Lx5JNPFgaDhFV+pq+GQGu89UJS5LdOtFOYR3MqTQb8X3755fI9kezV9fK3+Qi0N140OvWVq6oq3wuCFnNnvL797W+H7wYTSNhT183fESNA8mzNITyxVqhtbZBXJxsJ2QWT8EMeQUddV/1Rkb7ONZJQ/Tro/a/tj370oxCw/3+nQZJ2yCGHxK9//es6a7i/JC4MxklS2VYhXtgeisVKnTjchlkw2hBA/JFOMkS///77A9dKkomI7dOnTwgHMv/888ckk0wSJgO/Jgkqf5Ox8cWQ8Ma0qHLEI40nVWWnRQpKajf77LPHZJNNFtrE//5ZfFdfffUwmWNobC7BFmnRRRf9X40o6h5mA2LlkcyyW33kkUdKfF7vGK7ab0uDPOgQgcbvW2Xj4rdONuTAwGAOjB21s3EkQbdokOhRZ2Js2FLW7fK3cwi0R+j7rhpbM7syl8Lae09ihBDaeeedyzfpl6bDt4cpbGybx12LQHvjZe6qr4LpJpzhYEhdbayWXHLJIClnJiOMks1uqLSNV90uf3snAmP1zsfu2qf20W244YaByHAsVISF6le/+tVIX4gUDWGBI83JdKTha3oDEjK2P2OPPXaYTI0xO1ISNeph0nU3gVD025ioXqqqCjatfYeqL03cCBymHohVeRzpGts0HiNqeWtSmSGcOHIhmFpfi4QCB417ZjfNRlJ9DJTF3XUaid/Ga+RxjcCwv8xz2ID5JhH9bCWHrRHhXSCtNkYYCHENEabGi2SPmQaTDHZ5rdvm+YgR8B434m9zFHNtYysMJDU/YscxwhWzxiZPooUgEPBd+OYa2+Zx1yLACx0jTtgi0kW/fv0K0914Fap9jAS1NeZixx13DJJUtpT8NQh7mCkZr8Z2edz7EEhCtYvGHAeJQ0TAWLA4TrSeSNu7lDr9+/cPO1ORokqkdYykq6pqr0nmjUYE2JSSpFKjU6Fw4KBWpNZHzJhsqf+pVhAoiBqLJ9U+1TDbRROwOlQ1dhxD5LIZ0p9k4jTABIEAABAASURBVEbYaGfRZUJismdnyj5WdAntjz322Jh44omDBO/KK68sIXtM/AglNqveSfdx6qmnFvtKBCxCSVvRA0YjjD3u0r5tdl4kchZghJNF2PiIfcxRBK6wZ6bBPKPHPWQ3vmERNuBtnsQoinDhdpnOMJeBP0bR98Nxx1aRyjONHgQQoeYfJki+C9oid4LRZx7DRtWcZh4lQSUJV55pDEfgKz5eEqpfEbiuakZCg3v08V5zzTUhMYQmnTUxd9V1sp+uQQDnLzQVaSipDCnZIossUjpHEJqgnSBklPNCxojIIy3fZZddYuGFF3YaFl/mAltvvXXJq6oqOGchPPVbS94QSRy2MDUkRI51QELLW9a9OOfQR3IqRBKJqjwSW0S0PCo09y+f2YDfTJ1DwJjwJGfCIfQRdWV7LQXfXmeddUo8w9bl8uebb77W2XneSQSEK2KHSq3PtKa9Zn369CnxjWktWpf7bnxLrfPzvOsRMPeZ19hP/va3vy1zXXtXMT+x7RflprG8qqowxmzyzZuNZXnc+xBIQnU0jzlClX0jAgZxwsbt/PPPL3FYLYqj+fby8q0QsFAKc7PqqqvGgAEDit1nXYVDnTwJcSg0iGMTtQlbPap30jb5pKt1HntmeewaEa8WVETpPPPMU5z0lGmLEELsOPe+sI/WBwm+PAQotZvjOlFTU/Wzna3zSDu0yzRCBIYp5L0MP2plTIMF1PjU58owH96RYRr+70Rd4/e/0/wZSQS8szBmWgV/zXkt+2ac9x+qmfJdtiZ61JO8/zUj6TxTcxGo57+akXc1kUl8I1VVhXnM3EijpKx1wrRzDDW2rcvyvHchkITqaB7vqqqKraMQNtTD1MVUuj7y0XxreflEIBFIBBKBRCARSAS6EIGR7yoJ1ZHHrEtbkMrgMkm8OOVIIgEIH8R5pksvlp0lAolAIpAIJAKJQCLQgxBIQnU0DxailH0q54D11luv7I280047lbti51MO8k8ikAiMNgTywolAIpAIJAKjD4EkVEcf9i1XFimA8w37VOE4BIcX55KUtaVSHiQCiUAikAgkAolAItDzERipJ0hCdaTg6vrKvFPF+7PFIkeAlVZaKTjicMzp+qtlj4lAIpAIJAKJQCKQCPQcBJJQHc1jxaORN6t4mqP5VvLyiUAiMDwEMj8RSAQSgURgtCCQhOpogT0vmggkAolAIpAIJAKJQO9FoLNPnoRqZ5HKeolAIpAIJAKJQCKQCCQCoxSBJFRHKdx5sUQgEei5COSdJwKJQCLQ/RD4z3/+E+KwDxkyJEQSajx/7733Wm74X//6V7z++uvxyiuvlPotBd38IAnVbj5AeXuJQCKQCCQCiUAikAgMDwFbru+8886x1157BcL0L3/5Sxx99NFhx0s7IT7zzDOl6UMPPRQHHHBA2WZYSExEaykYnX86ce0kVDsBUlZJBBKBRCARSAQSgUSguyFw++23xymnnBII0tNPPz1+/OMfx6uvvtpybkOh448/Pr744ov47LPPCqF60kknxfe+97245ZZbutvjtHs/Sai2C0tmJgKJQCLQLgKZmQgkAolAt0Dggw8+CKEtZ5xxxrJZ0FFHHVVU/zPPPHP89Kc/DTHaF1tssXj33XdDXPb55psvRBiSP8sss8Qnn3zSLZ6jo5tIQrUjhLI8EUgEEoFEIBFIBBKBbobAs88+WySjCNVzzjknrr322iJN/c53vtNypy+//HIhYquqClu2V1UV//znP6NPnz7xi1/8oqXe6D0Y8dWTUB0xPlmaCLQg8NFHH8WgQYNKYhOk4MMPP4wrr7yy5P35z3+WNdx01llnhfp1hYsvvri0e/DBB4ORe53/dX7feuutuPfee+PLL7/8Ot1k23YQYOc1aOj4s/Nqb7yo4JTXySJSd3PRRReVsb7uuuvi73//e52dvyOBAHs62N5xxx3tfi/e+XoMHn300WF6vv/++wv+xoH93jCFedIUBMxFxmt46mVq6JtvvrmMy2WXXdbmHt5444144IEH2h3rNpV7aQanqJ/97GexzjrrxAwzzFCkpS+++GILGojUjz/+OJZccsmWPAcvvPBC/Pvf/45ZZ53VabdPSah2+yHKG+xOCDz//PNx5plnDkNsWPgQmyNzn9dcc01YPE0kCFhE8Mi070xd3p/DWyQ60z7r/D8CPGq32267knHqqadGIxFaMof+eeSRR4b+/e//Sy65JOrzxx57rHja/rck/35VBA488MDiKIKoueuuu9p0c/fdd8dVV11V8u3298QTT5Rj39b1119fjvPPqEEAEeQ7efPNNwNzJrW+8p133hlnnHFGfP7552VOvemmm1qqYASPPPLIuOCCC4r0r6UgD4ZBYOKJJy4SVJlU+ySpE0wwgdPAKNx6663Baapk/O+PyADWn1VWWSXsjPm/7G79k4Rqtx6evLnuhIBtbX/5y18WzpXdj3ujPllkkUVCqvPkt5fWXnvtUF8Z4nT22Wcv3pd/+MMfQt/yv26adNJJY+655y72SFdccUVYDL5un9k+irPCQgstFAMGDIh+/foVFVsjLhbkDTbYIJRLL730Uiy//PKlCkkgiYf8pZdeOr7//e+X/PzTeQQQOgiabbfdNpZaaqk49thj2zS+8cYbY8UVVyxjQIKEoVSJBBbhCn/bVP/whz+UnamJCGDCMfU77LBDrLzyymGOa7yc8ElPPvlkTDvttLHxxhvHbLPNFrfddltLFRor3ustGXnQLgLzzz9/IVRvuOGGIEm1y+VPfvKT4jhFWs0koM9QFb/xUI7hPvTQQ2PDDTcsaw4NRKOWr92LjLrM4V5prOGWZEEikAiMFAImX9IeBOlGG20Uk002WRx00EFFxUK6hnslQd11110DEbn55pvHJptsErhg0rqpppoqFlxwwbjnnnuCukY/uF5G71SWiy66aOy0006BIJ5nnnmC+gbRtNxyywU1GSkGr88tt9yySCrcg0lp8cUXLwsCIlYb13dvI/Vwvbwy8w74g4G6zATvuE48bccZZ5xyijAyJs45MZx//vnBucHYpNq/QDTSf9jeWZTZ2PkeSIRI3Ro7whz4luQhZmtTHFKlOeecMzASFmrlmZqLAOwx4t/61rcK42z+M0fVV62qKsxd5jpE1NNPPx1rrbVWKSYBp6Hq379/Oc8/w0eAFJW24He/+10hPs31pKy77757SKuvvnpMM800sf/++xfp6dZbb120DhgD6w0TGd7/w79C9yhJQrV7jEPexRiAgIXTpGHiJblBXFpQEZ2IFY+IWDVpWEhJhU4++eQiHZpyyikLR0zyw3MTh6wfklY2p9qRULzzzjtx4oknFnXZwQcfXFRjFoO333672KXWqk8E7vbbbx/bbLNNUKkhoC3WJBiIYdIn95OpcwiQmDbWLItuY0bDMbV0//79S854441Xxotkm4TD2DLJKIX5p9MIkEq3ruydb8zzHTz88MNFVfzpp58GRkH5brvtVr4tRK4Ykn/7299kZ2oiAu2NFw/1xkuaj0j/MN3mpOmmmy7UOfvss2PgwIGNVfN4BAgQQBCA3HfffUWIoaq1QV6dLr300hK2iiavzvO7xRZbhO9Gm+6cklDtzqOT99btEMDB1gtg65uzEP72t78Nkrc55pijRc2vngnBb+skPMhTTz1VvC/HGmus0I5UiNROPyZz11t22WWL4Tt1MmLTtagyqTGFGtGv33XXXddhm0Sih2giXeJ0MtFEE7WpkxnDR2DCCSccphDuw2T874S6UiItkmVMJNILi4cyNs3KMnUegfbwHn/88YfpAHNoIV5vvfVi3333jSWWWKKUY/aqqipSpT5D1aAkr6Ug/zQNgfbGq7XJi3FAZNE0YCAw1DQVmG1jKDYoSTpmv2k3mh13OwTau6EkVNtDJfMSgeEggEAUOLlRKkbahhgZTpMRZusP8fvKK6+Uet/4xjcCESmvZHThn6222ioOO+ywmGSSSaI14dWFlxkjuyIhffzxx8uzkUSwAy4nrf5wVGAG0HpRVo3kYuqpp26R9MnL1DkEFl544aCmVBvGpHAYOOd1Yj8uRM8+++wTNA/iS9ZlfjGAGIbW7ZRl6loEFlhggai/F9EyaIymmGKKloswU0KEmocw3BzlLrzwwsLkn3DCCbHnnnvGSiutFMw9pJaGedArEUhCtVcOez70V0XAZEt1SHJjwRQ+xbGJmI0qGzjlEvUwBxDqLAunchK1999/P6gmHSN42RUdfvjhoT+LMakPVb8+2Gvpw69wLgzf2T2SilJ9yieV5eFp8lemnTZsk9QnwVNGqmqxdk56+1Ux6I3t2M9ZSDmAXH755cEpyni+//77RV3pWLI4sxGrCVVMjffA2A4ePLjYLZPw9UYMv84z23WHKQzVPgkc+2v9Maupvy3fmW8CQ0YNagyY4/g24K+971MgdG0zNQ8BdvTmK5EYjjvuuGJbjwk3Xsaoqqqg4WGLKjoDG1bjYsyYA/Tt2zdIZdXJ76V549RTek5CtaeMVN5nt0CAtJPExoK31157BRvT3/zmN2GbOosiQpOUVEgitla8LnmzsldlFsCblWMOlRcil93iaqutVgzh9ac9tRcPTf0gKqnIOOgghuWzdyXRI5Gw+JJKiMmKKGILWVVVCYdkFxKEKvskiwaJHuN6Tg7dAswedBNMMdj8HnLIIcH+F4aYBePJcUQAbee8bueZZ56WJ3vttdfimGOOCWOLkOIB3VKYByOFAJtuNr7MYX71q1+VtpxxfGfwF8ro3HPPDUwfgkcFhNFpp51W8DdOAwYMkJ2pyQggSjlt2qoTAVozFghXsVWrqirf0fTTTx8cPjHvvq/G28JoM4VKproRlV5y3Ooxk1BtBUieJgIdIUCq+sc//jFIdjg2LbLIIqUJwpLKSv4KK6xQyh2zG+VA5XjnnXcOi6VjC2hNuLBhlYegQcQiMp0jjAR0ZqPqXD+8+hHIFgFEsnyJFNav1K9fv3Cfji0SPDst5iSuVP/lhvPPSCFASgRPtpCIftJpYacaz4VFssDWHbMnZn+nnZAwdX7+jjwCP//5z8s3BUffmh7gTcVvPETR8L004s+OlTMV/Nmt1gSstpmai4B5C+7mq/pKnEjNgc5JTEUxUcd8Jq9OVVUF8wHzm7Gt8/O3dyKQhGrvHPd86l6EAHU1r9q55por1lxzzV705KPsUfNCiUAikAgkAk1CIAnVJgGb3SYC3QUBZgm8adlPjjvuuN3ltvI+EoFEIBFIBBKB4SDw/9lJqP4/FnmUCCQCiUAikAgkAolAItCNEEhCtRsNRt5KIpAI9FwE8s4TgUQgEUgEuh6BJFS7HtPsMRFIBBKBRCARSAQSgUTg6yFQWiehWmDIP4lAIpAIJAKJQCKQCCQC3Q2BJFS724jk/SQCiUDPRSDvPBFIBBKBRKBLEUhCtUvhzM4SgUQgEUgEEoFEIBFIBLoKgSRUuwrJ7CcRSAQSgUQgEUgEEoFEoEsRSEKYbHbhAAAQAElEQVS1S+HMzhKBRCARSAQSgUQgEWgOAnYY3GSTTWKqqaaKJZZYIt54441yoSFDhoSdvOTPMcccJe+zzz6LgQMHRt++fcPuhs8//3z85z//ibfeeiuWXnrp0sdqq60Wn376aanfXf8kodpdRybvKxFIBBKBRCARSAQSgQYEHnzwwbBt88svvxw33nhjTDbZZPHvf/87bI+96667hvwHHnigtLj44otjvPHGixdffDFWXHHFOOOMMwLxesEFF8TUU09d6v7jH/+Iyy67rNTvrn8Kodpdby7vKxFIBBKBRCARSAQSgUTgvwgMGjQozjvvvLjyyiv/mzH07+effx4XXXRRHHDAAfHmm28Ozfnvf0Ttz3/+8/jGN74Rc845Z/zlL3+Jjz76KH7wgx/E3/72t3L8/vvvx2yzzfbfBt30bxKq3XRg8rYSgUSgRyOQN58IJAKJQJcj8Lvf/S623HLL2G677eKss84q/X/729+OddddNxZYYIFiDvDII4+U/C+++KJIWp1897vfDdJTpgPLL798OZ5llllin332iRlmmEGVbpuSUO22Q5M31gwEhgwZEkOGpg8//LBwk44lH7TrUZ84/+STT5y2Sf/6178KV/rll1+2KaszcKxSff5VfqlnGjnjr9JHtulaBEggvBsffPBBsfNq3bt3w5gNGfp+kXC0LifNeO2116K9stZ187wtAr4J2MKRnV3rGtSfytT5cOj33br873//e/n2/bYuy/OuR8B7bizMqe313jhef/3rX9tUMU7a++7aFPbijH79+gUpKQnqc889F++9915885vfjJlmmqkQsMcee2yxSwXR3HPPXQhROL7wwgsx9thjx3e+85341re+FT/72c+KyQDpbM+xUfVUmRKBMRgBEyMj9Pnnnz+uueaauP3222OppZaK5ZZbrtjqKD/++OMLZ/r444+3i4TJ8/LLLy/G6O1VoEbB6eqndbn+n3nmmdbZbc5d49BDDy331qYwM0YLAhiXHXbYIfbaa6/Ye++9WxwYGm/m5ptvjt122y222GKLOOiggxqLilru97//fRx22GHx0ksvDVOWJ51D4Oijj47tt9++LMIW6NatnnzyyTI+xojUqXYyUe/++++PPffcM9jwPfTQQ9Eeoatepq5BAL5nnnlmbLvttrHHHnuEsWnd8xNPPNEyXhtttFFhIuo6jz32WOy3336x4447BvV1nZ+//4/Aj3/84xh33HGjtdCkf//+ZT1Tk5R1pZVWKu/+wQcfHNNNN1306dMnzj///GK76lt4/fXX45xzzlG926aUqHbbockb62oExhprrNh///1j0UUXjfnmm694QZogF1544Zh88slDOQ9IBAV7nvauz7Zngw02iCmnnLK94hHmIY7//Oc/j7BOFnZPBC699NLyfpx++ukx6aSTxnXXXdfmRqnQJHUuueSSeOSRR0IldTE3G2+8cRx++OFFkiE/U+cR4Bzy6KOPBmwXWmihOOaYY9o0vvrqqwtzpw4155/+9KdSB1GLQNVOmd+qqkpZ/mkOAs8++2zcddddZbwQSrvsssswF8K033rrrWUuNiYce0499dRSh9bBvEyNrQzhVQryzzAIcJoyF/3whz8cJv+KK66IbbbZpiXPmrfyyivHKqusEquvvnowE+D9/+qrr8bEE08cM888c9ASRTf+l4RqNx6cvLWuR8CHTSUlPEdVVWHRoiakPiEFePvtt0vIDsbnVLkrrLBCOT/qqKPKzeA+LXrUKOqfcsoppVxIkGmnnTYsltNPP30xXl977bWD9Jbk9sQTT4x11lmnSBcOPPDAwgW7tnZ/+MMfgsROf7wx2Q21p+5SjhPWRrr22mvDgrDMMsuUe8Ad33TTTeWYtIJkVj0Jgex5SHo33HDDoBJy78cdd1ypr87DDz9cnlFbi4Q8yaJBUkwq4lxy3VK5l/w5++yzY8011yxPa0xvueWWclz/ofK3wGJ4JppoomIndtVVV4U8hOrHH38c6623Xvzxj3+sm+TvSCCAUfC9kCBhMn0HrZs/9dRTxQNa/oABAwJz4JgEfPbZZw+LN6n4hx9+KDtTExHwzlNFk94RDJiXGk0AzGW+DfOx2zBXImx9J8aL8IBmwlzb3Yko9z+qEuLSd2AOpnUTnooq3/wsTzI3r7HGGuWWaHBIUbUjXf3Rj35U8jfffPOiUVT/nnvuiU022aTkd9c/rQjV7nqbeV+JQNcgYGLEQd57772lQ4vW4MGDiyrf5ImAnXXWWUvZXkPVvD54khkEHRs5xuuIUUbp7HoQe3fccUf89re/LdIcxKjGJoitttqqhBFRTpqGmzUJ77TTTkWtZeG04DIHkBDJ+rv77rvDsX4ak2sikC3A++67b+y8886BKN5ss80KEcxzk/2S+/3JT34SuOgLL7ywXMukZgKjmrYYkO56NosFItZERdWGuKUiQjyTYFlsqFERCKsP5cYRaNSvvEsb721MPyaVI4nwnOzBnDtuTBgBUg556nBaeOWVV4ra0xhZrHnqPvLII6pkGgkEfB8WZE1gi9lsVO3LpwLFSPqO1YG/BVqe79536H23MKufqXkI+D7q8fLdmLsaCVVXNn9i8EhXCQaMlzoY5nHGGSduu+22woibh9TPFGFeh4t5hm3pJJNMUmCZcMIJi7pfPmasxp520FhwvqINLJWH/qERZA6jPpMl8/vQ7G77PwnVbjs0eWPNQoB0BfdugjRRkpqSepJimizr65500kklDAjicd555w0LHoJ0nnnmKVUQudQuVVWF8kbO3wQh6HKfPn1CPYtoaTT0D8ctxC67IBLO73//+1FVVSCGV1pppTD51NK7odVb/pvwEZpjjz12IX7qAtJRqhxEpmupR0KMAGdYL9YeThpnzeaLRMrEZjFAcGrXaDepf4nBPaN990v1iohHvDvWvr5+b/j93ve+N8xjGrPGDPZiiy++eFBxDho0qEgrjP8777wTs802W/HGHX/88YsThMWhsW0ed4xAa7y18P76rVP//v3DN3vyySeX79Z3Cn9Sboyib+AXv/hFsH/EVNTt8rfrEWhvvGriydWYWRkLc6Axw1z/9Kc/LYHnEU0DBw6MKaaYIoyhuVqbTL0XgSRUe+/Y99onp7q1gCHuSBeXXHLJIMW8/vrrS6y5GhiTKaklOynJRFqX+UVQkmJSoyBmSUjldzbtvvvuxYZL3/rhsTyiaAEWV3ZcJnUSpfo6VM0zzjhjULdpjyBShtg84ogjWq4hrzEhnkl+2VPWBLoFxg4mRx55ZGy66abFLnOG/4UuQay5V4mEtrGvMf0YIzJkyJDymKTdxqucNPxB9NvtBdFvkV1kkUWK44Kxgi+pkbEyLg3N8rATCMC/JvB9u97J+j2vm2Pu1l9//WKDx06PRgOj6Lvy7ZCyeoe941VV1c3ytwkIzDXXXFF/L6TYbCExDPWlqqoq2ibqaN8DTRUnVGNKc4XRVtd49Tam2HNnGhaB9gjVYWvkWSIwhiFgMmQHuthiixWnKqp+kyr70EZiFAGLkPP41IatvU9JZC2aHGRILk3O6g4v2UFEGckCaVtN2FJFijKAyGHXaBcR9qDU/PrWRiLZpPYXA8+9yasTSS9Pc1LfqaaaKvr27RuIIoStOqS1fhuTBUF/2rJJVSYPFtT7pKcWDwuF+7V4nHvuuaq168xSCsbQP4ig0047rdgS2+1FtIjWj0piRwKNMGWLapFFUCFcqa4xEST43r3WbfN8xAgwqWE2QWvBm3zAgAFtGpDE0RbQVjCzYDPOSYfaH3Pl/TUGNBCY0DYdZEaXIfDLX/6yRLeghSHhZufOwa3xAlTRCy64YPiehFrCbJsjSVoxysaL7SWpamO7PO59CCSh2vvGPJ94KAKIM/adCLBpppkmTJiID1KvocXlPyIP0YHwI0E0gZ5wwglxww03hImULRwik+RMe6p1i6gFFWHIAYRNKgJ48ODB0X+oavKQQw4J9p/6Q+jqG4GJeCbJJO2lQmaKwKFAeJdyM0P/UMW7Z/s5IyKZKlA1Dy0Ki7Kt8hDBnkFi2yWkj2uQKpEgc+py7xxP1FWGQLaYqO8X8Q0f9+uZSQrdK5smzg3auBfX7S2JAwNM2DcjNI0Not5YsunFRMCFoxx7MDZgsLHwigSw2WabBac3BG+jZEmdTB0jQM3v2yHJrqoqOARqhXlAtMKfZmPVVVctIah8i8p9B4hU7z1mjBaE6Y+yTM1DAJPGVhLjwO504FBVvqvRPLF1N5/QVpk7zS81MYqBYA5l/sP4+44wf9pm6r0IJKHae8e+Vz85gpATUQ0Cgg7XX5/7tchRIVI5IjpNoiRlzhGsJKwIE+cSL3zSm9rYnTOTfAkR7JqOES4m8roe1TvihWoS0aqOPKp891EnxCGiSDlTBZI6k3xdToqE6KzPqTjVlcQyJLFwLAn2TOXGntI5OzCErEVEaC5Eq3y/zhHsHKvkSRys6uv0ll9SPc9uAUbkkxAZy/pcGQcHav9GTDi4eVc4USH+G8vyuPMI8B6HMYKVHbaWVP3eYeNBq4HAwVwpa0wiWXAm5NTWmJ/HzUOgnu/MlfVVMBi+kXou5TiF+avL61+hq4w186M6L397LwLDJVR7LyT55IlA5xBg94ZQFXZIMgEj6jrXunvWIplC8JJOeSZ2tKRZpLXd847zrhKBRCARSATGZASSUB2TRzefrakIkBhQK9rxRsL9c95o6kWb3DnJLjWdUFWeiXqOeQQJSJMv3Ru6z2dMBBKBRCARGEkEklAdScCyeiJQI1BVVXFa4rgkTTDBBHVRj/2tqiqE3PI8krBLtZq1xz5U3ngikAgkAolAj0VgxIRqj32svPFEIBFIBBKBRCARSAQSgZ6OQBKqPX0E8/4TgUSgRyGQN5sIJAKJQCLQeQSSUO08VlkzEUgEEoFEIBFIBBKBRGAUItAJQnXYuxESp6qqqKpMVZUYVFViUFWJQVUlBlWVGFRVYlBViUFVJQZVlRhUVccYoCmHpTLbno00oWr3DwGWMw2IAQMGZEoM8h3Id2Dk34HELDHLdyDfgXwHAk3ZljQdNmekCVXbMgrJk+n0SAwSg3wH8h3IdyDfgXwH8h3Id+CrvQNoymHJ0rZnnSVU27bMnEQgEUgEEoFEIBFIBBKBRKCJCCSh2kRws+tEIBFIBIaPQJYkAolAIpAIdIRAEqodIZTliUAikAgkAolAIpAIJAKjBYGRIlRHyx3mRROBRCARSAQSgUQgEUgEeiUCSaj2ymHPh04EEoFugkDeRiKQCCQCicAIEEhCdQTgZFEikAgkAolAIpAIJAKJwOhDYOQJ1dF3r3nlRCARSAQSgUQgEUgEEoFehEASqr1osPNRE4FEoHsikHeVCCQCiUAi0D4CSai2j0vmJgKJQCKQCCQCiUAikAiMZgS+IqE6mu86L58IJAKJQCKQCCQCiUAiMMYjkITqGD/E+YCJQCLQIxDIm0wEEoFEIBFog0ASL8yz8AAAEABJREFUqm0gyYxEIBFIBBKBRCARSAQSge6AwNchVLvD/ec9JAKJQCKQCCQCiUAikAiMoQgkoTqGDmw+ViKQCPREBPKeE4FEIBFIBBoRSEK1EY08TgQSgUQgEUgEEoFEIBHoNgh8bUK12zxJ3kgikAgkAolAIpAIJAKJwBiFQBKqY9Rw5sMkAonAGIBAPkIikAgkAonA/xBIQvV/QORPIpAIJAKJQCKQCCQCiUD3QqBrCNXu9Ux5N4lAIpAIJAKJQCKQCPQKBB5++OEYPHjwMM86ZMiQGDRoUFx44YXD5N91110l/89//nP84x//GKasu54kodpdRybvKxFIBHo1AvnwiUAikAh0hMAVV1wR6623Xhx//PEtVf/+97/HCSecEB999FEor8vuvffeuPLKKwuBevbZZ8ctt9zS0qY7HySh2p1HJ+8tEUgEEoFEIBFIBBKB4SCw0EILxZZbbjlM6QMPPBBjjz12bLHFFrHrrrvG/vvvH3/5y1/ivPPOi1//+tex4YYbxjLLLBOkqojaYRp3w5MuJFS74dPlLSUCiUAikAgkAolAIjCGItCnT5+YeOKJh3m6l19+Ob7xjW+UNOOMM8aEE04YDz30ULz33nvxne98J6qqir59+8bf/va3+OSTT4Zp2x1PxuqON5X3lAgkAolAIhARCUIikAgkAiOJwA9+8IN4/PHH469//WtpiRj9/ve/H+OOO25cf/31Je+f//xnjDXWWIVoLRnd+M9Y3fje8tYSgW6FwAYbbBBvvPHGKL+n888/P2677bb417/+FRdccEFQ67S+iT/96U9xww03lOyrrrqqZTJ67bXX4swzz2yZsEqF/POVEKAmYwt20UUXFRuv1p18/PHHsfvuuxd7sWeffbYUk2AccMABJU/bbbfdNp566qlSln9GDoFHH3204HjKKaeERbZ1a/gfccQRpc7BBx/curh8Q8bAt/Sf//ynTXlmdC0C3nN4H3300e12/Omnn8bJJ59cxou9ZGOlp59+Ovbee++ctxpBGYnjeeedtxChAwYMKPh+61vfiplmminWWGONOOOMM0reXnvtFaSxCNiR6Hq0VO1qQnW0PEReNBFoNgJ33HFH+cCvueaaZl+qTf9LLrlkzDbbbHHJJZfEzjvvHK+88kqbOuyN5plnnuD9ucsuu5RflX70ox/FsssuG+ONN158/vnn0XpBUCdTxwi8+eabgcjcc889CxPw4IMPtmmEOKJmW3HFFWPllVcu5ey/Fl988dBuhx12iPHHH784OJTC/DNSCMBw/fXXL+//5Zdf3qbt1VdfHYjZHXfcMe688864+OKLW+rss88+4bvYeuuty7dUVVVLWR50PQKYag48CCMMBIK08SoYBePluzKuvh3n6phrF1100XCOmJWXaeQQmGSSSeK4446Lo446qsw5hx56aJCyzjfffAVX68giiyxS1obvfve7I9f5aKidhOpoAD0v2bMQ+Pe//x333Xdf+NhJNBulOSbc999/P4YMGVIW0PrJqFyGDM0zEdchQHhgynvrrbeKREjbd955p9gNffDBB6XpZ599VvpS94svvgh1XJ+KBgG06qqrFoLz9ddfj1dffbW08cc9ffOb34xf/OIX8dvf/ja+/e1vyy7tq6oqEkAenrhpUmFtEbwWAnZKzl2vNMo/bRAgqdtmm22CXddiiy0W11133TB14E+qveaaa8ZKK60Uk08+eZC8sh372c9+VtphFqjgpptuumHadnySNc4555yYaqqpYsEFFyyL64knntgGFM4iP/nJTwK+P/7xj8O3pxINA09nTNpkk00W44wzjuxMTUSA9gFjvMQSS8Qqq6wSBx54YNEI1Zc0p73wwgux1FJLlW9jt912K44+8hdYYIEwT9Z183fECGCG33333WJr+uGHH5Y53zqCQTj99NNj1llnjV/+8pelkw+HlpurTjrppII7yWtVdX+mLQnVMnz5JxEYPgJDhhKcCBTekojOZ555pqUyYo8KZY899ojZZ5+9TLCIPhMzSQHVF+kOwvWQQw4JdTfddNNC+CJOtTviiCOCxMckbVElEdppp52K8ftLL71UJvobb7yxXBMx6Xi//fYLUodHHnkknnvuuVhhhRXi2muvLXXqP6QaJqrtttsunnjiiTj88MODSvrYY4+NP/zhD0ECS8U2ePDg2GSTTQLxW7fN32ERgNEss8xSMhFMwryUk//9QRQZP4uzrLnmmqtI9773ve+FJM8YkKhybHCeqfMI3HrrrfHzn/+8NPAt3nPPPYHJKhn/+wNzdnlUzRxJ1lprrVLH+fLLLx8k2t5z39T/muRPkxAgFa2/lxlmmKEw8cyQ6sthwN9+++348ssvS5a65lLEVcnIP51GgBYN3hNNNFExbzEPWRP2GqratyatvfbaLX2RUsvHUP/mN79pye/uB00hVLv7Q+f9JQIjg4CPnkqXGp2dj4DJ2psQbr755jARIwhr9ZYAy9NPP30MGjSo2FmR7iAia1Xkiy++GCRypKkmahOJSQNhiZDcfPPNY7nllotpppnGZdok93LYYYeFeojcNhXaySBpQrCSuApVQi3nWSaddNJgo4TwHd712umu12WRmjc+NOld4znikx2YcZWvPqLUcZ0wGELJ1Of523kEWuOtJUbPb51mnnnm8J4z0UDMwp/3M9Xm0ksvHWxbSbjZqPp263b52/UItDdeJH/1laqqCt8LAgtzR9JHC5TS7hqhzv/SMlh/JAILTFq/fv2i8bzubZ111in5TMnqvJ7wm4RqTxilvMfRhgAO/7LLLgt2otNOO22x8XzsscfCpEsqQI1OwmZy+NWvfhWTTDJJkNggVKuqirnnnruoIoUGoQIjZaWGrNXIiFPqryFDpbZU9yYQTlsIR4sqQvKnP/1py/MLLeI+LL5sjEhqqTpdt6XS/w7cEykUQvR/WS0/FgSELpUqogo33lKYB20QwGw0ZlIhN56TmrKzY0PpfTjrrLOKirquQ+JK+j3nnHPWWSP726vrM6VoDUDrd57dsPeYmY6xwBiSunr/EbFML6aeeuoST9K327q/PO86BNobL4xDfQVz02qrrRY0SOZKc6M5FrFa18nfRKBGIAnVGon8TQTaQYAaHwFKbUU6QxqDOKQmr6oqEJekoCQ0Fj+mARNMMEFR7euOPRB7IRM3ohaBQ+pDmqAtNTyvfo4e6uOO9Yf4RMzKa0yuo095CGJEq+POJItDYz1OPrfffnuJq+eeG8vyeFgEOKQZe/gjOI3TsDUivCfsj0nu2DNbgOs62sLbuNd5+dt5BNjYwbDGn/Ng6/f5pptuKk6Dc8wxR7EnV3+KKaYokTp8s9q6IuaPzbfjTM1BgB03hsFcRQPVb6iEz9zXeDVMG4nq3XffXZh5Jk+N5XmcCNQINI9Qra+Qv4lAD0WAip5kTJgVkpn6MdhSHXPMMSXMEInp/fffXzwsOSqJUUcywCSA6p85APs60s8nn3wySHqo3YWSQmiyUWXzypbIpM7I/bTTTisBnNncqfP888+H/izMCF4Tu74RmZtsskmJl4cIds7mlC2ke0I0seWTx3GBNEm4JGYInHrGHnvsmH/++YMKlYS1fr78bYsA6Q+PcuNpAUY4GS/4kpaTjBsTRCpGg7dz3QvVJmZk9dVXr7PydyQRoLqHN09mktLNNtus9ODbRKDCn9TUt2aMLr300qIFIXVdd911g9mL785771usqqq0zz/NQQARaq7ide67YIPvSuZA5jGYegSsuU74Nk6qyiXzkXmX+YBQfOzy5WfqvQgkodp7xz6fvAMEqqoK9qAWwLoqFe9GG20UpDZVVQUPVcQi9SJCktSMtM3CqA1VJK9LEgULKBWyPhZeeOGwiHKIUg/Bqr180k32o7z3qfoHDBhQvPmVMwtgKqANiSvJq+Otttqq3JNj9q11HRJXTiXUnqRL6qkjkSohkC0qzjMNHwHqfFsRGg8mE43S0sZW7HzZgXkfGvO9M/pozPsqx725DdtquCI8MVitsSD1ZnpBfezbca6O74czlfA8VMwIKPmZmoeAuUUoMHOZscBotHc1c+HAgQPDPNlYTtvE4VP7xvw87p0IJKHaO8c9n7oTCJBAWuSEWKkljggReRJi0qKIMHXOKapPnz6l5/79+4c84aQQpzIRrPKkH/7whyWunQXUuTKTOwLYYiqvqqpgn+pY0oedRUjznE855ZSygsesayNIEdakecqpOOv+1aF2JhnU3gLBkYs5Q03sls7yz3AR4DQHV3bEmAaLqcgJ9bkyCzJiqrGTsYdKrqmqG/PyeOQRQGDCGHMIfz0I/YU5dO6dhrM6viHldeLl7BvxHdR5+dtcBMw/xoIEu76SOdM3UlVViOmJiOWIWJf7ZcuqncT5EHMoP1PvRaDJhGrvBTafPBHorghQrSG+SVIR1Bb57nqveV+JQCKQCCQCvRuBJFSbOP4CrI+o+47KR9Q2yxKBr4oAiYVQSWxZmQZ81X6y3WhCIC+bCCQCiUAvQiAJ1SYONls2wd7F4WydOGFsvPHGTbx6dp0IJAKJQCKQCCQCiUDPRmBUEKo9G6Gvcfe8rm0lx8u6MfF0XG+99YJX+dfoPpsmAolAIpAIJAKJQCIwRiOQhGoTh1e8TN7cvB9/97vfBccaO3BccsklZb9pOxg18fLZdSKQCIyxCOSDJQKJQCLQOxBIQrWJ4yzuop2NBg8eHPvuu2+stNJKYVejHXbYIcT+a+2Z2sRbya4TgUQgEUgEEoFEIBHocQiMMkK1xyHTBTfMYWXTTTcNuw4JXrz33nuHWJpiKnJo6YJLZBeJQCKQCCQCiUAikAiMsQgkodrEoRUG6N577w1xFnffffcQvzIDGDcR8Ow6EehdCOTTJgKJQCIwxiOQhGoTh3jFFVcsqn7bbLJRtb0iByvbcdpCromXzq4TgUQgEUgEEoFEYAxHgN/LkCFDAm3xj3/8o+VpHcury+qCd955J+o82xLX+d35d9QSqt0ZiSbc20EHHRR2r1lnnXXinHPOCbsRHX744XHqqafGDTfcEG+//XYTrppdJgKJQCKQCCQCicCYjsD7778fZ5xxRnDaJgy7/vrroyY+n3322bDt8F577RWEZLCwE+GAAQNizz33jO233z7Ecv/3v/+tqFunJFSbODyHHXZY7LbbbnHooYeWF0U4Kltxcq7iUOWFaeLls+tEIBHoBQjkIyYCiUDvRODdd9+NpZdeOk4//fT49a9/XZy0//73vwdpKiJ2u+22K2V/+MMfCkCcuG3yMmjQoLA74eDBg+PLL78sZd35TxKqTR6dN998Mz755JOWNNZYY8XPf/7zksRYbfLls/tEIBFIBBKBRCARGAMR+OlPfxrTTTdd2AZ7xhlnjO9973vxr3/9K5gXDhw4MH71q1/FK6+80vLkk002WYjpTuqKmOU/853vfKelvLsejAZCtbtC0fX3JY7qWWedFb///e+LmJ14vk7yZ5hhhmhD/r0AABAASURBVK6/aPaYCCQCiUAikAgkAmM8AkwLq6oKPi9/+9vfArE63njjxfe///0Qp32bbbaJBRdcMJ544omCxTLLLFMksMpff/31EDKzqqpS1p3/JKHaxNG577774vzzzy+E6q677lo4mUYx++OPP97Eq2fXXYEAcw1qEsnH/uqrrxbp+PD6pm6xPS6Odnh1WudT1Vx22WVx5plnFuc7E8gXX3zRulo5F+ZMJInPPvusnOefUYfA559/HkLO0ZK0d1VjxkbMu2JM6zrvvfdenHfeeXHBBReEuMp1fpf+9oLOfBewveOOO4rUqPUjm1tvv/32UEcM67qc5kpenZ5//vmysNfl+dscBDjywJwdZHtXaBwvc1pdx9ypnfTAAw+0O9Z13fz9LwLmntdeey3mnHPO+Na3vlUkrHxi1l133TjxxBPjkEMOKRXNXT/72c+KmcBDDz0Ud955Z6SNavTuf7ZKPfLII2OqqaYK0lNE61VXXdW7QelBT3/33XcH2x4ft9s2do3n8hoTlQtnObbJH374YWPRCI8RMLfeemuZMBybWJiLtG5EXaNv1/joo49aF+d5kxHgfLDtttu2SCdaX84YXnfddcGrdp999inFiNtjjjkmMDAI1mOPPTbklcL8M1IIHHjggQFDTN1dd93Vpq3v1TeqQPxqjCViqfFbxEg88sgjqmRqIgKIH07D5k7fhNT6coikerzEGr///vtLFd+IcTLHWUMJB0pB/hkuAieccELMNNNMMf3007epM/PMM7fkXXrppcUmddCgQbHCCivE4MGDoycIPUaXRLUFuDH5wG5UXiAL3MYbbxyIDGlMfuYx6dnY9yyyyCJRO75tsskmsfjii4f4uO09J/vj/v37x8hs5mAyNlnYpWzAgAGx4YYbxiyzzNJe94VL7j+0/z59+rRbnpnNRWCLLbaI+eeff7gXEeXDGDL1IVW3C92TTz5ZFoY111wzfvOb3xRC65UGm7HhdpYFwyCA0EHgYxSWWmqpQMwMU2HoCWm3kIDGYMkllywaCvZ36svzPc8+++zBrq+qur+6c+gj9dj/iE6Sa3PnyiuvXBj+xofB1JPoiS1ubMx/HILUueKKK8J6ufbaawd1Noch+ZnaR+Doo48uRGq/fv2CRqH1+nTUUUcV00OtCTsw1EwGrCM0P1XV/b+FJFSNXpMSwoWHHVE8DvOll15KlVOTsO7qbq+55poiBbPwGT/9+7BNnnPNNVdR/wvxQVqOmH3wwQejqqpQxySgPhUWZkUdnO4bb7xRpGmbbbZZCCUyzTTTlFBljOF5ZyJgJp544kLQ2BjCwmw3M+379u1bON8f/OAHxaPz4IMPDjbQO++8c5HEmnAsxFNOOWW4R5Inizv7JAT2RBNNVFRABxxwQAmTRnLrHv/6178G6ZNrTDHFFGVhkJ+pLQI//vGPi7NC25IIYV+YiXCU9L4stNBCZdHwzbMZ++53vxsifng/michau/Oxow8DiCYBN8WRs73idhpfDpYI0zlIU6p/H1H7PHkkXQzvfAtOc/UPARgjynwLYgjTopt/quvyKbS+be//e2StdhiixVNBek3JmO//faL5557rqiyc7wKRO3+OeKIIwKhirBHa9DecJLCJJvTJd9NLVXdeuutyxwvH2OASTAvtdt5N8pMQrWJg4FI2WuvvYKY3QuFYPBCNfGS2XUXIfD0008XwhPh2F6XuFTSUATKGmusUaSureuxS0SUqEOyMO+88wbbOUQtdQvChvTNe2GiRlTuuOOOodwiTNJKdab9euutF0899VS5hAl8nnnmCZwx2y7EJnMAHp3aK3ed448/Pizeyy23XJFAMUNhbM9eSZgSi4V4viY2RDeCuT1JVblo/hkhAmyHW1cQJxkD0WjG4VxqXTfPR4wA+9TWNeDbmIcoevjhh0scSUxi6wUYI8grGrPQ2C6Pux6B9sarUdJXVVVhsJlnmH+MFyYE4cq8yvxbM94/+tGPuv4Gx5Aet9lmm0LQWyMk5oXWrFlnnTWcSxymGh/X3C8fs8detbGsux6PVkK1u4LSVfe1+uqrh/AP7KmeeeaZIEkT66yr+s9+mocAyaXeEaN+WycSg2WXXbZk9x+qjm+9aCqoJZqOEbNUWKQ76iNK5UuM3gVmFpRZOftG0p+bb765eGyqg5CcY445HJbQZtprZ5JnSE/tyeTAQkxCSrKqHxORup6HtMmk5RoWBWogRG3pdOgfG1O4t6GH+X8kEYBv6ybGgyR17LHHbilqfd5SkAcjRMC73roCfBvzVl111RBNBVNHSCBOZGO5BRpD2JiXx81BoL3xMu/UV6NtZEZ18cUXh/Hafffdi/QUc3HuueeW+OO0TsYTs1+3y9/eiUASqk0cd4QBsTtVsUmUStaH2MRLZtddhMCiiy5aOH5Sy7pLEkhMx2233RbUuXb+UEaSQy3suDEhXnCu8ox7Vf3XNMB5nUhWEZmIWCovRCZik/ONa9Tt6/rD+1WXqm3AgAExYGhihze8unV+VVVl5zTctzY8RElq6/L87TwCU089dTGpqMdLxA8qz8knn7yYavBwxlBIJN+d73mka46RDRZeeOGijfBwQ4YMCe+pb8p5ndg72gEQg4bRo62oy/z6bvv16+cwU5MRWGCBBaKOakNIg3FmWlRftqqqYJ5B/YypYM9qcxyqf6psc6FIOb4fZgN1u/ztnQgkodrEcR88eHAgUk2cuEP2jkJINPGS2XUXIYDwoN5nusFT0uLIS/WSSy4J9oekj6ScnAYsgOLTIWQRmBwAqHdJCkhVSXJICdZaa62YcMIJi42QeogWbdjfUdm4hsndAsy2rv9QSS2pKsccZWLwkvDq3y+zAHasji3SzErUo853T3U50wDXQyyR/LLT04dfxK3ncw2Et4WjiyAc47rBTMCMmp802tjBlUpTwG3vhLG87LLLiuMbJlW0D/hbrIXpUZ9t8hgHTpMfiEbCt0a17x3daKONyhWNB6LUWBgH7/dWW20VGIVGCd4ZZ5wRNFw0CaVh/mkqAiTXmHCRGI477rjYaaedyjdhvIyRizv2PZi3zHOYbWYZ5kjmcr4Z3w5iVf1MvReB0U+ojsHYm0w5yfD8RQTw+sU9jsGPPEY9GukmlTwvVAQrmx72nR6SCv3QQw8NkzDbN9JydqUIPkSLSZbUh5fy2WefHRbZk08+uewSUlVVeB8wLRZOTAzpu2sgMk3cpG7zzTdfsF8VrkqZ+/EuUZuJL8hUgGMJ71nRAtjBqocgQiAhfhnYI5Qt8CR8YrVaKFzXtRDDCGjXYOeKuPJ8mdoicNFFF5WYjsbYIovwv/rqq4MZCMIVY4JBufzyy4vHuR6oQNmRwZ1Hru+fxFxZppFDgP005lGsSPaLWhsL3xf8OQhiCL3HjUSqesarbuM8U3MRMAeSiJ500kklyoK10BURrohQzsXmVfOOebFR2mpO5WhlHBG8zJe0zdR7EUhCtYljz3GFJMCkyU6NJM4H6JIcZkjCHGfqnghU1f+rp0hxEK2Nd0pdLp/xP696xB8pqzwx6hCUbJKd12HJeP9bcOUhIjkPyEOcyqsXYtfRHnMjX5p00kkD4etYuCOe/44RmghdhKpznv0M6h1LFgl1xSTkbGUBkU/CpN0GG2xQ9oMWSo1DlWtnaouACAtwM0Yk3rAjRWXWw/zDdw5fdRCodQ8kqNSZHCpHlWNIfe0x6VdEBdhiynxrno2KmIof/ptvvnkJbA5vZY1pyy23LFEyGvPyuLkIIDCNF+zrK1H3+47MbRg7XurMAupyv4hW7STmSNZO+Zl6LwJJqDZx7EmrhBfCNVqkqKRI2SxaHKsQDk28fHadCCQCiUAikAgkAolAj0agmxCqPRrD4d48O0D2alSDVK9sbxiNU9tSzQpPNNzGWZAIJAKJQCKQCCQCiUAvRyAJ1Sa+ALUqiu0UY/7WidqjiZfPrhOBRKA3I5DPnggkAonAGIBAEqpNHER2amwMxaqUGJA38XLZdSKQCCQCiUAikAgkAmMUAt2JUB2jgPUwducQ/4/jyyqrrBI8w5kDKMuUCCQCiUAikAgkAolAIjBiBJJQHTE+X6tU2CE7U4lpyROV97UdOL5Wp9k4EUgEEoFOI5AVE4FEIBHo2QgkodrE8bOL0X777Re2TXQZgYvF/XOcKRFIBBKBRCARSAQSgURgxAh0O0J1xLfbs0rt+iPWYn3XQlItscQS9Wn+JgKJQCKQCCQCiUAikAiMAIEkVEcAztctEj+Vx79+5phjjrDNpWDHzjMlAolAIjCKEMjLJAKJQCLQYxFIQrWJQ/enP/0peP1//vnnsf3224ctMO2o4tx2irkzVRPBz64TgUQgEUgEEoFegMCrr74adgCbaqqpQqx2WznbqfAnP/lJcOSunbht883Je7zxxosrr7yyxyDTPQnVHgPfiG/UblQ77bRT7LPPPsOkPffcMwYMGBB+R9xDliYCiUAikAgkAolAItA+AgjTTTbZJGacccZ4+eWX46c//Wm88MILsfzyy8eTTz4Z//nPf+Lggw8ujT/99NM46qijYpJJJinnPeVPEqpNHqmpp566vDhenjp5of7whz/Eggsu2OSrZ/eJQCKQCPwXgfybCCQCYxYCJKfMCaebbrq4//7744orrghS08kmmywWWmihGHfccUN4TMSsJ3d+0EEHhZ0xnfeUlIRqE0dq0KBBsdVWWwXpaXuJSL6Jl8+uE4FEIBFIBBKBRGAMRcA27G+88UZQ+fOBOfXUU+Ptt9+OiSaaqOWJqf2XW265lvOeeNCNCdWeCOew97ziiivGN77xjWEz82yMR4B65bXXXoshQ4bEO++8E87/+c9/jvHPPaY/IEmF8fzss8/afVQ7z1kkjHvjeH/xxRfhfZD+8Y9/tNs2MztGAO6w/ctf/lLUma1bwF+ZOh9++GFLsXFjwydZtFsK8qCpCPDFMBbvvvtuu9dpHK+//vWvw9TR7pVXXokcr2FgaXPyzDPPhFjtW2+9dSy22GLBB4a6v674ySefxI033likqnVeT/xNQrUnjlrec7dFACGz//77x6677hp77bVXULOwRX7ppZdG+p5N5CaikW1IHWSSH9l2WX/ECJxxxhmxwgorBEfI9mo++uijwSZ9u+22i7PPPrtUQZhyqvQOeB8uvvjikFcKR8efHnzNo48+ujilDhw4MJ577rk2T2KBhrFksxWSJpUuv/zy4FjimzzssMPigw8+kJ2piQiwizzzzDNj2223jT322KPYSra+3BNPPFHmSONlMxzEqTqDBw+OzTffPHbccccwXul0DJX2E1V+TeSPPfbYMc4445SkNtwuueSSOPLII5326JSEahOH75hjjhlh7ybeEVbIwh6FAMLytNNOi7feeqtMDqeffnr8/ve/D1xto4Stsw9lw4jLLruss9Vb6pno//znP7ec50HXIPCtb30rfvzjHw+3M4vqOuusE8b9lFNOCY4NVHMI2AMPPDD6/MhVAAAQAElEQVT23XffsEtdTUANt6MsaIPAAw88EHCELdu79ubWq6++OpZaaqmCv01WMAi+Rd+Cufbwww8vkm39tLlAZnQpAs8++2yYv4zXSiutFLvssssw/Zsrb7311lh00UXLePHloLYmETeHamfMXhkqVTX2wzTOkxYE5p577oD1Qw89VNadH/7wh9G3b9/CDGOoJ5100vjBD35QGDvfQkvDHnbQ3QnVHgbnsLdrVyoSFEbOrRMun5Rl2BZ51pMRQIxec8018atf/SommGCC8iiM2klVOdLJQESyJ5KoguWdf/75ceihh5bJ3CYR+sAJ6weBQyKB2F1rrbWKLRIpBW6ZtJWhPGN6/V177bVx5513Frto0ohllllG95m6CAGmPHBur7s333wz7r777uLAIPTLPPPME8baGFksSD6+//3vxySTTFII2Mh/I4XApZdeWrCF43zzzRe+mdYdPPXUU+F7kz9gwIAwxxoXUiZjYhH3HT788MOBUFIvU3MQuO6662KmmWaKPn36FGL0pptuikYTABJX859vwx2svfbahbBFdDmfeOKJi52l8XrsscdkZWoHgQknnDAwxexT+bwssMAChZnGGKy33nqx7rrrBkcrpgHef10svvjigXnDOKNL5HX3lIRqE0eIjY4XRCxVyeLlV9pmm20CsdHEy2fXoxgB0rP33nuvTLCNl0agkMa9//77RQ2GoCF9+9nPfhY33HBDHHfccSV8mUlGLDySoSWXXLJIYznj7b333nHWWWfF/PPPHySsrmNRZpOEoCWNMOmYnNTZcMMNi+kBgrfxPvK4eQiQkrKFNM6uMvZQNRxzD2pmTEZVVVFVVTDLaLSfVHfUp553RQR/vcsfOzxzK8wbn+TLL78MMSIRQepgHKuqKlJUuKs71lhjFa9odZxnag4CTDPq8fr2t79dJHyNhKqrst3HSGAa+HIYL+NDivrxxx+X78W5cvUztY8A4QaTM5oCxCrMDjnkkLAW+UZodggx6rkJ02BOuvfee2POOedsv9NulpuEahMHpF+/fkFt4UXBxSNYHNfJeRMvn12PYgSMp8nWgtnepS+66KKYddZZg1SNtFSIEBMydfEWW2wRyy67bBsit+4HQXrVVVfFEUccEbhodo6kFqRLq6222nDb1e3zt7kIUDW3vgJJnsVBqsscS/V5/nYOAd9W65rwbczr379/nHTSSXHyySfHeeedFwQDJOCkqbvttltQLVORyrOYN7bN465FoL3xqglXV4L/L37xizjnnHPKmDHTID01P04//fTBxnjQoEFx3333FS2SNpl6LwI9glDtqcNDUoYoQZiaKD1HHc+MLU5yihAZc9KPfvSjkEh/Gp/K4mjcSXEapejUW1SZjXVHdMzBgO2WxE5vRHWzbNQiMNVUUwWCiGTDlakwLcRUm1VVFYkS5kKZOcFvps4jMO+887aYTMAYkzf++OMP0wEzmPXXXz9I8MSTpI1Qx9xLW0GjgbhFwFZVNUzbPOlaBOaaa64YMmRI6ZSK31w3+eSTl3N/qqqKVVddtaimxx6qfaAx4oT4ve99LwYOHFhsjUlbqauNlzaZei8CSag2cewF9ideJzETWoXNqm1Ub7nlluLtSGTfxMtn16MYAZKybbbZpnh+k6C7PAKVAw2C1MRM7c/I3WJ7zz33xMILL6xauwmRUxdQ6SNUnVP768dxe8nibLFuryzzmoOABdZOMIMHD45HHnkkLNKLLLJIIKioMoXZefrpp4M5wLTTTtucmxi5XntUbXPolVdeWTz2eZOzQW39AL4xGgamM9SbNc5TTjllsIGUz7lxRA5xrfvM86+GAJtJ7zoHHhJuXv2ttQ6cfGx6w4+Dtsl66WrGB9MhKoAdlyaZZBLZmXoxAkmoNnHwhahZffXVQ7BdISI4w7BjQ7BccMEFJfxGEy+fXY9iBKqqil//+tdxwgknFEkBKRvC1fibbKnsDzjggLDokhI8MpSgEduRWt+7cuyxxwZ7VLal1PwWXYsy5ym2zohVfbJjRcQKlWQxGDhUAsFGFTEkBA+TE6q0WWaZZRQjMGZfbuONNw5OPTC2AGM+7TBne0KxUrfffvviwMOsw5hSdXLuMV7eAe+C0GV9+vQZs4FqwtORhLK7EzOyqqpgh+0yPMQRrfDHyJlbjQ8HEuXsuTEQvhsMIwefqkppKmyamTBuQoKZw9immqNcj+OPNZE2kRMoZs6caD5UzsZY5AbjxMyJVoKZgLJMvReBnkOo9sAxYndoErVA4fYtULxV2U+xL1x66aV74FPlLXeEAGc5nqpMPkhxaslOVf2XkJUvTTHFFMUjUx3nFloSeMcmc4utY2F1vDuIT+c8Nvv27VtUoc6VW4QtygghalLS2scff7yjW83ykUDAdwtvducYBRKiffbZp8ToRJQaIzZ36jSqOTEODz74YBhbBNNIXDKrNiAglBFsEay1xoCqny0j/DF6tBWIm7rZNNNMEySx2tV5+TtqEMBYwx3jXl8Rg+E7Qnz+8Y9/DJqnmWeeuS4O3831119fwikxF2gpyINejUASqk0cfl7dVP0m0foyPr4lllgiTKY+1jo/fxOBRCARGNUI5PUSgUQgEejuCCSh2sQRoibk/ShWmUTt38TLZdeJQCKQCCQCiUAikAiMUQj0MEK1Z2EvhtnPf/7z2GabbUJ8M/aLbBJ71lPk3SYCiUAikAgkAolAIjB6EEhCtYm4s1HltXjzzTcHJ4Ddd989pCZeMrtOBBKBRGDkEMjaiUAikAh0YwSSUG3i4Agj1NpG9YUXXmjiFbPrRCARSAQSgUQgEUgExhwEeiKh2mPQF2aj0S51mWWWiTXWWKPH3H/eaCKQCCQCiUAikAgkAqMTgSRUm4i+2KnCUbnEkksuGUcffXQwB3CeKRFIBBKB7oNA3kkikAgkAt0TgSRUmzgu9913X9lWUcBjMRdtm2qXmiZeMrtOBBKBRCARSAQSgURgjEGgxxKqPWEE2KgiUO1SI0j72GOPHXvuuWdPuPW8x0QgEUgEEoFEIBHoAQgceOCBsd5664Vdvj766KNh7thObe+///4weXYxtNshLe/f//73Ycq640kSqk0cleOOOy7mnHPOuPHGG8OuKrbys/NGEy+ZXScCiUAi8FURyHaJQCLQwxB44oknwk55hGCIVbHb60ewK9vJJ58cjcSo+nPPPXfYMW+11VaLccYZp67ebX+TUG3i0Ngidbvttgvci20tbbdot6omXjK7TgQSgUQgEUgEEoFegsBNN90UCy+8cPzwhz+MSSaZJL7xjW+UJx8yZEi8+eab8c1vfrOc+/PJJ5+Ebd1JXvnNjDfeeNETdsjs2YQq5LtxOuyww4Ln/8CBA+OnP/1pbLXVVsO8NN341vPWEoFEIBFIBBKBRKAbI/DFF1/EO++8E3/4wx+CUOyll14qd/uPf/wjnn/++Zh55pnje9/7Xsnz56yzzopZZpklmCNusMEG5fef//ynom6dklBt4vB88MEHRZp69913hwgAZ599dpx66qlNvGJ2nQgkAonA10MgWycCiUDPQWC//faLk046KX70ox/FZZddFp999lnce++9gVidfvrpWx4EUfvoo4/G1FNPHaeffnqxab3lllvi448/bqnTXQ+SUG3iyNg2lX0IG1XpjjvuiCmmmKKJV8yum4HARRddFLbCnWqqqeI3v/nNaP+wOemxLRrRs7799tsx00wzxXvvvTeialk2EgiceeaZ4R04+OCDw6TfuqmoHsx91BHxoy63GHh/5phjjnjkkUfq7PwdSQTs8Afb7bffPr788ss2rTmR/P73vy9jdNRRR7WU+wZ8CyRJ5557bkt+HjQXgTvvvLOMhd0Z27sSAqker4FDtY6NdYzzjDPOGOecc05jdh63QuA73/lOUd2PPfbYRaVPtQ9Xc9SGG25YdsQUaeiXv/xlIV6ZIc4zzzylDYKVmQCCtlW33e50DCBUux2mLTdkMmU/ct1114V0ww03xBFHHNFSngc9A4FVV101dtxxx0L4XXDBBSXkWHt3bnOHP/3pT/H666+3V/y18i6++OIgmdcJO+c6Pq/z9hJbpSeffDImmmiieOONN+L4449vr1rmdRIBzMEhhxwSL7/8ckm33nprm5Y777xz7LTTTnHppZfGr371q/j000/jueeeiyuuuCJE/eBhy+GB3VibxpkxQgRgybMZ7t7p0047rU196kyCAGN06KGHFumSRdjugJjNyy+/PDiXJLPQBrouzzBep5xySljzMAitCdH//Oc/YU6rx+uaa64JjCA1NCa8Hq+rrroqHnvssS6/vzGxQ5gyMeQc5V03zzz00EMx5ZRTxrXXXhsLLbRQWDseeOCB8vjoE+sEYrdkdOM/Sag2cXBMkD5UXnaSl4ZDVRMvmV2PAgR4UJoISHAGDRpUCEgLInXK4YcfHtdff30oe+utt0I5Kc5f//rX+Pe//x333HNPmbzZCuF8SR3UkepbJw298MILy8T9zDPPhIll//33L5PNww8/HMoHDx4cJnULr7avvfZaMDVBKLNNcj3SCNIkBBKGyUJQ11eOgEXwvvrqq/Wl83c4CDDdYQemmN25MXZcJ5IMxOgiiywSvGnnn3/+gK2x6Nu3b4w77rhBQmTRePbZZ+tm3fe3m92Zd5ck6Cc/+UmQWrdHqD744IPRv3//cue+F9+d78fiTUI32WSTlUXbXGxRLxXzT1MQQHiOP/74xTdjhRVWCJpFKun6YuZC8w4nY3l77713Yeh8Gwit2WefPXw3ouSY89TJ1BYBYaesFdaAq6++urzf5pq2Nf+bYyxoe3w/l112Wcw111zRp0+f/xZ2479JqDZxcEhTSFAGDBgQA4am9dZbr4SqauIls+smI4ALtWgaT/HpcK2klX4tfh9++GHLHey2227l+Pbbby9G64hE7UwsiEx9/fGPfyw2Rcccc0whcLU/77zzCjFKInrQQQcVyRzCUmfKEcP77ruv03jxxRdL3wglEl2LMOLUtTFKKiGsXc8xgpZ0n8TD9aml5WcaMQKk2X379i2VSCEwneXkf38sGPCnWpPFNoxjw+effx7KLMzf+ta3gorOGKqTqfMIwB+xqQVbPAQoxsx5nbz33mvn1JovvPBCwJ9Jhm+T5IhntG/VeKiXqTkIIIbq74XU1KY3jZom42Fc6vFSVx3finF0V9/+9reLRki+80wjRmDWWWeNmvCvawpVZQ2ZcMIJSxbnqoEDBxbV/xJLLBGLDGWsS0E3/zOmEKrdEmaSFy8INYZECkeV0S1vNm+qUwggNkjLJp544kCorr766mEBNOGuvfbaZWI17o8//niQbu61116BMOVIp524urhaBKt3g6Thd7/7XYhrR72MuDFR69eEQjK0wAILlNAj7Iyob+abb75ifiDsyMorr1zampCEGtEPLlm+B6ImpXozia2zzjrBjGGGGWYo0liE1eKLLx6kVOpmGj4CGIHGUsR/4/mPRAVAOQAAEABJREFUf/zjsLkHKbZ8xBCCFlHlm7cAYxYkY6VOps4j0BpvLX1zfuvkXWeaQ3LHE7rGn1Qbk4gxwySQIFVVVTfL3yYg0N54Ndp1C4nEG924qIsY9f1MN910QWPEbMBtGa8RSQjV6c3JGvLrX/86rCf9+/dvAwXG2FrQOOewl1dfeCqYt2nUDTOSUG3ioNx1113BAcCEWacJJpigcPmkbNTDTbx8dt0EBKqqih/84AeBYKVKrKqqcKfR6h/J5WKLLVbsGRGeJJfamXRNHlX134XSJG2HEEStLkjkSHsQvyYR741JXZnE+B1B6tc5ItOC7XoWZIGfEbAWbeWtEwKb+tm7iYBqb3Jr3SbPIyygCB1YIDadO64TzJlzkJAjWqnV2KliMiwkJBdU/ySBfut23fu3+9wd5qomdODvG/L9Nd7hDjvsEDQK7PDYOa6//vqFCWM7zGyDlJWwALPY+E019pHHXYOA76MeL9+N+dLcU/cOf84+pN4Yacz9GmusEeY7fhw82an9mc/4hup2+ds7EUhCtYnj7oPjXMEutTHV5gB+m3j57Ho0IIC4dNlpp522LJpDhgxxWo7LQcMfNloWVFJOHK4i0h75iFvqMZI5DA27LeXtJSochDBb1vaIIERvTdhqb5c0NmRVVRVJrbxMI0aAVAKRwxaZQw/Cs3ULkmwqTx637JURrOrssssuhWFFLGFKLMDyM3UeAUQ/u2DED1tg303r1hgwEjo7AvImF3VFHTatbB933333Em9SBAD5mZqHAMzZDNNEYIhpbhoJVVc2R9I0itCw0UYbxbrrris7EKZMmhCrnOLUKwX5p9ciMEYRqt1xFHHxjPkbE2LCorXgggt2x1vOe2qFgLBipGVi0FHl81bliCRgMqJl8ODBJT4uKQGJ6R577FGcZ9Zaa60Sq45tsjqIQxJ2XuHss1zGwjto0KDiMMVEgC0qiZD3g0RImXqkQCZuns+kdYhSziLKhD1CqFbVf6W9iFyLNfu8M844o8TNI0Fls4rotaAjomabbTbNM3UCAYQSSbexxDxYiI0de2WLLQJWGYkqiTcJkW5J+LbddtsS55CNZZ2vLFPnEfCO1yrL2267LTbffPPSmLOqjVXgf+yxx4bQVYgczjkqcGgcOHBg+Q6ZYTCfYfuoLFPzEGCjzdTJN2G+shOSq2E2fCO+JczcFltsEcInMXFSbl7EUJC2Mg2g8Rl77LEVZerFCCSh2sTBR2TYjYq0rE5sEOvjmuNv4i1k112AAC9uAZWZa2y88cbB1pMnKoJ0xRVXLE5QdgWh3mJbZWHEoJCYm4z9khaYdDkyIUJJTqnthQ1RTsWF8GRjirBBkMpnv8pkQBsOV/ohoUOY1kQP+yMEKcmsx+Vtqy0p0nLLLRfzzDNP8PLUTl112Da1NryXn6l9BDjhwJQTG+0ItTOCZ9lllw0mHlT/yqmfEUOk2HqaZpppgnSPJBVxK6+HpW5zu0LEIXh8GwghN+bdXnPNNcuOf2IcsxsnnfMtKjduvlljg9mo339lmZqHQFVVYRw48gjrVkuxzTkDBgwo5lJs9c1rxqceF/PiZpttFvV4mfuad5fZc09BIAnVJo4Ug36LlgWqTkIF+XgZiTfx0tl1FyJgEkVU9u3bN9iAsv90TDKJKHQsv6qqoN5SFxFDEqBMImk16TpWF2FTVVXZAEIedbD+qPglnrLyEZQepW7LhktSxt5ZmURCqp1jfSOk1NHevehfnyQWQmSR2qqnfqbOIWAMYWrMq6qKqqoCvvW5MmPTiCtTELgjmDp3law1PARgCWNYVlVVqpFek5RWVVUcGb3nNZOgAoLVt6Gd80yjDoF6/jMn1lc1Xr4R576deq50Lvl2jJe5sNFcSVmm3ovAmEeodqOxJF2jrmJDWCfqDB8gL+BudKt5K70AAU4oog8IQE9y2wseOR8xEUgEEoFEoIcjkIRqEweQzdRKK60U1MJ14oHqmEqwiZfOrhOBNgiQLlFDswdrlHK0qZgZiUArBPI0EUgEEoHRhUASqk1EngNMa4cV9jhNvGR2nQgkAolAIpAIJAKJwBiDwBhKqHaf8WFX1Xg37AUbz/M4EUgEEoFEIBFIBBKBRKB9BJJQbR+XzE0EEoFEIBFoRCCPE4FEIBEYDQgkoToaQM9LJgKJQCKQCCQCiUAikAh0jMCYTKh2/PSjoYa4lqPhsnnJRCARSAQSgUQgEUgEehwCSag2echsIff222+HbeQEh7erTZMvmd0nAolAItAkBLLbRCARSARGLQJJqDYRb1sr2illm222CTsHLb744mFbuSZeMrtOBBKBRCARSAQSgV6OgC20bdltN8yeDsUYT6iOzgG68cYb44033oj11lsvBP/fcsstY9pppx2dt5TXTgQSgUQgEUgEEoExGIF//OMfcc455xTa4+677+7xT5qEahOH8OCDD45DDz00Xn/99bjmmmvilltuifPOO6+JV8yuE4FEIBFoOgJ5gUQgEejGCHzzm98Mmw0ttNBC3fguO39rSah2HquRrjnWWGPFv/71r5KOO+642GqrreI73/nOSPeTDRKBRCARSAQSgUQgEegMAlVVxQQTTBDjjjtuZ6p3+zq9g1AdTcOw9957x2qrrRaPPPJI7LLLLnHnnXfGvvvuO5ruJi+bCCQCiUAikAgkAolAz0IgCdUmjteCCy4Ya6+9dsw777wxzjjjNPFK2XVXIPCf//wnLr744mLXw674uuuu64pu4y9/+Uvstttupa8vv/wyBg0aFA899FA5H5k/pPOnnnpquT9RJK6//vr46KOPRqaLDuu+8847cfrpp8d7773XYd3eVuFvf/tbMeV54okn2n30v//973HQQQeV8THmdaUXX3wx2KdzqoRvnd/Tf0f1/X/88cdx5JFHxsMPP9zupb/44ov405/+VPC//fbbh6mz6aabFo3WAw88MEx+njQPAXPT0UcfHffff3+7FzEXnnnmmWW8Ws+1O+64Y/lm7rvvvnbbZmbvQiAJ1SaO9/LLLx8bb7xxiJ36ox/9qNin7r777k284v+xdx7wURRvH38WLNiDCFKkq4DSxIaKSpciTXoRQhMUQRBRQJEmTVCQIlUIVekIooiA9KKAVGkqoSNIlWKhvPnO37n3crmQhGTDHffwYbK7s1P2frMz85unzGrRiUHAcRwpXry43HbbbZIzZ055+umnE1OcyYvnJbbKBCJuvPFG4b3InTs3lwkKK1eulNmzZ0vnzp0FstS4cWM5f/58gsq4UmIM8IcOHSoDBw4USNmV0obivdKlS8vgwYMlti3maBsm3/r165vJF4zYnq5Hjx5St25dqVGjhtD/ieOehoQh8M477wi7qOzdu9dvxp07d8qSJUsEksPC8Oeffzbp+vfvL7ly5TJtMGfOHNmzZ4+J1z/uIXDp0iXhvW/fvr1ERkb6rWhDlKaRcaxt27YycuRIWb16tUk3YsQII9ipXbu2zJ07V/bt22fi9U/oIqBE1cW2RwKQKlUqSZ06teTJk0caNGhgVomi/wIWAdoqLCxM0qRJI3feeWeM54TMQVQYfGlfEly4cMGQF+IOHTokXBOPhJY9dCEoEF8bxyCeMmVK4T6SyxMnTpjB+MiRIyQxgXPKswEChHThgQcekAwZMggTQJcuXYRyuEc9SPRIT3mUTeCcOJ7LFBz1B0kHkzUTPr8nKkoogx0qSpUqJY899hhRGnwQQIJdpUoVn9j/XdLmECIIabFixQyemPog/cucObM8/PDDkjtqccJ7hYT1f7n0b0IQ6NevnzRp0iTWLGwHiFCAsRaSM2XKFOEdX7NmjTRt2lTy5ctn+tyOHTtiLcPvDY1MMAL4Z3Tt2lXCw8P95mUMXLx4sTzxxBOSN29eob3QZjEejh49WrpEjW2FChWS48ePx0p0Rf/FigBj/8mTJ43AAUzPnTsXa9pguKFE1cVWeuONN8zqHXUf5KZ3795SuHBhT40QC8+FngQ8AnR+JDJINJHuNG/e3AyiqBNR7RLPwLxp0ybzW6ZOnSqoHFHXM2gQuXnzZqlYsaJs3LhRUE9yTj4kReXLlxfSQSLbtGljJKdIX6tXr26IMBI91Gg9e/aUXbt2mUkbMj158mR5/vnnBUkE7xzPduzYMTPIo4pmwoBAUS7Ela3SmAgaNWpkJLSoTJGktmvXToYNGya8rzyrhvgjwF6FqPuzZ89uMjEBQ5DY8QMp+k033WQcKXGmZEFgEumfJEWAPpUlSxZTZsGCBWX58uVmx5X06dObBR3mV3fddZf88ssvAlEyCfXPNUEA/Hfv3i333XefqR+yyqKOcc0u6hHy0J5WMm4S6p94IQC+S5cuFbz/6Rfbt2+PV75ATaRE1cWWmTVrllEBNmzYUCAwOFJhY2WrhMTY8yQ8alEuIYDUDJIHGcSOk2tI6D333GOc5DhHasZEuHXrVmGnB9Jhp8ygG9tjITlA3Y4ajAUNA0zGjBklIiJCsGtESsSAjlQOdRqSVd6pTp06RSsSm+jhw4cbu1XKQm1mbb94JrZGwzZ2xowZgiQVgspee9THYMazQrijFaoX8UIAKbVvQhYFmGZg/mHv+V7beD0mHgGkb96lWPzRFnjHIyBg0ekdp+fJj4Bvn6GdWOwxrno/jdVcecfp+ZURQNOGEIQxncAcc+UcgX1XiarL7YP0C8KCtIvV4ltvveWpUYmqB4qgOEGNiO0mJBLpDBJQJGZ8xAF1I/at2CnyY5CqIu1EmoOqnvTEIyHIkSMHp/Lkk08K5w899JAxDzGRUX+wp0NdE3UqqNBuueUWIxHivEiRIjJx4kRBDTpgwABhb15UmthAP/7448KR5+M5kb5Wq1ZNkNhCRlGbRkZGyqOPPmrixo8fb8g00kCI8O23326egzKoW0P8EWCx4puatgdTbJ7tPa4J9lqPSYcA/cy7NIu/7yIxLCxMHMfxThqPc02S1AikS5cuWpGMc4xDaB+8b2Au432t56GHgBJVF9scNTGqQDqgDRj6s0JkRZ8UzjouPr4W/R8CqJ5mzpwp2BriCIPKiltIJFFTYQuHJHXs2LHGYYZ7qHtRt5AeCQGqmPiqfFGDkR4Si1QU5xzHcYwzHnaP7I+HCQn2krZMdgSgHggqdUKIs2XLJrxrmTJlEs4dxzFEludmksiaNatAmlAPYTdJPOUg9UMaxe/QED8E6OcETDvAccmSJYKEG9xpEzClLTDtoG3iV6qmSggCLMC2bdtmsmBPjL017ziLSWz0kOAhsaOvsugzCfXPNUEA/Bk7cYBjLuRTn/QXxjzGIsYw+g2LaBbi1+QhtdKAQUCJqotNAbnBSBwnKhu+/PJL4w1pCYZb1Wu5CUeAARPVONJQPFBRvRP69Okj2LZB6JCiDhkyxKjlUcEjsWQiLFasmJFuMshia4VDEpJN7EWxVYWgIFlHqs7giyQUG0bO8X7ly2WQRIgo5BTJApJQ1P48EwM3RBRVPc9EIC+DO78UEqvugEQAABAASURBVITpAaYJkCNINRM1JJuvo5EelT+SX6S12KKiEuLZnnnmGbMdFaYpOKRAthYuXEixGrwQABNMOrAttu3Be0L70HYtWrQQ8MQ8A2l5/vz5jbc5m25//vnnQtukTZtWIE9exeppPBEAZ4gopMaq+Xm/FyxYYD6qQh9Ei8C7Tj966aWXBKlquXLlzLZixKdKlUogqvGsUpNdJQKMpYyjLNYXL14sOIdSFP0HcySIKl9NYqxk3CKOsRRpN+ZH3bt3NzsBICVnPCOvhtBFQImqi20PKcGJhe03bEBFWLJkSeNY4WLVWnQiEGCCY9KzRZQpU0YKFChg1PDYjEJWuYfDE5MeA2zRokWNeh4Pfxxp2D2AARjCShrIIkQQNXCzZs2Myh/ia88pD/MQpJ+QmQcffJAoExjskQah9qd+Exn1h/Q2HfXxbiEpxfkKqSuSWchu+vTpo1KL1KpVy2y71aVLF4FEI/Vlj1+eD1MCJBzsKsC5mqUYyKL9QSvCwhNco93474JFBaQfqRC4E435ByYXtA8mHZBZyBL3NCQMARzR6tSpI4888ojfjMTzjnMT50AruQ4PDxfMazBp4Zw+QpqrCJolAQgwvmCfj0mSv2z0Iz6IwzjYq1cvYRwiHX2MPIxRaJMYD4nXELoIKFF1se1ZwbMaRLJlAwMo59r5XAT+Kot2HEcwOmcy8w4MnNZOimPVqlWF+zg3URUTIgMuUnOIKip5JLAQP9JxzQTrOI7Zpog4yCSmH5yzcEHqwzlEZ/78+WbbFq4JEB+kqdmiVPkQSeIITL7UbwPPVa9ePSENcUgtIMqkJaDmJx5JHxMIAfJk40jD7+D9RApIvIb/R4AFi8WIBQcTLCYYvDM4L0CkXnjhBSGNxZrc9HUIFO8GxJU4DQlHgAUg2BJYiFECfYD+A/60B5Js7mM3zn0bEBjQBirNtoi4e3Sc6GOpXRwwZtKPqJ02o//QXsQTZwNjGe2VLWrMs3F6DF0ElKi62PYQAQZStvuxAXsbzpGwojZ0sXoRLTzoEIDgstUUkoTs2bMLAVU9ZNjfj8GkgH0H2RQbsusvjcYpAoqAIqAIKALBioASVRdbji8JoUZG0kbwPscmkS2JXKxeiw5SBJDKsTcrTlsENiuP7aeEhYWZL0mRjncqtnQarwgoAkmDgJaiCCgCyYuAElUX8UaayicTkZ4SKleuLJAQziGp7IXpYvVatCKgCCgCioAioAgoAkGNgBJVF5uvUqVKguMNNn+EVq1aCR6QnNvgYvX/Fa0HRUARUAQUAUVAEVAEghMBJaouthuejJDTDh06CF8RQv3vOLrRtIuQa9GKgCKgCLiPgNagCCgCyYaAElWXoT527JgcPnxY+DwcHth9+/Z1uUYtXhFQBBQBRUARUAQUgesDASWqLrYjG8OzqTqBTeP5fKrdOsjFav0VrXGKgCKgCCgCioAioAgEHQJKVF1sMvbO9P1usYvVadGKgCKgCCgCyYaAVqQIKALJgYAS1eRAWetQBBQBRUARUAQUAUXARQTYV3vPnj3Cvtq+1fDRGD49zNH3XqBfK1F1sYX4nvuVio/r/pXyXs09zaMIKAKKgCKgCCgC1x8CfEho5MiR0rVrV2HP9k2bNkX7kRs3bpTmzZvLoUOHosUHw4USVRdbqUWLFrJixQrZuXNnjMBXqV555RUXa9eiFQFFQBFQBFxGQItXBAICgb///lvKlCkjo0aNkpo1a8rSpUs9z3Xu3DmZMWOG/PXXX564YDpRoupia7FyYZN/PonpGypWrCi//vqri7Vr0YqAIqAIKAKKgCIQCghkzpxZHn74YUmRIoUhq+Hh4Z6fvXDhQnMvbdq0nrhgOlGi6nJrbd682YjaIa3eYd26dXLbbbe5XLuf4jVKEVAEFAFFQBFQBK4rBBzHMRrcxx9/XE6fPi0HDx40v48tMn/++WcpVKiQuQ7GP0pUXW619OnT+62B1U+9evX83tPIwETg1KlTEhERYcLRo0fNQ7I/7rJly0zc+PHj5fz58yY+vn927dolLFouXrwY3ywx0rFP7+LFi2PEa0TSIrB9+3bTzuvXr5crtdfatWtl3LhxsmPHDvMAaE4mTpwoBM5NpP5JMAL79+83+C9fvtwv/vQ9JEcREREyf/58T/knT56UCRMmmLw//vijJz4pT7SsmAggmImIaovYfDHoC9y34bvvvjOFLF682LSVjcd0ztzQP3Ei8Oyzzwrv+Msvvyx8cOjPP/+U8PBwuffee2XmzJnCfDN16tQ4ywm0BEpUXW4RbENiq2Lo0KGx3dL4AEWA9sRgvXv37tGe8Jtvvol27fYFts9//PGH29Vo+f8hgLds27ZtzdVnn33mIaEm4r8///77r0yfPl3o15AmolnQMGEg1Thx4oR8+umnou0GMgkP7EUNnrNmzZKVK1fGKIAF4+jRo83HVQYNGmSkSyT65JNPBHKLlAnC+ttvvxGtwUUELl26JPQTpHrz5s0Tgnd13GfhZ+NYbJOevgHBtfGMqzgB2Ws9xg+BZs2aCYIVUjds2JBDUAclqi43X2yrSZerjaN4vX01CNx1113C3rh0/Llz5xo7IPbJZRXLV8dYxd5yyy0JKpoPQDz66KOSMmXKBOXr1KmTnDlzxuRBal+0aFFzrn/cQQAHheeee85IJwoWLChMoL41MdkiVefDHk2bNpVcuXLJkSNHjAMDzg3VqlUTCCtxvnn1+soIQHRwBGnTpo288MILwsdUfHOg3gRzsM+ePbshs0iUIKlNmjQRNFgQpL179/pm1eskRgCpHtK7du3aGQ90xivvKhzHkXz58gnSPsLtt98u5cqVM0kqVKhg4qtUqSJ58+aV3Llzm3j9E38Evv76aylevLjccccdBn8wBk/mm+rVq8e/oABJqUTVxYb4/PPPBXWGv0nNxWq1aJcRyJAhg3z88cfy9ttvC1Ic7+qQ2rDbQ5YsWSRdunQCoZ0yZYrcd999grkHkyREBnKKl2bHjh3lww8/NEVERkaaPJSfKlUq48EJqalfv75kzZrV3MOTk8EfiSoDkVWPsYKmENKzm0S2bNkkY8aMhigxWZcuXVoGDBggJUuWFBz7VKoEWvEPc+bMMQM/OfLnzx+j3Ynv1q2bsTsvVaqU1KhRQ2hfVG5IWpEC7t69W3BmeOihh0iuIQEIMIY+88wzcsMNNxiCw0Tsa34Brphd/PDDDwJpZeHIRP3II49I//795ZdffpEcOXJInjx5ElBzApJqUg8CmGAwxt14443y5JNPyoYNG+TAgQOe+47jCGMkEbQjCw881u+55x6BtBLPXqA333yzZMqUiUsNcSDAXJM9aoFGQIjx+uuvR8tBW4AvfSjajSC4UKLqYiPR8fr27SsPPvig2YD38uXLLtamRScnAkya/fr1k3fffVcgILZuBggIJHvYvffee2ZLEEhL69atpX379mZw5ppFDGpMFjI2L4R0zJgxRk2GtAEpEuWxcwQTL/eZACiHd2rRokVGCsFWZ5gksJEzEj0mYiblzp07GykS6SKjSPC3334rqEQhzdu2bbPV6jEeCKDC9E7mPekSf/bsWVmwYIGR9mFzzDvBxMHEwITBe4Ikg/eA9BoShgD2qb45kGB7xz311FOG1BQuXFiQqqb/zz8A7OkD7LSCRInFg3c+PU96BPy1F6Yv/mqCxDKesbC395krWdTfeuutRipo4/UYOwLly5c3cxFjD3OEb8qcOXMK5jMIQnzvBfq1ElUXW4jJi0GyYcOGgugdyRmEwsUqE1K0pk0kAg0aNDCqKaSrOGxQHFLMqlWrChIf7BWJI2AygG0rzlfYL6ZOndpIPFEJc5+A2hK7IsgNElXikACxeTP2W0uWLCEqWmBwR4KH5AiiumfPHiMxYtVMnag6c0epzgioP5HMIqWIVohexIlAmjRpoqWhjbwjUOmDf9myZY3jAlLrn376yXjf4lSFnSoLlN69e6uNqjdw8Tz3xZts9CGONjBBMwljk9qjRw+PjSpOJO+//76wSGAhqNoEi5h7R3/tZSWlvrVOmjRJMOnwjmfc2rJli/FURxLofU/PQw8BJaoutjkSrVWrVgmD5MCBA41UFWN/F6vUopMRAexKUfMj7cQOjqpRV9WuXVtwvsEmiDgCBLZgwYJGWgqJ9Ddoozpu1aqVIBFCfU8+SA4rZYgSklriYguO4xhvaNT/pHEcR8LCwow6mmsNV48ANsBsNUcJECLUmZzbQPui1kcKRBxbzyHRo/2QGLHYwFQEG2ak26TREH8EIP7WqQb86CNI27xLwDyDNqBPEtjgHKnr2LFjpVatWtKyZUtjQkObeOdL2nMtDQSKFCkitr/gNMUCGdMn7nkHFvhof0jvHY85AFopFu/e8XoemggoUXWx3ZGCsRUEdoFsxIuNoBJVFwFPhqJR10MECVRHuzZv3lzuvPNOLs1iBBKKaQCEFKk6NqLcxI4Ur2McsCAsSFYhtNi1Iml97bXXjJMOdq84aBEPKUJKBMmFGFMWeakP9RrPgRSWeMp7+umnZdq0aYKt3uzZs41DD05gOKLgUYv6DXMCPreHBJDn0hA3Ajji0Je3bt0qX375pWDWg3oSSSqY0jYsUDC9YPsq0kCWaCcmbMgR5gK0FXFx16gpvBFAO4CDDlJqpKKo9rlPH+Fdpi3AFcEA2xmRDlUnC4a7775bGHchRPQz+gN5NbiHQIkSJcxWfbQHO1288847xmGU9mLxYGtmnGIRyFhp4zhiRoMWiAU61xpCGwElqi62P/ZQkAKq4IhdIpIXrgMl6HPEHwHUUdgd4tDkrdbH9pCJlJJo8/vvv1+QoCNdY1LEZpF7OBcw+GIjyjVqerZiQarAZFugQAGzfVHXrl2FwEIHr1feGexhccqBXCLBffXVV4UJe/LkyYKEiYEeJxLsWVE/45wA4UUFSv1M2hAoJL9M3tyjXp5DQ9wI4ECFExztgGkHbQnp4X3Abph2oU1oK+yAaTtKRSLUoUMHiYiIkOHDhxsPXOzxuKchYQjwTtOv2NDcaiuwz2Z/WvCvW7euWZhhYoUpDAs/jphdsFBAUEC7sUNHwmrW1AlFgIUbjqIjRowQxkS7sIC40hdsebRbsWLF7KXnyAKQXTI8EXoS0ggoUXWx+RkwfW1UW7du7WKNWrSbCPBpOtoPgui73QrSNOpG9Yj0gDS0P+QEiQH3kKSi3kdCyjVeyqQbPHiwsWVE2sA1AbtX0kBqyUMcjnlM1Ey0SPggQ6gz7X08+yGhECbSs58nZVA/5zwLNrHcI/izIyO9Bv8IICUCNyZQ7Oaw9cXBzV4jSUe9TxrIlC0F6SsTNoGtlWy8HhOGAAs5sMXWmoUZuXnn33zzTaE9kJwy3pKGvsJ9AgsDsCcefwH6IfEuBi06CgE7vjFGRV2a/7z/3o4+9A20Ream1x92bGBR7xWlpyGMgBJVFxuffRchEXhwY9OGWtDf6tHFR9CigwQBnKZwHmBrEQJR/SNnAAAQAElEQVQDeuPGjYPk6fUxFQFFQBFQBBQBdxBQouoOrqZU1KvYEGKrhjMAHv/srWhuBtIffZZrjgB2p/Pnz/dsL4IaH5u7a/5g+gCKgCKgCCgCisA1RECJqovgY/yPIwWOFdgcYqPKPmYuVqlFKwKKgCKgCAQAAvoIioAikDQIKFFNGhz9lsLXaCpVqmS+ToQql22qvD0e/WbSSEVAEVAEFAFFQBFQBBQBg4ASVQODO3/w+O7Zs6f5dB/bBrHZ9xNPPOFOZYkuVQtQBBQBRUARUAQUAUUgsBBQoupie7B3H57AbGvDfpt4XPOZPxer1KIVAUVAEVAEAgUBfQ5FQBFINAJKVBMNYewF4BDDvn1s/v7FF18I+1myfVDsOfSOIqAIKAKKgCKgCCgCioBFQImqRcKFIxuz483Np/zYFJzvt/NZVReqSqoitRxFQBFQBBQBRUARUAQCBgElqi42BZ+OY+Pv7777TooXLy4VKlQQNnB3sUotWhFQBBQBRSCgENCHUQQUgcQgoEQ1MejFkZevbrRt21b+/PNPwTa1e/fuwtc64simtxUBRUARUAQUAUVAEbgiAnzGedSoUZItWzYjDNuwYYNcvnxZ+Nw2X0bMlCmTpE+fXtauXWvK4bPdmCLyUZnmzZvL+fPnTXyg/1Gi6mIL8RlMtqXq0qWL+d5xq1atpEiRIi7WmDRFaymKgCKgCCgCioAiENgI7N69WyCjv/76q7AVJh+NuXTpkpw6dUrQ5C5atEjYvz08PFwuXLgg69evF/Z1h9AeOnRI5syZE9g/8L+nU6L6HxBuHHCcql+/vrA11Q033CA5c+aULFmyuFGVlqkIKAKKgCIQuAjokykCSY4AklSEYUhQIaiFCxeWlClTSooUKcRxHDlx4oScPHnScBAqL1mypNx9990CN3n22Wfl+PHjRAd8SBHwT6gPqAgoAoqAIqAIKAKKgCIQDYGbb75Zfv/9d8mVK5dMmTJF2LudBGFhYVKrVi0jZWX3oY8++kgQlt1xxx2GxGIecO+998qDDz5I8oAPSlQDvomu0QNqtTEQiIyMlEivgL3PX3/9FSNdQiLIf+zYMWE17C8fNkT+7l+8eFH27t0rHFHpHDhwwGRnAPrjjz/k3Llz5lr/JB0C2JrT/kgpwNm3ZNri4MGD5h2hXe192pC22rdvn/z99982Wo8JRAAcwZ/32x/+9CHukQYpki0eaRN9lfhgkSDZZw/mI30AzI8cOeL3Z3i3l2+7kI82o8/5zayRHgQyZMggO3fulGLFism4cePk7Nmz5l7GjBkFieuQIUPkzJkzJs7+OXr0qOzfv1+C5QNESlRty7lw/P77769Yalz3r5hZbyY7AmwthoPcK6+8Il27dpXq1asLg4AdGBL6QBCbwYMHS/v27Y2Kxjc/BBQHPOr1JZ6nT58WvnrGhDx37lx57LHHTHYm5dmzZ5uBi8mcSYLJ29zUP1eNAG3crl070+7dunUTuzDwLhB7sHfffVdef/11wZGBe0y048ePN/n48MfYsWOFCZx7GhKGwKBBgwQMsflnYvbNvXXrVoMzfbNZs2aeNmIP60aNGpl+Rn+6lv3B95mv12vGHkhTmzZt5P333xfaxve3btmyxdNeTZs2NQs80ixevFhatGghb7/9tuDngb0l8RpiRwDJ6osvviipUqUyi2FI6NSpU+Wrr76STz75RFD523EHwQdzRHh4uPBBothLDZw7SlRdbAs624oVKwxpYGD1DqtXrxYIj4vVa9FJjMBnn31mPCvZyWHMmDHCpDdx4kRBipbEVcVZXOrUqWXYsGEeVY/NcNNNNwmTcsGCBY3nJ5M00gl7X49Xh8DMmTONyox2R4Ixb968GAXxPhBIw97JGzZsMBM0xGjAgAHSo0cPWb58ueD4ECOzRlwRAbyWN27cKGD73HPPCQs83wws2LDXIw37V0+YMMFoFjh26tTJ5GHxt23bNt+sep3ECOzYsUNWrlxp2qty5crSoUOHaDUgTV2yZIkZT2mvHDlyCOMrfYUvOhLHwmTPnj0ej/VoBehFNATAE40NHv6o9+17jkADSSu4koGFM7sEsG0m4xiLBV9pK+kCLShRdbFF8Kqjkz7//PPiGypWrBgEE5aL4FwHRTOYpkmTxqhX+DkMDo0bNxa2/kAKStymTZuEiZU4pAbEESCZ2BUxaUIuiWMhQzoCklpsinDE4x7vC/ZE33zzDZfCxyTy5s1r7I7y58/vIaxIWFu2bCkMQA0bNhQmaPJWrVrVPBeLI9TQxaLURK+++qopS//EjQALkrp165qEbO/iqw1hscJEwT7JadOmlVKlShlpBvG0I23MJJIuXTrt9wbFhP1hoUA/YhKmT0yePDlGAfQJPKC5ER4lLcK7GQkTcUiWkCiBvzq0gpC7gYXcww8/LNhKsof4ggULBO2OrRWJK/0FskTcyy+/bIgtBJdr2ol+9MADDwhjKHEaYiLAe41QAkdtFmq89zfeeKPZCYD+QmC8gpxiXoGm4YMPPpBq1aqZ+YD5hPQxSw6sGCWqLrYHnRRDZggrARsrjgQ6LYOui9Vr0S4gQNtBWCAebAfCFiC2o0Nc+WQuk+q0adMEEsogCyFEgo4KGBUYg8OaNWuEQZlJk1Uvj4rkh49EoB6GGBFHQIXWv39/YbDp3bu3IFlCWg/h5L4NrKoZrHgO9tcbOXKk+cgEap7p06dLzZo1jbkCZAoThqFDh9qseowDARYRtDnJIJ5cc+4dMOVguxjiSMPCBe9bJmTag3jHccw+h5xriD8C27dvF9Sb5ABbSKev+QUYo/KEBJHG4t+nTx8jrWPCLlGihGTNmpVirl0IgZrpH7a96DeYJDHnef90zJlYyDFu4alu2wspKmOi4zhGi8F90X9+EUDdj+aGcYd5wnIKsGeuoI8Q+NgQNquTJk0y+7qTnoA5E2n9Fh5AkUpUXWyMESNGmFULVSBeZwClA3KtITgRQAKAzQ8SSlazLD7sL4mIiDD70nEfI3XavF69eoI6EhMQ0jEYs9cdUk4GZyTtd955J7cE20dUMuvWrYtmx8gWZ6yYKROpKhMyxNVk8voDKSpbtqwweHlFe07r1KkjSAI3b94s999/vydeT+JGgO1cvFP52nYxCWAHhoqT92DZsmXGbhipNySLRQYLFSZr3iHvsvQ8bgR88SbHrbfeysETihYtKoy5LNAweWGrHm7u2rVL6If0HyZzqwblngZ3EPDXXt6EiLHqkUceEYgTbcYiHekp2iE0TUj+6EcIerJnz+7OQ2qpQYOAElUXmwr1H8VDPNgeArLBcfHixUQHS9Dn9EHgnnvuMfaGkD4klfY2g+9rr71m7LKQauLgBGFB1WInR0gmkgRIrM1nj2y+TFq8ym2c7xHpLepM3/j4XDMBMIFAlJH8xiePpvkfAnxZLjIy0lwcPnxYChUqZM69//Tq1UuQVP/222+SOXNm44XLgoB4TETIxx6G+nU6b9Tidw7+SIBIzXY8uXPnFuy0ubYBTQf22Ujw0CLwgRX6Ek6HOLmhrUASiwTK5tGjOwiwKLD9BY0Cqnw0ObY2x3GM+rlBgwbGAYhFHLb/LAi7dOliFvdIWxlrCxcubLPpMUQRUKKaDA2fJ08e4XNlqHwhMpAE1MOQlmSoXqtwAQGM/5GcMgkuXbrU1IDdJ9IBpOao/SGe3377rflsLhMoiXDyKFCggKB2R8KO+p5BGpsuJKqUgUmBVeGQB3Um5BaHElRoTNLExxUgzth52XRM4OTF7MBXGmXT6NE/ApAgnDzw/mdxgpTcNyX7EtK2kCHUbky6pGHBgsQVSXbXrl2DxtOWZw+UgFaB/gTxRCqKLZ7vs9FnsF/F/AWbSBYJpKHv0EdZ5KG9cByH6Gscru/q0eywYMNUCgk39vmMfd6/mj0/2XSe/oSpFPMk9+kr9Dfs7Jk3se0mXkPoIqBENRnaHlLAJEbAdooBFZWgP6laMjyOVnGVCKCaYrHBdlLYmr700kuCCh+pAFuwEJAcQFYgptgFcY63N3mRMpCGfKRDwo6na+vWraV8+fJCOZwzqCN9QzqHGp98Tz75pJHiMuhzD+kD0gYWPpgBsD8n5IlNnyGibJHEuwZRZRuthQsXmi+V8N7hUIX99FXCEJLZcEqoXbu2gF++fPkEkw3MPnBW69u3r9kSBhUl9pA4s1k7SCTqSIS+/vprYScAiFJIApjIH80Y2q9fPyPJdhxHmjRpYkpk8QBp5f3HbhsnkY4dO3o2MkfqyqKBfoeqmXIgsyaz/nENARZp2D+CNeYuSEmpDBMYNqLH7pRxlIU5Ggf6CPdZlDOO4YjFThm0GQtu7mkIXQSUqLrY9qz+UfPjcEPAphHHGqqEuGCjyHkwBH1GEVSPhJ9++kkYSGk/JkHikIZCDCGeXK9atcoQQ0gi1wSkOpAVJASzZs0SJA5s0UJeiCPkknTr1683jlZIWcGdiZd4SDLXqJUhnuRHnTlw4EDzbEhvkUCQlq1heB6bFycSJLi8k0gykK5Slob4I4BUD2yZgLG3Q0LEdlT2mnvYRtI+tlQkRjjSsR+kjdPj1SGA9zgYQ1jt+8uCEFtG2oOdMlgYQG68a2Chh50weemvtJv3fT13BwGc18CcHU5sDSww6COQT9qCsZTFn72PeQDjGM5YLOZtvB5DGwElqi62PxMYtlHYpdrgYnVatCIQKwIsktg6CxJsVWyxJtYbioAikBwIaB2KgCIQDwSUqMYDpKtNgtQLCRvbDRFYSWKbeLXlaT5F4GoRQLLH3q6or1X9fLUoaj5FQBFQBBSB5EZAiaqLiGO/hn0OXtoE1FVsx+File4WraUHLQKo+7GbxMQgaH+EPrgioAgoAopAyCGgRNXFJsejG9U/zisEnGXYtsjFKrVoRUARUAQUgSBCQB9VEVAEroyAEtUr45Oou2xhdPToUWGrDgLe3mxPk6hCNbMioAgoAoqAIqAIKAIhgoASVRcbGo9/AtunEPD+5ctDLlaZDEVrFYqAIqAIKAKKgCKgCCQPAkpUXcSZTf2rVKki7IdJwKGKfTBdrFKLVgQUAUVAEQg2BPR5FQFFIFYElKjGCk3ib2TIkEHKlSsnpUqVMqFOnTrC11ESX7KWoAgoAoqAIqAIKAKKwPWPgBJVF9u4cuXKguc/XxEisMHxgQMHXKwx2YrWihQBRUARUAQUAUVAEXAdASWqLkL8+eefC1+kGT58uBD4lObmzZtdrFGLVgQUAUVAEQhOBPSpFYGrQwCewZcNbW6+QIg/TPbs2YVQsGBB+fXXX2XOnDlSoEABE0c8XzI8efKkzRawRyWqLjYNL8Ann3wiSFL5zOUff/whfP7PxSq1aEVAEVAEFAFFQBEIEQQGDRokffr0EXYYsj95165dwie3+YTtjh07hM8G86GXLFmyyIoVK8wntxGk5c6dW/jUts0XqEclqi62DDapa9eulQULFgifsBw3bpw89thjLtaYvEVrbYqAAJFvWgAAEABJREFUIqAIKAKKgCJw7RBo2bKljBo1KtoD5MyZU+655x4TB2EtWrSoQFQffPBBuf322+XixYuyfft2SZ8+vbk2CQP4jxJVFxunQ4cO8t5778lbb70l77//vrCH6rlz51ysUYtWBBQBRUARCGIE9NEVgUQjkCZNGk8Z06ZNkxo1asjNN99sJKvcOH/+vBw6dEgefvhhLgM+KFF1sYm6dOkiKVOmlA8//NCI5m+66SbBFMDFKrVoRUARUAQUAUVAEVAEZM+ePXLhwgXJly9fNDTOnj0rP//8s+TJkydafKBeKFF1sWWmT59uCOoDDzwgGTNmNJLVqVOnuljjNSg6xKr8559/pH379oKtD+qU77//Xr799ttkQeHy5cuyZs0aee6554wx/KxZs4yznq28dOnS8sMPP9hLPSYhAgz2PXv2NLi/8847curUqRil79y507QNfZ2+TwLyDRkyRHLnzm0mBZwqieOehoQhMGnSJMmcObM0adJEjh8/HiPzwYMHpXHjxqaN0GIhNULFyZiLyjNv3rzy5ZdfGrVnjMwakeQIgPV9990ntWvXltOnT8co37u9mjZtKpAnm4ixDkng22+/LX///beN1mMCEViyZIn5MqavHSpOVcwXvvEJLD7ZkitRdRFqxOpIVG0VAwYMEEirvdZj8CGAzTGD7rp16wRj9AkTJiTbj4Dg8BGJFi1aGGN4bIwwmrcPMH/+fHniiSfspR6TEAEG/JUrVxrc6dNg7Vs8BHbw4MGyfPlyYTGzb98++e2334xTA3bqBMqJjIz0zarXcSCAZAjCuX79evOOQ/59s7A4YBGHAwn9dOLEiXL48GG56667jPSob9++Atndu3evb9aAuw72B0KtzFjFwrlixYrSrl27aD/p0qVLxgO9SJEipk+x8BsxYoQnDR7qU6ZM8VzrScIR+Ouvv4T5AR7imxui+tRTT/lGB+y1ElUXmwaVPwOmrQIbka5du9pLPQYpAmwxduzYMeGDDh999JEgqeGn/Pvvv9KwYUMTILIMxkgV2DrkjTfekB9//FHwwIRoku6XX34hmzFqZ59dpEGLFi0ycUyyc+fOlf79+wu2zr///ruJR6ILQUX6wODfunVrE8+fZs2aCVIK3jkM7KmDMGDAACPRWLZsmXm2jh07ypEjR8iiIZ4I8EU5u/1LsWLFjIOkd1behw0bNkj+/PklR44cQpoZM2YYuzBMfiBPvA+MAdbJwTu/nl8ZgS+++EKeeeYZSZs2raDJGD9+fIwMmzZtMvhzg/6ERC516tTy/PPPG89mtuVBE3LmzBmSaHARARx4smbNajSJZcqUERYZ7IJjq0RiyoLBqp4ZH1nIWR8OhADqeGzRuvKRcR2TQhYFs2fP9mgMWNzdeuutQjt4l0A/4cND999/v3d0QJ+nCOinC/KHK1y4cDQv/9dee82s6IP8Z/l5/NCJYvAsVKiQFC9e3KgZw8LCJFOmTAYABmTUXA899JCMGTNGVq9eLeHh4UYdDwlF/chA0qBBA5MfFSaSBKRs1atXN6YhbGU2duxY86EI8lD2/v375bvvvjOTLUbxEFjqQGqRK1cuU/crr7wikFsmA0KjRo2MVO/PP/8UDOs5onbu3LmzMEkg7RD9F28EfvrpJ4MjGfCeZbDn3AYWDki8IazE3XvvvcJ2dKg+aTPao2bNmoJUj3eGNBrijwCSVEvwwR9JKcG7BPoS2g7iSMt2PUzULA6IwwyA/pQ+fXouNbiIAIs2xh2qYLHA+MN4xTWBMYhxyrYXaUnDQhzJKnMlfYi0Gq6MwCOPPGK8/jFDQ6OQIsX/aF327NkFocjdd98drQC0uiy8o0UG+MX/flGAP2SwPh5bRvB1KggMARUIe54haaWTBuvvCuXnRjrG6vXTTz+VhQsXyqOPPipITyEyEBPsfpB0ogJ+8sknpU2bNoaUImljAGH3B0gpRyZa8kZERBivTD63i9StYMGC8uKLLwqqZEgOEytqHMdxhMUPaufcuXML5WPDhSSXwd22S8mSJY1kCfUOE/XLL78s8+bNM6o2JH1Ip5AK2/R6TDwCSOqQXCA9j4yMNKpmJgRKZoECueL9QGoEoSVeQ9IiwKKNCXjbtm0CUQJ37xpwHoH8IJX1jg/Y8+v4wRgLWTQgOd2yZYswfkJWUVUznkFg6S8cWYBcx1Ak+qex3VS2bNkkW1QICwsTx3FMmcxVjDtgbSL++8Oeqiwe/rsMioMSVRebCaKKuqlUqVJiw2233WZ2ArCSFxer16JdQIBB88CBA8ICBDsq7HzYI5cBFXXKlapkcYJEFnKKKhgVPgMLEtpVq1YJcRAZ3hl/5UBiqRPyiQPX119/Laj5Kcc3PWQWCSvbo3EPaUWtWrVMHdQDkSZeQ/wQQGph+yxtzcLDN+dnn31mTC8wEcA+tXz58gZv7JhZKCBlR5LNZOybV6+vjAB9BOJCKvBn8Ubg2gbMXXC2QhDAe0872HuY3WDugsbDxunRPQQKFiwotr/wlaQ77rjDmErZGiFP4VHaJhZ3mE8h+SOwIGdBgYkczqJsTk+w+fQYmggoUXWx3TH4h9BgL2UDThacsyGvi1Vfi6JDok48u1GhQzodxzE2cxBHDNaRUjIhAgQ2Why9Q7p06YzaHVsi4iEvOOYwoCMBQh0GicEBh/u+AUkctnlLly41t3ASYdWcKlUqc23/IFGCiCJlxQN948aNwvuGaojnJx3kmqOG+CGA2h5JNBMpbYvU2jcnEj0IEmYhaE6QELFAYOJFSoSED2meejH7Ihf3NYssFmbYl0Jg6tatGyMT/QsNA5JsNB7YtJKIxRxmOCwcUD9ju0df454GdxBAg7h161azOwNSU7RILMq9a2NsQvsEWeXLSvSpZ599Vpg3WdRhGlW2bFlB0+SdT89DDwElqi62OSoNJAG2CtQb2BLaaz0GHwKoTCCHDMQ4zUBMmzdvbr4CwkTKZIhtEFJTtpLCGQoJD+SRfAzMSHpIg/QUZwK+YIbDFOV16dJFeG8gkgzemAhAkFjgYKeHzSNSOfKXKFFCsOUiX4UKFQRHBBZBH3zwgTFLwGwAAj1s2DDjKc27h2MJeSFOwYf+tXtiJkvsgiGjLC4w8WD7o06dOhm7U8gnuLItEkSV72zztOCP1B3cIU60H3Hc0xB/BCAzOAbSBvQlpKfkHj16tCCZA38kcrQT0jyIEfdZDGL60r17d2OmU6VKFcEx0XEcbgdBCM5HhISymEA7tHjxYtNH+CVoGVl0oB1iTHv66aeNk1zDhg25rUER8IuAElW/sCRN5Jtvvik4xuB9iiSMiQovcFs6293Ycz0GBwJ4SiI1Q0KDLSKEErUWTw9xRL2Iap00DMKovfDuh5A6jiOVKlUyWxaRpl+/fgJ5xXYZ5xziUNdzjVQVG9Zu3boJamTUntinImVA4kBazABIC3HCHpXBH2kfZSD5Iw3So6FDhwq2eZAoJErEM2HzzBrihwAOOUjrwBOHKKTZ2HpBgFh8cB9cWURAlFBtUjLtiwMbknKk2UzOcZmIkE9DTAQgnHiKI22zDiI4qWHjDf70C0xoIKsWfxaUEFvbL+m3uoVbTGzdiGGMYeximylsJamD8YuFN+2D4yjaHszifPfzdBxHcDplkU/bkldD6CKgRNXltme7Iby0OaI+ZPsgWyUEwp5fL0f9HYqAIqAIKAKKgCKgCCQVAkpUkwpJP+UgUYWM8tWNVq1aCc4W7Bdnk3LPnutREVAEFAFFQBHwg4BGKQIhjYASVRebH5tApKioe1FBobrCucJWybYS9lyPioAioAgoAoqAIqAIKALREVCiGh2PJL1CeorzDXZT2BOy9yUOMklaSSAWps+kCCgCioAioAgoAopAEiCgRDUJQIytCPa6ZIsgHHCQnrL5O57hsaXXeEVAEVAEFAFFwB8CGqcIhCoCSlRdbHn2hGMvOarAa7tXr16SN29eLjUoAoqAIqAIKAKKgCKgCMSBgBLVOABKzG0+tcmWNuyvyJdt2KaD/TITU2bw5NUnVQQUAUVAEVAEFAFFIHEIKFFNHH5XzM3nUvmUJZ+I+/jjj4U9VPk6zRUz6U1FQBFQBBQBRcAfAhqnCIQgAkpUXWx0NjxesmSJsCE8n4/jCyrLly93sUYtWhFQBBQBRUARUARCCQG+eskXwOxv5kttkydPFj6GQeAztqdOnbK3hQ/CcJ/PapPWcyNAT5SoutgwfOObz1kiUSXs379f2PzfxSoDrWh9HkVAEVAEFAFFQBFwCYHZs2cLn6D13pedryGePn3aU2PXrl3FXvNFxVdffVU2bNjguR/oJ0pUXWwhPqE5Y8YM8xnVsWPHyvDhw+Wpp55ysUYtWhFQBBQBReD6RkB/nSLw/wg899xz0rJly/+PiDq75ZZbpFGjRhIeHi6PPfaYceLOmDFj1B0R9nNH29upUycpVqyY3HzzzSY+kP8oUXWxdZ544gnhhUmVKpUQChcuLDhYuVilFq0IKAKKgCKgCCgCIYIAZoXp0qWL9mtz5MghKVOmNHGo/SGtXI8bN06KFCkifCnzmWeeEbbQvHz5skkXyH+UqLrYOgMGDJAGDRoIX6h67bXXpHnz5oL3v4tVBmTR+lCKgCKgCCgCioAikLwI8CXMLVu2yJNPPikXLlwQbFkxQcQ0AD4yZ84cj0lA8j5ZwmpTopowvBKcmk+nli1bVsqVKyfNmjUT3Z4qwRAGTIbDhw8bWyDsgbp3726e6+DBg9KlSxcTH5e0vGnTpnLgwAGTjz9sXUZZU6dONYMIcYkN2EVPmDBBzp49m9iiNL8PAgsXLjTtPG3aNPn333997ooZ8N977z2TZseOHZ77fJUO1Vzr1q3l999/98TrScIQ2Lhxo8F21KhRfvsL7/zIkSNNGuz2bOn0S/qZDUuXLpVgkCLZ54/lGPDRjEVgPmjQIL/Peu7cObHtNXHiRE+affv2mTZs27atQLI8N/QkwQisXr1a0OymSZPGOFAdPXpU6tevLzfccIMUKFBAcKTCsSrBBSdzBiWqLgLOwAo5xU6kbt26Urp0afEV0btYvRadxAjQ2VlwsDKtWrWqKT1t2rRSvnx5wayjdu3aJi62P5AY2/7Dhg0TvliGrRAT57Fjx2LLlqB4VD48IyYnDELeE0CCCtLE0RBgQcKX5Tp37mzUZevWrYt2n4u+fftKnjx5pFKlSvLSSy8RZRYMPXr0EPp/jRo1hHcAQmVu6p8EIQD2qDD37NljtvrzzYwak3YiXYsWLYR+xSKhePHiQhwhU6ZMcuLECXEcxze7XichAhcvXhSce+rUqWMWcBBS7+JZKOBYbNuLvsP1yZMnBWJLWz366KMycOBA017eefU8/ghAVJGmkoPtMrNkySL0Ca4hq8xHHLkO5KBE1cXW4aVIkUIhNhBfB39uvPFGyZAhg9lujA7OTyIOskogjtUppJMvkTGhonohHQM3R8dxhPuzZs0yUiEGEQZmyomMjBQGbirF/cMAABAASURBVCutIx0r4L179wrSB6RxDOSkOXTokDDYI304cuSI2PIvXbpENYYgffrpp7J27VojxaUMAmUfP35cqMsk1D/xQmDAgAGCRDRbtmxSokQJmTdvXrR8LF6QZENIK1euLPfdd58geUXVljlzZnn44Ycld+7cwmLHThTRCtCLKyIwadIk4cMpfO2PhSGOqb4ZVqxYIRUrVhTaqGPHjjJ+/HgjGGBBSNztt98ud911l+he1r7IJf012gfGwlKlSgmL+j59+njGKGpjnEL9/MILL5j2evfdd+WLL74Q4tFW0V6Ehx56SG666SayaIgFgTNnzghzAAtg5gfmBZKiAfznn3/MuMO14ziCdpe+xBi0YMECyZUrlxmTuB/IQVlUILeOPltQIcAgO2TIEEFy1rt3b2ELkG7dugmDBQbtEBVIJnZB27dvF1T+DBq7du2Snj17GhMCVMTsFIHajHIaN24sDNyQHiTyqMMokwmZ/EzIVsoECSUt0oiVK1cKxIm99QYPHiyvv/66IL2F+I4ZM8YY0wcVuNf4YcExX7585ikgTGvWrDHn9g/tSvszOROHug2NCvZgLEKYbPGuJXibf5BWQ9wILFmyxKgqSQmBQVLE4o1rG2gD+hrXBQsWlPXr15tFJdoF4pjMmcgRIHB9PYRA/Q3sF277C+Mei3YW1fZ5IVOWSBFHWtIg2KGPMN5FREQYrROSQNJo8I8Ai2HwRliCFoFxiJSMRThOsTjmmsBCjzmEbTNxrkL7Q3ygByWqgd5C+nxBiQBmAAwGP/74o5GG2h8BYQkPD5dChQoJDnaoKBmQGcwhkDjeIW1lECcPqmTIr3XCQ1IHUUWCx8CPWpkdJVBnkt4GPDqRZCDd69Wrl1HDMQmwRckDDzwgfCnNptVj3Agg3fZOhcTc+5rJAEJqJa2kT506tZw/f97Yhtm0vtc2Xo9XRsAXb1L7vvNITJHkQVYhpOBPOhtYHCLdVuJjEXHv6K+9kPzZGh3HEfoLBAtCRXsxNt56660mieM4RpLKlx25ZyL1j18EIJ/MHQQEGBBQEmaL0v4gsbYLNeIIkFPStmvXzmgYiAv0kCLQH1Cf73pCIPh/C6Twzjvv9PtDIIKPP/64UacwSHgPEOxX5y8TUiEkCaiGHceRnDlzChMphBS1DOUwgCNxIC5//vxGnclgxD69TM4M+JTNMbZ9erHNQyKLes1xHLn77rvJoiGeCEDwvZOCp/c1bYadHdJtJK6onVFR0z7cs2m5JthrPcYPAd5935Tp06ePFoUGY8qUKUa1z24rLNS8E0B6YuuH3un0PPEI+Gsv74UD41fNmjXlq6++MuMlbcX4xFhH7VyziEedjSScOA2hi4AS1dBte/3lV4EAZBBJjl3lYxuKmv5qyQdqLkgjqnoeB7UNKhxvkkv81QRfI/l69eoJHtPY0lLn1ZQZqnkgnVattmHDBkGK4YsFphXYDoMxX6KzCw3slJGk8mUY3htVPfsiF/c1tnXe+OMwCNnxzomWAic3zAI4h7ja+ywI6ae0iY27bo4B+EOw4/7hhx+MHT5jG6YYvos7FvVIVFetWmUWF+yC4v1TGFNxDkU44B2v56GHgBLV0Gtz/cWJQAAJJ9I1vFFR2eO9v3jxYiMVgGSi6mdrItSMVoXP5Ik0B1KLrR0DM45NTLykq1atmkyfPl0ojzyo5iE1lLN582aB/HDE3hF7pO+//954wuLlTPzu3buNZAIb1UWLFgllYjqAIwKkatmyZcZOFkkfW5IwaScCgpDMivQHm1O8l5mAIU6YX0CKsIWkbWk/SCrthrczQEGM7rjjDsFGmXssQrJmzcotDQlAoEyZMob04CBIX8FshuyQT5xCwH9xVD9EpYnjzsyZM7ntCdhpsz2cJ0JPXEUAEopU1Y6T2M5T4datW40jIjaqENjRo0cLpklIwrmPEAAbffoSdqqUwcKaexpCF4EUofvT9ZdfIwSCulq8hvmqR9GiRc3v4BqHJ0s+ICZ8og5pADsEtG/fXrC7Qu01YsQIj00Qzk182o5C+AQetqacY5OK2gspAuWwtQ7xBLZHsnm6dOnicTRo3ry52PqRXOB5jqQWO1XskchLcBzH2May7QvXGuKPACQfxzVwRSVJO/vLjekG+xTS/tzHTKRJkyZC+5OHvLQt9zQkDAEcDsEVtT7vtr/cYWFhQjv5aiToN0jF/eXRuKRHADOoN954w5gYYa/PQsNfLZjFMJaxgPO9z+4MLOK1v/giE3rXSlRDr831FycSAUgH+2SGh4cL6nQICEUyOL/44otCPJJLjgTsRnFq4pwBm2vOmXCtkxS2c8RVqFDBkFkID9cM1BBebFS5phzSQmKpmwmYeAJmCRwJOI3wnJyjpsb2C4kvamjIE8+rIWEI4PAGnqiVUTtjWsH+ufaae7QvZMq7ZCbhWrVqmd0gFHtvZBJ2jnQNjPFkBn9yozUoWbKk+Vxk0aJFhf4C3tzzDiz4rm9zF+9fGxjnjFu0F+OVfSJs8ekjjuMINvaQWBwR7X3GLDu2srCAyNp7egxdBJSohm7b6y8PEQRQt0FW2bMV6WuI/Gz9mYqAIqAIKALXAQJKVK+DRgzGn6DPnHwIIMXATnXTpk3JV6nWpAgoAoqAIqAIJAECSlSTAEQtQhFQBBQBRUARuMYIaPWKwHWJgBLV67JZ9UcpAoqAIqAIKAKKgCIQ/AgoUQ3+NgzeX6BPrggoAoqAIqAIKAKKwBUQUKJ6BXD0liKgCCgCioAiEEwI6LMqAtcbAkpUr7cW1d+jCCgCioAioAgoAorAdYKAEtXrpCGD92fokysCioAioAgoAoqAIuAfASWq/nHRWEVAEVAEFAFFIDgR0Ke+bhE4c+aMREZGysGDB+Wff/7x/M7Tp0+b+MOHD3viODl58qQn/uLFi0QJH3/5/fffTfzRo0dNXCD/UaIayK2jz6YIKAKKgCKgCCgCikAUAnxZcNKkSdK5c2fh4y3Tpk0zZPX8+fMyePBg6dq1q/C1r+3bt0elFkNm+/bta9K3bdtWVqxYYUgqe2rzaWfSN2vWTPbu3WvSB+ofJaqB2jKh9Vz6axUBRUARUAQUAUXgCggcOXJE+MLgmDFjBKK5ZMkSOXXqlBw/flxKlCghxOfNm1fWrVtnSpk5c6bkypXLxFeqVEnmzp0rSGQXLFggefLkEdLffPPNsmjRIpM+UP8oUQ3UltHnUgQUAUVAEVAErhoBzXi9IZA1a1bJnz+/pEiRQh544AG566675MKFC3LvvfcaArtnzx5DRKtUqWJ++rZt2yRDhgwmPV8ohNAilc2WLZv8/PPPgtqfNFWrVjXpA/WPEtVAbRl9LkVAEVAEFAFFQBFQBP5D4IYbbhDHccwVhBPyGRYWJsT/8MMPUqdOHfn+++/l77//NmlSpkwpO3fuNOeQ28uXLwuhXLlycs8990i6dOlk1KhRcscdd5g0gfpHiWqgtkwIPpfbP5kOGhERIRFRYcOGDbJ582ZzzjUqFe4vXLjQxGGs7u95sAXCzoeVqb/7xG3ZskUInF9twFD+yy+/vNrsms8FBLD74l1Zv369WKcE72p4N2gz0uCoYO/9+uuvMnHiRBM4t/F6TBgC+/fvN31z+fLlfvHHsWTZsmUmzcaNGz2F2z5NuxB27dplJmtPAj1xBYFDhw6ZtoA4+avAu73WrFnjSXLu3DmTj7Zau3at37b2JA7RE7D75ZdfpECBAnLLLbcYFJ544gljg/r8889L69atTVyFChVkyJAhBs8pU6bIrbfeasIff/whOXPmlJdeekn69OmjNqoGLf2jCAQIAqg5PvroIzlx4oSZrBgMv/jiC/n333/NEzK4rl692nNtIhPwJzIyUt5++22ZPXt2jFyQGybNGDd8IhhEOnbsKO+9957PHb28VgiwMMEZgfo/++wz2bFjB6fRAmQUJ4XffvtNWrVqZe6hWuvVq5ccO3bMvHOffvqp0L7mpv5JEAJMqOA5a9YsWblyZYy8q1atkq+++srEv/rqq2axSH/G69lERv359ttvhUVq1Gmo/L8mvxOvcvoJC+558+YJwfdBWPDb9urQoYP8+OOPJgnEinbC9nL06NEBT6LMQyfzH+asTJkyScGCBWPU/Morr3jGmJIlS0rPnj0N2acvYN965513mkXz3XffbQgspgE4ZcUoKIAiVKIaQI2hj+IuAo7jSP369eWpp54SbH3y5csn9erVM+d0Xsdx5LHHHpOKFSvK/fff7/dhWL0+88wzQif3lyBjxoymDFQx4vNvxIgRglTOJzrGZVhYmJQpUyZGvEZcOwRQjz333HMSHh4uTA7ffPNNjIfp37+/vPXWW/Luu+/Kvn37hEUJkvq//vpLatasKdWqVTOElbgYmTXiighAdMCxTZs28sILLxgpkW+G7777TnAYoY1Kly4t48aNExxFSE8cdnuPPvqose1znP+pT33L0OukQQDSieS6Xbt2RmrXqVOnaAWzaEczUbZsWdOnsJHEsYdELPIhWy+//LKg3tb+Air/H5CM3njjjfL0008braBV7dsUM2bMMCYA9rpy5cqClDpHjhwCzsxNOFQhrUblz6KCOJs+EI9KVAOxVUL5mVz+7Rids1JH8uk4jjzyyCPCQMjec6j+2aYDI3XHcYwnJRNe5syZpX379ubJMBdo0KCBIJmlg3fv3l24j1E75JWVLp2fOmrUqCGQYaQGw4cPl3feeUfef/99o2pBMsTqNkuWLIL0Bwkvg/cnn3wi2bNnl6VLl5r6vP9Q39ChQwUynDZtWkFigWSvaNGikjp1amHgnz9/vjGe5zmR7FE+K2aeC9U0zwARf/zxxwXizPPfd999hniTFwwWL14shQoVMgQeYs7vRSIIEeDZsGvyJ9Hyftbr7XzOnDlSvHhx87NwZkDFbC7++8N7c/bsWbnpppsMOcIDFxx535DWIwXcvXu30G4PPfTQf7n0EF8EWBiwQGRCpU99/fXXRkrknZ/3HWJKHOSUhQJ98vbbbydKMMdgr8ls2bKZa/3jHgJgz6IAQsU4hxT7wIEDngoZZ7imvxBJf8FcCuk3Y26PHj2MbSXjlLYXCP0vjB8/3mxBhcbtwQcflDfffFNuu+02syhmbCbkzp1b6tatazJ8/PHHQjrmJzR9qVKlMvGc0x9Iz9FqgMzNAPyjRDUAG0UfyT0EMCBnAEXtxGAJQWVFiu0gRBD1CESVJ6AzQ9hat25tVCV0aLb9gORyH3LJBIr0AAJaOWrlisSWe9hlffDBB4Jk4KeffjJbiSC97datmyGsXbp0kVKlSpmBhgEaD0zSscpFfczAQjnegeeDkPLsqJAHDhxoth6hLOrhWZnEWVGjFqpevbqR8PFMmDtAyJlAIK4cIaCQbOrk+SGuSDAgwAxwW7duNdIQiLTjOOa3sH8f5aLO9n626/0cFab3b2SS9b5msnAcx6O+hLRiD8b79vrrrxspK7jxLnnn0/P4IYB9qm9HE+jUAAAHQklEQVRK+q53HKSIPoQXNBIk8Pe+v2fPHjOph0VpLLzjQ+E8uX+jv/ZivLTP4TiO2c+TsY+FHO3FIgTiivQVQQAScDRfLPZsvlA/ImVmXGbRS0B4wFgP0eea0LRpUw9MEFnmN+YAT2TUCfMLiz3ST506NSomsP8rUQ3s9tGncwEBjM4hZxBVCAcG59ilYjuICYCtErsdpKNp0qQRJI+O4wjSS1aspHEcRxgkUEn6rvoZZCG8SHNQszB5kocAicGejgmT0LhxYyOVnTx5sjFLgIwWK1aMpNECA3mtWrWMzdaECRM89zBXYMDh+SmbdJBSBnt+D/vltWjRwjwr6meuiUf6FB4ebsgVTkAUSBxSP6SveIwikSUe8syRgJTDd+Aj/noOvAPevw8C6n3N/ebNm0vLli2lYcOGxka5aJSkGwkeUm+IPRL23r17e+zHvPPr+ZUR8MWb1PQTjjbwbiNxAn8WiSwE7T2O7B3JhM65BncR8NdejIW2VjzQsZ+cPn266S/Y4zOusLj4/PPPzcKOjehpT7QVNp8eQxMBJaqh2e4B/qvdfTxse1jdQ1YhmUxoqHIZNJG22tpRG1q7RAgdKm97jyMkk/RFihQRpJr9+vUjOs6AOh0y+eyzzwrlEiCEEFpMAmIrAMkDNl+QyvLly3uSMQEUiyK2EGvyI4FgwIdoko7yCZ4M/53w24lHIoy6jWiIKnZMqJaQzqIqQlJLHUg9SE9AOkj6UAmQTswp+L0sClBncu4d3njjDUE6gZ0kCwomXkgqak+8a5HQ0/ZWIu+dV8+vjACezNaTH/wKFy5svJe9c2HvyFd7WFSi/UCa5H0fcxrsi73j9NwdBBgTbX/BLh/JKNobW5vjOMbWGLtUFhXYs2Lbjep/0KBBwnjEGIRZEv3H5tNjaCKgRDU02z2kfzXqJQgoAVs2JKTYlPJ1DgijBYdP0XXt2lWYGCEgEFsGUtTjEF2kpAyibJeDDSPxkE2O3OccKSdlQ/KQsGLryT0m1UaNGpmyZ82aJagssSvClAC1POp9th/BQcc+D0QV4vPiiy8KxJFryuM+ElxMATBd4DcQIJh4fPL8TAakp24C25uw1x6kCzILgeIZyc/XTigLCRTmAcRDyCG/SAYpDxsy6g2VgNkG7wBqNxYKOLshkQd/8OQcVTRSbLbawamKxQiSayZs2g3pPe8CcaGCW1L9TvaHZEFFP4HcWPUmEmtIKfjTDrQB9nbsKUkfsfWPHTtWWDzQJjYu5I7J+IORXKOVQXOEmRK28SycaS/aiEfhnP6D2RJjL/2CxT/aCXZjgeCy2wZklfQaQhcBJaqh2/Yh/cuZ+PAg5vNyqO9Rv/NJOm9QkMwg9YSsMmhC/JgokTriuOQ4jtl8mfsE7E8hMQzQED4ICranEEIcPSDFEEM+Y8d2LBBE8mGnhbSB3QiYgFHrY2/Hdkg8o30m6kVahzoM0oxUCYLM/fTp0ws2skguuCbgKQ2xpg48PiHNmASgSmOygJxS3rBhw4w5AU5aTPCYOxBHPgJSKkwKcA7C3ok4sKCOUAk4UEE+kZojcYa4Q/ZpSxYWLFrABakfkjwmXLDh/aKtmXhxqEOyinMD9zQkDAG2LWIBhaSahRm5MdlhWzDwxxsatTGLO2+SSjpIkc3DtQZ3EYCUIhHF7p0FOuMaNUJc6QvY2+PdT58YOXKkMX3iPgFiy6KOdoTwqvMhqIR2UKIa2u0fyL/e1Wdj8EM6aCvBYQgpmb3miB0VzkNIcDDw5xr7N65xkGFShPByTWAwzpYtm/Tt21e4xsSAIwFiQ52cY9+JSv3DDz806bDPwqaLwR3CTBrikILyHDZ438fZiT0GUcPb+0hPqcdeQ0Qpi8BzQoY5JyBxRbIMqeKa8tiCiS+dkA9SQDwYMdFQJiSYOAISYeJCKTBp8tuxhWQhwcKBtrTX3GN3CGx8vXHhvWLCJrBY8b6n5/FHgM3NwZgFpZWMoiLmHaY9sMNmIcFG5r6lYjvMO+8br9fuIWDHO7C3tfD+00fsWDp48GCzu4i9z9F7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width=\"682\" height=\"776\"\u003e\u003c/p\u003e\n\u003cp\u003eWe pre-process the data to make sure that it is in a format that the model can understand. We make use of the natural language toolkit (NLTK) to pre-process the data and remove noise and irrelevant information. This pre-processing phase includes several important steps. First, case normalization is carried out to transform the entire text to lowercase, which aids in avoiding feature duplication. Stop-word removal is the next step, where words that do not aid in classification such as \u0026quot;am,\u0026quot; \u0026quot;are,\u0026quot; and \u0026quot;the\u0026quot; are removed. The noise removal stage removes any data that impair classification performance or confuse the model during learning, including numeric data, email IDs, and special characters. Then tokenization is performed split the natural text into representative sets of words. Encoding is then used to map each token to a unique integer value using a custom vocabulary. Padding and truncation are then employed to ensure that the input sequences are of a fixed length. To indicate the beginning and end of input sequences and to identify unknown words and padding, special tokens such as [CLS] (starting position of every sentence) and [SEP] (ending position of every sentence) are included in the sequences.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Evaluation Procedure\u003c/h2\u003e \u003cp\u003eWe evaluate the efficacy of our methods using common metrics such as Accuracy, Precision, Recall, and F1 score. We evaluate the model's performance using metrics such as true negatives, true positives, false positives, and false negatives. The true negative rate is defined as the number of times the model correctly identified the absence of a specific condition or characteristic in a sample. True positives correspond to number of instances where the model correctly identified the presence of a particular condition or characteristic in a sample. False positives arise when the model incorrectly predicts the presence of a condition or characteristic in a sample when it is not present, potentially leading to false alarms. False negatives arise when the model incorrectly predicts the absence of a condition or characteristic in a sample when it is present, resulting in missed opportunities for intervention or diagnosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccuracy = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{T}\\:\\text{P}\\:+\\text{T}\\:\\text{N}}{\\text{T}\\:\\text{P}\\:+\\text{T}\\:\\text{N}+\\text{F}\\:\\text{P}\\:+\\text{F}\\:\\text{N}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eRecall = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{T}\\:\\text{P}}{\\text{T}\\:\\text{P}\\:+\\text{F}\\:\\text{N}}\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e \u003cp\u003ePrecision = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{T}\\:\\text{P}\\:}{\\text{T}\\:\\text{P}+\\text{F}\\:\\text{P}\\:}\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/p\u003e \u003cp\u003eF1-Score = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{2.\\text{P}.\\text{R}}{\\text{P}+\\text{R}}\\)\u003c/span\u003e\u003c/span\u003e (4)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.8. User Reviews Classification Process\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e depicts the entire process for classifying the user reviews automatically. This process is broken down into six distinct phases, each with its own set of guidelines and techniques.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePhase 1: Input User reviews\u003c/b\u003e: The initial step in the process involved collecting user reviews from a well-known open-source project, as explained in section 3.5. \u003cb\u003ePhase 2: Pre-process User Reviews\u003c/b\u003e: In this phase, we manually analyzed each sentence in the user reviews and categorized them into front-end types (Accessibility, Uniformity, Interactivity, Readability, Content, Color Scheme, Navigation, Screen Size, and Loading Speed). We then preprocessed the user review sentences by converting them to lowercase, eliminating stop words, and removing noise. \u003cb\u003ePhase 3: Token Embedding Generation\u003c/b\u003e: The third phase involved generating token embeddings for every token in the input sequence to prepare the input for the BERT-FRIDE model. The token embeddings capture the meaning and context of each token in a high-dimensional vector format. The BERT-FRIDE model uses self-attention mechanisms to learn contextualized representations of each token based on their relationships to other tokens in the sequence. \u003cb\u003ePhase 4: User Reviews Augmentation\u003c/b\u003e: To address underrepresented classes in the dataset, a machine learning random oversampling technique and loss function is used. \u003cb\u003ePhase 5: Task-Specific Training\u003c/b\u003e: In this phase, the BERT-FRIDE model trained on our dataset specific to the downstream task. Tuning of hyperparameters, such as learning rate and batch size, was carried out to achieve optimal performance. The objective was to optimize the BERT-FRIDE model for the downstream task and enabling it to make accurate predictions on unseen and new data. \u003cb\u003ePhase 6: Model Evaluation\u003c/b\u003e: In the following phase, the performance of the model was evaluated using a variety of metrics, including accuracy, precision, recall, and F1 score. These metrics highlight areas for improvement and show how well the model can predictions n new, unforeseen data.\u003c/p\u003e \u003c/div\u003e"},{"header":"4.\tEXPERIMENTAL RESULTS","content":"\u003cp\u003eIn this section we present a comprehensive discussion of our experimental results and highlight their significance and relevance. Section 4.1. showed the evaluation of model on our dataset. Section 4.2, explain the model result with random oversampling technique. The Section 4.3 express the model results by addressed the underrepresented classes using our loss function. This helped to reduce biased model predictions, resulting in more accurate classification results. In section 4.4, we also compare the performance of our model with the other state of art deep learning models.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Evaluation of Model\u003c/h2\u003e \u003cp\u003eWe evaluate the performance of our model using various performance factors on our dataset. We used the scikit-learn \"classification report\" function in python to calculate the weighted averaged precision, recall, and F1-score. By leveraging these metrics, our purpose is to provide a comprehensive understanding of our proposed approach in classifying user reviews. The number of false positives, true positives, and false negatives is used to calculate the recall, precision, and F1 score for each class. The support column displays the number of instances of each class in the test set.\u003c/p\u003e \u003cp\u003eThe Table\u0026nbsp;3, showed the BERT-FRIDE model results with our dataset. For example, the precision for the class \"Content\" is 0.99, which means that 99% of the instances classified as \"Content\" were actually related to content. The recall is 0.96, indicating that the model correctly identified 96% of the instances related to Content. The F1-score is 0.97, which is a harmonic mean of precision and recall and a measure of the model's overall performance in that class. The support for \"Content\" is 631, indicating that there were 631 Content instances in the test set. Overall, our model achieved 94% of accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Model results with random oversampling\u003c/h2\u003e \u003cp\u003eTo enhance the performance of our BERT-FRIDE model, we implemented the machine learning random oversampling technique to balance the samples, we evaluated its performance using the classification report, as presented in Table\u0026nbsp;3.\u003c/p\u003e \u003cp\u003eThe accuracy of our model was also increased to 96%. This successful outcome demonstrates the effectiveness of the Random Oversampling technique in improving the accuracy of our model, as it overcame the challenge of the imbalanced dataset and enhanced the performance of model. Our evaluation of the BERT-FRIDE model has shown substantial improvements in its performance. Initially, we evaluated the BERT-FRIDE Model on an imbalanced dataset, achieving an overall accuracy of 94%. However, after implementing oversampling technique to address the challenges for underperforming classes, we observed a significant increase in accuracy to 96%. This cumulative improvement in accuracy is a testament to the effectiveness of the technique employed to overcome class imbalance and refine the performance of the model. Our efforts to address the underperforming classes by analyzing misclassified samples and implementing targeted measures have further contributed to model accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Model results with loss function\u003c/h2\u003e \u003cp\u003eTo enhance the performance of our BERT-FRIDE model, by incorporating the loss function. We evaluated its performance using the classification report, as presented in Table\u0026nbsp;3. The accuracy of BERT-FRIDE model increase to 98%. This successful outcome demonstrates the effectiveness of the loss function in improving the accuracy of our model, as it overcame the challenge of the underperforming classes. Our evaluation of the BERT-FRIDE model has shown substantial improvements in its performance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Comparison with state of art\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive evaluation of our BERT-FRIDE model in comparison to other cutting-edge deep learning models such as XLNet, RoBERTa, and GPT-2, using our dataset. Our findings revealed a consistent outperformance of our BERT-FRIDE model over the other deep learning models as showed in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of different deep learning models for text classification in the context of front-end design Issue\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF1-Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXLNet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoBERTa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPT-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBERT-FRIDE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBERT-FRIDE+\u003c/p\u003e \u003cp\u003eRandom Oversampling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBERT-FRIDE\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e+ Loss Function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.98\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese models are evaluated based on three crucial metrics: accuracy, recall, and F1 score. XLNet demonstrates a respectable accuracy of 0.77, with a corresponding recall score of 0.70, and an F1 score of 0.77. RoBERTa improves upon XLNet's performance with an accuracy of 0.86, a recall score of 0.86, and an F1 score of 0.87, indicating enhanced precision and the ability to capture relevant instances. GPT-2 further elevates the metrics with an accuracy and recall score of 0.89, along with an F1 score of 0.89, underscoring its effectiveness in accurately categorizing instances. However, BERT-FRIDE surpasses all others, boasting an accuracy of 0.94, a recall score of 0.92, and an F1 score of 0.93, indicating superior precision and the ability to identify a higher proportion of relevant instances. By incorporating random oversampling, achieves even better performance, with an accuracy of 0.96, a recall score of 0.96, and an F1 score of 0.96. Notably, the enhanced BERT-FRIDE model, using a proposed loss function, achieves remarkable performance improvements, with an accuracy of 0.98, a recall score of 0.97, and an F1 score of 0.98. These findings highlight BERT-FRIDE effectiveness in text classification tasks related to front-end design issues and underscore the significance of tailored optimization strategies, such as the specialized loss function, in achieving state-of-the-art results.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.\tTHREATS TO VALIDITY","content":"\u003cp\u003eThis section outlines potential risks associated with the study, provides a discussion of each, and proposed strategies to mitigate them.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eInternal Validity\u003c/strong\u003e \u003cp\u003eInternal validity of our study could have been threatened by the labelling the app review set by the authors because it might not accurately reflect how the development teams interpreted user feedback. Future research might involve closer author and development team collaboration to ensure a more accurate representation of the issues addressed in the dataset in order to lessen this.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eExternal Validity\u003c/strong\u003e \u003cp\u003eThe external validity in our study was the limited dataset consisting of design-related reviews for open-source Android applications. While the dataset provides valuable insights into front-end design issues, it may not cover the full spectrum of problems and considerations that mobile app users have. To enhance external validity, future research could expand the dataset to include reviews from a more diverse range of mobile applications, thereby increasing the generalizability of the findings.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConstruct Validity\u003c/strong\u003e \u003cp\u003eThe construct validity in our study was the potential for misinterpreting the construct of front-end design issues or failing to account for other factors that contribute to user comments. To address this, future research could refine the definition of front-end design issues and consider other factors that may impact user comments, such as app back-end functionality or user demographics.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAdditionally, we acknowledge that our investigation focused solely on English-language evaluations of open-source mobile apps, which may limit the transferability of the conclusions to commercially produced projects or reviews composed in different languages. Therefore, future research should consider the language and commercial context of mobile applications in their evaluation to increase the generalizability of the findings.\u003c/p\u003e"},{"header":"6.\tCONCLUSION AND FUTURE WORK","content":"\u003cp\u003eIn conclusion, our study proposed a novel automated approach for classifying front-end design issues in mobile applications using a dataset of 2398 samples across nine categories. Our model achieved an impressive 98% accuracy by incorporating loss function. This breakthrough represents a significant advancement in the field and has practical implications for product development, paving the way for more efficient and effective identification and resolution of wfront-end design issues in mobile applications. Despite the success of our approach, we acknowledge that our study has limitations, including the restricted dataset and language bias. Nevertheless, our findings have significant implications for software developers as they can use this approach to gauge user satisfaction with app design and make necessary adjustments to enhance user experience. In the future, we intend to improve the front-end review selection and analysis process by increasing the number of keywords and the sample size to address the limitation of not identifying all design problems. Additionally, we aim to develop an algorithm that can take these design issues and directly send them as an issue or improvement to the development Git site, facilitating the immediate integration of user feedback into the development environment. These findings are valuable to software developers as they provide insights into whether users are satisfied or dissatisfied with their app's design, enabling them to make necessary adjustments for better user experience.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical Approval:\u003c/h2\u003e \u003cp\u003eThis study did not involve human participants, data, or tissue; therefore, ethical approval was not required.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests:\u003c/h2\u003e \u003cp\u003eThe author declares no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNo funding was received for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author solely contributed to the conceptualization, methodology, analysis, and manuscript preparation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets that support the conclusions of this article are provided within the article itself.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNumber of apps available in leading app stores as of January 2024. https://www.statista.com/statistics/266210/number-of-available-applications-in-the-google-play-store/, last accessed 2024/05/13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. 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Marcelo Medeiros Eler, and Alberto Dumont Alves Oliveira, \" Do Android app users care about accessibility? an analysis of user reviews on the Google play store,\" in 18th Brazilian Symposium on Human Factors in Computing Systems (IHC '19), 2019, vol. 23.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Front-End Design Reviews, Deep Learning, Zero-shot annotation, User Review Classification","lastPublishedDoi":"10.21203/rs.3.rs-9141663/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9141663/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccording to recent research, app Stores like Google Play, Apple AppStore, and Windows Phone Store contain more than 5\u0026nbsp;million applications. The reviews provided by users on these platforms contain valuable information that can assist developers in enhancing their apps. The large volume of daily app reviews and their noisy nature make it difficult to extract front-end design issues. Accurately classification of reviews is the only viable option for achieving this goal. We proposed the BERT-FRIDE approach to effectively classify user reviews and extract valuable interface design insights. To achieve this objective, we developed a comprehensive dataset by utilizing our groundbreaking zero-shot annotation technique. The dataset contains nine specific categories of interface design issues. Furthermore, we proposed a tailored loss function to address challenges like underperforming classes, assigning higher weights as needed, resulting in an outstanding reported accuracy of 98%. Additionally, we compare our approach with state-of-the-art deep learning models such as XLNet, RoBERTa, and GPT-2, demonstrating superior performance. Our proposed approach has the potential to greatly benefit front-end developers and improve the overall quality of their products.\u003c/p\u003e","manuscriptTitle":"BERT-FRIDE: An Efficient Approach for Front-End Issue Detection and Extraction from User Reviews","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-22 15:33:29","doi":"10.21203/rs.3.rs-9141663/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"365c5981-5d84-49b4-a606-df045aecd85e","owner":[],"postedDate":"March 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-11T19:09:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-22 15:33:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9141663","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9141663","identity":"rs-9141663","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}