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              <text>Patterns in dryland vegetation were first officially reported in the 1940s when aerial images revealed strips of vegetation separated by bare soil in East Africa. These are suspected to be due to water scarcity being a limiting factor influencing vegetation. These large-scale patterns are not easily recognized from the ground; moreover, studying them is challenging due to harsh climates and political instability. A key characteristic of these patterns is their wavelength—the distance between stripes of vegetation, which are typically seen on sloped terrain. Mathematical models predict that the wavelength of these stripes should change over time as environmental conditions, such as precipitation levels affected by climate change, fluctuate. However, this phenomenon has yet to be observed through aerial or satellite imagery. This may be due to a lack of comprehensive studies or potential inaccuracies in the mathematical models. To address this, I have created a python algorithm which finds the dominant wavelength in satellite images given to it. Further automation of collecting the images from Google Earth and feeding them into this program would allow for quick verification of mathematical models and their predictions.</text>
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              <text>Murdoch’s The Sovereignty of Good is often interpreted as suggesting that love requires recognizing the individual reality of one’s beloved. However, through a closer reading, I argue that Murdoch’s characterization of love is more nuanced. Specifically, I show that, due to ambiguity in her writing, Murdoch leaves the exact relationship between love and the perception of reality unclear. In this paper, I begin by unpacking this ambiguity. Then, I propose that this ambiguity allows for two possible kinds of interpretations of her view on love: the objective view, and the positive light view. Finally, I argue that the positive light view offers a better interpretation of Murdoch’s position, concluding that love, while rooted in reality, also exceeds it and transcends it.</text>
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              <text>This project focuses on applying sentiment analysis to student feed- back from teaching lessons, employing natural language processing (NLP) techniques such as VADER and TextBlob to classify senti- ments as positive or negative. The dataset, sourced from a univer- sity in the Netherlands, exhibited a significant class imbalance, with a majority of positive sentiments. To address this issue, synthetic data generation via OpenAI’s batch request API was implemented to oversample the minority class (negative sentiments), ensuring a more balanced dataset for training. Post-balancing, machine learning models, particularly Random Forest, were employed to classify the data. The model achieved strong performance, with a balanced accuracy of 99 This report provides a detailed overview of the methodology used, including data preprocessing, model training, and evaluation. It also explores the limitations of the approach and proposes poten- tial directions for future research, including the use of alternative classifiers and more sophisticated text representation techniques to further enhance the accuracy and robustness of sentiment analysis in educational feedback contexts.</text>
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OpenAI API&#13;
Synthetic Data Generation&#13;
Machine Learning&#13;
Class Imbalance&#13;
Random Forest&#13;
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