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With the growing need for AI-driven solutions in power grid management, this work addresses the challenge of creating realistic synthetic operating scenarios essential for developing, testing, and validating AI-based decision-making systems. It uses spatial-temporal noise functions, predefined patterns, and optimal power flow to model renewable energy and conventional power plant generation, load, and losses. Quantitative and visual key performance indicators are proposed to evaluate the quality of the generated operating scenarios, and the validation highlights the framework's ability to emulate diverse and practical operating scenarios, bridging gaps in AI-driven power system research and real-world applications.

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This chapter explores the critical role of derivatives in optimizing cost functions and driving the backpropagation algorithm in neural networks, emphasizing their applications in the education field. The study examines the use of derivatives in personalized learning systems, particularly within the Khan Academy platform, and evaluates their impact on scalability, bias, and efficiency. Five research questions guide the analysis, ranging from environmental impact to fairness in AI- driven education. Employing methods like Experimental Performance Evaluation and Comparative Analysis, the study offers both technical insights and ethical considerations. While derivatives enable precise optimization, the chapter highlights how they can unintentionally reinforce biases in training data, raising critical concerns about fairness and representation in educational technologies. © 2025 Elsevier B.V., All rights reserved.

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Class overlap presents a significant challenge to machine learning algorithms, especially when class imbalance is present. These factors contribute substantially to the complexity of classification tasks, particularly in realworld scenarios. As a result, measuring overlap is crucial, yet it remains difficult to quantify due to its intricate nature, since it can manifest and be measured in multiple ways. To help mitigate this, recent research has conceptualized a new taxonomy of class overlap measures, divided into multiple families, which allows researchers to obtain a more complete overview of the complexity of the datasets. In line with recent research, we introduce a new Python package for class overlap measurement named pycol. This package implements 29 overlap measures, divided into four overlap families specifically designed to capture class overlap in imbalanced real-world scenarios. This makes pycol an essential tool for researchers dealing with complex classification problems, providing robust solutions to quantify the joint-effect of class overlap and class imbalance effectively.