Computers

In this paper, we describe the procedure of implementing a reinforcement learning algorithm, TD3, to learn the performance of a cooling water pump and how this type of learning can be used to detect degradations and evaluate its health condition. These types of machine learning algorithms have not been used extensively in the scientific literature to monitor the degradation of industrial components, so this study attempts to fill this gap, presenting the main characteristics of these algorithms’ application in a real case. The method presented consists of several models for predicting the expected evolution of significant behavior variables when no anomalies exist, showing the performance of different aspects of the pump. Examples of these variables are bearing temperatures or vibrations in different pump locations. All of the data used in this paper come from the SCADA system of the power plant where the cooling water pump is located.

This study examines how interaction data from Learning Management Systems (LMSs) can be leveraged to predict student performance and enhance academic outcomes through personalized study plans tailored to individual learning styles. The research followed three phases: (i) analyzing the relationship between engagement and performance, (ii) developing predictive models for academic outcomes, and (iii) generating customized study plan recommendations. Clustering analysis identified three distinct learner profiles—high-engagement–high-performance, low-engagement–high-performance, and low-engagement–low-performance—with no cases of high-engagement–low-performance, underscoring the pivotal role of engagement in academic success. Among clustering approaches, K-Means produced the most precise grouping. For prediction, Support Vector Machines (SVMs) achieved the highest accuracy (68.8%) in classifying students across 11 grade categories, supported by oversampling techniques to address class imbalance. Personalized study plans, derived using K-Nearest Neighbor (KNN) classifiers, significantly improved student performance in controlled experiments. To the best of our knowledge, this represents a novel attempt in this context to align predictive modeling with the full grading structure of undergraduate programs. These findings highlight the potential of integrating LMS data with machine learning to foster engagement and improve learning outcomes. Future work will focus on expanding datasets, refining predictive accuracy, and incorporating additional personalization features to strengthen adaptive learning.

This study explores the application of JAYA optimization algorithms to significantly enhance the performance of indoor optical wireless communication (OWC) systems. By strategically optimizing photo-signal parameters, the system was able to improve signal distribution and reception within a confined space using circular and randomly positioned diffuse spots. The primary objective was to maximize signal-to-noise ratio (SNR) and minimize delay spread (DS), two critical factors that affect transmission quality in OWC systems. Given the challenges posed by background noise and multipath dispersion, an effective optimization strategy was essential to ensure robust signal integrity at the receiver end. Key achievements of JAYA optimization include significant performance gains, such as a 29% improvement in SNR, enhancing signal clarity and reception, and a 23.3% reduction in delay spread, ensuring stable and efficient transmission. System stability also improved, with the standard deviation of SNR improving by up to 5%, leading to a more consistent performance, while the standard deviation of delay spread improved by up to 9.9%, minimizing variations across receivers. Resilience against environmental challenges: Optimization proved effective even in the presence of ambient light noise and complex multipath dispersion effects, reinforcing its adaptability in real-world applications. The findings of this study confirm that JAYA optimization algorithms offer a powerful solution for overcoming noise and dispersion issues in indoor OWC systems, leading to more reliable and high-quality optical wireless communications. These results underscore the importance of algorithmic precision in enhancing system performance, paving the way for further advancements in indoor optical networking technologies.