Federated Learning for Real-Time Demand Response by Data Centers Toward Energy Efficiency and Privacy Preservation by Mohannad Alhazmi, Alexis Pengfei Zhao, Wanzi Li, Chenlu Yang

“…Our novel method utilizes real-time data to generate effective and efficient actions in response to operational demands, in compliance with energy conservation and stringent privacy standards. We evaluate various demand response programs by the proposed method in different scenarios and case studies to demonstrate their efficacy in adapting to energy consumption patterns for optimal energy usage and cost reduction. …”
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Bioinformatics-based identification of key genes for Olaparib resistance in breast cancer: prognostic implications and therapeutic relevance by Kezhen Shen, Xiaozhe Li, Sihan Zhao, Xinyu Zhang, Le Yu, Jun Li, Lei Cai

“…Differential expression and drug sensitivity prediction were performed to identify resistance-associated genes, followed by pathway enrichment and protein–protein interaction (PPI) network construction. Kaplan–Meier survival analysis and Cox regression were conducted to evaluate the prognostic value of candidate genes. …”
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Examining the landscape of proctoring in upholding academic integrity: a bibliometric review of online examination practices by Manit Malhotra, Indu Chhabra, Salil Bharany, Ateeq Ur Rehman, Seada Hussen

“…The analysis concludes by presenting the citation network, country collaborations, and factorial map. …”
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Groundwaterscapes: A Global Classification and Mapping of Groundwater's Large‐Scale Socioeconomic, Ecological, and Earth System Functions by Xander Huggins, Tom Gleeson, Karen G. Villholth, Juan C. Rocha, James S. Famiglietti

“…All large aquifer systems of the world are characterized by multiple groundwaterscapes, highlighting the pitfalls of treating these groundwater bodies as lumped systems in global assessments. We evaluate the distribution of Global Groundwater Monitoring Network wells across groundwaterscapes and find that industrial agricultural regions are disproportionately monitored, while several groundwaterscapes have next to no monitoring wells. …”
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Devising a Breast Cancer Diagnosis Protocol through Machine Learning by Tooba Mujtaba, Saif Ullah Hashmi, Usama Bin Imtiaz, Sheikh Jameel Fathima Nusra

“…Upon following a predefined computational pipeline, we identified 369 genes that had distinct patterns of gene expression profiles in cases of ER-positive (ER + ) and HER2-negative (HER2-) breast cancer. …”
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A machine learning approach to identifying key predictors of Peruvian school principals' job satisfaction by Luis Alberto Holgado-Apaza, Dany Dorian Isuiza-Perez, Nelly Jacqueline Ulloa-Gallardo, Yban Vilchez-Navarro, Ruth Nataly Aragon-Navarrete, Wilian Quispe Layme, Marleny Quispe-Layme, Danger David Castellon-Apaza, Remo Choquejahua-Acero, Jaime Cesar Prieto-Luna

“…When using Generative Adversarial Networks to balance only the training set, better results were obtained in recall (0.74), precision (0.72), and F1 score (0.70). …”
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Analyzing and explaining the reflective teacher approach and its role in improving curriculum knowledge: Developing an educational discourse at Farhangian University by Reza Masouminejad

“…In fact, reflective activity allows teachers to take a moment to examine their past teaching experiences and through the tools of self-observation, self-analysis and self-evaluation, identify individual experiences and discover the truth about themselves and improve their professional life (Suphasri & Chinokul, 2021). …”
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From Tables to Computer Vision: Transforming HPDC Process Data into Images for CNN-Based Deep Learning by A. Burzyńska

“…The proposed study is founded on two principal pillars: the transformation of process tabular data (generated using the Conditional Tabular Generative Adversarial Network (CTGAN)), involving the mapping of features onto a fixed grid in a heatmap structure, and the configuration of the CNN algorithm to extract complex patterns in the data that are not readily apparent in the original tabular format. …”
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Exploring the Association Between the Doppler Findings of Endometriomal Wall Vascularization and Volume of the Endometrioma by Berna Seçkin, Ayla Sargın Oruç, Yunus Yıldız, Mustafa Uğur

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Energy Demand Forecasting Scenarios for Buildings Using Six AI Models by Khaled M. Salem, Francisco J. Rey-Martínez, A. O. Elgharib, Javier M. Rey-Hernández

“…Understanding and forecasting energy consumption patterns is crucial for improving energy efficiency and human well-being, especially in diverse infrastructures like Spain. …”
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Exploring New Paradigms in Time Series Prediction by Integrating Computer Simulations and Machine Learning by Pengcheng Wu, Yan Shi, Pengfei Zhao, Zhengzhao Gu

“…By merging data-driven learning with simulation-informed constraints, our approach enhances both predictive accuracy and interpretability. Empirical evaluations across diverse benchmark datasets validate the effectiveness of our framework, demonstrating its robustness, scalability, and ability to uncover meaningful temporal patterns in complex time series data.…”
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Land Use Characteristics of Commuter Rail Station Areas and Their Impact on Station Ridership: A Case Study of Japan Railways in the Tokyo Metropolitan Area by Yanan Gao, Xu Cui, Xiaozheng Sun

“…The key findings were as follows: (1) the land use characteristics around commuter rail stations exhibit distinct zonal patterns; within 250 m, public transport stops and public service facilities are the most densely concentrated; the highest residential population density is found between 250 and 750 m; and commercial facilities are mostly clustered in the 500 to 750 m range; (2) the impact of land use factors on ridership varies in intensity across different spatial zones; the density of public transport stops and street network density is most significant within 250 m, whereas commercial facility density is greatest within the 500–750 m PCA; (3) The land use characteristics within 500 m of stations have greater explanatory power for passenger flow, and the goodness of fit of the MGWR model surpasses that of the linear regression model.…”
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HSTCN-NuSVC: A Homogeneous Stacked Deep Ensemble Learner for Classifying Human Actions Using Smartphones by Sarmela Raja Sekaran, Ying Han Pang, Ooi Shih Yin, Lim Zheng You

“…In this model, multiple enhanced TCN networks with diverse architectures are organised parallelly to capture hierarchical spatial-temporal patterns from raw inertial signals. …”
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Machine Learning and SHAP-Based Analysis of Deforestation and Forest Degradation Dynamics Along the Iraq–Turkey Border by Milat Hasan Abdullah, Yaseen T. Mustafa

“…This study explores the spatiotemporal patterns and drivers of deforestation and forest degradation along the politically sensitive Iraq–Turkey border within the Duhok Governorate between 2015 and 2024. …”
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Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…The analysis utilized the “Concrete Compressive Strength” dataset, sourced from UC Irvine’s publicly available ML repository. The models evaluated include Gradient Boosting Regressor (GBR), Extreme Gradient Boosting Regression (XGBoost), Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Network (ANN), Multilayer Perceptron (MLP), Lasso, and k-Nearest Neighbors (KNN). …”
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A Temporal Attention-Based SARIMA-BiLSTM Residual Learning Model Tuned by Grey Wolf Optimizer for Parallel Urban Traffic Forecasting by Manas Kamal Das, Christopher Columbus Chinnappan, E. Elakiya

“…The SARIMA component captures underlying seasonal and linear patterns in traffic flow, while the residuals—representing unmodeled nonlinear dynamics—are learned by the BiLSTM module. …”
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Fractal Fuzzy-Based Multi-criteria Assessment of Sustainability in Rare Earth Use for Hydrogen Storage by Gang Kou, Serhat Yüksel, Serkan Eti, Hasan Dinçer, Merve Acar

“…Unlike linear fuzzy sets, fractals can capture the patterns of ambiguity found in expert evaluations. …”
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Stress Interferences between Fractures and Fracturing Stages of a Horizontal Well in a Sandy Conglomerate Reservoir in Junggar Basin, Northwest China by Jianmin Li, Binghui Du, Junxiu Ma, Ruining Liu, Can Shi, Botao Lin

“…The sandy conglomerate reservoirs in the Mahu oilfield located in the Junggar Basin of Northwest China are featured by a significant horizontal stress difference between two directions, making formations easy to form double-wing fractures upon hydraulic fracturing instead of creating a complex fracture network. In addition, as the well spacing or interval cluster spacing decreases, the stress interferences between hydraulic fractures strengthen accordingly, leading to more difficulties in the prediction of fracture propagation patterns. …”
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A Novel Hybrid Model for Loan Default Prediction in Maritime Finance Based on Topological Data Analysis and Machine Learning by Mohammad Amin Kheneifar, Babak Amiri

“…By constructing correlation-based networks of shipping firms and extracting topological persistence features, such as cyclical trends and structural interdependencies, via Vietoris-Rips complexes, the model captures nonlinear risk patterns overlooked by conventional metrics. …”
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Studying Forgetting in Faster R-CNN for Online Object Detection: Analysis Scenarios, Localization in the Architecture, and Mitigation by Baptiste Wagner, Denis Pellerin, Sylvain Huet

“…In this context, the widely used architecture Faster R-CNN (Region Convolutional Neural Network) faces catastrophic forgetting: the acquisition of new knowledge leads to the loss of previously learned information. …”
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