Analysis of Gender Issues in Computational Thinking Approach in Science and Mathematics Learning in Higher Education by Alejandro De la Hoz Serrano, Lina Viviana Melo Niño, Andrés Álvarez Murillo, Miguel Ángel Martín Tardío, Florentina Cañada Cañada, Javier Cubero Juánez

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The ab initio non-crystalline structure database: empowering machine learning to decode diffusivity by Hui Zheng, Eric Sivonxay, Rasmus Christensen, Max Gallant, Ziyao Luo, Matthew McDermott, Patrick Huck, Morten M. Smedskjær, Kristin A. Persson

“…These models predict the Li-ion diffusivity with speed and accuracy, offering a cost-effective alternative to expensive density functional theory (DFT) calculations. Furthermore, the process of computational quenching non-crystalline structures provides a unique sampling of out-of-equilibrium structures, energies, and force landscape, and we anticipate that the corresponding trajectories will inform future work in universal machine learning potentials, impacting design beyond that of non-crystalline materials. …”
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Application of deep learning for diagnosis of shoulder diseases in older adults: a narrative review by Sung Min Rhee

“…Shoulder diseases pose a significant health challenge for older adults, often causing pain, functional decline, and decreased independence. This narrative review explores how deep learning (DL) can address diagnostic challenges by automating tasks such as image segmentation, disease detection, and motion analysis. …”
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The Effect of Opinion 1/17 on the EU-ECHR Draft Accession Agreement: Lessons Learned? by Jennifer Buckesfeld, Ramses Wessel

“…. – IV. The DAA: Lessons learned from Opinion 1/17? – IV.1 The existence of the treaty’s dispute settlement mechanism outside the EU judicial system. – IV.2 The absence of any competence for foreign courts to interpret and apply EU law. – IV.3 The existence of safeguards against interference with the EU institutions’ proper functioning. – V. …”
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Machine Learning Forecasting of Commercial Buildings’ Energy Consumption Using Euclidian Distance Matrices by Connor Scott, Alhussein Albarbar

“…It uses readily available building attributes—such as floor area and functional type to position a new building within the matrix of existing data. …”
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Deep learning model for patient emotion recognition using EEG-tNIRS data by Mohan Raparthi, Nischay Reddy Mitta, Vinay Kumar Dunka, Sowmya Gudekota, Sandeep Pushyamitra Pattyam, Venkata Siva Prakash Nimmagadda

“…This study presents a novel approach that integrates electroencephalogram (EEG) and functional near-infrared spectroscopy (tNIRS) data to enhance emotion classification accuracy. …”
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Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings by Rosario G. Garroppo, Pietro Giuseppe Giardina, Giada Landi, Marco Ruta

“…Smart building applications require robust security measures to ensure system functionality, privacy, and security. To this end, this paper proposes a Federated Learning Intrusion Detection System (FL-IDS) composed of two convolutional neural network (CNN) models to detect network and IoT device attacks simultaneously. …”
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Federated Deep Learning for Scalable and Explainable Load Forecasting in Privacy-Conscious Smart Cities by Ibrahim Alzamil

“…By integrating explainable deep learning with privacy-preserving federated learning, HHCTE-FL establishes a scalable and trustworthy paradigm for intelligent electricity load forecasting—aligned with the demands of modern urban energy systems and smart city sustainability goals.…”
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Implementation of LABSTER virtual lab in immunology for innovative teaching and improved learning in veterinary degree by Javier G. Casado, Raquel Tarazona, Hector Cordero

“…In the case of Immunology in the veterinary degree, our prior observations indicate that veterinary students experience learning difficulties with topics related to molecules and effector functions of the immune system. …”
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Fall prediction in a quiet standing balance test via machine learning: Is it possible? by Juliana Pennone, Natasha Fioretto Aguero, Daniel Marczuk Martini, Luis Mochizuki, Alexandre Alarcon do Passo Suaide

“…Currently, clinical assessments of gait and posture include functional evaluations, objective, and subjective scales. …”
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Predicting Cognitive Decline in Motoric Cognitive Risk Syndrome Using Machine Learning Approaches by Jin-Siang Shaw, Ming-Xuan Xu, Fang-Yu Cheng, Pei-Hao Chen

“…Support Vector Machine (SVM) classifiers with different kernel functions were trained to predict cognitive decline. …”
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Bayesian optimized deep learning and ensemble classification approach for multiclass plant disease identification by Silpa Padmanabhuni, Pradeepini Gera

“…Bayesian optimization is used to identify and combine optimal activation functions, enhancing the network's capacity to learn complex disease patterns from tomato leaf images. …”
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Potential of Graphic Organizers in Developing Communicative Competence in Learning Russian as a Foreign Language by Sharofat T. Chorieva

“…In practical pedagogical work, graphic organizers perform illustrative, cognitive, communicative and developmental functions. As a result of their use, students learn to transform information into a plan, algorithm, table, diagram; skillfully present educational material in visual and verbal form; use a dialogical form of communication; transform information into a more accessible form for further use in life situations. …”
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Model Predictive Control With Reinforcement Learning-Based Speed Profile Generation in Racing Simulator by Min-Seong Kim, Tae-Hyoung Park

“…This paper introduces a Reinforcement Learning-based Speed Profile Model Predictive Control (RLSP-MPC) approach, which leverages deep reinforcement learning (DRL) to approximate global optima via an exploration-exploitation framework, eliminating the need for manual tuning. …”
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Improving Doppler Radar Precipitation Prediction with Citizen Science Rain Gauges and Deep Learning by Marshall Rosenhoover, John Rushing, John Beck, Kelsey White, Sara Graves

“…In this work, we leverage citizen science rain gauge observations to develop a deep learning framework that corrects biases in radar-derived surface precipitation rates at high temporal resolution. …”
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A bibliometric analysis of artificial intelligence and machine learning applications for human resource management by Güler Koştı, İsmail Kayadibi

“…Additionally, keyword co-occurrence network analysis identified three major thematic clusters: HRM functions, AI applications, and machine learning analytics, highlighting the field’s interdisciplinary nature. …”
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TEEMLEAP—A New Testbed for Exploring Machine Learning in Atmospheric Prediction for Research and Education by J. Wilhelm, J. Quinting, M. Burba, S. Hollborn, U. Ehret, I. Pena Sánchez, S. Lerch, J. Meyer, B. Verfürth, P. Knippertz

“…Abstract In the past 5 years, data‐driven prediction models and Machine Learning (ML) techniques have revolutionized weather forecasting. …”
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Reliable, efficient, and scalable photonic inverse design empowered by physics-inspired deep learning by Shao Guocheng, Zhou Tiankuang, Yan Tao, Guo Yanchen, Zhao Yun, Huang Ruqi, Fang Lu

“…To address the issue, a physics-inspired deep learning architecture termed electromagnetic neural network (EMNN) is proposed to enable an efficient, reliable, and flexible paradigm of inverse design. …”
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A novel approach to field data augmentation with remote sensing and machine learning in rangelands by Hailey E. Schmidt, Javier Osorio Leyton, Efrain Noa Yarasca, Sorin C. Popescu, Justinn J. Jones, Justin P. Wied, Xinyuan Wu

“…However, these ecosystems are increasingly threatened by woody plant encroachment (WPE), which impacts forage availability, habitat quality, and other ecosystem functions. Despite advances in remote sensing and machine learning (ML) for monitoring and managing WPE, a significant gap persists in providing effective, scalable, and high-quality training data to support early detection efforts. …”
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Trust Region Policy Learning for Adaptive Drug Infusion with Communication Networks in Hypertensive Patients by Mai The Vu, Seong Han Kim, Ha Le Nhu Ngoc Thanh, Majid Roohi, Tuan Hai Nguyen

“…Secondly, a model-free deep reinforcement learning (MF-DRL) algorithm is integrated into the NBC to adjust dynamically the coefficients of the controller. …”
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