MRCPs-and-ERS/D-Oscillations-Driven Deep Learning Models for Decoding Unimanual and Bimanual Movements by Jiarong Wang, Luzheng Bi, Aberham Genetu Feleke, Weijie Fei

“…Motor brain-computer interface (BCI) can intend to restore or compensate for central nervous system functionality. In the motor-BCI, motor execution (ME), which relies on patients’ residual or intact movement functions, is a more intuitive and natural paradigm. …”
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Objective Detection of Newborn Infant Acute Procedural Pain Using EEG and Machine Learning Algorithms by Jean‐Michel Roué, Amir Avnit, Behnood Gholami, Wassim M. Haddad, Kanwaljeet J. S. Anand

“…Preliminary changes in functional connectivity indicate infant pain processing. …”
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Neurodidactic analysis of the difficulty in learning the notion of rational number in the 3rd grade of Elementary School by Angelita Fulle, Daniela Maria Oliveira Bonci, Laerte Silva da Fonseca

“…The objective of this article was to analyze the monitoring and remediation of learning difficulties related to the ability to operate with the notion of rational numbers in the 3rd grade of Elementary School. …”
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Modeling and optimization of renewable hydrogen systems: A systematic methodological review and machine learning integration by M.D. Mukelabai, E.R. Barbour, R.E. Blanchard

“…Previous studies have included many aspects into their optimizations, including technical parameters and different costs/socio-economic objective functions, however there is no clear best-practice framework for model development. …”
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Crystal Composition Transformer: Self‐Learning Neural Language Model for Generative and Tinkering Design of Materials by Lai Wei, Qinyang Li, Yuqi Song, Stanislav Stefanov, Rongzhi Dong, Nihang Fu, Edirisuriya M. D. Siriwardane, Fanglin Chen, Jianjun Hu

“…These models have demonstrated superior performance in the generation, structure classification, and functional predictions for proteins and molecules with learned representations. …”
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The COVID‐19 Pandemic and Its Influence on Patients With Myotonic Dystrophy Type 1: Lessons Learned by Vera E. A. Kleinveld, Johanna E. Bruijnes, Samuel Labrecque, Danielle E. G. Jeurissen‐Bekkering, Catharina G. Faber, Corinne G. C. Horlings

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IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions by Clara Shionyu-Mitusyama, Satoshi Ohmori, Subaru Hirata, Hirokazu Ishida, Tsuyoshi Shirai, Tsuyoshi Shirai

“…Despite this potential, rational methodologies for IDR-targeted drug discovery remain underdeveloped, primarily due to a lack of reference experimental data.MethodsThis study explores a machine learning approach to predict IDR functions, drug interaction sites, and interacting molecular substructures within IDR sequences. …”
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Identification of neutrophil extracellular trap-related biomarkers in ulcerative colitis based on bioinformatics and machine learning by Jiao Li, Yupei Liu, Zhiyi Sun, Suqi Zeng, Caisong Zheng

“…Three machine learning algorithms successfully identified core NETs-related genes in UC (IL1B, MMP9 and DYSF). …”
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Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation by Juan Yang, Xiang Li, Li Ma, Jun Zhang

“…These genes were then subjected to functional enrichment analysis, including Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), and Gene Set Enrichment Analysis (GSEA). …”
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Robust ConvLSTM Model With Deep Reinforcement Learning for Stealth Attack Detection in Smart Grids by Ahmad N. Alkuwari, Abdullatif Albaseer, Saif Al-Kuwari, Marwa Qaraqe

“…The advent of modern electricity distribution systems, comprising digital communication technologies and principles, has triggered a new era of smart grids, in which advanced metering infrastructure plays a crucial role in functions, such as digital monitoring and billing. …”
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Reconstruction Techniques for the Medial Canthus and Lateral Nasal Wall: A Case-Based Learning Approach by Andrea Malagón-Liceaga, Alejandra Juárez-Flores, Ana Lilia Ruelas-Villavicencio

“…Conclusion: Successful reconstruction in this region requires a multidisciplinary approach that carefully integrates facial esthetic units while preserving the functional integrity. …”
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Creative Steps for Learning Islamic Religion (Classroom Management Study at Smk Muhammadiyah 2 Wates) by Muhammad Rizky Nur Risam, Arif Agus Mujahidin, Nanik Rahmanti, Fifin Al-Fionita, Sri Roviana

“…At SMK Muhammadiyah 2 Wates, researchers suggest that educators' roles and functions are vital in carrying out learning steps and classroom management. …”
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Shap‐Cov: An Explainable Machine Learning Based Workflow for Rapid Covariate Identification in Population Modeling by Logan Brooks, Rashed Harun, Jin Y. Jin, James Lu

“…Early work leveraging machine learning (ML) for covariate screening has shown promising results over traditional methods. …”
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Learning Our Limits: The Science Libraries at Duke University Retreat to Respond to Our Changing Environment by Anne Langley, Linda Martinez

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Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis by Hang Chen, Biao Wu, Biao Wu, Kunyu Guan, Liang Chen, Kangjie Chai, Maoji Ying, Dazhi Li, Weicheng Zhao

“…Following this, differential expression analysis and functional evaluations were carried out, after which various machine learning approaches were employed to determine significant diagnostic genes for atherosclerosis. …”
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Deciphering Immunometabolic Landscape in Rheumatoid Arthritis: Integrative Multiomics, Explainable Machine Learning and Experimental Validation by Dong Q, Wu J, Zhang H, Chen X, Xu X, Chen J, Shi C, Luo L, Zhang D

“…Explainable machine learning, PPI network analysis, and qPCR jointly identified four hub genes (PFN1, SRP14, S100A11, and SAP18). …”
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Integrative analysis of PANoptosis-related genes in diabetic retinopathy: machine learning identification and experimental validation by Han Chen, Han Chen, Enguang Chen, Enguang Chen, Ting Cao, Feifan Feng, Min Lin, Xuan Wang, Yu Xu

“…Differentially expressed genes (DEGs) were identified using the DESeq2 package, followed by functional enrichment analysis through DAVID and Metascape tools. …”
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Global atomic structure optimization through machine-learning-enabled barrier circumvention in extra dimensions by Casper Larsen, Sami Kaappa, Andreas Lynge Vishart, Thomas Bligaard, Karsten Wedel Jacobsen

“…The new degrees of freedom are incorporated in a machine-learning model through a vectorial fingerprint trained using density functional theory energies and forces. …”
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Curvature-Adaptive Learning Rate Optimizer: Theoretical Insights and Empirical Evaluation on Neural Network Training by Kehelwala Dewage Gayan Maduranga

“…To address these limitations, we propose the Curvature-Adaptive Learning Rate (CALR) optimizer, a novel method that leverages local curvature estimates to dynamically adjust learning rates. …”
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Quality of Life and Coping Strategies in Children with and Without Learning Disabilities from the Perspective of Their Parents and Caregivers by Ayoob Lone, Abdul Sattar Khan, Fahad Abdullah Saeed AlWadani, Abdullah Almaqhawi

“…Background: Children with learning disability (LD) often experience a poor quality of life (QOL) compared to their peers without a known history of LD. …”
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