Real-world validation of safe reinforcement learning, model predictive control and decision tree-based home energy management systems by Julian Ruddick, Glenn Ceusters, Gilles Van Kriekinge, Evgenii Genov, Cedric De Cauwer, Thierry Coosemans, Maarten Messagie

“…The OptLayerPolicy safety layer allows safe online training of a reinforcement learning agent in the real world, given an accurate constraint function formulation. …”
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Identifying Molecular Properties of Ataxin-2 Inhibitors for Spinocerebellar Ataxia Type 2 Utilizing High-Throughput Screening and Machine Learning by Smita Sahay, Jingran Wen, Daniel R. Scoles, Anton Simeonov, Thomas S. Dexheimer, Ajit Jadhav, Stephen C. Kales, Hongmao Sun, Stefan M. Pulst, Julio C. Facelli, David E. Jones

“…In the present study, data mining and machine learning techniques were employed to analyze HTS data and identify robust molecular properties of potential inhibitors of <i>ATXN2</i>. …”
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Machine-learning assisted discovery unveils novel interplay between gut microbiota and host metabolic disturbance in diabetic kidney disease by I-Wen Wu, Yu-Chieh Liao, Tsung-Hsien Tsai, Chieh-Hua Lin, Zhao-Qing Shen, Yun-Hsuan Chan, Chih-Wei Tu, Yi-Ju Chou, Chi-Jen Lo, Chi-Hsiao Yeh, Chun-Yu Chen, Heng-Chih Pan, Heng-Jung Hsu, Chin-Chan Lee, Mei-Ling Cheng, Wayne Huey-Herng Sheu, Chi-Chun Lai, Huey-Kang Sytwu, Ting-Fen Tsai

“…This study aims to elucidate the functional capability of gut microbiota to affect kidney function of DKD patients. …”
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From didactic explanations to co-design, sequential art and embodied learning: challenges, criticisms and future directions of patient pain education by G. Lorimer Moseley, G. Lorimer Moseley, Amelia Mardon, Amelia Mardon, Amelia Mardon, James Watson, James Watson, James Watson, Felicity Braithwaite, Felicity Braithwaite, Felicity Braithwaite, Monique V. Wilson, Monique V. Wilson, Monique V. Wilson, Trevor Barker, Trevor Barker, James Lawrence, Dianne Sheppard, Dianne Sheppard, Michiel F. Reneman, Michiel F. Reneman, Jennifer Stinson, Jennifer Stinson, Cormac G. Ryan, Cormac G. Ryan

“…The method of PNE has also been updated, with integration of educational frameworks, teaching strategies and tactics, patient resources and clinical tools that all aim to promote the likelihood that patients will learn key concepts and operationalise them to improve their pain, function and quality of life. …”
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A Feature-Enhanced Small Object Detection Algorithm Based on Attention Mechanism by Zhe Quan, Jun Sun

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Decentralized Consensus Protocols on <i>SO</i>(4)^<i>N</i> and <i>TSO</i>(4)^<i>N</i> with Reshaping by Eric A. Butcher, Vianella Spaeth

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Bridging the Connection between Fluency in Reading and Arithmetic by Reut Balhinez, Shelley Shaul

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An Improved YOLOv8-Based Method for Detecting Pests and Diseases on Cucumber Leaves in Natural Backgrounds by Jiacong Xie, Xingliu Xie, Wu Xie, Qianxin Xie

“…Finally, the original CIOU loss function of YOLOv8 is replaced with the Focaler-SIOU loss function. …”
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PEMOCS: effects of a concept-guided, PErsonalized, MOtor-Cognitive exergame training on cognitive functions and gait in chronic Stroke—a randomized, controlled trial by S. K. Huber, S. K. Huber, R. H. Knols, R. H. Knols, J. P. O. Held, J. P. O. Held, M. Betschart, M. Betschart, S. Gartmann, S. Gartmann, N. Nauer, N. Nauer, E. D. de Bruin, E. D. de Bruin, E. D. de Bruin

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Effect of Computer-Assisted Cognitive Training on Visual Perception in Children with Intellectual Development Disorders by LI Zhaoying, DENG Haiyin, HUANG Zhenming, HE Youze, TU Jingnan, CAO Lei, HUANG Jia, WU Jingsong

“…Compared with the baseline, the results of the between-group comparison in the follow-up period showed that the improvement effects of VPT [<italic>d</italic>=0.80, 95% <italic>CI</italic> (0.27, 1.32)], PPVT [<italic>d</italic>=0.79, 95% <italic>CI</italic> (0.27, 1.32)], and PSQ learning problems [<italic>d</italic>=-0.11, 95% <italic>CI</italic> (-0.62, 0.40)] in the experimental group were significantly superior to those in the control group (<italic>P</italic>&lt;0.01).ConclusionThe five-week computer-assisted cognitive training can be effective in improving visual-perceptual functioning in children with IDD, and also promote improvements in vocabulary, learning problems, and attentional behaviors.…”
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A Novel Prescribed-Time Convergence Acceleration Algorithm with Time Rescaling by Xuehui Mei, Pengrui Zhang, Haijun Jiang, Zhiyong Yu

“…In machine learning, the processing of datasets is an unavoidable topic. …”
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Novel fruit fly algorithm for global optimisation and its application to short-term wind forecasting by Yang Chen, DeChang Pi

“…According to the experimental results of eighteen well-known benchmark functions, the SEDMFOA is efficient and effective. …”
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Robot Variable Impedance Control and Generalizing from Human–Robot Interaction Demonstrations by Feifei Zhong, Lingyan Hu, Yingli Chen

“…This paper proposes a control strategy for learning variable impedance characteristics from multiple sets of demonstration trajectories. …”
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miRAW: A deep learning-based approach to predict microRNA targets by analyzing whole microRNA transcripts. by Albert Pla, Xiangfu Zhong, Simon Rayner

“…Using this data, we implemented and trained a deep neural network-composed of autoencoders and a feed-forward network-able to automatically learn features describing miRNA-mRNA interactions and assess functionality. …”
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Identification and experimental validation of biomarkers for chronic obstructive pulmonary disease complicated with pulmonary arterial hypertension based on bioinformatics and mach... by YANG Yan, TAO Chunrong, ZHU Youjun

“…Methods High-throughput sequencing data analysis was employed to identify differentially expressed genes (DEGs) in COPD-PAH. Functional enrichment analysis was then conducted to explore the biological functions of these DEGs. …”
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Deep learning in-depth analysis of crystal graph convolutional neural networks: A new era in materials discovery and its applications by Qureshi Nilam, Bang Jinhong, Doh Jaehyeok

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A new Tec family-based clinical model predicts survival in differentiated thyroid cancer patients via machine learning by Ziyu Luo, Wenhan Li, Jianhui Li, Ying Zhang

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Deep transfer learning approach for automated cell death classification reveals novel ferroptosis-inducing agents in subsets of B-ALL by Paweł Stachura, Zhe Lu, Raphael M. Kronberg, Haifeng C. Xu, Wei Liu, Jia-Wey Tu, Katerina Schaal, Ersen Kameri, Daniel Picard, Silvia von Karstedt, Ute Fischer, Sanil Bhatia, Philipp A. Lang, Arndt Borkhardt, Aleksandra A. Pandyra

“…In the current investigation, we classified brightfield microscopy images of tumor cells undergoing defined modes of cell death using deep transfer learning (DTL). The trained DTL network was subsequently combined with high-throughput pharmacological screening approaches using automated live cell imaging to identify novel ferroptosis-inducing functions of the polo-like kinase inhibitor volasertib. …”
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Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms by Xia Zhao, Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia, Qingfeng He

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Exploring fecal microbiota signatures associated with immune response and antibiotic impact in NSCLC: insights from metagenomic and machine learning approaches by Wenjie Han, Wenjie Han, Yuhang Zhou, Yuhang Zhou, Yiwen Wang, Yiwen Wang, Xiaolin Liu, Tao Sun, Tao Sun, Junnan Xu, Junnan Xu, Junnan Xu

“…Antibiotic exposure significantly influenced the abundance and functional potential of these key taxa. KEGG-based functional analysis revealed the enrichment of amino acid metabolism pathways in responders. …”
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