scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links by Gefei Wang, Jia Zhao, Yingxin Lin, Tianyu Liu, Yize Zhao, Hongyu Zhao

“…These technologies have significantly deepened our understanding of cell functions and disease mechanisms from various omics perspectives. …”
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Syngeneic Transplantation of Olfactory Ectomesenchymal Stem Cells Restores Learning and Memory Abilities in a Rat Model of Global Cerebral Ischemia by Antoine D. Veron, Cécile Bienboire-Frosini, Stéphane D. Girard, Kevin Sadelli, Jean-Claude Stamegna, Michel Khrestchatisky, Jennifer Alexis, Patrick Pageat, Pietro Asproni, Manuel Mengoli, François S. Roman

“…We observed significant impairments in learning and memory abilities following ischemia. However, 4 weeks after OE-MSC grafts, animals displayed learning and memory performances similar to those of controls, while sham rats did not improve them. …”
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Point Defect Detection and Classification in MoS₂ Scanning Tunneling Microscopy Images: A Deep Learning Approach by Shiru Wu, Guoyang Chen, Si Shen, Jiaxu Yan

“…We also perform density functional theory (DFT) calculations of representative defect structures to support interpretation of the STM features. …”
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Mechanical properties of 3D-Printed aluminum, nickel, and titanium using a hybrid machine learning and computational mechanics approach by Sara Samine, Mohamed Karouchi, Maria Zemzami, Nabil Hmina, Soufiane Belhouideg

“…By integrating first-principles simulations via the CASTEP code—grounded in Density Functional Theory (DFT)—with machine learning techniques, specifically Ridge regression, the approach aims to enhance prediction accuracy while minimizing computational costs. …”
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A multi-objective deep reinforcement learning method for intelligent scheduling of wind-solar-hydro-battery complementary generation systems by Yuanyu Ge, Jun Xie, Jiaqi Chang, Shuo Feng

“…Meanwhile, this work proposes a MODRL framework to learn the scheduling strategy for multiple competing objectives. …”
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Physical Property Prediction of High-Temperature Nickel and Iron–Nickel Superalloys Using Direct and Inverse Composition Machine Learning Models by Jaka Fajar Fatriansyah, Dzaky Iman Ajiputro, Agrin Febrian Pradana, Rio Sudwitama Persadanta Kaban, Andreas Federico, Muhammad Anis, Dedi Priadi, Nicolas Gascoin

“…Consequently, material modifications are crucial to ensure that gas turbine components meet their required properties. Machine learning (ML) and deep learning (DL) offer promising solutions for the design of materials with tailored tensile strength, hardness, and melting point properties. …”
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Identification of potential riboswitch elements in Homo sapiens mRNA 5'UTR sequences using positive-unlabeled machine learning. by William S Raymond, Jacob DeRoo, Brian Munsky

“…In this work, we set out to train positive unlabeled machine learning classifiers on known riboswitch sequences and apply the classifiers to H. …”
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Heart Rate Variability-Based Stress Detection and Fall Risk Monitoring During Daily Activities: A Machine Learning Approach by Ines Belhaj Messaoud, Ornwipa Thamsuwan

“…Data were collected from 14 healthy participants who wore a Polar H10 heart rate monitor and performed Berg Balance Scale activities as part of an assessment of functional balance. Machine learning techniques applied to the collected data included two phases: unsupervised clustering and supervised classification. …”
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A Novel Algorithm for Ice‐Water Discrimination in Large Lakes Using ICESat‐2 Altimetry and Data Driven Machine Learning by Isabella Peter, Eric J. Anderson, Matthew R. Siegfried, Nathan T. Kurtz

“…Abstract Large freshwater lakes are critical for human life, ecosystem functioning, and the global carbon cycle. However, consistent high‐resolution methods to characterize ice over large lakes remain limited. …”
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Integrated bioinformatics, machine learning, and molecular docking reveal crosstalk genes and potential drugs between periodontitis and systemic lupus erythematosus by Junjie Wang, Qingao Deng, Lu Qi

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Effect of cognitive behavioral intervention on electroencephalographic band powers of children with learning difficulty under eyes-closed and eyes-open conditions by Pratima Kaushik, Samanta Puspak Kumar Jena

“…Understanding EEG alterations in students with learning difficulties is crucial for evaluating cognitive functioning. …”
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Phosphorylation of GABAA receptor β3 subunit at Ser408–409 is essential for contextual learning at hippocampal CA1 synapses by Yuya Sakimoto, Yuheng Yang, Hiroyuki Kida, Dai Mitsushima

“…In behavioral experiments, bilateral microinjection of Tat-pep β3-SS into the CA1 region 60 min prior to inhibitory avoidance (IA) training significantly impaired contextual learning performance without affecting sensory, motor, and emotional functions. …”
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Machine-Learning-Guided Design of Nanostructured Metal Oxide Photoanodes for Photoelectrochemical Water Splitting: From Material Discovery to Performance Optimization by Xiongwei Liang, Shaopeng Yu, Bo Meng, Yongfu Ju, Shuai Wang, Yingning Wang

“…This review highlights recent progress in the machine learning (ML)-assisted development of nanostructured metal oxide photoanodes, focusing on bridging materials discovery and device-level performance optimization. …”
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Q-learning-based exponential distribution optimizer with multi-strategy guidance for solving engineering design problems and robot path planning by Fengbin Wu, Shaobo Li, Junxing Zhang, Liya Yu, Xuan Xiong, Libang Wu

“…To tackle these problems, we present a Q-learning and multi-strategy guidance-enhanced EDO (QMEDO), integrating three essential innovations into the EDO framework. …”
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METHODS OF MANAGING CHANGES IN EDUCATIONAL INSTITUTIONS OF UKRAINE DURING THE CRISIS (COVID-19, WAR): STRATEGIES FOR ADAPTATION AND ENSURING CONTINUITY OF LEARNING by Viacheslav Blikhar, Dmytro Hurkovskyi

“…Based on the study, the understanding of crisis management in education has been further developed, emphasizing the interaction of technological, pedagogical and human-centred approaches in ensuring the smooth functioning of educational systems under extreme pressure. …”
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A Fluid Flow‐Based Deep Learning (FFDL) Architecture for Subsurface Flow Systems With Application to Geologic CO2 Storage by Zhen Qin, Yingxiang Liu, Fangning Zheng, Behnam Jafarpour

“…Despite their powerful learning capabilities and computational appeal, deep learning models have important limitations, including lack of interpretability, extensive data needs, weak extrapolation capacity, and physical inconsistency that can affect their adoption in practical applications. …”
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On Robustness of the Explanatory Power of Machine Learning Models: Insights From a New Explainable AI Approach Using Sensitivity Analysis by Banamali Panigrahi, Saman Razavi, Lorne E. Doig, Blanchard Cordell, Hoshin V. Gupta, Karsten Liber

“…Abstract Machine learning (ML) is increasingly considered the solution to environmental problems where limited or no physico‐chemical process understanding exists. …”
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PLOD3 as a novel oncogene in prognostic and immune infiltration risk model based on multi-machine learning in cervical cancer by Lingling Qiu, Xiuchai Qiu, Xiaoyi Yang

“…To improve prognostic accuracy and therapeutic strategies, we developed a multi-machine learning prognostic model based on metabolic-associated genes. …”
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Enhancing Creativity and Validation in Explanatory Deep Learning-Based Symbolic Music Generation: A Hybrid Approach With LSTM and Genetic Algorithms by Ahmad Zainul Fanani, Arry Maulana Syarif, Ika Novita Dewi, Abdul Karim

“…This research proposes an explanatory deep learning-based music generation approach, where the output of a deep learning model is validated through a set of predefined musical rules, with a refinement process applied when inaccuracies are detected. …”
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Identification of immune and major depressive disorder-related diagnostic markers for early nonalcoholic fatty liver disease by WGCNA and machine learning by Yuyun Jia, Yanping Cao, Qin Yin, Xueqian Li, Xiu Wen

“…The intersection of shared DEGs across both conditions and WGCNA-identified genes was determined and subjected to functional enrichment analysis. Immune cell infiltration levels were quantified using single-sample gene set enrichment analysis (ssGSEA). …”
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