CPG-Based Control of an Octopod Biomimetic Machine Lobster for Mining Applications: Design and Implementation in Challenging Underground Environments by Jianwei Zhao, Haokun Zhang, Mingsong Bao, Boxiang Yin, Yiteng Zhang, Zhen Jiang

“…Central pattern generators (CPGs) have been extensively researched and validated as a well-established methodology for bionic control, particularly within the field of legged robotics. …”
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Deciphering the complex links between inflammatory bowel diseases and NAFLD through advanced statistical and machine learning analysis by Deepak Kumar, Brijesh Bakariya, Chaman Verma, Zoltán Illés

“…There is an urgent need for non-invasive methods to diagnose various stages of liver dysfunction and uncover hidden pattern based on individual disease characteristics. …”
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Quantitative assessment of factors that influence heat vulnerability in residential areas using machine learning and unmanned aerial vehicle by Jawoon Gu, Dongwoo Kim, Chulmin Jun, Seungwoo Son

“…High-resolution thermal imagery captured by unmanned aerial vehicles (UAVs) and interpretable machine learning (ML) techniques were used to model and analyze thermal patterns at the microscale. …”
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Federated Learning-Based Credit Card Fraud Detection: A Comparative Analysis of Advanced Machine Learning Models by Zheng Han

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Mathematical Modeling as a Aspect for Designing Agricultural Machines and Units (Development History Of Southern Urals Scientific School) by Yu. S. Tsench, E. V. Godlevskaya

“…(Research purpose) The objective of the study is to identify the patterns in designing tillage machines and units and, based on these patterns forecast their potential enhancements using mathematical modeling. …”
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Understanding household VMT generation: A comparative analysis with traditional statistical models and a machine-learning approach by Guang Tian, Bob Danton, Bin Li, Vijaya Gopu, Julius A. Codjoe

“…This study is the first to compare these methods’ indications of the impacts of land-use patterns on VMT generation using a large multiregional dataset. …”
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Synergistic bioinformatics and sophisticated machine learning unveil ferroptosis-driven regulatory pathways and immunotherapy potential in breast carcinoma by Lei Xia, Zhen Ye, Man Zheng, Zhaofeng Tan

“…Conclusions The integration of bioinformatics and machine learning in this study underscores a strong correlation between FRG expression patterns and BRCA prognosis, affirming their potential as precise biomarkers for personalized immunotherapy.…”
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Street View-Enabled Explainable Machine Learning for Spatial Optimization of Non-Motorized Transportation-Oriented Urban Design by Yichen Ruan, Xiaoyi Zhang, Shaohua Wang, Xiuxiu Chen, Qiuxiao Chen

“…The key findings reveal distinct nonlinear response patterns between HP and MA modes to built environment factors; for instance, a notable promotion in mechanically assisted NMT vitality is observed as enterprise density increases beyond 0.2 facilities per ha. …”
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Proteomics and machine learning: Leveraging domain knowledge for feature selection in a skeletal muscle tissue meta-analysis by Alireza Shahin-Shamsabadi, John Cappuccitti

“…Typically, proteomics research focuses narrowly on using a limited number of datasets, hindering cross-study comparisons, a problem that can potentially be addressed by machine learning. Despite this potential, machine learning has seen limited adoption in the field of proteomics. …”
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Integrating bioinformatics and machine learning to elucidate the role of protein glycosylation-related genes in the pathogenesis of diabetic kidney disease. by Ziyang Liu, Zengyuan Qin, Wenxin Bai, Shasha Wang, Chunling Huang, Na Li, Lei Yan, Yue Gu, Fengmin Shao

“…<h4>Results</h4>Unsupervised clustering of glycosylation-related genes revealed two distinct DKD molecular subtypes with differential pathway activation (e.g., extracellular matrix remodeling) and immune infiltration patterns. Six hub genes (S100A12, EXT1, SBSPON, ADAMTS1, FMOD, SPTB) were identified as critical to DKD pathogenesis through machine learning. …”
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Unveiling the role of coagulation-related genes in acute myeloid leukemia prognosis and immune microenvironment through machine learning by Liyun Ji, Yanxia Yang, Siyue Ma

“…This study aims to investigate the expression patterns of coagulation-related genes in AML and their clinical relevance. …”
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Application of Machine Learning Techniques for Predicting Heating Coil Performance in Building Heating Ventilation and Air Conditioning Systems by Adam Nassif, Pasidu Dharmasena, Nabil Nassif

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Identifying Antibiotic-Resistant Mutants in β-Lactamases for Class A and Class B Using Unsupervised Machine Learning by Soumya Lipsa Rath, Smaranika Mohapatra, Veena Gayathri

“…The current study incorporates the techniques of machine learning to cluster the patterns of the proteins which may be antibiotic resistant. …”
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Advancing Hydrogel-Based 3D Cell Culture Systems: Histological Image Analysis and AI-Driven Filament Characterization by Lucio Assis Araujo Neto, Alessandra Maia Freire, Luciano Paulino Silva

“…<b>Background:</b> Machine learning is used to analyze images by training algorithms on data to recognize patterns and identify objects, with applications in various fields, such as medicine, security, and automation. …”
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Comparison of Induction Machine Drive Control Schemes on the Distribution of Power Losses in a Three-Level NPC Converter by Carlos A. Reusser, Matías Parra, Gerardo Mino-Aguilar, Victor R. Gonzalez-Diaz

“…Each of these control strategies exhibit a particular operational profile which affects the switching pattern of the converter semiconductors, thus conditioning the switching and conducting losses of these power devices. …”
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Machine learning combined multi-omics analysis to explore key oxidative stress features in systemic lupus erythematosus by Hongwei Zhou, Xiaoqing Li, Yanyu Zhang, Feng Wei, Zhiyu Liu, Yan Zhao, Xubo Zhuang, Xia Liu, Haizhou Zhou

“…Correlation analysis underscored strong relationships among key genes, OS/AOS levels, and vital metabolites.ConclusionThis multi-omics and machine learning–based investigation uncovered major disruptions in OS-related metabolic pathways and metabolites in SLE, ultimately identifying six key genes with distinct expression patterns across immune cell subsets. …”
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Combination of gray level features with deep transfer learning for copra classification using machine learning and neural networks by A. Stephen Sagayaraj, T. Kalavathi Devi

“…Among the classifiers tested, Neural Network-based Pattern Recognition (NNPR) achieved the highest accuracy of 99.6%, sensitivity of 99.64%, specificity of 99.64%, F1-Score of 99.6, and a Kappa score of 0.99, demonstrating its superior performance. …”
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Annual global dengue dynamics are related to multi-source factors revealed by a machine learning prediction analysis. by Haoyu Long, Yilin Chen, Jingru Feng, Jian Chen, Xue Zhang, Wenjie Han, Min Kang, Xiangjun Du

“…Feature contribution pattern was different between hyperendemic and non-hyperendemic regions. …”
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Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations by Jie Yang, Fanyan Ou, Binbin Li, Lixiong Zeng, Qiuli Chen, Houyu Gan, Jianing Yu, Qian Guo, Jihua Feng, Jianfeng Zhang

“…This study, through the integrated application of computational biology and machine learning algorithms, discovered biomarkers of PCD patterns that affect cytokine storm-mediated inflammation and immunosuppressive effects in sepsis populations across different age groups (neonates, children, and adults). …”
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Automated long-term monitoring of stereotypical movement in polar bears under human care using machine learning by Matthias Zuerl, Philip Stoll, Ingrid Brehm, Jonas Sueskind, René Raab, Jan Petermann, Dario Zanca, Ralph Simon, Lorenzo von Fersen, Bjoern Eskofier

“…Finally, we classified the trajectory patterns as SB or normal behaviour using a lightweight random forest approach with an accuracy of 94.9 %. …”
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