Modeling and Analysis of a Cutting Robot for the “Excavation–Backfill–Retention” Integrated Mining and Excavation Equipment by Hongwei Ma, Wenda Cui, Chuanwei Wang, Xusheng Xue, Qinghua Mao, Haotian Wang, Limeng Xue, Hao Su, Zukun Yu, Jiashuai Cheng, Yifeng Guo, Kexiang Ma

“…Using a hierarchical solution method that combines local search and multi−objective genetic algorithms, the robot’s fundamental parameters were determined, enabling the development of a detailed 3D model. …”
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Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning by Kennedy C. Onyelowe, Viroon Kamchoom, Shadi Hanandeh, S. Anandha Kumar, Rolando Fabián Zabala Vizuete, Rodney Orlando Santillán Murillo, Susana Monserrat Zurita Polo, Rolando Marcel Torres Castillo, Ahmed M. Ebid, Paul Awoyera, Krishna Prakash Arunachalam

“…By harnessing the synergies between physics-based principles and data-driven algorithms, PIM-ML not only streamlines the design process but also enhances the reliability and sustainability of concrete structures. …”
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Breast tumors from ATM pathogenic variant carriers display a specific genome-wide DNA methylation profile by Nicolas M. Viart, Anne-Laure Renault, Séverine Eon-Marchais, Yue Jiao, Laetitia Fuhrmann, Sophia Murat El Houdigui, Dorothée Le Gal, Eve Cavaciuti, Marie-Gabrielle Dondon, Juana Beauvallet, Virginie Raynal, Dominique Stoppa-Lyonnet, Anne Vincent-Salomon, Nadine Andrieu, Melissa C. Southey, Fabienne Lesueur

“…Moreover, using three different deep learning algorithms (logistic regression, random forest and XGBoost), we identified a set of 27 additional biomarkers predictive of ATM status, which could be used in the future to provide evidence for or against pathogenicity in ATM variant classification strategies. …”
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Prospective Validation of Glial Fibrillary Acidic Protein, d‐Dimer, and Clinical Scales for Acute Large‐Vessel Occlusion Ischemic Stroke Detection by Yasir Durrani, Jakob V. E. Gerstl, Danielle Murphy, Ashley Harris, Imane Saali, Toby Gropen, Shashank Shekhar, Ari D. Kappel, Nirav J. Patel, Rose Du, Rodolfo E. Alcedo Guardia, Juan C. Vicenty‐Padilla, Adam A. Dmytriw, Vitor Mendes Pereira, Saef Izzy, Allauddin Khan, Mohammed A. Aziz‐Sultan, David S. Liebeskind, Jason M. Davies, Adnan H. Siddiqui, Edoardo Gaude, Joshua D. Bernstock

“…Critically, application of the biomarker and stroke scale algorithms ruled out all patients with hemorrhage. Conclusion The present work prospectively validated the potential utility of previously defined glial fibrillary acidic protein and d‐dimer cutoff levels (ie, 213 pg/mL and 600 ng/mL, respectively), demonstrating their value for discrimination of LVO stroke from differential diagnoses during code stroke workups. …”
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Rationale and design of the HERZCHECK trial: Detection of early heart failure using telemedicine and cardiovascular magnetic resonance in structurally weak regions (NCT05122793) by Sebastian Kelle, Anna Clara Nolden, Maximilian Leo Müller, Rebecca Elisabeth Beyer, Henning Steen, Bjoern Andrew Remppis, Johannes Wieditz, Hannah Kentenich, Alex Tuit, Mina Cvetkovic, Undine Ella Witt, Florian André, Sein Schmidt, Alexander Huppertz, Dusan Simic, Dirk Müller, Arim Shukri, Matthias Issing, Andre Glardon, Katrin Christiane Reber, Ulf Landmesser, Norbert Frey, Burkert Pieske, Stephanie Stock, Volkmar Falk, Tim Friede, Gisela Thiede

“…Yet established screening algorithms for asymptomatic pre-HF, allowing for early and effective preventive interventions, are largely lacking. …”
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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

“…Among eight machine learning algorithms evaluated, the optimal model was selected to construct a predictive framework for immunotherapy response.ResultsMicrobial α-diversity was significantly elevated in responders compared to non-responders, with antibiotic administration further amplifying this difference—most notably at the species level. …”
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Advanced classification of optical water types and ensemble learning models for Chl-a inversion in Dongting and Poyang lakes using Sentinel-2 remote sensing: assessing the impact o... by Kai Xiong, Bin Deng, Jiang Liu, Zhixin Guan, Weizhi Lu, Changbo Jiang, Wei Luo, Han Rao, Longbin Yin, Kang Yang

“…This study proposes an integrated framework combining optical water type (OWTs) classification and ensemble learning algorithms to enhance the re- mote sensing retrieval accuracy of Chl-a in Dongting Lake and Poyang Lake during 2020–2023, particularly during the extreme drought event of 2022. …”
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A novel machine learning models for meteorological drought forecasting in the semi-arid climate  region by Chaitanya Baliram Pande, Dinesh Kumar Vishwakarma, Aman Srivastava, Kanak N. Moharir, Fahad Alshehri, Norashidah Md Din, Lariyah Mohd Sidek, Bojan Đurin, Abebe Debele Tolche

“…The ensemble method played a novel and crucial role in significantly improving the accuracy of drought forecasting by developing ML models based on various algorithms that operate more efficiently, require fewer inputs, and exhibit less complexity than precise models, proving highly effective for drought warning systems. …”
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Habitat radiomics analysis for progression free survival and immune-related adverse reaction prediction in non-small cell lung cancer treated by immunotherapy by Yuemin Wu, Wei Zhang, Xiao Liang, Pengpeng Zhang, Mengzhe Zhang, Yuqin Jiang, Yanan Cui, Yi Chen, Wenxin Zhou, Qi Liang, Jiali Dai, Chen Zhang, Jiali Xu, Jun Li, Tongfu Yu, Zhihong Zhang, Renhua Guo

“…After applying four kinds of machine learning algorithms to select the key preoperative CT radiomic features, we used clinical, radiomics and habitat radiomic features to develop the clinical signature, radiomics signature and habitat radiomic signature for ICIs prognostics and irAEs prediction. …”
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Machine learning aids in the discovery of efficient corrosion inhibitor molecules by Haiyan GONG, Lingwei MA, Dawei ZHANG

“…Continuously optimizing ML algorithms and combining them with experimental validation should contribute to the efficient and high-precision discovery of corrosion inhibitor molecules in the future, leading to breakthroughs in materials science and industrial applications.…”
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Accuracy and clinical effectiveness of risk prediction tools for pressure injury occurrence: An umbrella review. by Bethany Hillier, Katie Scandrett, April Coombe, Tina Hernandez-Boussard, Ewout Steyerberg, Yemisi Takwoingi, Vladica M Veličković, Jacqueline Dinnes

“…Two SRs assessing machine learning-based algorithms reported high prognostic accuracy estimates, but some of which were sourced from the same data within which the models were developed, leading to potentially overoptimistic results. …”
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Benchmark Investigation of SARS-CoV-2 Mutants’ Immune Escape with 2B04 Murine Antibody: A Step Towards Unraveling a Larger Picture by Karina Kapusta, Allyson McGowan, Santanu Banerjee, Jing Wang, Wojciech Kolodziejczyk, Jerzy Leszczynski

“…Three essentially different algorithms were employed: forced placement based on a template, followed by two steps of extended molecular dynamics simulations; protein–protein docking utilizing PIPER (an FFT-based method extended for use with pairwise interaction potentials); and the AlphaFold 3.0 model for complex structure prediction. …”
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Home-Based Intervention Tool for Cardiac Telerehabilitation: Protocol for a Controlled Trial by Francesca Mastorci, Maria Francesca Lodovica Lazzeri, Lamia Ait-Ali, Paolo Marcheschi, Paola Quadrelli, Massimiliano Mariani, Rafik Margaryan, Wanda Pennè, Marco Savino, Giuseppe Prencipe, Alina Sirbu, Paolo Ferragina, Corrado Priami, Alessandro Tommasi, Cesare Zavattari, Pierluigi Festa, Stefano Dalmiani, Alessandro Pingitore

“…The secondary aims are to implement the system in a “real-life” context of postcardiac surgical rehabilitation, and to create a data set and a data collection methodology to prototype data analytics algorithms and artificial intelligence techniques for customizing the rehabilitation pathway. …”
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Machine Learning-Assisted NIR Spectroscopy for Dynamic Monitoring of Leaf Potassium in Korla Fragrant Pear by Mingyang Yu, Weifan Fan, Junkai Zeng, Yang Li, Lanfei Wang, Hao Wang, Feng Han, Jianping Bao

“…By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” …”
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Opportunities, Challenges, and Future Directions for Generative Artificial Intelligence in Library Information Literacy Education: A Scoping Review by Fan YUAN, Jia LI

“…However, the study also identifies several critical challenges, including concerns about data accuracy concerns, inherent algorithmic biases, risks to academic integrity, and the potential weakening of independent thinking skills due to over-reliance on AI systems. …”
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Research Progress of Intelligent Evaluation and Virtual Reality Based Training in Upper Limb Rehabilitation afrer Stroke by XIE Qiurong, LIN Wanqi, ZHANG Qi, SHENG Bo, ZHANG Yanxin, HUANG Jia

“…Automated assessment of upper extremity motor function based on machine learning algorithms with markerless sensing techniques has focused on the Fugl-Meyer assessment of upper extremity (FMA-UE), Brunnstrom stages, and Wolf motor function test (WMFT) scales and has been proved with high-scoring accuracy and time efficiency. …”
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System architecture and key technologies of intelligent, safe and efficient mining for rockburst coal seams by Zhigang DENG, Yunpeng LI, Shankun ZHAO, Yin WANG, Shaogang LI, Linghai KONG, Junjun JIANG, Zhenguo SU, Yizhe LI, Kai QIN

“…First of all, digital twin technology is used to build a fine digital model, deeply detect and analyze the geological structure of the area where the mine is located, accurately measure the characteristics of the regional geostress field and the physical and mechanical properties of coal and rock, and rely on advanced algorithms to carry out engineering distribution of formation change information to achieve “transparency” of geological information. …”
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Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in precision me... by Yutong Fang, Rongji Zheng, Yefeng Xiao, Qunchen Zhang, Junpeng Liu, Jundong Wu

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”
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Single-Cell Transcriptomics Unveils the Mechanistic Role of FOSL1 in Cutaneous Wound Healing by Jingbi Meng, Ge Zheng, Yinli Luo, Ling Ge, Zhiqing Liu, Wenhua Huang, Meitong Jin, Yanli Kong, Shanhua Xu, Zhehu Jin, Longquan Pi

“…Subsequently, we constructed Protein–Protein Interaction (PPI) networks via the STRING database. Machine learning algorithms were instrumental in identifying pivotal genes, a finding corroborated through animal modeling and Western blot analysis of tissue samples. …”
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Predicting Index Trend Using Hybrid Neural Networks with a Focus on Multi-Scale Temporal Feature Extraction in the Tehran Stock Exchange by Mohammad Osoolian, Ali Nikmaram, Mahdi Karimi

“…A wide array of predictive modeling techniques have been meticulously investigated, spanning from conventional statistical methodologies to more sophisticated machine learning algorithms. The primary focus of this research endeavor revolves around the predictive analysis of the Tehran Stock Exchange (TSE) Composite Index, wherein a novel hybrid neural network framework is employed. …”
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