Machine learning assists regulated cell death crucial biomarker selection in adenocarcinoma of the lung: biological data testing and cell assay determination by Han Ning, Ying Jiang, Mengli Zheng, Gao Yang, Lianjun Ma, Yachao Zhao

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Preliminary Development of Global–Local Balanced Vision Transformer Deep Learning with DNA Barcoding for Automated Identification and Validation of Forensic Sarcosaphagous Flies by Yixin Ma, Lin Niu, Bo Wang, Dianxin Li, Yanzhu Gao, Shan Ha, Boqing Fan, Yixin Xiong, Bin Cong, Jianhua Chen, Jianqiang Deng

“…In our previous study, we developed a GLB-ViT (Global–Local Balanced Vision Transformer)-based deep learning model for fly species identification, which demonstrated improved identification capabilities. …”
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Prediction of PD-L1 tumor positive score in lung squamous cell carcinoma with H&E staining images and deep learning by Qiushi Wang, Xixiang Deng, Pan Huang, Qiang Ma, Lianhua Zhao, Yangyang Feng, Yiying Wang, Yuan Zhao, Yan Chen, Peng Zhong, Peng He, Mingrui Ma, Peng Feng, Hualiang Xiao

“…Therefore, the application of deep learning models to segment and quantitatively predict PD-L1 expression in digital sections of Hematoxylin and eosin (H&E) stained lung squamous cell carcinoma is of great significance.MethodsWe constructed a dataset comprising H&E-stained digital sections of lung squamous cell carcinoma and used a Transformer Unet (TransUnet) deep learning network with an encoder-decoder design to segment PD-L1 negative and positive regions and quantitatively predict the tumor cell positive score (TPS).ResultsThe results showed that the dice similarity coefficient (DSC) and intersection overunion (IoU) of deep learning for PD-L1 expression segmentation of H&E-stained digital slides of lung squamous cell carcinoma were 80 and 72%, respectively, which were better than the other seven cutting-edge segmentation models. …”
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Association between accelerometer-measured physical activity volume and sleep duration in older adults: a cross-sectional interpretable machine learning analysis by XiaoTao Cai, Yi Xian, YuXin Zhou, TongYi Liu, Xinyue Zhang, Qing Chen

“…Analysis of the derivation cohort included weighted univariate analysis, weighted multivariate logistic regression, and interpretable machine learning analysis. The machine learning interpretability process involved dividing a 20% internal validation test set, using the grid search method and five-fold cross-validation to construct RF, GBDT, XGBoost, and LightGBM models, as well as a two-layer stacked ensemble model for model comparison, with external validation of the optimal model’s performance. …”
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Multiobjective integrated scheduling of disassembly and reprocessing operations considering product structures and stochastic processing time via reinforcement learning-based evolu... by Yaping Fu, Fuquan Wang, Zhengyuan Li, Guangdong Tian, Duc Truong Pham, Hao Sun

“…First, a stochastic programming model is developed to minimize maximum completion time and total tardiness. …”
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Editorial: Computational bioacoustics and automated recognition of bird vocalizations: new tools, applications and methods for bird monitoring by Yizhar Lavner, Cristian Pérez-Granados, Cristian Pérez-Granados

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Do pensamento computacional desplugado ao plugado no processo de aprendizagem da matemática / Computational thinking unplugged to plugged in the mathematic learning process by Ingrid Santella, Adriana Aparecida de Lima Terçariol, Elisangela Aparecida Bulla Ikeshoji

“…. /// This article is an excerpt from the dissertation entitled «Computational thinking in the mathematics learning process in the final years of elementary school», linked to the Master Program in Management and Educational Practices (PROGEPE) at Nove de Julho University, in particular, to the Research and intervention line learning methodology and teaching pratices (LIMAPE). …”
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Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery by Jose I. Bueso-Bordils, Gerardo M. Antón-Fos, Rafael Martín-Algarra, Pedro A. Alemán-López

“…Currently, there are numerous commercially available and free web-based programs for toxicity prediction, which can be used to construct various predictive models. …”
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Unveiling the role of IL7R in metabolism-associated fatty liver disease leading to hepatocellular carcinoma through transcriptomic and machine learning approaches by Priyadharshini Annadurai, Arnold Emerson Isaac

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Microcontroller Unit-Based Gesture Recognition System by Jakub Grabarczyk, Agnieszka Lazarowska

“…This article describes the design, construction, and programming of a microcontroller-based system, which uses hand gestures with machine learning algorithms to control an unmanned aerial vehicle (UAV). …”
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The Contribution of Linguistic Distance to L3 Learning Motivation: A Cross-L2 Comparison of University English as a Foreign Language Learners by Xuan Wang, Yilin Zhu

“…In this study, we respond to calls for more research on the motivation to learn a third language (L3), especially regarding how this motivation is influenced by linguistic distance in various second language (L2) contexts. …”
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Designing an overview Theory of Change for a multi-component support community for people affected by rare dementia by Mary Pat Sullivan, Paul M. Camic, Emma Harding, Joshua Stott, Gill Windle, Ian Davies-Abbott, Sebastian J. Crutch

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Collaborative 360° virtual reality training of medical students in clinical examinations by Jacob Gorm Davidsen, Dorthe Vinter Larsen, Sten Rasmussen, Lucas Paulsen

“…The study population consisted of 14 medical students in semester 5 of their Bachelor’s programme. The students were divided into three groups before watching and annotating a 360° video of an authentic learning situation inside a collaborative immersive virtual reality space. …”
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Machine Learning Modeling for Building Energy Performance Prediction Based on Simulation Data: A Systematic Review of the Processes, Performances, and Correlation of Process-Relate... by Damla Kömürcü, Ecem Edis

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Arduino-Based Mobile Robotics for Fostering Computational Thinking Development: An Empirical Study with Elementary School Students Using Problem-Based Learning Across Europe by Rolando Barradas, José Alberto Lencastre, Salviano Pinto Soares, António Valente

“…The eBooks serve as a guide to the construction and programming of a small Arduino-based robot. …”
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Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learn... by Ting Ge, Guixin He, Qian Cui, Shuangcui Wang, Zekun Wang, Yingying Xie, Yuanyuan Tian, Juyue Zhou, Jianchun Yu, Jinmin Hu, Wentao Li

“…Subsequently, we developed a novel machine learning framework that incorporated 12 machine learning algorithms and their 113 combinations to construct a LUAD CS-related signature (LUAD-CSRS), which were assessed in both training and validation cohorts. …”
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SOME PROBLEMS OF THE ORGANIZATION OF NETWORK INTERUNIVERSITY INTERACTION by E. K. GITMAN, M. B. GITMAN, V. Yu. STOLBOV, A. P. CHUGUNOV

“…Solutions are based on network education program being developed in module and competence form and individual education program constructed with the preference to student wishes. …”
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M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy by Nalini Schaduangrat, Hathaichanok Chuntakaruk, Thanyada Rungrotmongkol, Pakpoom Mookdarsanit, Watshara Shoombuatong

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Integrated machine learning identifies disulfidptosis-related and ferroptosis-related genes to evaluate survival prognosis and treatment efficacy in kidney renal clear cell carcino... by Yuan Xiang, Zijian Zhou, Tong Mu, Shunyao Zhang, Lei Xie, Yajie Zhou, Wenxiong Zhang, Liuxiang Fu

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Predicting student achievement through peer network analysis for timely personalization via generative AI by Ivica Pesovski, Petar Jolakoski, Vladimir Trajkovik, Zuzana Kubincova, Michael A. Herzog

“…The neutral group was given the option to request specific learning materials and self-assessment quizzes on demand to address their individual learning needs. …”
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