Data driven tensile strength prediction for fiber-reinforced rubberized recycled aggregate concrete using machine learning by Avijit Pal, Khondaker Sakil Ahmed, Nur Yazdani

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Optimization of dual-layer flow field in a water electrolyzer using a data-driven surrogate model by Lizhen Wu, Zhefei Pan, Shu Yuan, Xiaoyu Huo, Qiang Zheng, Xiaohui Yan, Liang An

“…Therefore, machine learning methods are adopted to accelerate the optimization. A data-driven surrogate model based on deep neural network (DNN) is developed and successfully trained using data obtained by the physical VOF method. …”
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Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data by Ke Niu, Jiayang Guo, Yijie Pan, Xin Gao, Xueping Peng, Ning Li, Hailong Li

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Using big data analytics to improve HIV medical care utilisation in South Carolina: A study protocol by Mohammad Rifat Haider, Bankole Olatosi, Jiajia Zhang, Sharon Weissman, Jianjun Hu, Xiaoming Li

“…These data will be linked to the RFA data and serve as sources for traditional and vulnerable domain Gelberg-Anderson Behavioral Model variables. …”
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Use of reagent test kits and fentanyl test strips among electronic music festival attendees in Colorado: prevalence, barriers, and behavior in response to drug checking by Cianna J. Piercey, Thomas E. Schlechter, Devin Henry, Mikayla Allen-Collins, Riley Ahern, Joseph Cameron, Bradley T. Conner, Jeffrey G. Snodgrass, Hollis C. Karoly

“…We also collected qualitative data on perceived barriers of using RTK and FTS within a festival setting using survey-based open-ended text response questions. …”
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Factors Influencing the Use of Recommendation Systems for Elderly Research in Thailand by A-Phorn Molee, Wirapong Chansanam

“…Employing an extended the Unified Theory of Acceptance and Use of Technology 2 model, the study investigates system use behavior (SUB) based on seven core factors: Performance expectancy (PEF), effort expectancy (EFF), social influence, personal innovativeness (INN), hedonic motivation (MOT), facilitating conditions (FAC), and intention behavior (IBV), alongside three additional factors—system quality (SQU), information quality (IQU), and trust. …”
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Model Predictive Control Using an Artificial Neural Network for Fast-Charging Lithium-Ion Batteries by Joris Jaguemont, Ali Darwiche, Fanny Bardé

“…The framework synergizes MPC’s predictive capabilities with the daptive learning of Artificial Neural Network (ANN) by training the ANN offline using MPC-derived input–output data. Validation against prior MPC results demonstrates MPCANN’s ability to replicate MPC behavior across temperatures, achieving strong alignment in current and temperature predictions. …”
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Using machine learning to identify key predictors of maternal success in sheep for improved lamb survival by Ebru Emsen, Bahadir Baran Odevci, Muzeyyen Kutluca Korkmaz

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Adolescent Perceptions and Use of E-Cigarettes as Smoking Cessation Tools and for Pleasure: Data Analysis from National Youth Tobacco Survey (NYTS), 2011, 2015, 2019, and 2023 by Olusoji Ibukun, Chesmi Kumbalatara, Wasantha Jayawardene

“…This study investigated demographic trends, usage frequency, initial and future use patterns, and quitting behavior. Descriptive statistics and latent class analysis (LCA) were employed to examine adolescent e-cigarette use patterns, with statistical significance determined at <i>p</i> < 0.05. …”
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Critical Fluctuations as an Early Warning Signal of Sports Injuries? A Proof of Concept Using Football Monitoring Data by Niklas D. Neumann, Jur J. Brauers, Nico W. van Yperen, Mees van der Linde, Koen A. P. M. Lemmink, Michel S. Brink, Fred Hasselman, Ruud J. R. den Hartigh

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Self-monitoring of health - user viewpoints on gathering data using consumer health technologies during leisure time by Nora Weinberger, Martina F. Baumann, Maria Maia

“…It focuses on attitudes toward health data collection, data sharing, privacy concerns, and the use of EEG-supported devices.ResultsFindings reveal a complex landscape of trust and concern. …”
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Scenic area attractiveness in Dali City and its influencing factors evaluated using multi-source spatiotemporal data. by Di Wang, Haizhong Qian

“…Developments in social media and multi-source spatiotemporal data provide a basis for studying complex tourist behaviors, overcoming the limitations of traditional interview survey data. …”
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Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data by Renjie Lu, Shiyun Wang, Pinghua Chen, Fangfang Li, Fangfang Li, Pan Li, Qian Chen, Xuefei Li, Fangyu Li, Suxia Guo, Jinlin Zhang, Jinlin Zhang, Dan Liu, Zhijun Hu

“…The 2015 CHARLS data were split randomly into a training set (70%) and a testing set (30%). …”
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A Dynamic Precision Evaluation System for Physical Education Classroom Teaching Behaviors Based on the CogVLM2-Video Model by Chao Liu, Fan Yang, Chengyu Ge, Zhiyu Shao

“…Leveraging the CogVLM2-Video model, the research presents a system for real-time data collection, automated annotation, and in-depth analysis of teaching behaviors. …”
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Cart-State-Aware Discovery of E-Commerce Visitor Journeys with Process Mining by Bilal Topaloglu, Basar Oztaysi, Onur Dogan

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Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction by Payam Boozary, Sogand Sheykhan, Hamed GhorbanTanhaei, Cosimo Magazzino

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Non-Linear Models in Economic and Social Research by L. F. Petrov

“…For simple natural scientific systems chaotic behavior is only one of possible forms of the dynamic process in line with determined behavior. …”
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Human dimensions in flood risk management: Exploring risk perception and climate change considerations among engineers in the US by Mohammad Pourmatin, Elham Ajorlou, Ali Farhadzadeh, Majid Ghayoomi, Elizabeth Hewitt

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Comparative effects of methamphetamine, cannabis, and polysubstance use on oral health by Osman Hasan Tahsin Kılıç, Duygu Kürklü Arpaçay, Onur Çağdaş Gezen, Zehra Nur Bayram, Aysel Başer

“…Sociodemographic data, substance use history, and oral health behaviors were collected using a researcher-developed questionnaire and the Turkish version of the Hiroshima University Dental Behavior Inventory (HU-DBI). …”
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Factors influencing high-risk fertility practices among women of childbearing age in Tanzania: using DHS 2022 by Hiwot Altaye Asebe

“…Women’s educational levels, Age of women, wealth index, media exposure, decision-making autonomy, contraceptive use, and rural residence were found to be predictors of high-risk fertility behavior. …”
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