Integrating Social Determinants of Health in Machine Learning–Driven Decision Support for Diabetes Case Management: Protocol for a Sequential Mixed Methods Study by Seung-Yup Lee, Leslie W Hayes, Bunyamin Ozaydin, Steven Howard, Alison M Garretson, Heather M Bradley, Andrew M Land, Erin W DeLaney, Amy O Pritchett, Amanda L Furr, Ashleigh Allgood, Matthew C Wyatt, Allyson G Hall, Jane C Banaszak-Holl

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Analysis and Optimization of Cutting Tool Coating Effects on Surface Roughness and Cutting Forces on Turning of AA 6061 Alloy by Mahir Akgün, Fuat Kara

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Experimental and Statistical Investigations for Tensile Properties of Hemp Fibers by Peyman Sadeghi, Quang Cao, Ragab Abouzeid, Mohammad Shayan, Meensung Koo, Qinglin Wu

“…Additionally, the Griffith model was employed to predict the strength and Young’s modulus based on fiber diameters, supporting the observation that thinner fibers generally exhibited higher tensile strength due to fewer defects. …”
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Efficient field correction of low-cost particulate matter sensors using machine learning, mixed multiplicative/additive scaling and extended calibration inputs by Slawomir Koziel, Anna Pietrenko-Dabrowska, Marek Wojcikowski, Bogdan Pankiewicz

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Transforming Screening, Risk Stratification, and Treatment Optimization in Chronic Liver Disease Through Data Science and translational Innovation by Tamer A. Addissouky

“…Evidence-based screening and risk models facilitate delivering tailored interventions. …”
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Efficient AI-Driven Query Optimization in Large-Scale Databases: A Reinforcement Learning and Graph-Based Approach by Najla Sassi, Wassim Jaziri

“…By employing proximal policy optimization for adaptive policy learning and using graph-based schema representations for relational modeling, GRQO effectively traverses the combinatorial optimization space. …”
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Multi-exit Kolmogorov–Arnold networks: enhancing accuracy and parsimony by James Bagrow, Josh Bongard

“…Kolmogorov–Arnold networks (KANs) uniquely combine high accuracy with interpretability, making them valuable for scientific modeling. However, it is unclear a priori how deep a network needs to be for any given task, and deeper KANs can be difficult to optimize and interpret. …”
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AI enhancing prefabricated aesthetics and low carbon coupled with 3D printing in chain hotel buildings from multidimensional neural networks by Gangwei Cai, Yin Lou, Feidong Lu

“…Using multidimensional algorithms within machine learning (ML), neural networks (NN), and statistical modeling (SM), this paper analyzes the impact of AI-driven prefabricated room renovations on tourist satisfaction and carbon emissions. …”
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QUANTUM INFORMATICS: OVERVIEW OF THE MAIN ACHIEVEMENTS by A. Sigov, E. Andrianova, D. Zhukov, S. Zykov, I. E. Tarasov

“…In the field of artificial quantum intelligence, attention is paid, first of all, to the “search” for a model of a quantum neural network that is optimal from the point of view of using all the advantages presented by quantum computing and neural networks, as well as machine learning algorithms. …”
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Review of Control Techniques for Dual Three-phase PMSM Drives with Low Carrier Ratios by Minrui Gu, Zheng Wang, Ming Cheng, Weifeng Su, Linyue Shang, Po Liu

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Model reduction of structural mechanical response in the time domain by Xin Yan, Xinyu Guo, Ningya He, Jinglong Shi, Daquan Zhao

“…Subsequently, road load spectrum signal tests extract vibration acceleration and strain signals from these areas, forming the foundation for model reduction training and validation sets. Comprehensive research into machine learning and model reduction techniques is conducted, with a focus on polynomial order in response surface models and kernel functions in Gaussian process models. …”
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High-performance machine-learning-based calibration of low-cost nitrogen dioxide sensor using environmental parameter differentials and global data scaling by Slawomir Koziel, Anna Pietrenko-Dabrowska, Marek Wojcikowski, Bogdan Pankiewicz

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Understanding the determinants of treated bed net use in Ethiopia: A machine learning classification approach using PMA Ethiopia 2023 survey data. by Abraham Keffale Mengistu

“…<h4>Conclusion</h4>This study demonstrates the superiority of machine learning (ML) models in capturing complex, nonlinear determinants of ITN utilization, providing actionable insights for targeted malaria prevention strategies. …”
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Surface Roughness Optimization of Polyamide-6/Nanoclay Nanocomposites Using Artificial Neural Network: Genetic Algorithm Approach by Mehdi Moghri, Milos Madic, Mostafa Omidi, Masoud Farahnakian

“…One of the key factors that affect the quality of polymer nanocomposite products in machining is surface roughness. To obtain high quality products and reduce machining costs it is very important to determine the optimal machining conditions so as to achieve enhanced machining performance. …”
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Optimized memory allocation in edge-PLCs using Deep Q-Networks and bidirectional LSTM with Quantum Genetic Algorithm by N. Naveen Kumar, S. Saravana, S. Balamurugan, P. Seshu Kumar, S. Suresh

“…The DQN component learns to optimize memory allocation policies based on immediate rewards and feedback, while the BiLSTM network captures long-term dependencies in data, enhancing predictive modeling for future data arrival rates. …”
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Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture by Haining Gao, Haoyu Wang, Hongdan Shen, Shule Xing, Yong Yang, Yinlin Wang, Wenfu Liu, Lei Yu, Mazhar Ali, Imran Ali Khan

“…The Ivy algorithm is utilized to optimize the hyperparameters of DBMA. The t-SNE algorithm is employed to visualize features extracted from different network layers of the chatter detection model. …”
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Dynamic bone recognition for robotic vertebral plate cutting via unit energy consumption and SVM optimized by PSO by Heqiang Tian, Jing Zhao, Ying Sun, Jinchang An

“…This study aims to improve robotic vertebral plate cutting by developing a bone recognition model that utilizes a unit energy consumption feature vector and support vector machines (SVM) optimized with particle swarm optimization (PSO). …”
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Modelling and Production of Injection Moulded Polyvinylchloride--Sawdust Composite by C.O. Aliyegbenoma, J.A. Apkobi, D.D. Olodu

“… This study focused on the modeling and production of the injection  moulded Polyvinylchloride-Sawdust (PVC-sawdust) composite. …”
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Thermite combustion: Current trends in modeling and future perspectives by Alain Esteve, Carole Rossi

“…Recent breakthroughs in machine learning will further accelerate the design and optimization of thermites by enabling the establishment of predictive quantitative structure–property relationships in complement of heavy detailed physical models. …”
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Modelling and Production of Injection Moulded Polyvinylchloride--Sawdust Composite by C.O. Aliyegbenoma, J.A. Apkobi, D.D. Olodu

“… This study focused on the modeling and production of the injection  moulded Polyvinylchloride-Sawdust (PVC-sawdust) composite. …”
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