Advances in impact force identification: A comprehensive review of techniques and mathematical innovations by Hamed Kalhori, Shabnam Tashakori, Ben Halkon, Mehrisadat Makki Alamdari, Bing Li, Morteza Saberi

“…A systematic taxonomy is established to evaluate impact force reconstruction techniques, including deconvolution, subspace state-space formulations, and data-driven models, as well as localization strategies, such as triangulation, similarity-based matching, and optimization-based algorithms. …”
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Evaluating feature selection methods in a hybrid Weibull Freund-Cox proportional hazards model for renal cell carcinoma by Shaymaa Mohammed Ahmed, Majid Khan Majahar Ali, Arshad Hameed Hasan

“… This paper reports a feature selection comparison between Lasso, Elastic Net, and Mutual Information-Support Vector Machine (MI-SVM) that are based on a hybrid Weibull-Freund-Cox Proportional Hazards (WFCPH) model when it is used with renal cell carcinoma (RCC) data. …”
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Prosumer Cost Efficiency and Ensuring Grid Stability Through a Hierarchical PPO Framework in Decentralized Community Energy Management by Ajay Singh, Bijaya Ketan Panigrahi

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Analysis of the Trends and Driving Factors of Cultivated Land Utilization Efficiency in Henan Province from 2000 to 2020 by Henggang Zhang, Chenhui Zhu, Tianyu Jiao, Kaiyue Luo, Xu Ma, Mingyu Wang

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Solid Oxide Fuel Cell Voltage Prediction by a Data-Driven Approach by Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Egor Sergeevich Mayorov, Oleg Evgenievich Babikov, Iliya Krastev Iliev

“…White box and gray box models are unable to achieve real-time optimization of control parameters. …”
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Construction of a Prediction Model for Energy Consumption in Urban Rail Transit Operations Using a Bottom–Up Approach by Boyu Chen, Ye Lin

“…The factors were grouped based on the scale of the urban rail transit network, and planned indicators were screened using stepwise regression and machine learning eigenvalue methods. Predictive models were then constructed using these planned indicators through multiple linear regression and random forest regression. …”
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A Rotational Speed Sensor Based on Flux-Switching Principle by Duy-Tinh Hoang, Joon-Ku Lee, Kwang-Il Jeong, Kyung-Hun Shin, Jang-Young Choi

“…Based on this model, the optimal dimensions of the proposed machine are investigated to achieve the best performance for speed measurement applications. …”
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Revolutionizing the construction industry by cutting edge artificial intelligence approaches: a review by Eliezer Zahid Gill, Daniela Cardone, Alessia Amelio

“…This study evaluates various AI and ML models, including Artificial Neural Networks (ANNs) and Support Vector Machines SVMs, as well as optimization techniques like whale and moth flame optimization. …”
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CellDynaMo-stochastic reaction-diffusion-dynamics model: Application to search-and-capture process of mitotic spindle assembly. by Evgenii Kliuchnikov, Artem Zhmurov, Kenneth A Marx, Alex Mogilner, Valeri Barsegov

“…We introduce a Stochastic Reaction-Diffusion-Dynamics Model (SRDDM) for simulations of cellular mechanochemical processes with high spatial and temporal resolution. …”
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Advanced methodology for maximum torque point tracking of hybrid excitation PMSM for EVs by Mahmoud M. Elymany, Nadia A. Elsonbaty, Aymen FLah, Lukas Prokop, Habib Kraiem, Mohamed A. Enany, Ahmed A. Shaier

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THEORY AND EXPERIMENTAL RESEARCH ON CUTTING PROCESSING OF AUTOMOBILE 6061 ALUMINUM ALLOY MATERIALS by WANG YunXia, QI XiaoMin

“…Automobile 6061 aluminum alloy materials has been taken as the research object,the samples have been obtained by the metallurgical technology by adding reinforcing phase fly ash,the cutting theory model under different process parameters have been established based on the grey multi response optimization theory,at the same time the cutting experiments under different process parameters have been conducted,the effects of different process parameters on the cutting of the automobile 6061 aluminum alloy materials have been studied by the theory and experimental methods. …”
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Constitutive Model and Hot Processing Map of Ni-Cr-Fe Heat-resistant Alloy for Advanced Ultra-Supercritical Boilers by Gao　Pei, Huang　Guijun

“…The results showed that the optimal fitting order of the material constants and strain of the improved constitutive model with coupled strain was 6， the correlation coefficient R was 0.988 89， and the absolute error δ was 5.905%. …”
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Frequency-Based Density Estimation and Identification of Partial Discharges Signal in High-Voltage Generators via Gaussian Mixture Models by Krissana Romphuchaiyapruek, Sarawut Wattanawongpitak

“…The optimal number of Gaussian components, determined as five using the Bayesian Information Criterion (BIC), ensures accurate modeling. …”
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A Novel Ionospheric Inversion Model: PINN‐SAMI3 (Physics Informed Neural Network Based on SAMI3) by Jiayu Ma, Haiyang Fu, J. D. Huba, Yaqiu Jin

“…The objective of this study is to investigate the feasibility of integrating physical models with machine learning for ionospheric modeling. …”
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Risk Assessment of High-Voltage Power Grid Under Typhoon Disaster Based on Model-Driven and Data-Driven Methods by Xiao Zhou, Jiang Li

“…Additionally, a power grid failure risk assessment model is built based on Light Gradient Boosting Machine (LightGBM), and the Borderline-Smoothing Algorithm (BSA) is used for the modeling of power grid faults. …”
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Modelling and Simulation for a Versatile Controllable Power Supply Configuration Appropriate for Magnetically Impelled Arc Butt (MIAB) Welding by G. C. Ganesha, M. Chaturvedi, S. Arungalai Vendan, S. Theodore Chandra

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A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals by Lei Liu, Ziyi Wang, Xiaohan Zhang, Yan Zhuang, Yongbo Liang

“…Among the regression models, the classification regression stack: SVM-Light Gradient Boosting Machine (LGBM) was the most effective, with a Mean Absolute Error (MAE) of 6.67 g/L, a Root Mean Square Error (RMSE) of 8.21 g/L, and an R-Square (R2) of 0.64. …”
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Enhancing Emergency Response in Road Accidents: A Severity Prediction Framework Using RF-RFE and Deep Learning Model by Chaimaa Chaoura, Hajar Lazar, Zahi Jarir

“…The attention mechanism further refines predictions by emphasizing critical features. This deep learning model significantly outperforms traditional machine learning methods, achieving accuracy score of 94.99%. …”
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Explicit and explainable artificial intelligent model for prediction of CO2 molecular diffusion coefficient in heavy crude oils and bitumen by Saad Alatefi, Okorie Ekwe Agwu, Ahmad Alkouh

“…The unique contributions of the developed model compared to existing models include: First, a simple and accurate mathematical correlation between inputs and CO2-diffusivity has been presented, making it easy to use particularly for individuals with limited understanding of machine learning. …”
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Enhancing Model Accuracy of UAV-Based Biomass Estimation by Evaluating Effects of Image Resolution and Texture Feature Extraction Strategy by Yaxiao Niu, Xiaoying Song, Liyuan Zhang, Lizhang Xu, Aichen Wang, Qingzhen Zhu

“…Maize AGB estimation models were established based on SIs only and combination of SIs and TFs using machine learning algorithms. …”
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