Effects of finger acupressure combined with lower limb rehabilitation training machine on stroke recovery by Xiaoxue Liu, Feng Zhang, Yanhong Li, Jieqiong Zhao, Yatao Du, Qian Zhang, Weifang Li

“…These participants were then randomly divided into two groups: the treatment group underwent finger acupressure combined with lower limb rehabilitation training machine, and the control group received basic rehabilitation therapy. …”
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A synthetic data-driven machine learning approach for athlete performance attenuation prediction by Mauricio C. Cordeiro, Ciaran O. Cathain, Ciaran O. Cathain, Lorcan Daly, Lorcan Daly, David T. Kelly, David T. Kelly, Thiago B. Rodrigues

“…IntroductionAthlete performance monitoring is effective for optimizing training strategies and preventing injuries. However, applying machine learning (ML) frameworks to this domain remains challenging due to data scarcity limitations. …”
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Prediction rotary drilling penetration rate in lateritic soils using machine learning models by Eugène Gatchouessi Kamdem, Franck Ferry Kamgue Tiam, Luc Leroy Mambou Ngueyep, Olivier Wounabaissa, Hugues Richard Lembo Nnomo, Abraham Kanmogne

“…The present paper investigated an accurate machine learning model for the penetration rates (ROP) prediction in lateritic soil covers layers. …”
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Decoding Motor Excitability in TMS Using EEG-Features: An Exploratory Machine Learning Approach by Lisa Haxel, Oskari Ahola, Paolo Belardinelli, Maria Ermolova, Dania Humaidan, Jakob H. Macke, Ulf Ziemann

“…We present a supervised machine learning framework that predicts individual motor excitability states from pre-stimulus EEG features. …”
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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…Although multiple regression analyses produced R² values exceeding 0.84, the multiple regression equations did not provide substantial insights into the impact of all petrographic parameters on engineering properties. To enhance predictive accuracy, advanced machine learning models, including Random Forest, Gradient Boosting, Multi-Layer Perceptron, and Categorical Boosting, were applied. …”
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Predicting biomass transportation costs: A machine learning approach for enhanced biofuel competitiveness by Ali Omidkar, Razieh Es’haghian, Hua Song

“…Consequently, this study explores the predictive capabilities of two alternative machine learning algorithms: random forests and artificial neural networks. …”
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The Effect of High Nitric Oxide Concentrations on Oxygenators in Cardiopulmonary Bypass Machines (Experimental Study) by A. M. Radovskiy, I. V. Vorotyntsev, A. A. Atlaskin, A. N. Petukhov, S. S. Kryuchkov, M. E. Atlaskina, A. N. Stepakova, A. O. Marichev, E. K. Barygin, V. V. Osovskikh, V. D. Selemir, S. N. Buranov, V. V. Golovanov, A. S. Shirshin, Yu. V. Valueva, V. V. Pichugin, S. E. Domnin, A. E. Bautin

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A Machine Learning Model for Predicting Prognosis in HCC Patients With Diabetes After TACE by Wu L, Chen L, Zhang L, Liu Y, Ouyang D, Wu W, Lei Y, Han P, Zhao H, Zheng C

“…Therefore, this study aimed to establish and validate a machine learning-based explainable prediction model of prognosis in patients with HCC and T2DM undergoing transarterial chemoembolization (TACE).Patients and Methods: The prediction model was developed using data from the derivation cohort comprising patients from three medical centers, followed by external validation utilizing patient data extracted from another center. …”
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A Hierarchical Machine Learning-Based Strategy for Mapping Grassland in Manitoba’s Diverse Ecoregions by Mirmajid Mousavi, James Kobina Mensah Biney, Barbara Kishchuk, Ali Youssef, Marcos R. C. Cordeiro, Glenn Friesen, Douglas Cattani, Mustapha Namous, Nasem Badreldin

“…This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed in the province of Manitoba, Canada. …”
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Evaluating the impact of waste marble on the compressive strength of traditional concrete using machine learning by Kennedy C. Onyelowe, Viroon Kamchoom, Ahmed M. Ebid, Shadi Hanandeh, Susana Monserrat Zurita Polo, Rolando Fabián Zabala Vizuete, Rodney Orlando Santillán Murillo, Rolando Marcel Torres Castillo, Siva Avudaiappan

“…Incorporating waste marble in concrete not only addresses environmental concerns related to marble waste disposal but also contributes to the sustainability of construction materials. Using machine learning (ML) to predict the impact of waste marble on the compressive strength of traditional concrete offers several advantages over repeated laboratory experiments. …”
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Analysis of Static Characteristic of Hydrodynamic Bearing with Different Surface Roughnesses by Hong Guo, Lanlan Song, Shaolin Zhang, Weipei He

“…When the working surface roughness of hydrodynamic sliding bearing reaches the same magnitude （micron） as the oil film thickness， the influence of surface roughness on the characteristic parameters of bearing cannot be ignored. In order to study the influence of roughness on the static characteristics of bearing， an experimental platform is built based on M2000 friction and wear testing machine to test the sliding friction pairs of 42CrMo steel， and a Reynolds mathematical model considering the surface roughness is established and solved. …”
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Numerical and Experimental Analysis of the Structural Behavior of an EPP Component by Carlo Sabbatini, Gianluca Chiappini, Veronica Ilari, Giacomo Zandri, Marco Sasso

“…We performed a uniaxial and simple shear test to calibrate the model’s parameters. In this work, a compression test was performed on a component, entirely made of expanded polypropylene, from a commercial machine. …”
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Evaluation of inflammatory serum parameters as a diagnostic tool in patients with endometriosis: a case-control study by Mariz Kasoha, Panagiotis Sklavounos, Istvan Molnar, Meletios P. Nigdelis, Bashar Haj Hamoud, Erich-Franz Solomayer, Gilbert Georg Klamminger

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Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning by Jian Li, Jian Lu, Hongkun Fu, Wenlong Zou, Weijian Zhang, Weilin Yu, Yuxuan Feng

“…Data analysis and parameter prediction were conducted using a variety of machine learning and deep learning models including Partial Least Squares (PLSs), Support Vector Machine (SVM), Random Forest (RF), and Long Short-Term Memory Networks (LSTM), among which the LSTM model demonstrated superior performance, particularly at multiple critical time points. …”
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Application of KTA-KELM in Fault Diagnosis of Rolling Bearing by Zhuo Wang, Wenjun Zhao, Tao Ma, Zhijun Li, Bo Qin

“…In the process of data-driven rolling bearing state identification model construction，the improper selection of the radial width parameter σ of the Gaussian kernel function in the Kernel Extreme Learning Machine（KELM）algorithm is very easy to cause poor classification accuracy. …”
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Algorithm for Identification Electromagnetic Parameters of an Induction Motor When Running on a Three-Phase Power Plant by D. S. Odnolko

“…The algorithm is based on the use of recursive least squares method, which ensures high accuracy of the parameter estimates for the minimum time. The observer does not assume prior information about the technical data machine and individual parameters of its equivalent circuit. …”
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A data-driven state identification method for intelligent control of the joint station export system by Guangli Xu, Yifu Wang, Zhihao Zhou, Yifeng Lu, Liangxue Cai

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Effect of aging on the translucency of lithium disilicate and zirconia reinforced lithium silicate ceramics: An in vitro study by Mohammed Ali Alasmari, Mohammad Ramadan Rayyan

“…All ceramic specimens were fabricated with shade that corresponded to A2 with high translucency (HT) and subjected to measurement of relative translucency parameter (RTP) with a spectrophotometer machine prior to and following thermocycling. …”
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Support Vector Regression Inverse System Control for Small Wind Turbine MPPT with Parameters’ Robustness Improvement by Hongru Wang, Zhigang Zhang, Wenjuan Zhang, Mengdi Li, Yang Zhang

“…Therefore, this paper introduces a support vector regression machine (SVR) method, especially for the inverse system model, which could solve the inaccurate parameters problems. …”
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Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles by Steven M. E. Demers, Christopher Sobecki, Larry Deschaine

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