Association Between Shift Working and Brain Morphometric Changes in Workers: A Voxel-wise ComparisonKey Messages by Joon Yul Choi, Sungmin Kim, Yongho Lee, Dohyeon Kim, Wanhyung Lee

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Tree-Based Machine Learning Approach for Predicting the Impact Behavior of Carbon/Flax Bio-Hybrid Fiber-Reinforced Polymer Composite Laminates by Manzar Masud, Aamir Mubashar, Shahid Iqbal, Hassan Ejaz, Saad Abdul Raheem

“…In this research, the effect of change in stacking sequences on the impact performance of bio-hybrid fiber-reinforced polymer (bio-HFRP) composite materials was analyzed and evaluated. …”
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Hedge-Algebra-Based Phase-Locked Loop for Distorted Utility Conditions by Dinko Vukadinović, Tien Duy Nguyen, Cat Ho Nguyen, Nhu Lan Vu, Mateo Bašić, Ivan Grgić

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A Rice Leaf Area Index Monitoring Method Based on the Fusion of Data from RGB Camera and Multi-Spectral Camera on an Inspection Robot by Yan Li, Xuerui Qi, Yucheng Cai, Yongchao Tian, Yan Zhu, Weixing Cao, Xiaohu Zhang

“…The model based on the LightGBM regression algorithm has the most improvement in accuracy, with a coefficient of determination (R²) of 0.892, a root mean square error (RMSE) of 0.270, and a mean absolute error (MAE) of 0.160. …”
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Intelligent traffic congestion forecasting using BiLSTM and adaptive secretary bird optimizer for sustainable urban transportation by Lalitha Krishnasamy, Siva C, Rajesh Kumar Dhanaraj, Mahmoud Ahmad Al-Khasawneh, Taher Al-Shehari, Nasser A. Alsadhan, Shitharth Selvarajan

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Data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performance by Harshada Mhetre, Sagar Pande, Babita Singla, Pavan Hiremath, Samriddh Sahu, Sarvesh Sorte, Ketan Kotecha, Nithesh Naik

“…These findings underscore the potential of machine learning in advancing electrospinning technology by minimizing trial-and-error experiments and boosting nanofiber production efficiency. …”
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Fine‐tuning established morphometric models through citizen science data by Veronika N. Biskis, Kathy A. Townsend, David L. Morgan, Karissa O. Lear, Bonnie J. Holmes, Barbara E. Wueringer

“…In the case of sawfishes, much previous research has relied on amputated trophy rostra or historical photographs to fill data gaps in distribution and population estimates. …”
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Mine pressure prediction based on empirical mode decomposition linear model by Yuwei ZHU, Pengfei WANG, Huixian WANG, Qiangqiang NIU, Liang XIN

“…Four evaluation metrics were used to assess the prediction results: Mean Absolute Error (EMAE), Mean Squared Error (EMSE), Symmetric Mean Absolute Percentage Error (EsMAPE), and R-squared (R2). …”
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Enhanced wind power forecasting using machine learning, deep learning models and ensemble integration by T. A. Rajaperumal, C. Christopher Columbus

“…The performance was evaluated using the Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R2) metrics. …”
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Monocular Unmanned Boat Ranging System Based on YOLOv11-Pose Critical Point Detection and Camera Geometry by Yuzhen Wu, Yucheng Suo, Xinqiang Chen, Yongsheng Yang, Han Zhang, Zichuang Wang, Octavian Postolache

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Automatic Recognition of Motor Skills in Triathlon: A Novel Tool for Measuring Movement Cadence and Cycling Tasks by Stuart M. Chesher, Carlo Martinotti, Dale W. Chapman, Simon M. Rosalie, Paula C. Charlton, Kevin J. Netto

“…<b>Background/Objectives</b>: The purpose of this research was to create a peak detection algorithm and machine learning model for use in triathlon. …”
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Predictive modeling of air quality in the Tehran megacity via deep learning techniques by Abdullah Kaviani Rad, Mohammad Javad Nematollahi, Abbas Pak, Mohammadreza Mahmoudi

“…R-squared (R2), root-mean-square error (RMSE), mean absolute error (MAE), and mean-square error (MSE) were used to assess and compare the models. …”
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An Optimal Approach for Assessing Weibull Parameters and Wind Power Potential for Six Coastal Cities in Pakistan by Ghulam Abbas, Arshad Ali, Faizan Rashid, Naveed Ashraf, Zohaib Mushtaq, Muhammad Zubair

“…Four statistical indicators, namely root mean square error, mean absolute error, coefficient of determination, and coefficient of efficiency, are taken into consideration to measure the accuracy offered by NEPFM-SSA and NEPFM. …”
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Development and validation of a novel AI-derived index for predicting COPD medical costs in clinical practice by Guan-Heng Liu, Chin-Ling Li, Chih-Yuan Yang, Shih-Feng Liu

“…Model performance was assessed with Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and R-squared (R²). …”
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Response Surface Methodology for Modelling and Optimizing Efficiency in Deep Well Pumping Systems by Nuri Orhan, Murat Çavuşlar, Meki Solmaz, Muhammet Emre Erdem

“…This study presents research on modelling the efficiency and flow rate of deep well pumping facilities using the response surface method, evaluating the models, and assessing optimization based on target flow rate. …”
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Marginal land identification and grain production capacity prediction of the coverage area of western route of China’s South-to-North Water Diversion Project by Heng Zhou, Jun Zhou, Jun Zhou, Kunming Lu, Minghui Niu, Chenyi Wang, Gaofeng Zhang, Jiawei Kou

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Prognostic machine learning models for thermophysical characteristics of nanodiamond-based nanolubricants for heat pump systems by Ammar M. Bahman, Emil Pradeep, Zafar Said, Prabhakar Sharma

“…The data collected from the experimental research were used to build prognostic models using modern supervised ML techniques, including Gaussian process regression (GPR) and boosted regression tree (BRT). …”
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Regression models for predicting the effect of trash rack on flow properties at power intakes by Shuguang Li, Sultan Noman Qasem, Hojat Karami, Ely Salwana, Alireza Rezaei, Danyal Shahmirzadi, Shahab S. Band

“…Thus, the LJA-GB model has the lowest mean absolute error (MAE) (0.3344), mean squared error (MSE) (0.1784), and root mean squared error (RMSE) (0.4223) values and highest R-squared ([Formula: see text]) (0.9899) and Willmott’s index (WI) values (0.9508) in the testing stage metrics for [Formula: see text] estimation and MAE (0.0061), MSE (0.0001), RMSE (0.0073), [Formula: see text] (0.9971), WI (0.9727) for [Formula: see text] estimation. …”
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Arctic Sea Ice Surface Temperature Retrieval from FengYun-3A MERSI-I Data by Yachao Li, Tingting Liu, Zemin Wang, Mohammed Shokr, Menglin Yuan, Qiangqiang Yuan, Shiyu Wu

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Optimizing Air Pollution Forecasting Models Through Knowledge Distillation: A Novel GCN and TRANS_GRU Methodology for Indian Cities by Sudhir Kumar, Vaneet Kour, Ankit Raj, Tagru Tapung, Shivendu Mishra, Rajiv Misra, T. N. Singh

“…The TRANS_GRU model achieves improvements of 2.36% in the R2 score, 15.23% in the mean squared error (MSE), 19.12% in the mean absolute error (MAE), and 7.93% in the root mean squared error (RMSE) compared to LSTM. …”
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