Harmonic distortion caused by non-linear household loads: Measurement and modelling by Vrindarani Nuñez-Ramírez, Néstor F. Guerrero-Rodríguez, Rafael Omar Batista-Jorge, Robert Mercado-Ravelo, F.A. Ramírez-Rivera, Julio A. Ferreira, Rubén D. Ramos-Ciprian

“…Specifically, RN_RMSE values ranged from 0.82 % for the microwave oven to 3.28 % for the smartphone charger, with the highest accuracy observed in the microwave oven and the largest deviation in the smartphone charger. …”
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Application of a Hybrid Model Based on CEEMDAN and IMSA in Water Quality Prediction by GUO Li-jin, WU Hao-tian

“…[Results] The study introduced four key innovations to the original Mantis Search Algorithm (MSA): (1) combining Logistic-Tent chaotic mapping for population initialization, ensuring uniform and random distribution of initial solutions to enhance global search capability and convergence speed; (2) nonlinear acceleration factor, refining MSA’s core update formula to transition from global exploration to local exploitation, mitigating local optima entrapment; (3) elite-guided adaptive update strategy, addressing the excessive randomness in the position update strategy when Mantis attacks fail, improving late-stage search efficiency while preserving some randomness; (4) opposition-based learning, generating individuals opposite to the current individual to enhance global optimization. …”
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Prediction of Flexural Ultimate Capacity for Reinforced UHPC Beams Using Ensemble Learning and SHAP Method by Zhe Zhang, Xuemei Zhou, Ping Zhu, Zhaochao Li, Yichuan Wang

“…The present ML models predict accurately the flexural ultimate capacity <i>M_u</i> of reinforced UHPC beams after optimization, with EL models providing a higher level of accuracy than the traditional ML models. …”
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An Analytic Policy Gradient-Based Deep Reinforcement Learning Motion Cueing Algorithm for Driving Simulators by Xiaowei Huang, Xuhua Shi, Peiyao Wang, Hongzan Xu, Xiaojun Tang, Gaoran Zhang

“…Unlike the online optimization employed in MPC, this algorithm as an offline optimization method, providing substantial computational advantages when integrated into the driving simulator. …”
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An interpretable stacking machine-learning model to predict the hot torsion flow characteristics of a micro-alloyed steel by Hojjat Emami, Mehdi Shaban Ghazani

“…Understanding hot flow behavior of these steels under complex thermomechanical conditions is of great significance for manufacturing process optimization. The current work investigates the hot torsion behavior of Ti-Nb micro-alloyed steel at temperatures from 850 to 1100∘C and strain rates from 0.01 to 1 s−1. …”
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Deep Learning-Based Vertical Decomposition of Ionospheric TEC into Layered Electron Density Profiles by Jialiang Zhang, Jianxiang Zhang, Zhou Chen, Jingsong Wang, Cunqun Fan, Yan Guo

“…It provides a novel tool for space weather warning and shortwave communication optimization. Current limitations include insufficient physical interpretability and prediction uncertainty in GNSS-sparse regions, which could be mitigated in future work through the integration of physical constraints and multi-source data assimilation.…”
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Six-Dimensional Spatial Dimension Chain Modeling via Transfer Matrix Method with Coupled Form Error Distributions by Lu Liu, Xin Jin, Huan Guo, Chaojiang Li

“…In tolerance design for complex mechanical systems, 3D dimension chain analyses are crucial for assembly accuracy. The current methods (e.g., worst-case analysis, statistical tolerance analysis) face limitations from oversimplified assumptions—treating datum features as ideal geometries while ignoring manufacturing-induced spatial distribution of form errors and failing to characterize 3D coupled error constraints. …”
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Application of Machine Learning Algorithms to Predict Gas Sorption Capacity in Heterogeneous Porous Material by Tasbiha Ibad, Syed Muhammad Ibad, Haylay Tsegab, Rabeea Jaffari

“…Statistical and graphical methods were used to compare the experimental results with the expected values. By comparison, the current work’s ANN-ABC and ANN-PSO models outperform all previous studies with higher R² values (0.9913 and 0.9954) and lower RMSE scores (0.0457 and 0.0420), respectively, indicating improved predictive accuracy and generalization ability. …”
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Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems by Samir A. Hamad, Mohamed A. Ghalib, Amr Munshi, Majid Alotaibi, Mostafa A. Ebied

“…The simulation was conducted on a 100 kW solar panel with an open-circuit voltage of 64.2 V and a short-circuit current of 5.96 A. Model performance was evaluated using metrics such as Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Mean Absolute Error (MAE). …”
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Estimation and Change Analysis of Grassland AGB in the China–Mongolia–Russia Border Area Based on Multi-Source Geospatial Data by Jiani Ma, Chao Zhang, Cong Ou, Chi Qiu, Cuicui Yang, Beibei Wang, Urtnasan Mandakh

“…An AGB estimation model that integrates SHAP-based feature selection with a particle swarm optimization-enhanced random forest model (RF_PSO) was proposed. …”
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Monitoring water quality parameters using multi-source data-driven machine learning models by Yubo Zhao, Mo Chen, Jinyu He, Yanping Ma

“…The MAE was less than 0.85, the RMSE was less than 1, and the MAEP was less than 15.65%. …”
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Estimation of Solar Diffuse Radiation in Chongqing Based on Random Forest by Peihan Wan, Yongjian He, Chaoyu Zheng, Jiaxiong Wen, Zhuting Gu

“…In particular, the fit was optimal for the model under overcast conditions, with R² = 0.70, MAE = 32.20 W/m², and RMSE = 47.51 W/m². …”
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Wind pressure characterization on ground-mounted solar PV systems: A combined experimental and numerical study by Krishna Debnath, Jagadish Barman, Chien-Chun Hsieh, Chao-Yang Huang, Rei-Cheng Juang, Chih-Wei Chiu, Chung-Feng Jeffrey Kuo

“…This study's main scientific contribution is the establishment of practical, verified design wind pressure coefficients for massive ground-mounted PV arrays, which closes a significant gap in current engineering standards. These insights significantly enhance structural optimization practices, ensuring material efficiency and reinforcing vulnerable panel zones, thereby contributing substantially to the resilience and economic sustainability of PV infrastructure under extreme wind conditions.…”
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Advancing in creep index of soil prediction: A groundbreaking machine learning approach with Multivariate Adaptive Regression Splines by Mohammed E. Seno, Husein Ali Zeini, Hamza Imran, Mohammed Noori, Sadiq N. Henedy, Nouby M. Ghazaly

“…The optimized MARS model was then applied to the test set, achieving excellent predictive accuracy. …”
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Centralized Measurement Level Fusion of GNSS and Inertial Sensors for Robust Positioning and Navigation by Mohamed F. Elkhalea, Hossam Hendy, Ahmed Kamel, Ashraf Abosekeen, Aboelmagd Noureldin

“…In the current era, which is characterized by increasing demand for high-precision location and navigation capabilities, various industries, including those involved in intelligent vehicle systems, logistics, augmented reality, and more, heavily rely on accurate location information to optimize processes and deliver personalized experiences. …”
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An Innovation Machine Learning Approach for Ship Fuel-Consumption Prediction Under Climate-Change Scenarios and IMO Standards by Bassam M. Aljahdali, Yazeed Alsubhi, Ayman F. Alghanmi, Hussain T. Sulaimani, Ahmad E. Samman

“…A sensitivity analysis identified vessel speed as the most critical factor, contributing 33% to the variance in fuel consumption, followed by engine power and current speed. Climate-change simulations showed that fuel consumption increases by an average of 22% for bulk carriers and 19% for container ships, highlighting the importance of operational optimizations. …”
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Processing of Polymers Stress Relaxation Curves Using Machine Learning Methods by Anton S. Chepurnenko, Tatiana N. Kondratieva, Ebrahim Al-Wali

“… Currently, one of the topical areas of application of machine learning methods is the prediction of material characteristics. …”
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Apple phenotyping using deep learning and 3D depth analysis: An experimental study on fruitlet sizing during early development by Giorgio Checola, Damiano Moser, Paolo Sonego, Cristian Iob, Franco Micheli, Pietro Franceschi

“…Current research in apple-growing focuses on collecting extensive biometric data to better understand physiological processes, improve orchard productivity and predict yields. …”
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Towards enhanced photovoltaic Modeling: New single diode Model variants with nonlinear ideality factor dependence by Martin Ćalasan, Snežana Vujošević, Gojko Krunić

“…This study introduces five novel SDM variants that incorporate the nonlinear dependence of the diode ideality factor on voltage, aiming to improve the accuracy of current–voltage (I-V) characteristic modeling. For each of the proposed models, an analytical current–voltage relationship was derived using the Lambert W function, ensuring precise representation of the system’s behavior. …”
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Machine learning application to predict binding affinity between peptide containing non-canonical amino acids and HLA-A0201. by Shan Jiang, Zhaoqian Su, Nathaniel Bloodworth, Yunchao Liu, Cristina E Martina, David G Harrison, Jens Meiler

“…Our model demonstrates robust performance, with 5-fold cross-validation yielding an R2 value of 0.477 and a root-mean-square error (RMSE) of 0.735, indicating strong predictive capability for peptides with NCAAs. …”
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