Multi-Objective Optimal Scheduling of Water Transmission and Distribution Channel Gate Groups Based on Machine Learning by Yiying Du, Chaoyue Zhang, Rong Wei, Li Cao, Tiantian Zhao, Wene Wang, Xiaotao Hu

“…Venant’s system of equations is built to generate the feature dataset, which is then combined with the random forest algorithm to create a nonlinear prediction model. An example analysis demonstrates that the optimal feedforward time of the open channel gate group is negatively connected with the flow condition and that the method can manage the water distribution error within 13.97% and the water level error within 13%. …”
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TPE-LCE-SHAP: A Hybrid Framework for Assessing Vehicle-Related PM2.5 Concentrations by Hamad Almujibah, Abdulrazak H. Almaliki, Caroline Mongina Matara, Adil Abdallah Mohammed Elhassan, Khalaf Alla Adam Mohamed, Mudthir Bakri, Afaq Khattak

“…It utilizes datasets comprising air quality, meteorological, and traffic data collected from strategically placed sensors along the Nairobi Expressway. …”
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Robust Closed–Open Loop Iterative Learning Control for MIMO Discrete-Time Linear Systems with Dual-Varying Dynamics and Nonrepetitive Uncertainties by Yawen Zhang, Yunshan Wei, Zuxin Ye, Shilin Liu, Hao Chen, Yuangao Yan, Junhong Chen

“…The feedforward component is used to make sure the tracking error converges as expected mathematically, while the feedback control part compensates for missing tracking data from previous iterations by utilizing real-time tracking information from the current iteration. …”
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The Correlation of Microscopic Particle Components and Prediction of the Compressive Strength of Fly-Ash-Based Bubble Lightweight Soil by Yaqiang Shi, Hao Li, Hongzhao Li, Zhiming Yuan, Wenjun Zhang, Like Niu, Xu Zhang

“…The Bayesian-optimized Random Forest model performed optimally in terms of the mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), and the prediction performance was best. …”
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Mapping forest types along ecological gradient in Pakistan by Naveed Ahmad, Syed Ghias Ali

“…Among the rest of variables, elevation (R = 0.6), sand contents (R = 0.8) and soil carbon (R = 0.6) contained useful information in order explain forest type spatial distribution. Analysis of regression models revealed that RF has achieved the highest correlation (R ^2 = 0.923) and lowest RMSE 0.54, followed by the SLR model in which R ^2 value has been progressively increased from 0.41 (error 2.02) to 0.917 (0.77) with respect four different predictors models, each separate developed for topographic (n = 5), soil (n-11), climatic (n = 11) and combined of all datasets (n = 27). …”
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Comparison of Machine Learning and Deep Learning Models Performance in predicting wind energy by Saswati Rakshit, Anal Ranjan Sengupta

“…The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. …”
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Crushing Force Prediction Method of Controlled-Release Fertilizer Based on Particle Phenotype by Linlin Sun, Xiubo Chen, Zixu Chen, Linlong Jing, Jinxing Wang, Xinpeng Cao, Shenghui Fu, Yuanmao Jiang, Hongjian Zhang

“…A principal component analysis was applied for data reduction, and the optimal parameters for the support vector machine (SVM) were selected using particle swarm optimization (PSO) combined with k-fold cross-validation. …”
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Development of an interpretable QSPR model to predict the octanol-water partition coefficient based on three artificial intelligence algorithms by Ao Yang, Shirui Sun, Lu Qi, Zong Yang Kong, Jaka Sunarso, Weifeng Shen

“…Using a dataset of 14,610 solvents (14,580 after data cleaning) and 21 molecular descriptors derived from SMILES representations, we rigorously evaluate these models based on R2, mean absolute error (MAE), root mean squared error (RMSE), and mean relative error (MRE). …”
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Machine learning approach for water quality predictions based on multispectral satellite imageries by Vicky Anand, Bakimchandra Oinam, Silke Wieprecht

“…The model performance was evaluated based on coefficient of determination (R2), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the Root Mean Square Error (RMSE) statistical indices. …”
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Study on the electrocatalytic CO2 reduction performance of covalent organic framework materials based on machine learning by Zhang Xiangying, Wang Sitan

“…In order to accurately predict the catalytic performance of covalent organic framework materials (COFs) for electrocatalytic carbon dioxide and analyze the influencing factors affecting the catalytic effect, this study collected COFs structure data and experimental data from 44 literatures, and used machine learning methods. …”
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Remaining useful life prediction of a small sample of aero-engine based on an improved gray Markov model by Dong-hai Li, Ji-liang Tu, Hui Liu, Nai-zhi Liao

“…To address this issue, this study presents a novel data-physics fusion framework using the NASA turbine engine public dataset as the operational data for the study. …”
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The Concept Design of Rice Quality Detection System Using Model-Based System Engineering Approach by Purwa Tri Cahyana, Titi Candra Sunarti, Erliza Noor, Hartrisari Hardjomidjojo, Noer Laily

“…Moreover, machine learning techniques were used to simulate rice quality data analysis using the decision tree classification with the Iterative Dichotomizer 3 (ID3) algorithm. …”
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Predicting soybean seed germination using the tetrazolium test and computer intelligence by Marcio Alves Fernandes, Izabela Cristina de Oliveira, Marcio Dias Pereira, Breno Zaratin Alves, Alan Mario Zuffo, Charline Zaratin Alves

“…The algorithms tested were REPTree, M5P, random forest, logistic regression, artificial neural networks and support vector machine, and the inputs tested were viability, vigor and vigor + viability (tetrazolium test) data. The data analysis used the correlation coefficient and mean absolute error as accuracy parameters of the algorithms. …”
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An improving secure communication using multipath malicious avoidance routing protocol for underwater sensor network by Vignesh Prasanna Natarajan, Senthil Jayapal

“…Due to the limitation of underwater radio wave propagation, nodes rely on acoustic signals to communicate. The data gathered by these nodes is transmitted to coordinating nodes or ground stations for additional processing and analysis. …”
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COMPARISON OF LEAST SQUARE SPLINE AND ARIMA MODELS FOR PREDICTING INDONESIA COMPOSITE INDEX by Any Tsalasatul Fitriyah, Nur Chamidah, Toha Saifudin

“…Spline is chosen because it can handle data that tends to fluctuate by placing knot points when data changes occur. …”
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Prediction of Water Quality Index of Island Counties Under River Length System—A Case Study of Yuhuan City by Cheng Zhang, Lei Wang, Chuan Lin, Minyuan Lu

“…Under the optimal input variable combination, the Extreme Gradient Boosting model demonstrated the best prediction performance, with a root mean square error (RMSE) of 0.7081, a mean absolute error (MAE) of 0.4702, and an adjusted coefficient of determination (Adj.R²) of 0.6400. …”
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Automating Monte Carlo simulations in nuclear engineering with domain knowledge-embedded large language model agents by Zavier Ndum Ndum, Jian Tao, John Ford, Yang Liu

“…In this work, we present AutoFLUKA, a novel framework that leverages domain knowledge-embedded large language models (LLMs) and AI agents to automate the entire FLUKA simulation workflow from input file creation to execution management, and data analysis. AutoFLUKA also integrates Retrieval-Augmented Generation (RAG) and a web-based user-friendly graphical interface, enabling users to interact with the system in real time. …”
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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

“…Methods: A total 111 healthy workers participated in this study and underwent brain MRI, with the analysis incorporating merged workers' health surveillance data from regional hospital workers. …”
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Feasibility and Accuracy of an RTMPose-Based Markerless Motion Capture System for Single-Player Tasks in 3x3 Basketball by Wen Zheng, Mingxin Zhang, Rui Dong, Mingjia Qiu, Wei Wang

“…The Bland–Altman graphs verified no proportional error and little bias. These results confirm the MMC system as a consistent, non-invasive method for gathering movement data in outdoor basketball environments. …”
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Bayesian-Optimized Multi-Task Gaussian Process Regression With Composite Kernels for Soybean Oil Futures Forecasting by Hui-Dong Yin, Yi-Yang Li

“…Comparative evaluations against Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Support Vector Regression (SVR) and Extreme Gradient Boosting (XGBoost) demonstrate MTGPR’s superiority, achieving a mean absolute percentage error (MAPE) of 0.85% and R2 of 0.9313. Key innovations include adaptive kernel selection, probabilistic uncertainty bands for risk-aware strategies, and cross-market transferability analysis. …”
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