Predicting day-one mobility in partial nephrectomy patients using preoperative and intraoperative clinical parameters by Meijuan Xu, Qiuxuan Zhang, Xiaohui Mo, Yanmei Liu, Man Peng, Xuexia Ma

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Machine learning approaches in the therapeutic outcome prediction in major depressive disorder: a systematic review by Veronica Atemnkeng Ntam, Tatjana Huebner, Michael Steffens, Catharina Scholl

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PCcGE: Personalized Chinese Couplet Generation and Evaluation Framework Based on Large Language Models by Zhigeng Pan, Xianliang Xia, Fuchang Liu, Minglang Zheng

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Predicting Transient Anomalous Transport in Two‐Dimensional Discrete Fracture Networks With Dead‐End Fractures by HongGuang Sun, Dawei Lei, Yong Zhang, Jiazhong Qian, Xiangnan Yu

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Predicting intensive care need in women with preeclampsia using machine learning – a pilot study by Camilla Edvinsson, Ola Björnsson, Lena Erlandsson, Stefan R. Hansson

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SiMBA-Augmented Physics-Informed Neural Networks for Industrial Remaining Useful Life Prediction by Min Li, Jianfeng Qin, Haifeng Fan, Ting Ke

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Enhanced Multi-Level Recommender System Using Turnover-Based Weighting for Predicting Regional Preferences by Venkatesan Thillainayagam, Ramkumar Thirunavukarasu, J. Arun Pandian

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Prediction of the digestibility and digestible energy content of hay for horses using an enzymatic degradability method by D. Andueza, W. Martin-Rosset

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The impact of exercising self-efficacy on physical education learning engagement: the mediating role of flow experience by Wenzhe Huang, Ruikai Wei, Bojin Cheng

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A framework for predicting zoonotic hosts using pseudo-absences: the case of Echinococcus multilocularis by Andrea Simoncini, Dimitri Giunchi, Marta Marcucci, Alessandro Massolo

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Generating learning guides for medical education with LLMs and statistical analysis of test results by Iván Roselló Atanet, Mihaela Tomova, Miriam Sieg, Victoria Sehy, Patrick Mäder, Maren März

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Exploring explainable machine learning algorithms to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023 by Mequannent Sharew Melaku, Nebebe Demis Baykemagn, Lamrot Yohannes, Adem Tsegaw Zegeye

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Development of a Competency-Based Training Model for Child Friendly Pesantren Teams by Abert Albert, Zulfani Sesmiarni, Arman Husni

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From Convolution to Attention: Transformer-Based Modeling for Multi-Day Wildfire Spread Forecasting by Parul Dubey, Nitin Rakesh, Pushkar Dubey, Pitshou Ntambu Bokoro

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Support Vector Regression and Beta Distribution for the Modeling of Incumbent Party vote for Presidential Elections by R., Kikawa, M, N. Ngungu, D., Ntirampeba, A., Ssematimba

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Theoretical Cognition and Practical Design of a High-Quality Teacher Education System for Building a Country Strongin Education: An Interview with Professor Liu Haimin from Northea... by LIU Haimin, MAN Ying

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Hybrid Symbolic Regression and Machine Learning Approaches for Modeling Gas Lift Well Performance by Samuel Nashed, Rouzbeh Moghanloo

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Predicting compressive strength of concrete at elevated temperatures and optimizing its mixture proportions by Jinjun Xu, Han Wang, Wenjun Wu, Lang Lin, Yong Yu

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Wave Run-Up Distance Prediction Combined Data-Driven Method and Physical Experiments by Peng Qin, Hangwei Zhu, Fan Jin, Wangtao Lu, Zhenzhu Meng, Chunmei Ding, Xian Liu, Chunmei Cheng

“…Results demonstrate that the GMM-GBR combined model achieves a coefficient of determination <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> greater than 0.91, outperforming a conventional, non-clustered GBR model. …”
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Predicting Sugarcane Yield Through Temporal Analysis of Satellite Imagery During the Growth Phase by Julio Cezar Souza Vasconcelos, Caio Simplicio Arantes, Eduardo Antonio Speranza, João Francisco Gonçalves Antunes, Luiz Antonio Falaguasta Barbosa, Geraldo Magela de Almeida Cançado

“…This study aims to evaluate the effectiveness of various modeling approaches, including a heteroskedastic gamma regression model, Random Forest, and Artificial Neural Networks, in predicting sugarcane yield based on satellite-derived vegetation indices and environmental variables. …”
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