Energy optimization control of extended-range hybrid combine harvesters based on quasi-cycle power demand estimation by Shuofeng Weng, Chaochun Yuan, Youguo He, Jie Shen, Lizhang Xu, Zhihao Zhu, Qiuye Yu, Xiaowei Yang

“…By segmenting harvesting processes into quasi-periodic cycles linked to machine dynamics, the method integrates component-specific power models (header, conveyor, drum) for accurate energy estimation. …”
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Human–Seat–Vehicle Multibody Nonlinear Model of Biomechanical Response in Vehicle Vibration Environment by Margarita Prokopovič, Kristina Čižiūnienė, Jonas Matijošius, Marijonas Bogdevičius, Edgar Sokolovskij

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Adaptive Production Rescheduling System for Managing Unforeseen Disruptions by Andy J. Figueroa, Raul Poler, Beatriz Andres

“…The approach begins by generating an optimal production plan through batch assignments to machines. …”
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Enhancement Material Removal Rate Optimization of Sinker EDM Process Parameters Using a Rectangular Graphite Electrode by Sukarman, Sumanto, Acim Maulana, Dodi Mulyadi, Khoirudin, Siswanto, Ade Suhara, Safril

“… This article discusses the optimization of sinker electrical discharge machining (sinker EDM) processes using SPHC material that has been hardened. …”
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Incorporating echo state network and sand cat swarm optimization algorithm based on quantum for named entity recognition by Fang Huang, Baocheng Wang, Jafar Safarzadeh

“…The main contribution of this study is the combination of QSCSO with ESN, which improves the model’s capacity to comprehend long-term dependencies and effectively optimize hyperparameters. …”
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Robust techno-economic optimization of energy hubs under uncertainty using active learning with artificial neural networks by Aya M. A. Heikal, Shady H. E. Abdel Aleem, Ragab A. El-Sehiemy, Almoataz Y. Abdelaziz

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One techno-economic analysis to rule them all: Instant prediction of hydrothermal liquefaction economic performance with a machine learned analytic equation by Muntasir Shahabuddin, Nikolaos Kazantzis, Andrew R Teixeira, Michael T. Timko

“…It is demonstrated that the reduced-order model’s predictions fall within 40% of the corresponding published values 95% of the time, and in the worst case, the associated discrepancy is 45.9%, suggesting that the accuracy of the machine learned model is indeed comparable to the TEAs that were used to build it. …”
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Optimizing stand-alone microgrids with lagrange multiplier technique: A cost-effective and sustainable solution for rural electrification by Rasha Elazab, Ahmed Mamdouh Ewais, Maged Ahmed Abu-Adma

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On the Utilization of Emoji Encoding and Data Preprocessing with a Combined CNN-LSTM Framework for Arabic Sentiment Analysis by Hussam Alawneh, Ahmad Hasasneh, Mohammed Maree

“…Furthermore, the Keras tuner optimized the CNN-LSTM parameters during the 5-fold cross-validation process. …”
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Predicting the high-strain-rate deformation behavior and constructing processing maps of 304L stainless steel through machine learning and deep learning by M. Ghaffari Farid, H.R. Abedi, R. Ghasempour, A. Taylor, S. Khoddam, P.D. Hodgson

“…The Random Forest model was optimized with various parameters, and the best performance came from a tree depth of 15, 150 estimators, and 150 leaf nodes. …”
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Machine learning-based predictive tools and nomogram for in-hospital mortality in critically ill cancer patients: development and external validation using retrospective cohorts by Kaier Gu, Saisai Lu

“…Among these models, the LR model and the eXtreme gradient boosting (XGB) model demonstrated the optimal efficacy. …”
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Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients by Yandong lian, Yibin Xu, Linlin Hu, Yuguo Wei, Zhaoge Wang

“…Furthermore, based on the optimal cut-off value of the model, subjects were categorized into low- and high-risk groups, and differences in the actual number of RBD patients between the two sets were compared to assess the clinical effectiveness of the model. …”
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Nitrogen Dioxide Monitoring by Means of a Low-Cost Autonomous Platform and Sensor Calibration via Machine Learning with Global Data Correlation Enhancement by Slawomir Koziel, Anna Pietrenko-Dabrowska, Marek Wójcikowski, Bogdan Pankiewicz

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Accelerated Prediction of Terahertz Performance Metrics in GaN IMPATT Sources via Artificial Neural Networks by Santu Mondal, Sneha Ray, Aritra Acharyya, Rudra Sankar Dhar, Arindam Biswas, Hiroaki Satoh, Gurudas Mandal, Vitaliy Maksimenko, Victor Krishtop

“…Mean square errors are observed to be on the order of <inline-formula> <tex-math notation="LaTeX">$10^{-4}$ </tex-math></inline-formula>&#x2013;<inline-formula> <tex-math notation="LaTeX">$10^{-6}$ </tex-math></inline-formula>, demonstrating the models&#x2019; high accuracy. Experimental validation shows strong agreement in terms of breakdown voltage, power output, and efficiency, supporting the potential of machine learning to streamline the design and optimization of high-frequency semiconductor devices.…”
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Association between accelerometer-measured physical activity volume and sleep duration in older adults: a cross-sectional interpretable machine learning analysis by XiaoTao Cai, Yi Xian, YuXin Zhou, TongYi Liu, Xinyue Zhang, Qing Chen

“…Analysis of the derivation cohort included weighted univariate analysis, weighted multivariate logistic regression, and interpretable machine learning analysis. The machine learning interpretability process involved dividing a 20% internal validation test set, using the grid search method and five-fold cross-validation to construct RF, GBDT, XGBoost, and LightGBM models, as well as a two-layer stacked ensemble model for model comparison, with external validation of the optimal model’s performance. …”
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The intelligent evaluation model of the English humanistic landscape in agricultural industrial parks by the SPEAKING model: From the perspective of fish-vegetable symbiosis in new... by Yiping He, Mingyue Gao, Luyao Wang

“…Comparative evaluations are conducted against five prominent translation models: Multilingual T5 (mT5), Multilingual Bidirectional and Auto-Regressive Transformers (mBART), Delta Language Model (DeltaLM), Many-to-Many Multilingual Translation Model-100 (M2M-100), and Marian Machine Translation (MarianMT). …”
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Comparative Analysis of Several Models for Churning Customer Prediction by Tan Zhaoyuan

“…This study compares three machine learning models: Random Forest, XGBoost Classifier, and Light Gradient Boosting Machine Classifier for predicting credit card customer churn using a dataset from Kaggle. …”
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GIS Analysis Model Integration and Service Composition Prospects by L. Ding, P. Cai, W. Huang, H. Zhang, F. Ding, W. Zhao, D. Tang, Z. Wang

“…Model ensemble techniques, rooted in machine learning and data mining, address limitations of single models by combining predictions from multiple base learners, thereby improving robustness and reducing overfitting. …”
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Machine Learning-Assisted Design of Doping Strategies for High-Voltage LiCoO₂: A Data-Driven Approach by Man Fang, Yutong Yao, Chao Pang, Xiehang Chen, Yutao Wei, Fan Zhou, Xiaokun Zhang, Yong Xiang

“…Experiments focusing on ion electronegativity design verified the effectiveness of the optimal combined model. We demonstrate the benefits of machine learning models in uncovering the core elements of complex doped LiCoO₂ formulation design. …”
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An explainable web application based on machine learning for predicting fragility fracture in people living with HIV: data from Beijing Ditan Hospital, China by Bo Liu, Bo Liu, Qiang Zhang, Qiang Zhang, Xin Li, Xin Li

“…The optimal model was integrated into an online risk assessment calculator.ResultsThe XGBoost model showed the highest predictive performance, with key features including age, smoking, fall history, TDF use, HIV viral load, vitamin D, hemoglobin, albumin, CD4 count, and lumbar spine BMD. …”
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