Machine learning for reparameterization of multi-scale closures by Hilary Egan, Meagan Crowley, Hariswaran Sitaraman, Lila Branchaw, Peter Ciesielski

“…Scientific machine learning (ML) is becoming increasingly useful in learning closure models for multi-scale physics problems; however, many ML approaches require a vast array of training data and can struggle with generalization and interpretability. …”
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Management of capital renewal at machine-building enterprises by N.A. Yefimenko

“…Scientific novelty and practical value. The model of structure formation in a rich creative process has been proposed for the upstream and downstream directions in order to introduce control over the totality of parameters for the renewal of capital in the machine industry. …”
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Transition state structure detection with machine learningś by Yitao Si, Yiding Ma, Tao Yu, Yifan Wu, Yingzhe Liu, Weipeng Lai, Zhixiang Zhang, Jinwen Shi, Liejin Guo, Oleg V. Prezhdo, Maochang Liu

“…We develop a machine learning approach that utilizes a bitmap representation of chemical structures to generate high-quality initial guesses for modeling transition states of chemical reactions. …”
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Twin Support Vector Regression Model Based on Heteroscedastic Gaussian Noise and Its Application by Shiguang Zhang, Ge Feng, Feng Yuan, Shuangle Guo

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Online Investor Sentiment via Machine Learning by Zongwu Cai, Pixiong Chen

“…They also outperform the traditional linear models, which shows a possible unobserved nonlinear relationship between online investor sentiment and risk premium. …”
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CONDITIONS OF REDUCED COST FOR MACHINE-BUILDING PRODUCTS by Dmytro NOVIKOV, Yury GUTSALENKO, Feodor NOVIKOV

“…The mathematical model for the cost of processing of machine parts was developed and the conditions for its reduction were determined. …”
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LLM-Enhanced Human–Machine Interaction for Adaptive Decision-Making in Dynamic Manufacturing Process Environments by Zozan Keskin, Dominik Joosten, Nils Klasen, Meike Huber, Chang Liu, Benny Drescher, Robert H. Schmitt

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EL-MTSA: Stock Prediction Model Based on Ensemble Learning and Multimodal Time Series Analysis by Jianlei Kong, Xueqi Zhao, Wenjuan He, Xiaobo Yang, Xuebo Jin

“…To improve the existing issues, this research introduces a novel stock prediction model based on a deep-learning neural network, named after EL-MTSA, which leverages the multifaceted characteristics of stock data along with ensemble learning optimization. …”
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Churn prediction for SaaS company with machine learning by Hugo Eduardo Sanches, Ayslan Trevizan Possebom, Linnyer Beatrys Ruiz Aylon

“…Design/methodology/approach – Through a preprocessing and normalization of data, seven machine learning algorithms were applied. The models were trained, and also cross-validation and parameter tuning techniques were applied to improve results. …”
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Artificial intelligence-driven ensemble deep learning models for smart monitoring of indoor activities in IoT environment for people with disabilities by Munya A. Arasi, Hussah Nasser AlEisa, Amani A. Alneil, Radwa Marzouk

“…This study develops and designs a metaheuristic optimization-driven ensemble model for smart monitoring of indoor activities for disabled persons (MOEM-SMIADP) model. …”
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Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines by E León, A Li, M A Bahena Schott, B Bos, M Busch, J R Chapman, G L Duran, J Gruszko, R Henning, E L Martin, J F Wilkerson

“…We train, optimize, and test our model on data taken from a natural abundance HPGe detector installed in the Full Chain Test experimental stand at the University of North Carolina at Chapel Hill. …”
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A Near-Real-Time Model for Predicting Electricity Disruptions in Texas During Winter Storms by Jangjae Lee, Sangkeun Lee, Supriya Chinthavali, Stephanie Paal

“…The developed models were broadly divided into two groups, with six models in each group - one group without optimization and another with optimization, totaling 12 trained models. …”
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The Application of Digital Twins in Precision and Ultra-precision Manufacturing by GENG Yanquan, GAO Yunli, LI Chen, YAN Yongda

“…By creating virtual replicas of physical entities, digital twins enable real-time monitoring, prediction, and optimization of the machining process, enhancing processing accuracy and efficiency. …”
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Development and Evaluation of Neural Network Architectures for Model Predictive Control of Building Thermal Systems by Jevgenijs Telicko, Andris Krumins, Agris Nikitenko

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Optimized Hydrodynamic Design for Autonomous Underwater Vehicles by Gang Fan, Xiaojin Liu, Yanan Hao, Guoling Yin, Long He

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Accurate modeling and simulation of the effect of bacterial growth on the pH of culture media using artificial intelligence approaches by Suleiman Ibrahim Mohammad, Hamza Abu Owida, Asokan Vasudevan, Suhas Ballal, Shaker Al-Hasnaawei, Subhashree Ray, Naveen Chandra Talniya, Aashna Sinha, Vatsal Jain, Ahmad Abumalek

“…The Coupled Simulated Annealing (CSA) algorithm was employed to optimize the hyperparameters of these models, enhancing their predictive performance. …”
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Economy Optimization by Multi-Strategy Improved Whale Optimization Algorithm Based on User Driving Cycle Construction for Hybrid Electric Vehicles by Jie Ma, Mingzhang Pan, Wei Guan, Zhiqing Zhang, Jingcheng Zhou, Nianye Ye, Haifeng Qin, Lulu Li, Xingjia Man

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Multi-Step Prediction of TBM Tunneling Speed Based on Advanced Hybrid Model by Defu Liu, Yaohong Yang, Shuwen Yang, Zhixiao Zhang, Xiaohu Sun

“…In this paper, a multi-step prediction model of TBM tunneling speed based on the EWT-ICEEMDAN-SSA-LSTM hybrid model is proposed. …”
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A novel mechanism-guided residual network for accurate modelling of scroll expander under noisy and sparse data conditions by Xiaoshuang Lv, Xin Ma, Wei Peng, Ke Li, Chengdong Li

“…Abstract Accurate and practical modelling of the scroll expander is essential to improve energy conversion efficiency and reduce energy losses. …”
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CKRT coagulation risk prediction and nursing feedback model based on intelligent algorithms by Xianrong Xu, Mou Chen, Lvjing Chen, Kaixing Huang, Shiqi Cao, Wenwen Gao, Kang Liu, Buyun Wu, Huijuan Mao

“…Extensive numerical experiments were conducted to optimize model parameters and evaluate performance, ensuring high accuracy, stability, and superior AUC values for reliable predictions. …”
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