Identifying Precipitation Types From Surface Meteorological Variables With Machine Learning by Adrienn VARGA-BALOGH, Ádám LEELŐSSY, Róbert MÉSZÁROS

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Using machine learning for the assessment of ecological status of unmonitored waters in Poland by Andrzej Martyszunis, Małgorzata Loga, Karol Przeździecki

“…The following study showcases usage of Machine Learning (ML) techniques as a complementary method for water status assessment of water bodies. …”
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Application of machine learning models in predictive maintenance of Francis hydraulic turbines by Júlio César Silva de Souza, Oswaldo Honorato Júnior, Geraldo Lúcio Tiago Filho, Otávio Augusto Salgado Carpinteiro, Hailton Silveira Domingues Biancardine Júnior, Ivan Felipe Silva dos Santos

“…Drawing upon vibration analysis and pressure coefficient parameter standards, such models are capable of identifying the vibratory state of a given machine, distinguishing its cavitating and non-cavitating states. …”
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Application of numerical methods to the analysis of the magnetic field in AC traction machines by A. S. Zuev, M. D. Gluschenko

“…This article examines the current approach to the design of rotating electrical machines. An overview is given of existing software packages for modelling electromagnetic and thermal processes using numerical finite element methods, designed to replace concentrated parameter analysis of electrical equivalent circuits. …”
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Machine tool FEM model correction assisted by dynamic evolution sequence by Weihao Lin, Peng Zhong, Xindi Wei, Li Zhu, Xuanlong Wu

“…Abstract In the simulation analysis of large-scale industrial instruments such as machine tools, in order to ensure simulation accuracy, model parameter correction is necessary. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

“…The algorithms (RF and STR) with the smallest Mean Absolute Error (MAE) and the highest residual error (RMSE) and the highest correlation coefficient (RP2) were selected for further parameter optimization and evaluation. A 5-fold cross-validation with 999 repetitions was performed on all trained machine learning models. …”
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Cheating Detection in Online Exams Using Deep Learning and Machine Learning by Bahaddin Erdem, Murat Karabatak

“…This study aims to identify the best deep learning and machine learning models to identify the unethical behavior patterns of learners using distance education exam data of an educational institution. …”
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Modelling the fatigue damage in power components using machine learning technology by Stoyan Stoyanov, Razia Sulthana, Tim Tilford, Xiaotian Zhang, Yihua Hu, Xingyu Yang, Yaochun Shen, Yangang Wang

“…Here, a novel physics-informed Machine Learning (ML) approach for developing computationally fast metamodels for predicting fatigue damage and its spatial distribution at common failure sites of power electronics components is developed, validated and demonstrated. …”
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Movement and distribution of particles in the tank of jet and agitation combined flotation machine by Tao WANG, Wei ZHOU, Zilei WANG, Shujie WANG, Lingling WANG, Liang LI

“…The uniformity of particle distribution does not increase with the increase of impeller stirring speed, but there is an optimal impeller working parameter. The results of simulation and test showed that the jet and agitation co-flotation machine has better particle dispersion performance under the condition of impeller speed of 6.25 r/s. …”
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Influence of the Drilling Parameters in the Single-Lip Deep-Hole Drilling Process on the Surface Integrity of Nickel-Based Alloy by Tao Wu, Fangchao Zhang, Haoguang Zhou, Dong Zhang

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Influence of Different Mining Parameters on Collaborative Bearing Characteristics Between Hydraulic Supports and Surrounding Rock Coupling System by Zhaosheng Meng, Jiacheng Wang, Xiaowan Lei, Penghui Xu

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Leveraging machine learning to proactively identify phishing campaigns before they strike by Kun Zhang, Haifeng Wang, Meiyi Chen, Xianglin Chen, Long Liu, Qiang Geng, Yu Zhou

“…These algorithms were chosen for their strong global search capabilities and adaptability to complex datasets, ensuring optimal parameter selection for improved model performance. …”
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Compression Index Regression of Fine-Grained Soils with Machine Learning Algorithms by Mintae Kim, Muharrem A. Senturk, Liang Li

“…These results indicate superior predictive accuracy compared to previous studies using traditional statistical and machine learning methods. Machine learning algorithms, specifically the gradient boosting regressor and random forest regressor, demonstrate substantial potential in predicting the <i>C_c</i> value for fine-grained soils based on multiple soil parameters. …”
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Machine learning in additive manufacturing——NiTi alloy’s transformation behavior by Lidong Gu, Kongyuan Yang, Hongchang Ding, Zezhou Xu, Chunling Mao, Panpan Li, Zhenglei Yu, Yunting Guo, Luquan Ren

“…Nevertheless, achieving precise control and regulation of the phase transition temperature poses a challenge, influenced by factors like powder composition and process parameter. In this study, a feature screening strategy and machine learning (ML) method were employed to predict and regulate the phase transition temperature of LPBF-NiTi alloy, offering a more efficient and cost-effective alternative to traditional experimental methods of regulation and control. …”
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Machine learning-assisted Ru-N bond regulation for ammonia synthesis by Zichuang Li, Mingxin Zhang, Xiaozhi Su, Yangfan Lu, Jiang Li, Qing Zhang, Wenqian Li, Kailong Qian, Xiaojun Lu, Bo Dai, Hideo Hosono, Yanpeng Qi, Miao Xu, Renzhong Tai, Jie-Sheng Chen, Tian-Nan Ye

“…However, their complex phase nature and the numerous controlling parameters pose major challenges for catalyst design and exploration. …”
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Machine learning model for random forest acute oral toxicity prediction by A.M. Elsayad, M.M. Zeghid, K.A. Elsayad, A.N. Khan, ِA.K.M. Baareh, A. Sadiq, S.A. Mukhtar, H.F. Ali, S. Abd El-kader

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METHODS OF DETERMINATION OF POWER AND FUEL-ECONOMIC RATES OF MACHINE AND TRACTOR UNITS by A. G. Arzhenovskiy

“…One of the most important tendencies of increase in agricultural industry efficiency is improvement of methods and means for  determination of the main parameters of the machine and tractor  units (MTU). …”
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Studies on Improving Seals for Enhancing the Vibration and Environmental Safety of Rotary Machines by Zhifei Yuan, Serhii Shevchenko, Mykola Radchenko, Oleksandr Shevchenko, Anatoliy Pavlenko, Andrii Radchenko, Roman Radchenko

“…The rotor of a multi-stage machine rotates in non-contact seals. Seals’ parameters have a great influence on vibration characteristics. …”
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Machine Learning-Based Methods for the Seismic Damage Classification of RC Buildings by Sung Hei Luk

“…The importance of different input parameters is studied. The results reveal that well-prepared machine learning models are also capable of predicting damage levels with an adequate level of accuracy and minimal computational effort. …”
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Heart rate variability in soccer players and the application of unsupervised machine learning by Wollner Materko, Sávio Andrei Medeiros Miranda, Thiago Henrique Lobato Bezerra, Carlos Alberto Machado de Oliveira Figueira

“…Aim: This study aimed to investigate the relationship between heart rate variability (HRV) parameters and performance in soccer players. Methods: This study used a cross-sectional design to assess HRV parameters in a cohort of twenty-nine male athletes, aged 18 to 20 years, randomly selected from the Macapá Sports Club team in the Amazon region. …”
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