Study on oil pilot circuit of adaptive hydraulic drive of tool advance in mobile drilling machine by V. S. Sidorenko, V. I. Grishchenko, S. V. Rakulenko, M. S. Poleshkin, D. D. Dymochkin

“…An adaptive hydraulic drive of the tool advance in a mobile drilling machine is studied on the example of the URB-2.5 installation. …”
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Machine learning helps reveal key factors affecting tire wear particulate matter emissions by Zhenyu Jia, Jiawei Yin, Tiange Fang, Zhiwen Jiang, Chongzhi Zhong, Zeping Cao, Lin Wu, Ning Wei, Zhengyu Men, Lei Yang, Qijun Zhang, Hongjun Mao

“…Model explainability results show that the feature parameters-emission response relationships for tire wear PM2.5 and PM2.5-10 are different. …”
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Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation by Srishti Gaur, Guler Aslan-Sungur (Rojda), Andy VanLoocke, Darren T. Drewry

“…In this study we present a systematic and comprehensive evaluation of machine learning (ML) models to assess the capability of meteorological and proximal sensing data for predicting LE at a half-hourly temporal resolution across multiple growing seasons for an agricultural system. …”
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Machine Learning-Based Classification of Suspension Droplet-Solid Wall Impacts for Control of Droplet Fragmentation by Mikhail Vulf, Dmitry Zharikov, Dmitry Kolomenskiy, Dmitry Eskin, Pavel Osinenko

“…It applies and compares multiple machine learning (ML) models for the classification of impact outcomes: splashing, breaking up, and rebound. …”
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Comparative Analysis using Electrical Discharge Machining to Determine the Impact of Powder Particles on Inconel-800 by Satish Kumar, Sujeet Kumar Chaubey, Harvinder Singh, Suyog Jhavar

“…Peak current ‘IP’, pulse-on-time ‘Ton’, and pulse-off-time ‘Toff’ were chosen as variable parameters for conducting experiments according to Taguchi L9 (33), and each experimental run was used for EDM and PMEDM for machining of Inconel-800 to perform the comparative evaluation in terms of material removal rate and tool wear rate. …”
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Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches by Farveh Vahidpour, Hossein Sabouri, Fakhtak Taliei, Sayed Javad Sajadi, Saeed Yarahmadi, Hossein Hosseini Moghaddam

“…The Bayesian algorithm was utilized to optimize the parameters of the machine-learning models. The results indicated that the Neural Network model accurately predicted powdery mildew disease resistance in barley lines. …”
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Prediction of Reservoir Flow Capacity in Sandstone Formations: A Comparative Analysis of Machine Learning Models by Micheal Ayodeji Ogundero, Taiwo Adelakin, Kehinde Orolu, Isaac Femi Johnson, Theophilus Akinfenwa Fashanu, Kingsley Abhulimen

“…Further analysis using the developed machine learning models revealed that geological formation thickness, reservoir thickness, and permeability are the most critical parameters influencing reservoir flow capacity and overall rock stability. …”
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MODAL ANALYSIS OF CARRIER SYSTEM FOR HEAVY HORIZONTAL MULTIFUNCTION MACHINING CENTER BY FINITE ELEMENT METHOD by Yu. V. Vasilevich, S. S. Dovnar, I. I. Shumsky

“…Reliability of FEM- estimations has been proved by in-situ vibration measurements.An effect for stabilization of resonance modes has been detected while making variations in design parameters of the machine tool. For example, a virtual replacement of cast iron for steel in machine structures practically does not have any effect on resonance frequencies. …”
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Optimizing Recycled Tunnel Boring Machine (TBM)-Excavated Materials as Aggregates in Shotcrete Mix Design by Wei Zhang, Rusheng Hao, Zhijun Men, Jingjing He, Yong Zhang, Wei Hu

“…Tunnel Boring Machine (TBM) excavation materials were recycled by sieving and separating particles into sizes 5–10 mm (coarse aggregates) and below 5 mm (manufactured sand) to explore their potential as aggregates in shotcrete production, with the aim of reducing environmental harm from waste disposal. …”
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Diagnosis of bipolar disorder based on extracted significant biomarkers using bioinformatics and machine learning algorithms by Hamid Mohseni, Massoud Sokouti, Akram Nezhadi, Ali Sayadi

“…The obtained gene expression data were trained by artificial neural network and decision tree method to identify the best models. Four parameters of sensitivity, specificity, accuracy, and area under the curve (AUC) were used to check the optimality of the model resulting from the training of machine learning algorithms. …”
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Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods by Yihao Yu, Yuqi Yang, Qian Li, Jing Yuan, Yan Zha

“…We aimed to develop and validate an explainable prediction model based on machine learning (ML) approaches for MASLD among the adult population. …”
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Coupled Induction Machine and HVAC Models for Simulating HVAC Performance Considering Grid Dynamics in Buildings by Viswanathan Ganesh, Zhanwei He, Jianjun Hu, Sen Huang, Wangda Zuo

“…This paper presents the development of novel models that integrate induction machines with HVAC equipment, such as pumps, heat pumps, and chillers, to analyze the impact of electrical parameters on the operational performance of thermo-fluid systems. …”
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Objective Detection of Newborn Infant Acute Procedural Pain Using EEG and Machine Learning Algorithms by Jean‐Michel Roué, Amir Avnit, Behnood Gholami, Wassim M. Haddad, Kanwaljeet J. S. Anand

“…Preliminary changes in functional connectivity indicate infant pain processing. Future machine learning algorithms can integrate physiological and behavioral parameters with EEG changes to accurately assess the complexity of infant pain responses. …”
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Machine learning models for performance estimation of solar still in a humid sub-tropical region by Farooque Azam, Naiem Akhtar, Shahid Husain

“…The output variable under consideration was the Hourly Yield, with four parameters serving as input variables: global solar radiation, water glass temperature difference, ambient temperature, and wind speed. 485 h of data were employed in the machine learning phase to assess the solar still’s performance. …”
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Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures by Tingting Liu, Lifan Zhong, Xizhe Sun, Zhijiang He, Witiao Lv, Liyun Deng, Yanfei Chen

“…The results demonstrated that the proposed system outperformed traditional Machine Learning models, such as Support Vector Machine and Random Forest, in terms of accuracy (98.6%), specificity (0.984), sensitivity (0.979), and F1-score (0.978). …”
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Managing Uncertainty in Geological Scenarios Using Machine Learning-Based Classification Model on Production Data by Byeongcheol Kang, Kyungbook Lee

“…The goal of this study is to develop a classification model for determining the proper geological scenario among plausible TIs by using machine learning methods: (a) support vector machine (SVM), (b) artificial neural network (ANN), and (c) convolutional neural network (CNN). …”
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Combination of machine learning and Raman spectroscopy for prediction of drug release in targeted drug delivery formulations by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…The considered drug is 5-aminosalicylic acid for colonic drug delivery, and its release was estimated using Raman data as inputs along with other categorical parameters. The models, including Kernel Ridge Regression (KRR), Kernel-based Extreme Learning Machine (K-ELM), and Quantile Regression (QR) incorporate sophisticated approaches like the Sailfish Optimizer (SFO) for hyperparameter optimization and K-fold cross-validation to enhance predictive accuracy. …”
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Enhancing concrete strength for sustainability using a machine learning approach to improve mechanical performance by Amir Khan, Aneel Manan, Muhammad Umar, Mudassir Mehmood, Kennedy C. Onyelowe, Krishna Prakash Arunachalam

“…This study leverages machine learning (ML) to predict the mechanical performance of RCA concrete and identify the key variables influencing its strength. …”
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Investigation of Relationship between the Surface Roughness and Residual Stress on Pearlitic Ductile Iron Face Machined by Olutosin Olufisayo Ilori, Gbemileke Akin Ogunranti, Toyese Friday Oyewusi, Opeoluwa Damilola Sola-Adeoye, Oluwaseun Adekola Fadare, Funmilayo Florence Adeyemi

“…These results can be used as a guide to improve the surface integrity of machined items. Thus, the study provided important information on the best cutting parameters for producing a significantly good surface finish during face milling operations in manufacturing industries. …”
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Identification of Ion-kinetic Instabilities in Hybrid-PIC Simulations of Solar Wind Plasma with Machine Learning by Viacheslav M. Sadykov, Leon Ofman, Scott A. Boardsen, Yogesh, Parisa Mostafavi, Lan K. Jian, Kristopher Klein, Mihailo Martinović

“…In this work, we explore machine learning (ML) and deep learning (DL) classification models to identify unstable cases of ion VDFs driving kinetic waves. …”
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