Increased Temporal Variability of Gait in ASD: A Motion Capture and Machine Learning Analysis by Katharine Goldthorp, Benn Henderson, Pratheepan Yogarajah, Bryan Gardiner, Thomas Martin McGinnity, Brad Nicholas, Dawn C. Wimpory

“…Because walking is a natural activity and gait timing is a metric that is relatively accessible to measurement, we explored whether autistic gait could be described solely in terms of the timing of gait parameters. The aim was to establish whether temporal analysis, including machine learning models, could be used as a group classifier between ASD and typically developing (TD) individuals. …”
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Development of a Small CNC Machining Center for Physical Implementation and a Digital Twin by Claudiu-Damian Petru, Fineas Morariu, Radu-Eugen Breaz, Mihai Crenganiș, Sever-Gabriel Racz, Claudia-Emilia Gîrjob, Alexandru Bârsan, Cristina-Maria Biriș

“…This work aimed to develop both a real implementation and a digital twin for a small CNC machining center. The X-, Y-, and Z-axes feed systems were realized as closed-loop motion loops with DC servo motors and encoders. …”
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A Lightweight Machine Learning Model for High Precision Gastrointestinal Stromal Tumors Identification by Xin Sun, Xiwen Mo, Jing Shi, Xinran Zhou, Yanqing Niu, Xiao-Dong Zhang, Man Li, Yonghui Li

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Analysis of surface roughness and machining performance of AZ91 magnesium alloy cut by WEDM by Levent Urtekin, Faik Yılan, İbrahim Baki Şahin, Kadir Gök

“…This study investigated the impact of wire electrical discharge machining (WEDM) parameters on material removal rate (MRR) and surface roughness (SR) using magnesium. …”
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Automated Detection and Classification of Sleep Spindles using Machine Learning and Signal Processing Techniques by Sindhu Faiza, Rehman Hafiz Zia Ur, Khan Zeashan, Moshayedi Ata Jahangir

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Predictive modelling of sustainable concrete compressive strength using advanced machine learning algorithms by Tejas Joshi, Pulkit Mathur, Parita Oza, Smita Agrawal

“…This research focuses on the development of machine learning (ML) models to predict concrete's compressive strength (CS) at 7 and 28 days. …”
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Modelling Soil Water Retention Using Support Vector Machines with Genetic Algorithm Optimisation by Krzysztof Lamorski, Cezary Sławiński, Felix Moreno, Gyöngyi Barna, Wojciech Skierucha, José L. Arrue

“…For the purpose of models’ parameters search, genetic algorithms were used as an optimisation framework. …”
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Evaluating groundwater potential with the synergistic use of geospatial methods and advanced machine learning approaches by Vicky Anand, Vishnu D. Rajput, Tatiana Minkina, Saglara Mandzhieva, Aastha Sharma, Deepak Kumar, Sunil Kumar

“…This study aims to evaluate and compare the predictive capabilities of six ensemble machine learning (ML) models; i.e., Random Forest (RF), AdaBoost, Neural Network, Decision Tree, k-Nearest Neighbors and Extreme Gradient Boosting. …”
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A Kp‐Driven Machine Learning Model Predicting the Ultraviolet Emission Auroral Oval by Huiting Feng, Dedong Wang, Yuri Y. Shprits, Artem Smirnov, Deyu Guo, Yoshizumi Miyoshi, Stefano Bianco, Shangchun Teng, Run Shi, Su Zhou, Yongliang Zhang

“…The input parameters of the models are the magnetic local time, magnetic latitude, and Kp index. …”
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Differentiating between obstructive and non‐obstructive azoospermia: A machine learning‐based approach by Abdolreza Haghpanah, Nazanin Ayareh, Ashkan Akbarzadeh, Dariush Irani, Fatemeh Hosseini, Farid Sabahi Moghadam, Mohammad Ali Sadighi Gilani, Iman Shamohammadi

“…Three machine learning models, including logistic regression, support vector machine and random forest, were evaluated for their accuracy in differentiating the two subtypes. …”
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Exploration of geo-spatial data and machine learning algorithms for robust wildfire occurrence prediction by Svetlana Illarionova, Dmitrii Shadrin, Fedor Gubanov, Mikhail Shutov, Usman Tasuev, Ksenia Evteeva, Maksim Mironenko, Evgeny Burnaev

“…The goal of this study is to explore the potential of predicting wildfire occurrences using various available environmental parameters - meteorological, geo-spatial, and anthropogenic - and machine learning (ML) algorithms. …”
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Enhancing shear strength predictions of UHPC beams through hybrid machine learning approaches by Sanjog Chhetri Sapkota, Ajad Shrestha, Moinul Haq, Satish Paudel, Waiching Tang, Hesam Kamyab, Daniele Rocchio

“…Abstract Ultra-high-performance concrete (UHPC) beam shear strength prediction is a complicated process due to the involvement of numerous parameters. The accuracy needed for precise predictions is frequently lacking in current empirical equations and traditional machine learning (ML) techniques. …”
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Application of machine learning techniques to predict the compressive strength of steel fiber reinforced concrete by Ala’a R. Al-Shamasneh, Arsalan Mahmoodzadeh, Faten Khalid Karim, Taoufik Saidani, Abdulaziz Alghamdi, Jasim Alnahas, Mohammed Sulaiman

“…This study presents a robust machine learning framework to predict the CS of SFRC using a large-scale experimental dataset comprising 600 data points, encompassing key parameters such as fiber characteristics (type, content, length, diameter), water-to-cement (w/c) ratio, aggregate size, curing time, silica fume, and superplasticizer. …”
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Development and evaluation of a machine learning model for osteoporosis risk prediction in Korean women by Minkyung Je, Seunghyeon Hwang, Suwon Lee, Yoona Kim

“…Abstract Background The aim of this study was to develop a machine learning (ML) model for classifying osteoporosis in Korean women based on a large-scale population cohort study. …”
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autoMEA: machine learning-based burst detection for multi-electrode array datasets by Vinicius Hernandes, Anouk M. Heuvelmans, Anouk M. Heuvelmans, Valentina Gualtieri, Dimphna H. Meijer, Geeske M. van Woerden, Geeske M. van Woerden, Geeske M. van Woerden, Eliska Greplova

“…However, this analysis remains time-consuming, user-biased, and limited by pre-defined parameters. Here, we present autoMEA, software for machine learning-based automated burst detection in MEA datasets. …”
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Scalability analysis of heavy-duty gas turbines using data-driven machine learning by Shubhasmita Pati, Julian D. Osorio, Mayank Panwar, Rob Hovsapian

“…In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. …”
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Non-invasive arterial input function estimation using an MRA atlas and machine learning by Rajat Vashistha, Hamed Moradi, Amanda Hammond, Kieran O’Brien, Axel Rominger, Hasan Sari, Kuangyu Shi, Viktor Vegh, David Reutens

“…A variational inference-based machine learning approach was employed to correct for peak activity. …”
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Evaluation of the Pile Group Response for Machine Foundation under Cyclic Load in Sandy Soil by Saif S. Abd Al-hafiz, Jasim Abbas

“…The machine load can reduce the lateral pile displacement, especially in case of large spacing. …”
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Production Lot Sizing and Process Targeting under Process Deterioration and Machine Breakdown Conditions by Muhammad Al-Salamah, Ali Abudari

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Research of the Virtual Machining Method of Vehicle Variable Ratio Gear based on Generation Method by Tan Jiangjiang, Li Gangyan, Niu Ziru, Kong Zhenbin, Hu Dawei

“…In order to solve the processing problem of the variable ratio gear（ also called variable ratio gear sector）,a machining method based on generation method is put forward. …”
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