Optimal Statistical Feature Subset Selection for Bearing Fault Detection and Severity Estimation by Chhaya Grover, Neelam Turk

“…The performance of bearing fault detection systems based on machine learning techniques largely depends on the selected features. …”
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Process of Solving Multi-Response Optimization Problems Using a Novel Data Envelopment Analysis Variant-Taguchi Method by Narong Wichapa, Narathip Pawaree, Pariwat Nasawat, Prawach Chourwong, Anucha Sriburum, Wanrop Khanthirat

“…Taguchi uses efficiency scores from the DEAV model to enable optimal parameter determination through Taguchi optimization. …”
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Deciphering Spatially Resolved Lyman-Alpha Profiles in Reionization Analogs: The Sunburst Arc at Cosmic Noon by Erik Solhaug, Hsiao-Wen Chen, Mandy C. Chen, Fakhri Zahedy, Max Gronke, Magdalena J. Hamel-Bravo, Matthew B. Bayliss, Michael D. Gladders, Sebastián López, Nicolás Tejos

“…Both regions also show a central Gaussian peak atop the double peaks, indicating directly escaped Ly$\alpha$ photons independent of LyC leakage. We introduce a machine learning-based method for emulating Ly$\alpha$ simulations to quantify intrinsic dynamics ($\sigma_{\mathrm{int}}$), neutral hydrogen column density ($N_{\mathrm{HI}}$), outflow velocity ($v_{\mathrm{exp}}$), and effective temperature ($T$) across continuous parameter spaces. …”
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AA5052-PVC-AA5052 (Al-PVC-Al) Sandwich Sheets Forming Analysis through In-Plane Plane Stretching Tests by P. Praveen Kumar Reddy, Chinmaya Prasad Padhy, P. Janaki Ramulu

“…The present work has investigated the formability of AA5052-PVC-AA5052 (Al-PVC-Al) sandwich sheets by considering the sheet rolling direction as a parameter. The mechanical properties of base metal and sandwich sheets were evaluated by conducting the uniaxial tensile tests. …”
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Constructing hidden differential equations using a data-driven approach with the alternating direction method of multipliers (ADMM) by Jye Ying Sia, Yong Kheng Goh, How Hui Liew, Yun Fah Chang

“…This paper attempted to illustrate the conceptual ideas and parameter discovery of the linear coupled first-order ODE. …”
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Probing the Inert Doublet Model via vector-boson fusion at a muon collider by Johannes Braathen, Martin Gabelmann, Tania Robens, Panagiotis Stylianou

“…Current dark matter data constrain the phenomenologically viable parameter space of the IDM and render certain collider signatures elusive due to tiny couplings. …”
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On Variable-Universe Fuzzy Control for Drive Chain of Front-End Speed Regulated Wind Generator by Hongwei Li, Kaide Ren, Haiying Dong, Shuaibing Li

“…The case study with the real machine parameter verifies the effectiveness of the proposal and results compared with conventional neural network controller, proving its outperformance.…”
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Modified Whale Optimization Algorithm for Multiclass Skin Cancer Classification by Abdul Majid, Masad A. Alrasheedi, Abdulmajeed Atiah Alharbi, Jeza Allohibi, Seung-Won Lee

“…The optimized features are fed into machine learning classifiers to achieve robust classification performance. …”
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Automation of Multi-Class Microscopy Image Classification Based on the Microorganisms Taxonomic Features Extraction by Aleksei Samarin, Alexander Savelev, Aleksei Toropov, Aleksandra Dozortseva, Egor Kotenko, Artem Nazarenko, Alexander Motyko, Galiya Narova, Elena Mikhailova, Valentin Malykh

“…This study presents a unified low-parameter approach to multi-class classification of microorganisms (micrococci, diplococci, streptococci, and bacilli) based on automated machine learning. …”
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APPLICATION OF SOFT SETS TO ASSESSMENT OF MATHEMATICAL MODELLING SKILLS by Майкл Воскоглоу

“…The soft set assessment model is applied for evaluating student mathematical modelling skills with respect to the parameters excellent, very good, good, mediocre, and failed. …”
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Exploring f(Q) gravity through model-independent reconstruction with genetic algorithms by Redouane El Ouardi, Amine Bouali, Safae Dahmani, Ahmed Errahmani, Taoufik Ouali

“…In this paper, we use a machine learning technique, specifically genetic algorithms, to reconstruct the functional form of f(Q) gravity in a model-independent manner. …”
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On the use of slurry as an alternative to dry powder for laser powder bed fusion of 316L stainless steel by Sebastian Meyers, Kopila Gurung, Yannis Kinds, Brecht Van Hooreweder

“…Two optimal parameter sets were obtained, resulting in component density of 99.4%. …”
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A review on scan strategies in laser-based metal additive manufacturing by Junaid Dar, Andre Georges Ponsot, Caden Jacob Jolma, Dong Lin

“…It also discusses recent advancements in MAM, including multi-laser systems, inline parameter control, machine learning, and in-situ monitoring, highlighting their roles in producing high-quality products and driving the mainstream adoption of metal additive manufacturing.…”
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On the effectiveness of neural operators at zero-shot weather downscaling by Saumya Sinha, Brandon Benton, Patrick Emami

“…Machine-learning (ML) methods have shown great potential for weather downscaling. …”
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Novel neuromuscular controllers with simplified muscle model and enhanced reflex modulation: A comparative study in hip exoskeletons by Ali Reza Manzoori, Sara Messara, Andrea Di Russo, Auke Ijspeert, Mohamed Bouri

“…The modifications consist firstly of simplifications to the Hill-type virtual muscle model, resulting in a more straightforward formulation and reduced number of parameters; and second, using a finer division of gait subphases in the reflex modulation state machine, allowing for a higher degree of control over the shape of the assistive profile. …”
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Online Modelling and Calculation for Operating Temperature of Silicon-Based PV Modules Based on BP-ANN by Honglu Zhu, Weiwei Lian, Lingxing Lu, Peter Kamunyu, Cao Yu, Songyuan Dai, Yang Hu

“…The operating temperature of silicon-based solar modules has a significant effect on the electrical performance and power generation efficiency of photovoltaic (PV) modules. It is an important parameter for PV system modeling, performance evaluation, and maximum power point tracking. …”
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Design and Experiment of a Dual-Disc Potato Pickup and Harvesting Device by Xianjie Li, Abouelnadar Salem, Yi Liu, Bin Sun, Guanzheng Shi, Xiaoning He, Dongwei Wang, Zengcun Chang

“…Through kinematic analysis, the disc inclination angle (12–24°) and operational parameters were optimized. Through coupled EDEM-RecurDyn simulations and Box–Behnken experimental design, the optimal parameter combination was determined with the potato loss rate and potato damage rate as evaluation indices: disc rotational speed of 50 r/min, disc inclination angle of 16°, and machine forward speed of 0.6 m/s. …”
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Concrete Dam Deformation Prediction Model Based on Attention Mechanism and Deep Learning by ZHANG Hongrui, CAO Xin, JIANG Chao, ZU Anjun, XU Mingxiang

“…Traditional statistical methods based on hydrostatic-season-time (HST) theory, while having clear physical meanings and being easy to implement, are limited by their inherent linear assumptions, resulting in constrained prediction accuracy. Machine learning models such as random forest, support vector regression, and extreme learning machine (ELM) extend statistical approaches but still lack the ability to establish temporal dependencies due to their static input-output mapping relationships. …”
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Large Scale MRI Collection and Segmentation of Cirrhotic Liver by Debesh Jha, Onkar Kishor Susladkar, Vandan Gorade, Elif Keles, Matthew Antalek, Deniz Seyithanoglu, Timurhan Cebeci, Halil Ertugrul Aktas, Gulbiz Dagoglu Kartal, Sabahattin Kaymakoglu, Sukru Mehmet Erturk, Yuri Velichko, Daniela P. Ladner, Amir A. Borhani, Alpay Medetalibeyoglu, Gorkem Durak, Ulas Bagci

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Metrics and extrapolation of resonant magnetic perturbation thresholds for ELM suppression by N.C. Logan, S.K. Kim, S.M. Yang, J.-K. Park, Q. Hu, N. Leuthold, C. Paz-Soldan, S. Gu, D. Weisberg, H. Wang, Y. Sun, P. Xie, G. Nina Montano, T. Wang, M.W. Kim, M. Willensdorfer, EUROfusion WPTE Team, the ASDEX Upgrade Team

“…This quantity does not exhibit clear power-law scalings for projection, but machine learning can assist in predicting thresholds within the existing parameter ranges and providing uncertainty quantification of those predictions. …”
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