The Stellar Abundances and Galactic Evolution Survey (SAGES). II. Machine Learning–based Stellar Parameters for 21 Million Stars from the First Data Release by Hongrui Gu, Zhou Fan, Gang Zhao, Huang Yang, Timothy C. Beers, Wei Wang, Jie Zheng, Jingkun Zhao, Chun Li, Yuqin Chen, Haibo Yuan, Haining Li, Kefeng Tan, Yihan Song, Ali Luo, Nan Song, Yujuan Liu

“…An increasing number of medium-band photometric surveys are presently used in estimating stellar parameters. In this study, we present a machine learning approach to derive estimates of stellar parameters, including [Fe/H], log g , and T _eff , based on a combination of medium-band and broadband photometric observations. …”
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Impact of Machining Parameters on Wire Arc Additively Manufactured Mild Steel Specimens of E6013 and E7018 Using an Electric Arc Heat Source by Muhammad Muzamil, Nabeel Ahmed Siddiqui, Kamal Kumar, Ghulam Hussain, Mohammed Alkahtani, Mohsin Sattar

“…This study investigates the machining parameters that affect the surface roughness of additively manufactured specimens employing wire arc additive manufacturing (WAAM) using electric arc welding as a heat source. …”
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E-scooter crash severity in the United Kingdom: A comparative analysis using machine learning techniques and random parameters logit with heterogeneity in means and variances by Ali Agheli, Kayvan Aghabayk, Matin Sadeghi, Subasish Das

“…We employed a random parameters logit model and investigated several machine learning algorithms, with XGBoost performing best. …”
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Comparison of 2D and 3D Surface Roughness Parameters of AlMgSi0.5 Aluminium Alloy Surfaces Machined by Abrasive Waterjet by Csaba Felhő, Krisztina Kun-Bodnár, Zsolt Maros

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Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Study by Weiwei Hu, Yulong Liu, Jian Dong, Xuelian Peng, Chunyan Yang, Honglin Wang, Yong Chen, Shan Shi, Jin Li

“…ObjectiveThis study aimed to develop a machine learning model using 24 routine blood parameters to predict influenza A and B infections and validate a deployable diagnostic tool for low-resource clinical settings. …”
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Experimental Investigation of Manufacturing Variables Effect on Electrical Discharge Machining of Titanium Alloy by Enzi Abass, Abdulameer Ahmed Ghazi, Hassoon Omar Hashim, Hamieh Abdallah

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A review of finite control set model predictive control for linear machines by Wei Xu, Han Xiao, Yirong Tang, Jian Ge, Leilei Guo, Abdul Khalique Junejo

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Device of identification of parameters and currents of the rotor three-phase asynchronous motor by A. J. Afanasiev, V. G. Makarov, Y. A. Yakovlev, V. N. Khannanova

Subjects: “…identification of parameters…”
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Experimental Determination of a Spoke-Type Axial-Flux Permanent Magnet Motor’s Lumped Parameters by Andre Mrad, Jean-François Llibre, Yvan Lefèvre, Mohamad Arnaout

Subjects: “…spoke-type axial-flux machine…”
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TO THE QUESTION OF OPTIMISING THE DYNAMIC CHARACTERISTICS OF A VIBRATIONAL TREE UPROOTING MACHINE by A. R. Mikhitarov, V. L. Savich, V. K. Khegai

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Sensitivity Analysis of Acoustic Field Parameters on a Change of Boundary Conditions in a Room by Katarzyna SUDER-DĘBSKA, Ireneusz CZAJKA, Mateusz CZECHOWSKI

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Determination of Operation Characteristics of a Synchronous Generator by Static Experimental Tests by ILINA, I.-D., TUDORACHE, T.

Subjects: “…electric machines…”
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Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models by Saad Hameed, Basheer Qolomany, Samir Brahim Belhaouari, Mohamed Abdallah, Junaid Qadir, Ala Al-Fuqaha

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Prediction and optimization of hardness in AlSi10Mg alloy produced by laser powder bed fusion using statistical and machine learning approaches by İnayet Burcu Toprak

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Application of machine deep learning technology in tight sandstones reservoir prediction: A case study of Xujiahe Formation in Xinchang, western Sichuan Depression by QIAN Yugui

Subjects: “…|machine deep learning|lithologic prediction|pre-stack inversion|resolution ratio|sensitivity parameter|quantitative prediction of reservoirs…”
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Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images by Saeideh Maleki, Nicolas Baghdadi, Hassan Bazzi, Cassio Fraga Dantas, Dino Ienco, Yasser Nasrallah, Sami Najem

“…Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). …”
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A hybrid bio-inspired augmented with hyper-parameter deep learning model for brain tumor classification by Morolake Oladayo Lawrence

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Optimization of key operating parameters to enhance performance and energy efficiency of a hammer mill for corn grinding by Maher Al-Eid, Ahmad Qabatty, Rayad Kubaisi, Abd Al Karim Jaafar

“…Their impact on ground corn productivity, energy consumption, machine efficiency, and compression ratio was assessed. …”
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Experimental estimation of technology parameters of TWT slow-wave structure elements manufacture by В. С. Предмирський, М. Г. Твердохліб

“…The list of technological parameters and modes of machining support rods held by molecular cohesion forces are defined. …”
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Determination of the state and functioning of the equipment of rod well pumping units in the operation process by the parameters of the wattmetrogram by V. A. Manakhov, A. N. Tsvetkov

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