Detecting sand gradation based on the two-dimensional sand particle features in sand images by Chuanyun Xu, Heng Wang, Yang Zhang, Song Sun, Gang Li

“…Images of sand particles with a single gradation are captured to extract five sand particle feature parameters, which are then used to train a mature network model. …”
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From data to decisions: Leveraging ML for improved river discharge forecasting in Bangladesh by Md. Abu Saleh, H.M. Rasel, Briti Ray

“…This study forecasted the annual river discharge forecasting in the Nilphamari district of Bangladesh, employing random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM) techniques. …”
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Pyrolysis Kinetics of Pine Waste Based on Ensemble Learning by Alok Dhaundiyal, Laszlo Toth

“…For the purposes of application, the ground form of pine cone was used to perform the thermogravimetric analysis at heating rates of 5, 10, and 15 °C∙min⁻¹. The supervised machine learning technique was considered to estimate the kinetic parameters associated with the thermal decomposition of the material. …”
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Investigating pedestal dependencies at JET using an interpretable neural network architecture by A. Gillgren, A. Ludvig-Osipov, D. Yadykin, P. Strand, JET contributors

“…A secondary objective is to provide a transparent alternative to current opaque, black-box machine learning models used to predict the pedestal in integrated modeling frameworks. …”
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Artificial intelligence-based multi-expression programming prediction of magnetized radiative nanofluid flow between coaxial deformable tubes by Bilal Ahmed, Dong Liu, Bouthaina Dammak, Naeem Ullah, Afraz Hussain Majeed, Hafedh Mahmoud Zayani, Binjuan Zhao

“…Eckert number depicts higher impact on the thermal features of fluid as compare to other parameters.…”
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Density matrix emulation of quantum recurrent neural networks for multivariate time series prediction by J D Viqueira, D Faílde, M M Juane, A Gómez, D Mera

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An artificial intelligence model for predicting an appropriate mAs with target exposure indicator for chest digital radiography by Jia-Ru Lin, Tai-Yuan Chen, Yu-Syuan Liang, Jyun-Jie Li, Ming-Chung Chou

“…This study aimed to establish a machine learning (ML) model for predicting an appropriate current–time product (mAs) using the target exposure indicator in chest digital radiography. …”
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The Interaction of Fitness and Fatigue on Physical and Tactical Performance in Football by Mauro Mandorino, Tim J. Gabbett, Antonio Tessitore, Cedric Leduc, Valerio Persichetti, Mathieu Lacome

“…This study integrates fitness and fatigue indices derived from a machine learning approach to develop a performance score based on Banister’s fitness–fatigue model. …”
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Severity Classification of a Seismic Event based on the Magnitude-Distance Ratio Using Only One Seismological Station by Luis Hernán Ochoa Gutiérrez, Luis F Niño, Carlos A. Vargas

“…We trained a Support Vector Machine (SVM) algorithm with seismograph data recorded by INGEOMINAS's National Seismological Network at a three-component station located near Bogota, Colombia. …”
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Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen by Mark Hutchinson, Jeffrey A. Ruffolo, Nantaporn Haskins, Michael Iannotti, Giuliana Vozza, Tony Pham, Nurjahan Mehzabeen, Harini Shandilya, Keith Rickert, Rebecca Croasdale-Wood, Melissa Damschroder, Ying Fu, Andrew Dippel, Jeffrey J. Gray, Gilad Kaplan

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Psoriatic arthritis in psoriasis: optimizing the current screening system for psoriatic arthritis based on serum data from U.S. and Chinese populations by Zheng Lin, Si-yi Pan, Yue-yi Shi, Xuan Wu, Yuan Dou, Ping Lin, Yi Cao

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GSPSO-LRF-ELM: Grid Search and Particle Swarm Optimization-Based Local Receptive Field-Enabled Extreme Learning Machine for Surface Defects Detection and Classification on the Magn... by Jun Xie, Jin Zhang, Fengmei Liang, Yunyun Yang, Xinying Xu, Junjie Dong

“…Machine vision-based surface defect detection and classification have always been the hot research topics in Artificial Intelligence. …”
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Heat Emissions from Mining Machinery: Implications for Microclimatic Conditions in Underground Workings by Artem Zaitsev, Oleg Parshakov, Mikhail Semin

“…This paper presents findings from a comprehensive study of the microclimatic air parameters in several nickel–copper and potash mines. …”
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Evaluating the effect of structural and parametric change of weft knitted polyester fabrics over the performance of sportswear by Muhammad Owais Raza Siddiqui, Salma Farooq, Muhammad Dawood Husain, Hassan Ali, Saira Faisal

“…Using the 100% polyethylene Terephthalate (PET) 75/72 DTY semi-intermingle yarn, three different weft-knitted structures are manufactured on similar gauge circular weft knitting machines. All the manufactured structures are tested using standard methods so that the influence of knitting parameters and optimization of structures on the overall performance of sportswear can be analysed. …”
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Research on the Stability Prediction and Optimization of CNC Milling Based on Bagging–NSGAⅡ Under the Influence of Multiple Factors by Congying DENG, Qian YOU, Yang ZHAO, Lijun LIN, Guofu YIN

“…The occurrence of chatter in the milling process is a key factor limiting the efficiency and quality of machining. The stability of milling depends mainly on the process parameters and the dynamic characteristics of the tool–workpiece system; however, the system dynamics vary with the machining position and tool properties. …”
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Surface Quality of CNC Face-Milled Maple (<i>Acer pseudoplatanus</i>) and Oak (<i>Quercus robur</i>) Using Two End-Mill Tool Types and Varying Processing Parameters by Ana-Maria Angelescu, Lidia Gurau, Mihai Ispas

“…The species-dependent machining quality implies that the selection of tool geometry and process parameters must be tailored per species.…”
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Predicting rheumatoid arthritis progression from seronegative undifferentiated arthritis using machine learning: a deep learning model trained on the KURAMA cohort and externally v... by Takayuki Fujii, Koichi Murata, Hirohiko Kohjitani, Akira Onishi, Kosaku Murakami, Masao Tanaka, Wataru Yamamoto, Koji Nagai, Ayaka Yoshikawa, Yuki Etani, Yasutaka Okita, Naofumi Yoshida, Hideki Amuro, Tadashi Okano, Yo Ueda, Takaichi Okano, Ryota Hara, Motomu Hashimoto, Akio Morinobu, Shuichi Matsuda

“…This study aims to build a machine learning (ML) model to predict the progression from seronegative UA to RA using clinical and laboratory parameters. …”
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Machine learning-driven risk prediction in severe acute kidney injury and increased mortality risk with early sodium bicarbonate use: a model-based post hoc analysis by Wanqiu Xie, Gerd Walz, Toma Antonov Yakulov

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A Two-Stage Three-Machine Flow Shop Assembly Problem Mixed with a Controllable Number and Sum-of-Processing Times-Based Learning Effect by Simulated Annealing Algorithms by Shang-Chia Liu

“…This study focuses on the two-stage three-machine flow shop assembly problem mixed with a controllable number and sum-of-processing times-based learning effect, in which the job processing time is considered to be a function of the control of the truncation parameters and learning based on the sum of the processing time. …”
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Integrated Neural Network Analysis of Machining Characteristics in Dry-Turned Al7075/FA0.9SiC0.9 Hybrid Composite Using PCD Inserts by Sunil Setia, Jarnail Singh, Sant Ram Chauhan, Nikunj Rachchh, Raman Kumar, Abhijit Bhowmik

“…Dry turning operations were performed using polycrystalline diamond (PCD) inserts to evaluate the influence of key input parameters—cutting speed, feed rate, depth of cut, and tool nose radius—on output machining regimes, namely, cutting force components and tool tip temperature. …”
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