Machine Learning for Dynamic Pressure Coefficient Prediction in Vertical Water Jets by Amin Salemnia, Seyedehmaryam Hosseini Boldaji, Vida Atashi, Manoochehr Fathi-Moghadam

“…This study aimed to predict the dimensionless pressure coefficient (C_p) of vertical water jets by examining the relationships between experimental parameters, such as Froude number, slope, and the ratio of waterfall height over the product of the Froude number and diameter, referred to as α, using machine learning models. …”
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Predicting Performance of Hall Effect Ion Source Using Machine Learning by Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe

“…Traditional methods rely on simplified scaling laws and computationally intensive numerical simulations. Herein, a robust machine learning model is introduced that uses a neural network ensemble to predict the performance of Hall effect ion sources based on design parameters such as discharge channel dimensions and magnetic field structure. …”
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A Physics-Informed Machine Learning Framework for Permafrost Stability Assessment by Polina Pilyugina, Timofey Chernikov, Maria Smirnova, Alexey Zaytsev, Alexander Bulkin, Evgeny Burnaev, Ilya S. Belalov, Nazar Sotiriadi, Albert Efimov, Yury Maximov, Oleg Anisimov

“…Although simpler models with fewer input parameters offer convenience, they generally lack accuracy. …”
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Predicting Atmospheric Dispersion of Industrial Chemicals Using Machine Learning Approaches by Maria Valle, Jairo A. Cardona, Cesar Viloria-Nunez, Christian G. Quintero M.

“…Machine learning techniques were rigorously trained and tested to predict atmospheric dispersion, emphasizing hyperparameter optimization to enhance model performance. …”
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Structural optimization and performance evaluation of a sugarcane leaf mulching machine by Weihua Huang, Shuo Wang, Chang Ge, Lijiao Wei, Dongjie Du, Zhaojun Niu, Ming Li, Zhenhui Zheng

“…Existing sugarcane leaf mulching machines struggle to process high-fiber, tough sugarcane leaves, leading to incomplete mulching and uneven residue distribution. …”
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Modeling Spectral LED Degradation Using an Unsupervised Machine Learning Approach by Alexander Herzog, Benoit Hamon, Paul Myland, Peter Foerster, Simon Benkner, Babak Zandi, Victor Guerra, Sebastian Schoeps, Willem D. van Driel, Tran Quoc Khanh

“…By combining the Arrhenius equation with the modeling parameters, the spectral characteristics can be modeled for 6000 hours of stress at four different stress test temperatures. …”
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Machine Learning Analysis of Maize Seedling Traits Under Drought Stress by Lei Zhang, Fulai Zhang, Wentao Du, Mengting Hu, Ying Hao, Shuqi Ding, Huijuan Tian, Dan Zhang

“…The maize seedlings were subjected to a 10-day period of water breakage following a standard watering cycle until they reached the third leaf collar (V3) stage. Parameters including plant height, stem diameter, chlorophyll content, and root number were assessed. …”
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Auxiliary Diagnosis of Breast Cancer Based on Machine Learning and Hybrid Strategy by Hua Chen, Kehui Mei, Yuan Zhou, Nan Wang, Guangxing Cai

“…Finally, five different machine learning models are used for classification prediction, the best combination of parameters for each model is found using a grid search method, and the final results of each model are derived using a 10-fold cross-validation method. …”
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A Multi-Objective optimization framework for the sustainable machining of Monel 400 by Binayak Sen, Prasadaraju Kantheti, Sachin Rathore, Bhavesh Kanabar, Ramachandran Thulasiram, Manoj Kumar, Abhijit Bhowmik, A. Johnson Santhosh

“…Analysis of variance revealed that feed and cutting speed are the utmost influential parameters influencing machining outcomes. Utilizing Multi-Objective Response Surface Methodology, the study established optimal machining conditions—cutting speed of 78.35 m/min, feed of 0.1 mm/rev, and depth of cut of 1 mm—attaining a composite desirability of 0.84. …”
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Microplastic Deposit Predictions on Sandy Beaches by Geotechnologies and Machine Learning Models by Anderson Targino da Silva Ferreira, Regina Célia de Oliveira, Maria Carolina Hernandez Ribeiro, Pedro Silva de Freitas Sousa, Lucas de Paula Miranda, Saulo de Oliveira Folharini, Eduardo Siegle

“…Through multivariate techniques, this study aims to investigate the role of the morphometrical parameters as independent variables in quantifying the distribution of MPs on the region’s sandy beaches. …”
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Machine Learning-Based Lithium Battery State of Health Prediction Research by Kun Li, Xinling Chen

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Using machine learning to study the population life quality: methodological aspects by E. V. Shchekotin, В. Л. Гойко, P. A. Basina, B. B. Bakulin

“…Assessment of the population life quality is an important and relevant sociological task. Machine learning as a classification tool of social network users’ digital traces makes it possible to create a base to calculate subjective life quality index. …”
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A comparative study on laser-assisted and conventional machining characteristics of waspaloy by Hamidreza Esrafili, Hossein Amirabadi, Javad Akbari, Farshid Jafarian

“…This was done using different cutting speed and feed rate settings while keeping the laser parameters constant. The heat-affected layers (HAZ) were then analyzed, followed by the machining process. …”
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Predicting Optimum Moisture Content by the individual and hybrid approach of machine learning by Yinghui Yang, Yahui Dai, Qunting Yang

“…The research demonstrates a strong relationship using GPR models between OMC and important soil parameters such as particle size distribution, linear shrinkage, and the kind and quantity of stabilizing chemicals. …”
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Effect of microstructure on machinability of extruded and conventional H13 tool steel by Stepan Kolomy, Martin Maly, Marek Doubrava, Josef Sedlak, Jan Zouhar, Jan Cupera

“…H13 tool steel samples were fabricated using material extrusion to explore their machinability, offering a promising alternative to laser powder bed fusion for producing complex parts like moulds and cores. …”
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Effect of Erosion on Surface Roughness and Hydromechanical Characteristics of Abrasive-Jet Machining by Vadym Baha, Jan Pitel, Ivan Pavlenko

“…The research was purposed by the pressing need to develop an inexpensive and effective working nozzle design of the air-abrasive unit which can be applied for surface processing before some technological processes are performed, as well as for surface coating, descaling after thermal treatment, processing of hollow holes of the crankshafts, smoothing of the inner surfaces of the narrow channels between the impeller blades after electric discharge machining for ultrahigh-pressure combination compressors. …”
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FEATURE-BASED IMPLEMENTATION OF MACHINE LEARNING ALGORITHMS FOR CARDIOVASCULAR DISEASE PREDICTION by H. Singh, R. Tripathy, P. Kumar Sarangi, U. Giri, S. Kumar Mohapatra, N. Rameshbhai Amin

“…This study employs various machine learning algorithms, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Random Forest, Decision Tree, and Naïve Bayes, to assess their accuracy in predicting cardiovascular disease and related conditions This paper makes use of the UCI repository dataset for coaching and testing including some basic parameters such as age and sex. …”
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Accelerating the Discovery of Steady‐States of Planetary Interior Dynamics With Machine Learning by Siddhant Agarwal, Nicola Tosi, Christian Hüttig, David S. Greenberg, Ali Can Bekar

“…We present a concept for accelerating mantle convection simulations using machine learning. We generate a data set of 128 two‐dimensional simulations with mixed basal and internal heating, and pressure‐ and temperature‐dependent viscosity. …”
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Development and validation of a machine learning model for prediction of cephalic dystocia by Yumei Huang, Xuerong Ran, Xueyan Wang, Defang Wu, Zheng Yao, Jinguo Zhai

“…We utilized basic patient characteristics, foetal ultrasound parameters, maternal anthropometric data, maternal psychological measurements, and obstetric medical records to train and test the machine learning models. …”
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Benchmarking In-Sensor Machine Learning Computing: An Extension to the MLCommons-Tiny Suite by Fabrizio Maria Aymone, Danilo Pietro Pau

“…This paper proposes a new benchmark specifically designed for in-sensor digital machine learning computing to meet an ultra-low embedded memory requirement. …”
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