Strength and Durability Characteristics of Soil-Cutting Working Tools by D. A. Mironov, S. A. Sidorov, I. V. Liskin

“…They have chosen basic materials for plowshares basing mainly on the strength parameters. They have conducted comparative laboratory tests of various materials and witness samples (including double-layer ones) for abrasive wear resistance on two bench installations; identifi ed materials for carbide coatings, clarifi ed the relative wear resistance coeffi cients of steel carbide layers recommended for use in the design of plowshares; and considered a technological method of hardening machine working tools. …”
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Optimization of compressor unit operation using artificial intelligence technologies by Alexander E. Vlasov, Lyubov V. Lazareva

“…The work examined the possibility of using neural networks and machine learning algorithms to optimize fuel gas consumption by a compressor unit. …”
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AISLEX: Approximate individual sample learning entropy with JAX by Ondrej Budik, Milan Novak, Florian Sobieczky, Ivo Bukovsky

“…We present AISLEX, an online anomaly detection module based on the Learning Entropy algorithm, a novel machine learning-based information measure that quantifies the learning effort of neural networks. …”
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Loan classification using logistic regression by U. I. Behunkou, M. Y. Kovalyov

“…Therefore, it is more important than ever for financial institutions to be able to identify reliable borrowers as accurately as possible. At the same time, machine learning is one of the tools for making such decisions. …”
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Implementation of SVM Algorithm to Predict Song Popularity based on Sentiment Analysis of Lyrics by Quiin Latifah Almatin Lubis, Arif Akbarul Huda

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Symbolic approximations to Ricci-flat metrics via extrinsic symmetries of Calabi–Yau hypersurfaces by Viktor Mirjanić, Challenger Mishra

“…In this paper, we analyse machine learning approximations to flat metrics of Fermat CY n -folds and some of their one-parameter deformations in three dimensions in order to discover their new properties. …”
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SAG-YOLO: A Lightweight Real-Time One-Day-Old Chick Gender Detection Method by Yulong Chang, Rongqian Sun, Zheng Yang, Shijun Li, Qiaohua Wang

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Modeling Parametric Forecasts of Solar Energy over Time in the Mid-North Area of Mozambique by Fernando Venâncio Mucomole, Carlos Augusto Santos Silva, Lourenço Lázaro Magaia

“…It highlights the essential importance of the exact management of the interferential power density of each parameter influencing the availability of super solar energy. …”
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YOLO-LSM: A Lightweight UAV Target Detection Algorithm Based on Shallow and Multiscale Information Learning by Chenxing Wu, Changlong Cai, Feng Xiao, Jiahao Wang, Yulin Guo, Longhui Ma

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A correlation for predicting the abrasive water jet cutting depth for natural stones by Irfan Engin

“…However, the effectiveness of AWJ cutting of natural stones is dependent on the rock properties and machine operating parameters. In this study, injection-type AWJ cutting was applied to 42 different types of natural stones to investigate the effects of rock properties and operating parameters on the cutting depth. …”
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Effortless alkalinity analysis using AI and smartphone technology, no equipment needed, from freshwater to saltwater by Zachary Y. Han, Zihan Zheng, Alan Y. Han, Huichun Zhang

“…Alkalinity is a crucial water quality parameter with significant environmental and engineered system applications. …”
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A Defect Detection Algorithm for Optoelectronic Detectors Utilizing GLV-YOLO by Xinfang Zhao, Qinghua Lyu, Hui Zeng, Zhuoyi Ling, Zhongsheng Zhai, Hui Lyu, Saffa Riffat, Benyuan Chen, Wanting Wang

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Interpretability in deep learning for finance: A case study for the Heston model by Damiano Brigo, Xiaoshan Huang, Andrea Pallavicini, Haitz Sáez de Ocáriz Borde

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Optimizing blood glucose predictions in type 1 diabetes patients using a stacking ensemble approach by Vincent B. Liu, Laura Y. Sue, Oscar Madrid Padilla, Yingnian Wu

“…Using the DiaTrend dataset, this study used stacking machine learning optimized by Grey Wolf Optimizer to construct and assess prediction models for blood glucose levels in type 1 diabetes patients. …”
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Stable phytoplankton community compositions in Lake Mead (Nevada-Arizona, USA) during two decades of severe drought by Charlotte van der Nagel, Deena Hannoun, Todd Tietjen

“…Additionally, we evaluated machine learning models for predicting changes in phytoplankton community structures. …”
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Heterogeneous Photocatalysis for Advanced Water Treatment: Materials, Mechanisms, Reactor Configurations, and Emerging Applications by Maria Paiu, Doina Lutic, Lidia Favier, Maria Gavrilescu

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Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio by Lulut Alfaris, Anas Noor Firdaus, Ukta Indra Nyuswantoro, Ruben Cornelius Siagian, Aldi Cahya Muhammad, Rohana Hassan, Rodulfo T. Aunzo, Jr., Reza Ariefka

“…Using annual temperature datasets and relevant oceanographic parameters, the data is carefully processed, cleaned and sorted into training and test subsets via the RStudio Platform. …”
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Speech Analysis as a Tool for Detection and Monitoring of Medical Conditions: A review by Magdalena IGRAS-CYBULSKA, Daria HEMMERLING, Mariusz ZIÓŁKO, Wojciech DATKA, Ewa STOGOWSKA, Michał KUCHARSKI, Rafał RZEPKA, Bartosz ZIÓŁKO

“…Information was extracted from each paper in order to compare various aspects of datasets, speech parameters, methods of applied analysis and obtained results. 110 research papers were reviewed and 47 databases were summarized. …”
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Research on Slope Stability Based on Bayesian Gaussian Mixture Model and Random Reduction Method by Jingrong He, Tao Deng, Shouxing Peng, Xing Pang, Daochun Wan, Shaojun Zhang, Xiaoqiang Zhang

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Abnormal nonlinear features of EEG microstate sequence in obsessive–compulsive disorder by Huicong Ren, Xiangying Ran, Mengyue Qiu, Shiyang Lv, Junming Wang, Chang Wang, Yongtao Xu, Zhixian Gao, Wu Ren, Xuezhi Zhou, Junlin Mu, Yi Yu, Zongya Zhao

“…Finally, the temporal parameters and nonlinear features of EEG microstate sequences were sent to three kinds of machine learning models to classify OCD patients. …”
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