Token-Based Digital Currency Model for Aviation Technical Support as a Service Platforms by Igor Kabashkin, Vladimir Perekrestov, Maksim Pivovar

“…The study introduces optimization techniques using machine learning to enhance token calculations, successfully standardizing heterogeneous services while maintaining flexibility and transparency. …”
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Evaluating Petrophysical Properties Using Digital Rock Physics Analysis: A CO₂ Storage Feasibility Study of Lithuanian Reservoirs by Shruti Malik, Pijus Makauskas, Ravi Sharma, Mayur Pal

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Recent Results on the Use of Artificial Intelligence Techniques Applied to Wireless Power Transfer Systems by Federico Amadei, Michele Quercio, Francesco Riganti Fulginei

“…This article reviews the application of machine learning (ML) techniques in wireless power transfer (WPT) systems, focusing on their role in optimizing system performance, enhancing safety, and improving efficiency. …”
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Multi-strategy enhanced artificial rabbits optimization for prediction of grades in tourism service communication courses by Xiaodan Qu, Zhuyin Jia

“…Furthermore, MEARO is used to optimize two critical parameters of the Kernel Extreme Learning Machine (KELM), significantly improving its classification performance. …”
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Enhanced Indoor Pedestrian Tracking Using UWB/PDR Fusion and NLOS Error Mitigation by Shoude Wang, Nur Syazreen Ahmad

“…First, a novel NLOS ranging error mitigation approach is developed by integrating an Extreme Learning Machine (ELM) in conjunction with Gaussian Process Regression (GPR), with the initial parameters of the ELM optimized via the Nutcracker Optimizer. …”
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Rapid prediction of poly(butylene adipate-co-terephthalate)/poly(glycolic acid) (PBAT/PGA) agricultural films based on UV-accelerated aging tests with applicability to the environm... by Zihan Jia, Minglong Li, Bo Wang, Dongsheng Li, Peng Guo, Mingfu Lyu, Zhiyong Wei, Lin Sang

“…The variation of performance parameters including haze, transmittance, tensile strength, elongation at break and melting temperature were monitored at varying degradation intervals. …”
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Evaluation results of the tribological properties of aviation oils for aircraft engines by M. V. Seleznev, K. I. Gryadunov, K. E. Balyshin

“…The specified friction machine has a variety of disadvantages. In this regard, the authors evaluated the anti-wear and anti-friction properties of domestic aviation oils using a versatile vibro-tribometer which allows for the operational properties of oils to be researched under the modes that are the most characteristic for the actual operation of aircraft engines compared with parameters of oil tests by a four-ball friction machine. …”
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Automated Line for Post-Harvest Processing of Root Crops and Potatoes by A. S. Dorokhov, M. A. Mosyakov, N. V. Sazonov

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Improving CNN predictive accuracy in COVID-19 health analytics by Tae-hoon Kim, Asadi Srinivasulu, Ravikumar Chinthaginjala, Dhakshayani J, Xin Zhao, Safia Obaidur Rab, Sivarama Prasad Tera

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RICD: Russian Intensive Care Dataset by A. V. Grechko, M. Y. Yadgarov, A. A. Yakovlev, L. B. Berikashvili, A. N. Kuzovlev, P. A. Polyakov, I. V. Kuznetsov, V. V. Likhvantsev

“…RICD also contains data on several vital parameters collected from bedside monitors and other equipment of ICUs, with up to 10 evaluations per hour.Conclusion. …”
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Visceral adiposity index as a predictor of metabolic dysfunction-associated steatotic liver disease: a cross-sectional study by Tuo Zhou, Xiang Ding, Linjie Chen, Qianxiong Huang, Linfang He

“…T1: 7.08, 95% CI: 4.35-11.5; P-trend=0.003). Machine learning models demonstrated robust predictive accuracy, with random forest (AUC=0.869) and gradient boosting machine (AUC=0.868) outperforming non-invasive scores. …”
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State-of-the-Art Review: Models and Algorithms for Optimal Power System Design, Stabilization, and Reliability Enhancement by Senele Njabulo Zwane, Bongumsa Mendu, Bessie Baakanyang Monchusi

“…The results also revealed that these algorithms are utilized for tasks such as optimal power system design, substation placement, parameter tuning for power system stabilizers, and load forecasting. …”
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ROUGHNESS OF PROCESSED SURFACES UNDER ABRASIVE POLISHING by Valentin SHKURUPY

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University proceedings. Volga region. Technical sciences by A.A. Ignatiev, V.A. Dobryakov, V.A. Revyakin

“…One of the dominant factors affecting the macro- and microgeometric accuracy parameters of parts is the dynamic quality of automated metal-cutting machines. …”

Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Induced by Microwave Radiation and Their Associated Strength Reduction Patterns: Employing Meta-Heuristic Opti... by Zhongyuan Gu, Xin Xiong, Chengye Yang, Miaocong Cao

“…This model was benchmarked against other prevalent machine learning frameworks, with Shapley additive explanatory methods employed to assess each parameter’s influence on UCSA. …”
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Hybrid LSTM–Transformer Architecture with Multi-Scale Feature Fusion for High-Accuracy Gold Futures Price Forecasting by Yali Zhao, Yingying Guo, Xuecheng Wang

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Calculating the Pitch of a Serrated-Type Rotary Tiller by P. I. Gadzhiev, M. M. Makhmutov, A. I. Alekseev, M. M. Makhmutov

“…(Materials and methods) The authors have noted that currently, there are no methods for selecting the main parameters and operation modes of serrated-type rotary tillers. …”
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Development of a model of an electrical complex for gas air cooling devices of gas field №1 gazprom dobycha Yamburg LLC with a centralized power supply system in the MATLAB/SIMULIN... by L. R. Maskov, V. Y. Kornilov

“…The comparative analysis of existing methods and calculation of parameters of the substitution schemes of the MMD ETK GP was carried out. …”
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Artificial Neural Network and Ensemble Models for Flood Prediction in North-Central Region of Nigeria by Sikiru Abdulganiyu Siyanbola, Aisha Olabisi Sowemimo, Zaid Habibu, Timothy Ebuka Eberechukwu

“…The collected data are the input parameters in training the machine learning models: Artificial Neural Networks (ANN), Adaptive Boosting (AdaBoost), Stochastic Gradient Boosting (GBM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF) models, for predicting flood occurrence in the region. …”
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Enhanced Farmland Extraction from Gaofen-2: Multi-Scale Segmentation, SVM Integration, and Multi-Temporal Analysis by Hang Yang, Hao Sun, Ke Wang, Jian Yang, Muhammad Hasan Ali Baig

“…This study proposes an object-oriented multi-scale segmentation method combined with a support vector machine, leveraging spectral reflectance, texture, and temporal differences between farmland and non-farmland plots. …”
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