Open Access
Issue
E3S Web Conf.
Volume 583, 2024
Innovative Technologies for Environmental Science and Energetics (ITESE-2024)
Article Number 01010
Number of page(s) 7
Section Geoinformatics, Mining Geology and Mineral Resources
DOI https://doi.org/10.1051/e3sconf/202458301010
Published online 25 October 2024
  1. Martyushev N. V. et al. Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption //Energies. – 2023. – Т. 16. – №. 2. – С. 729. [CrossRef] [Google Scholar]
  2. Shutaleva A. et al. Sustainability of Inclusive Education in Schools and Higher Education: Teachers and Students with Special Educational Needs //Sustainability. – 2023. – Т. 15. – №. 4. – С. 3011. [CrossRef] [Google Scholar]
  3. Rezanov V. A. et al. Study of melting methods by electric resistance welding of rails //Metals. – 2022. – Т. 12. – №. 12. – С. 2135. [CrossRef] [Google Scholar]
  4. Martyushev N. V. et al. Provision of Rational Parameters for the Turning Mode of Small-Sized Parts Made of the 29 NK Alloy and Beryllium Bronze for Subsequent Thermal Pulse Deburring //Materials. – 2023. – Т. 16. – №. 9. – С. 3490. [CrossRef] [PubMed] [Google Scholar]
  5. Kukartsev V. A. et al. Study of the Influence of the Thermal Capacity of the Lining of Acid Melting Furnaces on Their Efficiency //Metals. – 2023. – Т. 13. – №. 2. – С. 337. [CrossRef] [Google Scholar]
  6. Degtyareva, K. et al. Use of Computer Simulation Tools to Simulate Processes at the Foundry. In 2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-5). IEEE [Google Scholar]
  7. Degtyareva, K. et al. Automated System for Accounting of Customers and Orders. In 2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-4). IEEE. [Google Scholar]
  8. Golik V. I. et al. The mechanochemical activation of leaching processes in a disintegrator. MIAB. Mining Inf. Anal. Bull. 2023;(11-1):175-189. [Google Scholar]
  9. Panfilova T.A. et al. To the concept of leaching metal-containing raw materials in the dizintegrator. MIAB. Mining Inf. Anal. Bull. 2023;(11-1):239-251. [Google Scholar]
  10. Suprun E. et al. The use of artificial intelligence to diagnose the disease //BIO Web of Conferences. – EDP Sciences, 2024. – Т. 84. – С. 01008. [CrossRef] [EDP Sciences] [Google Scholar]
  11. Orlov V. et al. Development of a multifunctional cross-platform system for automation of energy data and resource management //E3S Web of Conferences. – EDP Sciences, 2023. – Т. 460. – С. 07002. [CrossRef] [EDP Sciences] [Google Scholar]
  12. Kravtsov K. et al. Workflow automation and performance improvement based on PostgreSQL //E3S Web of Conferences. – EDP Sciences, 2023. – Т. 458. – С. 09022. [CrossRef] [EDP Sciences] [Google Scholar]
  13. Tynchenko V. S. et al. Effective energy management tools: inventory management and monitoring of energy consumption by personnel //E3S Web of Conferences. – EDP Sciences, 2023. – Т. 458. – С. 01011. [CrossRef] [EDP Sciences] [Google Scholar]
  14. Nelyub V. A. et al. (2023). Correlation Analysis and Predictive Factors for Building a Mathematical Model. In Proceedings of the Computational Methods in Systems and Software (pp. 14-25). Cham: Springer International Publishing. [Google Scholar]
  15. Degtyareva K. V. et al. Automatic monitoring system designed to detect defects in PET preforms //E3S Web of Conferences. – EDP Sciences, 2023. – Т. 458. – С. 02002. [CrossRef] [EDP Sciences] [Google Scholar]
  16. Gantimurov A. et al. Investigation of the influence of geographical factors on soil suitability using a nonparametric controlled method of training and data analysis //E3S Web of Conferences. – EDP Sciences, 2023. – Т. 431. – С. 03005. [CrossRef] [EDP Sciences] [Google Scholar]
  17. Tynchenko V. S. et al. Using software to shape safety on the construction site //E3S Web of Conferences. – EDP Sciences, 2023. – Т. 458. – С. 07003. [CrossRef] [EDP Sciences] [Google Scholar]
  18. Tynchenko V. et al. Application of U-Net Architecture Neural Network for Segmentation of Brain Cell Images Stained with Trypan Blue. In International Conference on High-Performance Computing Systems and Technologies in Scientific Research, Automation of Control and Production (pp. 170-181). Cham: Springer Nature Switzerland. [Google Scholar]
  19. Martyushev, N. V. et al. Production of Workpieces from Martensitic Stainless Steel Using Electron-Beam Surfacing and Investigation of Cutting Forces When Milling Workpieces. Materials, 16(13), 4529. [Google Scholar]
  20. Martyushev, N. V. et al. Production of Workpieces from Martensitic Stainless Steel Using Electron-Beam Surfacing and Investigation of Cutting Forces When Milling Workpieces. Materials, 16(13), 4529. [Google Scholar]
  21. Tynchenko V. et al. Mathematical Models for the Design of GRID Systems to Solve Resource-Intensive Problems. Mathematics, 12(2), 276. [Google Scholar]
  22. Rogova D. et al. Software System for Modeling Temperature Distribution During the Electron Beam Welding. In 2022 IEEE International Conference on Design & Test of Integrated Micro & Nano-Systems (DTS) (pp. 1-6). IEEE. [Google Scholar]
  23. Kurashkin S. et al. Mathematical modelling of waveguide paths by electron-beam welding. Procedia Computer Science, 200, 83-90. [Google Scholar]
  24. Tynchenko V. et al. Software for optimization of beam output during electron beam welding of thin-walled structures. Procedia Computer Science, 200, 843-851. [Google Scholar]
  25. Tynchenko V. et al. Software for modeling brazing process of spacecraft elements from widely used alloys. In 2022 21st International Symposium INFOTEH- JAHORINA (INFOTEH) (pp. 1-5). IEEE. [Google Scholar]
  26. Orlov V. et al. Development of a multifunctional cross-platform system for automation of energy data and resource management //E3S Web of Conferences. – EDP Sciences, 2023. – Т. 460. – С. 07002. [CrossRef] [EDP Sciences] [Google Scholar]
  27. Kravtsov K. et al. Workflow automation and performance improvement based on PostgreSQL //E3S Web of Conferences. – EDP Sciences, 2023. – Т. 458. – С. 09022. [CrossRef] [EDP Sciences] [Google Scholar]
  28. Tynchenko V. S. et al. Effective energy management tools: inventory management and monitoring of energy consumption by personnel //E3S Web of Conferences. – EDP Sciences, 2023. – Т. 458. – С. 01011. [CrossRef] [EDP Sciences] [Google Scholar]
  29. Semenova E. et al.. Using UML to Describe the Development of Software Products Using an Object Approach. In 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) (pp. 1-4). IEEE. [Google Scholar]
  30. Tynchenko V. S. et al. Energy distribution computation for induction soldered construction elements. In AIP Conference Proceedings (Vol. 2700, No. 1). AIP Publishing. [Google Scholar]
  31. Chernykh N. et al. Comparative Analysis of Existing Measures to Reduce Road Accidents in Western Europe. In 2023 22nd International Symposium INFOTEH- JAHORINA (INFOTEH) (pp. 1-6). IEEE. [Google Scholar]
  32. Volneikina E. et al. Simulation-Dynamic Modeling Of Supply Chains Based On Big Data. In 2023 22nd International Symposium INFOTEH-JAHORINA (INFOTEH) (pp. 1-6). IEEE. [Google Scholar]
  33. Filina O. A. et al. Increasing the Efficiency of Diagnostics in the Brush-Commutator Assembly of a Direct Current Electric Motor //Energies. – 2023. – Т. 17. – №. 1. – С. 17. [CrossRef] [Google Scholar]
  34. Boychuk, I. P., Grinek, A. V., Martyushev, N. V., Klyuev, R. V., Malozyomov, B. V., Tynchenko, V. S., ... & Kondratiev, S. I. (2023). A Methodological Approach to the Simulation of a Ship’s Electric Power System. Energies, 16(24), 8101. [CrossRef] [Google Scholar]
  35. Golik V. I. et al. Reuse and Mechanochemical Processing of Ore Dressing Tailings Used for Extracting Pb and Zn //Materials. – 2023. – Т. 16. – №. 21. – С. 7004. [CrossRef] [PubMed] [Google Scholar]
  36. Malozyomov B. V. et al. Designing the Optimal Configuration of a Small Power System for Autonomous Power Supply of Weather Station Equipment //Energies. – 2023. – Т. 16. – №. 13. – С. 5046. [CrossRef] [Google Scholar]
  37. Malashin I. P. et al. Estimation and Prediction of the Polymers’ Physical Characteristics Using the Machine Learning Models //Polymers. – 2023. – Т. 16. – №. 1. – С. 115. [CrossRef] [PubMed] [Google Scholar]
  38. Malozyomov B. V. et al. Overview of Methods for Enhanced Oil Recovery from Conventional and Unconventional Reservoirs //Energies. – 2023. – Т. 16. – №. 13. – С. 4907. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.