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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
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Ab Initio Study of the Influence of Spin and Orbital Magnetic Moments on the Stability of Magnetic and Charge Distribution in Co:ZnO Monolayer

The Journal of Physical Chemistry Letters. 2024. Vol. 15. No. 41. P. 10295–10300.
Prokhorenko A., Chibisov A., Gnidenko A., Chibisova M., Obrazcov K., Andrey S. Vasenko, Srivastava A.

The development of ultrathin magnets with tunable magnetic properties is essential for advancing quantum computing technologies. In this study, density functional theory (DFT) calculations were employed to investigate the atomic and electronic structures of a ZnO monolayer embedded with cobalt atoms. The impact of spin dynamics on charge transfer within the Co:ZnO system was thoroughly examined. Results revealed that the orbital magnetic moment of the cobalt atoms plays a crucial role in stabilizing the magnetic and charge distributions across the system. These findings offer valuable insights for the design and fabrication of quantum devices, thereby highlighting the potential of Co-doped ZnO monolayers in quantum computing applications.

Research target: Physics
Language: English
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Keywords: Теория функционала плотностиквантовый компьютерDensity functional theory (DFT)ultrathin magnetsZnO monolayerquantum computing technologiesультратонкие магнетикимонослой ZnO
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