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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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Blended Learning in Russian Higher Education: A Comparative Analysis of Three Cases

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Shcherbakova I. O., Skripnik I. L.

Blended learning has become a popular technology which combines traditional methods and tools with e-learning strategies. This paper compares and contrasts three cases of blended learning implementation at Russian higher education institutions. The cases were selected to represent three different curriculum specialisations in higher education: in emergency control technologies, in marine technologies and commerce, and in social studies and humanities. The authors use the official data and methodological documents and describe the latest practices applied in the educational process in the context of digitalisation of education. The paper finds some difference in blended learning implementation at these institutions and provides some explanation.

Language: English
Full text
DOI
Keywords: digitalised higher educationblended learning of foreign languages

In book

Proceedings of the 2nd International Scientific and Practical Conference on Digital Economy (ISCDE 2020)
Atlantis Press, 2020.
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