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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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Особенности метрики стихов Н. Е. Горбаневской

Труды института русского языка им. В.В. Виноградова. 2022. № 3(33). С. 59–95.
Баркова Л. А.

This article analyzes stanzaic, metric, and rhyming structure in N. E. Gorbanevskaya’s poems, and shows their specifi c features by comparing them with other poems, written in the second half of the 20th century and in the beginning of the 21st century. The source of the present study is the data from the Poetic sub-corpus of Russian National Corpus. There are many phenomena, which are widespread in Gorbanevskaya’s poems, but rare in other poems written at the same time: wide range of meters in sonnets; several accentual-syllabic meters in one poem; modifi cations of meter, connected with caesura; rare types of rhymes; etc. Studying prosody in Gorbanevskaya’s poems enriches our notion of how may the regular verse in Russian poetry of the second half of the 20th century and the beginning of the 21st century be arranged.

Research target: Philology and Linguistics
Language: Russian
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Keywords: метрикаНациональный корпус русского языкаprosodyRussian National Corpusсовременная поэзияРусский стихRussian versecontemporary poetryN.E. GorbanevskayaН. Е. Горбаневская
Publication based on the results of:
Indigenous languages and cultures of the Arctic (2022)
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