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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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Диалект села Старошведское: опыт составления словаря неизученного языка (lägge — mūär)

Вестник Православного Свято-Тихоновского гуманитарного университета. Серия 3: Филология. 2020. № 62. С. 108–132.
Mankov A.

The Dialect of Gammalsvenskby: compiling a dictionary of an unexplored language (lägge — mūär)

This paper presents new material for the dictionary of the present-day dialect of Staroshvedkoye (Gammalsvenskby), the only Scandinavian dialect in the territory of the former Soviet Union. The present-day state of this dialect has not been described in linguistic literature. The only source of data on Gammalsvenskby is fi eldwork with speakers of the dialect. The main objective of this work is to present material recorded in the interviews in the most complete way possible and to describe the state of the vocabulary and inflection in the dialect. The entries include the following information: type of inflection; translation; phrases, sentences and short texts illustrating the usage (with initials of the informants). In many cases full paradigms are given as well. They include all phonetic and morphological forms that have occurred in the interviews.

Language: Russian
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Keywords: endangered languagesполевая лингвистикаСтарошведскоеGammalsvenskby
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