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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.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Русский язык в неславянском окружении: модель языковой конкуренции в регионах тесного языкового контактирования

Русин. 2023. № 71. С. 239–250.
Artemenko E.

Language competition is mostly relevant in regions of close language contact. The study allows predicting the loss of the minority language by bilinguals and preventing the final transition to monolingualism, thus preserving the bilingual community. The article analyzes the existing models of language competition and assesses the predictors of its dynamics, taking into account the specificity of the linguistic situation of Turkic-Russian bilingualism in South Siberia. The predictors are sociodemographic, including institutional, sociocultural and some motivational factors. Modeling is based on the Turkic-Russian bilingualism database, which includes three groups of linguistic interaction: Tatar-Russian, Shor-Russian, and Khakass-Russian. The database contains a total of 25,145 observations from 235 bilinguals. The proposed modeling and its results have shown that language competition is provided by mutually directed processes. Thus, the most influential factors ensuring high proficiency of bilinguals in their native minority language and, therefore, leading to a balanced language situation in the conditions of language competition, are sociodemographic factors: experience of preschool residence (in the village or in the city) and place of residence at the present time. The age as well as a group of socio-cultural factors (the language of rites and songs) is insignificant. Motivation, on the contrary, leads to an unbalanced bilingual situation, reducing the functional role of the minority language. Bilinguals emphasize that their own intention to use Russian is higher than the intention to use their native language. Thus, the maintenance of a balanced bilingual situation largely depends on demographic factors and motivation of bilinguals.

Research target: Philology and Linguistics Sociology (including Demography and Anthropology
Language: Russian
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Keywords: bilingualismбилингвизмбилингвыlanguage competitionTatar-Russian bilingualismunbalanced bilingualismКонкуренция языковНесбалансированный билингвизмТатарско-русский билингвизмБилингвальная языковая ситуация
Publication based on the results of:
Innovative methods of data collection and analysis in the modeling of communicative behavior of Internet users and the development of respective technological solutions (2023)
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