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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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Национальные и институциональные связи депутатов Народного собрания Республики Дагестан VII созыва

Полития: Анализ. Хроника. Прогноз. 2025. Т. 118. № 3. С. 80–107.
Андриянов Д. А., Брагина А. Б., Kechik U., Chebanova D.

It has long been generally accepted in Political Science that distributed elite networks, rather than institutions, are crucial to the functioning of the Russian political system. One of the important factors in the formation of such networks is ethnicity. Of particular interest is the ethnic dynamics in those regions, where there is no dominant ethnic group and, consequently, mechanisms for balancing interethnic relations are needed. In Russia an extreme example of such regions is Dagestan. The article attempts to test the role of the ethnic factor in forming network relations between the deputies of the People’s Assembly of the Republic of Dagestan. In addition to ethnic ties, it examines institutional and bureaucratic connections based on factional affiliation and parliamentary committee membership. When solving this tasks, the authors employed two unique datasets that they compiled on the basis of the information from People’s Assembly official web page and a number of its archived versions, using network analysis and the exponential random graph models. The research has shown that the main role in structuring People’s Assembly deputies belongs to political party factions and especially to committee affiliations, which have a decisive influence on the formation of relations between deputies, including those that lead to active joint lawmaking. According to the authors’ conclusion, when forming ties between the deputies of Dagestani legislature, the ethnic factor is overall less significant than institutional factors, and the mechanism of the formation of such ties needs further investigation.

Research target: Political Science, International Relations, and Public Administration
Language: Russian
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Keywords: сетевой анализlawmakingзаконотворчествоэтничностьгоризонтальные связиnetwork analysisethnicityRepublic of DagestanРеспублика Дагестанhorizontal relationship
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