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News
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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Действия россиян по улучшению собственного материального положения в эпоху COVID-19

Мониторинг общественного мнения: Экономические и социальные перемены. 2021. № 2(162). С. 121–137.
Karavay A.

The study is devoted to the analysis of Russians' actions to improve their financial situation in the conditions of the economic crisis caused by the pandemic. The empirical basis of the analysis was the data of surveys conducted by the Institute of Sociology of the Russian Academy of Sciences for several years, including a survey conducted in September 2020. Changes in the behavior of Russians in the conditions of various economic crises are demonstrated, in particular, the decline in the popularity of the most universal ways to improve their financial situation, which were formed in the 1990s, related to casual earnings and subsidiary farming. It is also shown that, in contrast to the crisis of 2014-2016, in 2020 there was such a serious reduction in the sources of income of many previously well-off representatives of the mass strata of society that they had to change their behavior and look for additional sources of income. As a result, among the ways to improve their financial situation among representatives of the mass strata of the population, actions characteristic of relatively high — resource social groups have become more widespread-part-time work, overtime employment, the use of previously accumulated material and financial assets, as well as the widespread attraction of help from close associates in different strata. However, even the relatively wealthy representatives of the mass strata of the country's population gradually have fewer and fewer resources of their own, and for an increasing number of them, the only way to improve their financial situation is to intensify employment for hire. However, an external barrier to its spread is the limited number of jobs that involve highly skilled and highly paid work and/or overtime pay.

Priority areas: sociology
Language: Russian
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Keywords: адаптацияadaptationbehavioral patternsповеденческие паттернытрудовые стратегииadaptation strategiesадаптационные стратегииpopulation’s resources labor strategiesthe impact of the crisis on societyресурсы населениявлияние кризиса на общество
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