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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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Будущая работа глазами студентов российских вузов: дифференциация образа работы в межрегиональной перспективе

Мир России: Социология, этнология. 2023. Т. 32. № 1. С. 61–86.
Poplavskaya A.

This article identifies the perception of future work by undergraduate students and the differentiation of work vision depending on individual, institutional, and regional characteristics. It uses data from a survey of Russian university students, commissioned by the Russian Ministry of Science and Higher Education in the summer of 2021. The sample comprises 14,800 students aged up to 29 y.o. studying in full-time bachelor’s programs at Russian universities. The results demonstrate that there is no unified image of work among students. This falsifies some generally accepted judgments about the attitudes of students towards future employment. Contrary to the initial hypotheses, younger generation of students appears to be less instrumental and individualistically oriented than millennials; STEM students, however, more often agree with the statement that “work is just a means of earning money”; and paternal rather than maternal cultural capital has an influence on work attitudes. Students with a high level of economic and human capital are more likely to focus on traditional, rather than flexible, work arrangements and on classic, rather than multiple, career paths. The inclusion of second-level predictors in the analysis made it possible to conclude that in relatively less prosperous regions students are more likely to orient towards entrepreneurial activity, and in regions with higher unemployment rates the likelihood of perceiving work as a priority and willingness to devote more time to it increases.

Research target: Sociology (including Demography and Anthropology Social Sciences
Language: Russian
Full text
DOI
Text on another site
Keywords: молодежьlabour marketсоциология трудароссийский рынок трудатрудовые ценностиRussian labour marketsociology of workwork valuesбакалаврыbachelorработающие студентыwork attitudescareer expectationsбудущая карьераТрудовые установкиemployed youth​youth
Publication based on the results of:
Russians’ Daily Social Practices under Exogenous Shocks (2024)
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