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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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Changes and Continuities in Patterns of Gendered Organizations During the COVID‑19 Pandemic: Evidence from the Russian Public Sector

Journal of Social Policy Studies. 2023. Vol. 21. No. 1. P. 95–106.
Davidenko M., Poretskova A., Utkina V.

In response to the first wave of the spread of the COVID-19 virus, the Russian federal government responded by introducing several reforms, including the transfer to remote work. This article conceptualizes the pandemic as an external shock that has affected working interaction in organizations at both the individual and structural levels. Focusing on women civil servants working in federal executive bodies, this article explores the following question: what are the features of gender-sensitive processes in the public civil service, and what significant changes have occurred in state bodies after introducing measures in response to the pandemic? To answer this question, we analyze the experience of women civil servants and their perception of the changes that have occurred in the workplace after the spread of coronavirus infection. Using analysis of the in-depth semi-structured interviews and detailed operationalization of Acker's theory (Acker 1990) on gendered organizations, the article compares gendered processes that have remained unchanged and have undergone changes in the introduction of measures against the spread COVID-19. Thus, the study's novelty lies in the conclusions on the main patterns and changes in organizational gendered processes in the Russian public sector.

Research target: Political Science, International Relations, and Public Administration Sociology (including Demography and Anthropology
Language: English
DOI
Keywords: Россиягосударственная гражданская службаRussiapublic sectorпандемияpandemicCOVID-19theory of gendered organizationsтеория гендеризированных организацийCOVID-19
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