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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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Гендерная сегрегация в трудовых отношениях на государственной гражданской службе РФ

С. 857–863.
Utkina V.

Nature of the civil service determines labor relations in public authorities. However, the external environment has a significant impact on human resource management in public authorities, as well raising questions that were not previously problematized. Today in Russian public authorities more than 70% of the employees are women, but most of them face difficulties in career progression. The article deals with the specifics of gender segregation in the state civil service of modern Russia. The author aims to identify peculiarities of the status of women holding on civil service positions, as well as identify the causes of gender discrimination. An empirical study based on a series of semi-structured interviews with female civil servants revealed that both vertical gender segregation ("glass ceiling") and horizontal segregation (“glass walls”) are represented in Russian public authorities. An effort was made to identify the reasons of it. According to the research all that’s happening caused not only by normative consolidation of discriminatory measures against women on civil service, but also by "double employment" of women (ie the need to combine professional activities with unpaid domestic work) and self-discrimination of women

Language: Russian
Text on another site
Keywords: государственная гражданская службаgender segregationгендерная сегрегацияstate civil serviceWomen in Powerженщины в управленииwomen in public administrationженщины во властных структурах
Publication based on the results of:
Сравнительный анализ положения женщин и мужчин в органах власти (2018)

In book

Модели государственного и корпоративного управления: традиции и перспективы
Издательство "Перо", 2018.
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