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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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Российские разработчики о создании и востребованности отечественного индустриального ПО: результаты пилотного обследования

Вопросы статистики. 2026. Т. 33. № 2. С. 58–68.
Shashnov S. A., Bogdanov T., Abdrakhmanova G.

The article presents the results of a study of the Russian industrial software market. The relevance of the topic stems from the significance of this segment for technological sovereignty and the comprehensive digitalization of industrial sectors and the economy as a whole.
The aim of the study was to collect and analyze primary data refiecting the positions, priorities, and assessments of software development companies. Conceptual framework, the system of indicators was created and original tools for a pilot survey were developed to study the key areas of domestic industrial software development. As a result, an analysis of the data obtained from more than 60 Russian organizations – developers of various classes of industrial software – was conducted.
The results of the pilot survey revealed a clear orientation of the vast majority of developers toward the domestic market. Companies highlight the development of the functionality of created solutions and products to meet the specifics of Russian customers, and the improvement of sales, promotion, and implementation channels for their products in the domestic market as key strategic priorities.
According to the authors, the conducted research has significant scientific and practical value. The results obtained do not exhaust the entire range of issues; they form a theoretical and methodological basis for the analysis of the industrial software market. The high dynamics of this market, manifested in the emergence of new products, changes in the composition of players and government support measures, necessitates further research taking into account the positions of developers, integrators, and end users.

Research target: Economics and Management
Language: Russian
DOI
Text on another site
Keywords: технологическое развитиемеры государственной поддержки technological developmentgovernment support measuresindustrial softwaredeveloper organizationssoftware marketиндустриальное программное обеспечениеорганизации-разработчикирынок ПО
Publication based on the results of:
Monitoring of artificial intelligence technologies and digital transformation of economy and society (2025)
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