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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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Барьеры реализации трансформации в университетах и факторы их преодоления: взгляд руководителей вузов и экспертов

Университетское управление: практика и анализ. 2025. Т. 29. № 4. С. 129–143.
Krestinin V.

The aim of this article is to analyze the institutional barriers to implementing transformation in universities, as well as the factors enabling their overcoming, through the lens of those leading these processes. To this end, the author conducted 22 in-depth expert interviews with university leaders (primarily rectors and vice-rectors) participating in the “Priority 2030” program, as well as with experts in higher education management. The interpretation of this qualitative study was grounded in an institutional theoretical framework and employed thematic analysis methodology, which enabled a structured identification of key themes and meanings within managerial discourse. University leaders identified the main barriers to change implementation as: misalignment between new objectives and existing practices, fragmentation and siloed nature of university structures, staff rigidity, symbolic compliance with required activities, and increasing bureaucratization. The factors used to overcome these barriers include: the creation and maintenance of internal communication channels, the managerial resource of rectoral leadership, the legitimization and adoption of project-based management practices, and strategic personnel changes. Particular attention is given to the role of the management team in strategic stakeholder engagement, where the importance of simultaneously meeting external expectations and articulating an autonomous institutional agenda is emphasized. Thus, this study contributes to the ongoing discourse on contemporary university transformation by offering the perspective of those directly responsible for its implementation. The findings may be valuable to higher education researchers and university administrators, as well as to those involved in designing organizational change programs and providing educational or consulting support for their implementation.

Research target: Education
Language: Russian
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Keywords: трансформация университетовuniversity transformationleadership in higher educationлидерство в высшем образованииmanagement practices in higher educationуправленческие практики в высшем образовании
Publication based on the results of:
The formation of a national higher education system: key changes and their effects (2025)
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