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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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?

Russia's transition to an innovation-based growth: problems and opportunities

Ch. 5. P. 129–166.
Simachev Y. V., Kuzyk M.

For at least the last 10 years, the Russian authorities have been declaring the need to move to an innovative path of economic development. The government actively initiates and applies various instruments and measures to promote innovation. However, the effectiveness of the Russian innovation policy is still in question. The chapter examines the evolution of state policy to foster innovation growth in Russia since 2000 and describes some sets of achievements and problems for different stages of this policy. In addition to analysis of changes in the innovation sphere at the macro-level, we discuss the primary motivations and limitations at the micro-level (firm level). As a result, the critical institutional barriers to innovation-based growth are revealed. In the same time, certain successes have been achieved in some sectors, and we consider various opportunities to improve Russian technological and innovation policy.

Language: English
DOI
Keywords: science, technology and innovation policyinnovation-based growth

In book

Modeling economic growth in contemporary Russia
Emerald Group Publishing Limited, 2019.
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