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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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Nanotechnology Research and Innovation in Russia: A Bibliometric Analysis

2014.
Karaulova M., Shackleton O., Gök A., Kotsemir M. N., Shapira P.
Available in SSRN: https://ssrn.com/abstract=2521012 This work was supported by the Economic and Social Research Council [grant number ES/J012785/1] as part of the project Emerging Technologies, Trajectories and Implications of Next Generation Innovation Systems Development in China and Russia.   The project addresses research, management and policy issues related to advanced technological development in driving the growth of Rising Powers, with a focus on nanotechnology in China and This working paper presents findings from analyses of Russian nanotechnology outputs in publications and patents focusing on developments over the period 1990 through to 2012. The investigation draws on bibliometric datasets of scientific journal publications and patents and on available secondary English-language and Russian sources.    The document provides both an overview and detailed analyses of nanotechnology research and innovation in Russia. The examination of publications highlights sectoral trends, leading authors and organizations, and acknowledgements to funding sources. The analysis of patents adds further evidence about patterns of invention and ownership of intellectual property emanating from research and development in Russian nanotechnology.   The analyses in this paper have been undertaken to provide an information base for further research on the current state and trajectory of nanotechnology in Russia and on the broader development of Russia’s innovation system. Comparisons with Chinese publication and patent outputs can be seen in a parallel report on Nanotechnology Research and Innovation in China (2014).
Research target: Sociology (including Demography and Anthropology
Priority areas: economics
Language: English
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Text on another site
Keywords: нанотехнологииинновацииRussiainnovationпубликационная активностьnanotechnologyinnovationsпатентная активностьscientometricsнаукометрияnanotechnologiespatent activityBibliometricsбиблиометрияpublication activityemerging technologiesпатентоведениенаукометрические исследованиябиблиометрический анализnanopublicationнаука и технологииразвитие нанотехнологийbibliometric analysispatent analysis
Publication based on the results of:
Assessment of effectiveness, forecasting and determination of strategic priorities for R&D sphere (2014)
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