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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
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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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Факторы результативности научно-исследовательского сотрудничества вузов и бизнеса

Университетское управление: практика и анализ. 2024. Т. 28. № 2. С. 96–110.
Melikyan A.

The article presents the results of a study aimed at identifying the factors influencing the effectiveness of university-business research collaboration. The empirical basis of the research consisted of quantitative data on the activities of 600 Russian universities in the year 2022. Regression models were constructed to achieve the research objective. The dependent variables in the models characterize the size of extrabudgetary income from research and development projects, its share in the total income from scientific research, and the number of publications prepared by the university in collaboration with companies. The independent variables considered indicators characterizing publication activity, composition and salaries of academic staff, material resources, the revenue structure of the university, the university’s participation in state projects aimed at supporting and developing partnerships with businesses, ownership form, territorial location, and specialization of the university. The analysis revealed that human resources within the university play a paramount role in achieving high performance in university-business collaboration in scientific research and development. The qualifications and degree of internationalization of academic staff, as well as their publication activity, were found to be of significant importance. Additionally, the university’s orientation towards generating income primarily from extrabudgetary sources and considering scientific research as a priority area for attracting financial resources were also identified as crucial factors. The conclusions drawn are based on statistically significant regularities identified through econometric analysis of relevant quantitative data on the performance indicators of most Russian universities. The research findings may be of interest to universities seeking to develop partnerships with business companies.

Research target: Education
Language: Russian
DOI
Text on another site
Keywords: research projectsнаучно-исследовательские проектыtriple helix modelмодель тройной спиралиuniversity-business collaborationfinancing of scientific researchсотрудничество вузов и бизнесафинансирование научных исследований
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