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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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Психологическая саморегуляция и ее значимость для развития внутренней мотивации, психологического благополучия и профессионального мастерства в спорте

Психология. Журнал Высшей школы экономики. 2024. Т. 21. № 1. С. 167–183.
Якушина А. А., Леонов С. В., Пшеничнюк Д. В., Седогин Е. А., Polikanova I.

Self-regulation is one of the main resources contributing to the productive realization of activity and getting satisfaction from life. The aim of the study was to investigate the role of different aspects of self-regulation in the development of intrinsic motivation to practice sports and the level of life satisfaction in athletes. To study different aspects of self-regulation, the «SelfGovernment Test» of J. Kuhl and A. Fuhrman was used. To study the level of intrinsic motivation «The Sport Motivation Scale» was used. The «Life Satisfaction Scale» and «The Negative and Positive Emotion Scale» by E. Diener were used to test the degree of life satisfaction. The research involved 178 athletes aged from 17 to 33 years (M = 19,38; SD = 1,88), having titles from junior to Master of Sports International Class. Regression analysis showed that aspects of selfregulation such as planning, sensitivity to own values and emotional regulation in athletes contribute to a higher level of life satisfaction. A comparison of groups with different levels of selfregulation showed that athletes with higher levels of self-regulation skill are characterized by a more stable internal motivation to do sports and less expression of negative experiences; they experience positive emotions more often. In addition, it has been demonstrated that the most developed indicators of volitional regulation are characteristic of athletes at the initial and professional level of skill, as compared to those who are at the intermediate stage. Thus, self-regulation is one of the most important resources of athletes, important not only for their professional performance, but for a sense of well-being and life satisfaction.

Research target: Psychology
Language: Russian
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Keywords: саморегуляцияself-regulationвнутренняя мотивацияспортивная психология intrinsic motivation sport psychologyvolitional regulationsports motivationволевая регуляцияспортивная мотивация
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