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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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Психометрический анализ русскоязычной версии шкалы тяжести усталости (Fatigue Severity Scale, FSS)

Теоретическая и экспериментальная психология. 2025. Т. 18. № 4. С. 178–197.
Zolotareva A.

Background. The Fatigue Severity Scale (FSS) is used by modern specialists as the main instrument for screening and monitoring studies of fatigue in clinical and population samples. Objectives. The aim of this study was to adapt the full (Fatigue Severity Scale-9, FSS-9) and two short Russian versions of the Fatigue Severity Scale (Fatigue Severity Scale-7, FSS-7; Fatigue Severity Scale-3, FSS-3) in a non-clinical sample. Study Participants. The study involved 526 respondents (264 men and 262 women) aged 18 to 74 years (M = 48.2; Me = 46; SD = 13.9). Methods. In addition to the FSS, the study participants completed the Depression, Anxiety and Stress Scale-21 (DASS-21). Results. The full and two short versions of the FSS have a one-factor structure in which all points on the scale are included in the general factor. The internal reliability is proved by means of adequate Cronbach’s α and Mc-Donald’s ω coefficients. Convergent validity is confirmed by positive correlations of fatigue severity indicators according to all FSS versions with depression, anxiety, and stress indicators according to DASS-21. Conclusions. The Russian versions of the Fatigue Severity Scale are psychometrically consistent and can be recommended for use in scientific research with population samples.

Research target: Psychology
Language: Russian
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Keywords: factor structureфакторная структурапсихометрический анализfatigueусталостьpsychometric analysisinternal reliabilityconvergent validityвнутренняя надежностьконвергентная валидностьFatigue Severity Scaleшкала тяжести усталости
Publication based on the results of:
Развитие психосоматических исследований в России (2025)
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