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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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Факторная структура русскоязычной версии шкалы соматических симптомов (Somatic Symptom Scale-8, SSS-8)

Консультативная психология и психотерапия. 2022. Т. 30. № 3. С. 8–20.
Zolotareva A.

The Somatic Symptom Scale-8 (SSS-8) is one of the most common and psychometrically sound instruments for self-reporting somatization and eight specific somatic symptoms [7]. Objective. This study was aimed to adapt and examine the factor structure of the Russian SSS-8. Methods. A total sample of 167 care home residents completed the Russian version of the SSS-8 and questionnaire covering socio-demographic information. Results. Exploratory and confirmatory factor analyses showed that the adapted scale demonstrated a single-factor structure evaluating the somatization total score via medically unexplained symptoms (stomach or bowel problems; back pain; pain in arms, legs, or joints; headaches; chest pain or shortness of breath; dizziness; feeling tired or having low energy; trouble sleeping). The Cronbach’s α coefficient for the SSS-8 total score was 0,76, indicating the good internal consistency. Conclusions. The Russian version of the SSS-8 is a reliable and valid measure assessing somatic symptoms in care home residents.

Research target: Psychology
Language: Russian
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Keywords: factor structureфакторная структурапсихосоматическая медицинасоматические симптомыpsychosomatic medicineSSS-8somatic symptomsSSS-8
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