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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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Адаптация краткой и сверх-краткой версий вопросника Big Five Inventory-2: BFI-2-S и BFI-2-XS

Психологический журнал. 2022. Т. 43. № 1. С. 95–108.
Mishkevich A., Shchebetenko S., Kalugin A., Сото К., Джон О.

The paper is aimed to examine psychometric properties of short (BFI-2-S; 30 items) and extra- short (BFI-2-XS; 15 items) forms of the Russian version of the Big Five Inventory-2. Two samples were employed, which comprised Internet users (N = 1787) and students (N = 1024). Scores on the BFI-2-X/BFI-2- XS scales and subscales were distributed normally. The methods demonstrated adequate internal consistency and retest reliability (n = 90). The factor structure measured by the principal components analysis and the Random Intercept Exploratory Factor Analysis showed that BFI-2-X/BFI-2-XS’s data reproduced the structure previously obtained in other cultures. The BFI-2-X/BFI-2-XS subscales correlated with the IPIP subscales (Goldberg, 2001), such that they supported convergent and discriminant validity. Therefore, the Russian BFI-2-X and BFI-2-XS represent psychometrically solid instruments.

Research target: Psychology
Language: Russian
DOI
Keywords: личностьreliabilitypersonalityпсихометрикаvalidityнадежность теставалидность опросникаpsychometricsпятифакторная модель личностиfive-factor theoryBig Five Inventory-2short forms of questionnairesBig Five Inventory-2краткие версии вопросников
Publication based on the results of:
Психометрические испытания русскоязычной версии вопросника "Big Five Inventory-2" (2019)
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