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News
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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Wellbeing Dynamics across Cultures During the Pandemic: A Five Country Exploration

2024.
Hartle L., Bortolini T., Karl J., Sokolov B., Fischer R.
Wellbeing levels have been a global concern during the COVID-19 pandemic, but there is a lack of attention to invariance questions that allow a robust examination of wellbeing dynamics across cultures. Questions of temporal stability that are crucial for examining the impact of the pandemic on wellbeing have received even less attention. Some studies suggested that measures may not be stable after the onset of the pandemic. We examine invariance parameters, the factorial structure and variability of wellbeing variables (life satisfaction, pandemic worries, anxiety and depression screenings) across five different cultural contexts from 2020 to 2022 (N = 4387, total observations = 13,161). A three-factor model separating life satisfaction, worry, and distress performed best in terms of model fit and parsimony. We observed scalar invariance across times and identified little variability of wellbeing measures during the pandemic, suggesting that wellbeing levels remained stable during the pandemic in each of the countries sampled. In contrast, we only identified metric invariance across countries at each time point, and found a weakening of correlations between life satisfaction and a depressive/anxious symptoms scale in lower income countries. We discuss implications of our findings for discussions of wellbeing dynamics.
Research target: Sociology (including Demography and Anthropology Psychology
Priority areas: sociology
Language: English
Full text
DOI
Text on another site
Keywords: кросс-культурные исследованияблагополучиефакторная структураinvariancefactorial structureлонгитюдwellbeingизмерительная инвариантностьlongitudinalcross-cultural
Publication based on the results of:
Comparative studies of culture: theoretical foundations, methodological problems and applications to problems of socio-economic development (2024)
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