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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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Examining Self-Help and Self-Improvement Practices in Mental Health: Youth Narratives vs. Literature and Online Sources

International Journal of Adolescence and Youth. 2025. Vol. 30. No. 1. Article 2474039.
Mikhaylova O., Ekaterina Gulina, Fomicheva E., Elizaveta Ivashkina, Alina Nagoryanskaya, Jasmin Jyrgalbek, Diana Shelkanova, Darina Valiahmetova, Tsyganova E., Kudinova V.

This mixed-methods study examines self-help and self-improvement practices for youth mental health across 75 websites (Russian/English), 1,970 PubMed articles (2018–2023), and interviews with 34 Russian youths (aged 14–34). Guided by self-determination theory and Boltanski and Thévenot’s justification worlds framework, practices were categorized into competence, autonomy, and relatedness needs. Chi-square tests revealed significant disparities: relatedness-oriented practices constituted 50% of youth narratives versus 32% in literature (p<0.01, Cramer’s V=0.21). Only 8% of websites cited experimental evidence for effectiveness, compared to 34.9% of articles. Youth evaluations prioritized subjective comfort (domestic world: 45%) or emotional resonance (inspired world: 30%), with no references to peer-reviewed evidence. Results underscore gaps in relatedness coverage across sources and inconsistent effectiveness reporting. We advocate enhanced transparency in scientific/online mental-health resources and youth-targeted psychoeducation to bridge lay and clinical efficacy assessments.

Research target: Social Sciences
Language: English
Full text
DOI
Keywords: effectivenessyouthself-improvementmental healthself-help
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