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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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Логический компонент мышления как предиктор принятия решений о наличии в ситуации коррупционного риска

Психологический журнал. 2021. Т. 42. № 6. С. 25–34.
Погожина И. Н., Sergeeva M.

The links between elements of the decision-making system on the presence of corruption risk (CR) in a situation with the logical component of thinking as a predictor are considered. The hypothesis of the role of logical reasoning component as a predictor of (1) perceptions of corruption, (2) indicators of emotional intelligence and (3) moral judgement was tested on a sample of Moscow university students (N = 134; M = 35±11 years old). The following diagnostic tools were used: (1) the author’s test for recognising CR situations, (2) the method for assessing the content of ideas about corruption (Pogozhina, Pshenichnyuk, Sergeyeva), (3) D. Lucin’s EmIn questionnaire, (4) Molchanov’s Justice-Care technique. Correlation analysis and structural modeling were used to process the data. The logical component of thinking was a significant positive predictor of the level of development of perceptions of corruption and understanding one’s own emotions and those of others. Also, the logical component significantly negatively predicted moral judgments based on instrumental individualism, reflexive empathic orientation and unconscious but internalized moral values. The findings suggest that the logical component will play a leading role in the CR decision-making system and should be specifically shaped.

Research target: Psychology
Language: Russian
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Keywords: коррупцияdecision-makingпринятие решенийcorruptionstructural equation modelingструктурное моделированиеэмоциональный интеллекткоррупционные рискиcorruption risks logical thinking emotional intelligenceлогическое мышлениеморальные сужденияmoral judgments
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