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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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Разработка методики для измерения представлений об идеальном государстве

Социальная психология и общество. 2021. Т. 12. № 3. С. 103–127.
Prusova I.

Objective. Develop a questionnaire to measure attitudes about ideal government and analyze the psychometric properties of this tool. Background. Psychological studies showed the key role of political attitudes (conservatism/liberalism) in different forms of political activity. Conservatism-liberalism dichotomy did not correspond with actual political “agenda” in Russia that induced the question about the structure of political attitudes. Study design. To indicate the attitudes about ideal government, an online survey was conducted. The structure of political attitudes was analysed through confirmatory factor analysis, and analyses of reliability, validity, and invariance. Participants. The research consisted of 2 independent studies. About 610 participants took part at the study 1 (353 men and 257 women) over 18 years old (M=34.28; SD=13.82); and 378 participants (196 men and 182 women) over 18 years old (M=35.26; SD=10.54) at the study 2. Methods. Russian-language versions of the scales of Needs for cognitive clousure, Openness to experience (“Big Five”), Right-wing Authoritarianism, System Justification, and the author’s questionnaire of attitudes about an ideal government. Additionally, participants completed the self-categoriaiton scales about political orientations (“left-right”; “liberal-conservatives”). Results. The structure of attitudes about the ideal government consisted of “respect” and “support”. “Political respect” was correspondent to “liberal” and “left-wing” self-identification that indicated the criterion validity. At the same time, the analysis of convergent validity showed that “respect” and “support” were linked with right-wing authoritarianism and need for cognitive clousure. Conclusions. The structure of political attitudes in Russia consisted of “governmental support” and “political respect”. The current perspective discussed in the context of liberal/conservative political orientations.

Research target: Psychology
Language: Russian
DOI
Text on another site
Keywords: консерватизмлиберализмгосударственная поддержкаpolitical supportliberalismполитические представленияconservatismидеальное государствоpolitical attitudesideal governmentpolitical respect
Publication based on the results of:
Связь показателей социально-экономического развития регионов России и уровня предрассудков: роль оправдания социальной системы (2020)
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