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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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Построение модели воздействия кредитной нагрузки на политическое поведение российских граждан

Власть. 2021. № 3. С. 76–85.
Kozlov N., Парма Р. В.

This article examines the impact of credit obligations of citizens on their cognitive attitudes and political behavior. Based on the statistics of the Central Bank of the Russian Federation, the cyclical nature of the development of the retail lending market is determined, which leads to a periodic increase in the risks of destabilizing the political situation. By analyzing the results of scientific research, the authors show how difficulties in paying off credit debt can provoke a state of stress, emotions of anxiety and anger, form pessimistic attitudes about the future, and cause political manifestations of discontent and protest. The article defines the main variables of the dynamic model of the relationship between the credit load and the political behavior of citizens.

Research target: Political Science, International Relations, and Public Administration Economics and Management Sociology (including Demography and Anthropology
Language: Russian
Text on another site
Keywords: заемщикиэмоциипротестные настроенияemotionsполитическое поведениеpolitical behaviorprotest moodscredit burdenCognitive attitudeshousehold debt burdenзакредитованностьдолговая нагрузка домохозяйствкредитные установки
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