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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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Факторы, влияющие на потребление алкоголя в России

Вопросы статистики. 2023. Т. 30. № 6. С. 51–64.
Sadykova E.

The main purpose of the study is to determine the differences in factors influencing alcohol consumption in Russia (among urban and rural residents). The first part of the article provides the author's assessment of studies in which place of residence is considered as a specific factor influencing alcohol consumption. Attention is paid to works that focus on differences in factors affecting alcohol consumption depending on the place of residence. The main section of the article contains a description of the empirical testing of the hypotheses based on statistical data for 2012–2021 from the Russian Longitudinal Monitoring Survey (RLMS) NRU HSE. The author used panel logistic regression with random individual effects for the probability of alcohol consumption and a panel Tobin model for the volume of alcohol consumption. The study revealed differences in the degree of influence of the level of education and religiosity, marital status and the presence of children on the level of alcohol consumption among urban and rural residents. Thus, a higher level of religiosity reduces the likelihood of alcohol consumption by rural residents; the volume of alcohol consumption decreases with a rise in the level of religiosity in the city. It is also shown that the presence of children in families reduces per capita alcohol consumption only in villages. According to the author, the findings help make more effective and targeted recommendations for implementing state alcohol policy

Research target: Economics and Management
Language: Russian
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Keywords: здоровый образ жизнипотребление алкоголяalcohol consumptionhealthy lifestyleсоциально-экономические факторы socio-economic factors
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