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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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Пространственная картина центро-периферийных миграционных взаимодействий в России

Региональные исследования. 2024. № 1(83). С. 19–33.
Mkrtchyan N. V.

The migration appeal of major cities and their suburbs (centers) and the outflow of the population from the periphery in Russia is a well-documented phenomenon. Utilizing detailed spatial data, this study examines the spatial diversity of the migration balance of 137 identified centers, represented by cities with a population of 100,000 or more and their suburbs, and 82 peripheral areas for the 33 years 2011-2020. The analysis encompasses not only the flow between centers and the periphery but also among individual centers, as well as migration involving the centers and peripheries within their regions and in interregional exchanges. Out of the variety of centers, 21 main ones are distinguished, concentrating the primary flow of population from peripheral territories and attracting populations from other centers. Meanwhile, half of the centers experienced migration loss. The centers leading the migration flow in each federal district are identified, with particular attention given to the migration balance characteristics of the largest centers. The data used allowed for analysis with and without considering auto-return, which impacts the scale of migration gain/loss, but, as it turned out, virtually does not alter the spatial pattern of population flow between centers and periphery.

Research target: Social and Economic Geography (including Urban and Transportation Studies) Sociology (including Demography and Anthropology
Language: Russian
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Keywords: внутренняя миграцияРоссияRussiaстатистика миграциикрупные городаperipheryпериферияinternal migrationmigration statisticsцентрыLarge citiescenters
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