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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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Российское отходничество: вехи многовековой истории

ЭКО. 2019. № 11. С. 94–115.
Plusnin J.

Abstract. Otkhodniks are the self-name of Russian internal temporary work migrants. They commonly are seasonal workers, but they are different from seasonal agricultural workers, as well as from circulatory migrants and gastarbeiters. A lot of them are small business entrepreneurs. But, they are mostly hired workers. The key attributes of the otkhodniks are following. They live permanently in the villages and small towns, but they seek work in the capitals (Moscow and St. Petersburg), or in industrial centers in the North and in Siberia. These people do not want to change their place of permanent residence in order to move closer to the working place. The main labor motivation of migrant workers is to increase the family’s welfare. Otkhodniks have the basic life motivations - independence, initiative, self-activity.

In this paper I briefly reviewed the 5-century history of Russian otkhodnichestvo. I have identified the main causes of this type of migration in the imperial period (XVII-XX century) and in our time over the past 30 years. I also identified factors that stimulate or suppress the migration activity of provincial residents. I have the author's estimates of the otkhodniks’s current number presented, and compared them with the estimates of other authors for the XVII – XX centuries. This allowed me to describe the secular dynamics. I have also estimates of the otkhodniks’s  structure of employment from the 1990s provided. The evolution of modern working migration has been described. The most important features of the evolution are dynamism, variability and expansion. Seasonal migrants-otkhodniks, are a new class in Russia – the precariat.

Research target: Sociology (including Demography and Anthropology Economics and Management
Priority areas: economics sociology
Language: Russian
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Text on another site
Keywords: неформальная экономикасамозанятостьотходникиotkhodniksprecariatinformal economicsself-emploimentinternal seasonal labor migrationhistory of Russian otkhodnichestvolabor migration structureвозвратная сезонная трудовая миграцияистория российского отходничестваструктура и виды отхожих промыслов
Publication based on the results of:
Промыслы провинции: организация неформальных экономических практик населения (2019)
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