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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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Кадровая политика попечителей Казанского учебного округа и динамика возрастного состава профессоров Казанского университета в 1804–1884 годах

Ученые записки Казанского университета. Серия: Гуманитарные науки. 2017. Т. 159. № 4. С. 925–941.
Костина Т. В., Kouprianov A. V.

Dynamics of the age structure of the professoriate in Kazan, St. Petersburg, Moscow, and Dorpat universities is analysed from their origins through 1884. The study is based on a prosopographical database containing records on the principal events of university professors' lives and careers compiled by the authors. New methods of data retrieval and visualisation based on scripts written in R programming language allow to analyse the temporal dynamics of the age structure in finest details. From the perspective of the temporal dynamics of the age structure, the history of university can be subdivided into periods of gradual concerted aging of a nearly stable population and much shorter periods of rather dramatic rejuvenation. The latter are usually (un)intended by-procucts of active interventions by Educational district Curators or the Ministry of Education in the routine process of hiring and firing the university professors. The analysis reveals a peculiar pattern of the aging / rejuvenation periods in Kazan university that differs significantly from more or less concerted dynamics of the remaining three universities under study. All remarkable rearrangements in the body of professors of Kazan University as well as their effects on the age structure are discussed in detail. A number of factors contributing to the unique character of aging / rejuvenation patterns in Kazan university (shortages of the early period of Kazan university history, constant deficiency of qualified candidates to fill the vacant positions, and active interventions by Educational district Curators) are discussed.

Research target: History and Archeology Sociology (including Demography and Anthropology Education
Priority areas: humanitarian
Language: Russian
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Keywords: Russian historyистория университетаrussian higher educationистория России XIX векаистория университетовhistory of universitiesисторическая демографияKazan UniversityИмператорский Казанский университетHistory of universityRussian history, 19th - 20th centuryRussian History, 18th - 19th centuryhistorical demographyRussian higher educationистория России 19 векаRussian History
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