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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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ОБРАЗОВАТЕЛЬНАЯ ДИФФЕРЕНЦИАЦИЯ РОССИЙСКОГО ДВОРЯНСТВА ЕЛИЗАВЕТИНСКОЙ ЭПОХИ

Вестник Санкт-Петербургского университета. Серия 2. История. 2022. Т. 67. № 2. С. 333–357.
Феофанов А. М.

This article analyses the educational differentiation of the nobility during the reign of Elizabeth expressed in the status of their property (number of “souls”); the geographical location of their estates; the educational institutions where they were sent; and the age when they began education. It is based on a wide array of historical sources, among which archival records from the collections of the Russian State Archive of Ancient Acts. The data of registration of gentry were supplemented by materials from educational institutions (The Noble Land Cadet Corps, The Naval Cadet Corps, artillery and engineering schools, Gymnasium of Saint Petersburg Academy of Sciences). The article demonstrates the career achievements; establishes which schools gave a higher percentage of students who replenished the elite of the Russian Empire. The median value of the estate for all gentry was 20 souls, including those who did not have peasants. The poorest representatives of the “noble proletariat” were students of garrison schools, more than half of whom did not have peasants at all. The highest was the median value of serf-ownership among the students of the Noble Land Cadet Corps. Average and big noble landowners (owners of hundreds and thousands of “souls”) studied in the Noble Land Cadet Corps. The percentage of generals from the families of major and middle-rank land-owners was also the highest among graduates of the Noble Land Cadet Corps. The presented figures clearly show the preferences of different layers of nobility, their educational strategies, the results of their career advancement.

Research target: History and Archeology
Language: Russian
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DOI
Text on another site
Keywords: nobilityпросопографияprosopographyСухопутный шляхетный кадетский корпусдушевладениепомещикидворянские недорослиserf-ownershipNOBLE MINORSLANDLORDSNOBLE LAND CADET CORPS
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