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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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Регион победившей лесоохраны: лесное хозяйство Челябинской области в 1940-х — 1980-х годах

Новейшая история России. 2024. Т. 14. № 3. С. 686–699.
Косенко В. В., Petrova A., Popov A.

The presence of vast forest areas was one of the factors in the industrial development of the Urals in the 18th — 19th centuries. Wood was at the base of the ‘great construction sites’ of industrialization in the 1930s. In the second half of the 20th century, however, the role of the forest as a source of industrial resources changed dramatically as businesses switched to alternative sources of fuel and building materials, and people were able to buy wood from neighboring regions. This situation differed significantly from the development vectors of the forest industry in the forest-rich regions of Siberia, the Middle Urals, and the Russian North. In this article, on the example of the forest management of Chelyabinsk region, we consider an atypical for the Soviet period model of the use of forest resources in the old industrial region in the second half of the 20th century. Based on the analysis of archival documents and materials of personal origin, the authors analyze the impact of state reforms of the forest management system on the specifics of forest protection in the region. The article shows how the role and perception of the value of forests changed because of technological changes in the economy, as well as increased access to alternative suppliers and energy sources of factors. As a result, much of the region’s forests have been reclassified as protected from industrial logging or listed as specially protected areas.

Research target: History and Archeology
Language: Russian
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DOI
Text on another site
Keywords: лесная промышленностьлесное хозяйствоforestry industryэкономическая история СССРEconomic Hisotry of USSRсоветское природопользованиеSoviet managementRussian forestry
Publication based on the results of:
Индустриальная эволюция лесной отрасли в старопромышленных регионах на примере Урала во второй половине XX в.  (2022)
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