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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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Донные осадки как природная летопись дегляциации в бассейне озера Сылтранкёль, Приэльбрусье

Криосфера Земли. 2024. Т. 28. № 2. С. 33–46.
Иванов М. М., Gurinov A., Голосов В. Н., Кузьменкова Н. В., Александрин М. Ю., Успенский М. И., Шоркунов И. Г., Гаранкина Е. В.

Deglaciation history of the Syltrankel high-mountain lake basin (Mt. Elbrus region, Northern Caucasus) from the end of the 19th to the end of the 20th centuries was reconstructed. In 2022, a comprehensive examination of the area was carried out, including sampling of the bottom sediments and following radioisotope dating, structural and textural analysis. Simultaneously, a set of published sources; topographic maps; ground, satellite, and aerial photographs; and field observations was analyzed to determine the positions of the edges of glaciers at different times. In the formation of bottom sediments, four stages were identified correlating with the state of mountain glaciation and changes in the configuration of the lake’s drainage area. Converging results obtained on the basis of independent sources indicate the high methodological value of studying bottom sediments of mountain lakes as one of the few environmental archives that record glaciation changes in the dynamic conditions of high mountains.

Research target: Earth Sciences
Language: Russian
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DOI
Text on another site
Keywords: Северный Кавказbottom depositsдонные отложенияbasin component of sediment yieldсток наносоврадиоизотопыNorthern Caucasusmountain lakesmountain glaciersdeglaciationradioisotopesvarvespaleoarchivesгорные озерагорные ледникидегляциацияварвыпалеоархивы
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