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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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К вопросу о формировании и эволюции плазменно-пылевых структур в ионосферах Земли и Марса

Физика плазмы. 2019. Т. 45. № 10. С. 913–921.
Дубинский А. Ю., Резниченко Ю. С., Попель С. И.

A self-consistent model of the formation and evolution of dusty plasma structures in the ionospheres of the Earth and Mars is presented. The model allows describing the formation of a stratified dust structure as a result of dust cloud evolution in the Earth's ionosphere. The structure forms due to the splitting of the primary cloud and is characterized by the presence of a cluster of dust grains at altitudes corresponding to noctilucent clouds and polar mesosphere summer echoes. The characteristic formation time of polar mesospheric clouds in the Earth’s ionosphere obtained within this model agrees with observational data. The possibility of the formation of oversaturated carbon dioxide clouds in the Martian ionosphere, similar to noctilucent clouds in the Earth's ionosphere, is shown. It is demonstrated that phenomena similar to polar mesosphere summer echoes on the Earth can also take place in the Martian ionosphere. The theoretically estimated dimensions and charges of dust grains in the Martian ionosphere agree with observational data.

Research target: Physics Earth Sciences
Priority areas: engineering science
Language: Russian
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Keywords: структураПылевая плазмаМарсDusty plasmasEarthstructureоблакаcloudsMarsЗемля
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