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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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First principle study of the CuCrTiS4: Spin-glass state and electronic transport

Computational Materials Science. 2024. Vol. 244. Article 113192.
Argunov E., Alexey I. Kartsev

Magnetothermopower and giant magnetoresistance in spinel-type compound  has attracted a lot of attention recently. Ab initio computations of the electronic structure and the Seebeck coefficient of the  structure are reported. The mixed nature of magnetism was identified to be caused by the competition between ferromagnetic and anti-ferromagnetic impacts, with the corresponding determined exchanged parameters:  meV,  meV,  meV. Seebeck coefficient and electrical conductivity of the  compound in the temperature range of  K along with its low temperature thermoelectric capabilities were calculated. The direct 0.4 eV band gap was determined by taking in account strong correlations effect via the Hubbard correction. The calculated Seebeck coefficient is in a good agreement with the available experimental data for a wide carriers concentrations range. This first detailed study of  reports that this thiospinel poses a mixed nature of magnetism along with unique thermoelectric properties.

 

Research target: Physics Materials Technologies
Language: English
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Keywords: thermoelectric effectlayered materialsAb initio band structure
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