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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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Два подхода к дифференциации терминов миграционных исследований (по данным корпусного анализа)

Новые исследования Тувы. 2025. № 4. С. 122–136.
Permyakova T. M., Smirnova E. A.

The article presents a quantitative and qualitative analysis of English-language terms related to the study of migration.The sources used were research articles in the social sciences published between 2018 and 2020 in international first-quartile journals indexed in the Scopus database. The corpus-linguistic study addresses two objectives: to identify functioning systems of terms in scientific articles and to describe their disciplinary affiliation based on the criteria of semantic fields. A corpus of texts amounting to approximately 280,000 words was compiled and processed using the concordance and keyword list functions of the AntConc software. Within the texts, two-word combinations containing the units migration and migrant(s) were identified. The qualitative analysis of the semantics of these units served as an additional verification of their semantic differentiation. The results of the study indicate that, from the perspective of semantic analysis of terms, two approaches to the study of migration are currently emerging: first, a time-oriented approach, and second, a subject-oriented approach within specific disciplines. The analysis further establishes that the field of migration studies is structured, on the one hand, around the study of communities or individual experience, and on the other, around temporal or subject-based approaches, which often intersect. It was also found that established terminology is characteristic of economics, education, and public administration, while fields such as history, geography, and psychology have yet to develop their own consistent conceptual frameworks for this phenomenon.

Research target: History and Archeology Philology and Linguistics
Language: Russian
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Keywords: миграциякорпусная лингвистикамиграционный дискурсколлокация семантическая категориятермин миграции
Publication based on the results of:
Миграции в социально-экономическом, демографическом, культурном и человеческом измерении (кейсы Пермского края и Республики Тыва в 1920-х – начале 1960-х гг.) (2026)
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