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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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Эмблема в визуальной репрезентации вуза: классификация и семиотика визуальных образов

Университетское управление: практика и анализ. 2018. Т. 22. № 6. С. 8–23.
Demin P.

The research article presents the study of the main attribute of the university's visual representation – an emblem. Among the few works devoted to the process of university branding, there is no comprehensive analysis of the functioning of their visual representation, while the observed growth of branding work in various universities increasingly involves the emblem as a graphic identifier of the university. Using a combination of quantitative and qualitative methods, a structural and semiotic analysis of the emblems was carried out on a complete sample of Russian universities (917 cases), which allowed to characterize their structure and range of visual images, as well as to compile a typology of semiotic values. In the results, a characteristic of the semiotic bases of visual representation of Russian universities is given, as well as its functions in the process of university branding are defined. The research results have a wide practical application as a source of information about the use and purpose of university emblems in the higher education system, both for specialists involved in university branding (marketing specialists, sociologists, designers, etc.) and for PR departments, media centers and administrations. Nevertheless, the interpretative nature of the semiotic typology and the non-exclusive character of the classification of visual images allow only to designate the semantic directions of the universities positioning, but not to characterize specific educational institutions in terms of their models of visual communication. Additional studies are needed to determine the validity of the classification, as well as to clarify the semiotic bases for the proposed typology.

Research target: Media and Communications Sociology (including Demography and Anthropology Education
Priority areas: sociology management
Language: Russian
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Keywords: семиотикафирменный стильsemioticsвизуальная репрезентацияvisual representationsuniversity brandingэмблемабренд университеталоготипlogotypeemblemuniversity brandcorporate identityбренд вузабрендирование
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