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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.
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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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Role of social communities in tourism: creation and support of a territorial brand

Современные проблемы сервиса и туризма. 2022. Vol. 16. No. 3. P. 39–50.
Elena R. Shark, Sharko O.

This article examines the essence and importance of social media marketing for the promotion of tourist services in an online environment. The purpose of the study is to identify promising ways to boost the target audience by communities in social networks adapted for tourism. A systematic and situational approach, as well as classification, formal and logical analysis and desk research are the main research methods. Based on a literature review on the approaches to understanding the types of social communities and the importance of the correct positioning of advertisements in the formation of loyalty to the territorial branding of customers, the authors present their definition of "community in a social network", give a classification of existing communities, and reveal the features of each type. The practical value of the research lies in the development of ways to use the advantages of each type of social community for the development of tourism in local territories. The authors provide practical tools for identifying and aggregating the target audience in popular social networks in Russia. The main paths of further research are a comparative analysis of the best practices of using social communities in tourism, in order to effectively interact with the target audience and develop recommendations for a content plan of a marketing strategy for promoting a territorial brand and tourist products of a specific local recreation.

Research target: Economics and Management
Language: English
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Keywords: туризмсообществоцелевая аудиторияtourismcommunityTarget Audienceмаркетинг в социальных сетяхterritorial brandingSocial Media Marketingtargeted advertising таргетированная реклама территориальный брендинг
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