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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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Intangible-driven performance: Two decades searching for the Philosopher's Stone

Russian Management Journal. 2020. Vol. 18. No. 3. P. 433–456.
Shakina E., Molodchik M., Parshakov P.

The study offers a structural literature review on the twenty years the evolution of the fast-growing research topic of intellectual capital (IC) and intangible-driven performance. Despite a rather short independent history, the IC concept has undergone a substantial transformation, bringing to the discussion vast empirical and methodological literature. Several endeavors carrying out literature review studies could only partially satisfy the needs of the systematization of the relevant research. Hence, there is still a large room for such kind of analysis due to the increasing number of new papers published in the area and puzzle of IC-related sophistications. To draw a holistic picture of the landscape of IC and associated corporate performance, this study departures from the mixed research methodology embracing elements of machine-learning tools and in-depth qualitative interpretation of obtained results and critical discussion of the most influential studies in the field. Our findings demonstrate that four professional outlets selected for the examination have generated four relatively isolated research topics: “Human capital and performance”, “Knowledge sharing, organizational learning: processes that drive performance”, “IC and knowledge management for business performance”, “Measurement, disclosure of IC and knowledge for business performance”. All these research lines are welcomed by all leading journals in the field, having high potential to create a dense flow of conceptual and empirical contributions and practical value for a knowledge-intensive business.

Language: English
Full text
DOI
Keywords: интеллектуальный капиталtext miningtopic modelingliterature reviewлитературный обзорintangible-driven economyтекст-майнинг
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