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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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Toward an Understanding of Cultural Orientations Through the Lens of Human Behavioral Ecology

Evolutionary Behavioral Sciences. 2025. Vol. 19. No. 3. P. 205–226.
Gallyamova A., Grigoryev D.

This article introduces a Cultural Orientations Model Based on Human Behavioral Ecology (COM–HBE), which redefines the study of cultural orientations. Our research program formulates and intends to describe a unified framework that integrates cultural orientations, mechanisms of cooperation and social conformity, and principles from life history theory. This integrated approach has the potential to bridge evolutionary science and social science, offering a more comprehensive understanding of both human behavior and culture. The proposed framework encapsulates established patterns at both the group and individual levels, providing a holistic understanding of human behavior and culture within an ecological and evolutionary context. Furthermore, our model is aimed to capture the mechanisms of cooperation and social conformity across all levels, from neurophysiological to cultural. This comprehensive approach not only deepens our understanding of cultural diversity and commonality but also enhances our ability to predict and manage the adaptive behaviors that arise in response to ecological pressures.

Research target: Social Sciences
Language: English
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Keywords: individualismcooperationcross-cultural researchcollectivismindividualism-collectivismsocial conformitycultural orientationslife history theoryhuman behavioral ecology
Publication based on the results of:
Адаптивные основы культуры: к пониманию культурных ориентаций через призму компромиссов истории жизни (2024)
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