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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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Метод моментальной экологической оценки в социальных исследованиях: возможности и ограничения

Вестник Санкт-Петербургского университета. Серия 12: Социология. 2025. Т. 18. № 1. С. 21–38.
Шарин Н. А., Eritsyan K.

This article focuses on the analysis of the current state of Ecological Momentary Assessment (EMA), a research methodology characterized by frequent real-time measurements in partici-pants’ everyday lives. Based on a review of contemporary scientific literature, the article high-lights the key advantages and challenges associated with the practical application of EMA in empirical social research. The advantages of EMA include the minimization of retrospective bias, high ecological validity, and highly detailed data resolution. Challenges associated with the method involve burden, fatigue, measurement reactivity, decreased motivation and en-gagement, data missingness, and the need to account for meso-context. The paper also identi-fies key solutions to these challenges and assesses their empirical support. Among the most supported solutions are the provision of financial incentives and personalized feedback, which have been shown to effectively address some of the method’s difficulties. On the other hand, solutions such as survey personalization and integration with other methods have not yet been empirically validated and require further investigation. Additionally, the article discusses potential directions for advancing EMA methodology and its applications within the social sciences. Despite EMA’s current limited popularity in this field, its considerable advantages make it particularly valuable for studying practices and interactions within groups and organi-zations. It is also useful for analyzing the impact of social context (such as physical locations and social ties) on individuals’ lives. Further development of the method, including empiri-cal validation, refinement, and comparative analysis of difficulties’ solutions, is anticipated to strengthen its benefits and enhance its overall applicability in empirical social research

Research target: Psychology Sociology (including Demography and Anthropology Other social sciences
Language: Russian
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Keywords: surveyопроссмартфонsmartphoneecological momentary assessmentexperience sampling methodologyecological momentary assessmentExperience Sampling Methodologyмоментальная экологическая оценка
Publication based on the results of:
Mental health research: behavioral and neurophysiological foundations (2025)
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