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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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Networks under deep uncertainty concerning food security

The Journal of the New Economic Association. 2024. No. 3(64). P. 12–29.
Aleskerov F. T., Dutta S., Egorov D., Tkachev D.

We propose new models to find the vulnerable countries in terms of food security. These models are based on network analysis under deep uncertainty. The conditions of deep uncertainty affect the supply and demand of food, namely, carbohydrates of the countries. Under such conditions networks of the carbohydrate supply between countries are constructed. The countries vulnerability in terms of food security were studied by new centrality indices taking into account carbohydrate consumptions of countries and the possibility of group influence of countries to a country. Also, our models show direct and indirect dependence on import of carbohydrates from other countries. The scenario of one of such situations was constructed, and our models for studying this situation were tested. The vulnerable countries are identified in terms of carbohydrate consumption from main crops in different scenarios based on real data using our new models. The developed models can make the food policy of countries more efficient.

Research target: Economics and Management Mathematics
Language: English
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Keywords: продовольственная безопасностьсетевой анализсценарный анализnetwork analysisscenario analysisfood insecurityглубокая неопределенностьdeep uncertainty
Publication based on the results of:
Study of the models and methods of decision-making under conditions of deep uncertainty. Extension and testing of developed methods (2024)
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