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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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Трансформация структуры государственного долга по составу кредиторов в странах Центральной Азии и Южного Кавказа

Финансовый журнал. 2024. Т. 16. № 5. С. 8–26.
Никонов И. В., Арустамян М. Г., Mukhametov O., Вотинов А. И., Radionov S.

The rapid growth of public debt in developing countries in recent years and the increasing risks associated with this process emphasize the importance of research in this area. The article examines one of the key aspects of public debt management — the structure of creditors’ debt portfolio. The object of the study is the public debt of four countries of Central Asia and three countries of the South Caucasus. The states of this region are important foreign economic partners of Russia, their contribution to mutual trade and realization of joint projects is growing. Their role in the global and regional economy is also expanding, including participation in major transport and logistics projects designed to increase the connectivity of Eurasia. The study is based on the data of international organizations on the total volume of public debt, debt to individual external creditors and the dynamics of macroeconomic indicators. The article examines changes in the structure of the main groups of creditors for each country over three long-term periods and analyzes similarities and differences in the observed structural shifts. It also decomposes the indicator of public debt dynamics and estimates the contribution of the economic growth factor and the effects of changes in countries’ debt to certain groups of creditors. The study identified the following key trends in public debt structural changes: 1) the declining role of bilateral loans from developed and developing countries since the 2000s; 2) the continued strong influence of debt raised from international financial organizations and development banks in most countries throughout the period under study; 3) in a number of countries, a gradual increase in market sources of debt financing (domestic and Eurobonds); the increasing role of loans from China — in the Kyrgyz Republic and the Republic of Tajikistan, etc. The study identifies reasons for these structural changes, and concludes with a discussion of the risks associated with the observed shifts in the structure of public debt.

Research target: Economics and Management
Language: Russian
Full text
DOI
Keywords: государственный долгвнешний долгЦентральная АзияCentral AsiaSouth CaucasusЮжный Кавказexternal debtpublic debt
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