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News
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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Выбор структуры капитала компаниями на развивающихся рынках с учетом бизнес-циклов экономики

Корпоративные финансы. 2015. Т. 4. № 36. С. 72–87.
Kokoreva M. S., Никифоров М. С.

This article presents the results of a study of corporate capital structure in emerging capital markets, taking into account the business cycles of the economy. Our study was conducted on the data of 581 companies from BRICS countries for the years 2002-2014. We revealed that the target capital structure is dependent on set of factors which is the same for both periods of economic growth and recession. The speed of adjustment to the target capital structure is dependent upon the stage of the business cycle of the economy and is higher for in economic growth periods. The study also found out that the direction of the impact of the determinants of the speed of adjustment varies depending on the state of the economy (the deviation from the target capital structure leads to an increase in the speed of adjustment in periods of growth and a fall - in times of recession).

Research target: Economics and Management
Priority areas: economics
Language: Russian
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Keywords: переключающиеся режимыструктура капиталаemerging capital marketsspeed of adjustmentскорость приспособления развивающиеся рынки капиталаdynamic capital structureswitching regimesдинамическая компромиссная теория
Publication based on the results of:
Empirical Analysis of Corporate Financial Decisions of Companies of Emerging Capital Markets under the Pressure of Global Markets Transformation and Knowledge Economy Development (2015)
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