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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.
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Влияние алгоритмической торговли на устойчивость развития мировых фондовых рынков

Финансы и кредит. 2017. Т. 23. № 20. С. 1184–1195.
Volodin S., Якубов А. П.

Subject. Various aspects of new market segment’s influence – algorithmic trading – on the development of world stock markets. Authors analyze in detail the most significant cases of algorithmic systems’ impact on market mechanisms and participants of the trading process.

Aims. Authors set the following goal – to study the peculiar features of algorithmic trading’s segment on the sustainable development of stock markets. The research of different influence characteristics allows estimating overall perspectives of development of world stock markets as a whole as well as algorithmic trading itself in particular. Contemporary, this issue obtained a special actuality due to wide spreading of algorithmic systems for stock market operations.

Methodology. Relevant stock market statistics, regulatory norms, legislative acts and opinions of experts in stock market sphere were analyzed; analytical, scientific and practical information in the studied field was systematized.

Results. It was shown that algorithmic trading exerts a significant impact on stock market indices’ dynamics, market liquidity and efficiency, and also on some other features which characterize sustainable market development.

Findings. The results of analysis allows stating that algorithmic trading’s segment has a marked negative effect on stock markets. And it has already worried market specialist as well as employees of stock markets and state regulators. Besides, adopted legislative and technological measures cannot provide a significant restrictive influence on the revealed negative aspects of algorithmic trading’s impact. That is why the increase of the claimed negative effects is expected in the nearest future. The obtained results may be useful for researchers of stock market sustainability, representatives of regulators and direct participants of the trading process.

Research target: Economics and Management
Priority areas: economics
Language: Russian
Full text
Keywords: фондовый рынок алгоритмическая торговляalgorithmic tradingrisks of algorithmic tradingриски алгоритмической торговлиstock marketsustainable development of stock marketsустойчивость развития фондовых рынков
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