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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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Acceleration effect of intangibles in the recovery of corporate performance after-crisis

Research in International Business and Finance. 2017. Vol. 42. P. 1115–1122.
Barajas A., Shakina E., Fernandez-Jardon Fernandez C. M.

This study explores the recovery in the Market Value Added (MVA) of European companies after the recent global economic crisis in 2008–2009. It introduces empirical evidence that intangible-intensive strategy in human and relational capital reinforces speed of the after-crisis correction for companies. Based on a panel dataset of more than 1600 listed corporations this research aims to discover drivers of Market Value Added trends in 2011–2013. The established results contribute to the understanding of the advantages that companies can exploit for the recovery after systematic shocks of markets. Our study demonstrates that intangible-intensive strategy not always enabled faster recovery speed. Meanwhile, it provided year-to-year acceleration of MVA growth after crisis.

Priority areas: economics management
Language: English
DOI
Text on another site
Keywords: strategycorporate performanceintangiblesrecoverymarket value addedCrisis
Publication based on the results of:
Optimal strategies for intangibles (2017)
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