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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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May 25, 2026
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Monitoring the Performance of Educational Institutions: A Spur for the Implementation of Systemic Changes in Higher Education. Part One

Russian Education and Society. 2016. Vol. 58. No. 4. P. 260–282.
Sorokin S., Sobolev A.

The article discusses the deployment of a comprehensive reporting and monitoring framework used to evaluate the performance of state and private higher education institutions in Russia. By referring to diversified indicators including organizational, financial and economic, training, research, graduate employment, and other metrics, the authors spotlight key developments taking place in the Russian higher education system as well as areas where reorganization/optimization measures are required.

Research target: Education Economics and Management
Priority areas: management
Language: English
DOI
Text on another site
Keywords: высшее образованиевысшее образование в России Russian Universitieshigher education monitoringмониторинг высшего образованияhigher educationмониторинг эффективности вузовmonitoring the efficiency of universities
Publication based on the results of:
Научно-методологическое обеспечение формирования системы мониторинга эффективности деятельности  учреждений высшего профессионального образования, в том числе входящих в образовательные кластеры (2012)
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