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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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Energy Transition as a Response to Energy Challenges in Post-Pandemic Reality

Energies. 2022. Vol. 15. No. 3. Article 812.
Gribkova D. E., Milshina Y.

The concept of energy transition has been adopted by national governments, international and regional organizations. The COVID-19 pandemic triggered increasing attention to climate policy, energy transition, and economic resilience. This paper looks at energy transition in selected countries with the use of case studies—China, the European Union, India, Russia, Saudi Arabia, and the United States. The aim of the study is to analyze the main approaches to the energy transition in these countries and the barriers which may influence this process. The study applies foresight methods such as trend monitoring, case study, and STEPE methods for barrier analysis. The outcomes include identification of energy priorities in the national context, key directions for future transformation, and selected countries’ approaches to the energy transition after the COVID-19 pandemic. All considered countries are taking measures for the energy transition, but the motives, pace, and priorities differ. Their approaches were grouped into ideological (the European Union, the United States) and pragmatic (Saudi Arabia, China, and India). Both groups attribute priority to the gradual shift to clean technologies and carbon-neutral energy generation. Russia occupies an intermediate position due to attention to consumers as well as high interest in diversification of budget revenues and the shift from fossil fuel. The list of barriers to energy transition includes an inappropriate level of technological development, inconsistency of renewable power generation, substantial/rapid increase in rare earth metals prices, economic sectors’ dependence on fossil fuels, lack of investment in renewables, restricted access to technologies, trade barriers, lack of political will, use of access to advanced technology market as a political tool, and renewables’ dependence on weather regimes, etc. The novelty of the study is in the scope of selected cases that give a broad overview of existing approaches to the energy transition, identification of two energy transition patterns, as well as trends, wild cards, and barriers on the way.

Research target: Economics and Management
Language: English
Full text
DOI
Text on another site
Keywords: case studyglobal trendsGrand challengesWild cardsenergy transitionsustainable developmentpandemic
Publication based on the results of:
Study of the mutual influence of global trends using big data analysis methods (2022)
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