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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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?

Waste-to-Energy Generation: Complex World Project Analysis

Sustainability. 2024. Vol. 16. No. 9. Article 3531.
Vukovic N., Makogon E.

Sustainable development and the circular economy mandate efficacious management of waste. The annually increasing volumes of municipal solid waste pose a formidable global challenge. Waste-to-energy conversion, utilizing thermochemical or biochemical technologies, presents a viable solution for mitigating waste disposal concerns. This study conducts a thorough analysis of extant projects to evaluate the economic viability and environmental benefits across various technologies. Employing a self-compiled, unique database, our examination spans enterprises operational from 1980 to 2022, including 37 of the most representative facilities across Europe, North America, and East and Southeast Asia. Economic efficiency is gauged through the levelized cost of electricity generated by these installations, while environmental impacts are assessed based on the statistics on prevented greenhouse gas emissions. The methodology encompasses correlation and techno–economic analyses and expert evaluation. Contrary to conventional wisdom, our findings challenge the ubiquity of scale effects among technologies and the presumed decline in electricity generation costs with newer technologies. However, they corroborate the enhanced environmental benefits of recent technological advancements. The insights derived from this research are poised to inform strategic municipal solid waste management planning in Russia and beyond, offering a foundation for the design of new facilities. The scientific novelty of this work lies in its holistic approach to analyzing the ecological and economic efficiencies of all extant technologies.

Research target: Economics and Management
Language: English
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Keywords: waste management
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