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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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An ecological perspective to master the complexities of the digital economy

npj Complexity. 2025. Vol. 2. No. 1. Article 16.
Rovenskaya E., Ivanov A., Hathiari S., Kotova D., Scharler U., Boza G.

Economic and social interactions are shifting to digital platforms which grow into vast ecosystems where user engagement creates value for members while ecosystem orchestrators harvest massive revenue. The digital ecosystem business model succeeds by adeptly navigating fast-changing environments, including new technologies and volatile demands, through dynamic innovation in a decentralized decision-making setting. This renders digital platform ecosystems complex adaptive systems. Recognizing that natural ecosystems are a prime example of complex adaptive systems, we propose a systematic hierarchical framework for describing and understanding digital ecosystems, rooted in ecology and evolution. Our framework compares digital ecosystems hosted by societies to natural ecosystems embedded in biomes, products to species, and technologies and elements of business strategy to the genetic makeup of a species. As digital platforms face heightened scrutiny about their socio-economic power and societal value, our approach contributes to the development of deeper understanding and sustainable governance of the digital economy.

Research target: Mathematics Law Economics and Management
Language: English
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Keywords: digital economy
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