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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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Industry Choice by Young Entrepreneurs in Different Country Settings: the Role of Human and Financial Capital

Journal of Business Economics and Management. 2016. Vol. 17. No. 4. P. 613–627.
Dmitri Knatko, Shirokova G., Bogatyreva K.

Entrepreneurial entry happens as a consequence of a general choice of an individual to become an entrepreneur. While most entrepreneurial entry studies rarely consider an industry choice to be an aspect of entrepreneurial decision making process, we address this issue taking into account individual, industrial, and country specific attributes. Using data from the Global University Entrepreneurial Spirit Students’ Survey (2013–2014) on young nascent entrepreneurs and extending it with objective indicators derived from World Bank, Global Entrepreneurship Monitor, and International Property Rights Index datasets, we investigate how various factors impact the choice between knowledge-intensive and capital-intensive industries. Drawing on the RBV and contingency approach, we link an industry choice to the level of human capital development and access to financial capital testing for possible country-specific moderation effects. Our study contributes to entrepreneurial entry research stream extending the understanding of entrepreneurial entry decision making nuances related to individual access to resources and both industryand country-level contingencies.

Priority areas: management
Language: English
DOI
Text on another site
Keywords: innovation-driven economyentrepreneurial entryindustry choiceyoung entrepreneurshipefficiency-driven economyGUESSS
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