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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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?

New venture internationalization from an emergent market: Unexpected findings from Russia

Journal of East-West Business. 2015. Vol. 21. No. 14. P. 1–35.
Michailova I., Shirokova G., Laine I.

This article offers new insights on determinants of degree and scope of new venture internationalization from emerging markets. We set our study by development of a theoretical framework and hypothesis integrating entrepreneur-, firm-, industry-, and institutional factors to be tested in the Russian empirical context. The results indicate that institutional factors have the strongest relationship with the degree and scope of internationalization in Russian NVs. However, contrary to our expectations and existing literature, entrepreneur-, firm-, and industry-related factors are not associated with new venture internationalization. These unexpected findings have motivated us to implement the second round of empirical research using qualitative approach. As an outcome, we were able to explain the underlying forces behind the inconsistencies in the survey results and to develop four puzzles that provide a foundation for the theoretical extension of new venture internationalization from emerging markets.

Language: English
DOI
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Keywords: Russiainstitutionsinternationalization emerging marketsNew venture
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