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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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?

Knowledge Triangle Targeted Science, Technology and Innovation Policy

Ch. 1. P. 3–15.
Meissner D., Gokhberg L., Kuzminov Y., Cervantes M., Schwaag Serger S.

During the last decade, the concept of the Knowledge Triangle (KT) in the form of change processes that foster greater interaction between education, research and innovation activities has left the academic community and diffused to the higher education and research policy arena. As a result, numerous policy measures have been developed and implemented aiming at strengthening interaction between the different sides of the knowledge triangle. Similarly, structured and systematic efforts have been taken to describe and understand the important role of universities in the innovation landscape. Universities fulfil numerous missions but they also face the challenge of meeting diverging expectations by different stakeholders. Furthermore, this challenge is complicated by the fact that universities and their surrounding environments are not static but co-develop continuously.

The book presents a number of case studies showing how universities react to these changing conditions. It shows examples of aligning universities to the Knowledge Triangle.

Language: English
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Keywords: change management performance evaluationKnowledge Triangle entrepreneurial universityuniversity missions
Publication based on the results of:
Research on the efficiency of innovation generation processes: resources and factors (2021)

In book

The Knowledge Triangle. Changing Higher Education and Research Management Paradigms
Aanstad S., Benner M., Borlaug S. B., Čadil V., Cervantes M. Switzerland: Springer, 2021.
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