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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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?

Predictive Model of Strategic Development of a University

P. 459–467.
Nadezhda Titova, Aleksey Shutov

The paper presents an economic and mathematical model designed to forecast development of a university depending on social and economic changes in the country and the amount of public funding of education. The university's internal decisions how to distribute its budget among operational activities, development, supporting science, and improving educational services and assumed as endogenous variables. The development of the university is viewed as a phase space trajectory defined by the four characteristics: quality of educational services, level of development of R&D and consulting activities, image and financial performance of university.

The choice of parameters meets the major interests of the most important stakeholders: state and society, business and science, labour market, prospective students, and the university staff. The model describes relationships between: a) funds obtainin and spending, b) results of university development along various axes and the investments, c) finance obtained and the results of university development, d) results of the university development in adjacent time intervals. The strenght of the model consists in its practical use confirmed by the first results of modeling.

Language: English
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Keywords: университетпрогнозuniversityquality of educationbalanced scorecardкачество обученияstrategic developmentразработка стратегииprognosiseconomic and mathematical modelrecursive relationsscientific activity of a universityfinancial situation of a universityimage of a universityэкономическая и математическая модельрекурсивные связинаучная работа университетафинансовое состояние университетаимидж университета

In book

Procedia Computer Science. 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014. National Research University Higher School of Economics (HSE) in Moscow (Russia) on June 3-5, 2014
Procedia Computer Science. 2nd International Conference on Information Technology and Quantitative Management, ITQM 2014. National Research University Higher School of Economics (HSE) in Moscow (Russia) on June 3-5, 2014
Vol. 31. , Amsterdam: Elsevier, 2014.
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