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August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
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Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Кластерный анализ российских вузов на основе динамики показателей их деятельности

Вопросы статистики. 2021. Т. 28. № 5. С. 58–68.
Melikyan A.

The article presents the results of a study, the aim of which is to investigate the dynamics of the development of Russian higher education institutions based on an analysis of their performance indicators. The sample includes 535 universities from 80 constituent entities of the Russian Federation. It presents the results of the clustering of universities on the basis of changes in indicators of six variables characterizing the key areas of activity of universities for the period from 2013/2014 to 2017/2018 academic years. The characteristics of each cluster are described, an inter-cluster comparison of quantitative indicators of the activities of universities is presented.

As a result of the calculations of the average annual growth rates of six key indicators characterizing the activities of universities, fve clusters were identifed that differ in their development trajectories. Thus, the universities that belong to Cluster 1 over a fve-year period retained or improved their positions in the main areas of educational activity and managed to maintain income growth at an average level.

However, their indicators in such areas as international and research activities, as well as infrastructure development (provision of educational and laboratory facilities) have decreased. In universities from clusters 2 and 3, all performance indicators changed in a fairly balanced way. At the same time, the indicator of an increase in the internationalization of the students’ body was signifcantly higher in Cluster 2, while educational organizations from Cluster 3 showed a signifcant increase in the publication activity of academic staff.

The results of the activities of universities in Cluster 4 were positive in all considered areas. This cluster had the highest average annual growth rates in the average Unifed State Exam (USE) score and publication activity of the academic staff. Educational organizations from Cluster 5, on the one hand, achieved the best results in attracting foreign students, increasing the proftability of their activities and the provision of teaching and laboratory facilities but, on the other hand, their average annual growth rate of the average USE score has signifcantly decreased.

The study represents one of the frst attempts to cluster Russian universities based on the analysis of changes in their performance indicators. Previous studies on the clustering of universities were mainly based on the analysis of one-time indicators. The approach proposed by the author makes it possible to compare indicators of the dynamics of development of higher educational institutions of different size and scope. Further research in this area could be aimed at analyzing a larger number of performance indicators of universities and studying in detail their strategies for a deeper understanding of the reasons for the differences in their effectiveness.

Research target: Education
Language: Russian
DOI
Text on another site
Keywords: российские вузыRussian universitiesдинамика развития вузовhigher education statisticsстатистика высшего образованиярезультативность деятельности вузовuniversities’ development dynamicsuniversities’ performanceuniversities’ clustersкластеры вузов
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