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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
Researchers Develop Method for Direct Generation of Regulatory DNA
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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Исследование взаимосвязи бизнес-моделей банков и стабильности экономического развития

Финансы: теория и практика. 2023. Т. 27. № 2. С. 108–118.
Господарчук Г. Г., Aistov A., Suchkova E. O.

Depending on the chosen business model, banks can act as both shock absorbers and crisis catalysts. In this regard, the analysis of the relationship between banks’ business models and financial cycles becomes a useful tool for diagnosing and predicting crisis phenomena. The purpose of the research is to identify the relationship between the volume of debt of the banking and the debt burden of the economy. The research uses econometric methods. The key result of the research is two new econometric models, which were calibrated for the Russian economy. The models differ from each other by the types of bank liabilities used in the calculation of independent variables. The models also differ from the existing models by the calculation algorithm of independent variables. The source of information is the official statistics of the Bank of Russia for the period 2008–2019. The tests of the models confirmed the presence of a statistically significant cointegration relationship between the debt burden of the banking sector and the debt burden of the economy. Coupling coefficients in the models are identified as debt multipliers of the banking sector and characterize the multiplier effect of changes in the debt burden of banks. For the model containing banks’ balance sheet liabilities, the debt multiplier for the Russian economy was 6.7; and for the model using banks’ total liabilities was 3.1. The developed models are easy-to-use for forecasting financial cycles.

Research target: Economics and Management
Language: Russian
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Keywords: банкибизнес-моделиbanksbusiness modelseconomic stabilityдолговая нагрузкакредитные циклыcredit cyclescredit burdenстабильность экономики
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