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November 10, 2026
Physicists Train AI to Optimise Particle Beams in Accelerators
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science and the Budker Institute of Nuclear Physics at the Siberian Branch of the Russian Academy of Sciences have developed RLABC (Reinforcement Learning for Accelerator Beamline Control), a framework that uses reinforcement learning to identify optimal settings for a particle accelerator. In a computer simulation, RLABC optimised the accelerator's parameters to achieve a particle transmission rate of 70%. The study has been published in Computer Physics Communications.
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The Laboratory of Molecular Physiology at the HSE Faculty of Biology and Biotechnology investigates how molecular regulators known as microRNAs control gene expression and contribute to the development of diseases, including cancer, viral and inflammatory conditions, and pregnancy complications. At HSE University, researchers develop organ-on-a-chip models: miniature devices containing living cells that mimic the functions of human organs, such as the placenta and intestines. These models help researchers understand how diseases develop and how drugs affect them. HSE News Service spoke with Evgeny Knyazev, Head of the Laboratory.
November 9, 2026
Flattened Terrain Models Underestimate Vehicle-to-Vehicle Communication Outages
Researchers at HSE MIEM have shown that widely used simulators of vehicle-to-vehicle communication significantly overestimate its reliability because they assume flat terrain. When terrain features, bridges, and tunnels are taken into account, the outage rate on a mountain serpentine road rises from 21% to 30%, on an overpass from 20% to 33%, and in a tunnel from 0% to 80%. The results have been published in Future Transportation.

 

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Fundamental factors affecting the MOEX Russia Index: structural break detection in a long-term time series

NRU Higher School of Economics , 2019. No. 77.
Agata M. Lozinskaia, Anastasiia D. Saltykova
This paper studies how the influence of the fundamental factors on the Russian stock market changes retrospectively. We empirically test the impact of daily values of fundamental factors (indexes of foreign stock markets, oil price, exchange rate and interest rates in Russia and the USA) on the MOEX Russia Index over long time interval from 2003 to 2018. The analysis of the ARIMA-GARCH (1,1) model with a rolling window reveals the changes in the power and direction of the influence of the fundamental factors which are probably caused by the structural instability revealed earlier in Russia and other stock markets. The Quandt-Andrews breakpoint test and Bai-Perron test identify the number and likely location of the structural breaks. We find multiple breaks probably associated with dramatic falls in the stock market index, for example with the significant falls of the then MICEX index in the spring of 2006 and the global financial crisis of 2008-2009. The results of the regression models over the different regimes, defined by the structural breaks, can vary markedly over time.
Research target: Economics and Management
Priority areas: economics
Language: English
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DOI
Text on another site
Keywords: российский фондовый рынокRussian stock marketструктурные сдвигифундаментальные факторыrolling regressionскользящая регрессияstructural breaksthe MOEX Russian indexfundamental factorslong-term time seriesbreakpoint testsиндекс МосБиржи
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