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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.
November 9, 2026
'Organ-on-a-Chip Technology Could Eliminate the Need for Animal Testing'
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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Миграция в России и социально-экономическое развитие регионов: анализ взаимного влияния

Проблемы прогнозирования. 2020. № 4. С. 87–97.
Карцева М. А., Mkrtchyan N. V., Florinskaya Y.

The article presents an empirical study of the cross-impact of socioeconomic development in Russian regions and migration in Russia. The employed data include state statistics, sample surveys, and administrative statistics for 2017. It is shown that none of the migration types (long-term international and domestic, international and domestic labor migration) has a statistically significant effect on the indicators of socioeconomic development in Russian regions. At the same time, labor migration, both international and domestic, is most dependent on the integrated development of regions, in contrast to long-term types of migration.

Research target: Social and Economic Geography (including Urban and Transportation Studies)
Priority areas: economics
Language: Russian
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DOI
Text on another site
Keywords: трудовая миграциясистема одновременных уравненийрегионы Россииsocioeconomic developmentregions of Russia labor migrationдолговременная миграцияlong-term migrationсоциально - экономическое развитиеsystem of simultaneous equations
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