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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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Against Putin and Corruption, for Navalny and the “Revolution”? The Dynamics of Framing and Mobilization in the Russian Political Protests of 2017–18

Communist and Post-Communist Studies. 2022. Vol. 55. No. 1. P. 99–130.
Fomin I., Nadskakuła-Kaczmarczyk O.

This article seeks to provide a better understanding of the dynamics of the nationwide protests that appeared in Russia as a result of the large-scale political campaigns of 2017–18. On the basis of an original database devoted to six protests, organized in this period by different anti-systemic opposition leaders and organizations, the study explores the turnout and geographic scope of these events and the repertoire of frames that were used to mobilize the protesters. The analysis contrasts three types of frames (an anti-corruption protest frame, election campaign event frame, and anti-systemic protest frame) and demonstrates that appropriate framing was a necessary condition of successful protest mobilization. In combination with other factors, such as the quality of protest organization and the impact of repressive actions of the authorities, the changes of protest frames contributed to the protests’ turnout dynamics. Alexei Navalny, the most popular anti-systemic leader, succeeded in organizing the initial mobilization by framing it as an anti-corruption protest, but then, under increasing repression, the opposition failed to convert this dissent into a longer-term campaign with broader electoral or anti-systemic frames.

Research target: Political Science, International Relations, and Public Administration
Language: English
Full text
DOI
Keywords: РоссияRussiaакции протестаprotestframingpolitical protestprotest slogansprotest symbolsprotests turnoutпротестные слоганыпротестные символычисленность протестующих
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