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News
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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Affiliations based bibliometric analysis of publications on parkinson’s disease

Computational Management Science. 2024. Vol. 21. Article 13.
Fuad Aleskerov, Khutorskaya O., Viacheslav Yakuba, Stepochkina A., Zinovyeva K.

Parkinson’s disease is the second most common neurodegenerative disorder in the world. Thousands of scientific works are published every year. We have analyzed more than 3 thousand organizations, who have published works on various aspects of parkinson’s disease in the period from 2015 to 2021. We have evaluated 4 classical centrality indices (In-degree, Eigenvector, Pagerank and Betweenness) and 2 new centrality indices. The new indices allow to take into account group influence and to identify pivotal nodes. Using the method, we have extracted the most influential organizations in the scientific area of parkinson’s disease. Stability analysis allows us to measure the value of dynamic changes in the network during the period under consideration.

Research target: Mathematics
Language: English
DOI
Text on another site
Keywords: network analysisParkinson’s diseasecitations
Publication based on the results of:
Models of data analysis and decision making in the socio-economic sphere (2024)
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