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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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Спрос и предложение на рынке труда: поиск источников конфликта

Социологический журнал. 2024. Т. 30. № 2. С. 82–101.
Ternikov A., Sheveleva D.

Classical approaches to conflict categorization within the field of social sciences may not be applicable to the labor market context because they are beyond economic theories. To the best of our knowledge, currently, in either national or foreign literature there has been no conceptual systematization of conflicts that occurs during the hiring process. Such a systematization would unite factors of conflicts at the intersection of economic, sociological, and conflict theories. The article suggests a categorization that utilizes the prominent conflict theories and considers potential employment conflicts within the labor market. The proposed categorization groups market participants (supply and demand sides) and sorts conflicts by type (systemic and non-systemic conflicts). Four primary sources of conflict are identified, including market mechanisms and salary expectations (systemic conflicts), company values and human capital (non-systemic conflicts). Systemic factors of conflicts such as supply/demand matching and wage expectations are determined by market mechanisms. These economic factors are determined by objective and predictable processes, but sociological theories of conflict are commonly excluded, which can result in a biased portrayal of macroeconomic processes. In addition, the policy of a particular company and job seeker expectations for a specific vacancy are non-systemic, they depend on such types of factors as company values and human capital. This categorization enables early identification of the conflict source and potential reduction of its destructive effects.

Research target: Sociology (including Demography and Anthropology Economics and Management
Language: Russian
Full text
DOI
Keywords: рынок трудачеловеческий капиталспрос и предложениеsupply and demandhuman capitallabor marketfactors of conflictcategorization of conflictssalary expectations of job seekersemployers’ valuesконфликтогенные факторыкатегоризация конфликтовзарплатные ожидания соискателейценностные установки работодателей
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