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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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Heterogeneous effects of individual socio-economic characteristics and regional environmental conditions on self-reported health

Economy of Regions. 2022. Vol. 18. No. 3. P. 687–698.
Liudmila A. Leonova, Valeriya V. Lakshina, Anna S. Aladyshkina

Health is a comprehensive phenomenon with many determinants. The influence of environmental
conditions on human health poses challenges for public health scientists. One of the main issues is the availabil-
ity and relevance of the data on public health, including such indicators as morbidity and decease prevalence. In
this study, we overcome this obstacle by using micro data on self-reported health from the Russia Longitudinal
Monitoring Survey of Higher School of Economics. The study analyses the influence of environmental condi-
tions on individual self-rated health in Russia, taking into account the heterogeneity of coefficients among self-
rated health levels. We used generalised ordered probit model with random effects and embedded procedure for
parallel line assumption violation testing. The characteristics of air, water and the quantity of pollutants in each
region were considered as independent factors. The study showed significant negative influence of environmen-
tal factors on self-rated health throughout Russia. It is also demonstrated that higher self-rated health estimates
are given by respondents whose level of education is no lower than that of vocational or trade school, who are
married, and who are regularly engaged in physical activity. The results obtained can be used to improve re-
gional state programmes aimed at improving the quality of life of the population in groups with different levels
of health, for example, to the development and targeting of a set of health policy measures. Additionally, these
findings can be utilised in programmes to improve the quality of the environment, which can increase the over-
all level of self-assessment of health in a particular region.

Research target: Economics and Management
Language: English
Full text
DOI
Text on another site
Keywords: окружающая средаenvironmentRLMS-HSEзагрязнениегетерогенностьheterogeneityRLMS-HSEсоциально-демографические факторыpollutionself-rated healthsocioeconomic factorsсамооценки здоровьяsociodemographic factors
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