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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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Схема оперативного краткосрочного комплексного прогноза приземной температуры и влажности воздуха

Метеорология и гидрология. 2018. № 8.
Bagrov A., Bykov P. L., Gordin V. A.

The following input data were used by our scheme: the operative weather forecasts with lead-time 3-5 days from several best forecasting hydrodynamic models. Also our scheme uses the archives of these models’ forecasts and of the meteorological measurements in 2800 cities of Russia, East Europe, and Central Asia. The list of the original hydrodynamic schemes can vary. The output of our scheme includes the air temperature forecast for the standard observations moments with period 6 hours and extreme temperatures for lead times from 12 up 120 hours.

The results of the operative forecasting (about ~280 Russian and Belarusian cities and separately 58 localities of Central Asia) are representing on the site of Hydrometeorological Center of Russia every day at 8.30 (a.m. and p.m.) of the Moscow time. Besides temperature we represent on the site amount of precipitation, wind (including gusts), and dew-point temperature with lead-time from 12 up 72 hours with period 6 hours.

We estimate the errors of the complex forecast of the temperature and of the dew-point temperature in the standard moments as well as extreme temperature with various lead-times during the period from June 2014 up to June 2017. Also we compare the accuracy of our operative version (it was improved during the period) with the accuracy of the last version of the forecasting. The accuracy of these forecasts for the standard moments is much better that the forecasts of the original hydrodynamic models. Also we compare the estimations for our forecasting extreme temperature with the similar results of the forecasting scheme “weather element computation” (WEC) and with forecasts of meteorologists of regional meteorological centers.

Research target: Mathematics Earth Sciences
Priority areas: IT and mathematics
Language: Russian
Full text
Keywords: forecasting modelпрогностическая модельair temperatureerror of forecast lead-time extreme temperature dew-point temperatureтемпература воздухапогрешность прогнозазаблаговременностьэкстремальная температура температура точки росы
Publication based on the results of:
Математические модели. Дифференциальные уравнения и большие массивы информации. Аналитические и вычислительные методы. Практические приложения (2018)
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