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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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?

Прогнозирование успеваемости в вузе по результатам ЕГЭ

Прикладная эконометрика. 2011. Т. 1. № 21. С. 56–69.
Poldin O.

This paper presents the results of econometric study on predicting first-year grade point average and dropout probability with national examination (EGE) results at the bachelor program in Economics in the Higher School of Economics. The use of a sum of four exams - math, social studies, Russian language and foreign language - shows worse fitting than a sum of three exams, excluding foreign language. In models with separate exams as regressors, the greatest effect on dependent variable provides math grades.

Research target: Education Economics and Management
Priority areas: economics sociology humanitarian
Language: Russian
Full text
Text on another site
Keywords: экономика образованияЕдиный государственный экзамен (ЕГЭ)прием в вузыuniversity admissionsentrance examinationpredicting GPAпрогноз успеваемости
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