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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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Industry-specific effects of IT systems: uncovering the winners and losers

Eurasian Business Review. 2026. Vol. 16. P. 95–122.
Ivanov E., Molodchik M.

This study investigates the industry-specific effects of IT system adoption on company productivity, emphasizing how different industry conditions shape IT-driven performance. Using hierarchical linear modeling on a dataset of 1,500 companies from 12 industries over the period 2008–2021, we analyze the impact of Enterprise Resource Planning (ERP) and Business Process Management (BPM) systems. Our findings confirm that both ERP and BPM systems generally enhance productivity, but their effectiveness varies across industries depending on the interplay of dynamism, complexity, and munificence. By decomposing the overall performance effects into industry-specific outcomes, we reveal substantial heterogeneity, with some industries capturing greater IT benefits than others. These results underscore the importance of industry context in digital transformation and provide new insights into how firms can align IT investments with their competitive environments to maximize performance gains.

Research target: Economics and Management
Language: English
DOI
Keywords: productivityпроизводительностьBPMERPERPIT systemBPMмногоуровневый анализmultilevel analysisindustry effectsИТ системыОтраслевые эффекты
Publication based on the results of:
Strategic behavior of companies and individuals under conditions of technological transformation in Russia (2025)
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