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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.
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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.
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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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How complex is professional academic writing? A corpus-based analysis of research articles in ‘hard’ and ‘soft’ disciplines

VIAL - Vigo International Journal of Applied Linguistics. 2023. No. 20. P. 149–183.
Perez-Guerra J., Smirnova E. A.

This study focuses on the analysis of linguistic complexity in professional academic writing in light of the empirical evidence provided by a 1,597,000-word corpus of ‘hard’ (life and physical sciences) and ‘soft’ (arts and social) scientific research articles published in leading peer-review journals. Specifically, this investigation aims both to describe the complexity features of texts written by professional authors and to test the hypothesis that linguistic complexity varies across disciplines. Since previous studies have revealed that automatic complexity indices do not sufficiently succeed in providing a comprehensive description of complexity of texts, in this paper complexity has been measured in two ways: quantitatively through the indexes provided by Lu’s (2010) L2 Syntactic Complexity Analyser, and through the more qualitative analysis of a selection of metrics associated with clausal and phrasal complexity in seminal studies. The data show, first, that syntactic complexity indices (basically, strategies of coordination and subordination) are statistically relevant to the characterisation of specifically the soft-science disciplines; second, that there is a continuum across subdisciplines within the broad distinction of soft versus hard genres; and, third, that the soft genre demonstrates a more stable productivity of clausal-complexity strategies, while phrasal-complexity features are more pervasive in the hard-science subcorpus.

Research target: Philology and Linguistics
Language: English
Full text
DOI
Keywords: academic writingcorpuscomplexityhard sciencessoft sciences
Publication based on the results of:
Automated Detection of Writing Inaccuracies for Students of English in Russia (2021)
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