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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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Ecoacoustic methods application for urban biodiversity monitoring

Urban Ecosystems. 2025. Vol. 28. Article 4.
Levik A., Dobromyslov I., Goretskaia M., Matasov V., Filyushkina A.

Passive acoustic monitoring (PAM) is an effective tool for ecosystem assessment. However, it is challenging to apply in an urban environment due to high levels of noise pollution. There is a lag in the number of studies applying PAM in an urban context, and such application therefore lacks proper methodological guidance. This study aims to fill this gap by examining the performance of acoustic methods for biodiversity and acoustic activity assessment. Over 1,000 h of audio data were collected in an urban green area in Moscow, Russia using four AudioMoth recorders and one SongMeter recorder. The sensors were installed at different distances from the road. The species lists obtained through PAM, a route census, and citizen science approaches were compared to evaluate the effectiveness of the acoustic methods. Of the studied methods, PAM produced the best results. Moreover, the correlations between species richness and four common acoustic indices were checked: the Acoustic Complexity Index (ACI), the Acoustic Diversity Index (ADI), the Normalized Difference Soundscape Index (NDSI), and the Bioacoustic Index (BI). None of the studied indices showed good performance as an indicator of species diversity. The biophony level and all acoustic indices were spatially heterogeneous; however, the nature of distribution requires further research. Finally, the dynamics of vocalizing animals’ activity and its correlation with the ACI, ADI, NDSI, and BI was assessed. Both the NDSI and BI proved to be suitable indicators of the daily dynamics of acoustic activity in an urban environment, although with some limitations. The BI could also be utilized to assess the seasonal dynamics of biophony in urban green areas.

Research target: Earth Sciences Biology
Language: English
DOI
Text on another site
Keywords: Urban Ecologybiodiversityurban greenSoundscapeAcoustic indicesEcoacoustics
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