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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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?

An Approximate Method for Solving Scheduling Problems

P. 23–23.
Lazarev A. A.

We consider the scheduling problems α|β|Fmax: A set of n jobs J1,…,Jn with release dates r1,…,rn, processing times p1,…,pn and due dates d1,…,dn has to be scheduled on a single or many machines. The job preemption is not allowed. The goal is to find aschedule that minimizes the regular function F(C1,…,Cn), that Cj is the job jcompletion time. We have suggest an approximation scheme to find approximate optimal value of the objective function.

Language: English
Keywords: оптимизацияoptimizationмоделиmodelsscheduling theoryтеория расписанийприближенные алгоритмыapproximation algorithms

In book

European Chapter on Combinatorial Optimization (ECCO 2009)
Jerusalem: [б.и.], 2009.
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