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July 9, 2026
HSE Economists Use Search Queries to Forecast Birth Rates
Researchers from the HSE Faculty of Economic Sciences have shown that the accuracy of birth rate forecasts for Russia can be improved by almost 50% by incorporating the dynamics of online search queries related to pregnancy and childbirth into forecasting models. In the best-performing models, the forecasting error fell from 4.6% to 3.2%. The findings have been published in Populations and Economics.
July 8, 2026
HSE Researchers Discover Who Eats Out in Russia-And Why
Around one-third of Russians (31.3%) rarely eat out or buy ready-made meals. The core group of active consumers—those who eat out or purchase prepared food almost every day or several times a week—accounts for only about 9% of the population. These are the findings of a study conducted by the HSE Institute for Social Policy. According to the researchers eating out is no longer a marker of high social status in Russia.
July 8, 2026
HSE University and RREDA Join Forces to Support 2026 Renewable Energy of the Planet Competition
HSE University and the Russia Renewable Energy Development Association (RREDA) have signed a partnership and information cooperation agreement to support Renewable Energy of the Planet—2026, a national competition with international participation for students and early-career researchers. Applications are open on the competition's website until September 20, 2026.

 

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Новая программная платформа для моделирования транспортных потоков с участием беспилотных автомобилей

Вестник ЦЭМИ. 2023. Т. 6. № 1. Статья 5.
Beklaryan A.

The article presents a new software platform for modelling traffic flows involving unmanned vehicles, using a number of advanced technological solutions, in particular, the FLAME GPU supercomputer agent modelling framework, intelligent software modules based on fuzzy and hierarchical clustering, genetic optimization algorithms, a subsystem for visualizing the state of agents-vehicles based on OpenGL, etc. As a result, it is possible to simulate the dynamics of road accidents and output traffic for various configurations of digital road networks within the “smart city” scale. The possibility of using the proposed software platform (implemented using C++ and FLAME GPU) is demonstrated for the problem of estimating the sensitivity of the total number of accidents in relation to important control parameters: arrival rate and average speed of unmanned ground vehicles interacting with usual ground vehicles and other agents of the intelligent transport system.

Research target: Mathematics Computer Science
Language: Russian
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Keywords: кластеризацияgenetic algorithmsгенетические алгоритмыclusteringинтеллектуальные транспортные системыagent-based simulationIntelligent transportation systemsFLAME GPUFLAME GPUunmanned vehiclesмультиагентное имитационное моделированиеautonomous transportбеспилотные автомобилиавтономный транспорт
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