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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Мультисекторная модель ограниченного соседства: сегрегация агентов и оптимизация характеристик среды

Математическое моделирование. 2021. Т. 33. № 11. С. 95–114.
Akopov A. S., Бекларян Л. А., Beklaryan A.

This article presents an approach to studying the effects of segregation using the developed multi-sector bounded-neighbourhood model. A model of the evolutionary dynamics of a community consisting of a local (natives) and external population (migrants) interacting in an artificial socio-economic system is proposed, in which the key sectors of the economy are highlighted: mining of raw materials (the primary sector, which attracts mainly migrants), the manufacturing sector (the secondary sector, which attracts mainly indigenous people), and the sphere of low-tech and high-tech services (the tertiary and quaternary sectors of the economy, which attract migrants and indigenous people, respectively). Formation of jobs in these sectors of the economy is carried out centrally using the previously proposed fuzzy clustering algorithm. Simulation experiments were carried out and the effects of segregation were investigated due to the desire of agents to search for the most preferable jobs in a bounded-neighbourhood under various scenario conditions. Using the proposed genetic algorithm, an important optimization problem was solved to maximize the GDP growth rate and minimize the level of population segregation.

Research target: Mathematics Computer Science
Language: Russian
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Keywords: генетический алгоритмgenetic algorithmbounded-neighbourhood modelsagent-based modelling of migration and socioeconomic processesmodels of tolerant threshold behavioursegregation effectsagent clusteringмодели ограниченного соседстваагентное моделирование миграционных и социально-экономических процессовмодели толерантного порогового поведенияэффекты сегрегациикластеризация агентов
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