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Subject
News
October 19, 2026
'Cultural Studies Seeks to Understand What Lies Beneath the Surface of Everyday Things'
Maxim Zhiganov has been exploring different aspects of sound for many years—first in relation to his native Perm and then on a broader, global scale. In this interview for the HSE Young Scientists project, he talks about sound maps, the special issue of the journal Logos, and why it is important to make the familiar less clear and obvious.
October 14, 2026
At HSE University–St Petersburg, Experts Discuss How AI Changes Tourist Routes and Cultural Heritage
Neural networks are no longer simple travel assistants; now they decide what to include on the route and what to skip. Artificial intelligence changes how people perceive a place and shapes the image of tourist centres. At the II International Scientific and Practical Conference ’Information Technologies in the Preservation and Promotion of Cultural Heritage,’ researchers discussed who controls algorithmic solutions and in what ways, and whether authentic traditions get lost in them.
October 12, 2026
Road Injuries Leading Cause of Death Among Men Aged 10 to 39
Worldwide, road injuries are the leading cause of death among males aged 10–39. An international team of researchers reached this conclusion as part of the Global Burden of Disease Study 2023, analysing data from 204 countries between 1990 and 2023. Their findings have been published in The Lancet Public Health, with HSE University Professor Vasily Vlassov listed among the collaborators.

 

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Возможности моделирования предрасположенности к наркозависимости методами искусственного интеллекта

Вестник Пермского университета. Философия. Психология. Социология. 2015. № 1. С. 61–71.
Yasnitsky L., Грацилев В. И., Куляшова Ю. С., Черепанов Ф. М.

A computer program designed to determine the degree human predisposition to drug addiction. The program is based neural network trained on the results of sociological surveys. Error of neural network model was less than 1%. With the help of neural network model evaluated the importance of factors that can influence the predisposition to drug addiction. The most important factors were: the level of education, having friends who use drugs, temperament type, number of children in the family, financial situation. Neural network model allows to evaluate the effect of varying the parameters characterizing the man and his predisposition to addiction and select the optimal combination of these parameters for each individual and thus receive individual recommendations for reducing drug addiction.

Research target: Psychology Computer Science
Priority areas: sociology
Language: Russian
Full text
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Keywords: нейронная сетьпрогнозэкономико-математическое моделированиеmathematical modelingartificial neural networksdrug addictionpredictionartificial intelligence
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