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June 25, 2026
HSE Researchers Make Aldehydes Perform Dual Function
Chemists from HSE University have discovered a way to carry out a reductive addition reaction without using an external reducing agent. Instead, the required 'resource' is supplied by the aldehyde itself, one of the reaction participants. This approach helps prevent unwanted side reactions, reduces toxicity, and simplifies the production and synthesis of organic molecules, including those used in the manufacture of medicines. The study has been published in Journal of Catalysis.
June 25, 2026
HSE Scientists Explain Why Findings in Autism Research Differ
Researchers from the Cognitive Health and Intelligence Centre at HSE University conducted the first-ever systematic review of studies on the specifics of emotion-from-motion perception in autism. The review showed that differences found between autistic and non-autistic individuals are largely associated with the experimental design and the types of tasks given to study participants. The review findings have been published in Research in Autism.
June 22, 2026
‘In Science, You Are Your Own Boss
Polina Nasledskova is interested in identifying gaps in linguistics and topics that have been overlooked by other researchers. In an interview for the  Young Scientists of HSE University project, she spoke about rare ordinal numerals in Nakh-Daghestanian languages, the benefits of knitting for concentration, and the beauty of the Patriarshy Bridge.

 

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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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