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News
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.
September 7, 2026
Biologists Discover 'Molecular Fingerprint' of Preeclampsia
Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.
September 7, 2026
‘Speech, Facial Expressions, and Gestures Cannot Lie
Would you like to know whether a speaker’s trembling voice or an accidental gesture can give them away? At HSE University in Nizhny Novgorod, researchers are developing an algorithm that analyses speech, facial expressions, and gestures, and determines whether information is truthful with 92% accuracy. The project has applications ranging from forensic examination and bank recruitment to fundamental research. Anna Khomenko, head of the research group and Senior Research Fellow at the Centre for Language and Brain at the HSE Faculty of Humanities in Nizhny Novgorod, explains how students and researchers are working together to create a corpus of video recordings, train a classifier, and prepare to introduce computer vision technology.

 

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Associations Between Depression Literacy and the Use of Traditional and Digital Media Among Students of Moscow Universities

JOURNAL OF HEALTH LITERACY. 2026. Vol. 11. No. 2. P. 84–100.
Oxana Mikhaylova, Polina Katznelson, Daria Lukasheva

Background: Depression literacy refers to knowledge and beliefs that facilitate the recognition, management, and prevention of depression, and traditional versus digital media are key channels through which mental health information is accessed.

Aim: The study examined relationships between depression literacy and media consumption patterns among undergraduate students in Moscow universities.

Methods: In May 2022, a cross-sectional online survey of 420 undergraduate students at Moscow universities was conducted using convenience and snowball sampling. Depression literacy was assessed with a researcher-developed instrument grounded in the Mental Health Literacy framework and ICD-11 criteria. Kendall’s tau correlations were computed in RStudio with significance set at p<0.05.

Findings: Students demonstrated moderate depression literacy, with higher consumption of television and radio associated with greater literacy and higher use of social networks and messaging apps associated with lower literacy.

Conclusion: Differential associations between traditional and digital media consumption and depression literacy suggest tailoring mental health interventions to specific media channels and student populations.

Research target: Social Sciences
Language: English
Full text
DOI
Keywords: university studentsmedia consumptionSocial networks RussiaMental Health Literacy (MHL)Depression Literacy
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