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Subject
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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?

Impact of Source Credibility on Medical Information Persuasiveness: A Pilot EEG Study

P. 74–77.
Monahhova E., Morozova A., Gorodnicheva Y., Zinchenko O., Mening S., Rudnenky N.

This study examined behavioral and electrophysiological responses to medical sources with varying credibility levels regarding expertise and trustworthiness dimensions. We investigated how a fictional doctor's work experience (expertise), patient ratings (trustworthiness) and participants' individual characteristics influence source persuasiveness and opinion changes about true and fake medical statements. Overall, 19 participants performed a pilot study: they evaluated their agreement with medical statements, viewed information about fictional doctors with different expertise and trustworthiness levels providing a cue towards the statements, and had opportunities to revise their assessments depending on the source credibility and perceived persuasiveness. Participants also completed questionnaires on analytical thinking, need for cognition and conformity. Additionally, we employed electroencephalography (EEG) to record participants' brain responses, comparing the feedback-related negativity (FRN) response between congruent and incongruent trials based on cue consistency with participants' initial evaluations. The behavioral results suggested that patient ratings (trustworthiness) had a stronger effect on the opinion change than work experience (expertise). Higher conformity and higher cognitive reflection had a trend toward greater opinion change, while higher need for cognition correlated with less opinion change. EEG results revealed no significant amplitude difference between congruent and incongruent trials, with peaks occurring for both conditions at 200-300 ms after cue onset, alike to P300.

Language: English
DOI
Text on another site
Keywords: cognitiondisinformationTime-frequency analysis (TFA)

In book

2025 Seventh International Conference Neurotechnologies and Neurointerfaces (CNN)
IEEE, 2025.
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