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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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Switching Attention Deficits in Post-Stroke Individuals with Different Aphasia Types

Aphasiology. 2023. Vol. 37. No. 2. P. 260–287.
Kuptsova S., Dragoy O., Ivanova M.

Background: Previous studies have shown that individuals with aphasia have impairments in switching attention compared to healthy controls. However, there is insufficient information about the characteristics of switching attention within one task and whether attention deficits vary depending on aphasia type and lesion location. We aimed to address these knowledge gaps by investigating characteristics of switching attention within one type of task in participants with different types of aphasia and distinct lesion sites.

Method: Forty individuals with post-stroke aphasia (20 with non-fluent aphasia and frontal lobe damage, and 20 with fluent aphasia and temporal lobe damage) and twenty neurologically healthy age-matched individuals performed an attention switching task. They listened to sequences of high-pitched and low-pitched tones that were presented to them one by one, tallied them separately, and, at the end of each sequence, had to say how many high- and low-pitched tones they had heard.

Results: Participants with aphasia performed significantly worse on the task compared to healthy controls, and the performance of two aphasia groups also differed. Specifically, individuals with both aphasia types made more errors than healthy individuals; and the participants with non-fluent aphasia responded more slowly than controls, while reaction times of the participants with fluent aphasia did not differ significantly from those of controls. Also, the two groups of participants with aphasia differed significantly in accuracy, with individuals in the non-fluent group making more errors.

Conclusions: The data demonstrated that people with different types of aphasia have distinct impairments in switching attention. Since cognitive deficits impact language performance, this information is important for differentially addressing their language problems and selecting more specific and optimal rehabilitation programs that target different underlying mechanisms.

Research target: Psychology Philology and Linguistics Clinical Medicine
Language: English
DOI
Text on another site
Keywords: вниманиеattentionафазияпереключение вниманияfluent aphasiaбеглая афазиянебеглая афазияaphasianon-fluent aphasiaswitching attention
Publication based on the results of:
Язык и мозг: предотвращение, диагностика и коррекция речевых расстройств (2020)
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