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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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Combination of Verb Network Strengthening Treatment with TMS and tDCS: Preliminary Results

The Russian Journal of Cognitive Science. 2021. Vol. 8. No. 3. P. 33–45.
Razmyslovich A., Buivolova O., Samoukina A., Abramova T., Iskra E., Ivanova E., Ivanova M., Pakholiuk O., Pozdniakova V., Shlyakhova A., Soloukhina O., Voronkova Y., Ivanova G., Malyutina S., Dragoy O.

Non-invasive brain stimulation (NIBS) techniques combined with behavioral speech/language therapies have recently been suggested as a promising method for language recovery in people with aphasia (PWA). Repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) are currently the most common types of NIBS for aphasia rehabilitation. In this study, we combined either rTMS or tDCS with a behavioral therapy approach, Verb Network Strengthening Treatment (VNeST), that has neither been used before in a Russian-speaking post-stroke population nor combined with any type of NIBS. In a case series study, we demonstrate the results of two groups of participants: 1) 4 PWA who received one daily session of VNeST preceded by 20-minutes of rTMS for 8-12 days; and 2) 4 PWA who received 2 sessions of VNeST a day, with the first 20 minutes of the first daily session combined with tDCS, for 10 days. We assessed the accuracy of action naming and sentence production and found a significant improvement in sentence production in the tDCS + VNeST group, while the other group did not show a significant improvement in these tasks. Interestingly, we found a correlation between improvement in sentence production and the number of sessions regardless of the stimulation type and another correlation between improvement in action naming and initial aphasia severity. This study is a first attempt to apply the combination of these techniques in Russian. Our preliminary findings prompt larger-scale research to study the potential of this method.

Research target: Health Studies Philology and Linguistics
Language: English
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Keywords: русский языкRussianафазияинсультstroketranscranial direct current stimulationтранскраниальная микрополяризацияaphasianon-invasive brain stimulationrepetitive transcranial magnetic stimulationVerb Network Strengthening Treatment (VNeST)неинвазивные методы стимуляции мозгаритмическая транскраниальная магнитная стимуляция«Укрепление сети глаголов» (VNeST)
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