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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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Функциональная связанность в височных областях головного мозга и конверсия в болезнь Альцгеймера при мягком когнитивном снижении амнестического типа

Психиатрия. 2025. Т. 23. № 1. С. 6–17.
Паникратова Я. Р., Комарова А. Ю., Абдуллина Е. Г., Божко О. В., Cherkasov N., Гаврилова С. И., Колыхалов И. В., Лебедева И. С.

Background: structural and neurochemical abnormalities in temporal lobe and temporoparietal junction (T-TPJ) are seen not only in Alzheimer’s disease (AD) but also in amnestic mild cognitive impairment (aMCI). At the same time, studies of conversion to dementia focused on functional characteristics of these regions are lacking. 

The aim was to search for patterns of functional connectivity (FC) within T-TPJ that differentiate patients with aMCI with future conversion to AD from stable aMCI patients and healthy controls. 

Patients, Comparison Group and Methods: patients with aMCI who further converted to dementia due to AD (converters, n = 15), patients with stable aMCI (n = 12), and healthy individuals without cognitive deficits (n = 29) underwent resting-state functional magnetic resonance imaging. FC between cortical T-TPJ structures was compared between groups separately for each hemisphere (one-way ANCOVA and post hoc between-group comparisons). 

Results: an increased FC between posterior parts of left middle and inferior temporal gyri was observed in converters compared to other groups. There was an inverse correlation between this FC and delayed recall of words (MoCA scale) in the entire sample, however, this correlation did not reach the level of statistical significance (p = 0.055). 

Conclusions: the posterior parts of left middle and inferior temporal gyri are involved in auditory verbal memory and storing of visual images associated with a word, respectively. Therefore, а pattern of increased FC observed in future converters to AD may be a consequence of pathological processes that have already started and/or compensatory mechanisms.

Research target: Medical and Health Sciences Clinical Medicine Basic Medicine
Language: Russian
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Keywords: functional connectivitydementiaдеменцияresting-state fMRIфМРТ покояAlzheimer’s diseaseболезнь Альцгеймераamnestic mild cognitive impairmentмягкое когнитивное снижение амнестического типафункциональная связанность
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