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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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Structural Features Of The Brain In Amnestic Mild Cognitive Impairment And Their Correlation With Mild Behavioral Impairment

Neuroscience and Behavioral Physiology. 2025. Vol. 55. No. 9. P. 1681–1687.
N. S. Cherkasov, Tomyshev A. S., Lebedeva I. S., Abdullina E. G., Bozhko O. V., Gavrilova S. I., Kolykhalov I. V.

Objective. To identify the structural features of the brain in elderly patients with amnestic mild cognitive impairment (aMCI) and the correlations of these features with mild behavioral impairment. Materials and methods. Fifty-three patients with aMCI (age 73.0 ± 8.5 years, nine men) and 34 mentally healthy subjects (66.8 ± 9.6 years, four men) were examined. Cognitive status was assessed using the Montreal Cognitive Assessment (MoCA) scale and the severity of mild behavioral impairment was evaluated using the MBI-C (Mild Behavioral Impairment Checklist) questionnaire. Structural MRI images were obtained on a 3T Philips Ingenia instrument. Cortical gray matter thickness, subcortical volumes, and their correlations with MBI-C and MoCA scores were analyzed. Results. Patients with aMCS were found to have smaller cortical thickness in the left superior frontal gyrus and right precuneus (p < 0.05), these parameters correlating negatively with MBI-C scores. In addition, this group had smaller amygdalar and hippocampal volumes in the left hemisphere (p < 0.05), with a positive correlation between the volume of the left amygdala and MoCA scores. Conclusion. The changes in structural MRI parameters and their correlations with neuropsychiatric and neurocognitive symptoms found here reflect the complex nature of the cerebral substrate of cognitive and behavioral impairments in aMCI. These results indicate the need for further work seeking clinically significant phenotypes in patients with aMCI, including those associated with an increased risk of Alzheimer’s disease.

Research target: Clinical Medicine Basic Medicine
Language: English
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Keywords: old agecerebral cortical thicknessamygdalastructural MRImild cognitive impairmentmild behavioral impairment
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