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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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Стресс-тестирование нефинансовых организаций: подход к обратной задаче на основе аналитического решения

Проблемы управления. 2021. № 6. С. 15–29.
Богомолов А. С., Dvoryashina M. M., Дранко О. И., Кушников В. А., Резчиков А. Ф.

This paper considers an approach to stress testing of nonfinancial organizations. It includes the problem statement and a methodology for solving the reverse problem. The mathematical model is based on open-source data (the financial statements of companies). The relevance of this approach is increasing due to different-nature crises (economic crisis, the COVID-19 pandemic, etc.). The resilience of companies (especially “backbone” ones) to shock situations is tested, and preventive management measures are developed. The direct problem statement involves determining the company’s financial model parameters that ensure a nonnegative level of cash balance in the forecast period. The reverse problem is to find the characteristics of the financial and economic state of the enterprise that correspond to different critical combinations of its financial result parameters. We develop an original stress-testing methodology that significantly reduces the labor intensity and computational complexity compared to stress-testing technologies for financial institutions. A new analytical model is used. The model results are illustrated by an example: stress testing of a backbone enterprise in the real economy sector, which was significantly affected by restrictive measures in the COVID-19 pandemic. Model calculations employ open data from the organization’s financial statements.

Research target: Economics and Management
Language: Russian
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Keywords: стресс-тестированиеbusiness modelingStress-testing28.17.31 Моделирование процессов управленияметоды риск-менеджментаanalysis and risk managementoperational efficiencyпланирование и операционная эффективность
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