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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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The impact of transparency constraints on the efficiency of the Russian healthcare system: Systematic literature review

Journal of Medical Economics. 2023. Vol. 26. No. 1. P. 95–109.
Timofeyev Y., Dremova O., Jakovljević M.

transparency a powerful tool of healthcare systems. This study addresses how the availability and accessibility of information about medical services to the general population affects healthcare outcomes in Russia. A systematic review was conducted and reported according to the Preferred Reporting Items for Systematic Reviewing and Meta-Analysis (PRISMA) guidelines. Transparency indicators of health facilities used in the world’s most efficient healthcare systems are also reviewed. Although the increase of transparency in the Russian healthcare system
is considered as a tool for improving its efficiency, very little has beenis done to improve the actual level of transparency. The existing institutional specifics of the Russian healthcare system imposes serious restrictions on acceptable levels of transparency. In the reviewed empirical Russian studies, transparency is often
viewed simplistically as either information available onat the websites of medical organisations or issues related to the amount of accessible indicators of compulsory medical statistical reporting. The novelty of this study consists in (a) reviewing the most recent studies on the topic and (b) including studies in Russian in the analysis.
We elaborated on general and specific policy implications for improving transparency-driven outcomes inof the Russian healthcare system.

Research target: Health Studies
Language: English
Full text
DOI
Keywords: раскрытие информациипрозрачностьinformation availabilityinformation disclosureдоступность информацииtransparencyсистема здравоохраненияhealthcare systemhealthcare outcomespublic reportinghealthcare facility
Publication based on the results of:
The impact of institutional transparency on the stability and effectiveness of the health system (2022)
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