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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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Managing Medical Technological Innovations: Exploring Multiple Perspectives

Vol. 5. World Scientific, 2020.
Under the general editorship: Daim T., A. Brem

This book addresses the issue of modern medical innovations management through an inductive approach by looking into cases before putting forward solutions in terms of strategies and tools. It provides a model for the designing and implementation of effective healthcare technology management (HTM) systems in hospitals and healthcare provider settings, as well as promotes a new method of analysis of hospital organization for decision-making regarding technology to show how systematic management using a strategy that balances bottom-up and top-down driven innovations, can deliver better medical technological advances.

Managing Medical Technological Innovations is organized in three parts. Part 1 covers innovation strategies, laying the groundwork and concepts in design thinking. Part 2 follows by presenting the tools available for implementation. And finally, Part 3 uses the case studies of pharmaceutical firms in China and hospital medical record management in Holland to illustrate how these ideas and methodologies have been applied.

Priority areas: management
Language: English
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Keywords: technological innovationsтехнологические инновацииуправление инновационной деятельностью medical innovationsinnovations managementhealthcare technology managementHTMинновации в сфере медициныуправление технологиями здравоохранения
Publication based on the results of:
Measuring and modelling innovation in the context of evidence-based policymaking (2020)
Managing Medical Technological Innovations: Exploring Multiple Perspectives
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