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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
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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
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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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Perceived Job Flexibility and Innovative Work Behaviour: The Roles of Supervisor Communication and Knowledge Hiding

Journal of Chinese Human Resource Management. 2025. Vol. 16. No. 4. P. 101–123.
Kolawole Ojo

Offering employees more flexible working options is now a popular human resources management practice for many companies. Although there are strong business cases that favour these non-traditional working arrangements, some business executives are concerned that they might slow employee innovation because they reduce interpersonal and social interactions. According to the available literature, job flexibility stimulates employee innovative work behaviour. However, the theoretical explanation of why and how is not entirely clear. Based on social exchange theory (SET), this study posits that higher levels of employee perception of job flexibility will be associated with higher levels of innovative work behaviour. Employees who perceive their jobs as highly flexible are likely to reciprocate these working opportunities with reduced knowledge hiding, and they would also increase job-related communication to mitigate the career risks of flexible working. Surveying 507 employees from 13 industries in Nigeria and using structural equation modelling (SEM), the findings suggest that supervisor-subordinate communication and subordinate knowledge hiding mediate the effect of perceived job flexibility on the innovative work behavior of the employee. The implications of the findings are useful for the design of effective, employee-centered flexible work arrangements that support knowledge and innovation management.

Research target: Economics and Management
Language: English
DOI
Keywords: NigeriaStructural Equation Modellingknowledge hiding behaviorInnovative Work BehaviourPerceived Job FlexibilitySupervisor Communication
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