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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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SERS effect on the surface of ZnO nanorods coated with CsPbBr3

Physics of Complex System. 2026. Vol. 7. No. 1. P. 3–15.
Гущина В. А.

Heterostructures based on ZnO nanorods and CsPbBr3 nanocrystals were investigated for their potential as semiconductor SERS substrates. We found that ZnO morphology governs the efficiency of interfacial energy transfer, leading to enhanced photoluminescence under 390 nm excitation and a noticeable reduction of the bandgap in the composites. Raman analysis revealed a pronounced intensity enhancement and the emergence of low-frequency CsPbBr3 modes isolated via Gaussian deconvolution, which confirms the SERS-like behavior of the hybrid structures. Our results highlight the strong prospects of ZnO/CsPbBr3heterostructures for sensing and optoelectronic applications.

Research target: Chemistry Physics Chemical Technologies Materials Technologies Nanotechnologies
Language: English
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Keywords: гетероструктурыlow-frequency phonons energy transfer heterostructures Raman enhancementSERS effectZnO nanorodsCsPbBr3 quantum dotsнизкочастотные фононы перенос энергииусиление рамановского рассеянияэффект SERSквантовые точки CsPbBr3Наностержни ZnO
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