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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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?

Identifying optimal cloud cover for enhanced forest carbon uptake: Periodic-case NEE-overshoot modelling

Ecological Modelling. 2024. Vol. 498. Article 110904.
Sergey N Kivalov

On certain kinds of cloudy days, many forested ecosystems exhibit enhanced carbon uptake and water-use effi-
ciency-the cloudy-day forest flux anomaly. Using ensemble methods to analyze eddy-covariance fluxes, we have
diagnosed net ecosystem exchange (NEE) and water-use efficiency (WUE) of a temperate broadleaf forest and a
tropical evergreen forest as they responded to natural fluctuating-light regimes. Here we apply average NEE and
evapotranspiration solutions of a first-order dynamic model to describe the observed whole-canopy sensitivity to
periodic light. On partly-cloudy days, maximum overall NEE enhancements over conventional steady-state equi-
librium estimates are ≈ 25% for a midlatitude deciduous forest and ≈ 15% for a tropical evergreen forest. This
finding supports our conclusion that in many cases the cloudy-day anomaly is a consequence of a dynamic re-
sponse by the trees responding to fluctuating-light regimes occasioned by passing cumulus clouds.

Research target: Earth Sciences
Language: English
DOI
Text on another site
Keywords: NEE-overshoot modellingenhanced forest carbon uptakeoptimal cloud cover
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