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August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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A Semi-empirical Approach for Decomposition of Remotely Sensed Leaf Area Index into Overstory and Understory Components over Russian Forests

IEEE Transactions on Geoscience and Remote Sensing. 2023. Vol. 61. Article 4405717.
Shabanov N., Sergey A. Bartalev, Kobayashi H., Shin N., Khovratovich T., Vasily O. Zharko, Andrei A. Medvedev, Natalya O. Telnova

Forest is a multi-layered canopy, where overstory and understory implement different biogeochemical cycles, phenology and functional role. Remote sensing products typically estimate forest total Leaf Area Index (LAI), while few quantify its components. The theoretical understanding of foliage distribution between layers is still quite limited. In this study we’ve developed a semi-empirical model for decomposition of forest total LAI between layers. Decomposition was implemented over the full extent of Russian forests, exhibiting a wide dynamic range of the forest total LAI. This paper addresses both the theoretical and practical aspects of the problem. In terms of theory we formalized the principles of forest layers "biological/radiometric coupling" into a parametric model allowing to analyze the relationship between overstory/ understory LAI and overstory crown fraction. The model captures various features of layers foliage growth, including the "understory seasonal dip effect". In terms of practical aspects, we generated time series of the MODIS layered LAI product for 2001-2020 at the spatial resolution of 230-m over Russian forests. We calculated mean layered LAI of species and contrasted with typical values from the literature surveys. According to our estimates relative contribution of understory LAI increases from South to North- 28% of forests pixels have understory LAI which exceeds that of overstory, those pixels are located in the northern part of the eastern Siberia and occupied by larch forests. The layered LAI product was intercompared/validated with multiple ground and remote sensing data.

Research target: Earth Sciences Computer Science
Language: English
DOI
Text on another site
Keywords: forestryRemote SensingTime series analysissatellitesvegetationMODISLeaf Area Index
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