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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.
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Ways to monitor FLW: Review and recommendations on data collection and reporting for the Russian context

Russian Journal of Economics. 2022. Vol. 8 (1). P. 81–94.
Galaktionova E., Kok M., Bos-Brouwers H.

In 2015, all 193 UN member countries agreed to halve global food loss and waste by the year 2030. At national level, the first step is usually to measure the extent of the problem and set targets. Countries that initiate the inventory of their national food loss and waste (FLW), frequently find out that first, the amount of FLW is bigger than was initially anticipated, and second, that there are massive data gaps, including quality, granularity, representativeness, collaboration and prioritization etc. Russia is no exception. In this article, we will make an overview of what is already happening in Russia regarding the FLW issue and what can be learned from international examples—mainly, the Netherlands. Despite many existing methodologies and practices being limited in their scope, reach, accuracy etc., it is more important to select what is appropriate and/or feasible now than wait for an invention of a perfect quantification methodology in an unforeseeable future.

Research target: Economics and Management Agricultural Sciences
Language: English
DOI
Text on another site
Keywords: государственная статистикаметодика расчетаfood loss and wastemunicipal solid wasteТвердые коммунальные отходыпродовольственные потерипищевые отходыFLW Monitoringnational statisticsmeasurement methodologyмониторинг ПППО
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