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July 6, 2026
Ancient Craniiform Brachiopod: A Newly Discovered Species with a Unique Shell Shape and Lifestyle
Scientists from HSE University, MSU, and Tallinn University of Technology have studied a fossil species of ancient brachiopods that lived in a warm sea in what is now northern Estonia more than 445 million years ago. These ancient brachiopods developed a cup-shaped shell with a protective 'cap' that shielded them from overgrowth by other marine organisms. The study has been published in Palaeogeography, Palaeoclimatology, Palaeoecology.
July 2, 2026
Researchers Discover How Spelling Errors Slow Down Reading in Russian
Psycholinguists from the Centre for Language and Brain at HSE University–St Petersburg have shown that words that are frequently misspelled are processed more slowly by readers, even when presented with the correct spelling. The researchers confirmed this effect for the first time using Russian-language materials and found that response speed is most strongly linked to how confidently individuals can distinguish the correct spelling of a word from an incorrect one. The study has been published in The Mental Lexicon.
July 2, 2026
HSE Develops App for Assessing Phonological Processing in Children
Researchers at the HSE Centre for Language and Brain have developed a new digital tool for assessing children's phonological processing skills—the ZARYA (Sound Analysis of the Russian Language) test battery. It is the first standardised application in Russia designed to provide a fast and reliable assessment of children's ability to distinguish speech sounds, retain them in working memory, and perform phonemic analysis. The app runs on Android tablets and smartphones and is available for download from RuStore. Details of the test validation have been published in the Journal of Speech, Language, and Hearing Research.

 

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Knowledge Generation in Raw Material Industries

Studies on Russian Economic Development. 2022. Vol. 33. No. 3. P. 257–266.
Krukov V. A., Milyaev D. V., Dushenin D. I., Savel’eva A. D., Skuzovatov M. Y.

Abstract— The article presents a mathematical toolkit for forecasting the development of innovative technologies that can be used for cost-effective development of hard-to-recover hydrocarbon reserves. The proposed approach is a symbiosis of agent-based models, Bayesian networks, learning curves, statistical analysis, and numerical simulation modeling methods. These techniques have not been used as a set in research related to the oil and gas industry ever before. The practical significance of the modeling toolkit is based on the prioritization of measures aimed at innovative growth in the oil and gas sector. To test the approach, calculations are performed for three objects: complex structures of the Predpatomsky trough in eastern Siberia and the Republic of Sakha (Yakutia), deposits of the Pre-Jurassic complex in the south-east of western Siberia, and natural bitumen in the Republic of Tatarstan. The calculations confirm the working hypothesis about the practicability of forming technological clusters for efficient extraction of hard-to-recover hydrocarbons.

Research target: Economics and Management Mathematics
Language: English
DOI
Text on another site
Keywords: bayesian networkeconomic and mathematical modelingInnovation Economics agent-based modeling hard-to-recover reserves
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