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September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.
September 7, 2026
Biologists Discover 'Molecular Fingerprint' of Preeclampsia
Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.

 

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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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