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News
September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
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.

 

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Impact of COVID-19 on the efficiency of business in Southern Siberian regions: Spatial and sectoral aspects

Regional Science Policy & Practice. 2024. Vol. 16. No. 7. Article 100065.
Anastasia Sherubneva

The COVID-19 pandemic was one of the biggest global crises of the 21st century, affecting all economic sectors, and Russia was no exception. Russian researchers have shown that the losses from the pandemic varied widely across sectors and regions: the service sector and large cities with high population density suffered the most. However, spatial and agglomeration effects at the micro level have been little studied in the context of COVID-19. Here, we show that during the pandemic, the influence of neighbouring firms on each other decreased, but the positive impact of agglomeration effects on firm performance increased. Using multilevel models and geographically weighted regressions, we constructed separate regressions for 2019 and 2020 explaining the sales margin of South Siberian firms by their individual, spatial and regional characteristics. Entertainment, service and transport companies were the most affected, which is consistent with the results of other studies. We also obtained new results: neighbours’ performance affected positively the firm performance in 2019, but during the pandemic the influence of neighbours became insignificant, a high level of digitalisation helped firms to survive the pandemic, and quarantine restrictions in the region had little impact on business performance. The findings can help develop an effective strategy to combat the economic impact of the pandemic and support businesses. We expect our study to improve understanding of what happens to firms during a pandemic and quarantine. The results may be useful outside the COVID-19 context: spatial and agglomeration effects also operate for firms outside the pandemic.

Research target: Economics and Management
Language: English
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DOI
Text on another site
Keywords: бизнесСибирьпространственная экономикаspatial economicsэконометрикаeconometricsрегионы РоссииSiberiaregions of RussiabusinessCOVID-19COVID-19
Publication based on the results of:
Оценка влияния макрошоков на социально-экономические процессы в регионах России (на примере COVID-19) (2023)
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