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August 13, 2026
‘Working with AI Solves a Wide Range of Engineering Problems
Artificial intelligence is a working tool based on a balanced combination of algorithms and engineering. Experts and doctoral students from the HSE Moscow Institute of Electronics and Mathematics explain how AI technologies can improve an application, device, or system, and what engineering tasks are solved in the process.
August 12, 2026
‘I Would Like My Research to Help Make the World a Calmer and Better Place
Whatever task Saraa Ali, Junior Research Fellow at the Laboratory of Methods for Big Data Analysis (LAMBDA) of the AI and Digital Science Institute (HSE Faculty of Computer Science), is working on, she thinks about how it can benefit people. She told the Young Scientists of HSE University project about her large family, diagnosing three-phase motors, and her dream of building a children’s home in her native country.
August 11, 2026
‘The Peak of Stupidity and ‘The Valley of Despair: HSE Economists Propose an Explanation for the Dunning–Kruger Effect
The Dunning–Kruger effect, which describes a sharp surge in self-confidence among beginners followed by an equally rapid decline as they gain experience, can be explained by the nature of the learning process and the acquisition of new knowledge. This conclusion was reached by Andrey Vorchik of the HSE Faculty of Economic Sciences together with independent researcher Murat Mamyshev. They developed a mathematical model of learning and demonstrated how subjective confidence is formed and changes as knowledge accumulates, as well as how teachers can reduce the ‘valley of despair’ experienced by learners.

 

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University Campuses as Vital Urban Green Infrastructure: Quantifying Ecosystem Services Based on Field Inventory in Nizhny Novgorod, Russia

Land. 2025. Vol. 14. No. 10. Article 2073.
Yakimov B., Zaznobina N., Kuznetsova I., Bolshakova A., Kovaleva T., Markelov I., Onishchenko V.

This study provides the first comprehensive, field-inventory-based assessment of urban ecosystem services within a Russian university campus, focusing on the woody vegetation of the Lobachevsky State University of Nizhny Novgorod. Utilizing a detailed field tree inventory combined with the i-Tree framework (including i-Tree Eco, i-Tree Canopy, UFORE, and i-Tree Hydro models), we quantified the campus’s capacity for carbon storage and sequestration, air pollutant removal, and stormwater runoff mitigation. The campus green infrastructure, comprising 1887 trees across 32 species with a density of 145.5 stems per hectare, demonstrated significant ecological value. Results show a carbon storage density of 26.61 t C ha−1 and an annual gross carbon sequestration of 11.43 tons. Furthermore, the campus trees removed 1213.7 kg of air pollutants annually (a deposition rate of 9.35 g m−2), with ozone, particulate matter, and sulfur dioxide showing the highest deposition. The campus also retained 956.1 m3 of stormwater annually. These findings, particularly the high carbon sequestration rates, are attributed to the dominance of relatively young, fast-growing tree species. This research establishes a critical baseline for understanding urban ecosystem services in a previously under-researched geographical context. The detailed, empirical data offers crucial insights for urban planners and policymakers in Nizhny Novgorod and beyond, advocating for the strategic integration of ecosystem services assessments into campus planning and broader urban green infrastructure development across Russian cities. The study underscores the significant role of university campuses as vital components of urban green infrastructure, contributing substantially to environmental sustainability and human well-being.

Research target: Biology Environmental Biotechnologies
Language: English
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Keywords: Russiaair pollutionNizhny Novgorodcarbon sequestrationurban vegetationi-Tree frameworkstormwater mitigation
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