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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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Зависимость запасов древесины в лесах России от климатических параметров

Лесоведение. 2019. № 2. С. 83–92.
Грабовский В., Zamolodchikov D.

Mean timber storages across different species and age groups of forests based on the State Forest Inventory 2013 data were correlated by means of regression analysis to climatic variables, averaged over 1981–2000. The following species categories were predefined: all species, conifers, hardwoods, softwoods, and others. The following age groups were predefined: all ages, young growth, middle-aged, pre-mature, mature, and oldgrowth forests. Finally, the correlation and regression analyses were undertaken for each of the 20 age and species groups of forests. Mean annual temperature, difference of mean July and mean January temperatures, and annual precipitation were independent variables. Mean timber storages positively correlate to the mean annual temperature and humidity, and negatively correlate with the difference of mean July and mean January temperature. Coefficient of correlation between the mean storages and the mean annual temperature had pronounced growing trend across the species groups, as follows by value: other species, conifers, hardwoods, softwoods. Multiple polynomial regression equations were found for every species and age group. They had all possible linear and squared couplings between independent variables. Coefficients of determination had the highest values (R2 > 0.7) for all forests combined, for hardwood species, and also for mature and middle-aged forests of all species combined and hardwood species. Other forest species and all young-growth forests were the least predictable (R2 < 0.5). The regression equations could be used to forecast changes in the mean storages of timber under changing climate for all forests combined and forests of different age and specie composition.

Research target: Agriculture, Forestry, and Fisheries
Priority areas: engineering science
Language: Russian
DOI
Keywords: статистический анализ statistical analysisgrowing stocksclimate parametersзапасы древесиныклиматические характеристики
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