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June 4, 2026
Machine Learning Models Can Help Reduce Volatility and Boost Stock Market Returns
The use of machine learning models makes it possible to achieve greater accuracy in predicting risks in the Russian stock market compared to classical econometric approaches. The predictive power of these models increases by 23%, while the average investor’s return can reach up to 13% per annum. These conclusions were drawn by Nikita Lysenok from the Department of Financial Market Infrastructure at the HSE Faculty of Economic Sciences. The paper has been published in Fundamental and Applied Mathematics.
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June 3, 2026
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К вопросу о возможности радикального повышения теплопроводности сред введением дисперсных частиц (обзор)

Журнал прикладной химии. 2020. Т. 93. № 12. С. 1696–1715.
Макарова В. В., Горбачева С. Н., Антонов С.В., Ильин С. О.

Theoretical and experimental data on the eff ect of additions of micro- and nanosized solid particles on the thermal conductivity of liquid media (nanofl uids) are considered. According to theoretical calculations, a manifold increase in the thermal conductivity is possible only for resting media in which percolation structures are formed from the modifi er nanoparticles, whereas in the case of the coolant circulation and/or random distribution of particles in its volume the increase in the thermal conductivity cannot increase several tens of percents. The primary factors for increasing the thermal conductivity are high volume fraction of particles and isotropicity of their properties, whereas the specific thermal conductivity of the particle material is not a key factor. The available experimental data were obtained using diff erent measurement methods and diff erent dispersions whose structure was not duly controlled. Therefore, the data are characterized by large scatter, which does not allow unambiguous identifi cation of the acting averaging law, although the majority of data are characterized by positive deviation from Maxwell’s law. On the other hand, the available data do not exceed the values expected on the basis of the rule of logarithmic additivity of the component thermal conductivities.

Research target: Chemistry Materials Technologies
Language: Russian
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Keywords: моделированиеmodelingthermal conductivityтеплопроводностьDisperse systemssuspensionsдисперсные системысуспензии
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