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June 2, 2026
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.
June 2, 2026
Discovering Science through Russian Language: HSE Prep Year Students Present at International Conference in Kazan
On May 23, 2026, the V International Scientific and Practical Conference ‘Discovering the World of Science’ took place in Kazan at the Preparatory Faculty for International Students of Kazan Federal University. Four students of the HSE International Preparatory Year took part in the event: two delivered their presentations in person, while two participated online. Their work was supervised by Acting Director of the International Prep Year Irina Isaeva and lecturer Ekaterina Kozhemyakova.
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.

 

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Algorithm for density of angle distribution in random sections of polyhedron

Expert Systems with Applications. 2024. Vol. 235. Article 121195.
Elizaveta P. Konstantinova, Lev N. Shchur

Scanning electron micrographs of solid materials are a source of analysis for hardness, defects, etc. A micrograph is a two-dimensional image of a material being cut. Typical photomicrographs contain about a hundred thousand structural elements, such as grain boundaries, boundary junctions, etc., which are not easy to analyze with the naked eye. Currently, computer vision can be used to analyze the geometric properties of structural elements. One of the important properties is the cross section of the grain, which is the result of cutting the material. In the article we turn to one of the geometric properties of random grain cuttings. The statistical properties of the cross sections of grains in an alloy are equivalent to the statistical properties of the cross sections of individual grains by random planes if the grains do not touch each other. We present two stages of mathematical analysis of the grain cross-sections. First, using the algorithm for generating random planes with a uniform distribution, a method is proposed for generating random sections of polyhedra. The method can be useful in stochastic geometry and computational geometry, as well as in materials science. Second, sections of polyhedra with random planes are considered, and a numerical algorithm for estimating the distribution density of angles of convex polyhedra is also implemented. Examples of distribution densities in random sections for a cube, a regular triangular prism, and a truncated prism are given. The developed algorithm and the obtained distribution densities are of practical use in the analysis of micrographs of thin sections of crystalline alloys.

Research target: Engineering and Technology
Language: English
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Text on another site
Keywords: numerical algorithmsrandom planepolyhedra random sectionangle distributioncrystalline alloysслучайная плоскостьслучайное сечение многогранникачисленный алгоритмплотность распределения угловкристаллический сплав
Publication based on the results of:
Methods of mathematical modeling of actual physical processes (2023)
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