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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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University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
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Creative Work as a Remedy for Burnout
The creative, supportive atmosphere and innovative methods at the Centre for Sociocultural Research make it appealing to early-career scholars. Over years of working at HSE University, they grow into researchers and lecturers recognised both in Russia and abroad. Chief Research Fellow Zarina Lepshokova and Leading Research Fellow Ekaterina Bushina spoke about their journey at the centre and at HSE, their research, and the role of mentors in their academic success.

 

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Аналитическая модель протокола множественного доступа с прослушиванием канала для приложений индустриального интернета вещей

Системы и средства информатики. 2021. Т. 31. № 2. С. 16–25.
Царев А. С., Хайров Э. М., Гайдамака Ю. В., Шоргин С. Я.

Industrial Internet of Things (IIoT) is one of the key features of Industry 4.0 that stands for a new approach to design industrial processes using the latest technological advances. However, before implementing new processes, it is necessary to understand the characteristics and limitations that arise due to technical complexity of the system. To solve this problem for some generalized IIoT use case, a mathematical model of the multiple access protocol with channel sensing is proposed. This model allows one to estimate the average sensor's data transmission cycle time, successful data transmission probability, and probability of a collision during data transmission. Analytical expressions for calculating key performance metrics at the access layer are proposed.

Research target: Computer Science Mathematics Electronics and Electrical Engineering
Language: Russian
DOI
Text on another site
Keywords: Markov chainцепь Марковамножественный доступmultiple accessиндустриальный интернет вещейIndustrial Internet of Things (IIoT)Collision probabilityMAC layerchannel sensingуправление доступом к средепрослушивание каналавероятность коллизии
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