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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.
June 3, 2026
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
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.
June 3, 2026
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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Exploiting bacterial properties for multi-hop nanonetworks

IEEE Communications Magazine. 2014. Vol. 52. No. 7. P. 184–191.
Balasubramaniam S., Lyamin N., Kleyko D., Skurnik M., Koucheryavy E.

Molecular communication is a relatively new communication paradigm for nanomachines where the communication is realized by utilizing existing biological components found in nature. In recent years researchers have proposed using bacteria to realize molecular communication because the bacteria have the ability to swim and migrate between locations, carry DNA contents (i.e. plasmids) that could be utilized for information storage, and interact and transfer plasmids to other bacteria (one of these processes is known as bacterial conjugation). However, current proposals for bacterial nanonetworks have not considered the internal structures of the nanomachines that can facilitate the use of bacteria as an information carrier. This article presents the types and functionalities of nanomachines that can be utilized in bacterial nanonetworks. A particular focus is placed on the bacterial conjugation and its support for multihop communication between nanomachines. Simulations of the communication process have also been evaluated, to analyze the quantity of bits received as well as the delay performances. Wet lab experiments have also been conducted to validate the bacterial conjugation process. The article also discusses potential applications of bacterial nanonetworks for cancer monitoring and therapy.

Priority areas: business informatics
Language: English
Full text
Keywords: wireless technologiesbiological tissueInternet communications
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