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News
July 15, 2026
HSE MIEM Students to Develop Two Satellites from Scratch for Orbital Experiments
The devices, created by student teams, will conduct space research on the properties of promising solar cells, on-board energy storage systems, and serial electronics for student satellites.
July 13, 2026
Biologists Discover Unique Properties of MiR-93-5p MicroRNA in Prostate Cancer
Researchers at the International Laboratory of Microphysiological Systems of the HSE Faculty of Biology and Biotechnology investigated how different isoforms of the same microRNA influence gene function in prostate adenocarcinoma. The study found that in some cases, microRNAs can reinforce each other’s effects by targeting and suppressing the same genes. This finding offers a fresh perspective on the molecular mechanisms underlying tumour development and on the search for disease biomarkers. The results have been published in PeerJ.
July 13, 2026
'My Goal Is to Become a Tenured Professor'
Mikhail Samatov focuses on the theoretical study of perovskite solar cells. In this interview for the HSE Young Scientists project, he talks about working on HSE University’s supercomputer, collaborating with Peking University, and making furniture.

 

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Новые технологические тренды: выявление в текстах на базе использования гибридных моделей и анализа временных рядов паттернов данных

Информационно-измерительные и управляющие системы. 2013. Т. 11. № 5. С. 25–34.
Khoroshevsky V. F.

New technological trends identification is one of the most sophisticated, as well as the most important, tasks in the domain of S&T analysis. Nowadays, the leading methodologies within the domain are focused mainly on technological roadmapping, Foresight, data patterns and time series analysis, which is used to specify current and projected trends. The paper presents intelligent tools for trend identification in texts collections with hybrid approach based on the integration of classical statistical methods and the methods of information extraction. Several existing approaches are combined to be used for multilingual text collections of various genres. Ontologies driving text processing as well as documents’ characteristic vectors containing multiword terms are used. The results of statistical analysis of document collections are presented in the form of data patterns time series that are analyzed with structural methods of image analysis. OWL representation of extensional part of the trend ontological model is generated.

Research target: Philology and Linguistics Computer Science
Priority areas: business informatics
Language: Russian
Keywords: технологический трендизвлечение информации из текстовгибридный подход
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