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News
August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Ценностное предложение ресторанного бизнеса: интеллектуальный анализ онлайн-отзывов клиентов

Вестник Санкт-Петербургского университета. Серия 8. Менеджмент. 2022. Т. 21. № 1. С. 48–75.
Serova E., Fainshtein E.

The purpose of the paper is to study value proposition factors in online customer reviews that
characterize consumer inquiries in the catering industry by using text-mining techniques. Research
design focuses on the analysis of chain restaurants, which focuses on the usefulness of the
service indicated in a quantitative system of scores, and pleasure factors for customers. Empirical
data from 201 online reviews were collected from TripAdvisor service, which contains information
about consumer preferences from St. Petersburg, Russia. In addition to text mining, the
study used a correlation analysis of rating scores for chain restaurant customers, comparing the
online reviews of satisfied and dissatisfied customers. The results of the study identified certain
categories of factors of high importance for customers, which were revealed in both positive
and negative online reviews. The results of the analysis help the restaurant management system
to manage the value proposition properly taking into account various consumer factors of thepreferences indicated in reviews. The research implications allow managers to purposefully select
those factors that will achieve the desired effect in the relationship marketing. This study gives
an opportunity to management specialists of the company explore and account for the emotional
sentiment of online reviews.

Research target: Economics and Management Mathematics Computer Science
Language: Russian
Full text
DOI
Text on another site
Keywords: семантический анализкорреляционный анализtext miningsemantic analysis correlation analysisценностное предложениеchain restaurantsинтеллектуальный анализ текста (text mining)сетевые рестораныvalue proposition.online customer reviewsонлайн-отзывы клиентов
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