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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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Образ старшего поколения в российском цифровом дискурсе о семье

Знак: проблемное поле медиаобразования. 2026. № 1(59). С. 92–101.
Соколова Е. Н., Grigoreva M.

The article analyzes representations of grandmothers’ and grandfathers’ images in the digital family discourse of the Russian social media segment. Based on a corpus of more than two million public posts from September 2023 to September 2024 collected via Brand Analytics, we extracted a subcorpus of 82 138 posts mentioning the older generation. The study employs automated topic modeling (Latent Dirichlet Allocation, LDA), descriptive statistics (frequencies and proportions of topics and coded categories),92content analysis (manual coding), and qualitative interpretation with expert validation. The findings show that online discussions are dominated by a traditional model: the grandmother is associated with care, domestic comfort, and the preservation of family memory, while the grandfather is linked to experience, a historical role, and episodic participation in everyday life. Alternative images of active and digitally engaged older adults appear, but remain marginal and are often stabilized through cultural clichés. Negative storylines are rare and typically relate to intergenerational expectation conflicts, the division of childcare responsibilities, and boundaries on elders’ influence over parental decisions. We identify gender differences in roles and in the evaluative (emotional) tone of mentions (manual coding) regarding grandmothers and grandfathers. The results refine the ambivalence of representations of the older generation and demonstrate the inertia of family archetypes online. Practically, the conclusions can inform communication strategies and social initiatives aimed at strengthening intergenerational ties, preventing ageism, and promoting active aging programs. Further steps include analyzing the self-presentations of older users, conducting cross-cultural comparisons, and tracking the dynamics of these images in connection with changes in family policy and demography.

Research target: Media and Communications Sociology (including Demography and Anthropology
Language: Russian
Full text
DOI
Keywords: valuesценностистереотипыfamilytopic modelingтематическое моделированиеuser-generated contentstereotypesintergenerational relationshipsстаршее поколениепользовательский контентмежпоколенческий конфликт older generation семья
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