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May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Occasionalisms in Social Networks During the Pandemic

Journal of Language and Education. 2023. Vol. 9. No. 4. P. 46–60.
Gabrielova E., Vitalia Lopatina

ABSTRACT
Background: This study explores and analyses occasionalisms that were created by social
network users during the COVID-19 pandemic. The theoretical framework of this research is
based on observing the concepts of occasionalism, neologism and nonce word.
Purpose: To indicate occasionalisms in the Russian and English languages and compare them in
terms of word-formation, frequencies, part of speech and meanings, thus making a contribution
to the understanding of how occasionalisms emerge in these languages.
Method: A qualitative and quantitative content analyses are used for data collection. The sample
is obtained from different social networks (‘Facebook1’, ‘Twitter2’, ‘Reddit’ and ‘VK’). Results:
Occasionalisms are created mostly through blending, compounding, and affixation. Mixed
word-formation methods are also used. The most used parts of speech appear to be nouns and
adjectives. The most Russian occasionalisms are ironic, expressive and attract attention, while
English occasionalisms are not that expressive as they refer to medical or political concepts.
Borrowings from English in the Russian occasionalisms are also indicated. The glossary of 106
occasionalisms in the Russian and English languages is introduced at the end of the study.
Conclusion: The results might expand the knowledge of new vocabulary items in the field of
lexicology and stylistics, and have practical implications in language teaching and translations,
as occasionalisms may frequently create obstacles for foreign language students and translators.
They also may serve as a basis for further studies on neologisms, occasionalisms and nonce
words generated during the pandemic and cover the gap in comparative studies of the Russian
and English occasionalisms. In addition, the obtained results might be beneficial for future
research in sociolinguistics and sociocultural linguistics providing greater awareness of the
linguistic and sociocultural factors that impact the adoption and use of occasionalisms in both
languages.
 

Research target: Philology and Linguistics
Language: English
Full text
DOI
Text on another site
Keywords: word-formationnonce wordsCOVID -19occasionalismslanguage of the internetword coinage
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