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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
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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?
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Are emotionally intelligent people more emotionally stable? An experience sampling study

2018.
Lyusin D., Mohammed A.
The temporal dynamic characteristics of mood play an important role in various aspects of our lives including our psychological health and well-being. It is assumed that the individuals with high emotional intelligence (EI) are characterized by more positive and stable moods. However, most studies analyze how EI is related to emotional traits or momentary assessments of mood; there are almost no findings on EI relationships with mood dynamics. The present study fills this gap. Two research questions were asked. How mood dynamics characteristics are related to each other and to what extent are they independent? Which aspects of EI are related to particular characteristics of mood dynamics? Method. To collect data on mood dynamics, an experience sampling procedure was implemented. Twenty-six female participants reported their mood for two weeks, three times a day, using the EmoS-18 questionnaire. Their emotional intelligence was measured with the EmIn questionnaire. Mean mood scores calculated across all measurement points were regarded as static characteristics showing a mood background typical for the participant. Also, three dynamic characteristics of mood were calculated, namely variability, instability, and inertia. Results. Mood variability and instability were found to be very closely related to each other, measuring essentially the same construct. Inertia is relatively independent. EI was not related to mean mood scores which contradicts the results of other studies and can be explained by the use of the experience sampling procedure. EI was positively related to the inertia of a positive mood with high arousal and a negative mood with low arousal. In addition, a negative relationship between EI and the instability of tension was found. Most of the correlations were low. Further studies with higher statistical power are needed for more decisive conclusions. However, the results show that experience sampling provides new important insights on the role of EI in mood
Research target: Psychology
Language: English
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Keywords: emotional intelligenceэмоциональный интеллектmood dynamicsmood variabilitymood instabilitymood inertiaнеустойчивость настроения
Publication based on the results of:
Структура эмоциональных состояний (2016)
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