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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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ЭмоС-15: самоотчётная методика для диагностики ядерного аффекта

Психологический журнал. 2019. Т. 40. № 5. С. 97–106.
Lyusin D.

The article presents the development of the new self-report questionnaire EmoS-15 measuring core affect. Core affect is defined as the key component of any emotional phenomenon, elementary consciously accessible affective feeling not directed at anything. The EmoS-15 is based on the structure model of core affect derived from the Russian data. The model distinguishes three dimensions of core affect, namely positive affect with high activation, negative affect with low activation, and tension. The EmoS-15 consists of three respectively named scales. The questionnaire comprises 15 items each of them being an emotion name. The participant must assess to which extent each item matches her emotional state using Likert format of responses. The EmoS-15 was administered to 303 participants. All scales have satisfactory reliability (Cronbach’s alphas higher than .85); the questionnaire factor structure fits the three-dimensional model. To assess validity, EmoS-15 scales were correlated with personality questionnaires that measure similar psychological constructs (N=83). The first scale yielded the highest correlation (r = .50) with the aggregated score consisting of Diener’s Satisfaction With Life Scale and Lyubomirsky’s Subjective Happiness Scale. The second scale yielded the highest correlation with the Beck Depression Inventory (r = .63) and the third scale with the state scale of Spielberger’s State-Trait Anxiety Inventory (r = .57). This pattern of correlations confirms satisfactory validity of the EmoS-15. The new questionnaire can be applied to a wide array of research and practical tasks including measurement of the actual core affect and emotional state, assessment of the affective background typical for longer time periods, experience sampling, and control of participants’ emotional states during the laboratory experiments.

Research target: Psychology
Language: Russian
Full text
DOI
Keywords: напряжениепсиходиагностикаcore affectpositive affectnegative affecttensionядерный аффектpsychological measurementположительный аффектотрицательный аффект
Publication based on the results of:
Структура эмоциональных состояний (2016)
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