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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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Тест самообладания: психометрические характеристики и перспективы применения

Психологический журнал. 2018. Т. 39. № 1. С. 103–114.
Zolotareva A.

The article describes the development and validation of a new measure called the Self-Possession Scale. The evidence for the factor structure and psychometric properties of the test is presented in the three series of studies with data from a sample of 678 subjects (325 males and 353 females) aged 16–68 (M = 30.32, SD = 10.86). Cluster, exploratory, and confirmatory factor analysis verified the three-factor solution: 1) personal self-possession (i.e., the ability to maintain moderation and composure in circumstances of everyday life); 2) eventual self-possession (i.e., the ability to maintain fortitude in difficult or unpredictable life situations); 3) existential self-possession (i.e., the ability to achieve harmony between oneself and the outside world in all circumstances). It was shown that the Self-Possession Scale shows good discriminatory power, internal reliability and test-retest reliability, and demonstrates the predicted correlations with total scores on questionnaire “Style of Behavior Self-Regulation” by V.I. Morosanova, Self-Determination Scale by K. Sheldon and E. Deci, Self-Monitoring Scale by M. Snyder, Self-Control Scale by J. Tangney, R. Baumeister and A. Boone, Self-Control Subscale from the questionnaire of volitional self-control by A.G. Zverkov and E.V. Eydman. We described the procedure of introducing the filler items for the correction of the social desirability, previously measured Balanced Inventory of Desirable Responding by D. Paulhus. The results provide support for the Self-Possession Scale as a valid and reliable scale for measuring selfpossession, which can be used in scientific and practical purposes, including the situations with high social desirability (e.g., when applying for a job, in the competitive selection, etc.).

Research target: Psychology
Priority areas: humanitarian
Language: Russian
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Keywords: самоконтрольсаморегуляциясамообладаниеличностное самообладаниесобытийное самообладаниеэкзистенциальное самообладание
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