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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.
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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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Applicability of the Online Short Spatial Ability Battery to university students testing

Образование и наука. 2023. Vol. 25. No. 10. P. 183–204.
Bartseva K. V., Likhanov M., Soldatova E. L., Tsigeman-Gorenko E., Alenina E., Kovas Y.

Introduction. Multiple studies advocate an importance of spatial abilities (SA) for
educational and occupational success, especially in STEM. Recently an Online Short Spatial Ability
Battery (OSSAB) was developed and normed for SA testing in adolescents. The battery includes
mechanical reasoning, paper folding, pattern assembly, and shape rotation tests. The battery has shown
good psychometric characteristics (high reliability and validity, low redundancy, discriminative power),
and is available in open access and free to use.
Aim. The present research aims: 1) to examine the applicability of the OSSAB for university student
testing; 2) to describe its psychometric properties and structure; and 3) to investigate links between SA
and educational performance.
Methods. A total of 772 university students (aged from 18 to 26, mean age (SD) = 19.55 (1.51), 63.1%
females) participated in the study. Participants provided information about their age, gender, university
major, and academic achievement, and completed a battery of tests that included the OSSAB tests.
Results. The study reports psychometric norms for using the OSSAB in university students.
Students’ performance in the OSSAB was similar to that shown in previous research in adolescents in
terms of means and variance. The OSSAB showed adequate psychometric properties in this sample:
no floor or ceiling effects; low redundancy; moderate to high internal consistency; high discriminative
power across university majors; and high external validity. The results indicated that around 6% of the
students showed very high levels of SA (higher than 1.5 SD above the mean), and around 8% of students
showed very low levels of SA (lower than 1.5 SD below mean). In addition, the OSSAB scores were linked
to educational profile choice and exam scores, with small-to-medium effect sizes.
Scientific novelty. The study provides psychometric norms for a short online open measure of spatial
ability in university students.
Practical significance. The OSSAB can be used to provide individual recommendations to students
(e.g. SA training), to identify spatially gifted students, and for research purposes in university contexts.

Research target: Psychology
Language: English
Full text
DOI
Text on another site
Keywords: образованиеeducationкомпьютерное тестированиеоценкаuniversity studentsпсихометрикаassessmentpsychometricsspatial abilityspatial intelligencecomputerised testingпространственные способностипространственный интеллектстуденты университетов
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