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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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Confirming the structure of the dual process model of diversity amongst public sector South African employees.

Journal of Psychology in Africa. 2018. Vol. 28. P. 182–191.
Jackson L. B., van de Vijver F.

We set out to validate the structure of the Dual Process Model of Workplace Diversity in a South African work setting contrasting black and white African employees. The study participants were a convenience sample of 296 public service employees (black = 73.3%, female = 59%, 90.7% between the age 18 and 40 years; white = 25.7%, female = 58%, 67.1% between the age of 18 and 40 years). They completed measures of acculturation antecedents (positive and negative conditions), mediators (integration as positive strategy to deal with diversity and separation as negative strategy), and outcomes (work success and ill-health). Using multi-group path analysis, we found race-based invariance of the Dual Process Model, with black employees self-reporting diversity relations in a more favourable way compared to white employees. We conclude that the Dual Process Model is supported for research use in the South African workplaces.

Language: English
DOI
Keywords: integrationseparationdiversitydual process model
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