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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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An Experiment on Gender Representation in Majoritarian Bargaining

Management Science. 2024. Vol. 70. No. 10. P. 6622–6636.
Baranski A., Geraldes D., Kovaliukaite A., James Tremewan

Women are underrepresented in business, academic, and political decision-making bodies across the world. To investigate the causal effect of gender representation on multilateral negotiations, we experimentally manipulate the composition of triads in a majoritarian, divide-the-dollar game. We document a robust gender gap in earnings driven largely by the exclusion of women from alliances rather than differential shares within alliances. Experiments with different subject pools show that distinct bargaining dynamics can underlie the same inequitable outcomes; gender-biased outcomes can be caused by outright discrimination, but they can also be driven by more complex dynamics related to differences in bargaining strategies. Although replacing the male with a female majority all but eliminates the gap in one pool, it has minimal effect in the other. These findings show that there is no “one-size-fits-all” solution to the gender gap we uncovered and highlight the importance of studying bargaining dynamics in detail.

Research target: Economics and Management
Language: English
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Keywords: Experimental Economics
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