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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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Quantifying the asymmetric information flow between Bitcoin prices and electricity consumption

Finance Research Letters. 2023. Vol. 57. No. 2023. Article 104163.

The present study uses transfer entropy and effective transfer entropy to quantify the asymmetric
information flow between monthly Bitcoin prices (Price) and total Bitcoin electricity consumption
(Electricity). Both the Shannon and R´enyi estimates confirm a statistically significant information
flow from Price to Electricity. However, Shannon and R´enyi methods yield mixed results in
quantifying the information flow for Electricity to Price. That indicates a possible non homogeneous
and chaotic impact of total Bitcoin electricity consumption on Bitcoin prices. However, the
R´enyi transfer entropy value converges with Shannon’s as the value of q approaches to 1. The
study findings are highly useful for managing the energy mix and carbon emissions associated
with Bitcoin & other cryptocurrency mining.

Language: English
Full text
DOI
Text on another site
Keywords: Shannon entropyRenyi entropyBitcoinenergy mixelectricity consumptionTransfer entropy
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