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May 25, 2026
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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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Modeling Social Self-Organization and Historical Dynamics. Agrarian Society

P. 309–335.
Malkov S., Kovaleva N., Leonid Grinin, Korotayev A.

This chapter presents the results of applying the set of basic models described in the previous chapter to model the dynamics of an agrarian society. Both interactions in the “society–nature” system and social interactions within society between the main social groups (including economic and political interactions) are considered. It is shown that agrarian society is characterized by cyclical demographic and economic dynamics due to the limited resource base (primarily limited land resources) and the low level of technological development. Based on the modeling of social interactions, the main structural and functional features of a typical agrarian society are revealed. It is shown that in agrarian societies, the mechanisms of social self-organization mainly lead to the formation of social structures of the so-called X-type.

Language: English
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Keywords: demographic dynamicseconomic dynamicsagrarian society

In book

Reconsidering the Limits to Growth. A Report to the Russian Association of the Club of Rome
Reconsidering the Limits to Growth. A Report to the Russian Association of the Club of Rome
Springer, 2023.
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