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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.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
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Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
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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Многономенклатурная оптимизация параметров стратегии поставок при возможности выбора транспортных средств с учетом их грузовместимости

Логистика и управление цепями поставок. 2017. № 5 (82). С. 103–116.
Gerami V., Kolik A., Shidlovskii I.

A new improved algorithm to optimize the parameters of a diversified supply strategy considering the transportation features for the EOQ-models is proposed. When optimizing decisions it will allow for the first time to take into account the following attributes of the simulated supply chain: 1) cargo capacity of the vehicles used; 2) a possibility of selecting their types when delivering goods; 3) the format of paying storage costs (rent or paying only for occupied places at the warehouse); 4) expediency of deliveries using several vehicles if there are discounts on the cost of such deliveries; 5) factor of time value of money taking into account the efficiency of cash flows of the supply chain itself; 6) assessment of the expected return on the working capital of the simulated supply chain; 7) specificity of such a profitability indicator taking into account the utilization of the cargo capacity of the vehicles used at the deliveries. This algorithm was developed with reference to special EOQ-models. Namely, it relates to a class of models interesting and important for business when profit can be made at each reorder interval. The models in question are those where profit during the reordering interval can be used both to cover part of the costs at that interval and to make payment of the next delivery. Therefore, the format of the algorithm will consider necessary and sufficient conditions fulfillment of which allows to ensure the possibility of these payments from the profit during the supply chain operation.

Research target: Economics and Management
Priority areas: management
Language: Russian
Full text
Keywords: управление запасамиалгоритм оптимизацииinventory managementeconomic order quantitytime value of moneyвременная стоимость денегEOQoptimization algorithmvehicle capacitysupply optimizationEOQ-моделиэкономичный размер заказаmultiproduct modelоптимизация поставокучет грузовместимостимногопродуктовая модель
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