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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.
May 25, 2026
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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Прогнозирование интенсивности послеоперационного болевого синдрома у пациенток, перенесших экстирпацию матки

Регионарная анестезия и лечение острой боли. 2018. Т. 12. № 3. С. 167–174.
Смирнова О. В., Генов П. Г., Тимербаев В. Х., Тукибаева Т. Ф., Rebrova O.

The problem of postoperative analgesia don’t lose it’s relevance despite the large implementation in practice the multimodal analgesia strategy. In prescribing the analgesia in the most cases don’t consider the predictors of intensive postoperative pain, which could to contribute the choice of ineffective postoperative analgesia. Purpose. The determination of predictors of intensive pain after hysterectomy. Materials and methods. We have observed women from 18 to 70 years old which have undergone a hysterectomy under general anesthesia. We have studied socio-demographic data, the presence of chronic abdominal pain before surgery, pain threshold and pain tolerance, type of surgical access and pain expectation. Results. A mathematical model was developed for predicting a moderate and severe (> 40 mm visual analogue scale) dynamic pain 2 hours after the operation with a 60% cut-off point, implemented as a calculator in MS Excel. As a set of predictors, the following signs were used: the presence of pain in the lower abdomen before the operation, tolerance to pain, the expected pain intensity and the type of surgical access. The predictive value of the positive model result was 79%, CI [69%, 86%]. Conclusion. Women who have a prediction of moderate and severe pain after the extirpation of the uterus are 60% or more likely to develop it, in order to achieve adequate analgesia, it may be recommended to use more intensive postoperative analgesia, including using regional techniques, which will improve the quality of postoperative analgesia.

Research target: Clinical Medicine Medical Technologies Computer Science Mathematics
Priority areas: IT and mathematics
Language: Russian
Text on another site
Keywords: painбольобезболиваниеhysterectomyэкстирпация маткиpredictorsпослеоперационная больpostoperative painanalgesiaфакторы прогноза
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