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News
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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Опыт применения носимых технологий в практике управления производительностью труда сотрудников массовых специальностей финансового сектора

Организационная психология. 2019. Т. 9. № 1. С. 34–56.
Strizhova E., Evdokimenko A.

Purpose: is to try and to show the opportunities and prospects of wearable technologies in the performance management and HR practice.

Approach: the study combines HRV monitoring data, KPI data and psychological diagnostics data. Among the indicators of HRV, the stress index is one of the most informative for characterizing the stress states of the central nervous system. The stress index was monitored in real time using a wearable device (Alpatov, Kuryshev, 2016) during the working day. Changes in performance indicators were also monitored during the working day at 30-minute intervals. Personal anxiety of employees (Charles D. Spielberger test in the adaptation of Y. L. Khanin) and involvement ("Test of Resilience" in the modification of E. N. Osin, E. I. Rasskazova) were also assessed.

Findings. Evaluations of the relationship between HRV indicators and objective indicators of the working process and psychological diagnostics data are presented.

Implications for practice. The original methodology of organizational and psychological research with the use of wearable technologies is presented, as well as the practical possibilities of using such technologies in organizational practice.

The results were interpreted for four subgroups of employees depending on the level of KPI (low or high) and the level of personal anxiety (low or high). For each subgroup recommendations for performance manager are given.

A subgroup of employees with high KPI and high personal anxiety unites employees who are able to act as agents of task performance control. Employees with high KPI and low personal anxiety are a stable work resource within the business process. Employees with low KPI and high personal anxiety are united by the need for a mentor (coach). Employees with low KPI and low personal anxiety are ready to leave the organization

Research target: Psychology
Language: Russian
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Keywords: производительность трудаэмоциональное выгораниеstressemotional burnoutКПЭKPIlabor productivityконцепт «стресс»heart rate variabilityвариабельность сердечного ритма
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