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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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Influence of Cultural Factors on Organizational Performance of Multinational Corporations: A Bibliometric Review from 1983 to 2020

Qeios. 2024.
Wang F., Barajas A.

Cultural factors are critical in shaping the organizational performance of multinational corporations (MNCs) operating across diverse countries and cultures. This study conducts a comprehensive bibliometric analysis to explore scientific developments in this research area from 1983 to 2020, based on 856 documents published across 195 journals indexed in the Web of Science. The analysis provides insights into (i) productivity and performance trends within the field, (ii) the most influential countries, regions, journals, authors, and citations, and (iii) frequently occurring keywords that highlight future research directions. This study captures the historical evolution of research on cultural factors, providing a valuable baseline for understanding the pre-COVID-19 context and enabling MNCs to better navigate cross-cultural communication and collaboration in an increasingly complex global environment.

Research target: Economics and Management Social Sciences
Language: English
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DOI
Text on another site
Keywords: международный бизнескультурные факторыsystematic literature reviewcultural factorsсистематический обзор литературы International businessBibliographic StudyMultinational Corporations (MNCs)Web of Science (WoS)Библиографическое исследованиеТранснациональные корпорации (ТНК)Web of Science (WoS)Pre-COVID-19Пре-COVID-19
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