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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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Новые возможности анализа революций: представление базы данных революций XXI века

Социологическое обозрение. 2026. Т. 25. № 1. С. 9–61.
Вадим Устюжанин, Дмитрий Семичев, Леонид Гринин, Андрей Коротаев

The revolutionary process in the 21st century has undergone significant changes, and with the destabilization of democracy and the transformation of the world order, the number of revolutionary episodes has only increased, acquiring new goals and forms. In the countries of the Global South, as the population grew, millions of people began to come out to revolutionary protests, and in the Middle East and the Sahel, the rise of radical Islam led to bloody Islamist armed uprisings. Existing data sources are focused on a longer historical period, which complicates the analysis of modern processes, they have a number of disadvantages, such as, for example, streetlight effect, that is, skipping cases, which can distort the effects associated with these cases. We present our own database describing successful and unsuccessful revolutionary events from 2000 to 2024 with their unique characteristics, such as goals, tactics, number of participants, the role of the military, other states and ethnic minorities. The article describes with examples all the variables presented, their operationalization and the data collection process, as well as ways to minimize problems such as selection bias and description bias. Empirical tests of conventional factors of occurrence, success of revolutions and their consequences are also conducted for validation, which shows that the results correspond to the general theory of revolutions, but also gives an understanding of changes in the scale of factors and consequences. As a result, the database expands the possibilities in the field of research of revolutions, which are integral elements of the changing world order and the transformation of the World System, which can greatly affect the idea of revolutions in the future.

Research target: Political Science, International Relations, and Public Administration
Language: Russian
Full text
DOI
Keywords: базы данныхпротестыprotestsМир-СистемаrevolutionsWorld Systemсмена режимаdatabasesmass mobilizationреволюциимассовая мобилизацияuprisingsregime changeвосстания
Publication based on the results of:
State Resilience and National Security of Russia in the Context of the World Order Transformation (2025)
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