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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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Statistics on International Migration. A Practical Guide for Eastern Europe and Central Asia

Geneva : United Nations Economic Comission for Europe, 2012.
Choudinovskikh O.
Academic editor: Valente P.

Migration is a powerful driver and important consequence of economic, political and social change. Because of its great impact on societies, migration needs to be adequately measured and understood. Reliable statistical data is the key to the basic understanding of this important phenomenon. Yet, in many countries, even the most general statistics on migration are incomplete, out-of-date or do not exist. Improvement in this area requires knowledge of the principles of collecting, compiling and analyzing migration statistics. Likewise, policymakers and other users need to be aware of the definitions and measurement issues related to the data to be able to interpret them. The present guide was prepared under the responsibility of the United Nations Economic Commission for Europe in the framework of the project “Strengthening national capacities to deal with international migration: maximizing development benefits and minimizing negative impacts”. The project involved all five regional commissions of the United Nations and was financed from the United Nations Development Account. The guide is intended for practitioners and professionals whose work is related to migration and migration statistics. It focuses on the specific context of migration processes in Eastern Europe and Central Asia. We expect that the practical examples and international recommendations presented herein stimulate interest and improve understanding and facilitate production, dissemination and use of statistics on international migration.

Priority areas: economics sociology
Language: English
Full text
Keywords: миграцияmigrationsources of data on migrationstatistics on migrationстатистика миграцииисточники данных о миграции
Statistics on International Migration. A Practical Guide for Eastern Europe and Central Asia
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