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News
September 11, 2026
How to Assess Students Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
September 9, 2026
‘Balkan Hospitality Opens Doors: Studying Dialects on the Verge of Extinction
You cannot study spoken dialects from books. Instead, you need to go to a village, seek out its elders, and earn the trust of local residents before you can record hours of spontaneous stories. This is how Natalia Muravleva, Associate Professor at the Faculty of Humanities, conducts her research. Her internship in Serbia continued her long-standing study of dialects spoken by Macedonian settlers. In this interview, she discusses how diaspora cultural centres help researchers reach informants, why native speakers need to be interviewed only in their own language (otherwise, as she puts it, they may 'break'), and how a single field season helped her finalise her monograph. She also shares warm memories of autumn in Belgrade and of colleagues with whom grammar can be discussed in three languages at once.
September 9, 2026
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.

 

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?

Caspian Pipeline Consortium Equipment Monitoring System

P. 169–173.
Belov A. V., Irina V. Arutyunova

The equipment condition assessment system of the Caspian Pipeline Consortium is purposed to enhance the efficiency of monitoring and to manage the operation of pipeline systems. The primary objective of this initiative is to develop an information system that enables continuous monitoring of pipeline conditions, thereby minimizing maintenance costs and reducing risks associated with potential failures. To achieve this goal, several tasks have been defined, including the analysis of existing pipeline condition assessment methods, the collection and structuring of data, the application of statistical analysis to identify patterns, and the development of a predictive model for equipment condition. The most important aspect is the evaluation of the effectiveness of the predictive model, which will justify its feasibility and advantages over traditional approaches.

Language: English
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Keywords: monitoringpredictive analyticsinformation systems Digital twinCaspian Pipeline Consortium

In book

Proceedings of the 2025 INTERNATIONAL CONFERENCE "QUALITY MANAGEMENT, DIGITAL SECURITY, INFORMATION TECHNOLOGIES" (2025 QM&DS&IT)
Proceedings of the 2025 INTERNATIONAL CONFERENCE "QUALITY MANAGEMENT, DIGITAL SECURITY, INFORMATION TECHNOLOGIES" (2025 QM&DS&IT)
IEEE, 2025.
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