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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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Разработка инструмента оценки эффективности экспериментальной деятельности подростков на примере задач с физическим содержанием

Образование и саморазвитие. 2024. Т. 19. № 4. С. 133–147.
Адамский А. И., Kolachev N.

This article focuses on addressing the problem of enhancing the effectiveness of natural science education in schools, particularly in physics, by applying the principle of problem-based learning and designing lessons centered on experimental activity. The foundational unit of this model is a student's action that is motivated, deliberate, and aimed at attempting to solve a task, exploring solutions, and engaging in reflection and generalization of goal-directed actions. However, assessing the effectiveness of such actions requires high-quality research instruments capable of collecting reliable data. To address this challenge, we developed an assessment instrument and empirically tested some psychometric requirements. We hypothesized that the key requirements for such an instrument are: (1) alignment with the structure of activity; (2) multifunctionality and robustness against changes in the evaluator. These requirements were formulated by the authors based on V.V. Davydov's theory of learning activity and the theory of psycho-pedagogical measurement. Data collection was conducted through non-participant observation, with student activity evaluated by independent experts—assessors—using audio and video recordings of experimental lessons in three schools in Yuzhno-Sakhalinsk. The validation of the instrument's requirements was carried out through the analysis of expert judgments and the application of consistency metrics, such as the proportion of matching results and Kendall's nonparametric concordance coefficient. The analysis of the collected data demonstrated that: (a) the developed instrument effectively captures actions related to task-solving in accordance with the theoretical model; (b) the assessments by independent observers yielded similar results, as reflected in acceptable consistency metrics for their evaluations.

Research target: Education
Language: Russian
DOI
Text on another site
Keywords: физиказадачаphysicsobservationнаблюдениесистема оцениванияevaluation systemscience literacyестественно-научная грамотностьэкспериментальная деятельностьинструмент оценкиphysical concept of the worldexperimental activityassessment toolфизическая картина мира
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