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June 3, 2026
Pocket Money, Personal Interest, and Family Practices: What Shapes Students Economic Literacy?
University students' economic literacy depends not only on their field of study but also on their interest in economics, the learning environment, and family financial practices. For example, students who received pocket money irregularly tend to perform better on economic literacy tests than their peers who received financial support on a regular basis. These findings come from a study conducted by HSE University involving more than 1,100 students from five Russian universities. The findings have been published in Cakrawala Pendidikan.
June 3, 2026
Creative Work as a Remedy for Burnout
The creative, supportive atmosphere and innovative methods at the Centre for Sociocultural Research make it appealing to early-career scholars. Over years of working at HSE University, they grow into researchers and lecturers recognised both in Russia and abroad. Chief Research Fellow Zarina Lepshokova and Leading Research Fellow Ekaterina Bushina spoke about their journey at the centre and at HSE, their research, and the role of mentors in their academic success.
June 2, 2026
HSE Study Reveals Imbalance in the Generative AI Market
Researchers at HSE University analysed how effectively the global generative artificial intelligence market converts investment into real revenue, concluding that AI is currently developing faster than it is paying off. The results have been published in the journal Foresight and STI Governance.

 

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Towards Recognition of Pleural Effusion Images

P. 77–83.
Zamyatina E., Veselov I., Plaksin S., Farshatova L.

Early diagnosis of patients' diseases allows to prescribe effective treatment in a timely manner. This article presents the results of research related to the recognition of images of pleural effusions. The purpose of the research is the recognition of images characteristic of pathologies associated with oncological diseases. When recognizing images, convolutional neural networks were used. When developing software, the authors used the TenzorFlow and OpenCV libraries. Image recognition accuracy is 95%. The studies are incomplete; the authors are trying to improve the results of research by replenishing the training sample with new copies of images of pleural effusions and using combinations of pattern recognition methods.

Language: English
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Keywords: segmentationclassificationconvolution neural networkpleural effusion

In book

The 13th IEEE International Conference on Application of Information and Communication Technologies (AICT2019): / Conference Proceedings
The 13th IEEE International Conference on Application of Information and Communication Technologies (AICT2019): / Conference Proceedings
Baku: The Institute of Electrical and Electronics Engineering, Inc., 2019.
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