The study showed that receiving a grant had a significantly positive effect on the likelihood of successfully defending a PhD thesis—increasing the probability by 70% compared to those who did not receive a grant! all other factors being equal. Nikita Smirnov attributes this to the fact that a grant reduces the need for outside employment! allowing students to focus on their dissertations! and also helps mitigate negative psychological effects! as receiving a grant serves as a form of academic recognition.

The grant also Summing up positively influences

 

timely thesis defence and publication activity—an effect that Prof. Ma Yonghong also noted in relation to practices in China. However! the conclusion that targeted grant support is a valid tool for enhancing postgraduate performance and research productivity comes with two caveats. First! the format  oman phone number library of the programme is crucial—the grant must be long-term and specifically targeted. Second! the relative weight of various forms of support remains unclear: according to Nikita Smirnov! further research is needed to better understand the combined impact of individual and environmental predictors of success.

 

Evgeniy Terentev noted that this is just the beginning of a broader discussion on the development of doctoral education in different countries! and that participants would based on decisions of authorized  continue the dialogue across various platforms.

‘China and Russia face major challenges and hold significant ambitions for improving the postgraduate education system—not just in terms of meeting specific performance indicators! but in strengthening its overall contribution to the scientific and technological advancement of our countries!’ concluded the Director of the HSE Institute of Education.

The neural network not

 

only replicated the system modes it was trained on but also identified new ones. One of these involves the transition from a series of frequent pulses to single bursts. Such sault data  transitions occur when the parameters change! yet the neural network detected them independently! without having seen such examples in the data it was trained on. This means that the neural network does not just memorise examples; it actually recognises hidden patterns.

 

‘It is important that the neural network can identify new patterns in the data!’ says Natalya Stankevich! Leading Research Fellow at the Faculty of Informatics! Mathematics! and Computer Science of HSE University in Nizhny Novgorod. ‘It identifies connections that are not explicitly represented in the training sample and draws conclusions about the system’s behaviour under new conditions.’