Dear authors of Russian Journal of Nondestructive Testing!
When you writing articles, please pay special attention to indicating the relevance of the problem under consideration, as well as the novelty and significance of the results obtained. The base for this should be a review of articles published in periodicals by other authors from different countries.
You have to remember that impact-factor of our journal depends on quantity of citations of articles in two last years from other WoS-journals. Here is some related journals from the WoS list:
1. NDT&E International (https://www.journals.elsevier.com/ndt-and-e-international)
2. MATERIALS CHARACTERIZATION (USA)
3. MATERIALS EVALUATION (USA)
4. MATERIALS LETTERS (NETHERLANDS)
5. STEEL RESEARCH INTERNATIONAL (GERMANY)
6. STRAIN (USA)
7. RESEARCH IN NONDESTRUCTIVE EVALUATION (USA)
8. ENGINEERING FAILURE ANALYSIS (ENGLAND)
9. NONDESTRUCTIVE TESTING AND EVALUATION (ENGLAND)
10. Insight – Non-Destructive Testing and Condition Monitoring (ENGLAND)
11. EXPERIMENTAL TECHNIQUES (USA)
12. PHYSICAL MESOMECHANICS Tri-annual ISSN: 1029-9599 ELSEVIER SCIENCE BV, PO BOX 211, AMSTERDAM, NETHERLANDS, 1000 AE (editorial@ispms.tsc.ru, http://www.ispms.ru)
13. EUROPEAN JOURNAL OF CONTROL (FRANCE)
14. JOURNAL OF STRAIN ANALYSIS FOR ENGINEERING DESIGN (ENGLAND)
15. JOURNAL OF TESTING AND EVALUATION (USA)
Warning!
The rapid development of neural network technology and their active application for the recognition of complex graphic data and video images has recently led to a sharp increase in publications in this field of research. In recent years, an exponential growth of manuscripts on the theme of the use of neural network approaches in the processing of various experimental signals obtained by such methods of non-destructive testing as mainly optical, acoustic and thermal methods has been observed in the Russian Journal of Nondestructive Testing. Certainly the application of correct algorithms for processing experimental data is necessary. However, many similar articles do not contain fundamental novelty, authors do not describe new physical phenomena and do not analyze the microparameters of the mechanisms that underlie the monitoring and diagnostics of materials and products.
The editorial board informs the authors that such articles concerning with the use of neural networks and related computer methods for processing non-destructive testing data will be considered as articles that scarcely correspond to the main profile of the RJNDT - the physical basis of methods and means of non-destructive testing and diagnostics. Modeling papers should bring new modeling techniques to the NDT field and be supported by experimental results.