No abstract

The reliability of the human operator is an essential indicator of the safe operation of nuclear power plants. Mistakes can be made during performance checks, maintenance, at the stage of accident management, etc.The need to take into account the human factor in the analysis of safety and reliability of nuclear power plants is justified by the fact that erroneous actions of personnel and operators of nuclear power plants directly or indirectly can lead to accidents. Therefore, the analysis of personnel reliability (ANP) when performing a probabilistic safety analysis (PSA) and risk assessments afford to identify the most likely erroneous actions of NPP personnel and to develop a set of measures to reduce them.Depending on the likely consequences of the accident and the time available to the operator for the intervention, the personnel will be exposed to various levels of stress, which will directly affect the personnel's actions to troubleshoot in the event of an emergency. If an accident has occurred, then in a short period of time the operator needs to make a number of decisions, the correctness of which can both save and aggravate the situation.This paper considers the issues of visualization of bibliometric networks of scientific publications on the study of the human factor in the operation of nuclear power plants.

In this paper, a systematic study of the microstructure damage process of metals and alloys was carried out. The main elements of the microstructure surface image, as well as the rules for the formation and interaction of rough slip traces and cracks to determine the model of damage accumulation on the image of the microstructure surface under cyclic loading are determined. A classifier that allows to determine the number of loading cycles before a sample goes out of service is proposed. A modernized structure of the convolutional neural network was developed to classify images of the damaged microstructure of the metals and alloys surface. The proposed classifier for determining the number of loading cycles made it possible to achieve a classification accuracy of 78.43%.

The accelerating evolution of scientific terms connected with 4P-medicine terminology and a need to track this process has led to the development of new methods of analysis and visualization of unstructured information. We built a collection of terms especially extracted from the PubMed database. Statistical analysis showed the temporal dynamics of the formation of derivatives and significant collocations of medical terms. We proposed special linguistic constructs such as megatokens for combining cross-lingual terms into a common semantic field. To build a cyberspace of terms, we used modern visualization technologies. The proposed approaches can help solve the problem of structuring multilingual heterogeneous information. The purpose of the article is to identify trends in the development of terminology in 4P-medicine.

Due to the increasing popularity of new research in medicine thisstudy was conducted to determine recent research trends of predictive, preventive and personalized medicine (PPM). We identified the terms relevant to PPM using own search engine based on neural network processing in PubMed database. We extracted initially about 15000 articles. Then we carried out the statistical analysis for identifying research trends. The article presents the results of solving the problem of evaluating research topics at the level of thematic clusters in a separate subject area. An approach based on the analysis of article titles has been implemented. Identification of terms, connections between them and thematic clustering were carried out using the free software VOSViewer, which allows to extract terms in the form of noun phrases, as well as to cluster them.