Nina Trifonova

The Arctic region has significant potential for the extraction of mineral and energy resources. However, effective resource development requires careful and balanced decisions that minimize environmental impact and ensure sustainable resource management. This article discusses the development of a conceptual model of the ontology of the Arctic zone of the Russian Federation, focused on logistics and digital infrastructure. Ontology is a powerful tool for organizing areas of knowledge, providing a formal representation of concepts and their interrelationships. The main classes, relations, attributes and rules of functioning of the ontological model are defined. The significance of digitalization and logistic routes in the extreme climate of the Arctic is demonstrated. The proposed ontology systematizes information about logistics and digital components of the Arctic region of Russia. It allows creating a structured knowledge base that will enable stakeholders to analyze complex interdependencies, develop effective management strategies and make informed decisions that contribute to sustainable development in this vulnerable ecosystem. In developing the ontology, we aim to provide a unified view of the Arctic’s logistics and digital sphere, which helps stakeholders better navigate the complex system of challenges and opportunities.

Vaccination is critical to preventing the spread of diseases. It stimulates the immune system to produce antibodies that fight specific diseases, eradicating and reducing their incidence. However, despite the proven benefits, there is hesitation and skepticism in some areas due to side effects and lack of knowledge. Developing a data collection and processing system to analyze vaccination is critical in today’s world. Vaccines are necessary to minimize morbidity and mortality, but success depends on analyzing data on vaccine use and efficacy. This system can identify potential side effects and adverse reactions, ensuring vaccine safety and building public confidence. This research focuses on IT support for analyzing vaccination side effects. The aim of this work is to develop an architecture model of the system to collect and process data on the health status of vaccinated patients. The research methodology consists of analyzing sources on the consequences and side effects of vaccination. On the basis of this knowledge, the key attributes (stakeholders, sources of information, input data, data analysis processes) of the data collection and analysis system were analyzed using an enterprise architecture approach. As a result, a general model of the architecture of the data collection and analysis system was proposed.