The article presents work on developing a software application for test creation using artificial intelligence and large language models. Its main goal is to optimize the educators’ work by automating the process of test generation and evaluation, with the tests being stored for subsequent analysis and use. The application can generate test questions based on specified criteria such as difficulty level, Bloom’s taxonomy level, question type, style and format, feedback inclusion, and more, thereby providing opportunities to enhance the adaptability and efficiency of the learning process. It is developed on the Google Firebase platform, utilizing the ChatGPT API, and also incorporates cloud computing to ensure scalability and data reliability.

In this article the effectiveness of one of the main methods for assisting the information search in large data sets is analyzed, namely the method based on the social approach. This method analyzes the behavior of multiple users when searching for information, particularly the keywords they use. Based on the obtained results, the relevant algorithms in the searching engines of the organizations can be optimized. The studies are based on data extracted from the databases of two relatively large information systems based at the University in Library Studies and Information Technologies in Sofia, Bulgaria and University of Economics in Varna, Bulgaria. The numerical and experimental results are analyzed and discussed.

The area of cybersecurity problems has reached the stage of becoming a science. This raises questions about the connection between the mathematical theories used in cybersecurity research and their relation to the methodology for experiments and conceptual models synthesized from the academic community. This research proposes an analytical review of the mathematical ideas used in applied cyber-security and theoretical explorations. This meta viewpoint is dedicated to standard mathematical theories applied in cybersecurity issues. The ground of the work is methodological problems relating to the validation of experiments and models with mathematical ideas in the cybersecurity exploration of digital space. This research emphasizes the application of game theory, catastrophe theory, queuing systems, and Markov chains. The methods are shown without claiming to be exhaustive. The goal is to review the currently established implementation of mathematical approaches to cybersecurity. A spectrum of possibilities for applying mathematical apparatus in future research for cybersecurity is given. After a review of the literature for each presented mathematical approach, we expose a list of problematic areas in which this has already been implemented.

Capacity planning of the infrastructure is a difficult task performed during the virtualization of the IT infrastructure. It is the planning of the necessary hardware to which the existing systems will be transferred, and later the future systems that will work in a virtualized environment. Infrastructure capacity planning in digitalization of educational services is a process in which the IT infrastructure is needed to meet not only the current workload but also the future workload and to follow further requirements. In case the process of planning is well performed, it could more fully support the development of emerging technologies in learning. The traditional IT infrastructure, bound by the physical limitations of the devices, does not meet the requirements for flexible changes, for quick recovery after a problem. This leads to difficult management, to weaknesses in the implementation of educational processes. Virtualization could solve most of these problems for modern IT infrastructures. It provides highly efficient and reliable operation of operating systems and applications that do not depend on the computer system.

The consortium of “The Future is in Applied Artificial Intelligence” Project designed the first competency-based applied artificial intelligence curriculum at the higher-education institution level. The development was based on advanced system research on existing artificial intelligence-related resources and surveying target groups of teachers, information technology students, and employers, which should enhance the performance of implementing artificial intelligence education. A review of applied artificial intelligence was prepared in the form of keyword clustering. The initial data were collected with the help of surveying by identifying job offers, existing artificial intelligence training courses, scientific projects, and real cases. A synthetic analysis of the textual information from the studies was conducted using the word clouds technique. A tensor-based approach was used for the presentation of the competency-based course. The specific numerical requirements for the course in the form of priorities followed from the solution to decision-making problems using the analytic hierarchy process technique. Based on a comprehensive study of surveys, educational experience, scientific projects, and business requirements, and a meta-analysis of the recent references, we specified the criteria for a training course in the form of a tensor-based representation of competencies in relation to content and educational modules.

During operation, mobile phones emit energy in the form of electromagnetic fields that can be absorbed by the user's head. However, it is not yet clear whether this electromagnetic energy causes biological consequences or adverse effects on human health. This article aims to determine whether radio frequency pulsed exposure from mobile phones can affect a person's brain activity. For this purpose, the influence of mobile phone exposure on the nervous response was studied by analyzing electrophysiological measurements of brain wave activity. Electroencephalogram (EEG) test measurements were taken in two situations when the participant was not using and when using a mobile phone.