Based on the synthesis of complex analysis methods, perturbation theory, and characteristics, a new approach has been developed for accounting for osmosis and temperature in predicting the migration processes of radionuclides under non-isothermal conditions in quasi-ideal porous media (in curved regions) bounded by streamlines and equipotential lines. The solution to the corresponding degenerate problem was obtained based on the developed method of nonlinear inversion of boundary value problem solutions into quasi-conformal mappings. An algorithm for calculating a uniformly dynamic grid, streamlines, and speed of ideal filtration field was constructed. With the developed method of characteristics, formulas for the approximate solution of diffusion-convection mass transfer problems in perturbed osmosis and heat filtration fields were obtained.
This article aims to discover how AI-powered systems facilitate auditing, what risks emerge for AI-assisted audits and how to deal with these new risks. The paper studies the impact of cognitive computing on audit risk. AI-powered software is capable of self-learn so that it can identify patterns in data and codify them in predictions, rules and decisions. This self-learning ability can become both a benefit and, at the same time, insecurity. Although AI-self-learning helps make the process more efficient and calculations more accurate by improving the algorithm, eliminating errors and reducing risks, it creates new previously unknown threats. We discovered inherent limitations of cognitive-based technologies and risks for the audit process associated with using AI systems. We also proposed a complex security model that can reduce the uncertainty of AI-enabled audit and provides insight into future research opportunities.
This research demonstrates the effectiveness of using project-based learning to develop virtual and augmented reality applications using available platforms such as Vuforia and Unity 3D. In project-based learning, students acquire knowledge and skills by researching and responding to interesting and complicated questions, problems or issues. The research involved 54 students from two universities. The students who participated in the research were separated in an experimental and a control group. Control work of the same level was carried out in both groups. In the post-test, 56% of the participants in the experimental group scored above 93, while in the control group only 36% of all participants scored above 80. The research showed that the knowledge and skills acquired by students in the experimental group in developing applications of virtual and augmented reality were higher than in the control group.
This paper is aimed to the analysis of the concept of self-improvable computer systems design through the use of reconfigurable hardware, specialized processors high-level automatic design and synthesis tools, and Artificial Intelligence technologies. It highlights FPGA as the hardware basis for self-improvable computer systems and considers the development directions for FPGA-based computer systems in the future. The Artificial Intelligence technologies are used to identify what and how the computer system can be improved by itself. The software for specialized processors high-level design tools serves as the basic acting mechanism for computer system self-improvement. Finally, the method of computer system self-improvement is presented in the paper.
Control of users’ access to the information system is the main part of the security system. Moreover, it must work in real time and be easily reconfigured. Such controller can be built by using fuzzy logic. In this paper, the authors propose a fuzzy controller for access distribution to the student assessment system. This approach allows applying the proposed fuzzy controller to any evaluation system, as it can be easily reconfigured. Hardware implementation of this controller allows to increase the stability of the information security system because of subjectivity exclusion.
