Preventive maintenance (PM) in the production industry is one of the most essential measures to eliminate accidental machinery failures by replacing/repairing worn out machines or parts. The decision of when and where to perform preventive maintenance is non-trivial due to the complex and stochastic nature of the industry where PM is implemented. This article deals with the theoretical and practical implementation of preventive maintenance based on a unique modification of the total productive maintenance (TPM) methodology. The innovative approach of preventive maintenance management was implemented in the real production hall of ITT (Czech Republic) and has been verified. Within preventive maintenance, the new concept brings in an innovative method of managing the maintenance process as a whole, from abstract methodical conception to practical usage. The whole new approach has been verified and implemented on industrial equipment. A challenging task while implementing Industry 4.0 technologies is the issue of how to fully gather and analyse operational data from various items of equipment and users under various conditions, which would result in innovative services of equipment maintenance for clients. The solution to this problem is based on an innovative approach to preventive maintenance of complex equipment and could help many industrial companies to increase production and maintenance efficiency.

The article is devoted to the issue of the construction of an intelligent tutoring system which was created by our university for implementing distance learning and combined forms of studies. Significantly higher demand for such tools occurred during the COVID-19 pandemic when distance learning was used by students in their full-time studies. Current Learning Management Systems (LMS) do not address students’ individuality regarding their various levels of input knowledge and skills or their different learning styles, which, in our case, are based on sensory preferences. Therefore, this article proposes a model of an intelligent tutoring system to control learning by accentuating the individual needs of a student. The foundation stones of this system are an expert system and adaptation mechanisms. The expert system acts as a tool for the identification of students’ needs from the point of view of input knowledge and sensory preferences. Sensory preferences influence the student’s learning style. The implemented adaptation mechanisms control the progress of the student through a study unit. The model was implemented in the LMS Moodle environment. Regarding the focus of the research content, our model is oriented on the study of the English language, where each student receives a unique study plan, which is continuously adapted based on achieved results. We consider the focus on the individuality of the student to be an innovative approach that can be achieved automatically on a mass scale.

The study is focused on monitoring the influence of selected parameters on the zeta potential values of titanium dioxide nanoparticles. The influence of pH, temperature, ionic strength, and mass content of titanium dioxide in the suspension was assessed. More than a thousand samples were measured by combining these variables. On the basis of results, the model of artificial neural network was proposed and tested. The authors have rich experiences with neural networks applications and this case shows that the neural network model works with a very high prediction success rate of zeta potential. Clearly, pH has the greatest effect on zeta potential values. The influence of other variables is not so significant. However, it can be said that increasing temperature results in an increase in the value of the zeta potential of titanium dioxide nanoparticles. The ionic force affects the zeta potential depending on the pH; in the vicinity of the isoelectric point, its effect is negligible. The effect of the mass content of titanium dioxide in the suspension is absolutely minor.

The study focuses on evaluating the performance of AI-based tools, specifically ChatGPT versions 3.5 and 4.0, in comparison to human students in the field of Theoretical Computer Science Education. The experiment aims to assess the capabilities of both AI and human subjects in solving learning tasks based on Bloom’s Taxonomy. The primary objectives of the study are to determine the educational performance of AI and students, identify areas where students may outperform AI in learning tasks, and evaluate the normalized overall results of educational performance with equal assignment weights. The assessment included testing on various types of Bloom’s Learning Task Taxonomy (LTT) based on specific Learning Objectives (LOs). Hypotheses were formulated for quantitative and qualitative analysis to compare the performance of AI and human subjects. Quantitative analysis revealed engaging results regarding the educational performance evaluation with AI-based tools. While some differences were observed in the performance of AI and students, the normalized overall results with equal assignment weights did not show statistically significant differences. The study highlighted the advantages and disadvantages of both humans and AI bots in solving learning tasks. The study concludes that there are areas where students may have an advantage over AI in tasks requiring understanding, evaluation, and creative thinking. Recommendations are provided for educators on utilizing AI-based tools in education, emphasizing the coexistence of AI possibilities with well-designed assignments. The findings suggest that AI has a valuable role in education, and a thorough analysis of teaching approaches and student evaluation is essential in leveraging artificial intelligence effectively.