With the progress of information technology, especially the widespread use of artificial intelligence technology, it has shown an important role in promoting economic and social development. Art and design in universities is a new discipline that combines modern technology with humanities and art. Only by emphasizing the development of science and technology, adapting to the requirements of the times, and closely integrating humanities and art with science and technology, can we gradually expand the educational channels for cultivating composite and innovative talents. Effectively organizing different types of scientific research activities, building a sound and comprehensive education system, plays an important role in adjusting teaching relationships, innovating teaching models, enhancing students' professional and comprehensive qualities, and improving their academic performance and employment competitiveness.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

Simulation training in dental medical eduaction is a modern high-tech approach in providing quality higher education. Simulation training immerses students in realistic scenarios, allowing them to develop both technical and non-technical skills essential for effective patient care. This study highlights key contemporary issues in high-tech simulation training for dental education and consolidates its rationale and benefits. We searched the databases PubMed, Scopus, Web of Science, and ResearchGate. This review includes 36 articles published in English, Russian, and Ukrainian from 2020 to 2024. Non-peer-reviewed papers or those not published in indexed journals were not considered. Simulation training was found to impact integration of theory and practice, training a wide range of psychomotor skills, development of complex clinical competences, cultivating confidence, empathy and patient-oriented care, neuroplasticity of the brain and the cognitive load. Pedagogical benefits and the place of simulation training in the curriculum were also discussed.

Objective/Aim: In the context of a constantly changing legislative environment and the necessity for professionals to develop their skills, the research focuses on identifying effective methods and tools that facilitate efficient learning and professional development in the field of labour law. This study aimed to propose a pedagogical technology for the preparation and training of specialists in the field of labour law and to assess the effectiveness of the training based on the specified technology. Method: The study involved 124 participants, with 63 in the experimental group and 61 in the control group. Statistical analysis was performed using Microsoft Excel. The student’s t-test indicated significant improvements in the experimental group’s training effectiveness, confirming the proposed pedagogical technology’s efficacy. Results: Consequently, implementing training and education technology for specialists in the labour law field was proposed to enhance the indicators. The criteria for the preparation of specialists in the field of labour law were delineated, including knowledge of labour legislation, consulting and support skills, analytical skills, communication skills, and continuous learning. According to the criteria above, levels of preparation for specialists in the field of labour law were established, namely high, medium, and essential. The proposed training and education technology for specialists in the field of labour encompasses the following tools: The utilisation of online platforms and educational resources, virtual classes and simulations, the incorporation of multimedia materials, the integration of adaptive learning technologies, the implementation of project- and problem-oriented teaching methodologies, the incorporation of interactive methodologies, the incorporation of cloud technologies and mobile applications, and the provision of assessment and feedback. Conclusion: The proposed pedagogical technology effectively enhances the training and education of labour law specialists. The experimental group’s significant improvement in learning outcomes confirms the technology’s efficacy. Implication: The findings of this research hold significant social implications. Improved training and education of labour law specialists leads to a more competent and effective legal workforce. This, in turn, ensures better protection of workers’ rights and fairer employer-employee relations, contributing to overall social stability.