This study aims to determine the extent to which talent identification is implemented in talent management. A Systematic Literature Review (SLR) was conducted to summarize the application of talent identification in the last six years. Researchers use Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) to process scientific articles. The literature reveals that while topics related to talent management garner significant attention, research on talent identification within talent management remains relatively scarce despite a gradual increase each year. We compared documents indexed by Scopus Q1 and Q2. The results show that the United States accounted for a significant portion of research on talent identification, representing 16% of the total existing research. Researchers have conducted extensive studies on the medical and pharmaceutical sectors, public services, tourism, and hospitality. The number of citations varied greatly from 1 to 93, with a median value of 20. These studies have also used various research methods with different theoretical bases and produced different analyses. This finding enriches the perspective of talent identification.

Tourist visits to a destination or attraction as a result of the destination being featured on television, video, or the cinema screen were the ones, that stimulated the creation and development of film tourism, which quickly established itself in global conditions. The main objective of the paper was focused on the identification and the perception of the conditions of film tourism development in Slovak republic. So far, a lot of film production has been realized in the country, but this potential has not yet been properly used for the creation of tourism products. Implementation of the study from a methodological point of view took place using several research methods. The pilot scientific abstraction of the issue was followed by the analysis of film conditions in the territory of Slovak Republic and their categorization. The given starting points were followed by the implementation of questionnaire research, the results of which were verified using several research methods such as Doornik-Hansen test, Kruskal-Wallis test. The results of the questionnaire research show a significant positive perception of the potential of filmmaking as a significant factor in the creation of new tourism products. At the same time, they identify key destinations that could potentially become objects of product realization. Due to the fact that this issue has not received adequate attention in domestic conditions, the study brings a new, more comprehensive view of the topic and emphasizes the power of the potential for further development.

The power of Artificial Intelligence (AI) combined with the surgeons’ expertise leads to breakthroughs in surgical care, bringing new hope to patients. Utilizing deep learning-based computer vision techniques in surgical procedures will enhance the healthcare industry. Laparoscopic surgery holds excellent potential for computer vision due to the abundance of real-time laparoscopic recordings captured by digital cameras containing significant unexplored information. Furthermore, with computing power resources becoming increasingly accessible and Machine Learning methods expanding across various industries, the potential for AI in healthcare is vast. There are several objectives of AI’s contribution to laparoscopic surgery; one is an image guidance system to identify anatomical structures in real-time. However, few studies are concerned with intraoperative anatomy recognition in laparoscopic surgery. This study provides a comprehensive review of the current state-of-the-art semantic segmentation techniques, which can guide surgeons during laparoscopic procedures by identifying specific anatomical structures for dissection or avoiding hazardous areas. This review aims to enhance research in AI for surgery to guide innovations towards more successful experiments that can be applied in real-world clinical settings. This AI contribution could revolutionize the field of laparoscopic surgery and improve patient outcomes.

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.

With the advancement of the green economy, the labor market is experiencing the emergence of new employment forms, positions, and competencies. This arises from the special relationship between the green job market and the transforming energy sector. On the other hand, the energy sector’s influence on the green labor market and the creation of green jobs is particularly significant. It is because, the energy sector is one of the fundamental foundations of any country’s economy and impacts its other sectors. Key components of this influence include green employment and green self-employment. The purpose of this study is to identify elements of the green labor market within the context of the green economy and the energy sector. The methodology employs a hybrid literature review, combining a systematic literature review facilitated by the use of VOSviewer software. Exploring the Scopus database enabled the identification of keywords directly related to the green economy and the energy sector. Within these identified keywords, elements of the green labor market were searched. The main result is the empirical identification of the crucial term ‘green skills,’ which links elements of the green labor market, as presented in bibliometric maps. The research results indicate a gap in the form of insufficient discussion on green self-employment within the energy sector. Aspects of green jobs and elements of the green labor market are prominently featured in current research. However, there is a notable gap in the literature regarding green self-employment, presenting promising avenues for further research.