This study delves into the intricate dynamics of tourism-induced water pollution through a systematic literature review, aiming to unravel complexities using a system dynamics (SD) modeling approach coupled with the PRISMA analysis methodology. Employing a comprehensive PRISMA analysis of 68 pertinent articles, the study establishes a metamodel for comprehending plastic pollution in water ecosystems resulting from tourism. The methodology emphasizes economic and environmental dimensions, causal conditions, and interventions, with a specific focus on the role of Information and Communication Technology (ICT). The results highlight integrated strategies as crucial in mitigating tourism-induced water pollution. These strategies advocate for the incorporation of environmental conservation and sustainable management practices. The study underlines the pivotal role of environmental education, awareness, and investments in protection as effective interventions. The findings offer valuable insights for policymakers and stakeholders in the tourism industry, emphasizing the necessity for proactive planning and management. The study advocates for knowledge-based decision-making to optimize tourism's environmental impacts and underscores the significance of quick and flexible responses to environmental challenges.

This systematic review provides a comprehensive overview of tsunami evacuation models. The review covers scientific studies from the last decade (2012–2021) and is explicitly focused on models using an agent-based approach. The PRISMA methodology was used to analyze 171 selected papers, resulting in over 53 studies included in the detailed full-text analysis. This review is divided into two main parts: (1) a descriptive analysis of the presented models (focused on the modeling tools, validation, and software platform used, etc.), and (2) model analysis (e.g., model purpose, types of agents, input and output data, and modeled area). Special attention was given to the features of these models specifically associated with an agent-based approach. The results lead to the conclusion that the research domain of agent-based tsunami evacuation models is quite narrow and specialized, with a high degree of variability in the model attributes and properties. At the same time, the application of agent-specific methodologies, protocols, organizational paradigms, or standards is sparse.

The research was motivated by the growing importance of visitor management in protected areas, which can be based on knowledge management, system modelling of processes and phenomena, and a deeper knowledge of the experience of visitors in connection with the concept of psychological carrying capacity. The work builds on previous publications and research by the authors, focused on the optimization of tourism impacts, visitor management and the development of the theory and applicability of the concept of carrying capacity. It emphasizes the overview analysis of the possibilities of using agent-based modelling and visualization of visitor flows in protected areas. The analysis of suitable sources was based on the PRISMA method, which showed the main research directions for the use of the agent-based approach in visitor management. For the practical application of modelling, the NetLogo environment was chosen, in which the visitor flows of the model area were simulated. The visitor attendance was evaluated in relation to the psychological carrying capacity. Subsequently, visitor management measures were implemented in the model, and repeated simulations of visitor attendance, based on monitored flows, were run for a specific location around Oheb Castle (the Železné hory/Iron Mountains, Bohemia). The main result is the innovative use of agent-based modelling in visitor management in the context of visitor experience, visitor satisfaction and psychological carrying capacity. The contribution of the presented research is also the proposal of future research directions for more accurate use of psychological carrying capacity in visitor management.

Various organizations and institutions store large volumes of tsunami-related data, whose availability and quality should benefit society, as it improves decision making before the tsunami occurrence, during the tsunami impact, and when coping with the aftermath. However, the existing digital ecosystem surrounding tsunami research prevents us from extracting the maximum benefit from our research investments. The main objective of this study is to explore the field of data repositories providing secondary data associated with tsunami research and analyze the current situation. We analyze the mutual interconnections of references in scientific studies published in the Web of Science database, governmental bodies, commercial organizations, and research agencies. A set of criteria was used to evaluate content and searchability. We identified 60 data repositories with records used in tsunami research. The heterogeneity of data formats, deactivated or nonfunctional web pages, the generality of data repositories, or poor dataset arrangement represent the most significant weak points. We outline the potential contribution of ontology engineering as an example of computer science methods that enable improvements in tsunami-related data management.

Chatbots are rapidly growing application area of conversational artificial intelligence. The aim of the paper is to explore the evaluation of user experience with chatbot applications in museums and galleries. Introduction to principles of chatbots, their creation and testing is provided. Methods of user experience evaluation are explained and the indicators that can be used to assess user experience with chatbots are listed. History and classification of museum chatbots is briefly summarized. A systematic review according to the PRISMA methodology was conducted to map the latest trends in museum chatbots’ development and namely to answer two research questions: (1) What chatbots have been developed for the needs of museums and galleries? and (2) Was the visitor experience with these chatbots evaluated? The research gap in measuring visitor experience with chatbots was identified.

Immense numbers of textual documents are available in a digital form. Research activities are focused on methods of how to speed up their processing to avoid information overloading or to provide formal structures for the problem solving or decision making of intelligent agents. Ontology learning is one of the directions which contributes to all of these activities. The main aim of the ontology learning is to semi-automatically, or fully automatically, extract ontologies—formal structures able to express information or knowledge. The primary motivation behind this paper is to facilitate the processing of a large collection of papers focused on disaster management, especially on tsunami research, using the ontology learning. Various tools of ontology learning are mentioned in the literature at present. The main aim of the paper is to uncover these tools, i.e., to find out which of these tools can be practically used for ontology learning in the tsunami application domain. Specific criteria are predefined for their evaluation, with respect to the “Ontology learning layer cake”, which introduces the fundamental phases of ontology learning. ScienceDirect and Web of Science scientific databases are explored, and various solutions for semantics extraction are manually “mined” from the journal articles. ProgrammableWeb site is used for exploration of the tools, frameworks, or APIs applied for the same purpose. Statistics answer the question of which tools are mostly mentioned in these journal articles and on the website. These tools are then investigated more thoroughly, and conclusions about their usage are made with respect to the tsunami domain, for which the tools are tested. Results are not satisfactory because only a limited number of tools can be practically used for ontology learning at present.