Ensuring cybersecurity in smart cities (SCs) presents a complex, multidimensional challenge that requires robust methodologies for threat identification, risk assessment, and the development of effective countermeasures. This article presents a web-based expert system powered by ontologies, designed to assist cybersecurity architects in modeling and analyzing security scenarios in SC environments, with a particular emphasis on open data. The system employs formal ontologies to structure cybersecurity knowledge, including assets, threats, vulnerabilities, and countermeasures, and uses automated reasoning to support informed decision-making and risk mitigation. The case study is based on Business Process Model and Notation extended for Smart Cities (BPMN-SC), tailored to SC services and data flows. The results demonstrate that an ontology-driven expert system can enhance transparency, consistency, and formal rigor in cybersecurity governance within SC initiatives driven by open data.

Modern Smart Cities generate vast amounts of heterogeneous data from urban infrastructure, IoT ecosystems, cybersecurity events, and AI analytics. Existing solutions often treat these domains separately, resulting in fragmented knowledge that limits cross-domain reasoning and context-aware cybersecurity decision-making. Current ontologies are domain-specific, and knowledge graphs typically lack semantic integration and AI-supported reasoning. This paper presents an ontology-driven knowledge graph framework that unifies Smart City, cybersecurity, and AI-related data into a coherent semantic model. Organized into four layers—Data Acquisition, Information Structuring, Semantic Modeling, and Knowledge Reasoning—the framework transforms raw data into actionable, interpretable knowledge. By explicitly modeling entities, relationships, constraints, and dependencies, it supports context-aware reasoning, explainability, interoperability, and modular extensibility. Remaining challenges include scalable reasoning over large graphs, automated alignment of heterogeneous data, and continuous ontology evolution to accommodate changing urban systems and AI models. Addressing these challenges will strengthen the framework and advance ontology-driven, adaptive, and explainable cybersecurity governance in complex Smart City environments.

This study examines the effect of synthetic data generation for balancing class distributions on the performance of classification algorithms in smart city network systems. Contrary to the assumption that data balancing improves classification performance, the analysis reveals a more complex impact. Using three publicly available network traffic benchmark datasets and four different balancing techniques, the study evaluates the performance of five classifiers on 65 classification tasks. The findings indicate that, for smaller datasets, classifiers that achieved the highest accuracy on unbalanced data did not benefit from synthetic data generation for minority classes. Although neural network-based classifiers showed improved performance with balanced data, these improvements came at the cost of lower overall classification scores. For larger datasets, balancing through random oversampling of minority classes and undersampling of majority classes helped improve classification. However, these improvements were limited to precision, with no significant gains in recall. The study offers valuable insights into using synthetic data for intrusion detection, emphasizing the challenges of intricate dependencies in network traffic data for generative models. The results align with previous research showing mixed effects of data balancing on classifier performance, contributing to a broader understanding of the limited efficacy of synthetic data in real-world network contexts. This experimental study highlights the need for a systematic benchmarking framework for synthetic data research, ensuring consistency in data balancing and classification processes. This work contributes to the ongoing discourse on the intersection of machine learning and cybersecurity, emphasizing the critical role of data in developing resilient intrusion detection systems.

Fast and reliable identification of cyber attacks in network systems of smart cities is currently a critical and demanding task. Machine learning algorithms have been used for intrusion detection, but the existing data sets intended for their training are often imbalanced, which can reduce the effectiveness of the proposed model. Oversampling and undersampling techniques can solve the problem but have limitations, such as the risk of overfitting and information loss. Furthermore, network data logs are noisy and inconsistent, making it challenging to capture essential patterns in the data accurately. To address these issues, this study proposes using Generative Adversarial Networks to generate synthetic network traffic data. The results offer new insight into developing more effective intrusion detection systems, especially in the context of smart cities' network infrastructure.

Modeling and simulation have been used to study tsunamis for several decades. We created a review to identify the software and methods used in the last decade of tsunami research. The systematic review was based on the PRISMA methodology. We analyzed 105 articles and identified 27 unique software and 45 unique methods. The reviewed articles can be divided into the following basic categories: exploring historical tsunamis based on tsunami deposits, modeling tsunamis in 3D space, identifying tsunami impacts, exploring relevant variables for tsunamis, creating tsunami impact maps, and comparing simulation results with real data. Based on the outcomes of this review, this study suggests and exemplifies the possibilities of system dynamics as a unifying methodology that can integrate modeling and simulation of most identified phenomena. Hence, it contributes to the development of tsunami modeling as a scientific discipline that can offer new ideas and highlight limitations or a building block for further research in the field of natural disasters.

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.

In areas often decimated by natural disasters, indigenous peoples have developed specific knowledge through generations of stories based on their past experiences. This indigenous knowledge has enabled them to reduce the risk associated with natural phenomena, including tsunamis, disasters with highly destructive potential that occurrence is complicated to predict. At the beginning of the 21st century alone, tsunamis claimed hundreds of thousands of lives and caused enormous material damage, but many indigenous communities survived them with minimal human casualties. This study aims to identify, describe and analyze the indigenous knowledge that indigenous peoples use to reduce the risks resulting from the possible effects of tsunamis and earthquakes and reduce their negative impacts. This area is mapped in detail based on the available relevant literature and its analysis. Here, individual findings are linked to specific localities threatened by tsunamis or earthquakes, which are primarily situated in the Ring of Fire. The search results revealed various forms of indigenous knowledge ranging from intangible cultural values and stories to tangible stones or musical instruments. There is an intention to generalize the view of traditional knowledge, especially by analyzing the similarities and dependencies found between the identified knowledge. Some examples of failure to use indigenous knowledge, or ignorance of it on the part of municipalities, are also presented. Acquired results demonstrate that procedures and methods based on indigenous knowledge serve as effective tools in risk management when dealing with the danger of tsunamis or earthquakes and reducing their negative impacts.

Like many research areas, tsunami research has plenty of related topics that lack unified terminology. Some particular sub-topics may not have enough attention or do not share the same terminology as different views on the phenomenon. This issue can be tackled by an ontology that puts knowledge from different related topics into a formal structure that connects concepts with relationships. This paper proposes the development process of Tsunami-Related Ontology (TRO) that would aid the research in this field. The proposed semi-automatic ontology development methodology applies to any research field and does not require specific algorithms or programming skills to achieve its goal. This paper particularly focuses on three research gaps related to tsunami that are expected to benefit significantly from an ontology: meteorological tsunami, community resilience, and physical vulnerability. For these topics, the created ontology provides a formal taxonomy that links individual concepts to equivalent or related concepts, providing an easy-to-understand overview of the area.

Tsunami disasters can have a significant impact on human well-being, lives, and infrastructure. Computer simulations and models have the potential to bring a significant contribution to the study of such disasters and improve evacuation procedures. They can also be used to help design appropriate countermeasures to mitigate risks to lives or critical infrastructure. After a systematic study of papers focused on agent-based tsunami evacuation models were identified certain similarities and patterns in these models` structure and elements they had been constructed upon. This paper describes the meta-model capturing these similarities in agent-based models dealing with tsunami evacuation topics and can be used as the referential framework for future research in this area.

Fuzzy algebra is a special type of algebraic structure in which classical addition and multiplication are replaced by maximum and minimum (denoted circle plus and circle times, respectively). The eigenproblem is the search for a vector x (an eigenvector) and a constant lambda (an eigenvalue) such that A circle times x = lambda circle times x, where A is a given matrix. This paper investigates a generalization of the eigenproblem in fuzzy algebra. We solve the equation A circle times x = lambda circle times B circle times x with given matrices A;B and unknown constant lambda and vector x. Generalized eigenvectors have interesting and useful properties in the various computational tasks with inexact (interval) matrix and vector inputs. This paper studies the properties of generalized interval eigenvectors of interval matrices. Three types of generalized interval eigenvectors: strongly tolerable generalized eigenvectors, tolerable generalized eigenvectors and weakly tolerable generalized eigenvectors are proposed and polynomial procedures for testing the obtained equivalent conditions are presented.

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.

Diagnostics-related errors made by medical students represent a considerable issue for the field, as they very often have negative consequences that reduce learning effectiveness. This study proposes a means of ameliorating problems with diagnostic reasoning and improving the learning process. The description of a virtual case, containing necessary information, is used within a knowledge-based system for evaluation of medical students' diagnostic abilities. This system is grounded in a branch-oriented model, where every decision reveals part of the information about the patient in each iteration, depending on the diagnostician`s choice, which supports evaluation of the flow of students' thoughts. By fostering the acquisition of "negative knowledge" about typical cognitive errors in the medical reasoning process, the system helps eliminate learners` errors specific to any type of situation that the system might be set up to address in any given case. Descriptions of teachers' inputs, the process model, the evaluation procedure for student's performance, and a method for configuring virtual patient cases are provided. Apart from the accuracy of the diagnosis, other aspects such as patients' comfort and the cost of the diagnosis are also taken into consideration when evaluating students' diagnostic process.

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.

The optimization problems, such as scheduling or project management, in which the objective function depends on the operations maximum and plus, can be naturally formulated and solved in max-plus algebra. A system of discrete events, e.g., activations of processors in parallel computing, or activations of some other cooperating machines, is described by a systems of max-plus linear equations. In particular, if the system is in a steady state, such as a synchronized computer network in data processing, then the state vector is an eigenvector of the system. In reality, the entries of matrices and vectors are considered as intervals. The properties and recognition algorithms for several types of interval eigenvectors are studied in this paper. For a given interval matrix and interval vector, a set of generators is defined. Then, the strong and the strongly universal eigenvectors are studied and described as max-plus linear combinations of generators. Moreover, a polynomial recognition algorithm is suggested and its correctness is proved. Similar results are presented for the weak eigenvectors. The results are illustrated by numerical examples. The results have a general character and can be applied in every max-plus algebra and every instance of the interval eigenproblem.

In max-min fuzzy algebra, the study of eigenvectors is important because it can help us to recognize the steady states of systems working in discrete steps. This paper investigates the properties of steady states described by max-min matrices and vectors with interval coefficients. The characteristics of the eigenspace structure and polynomial-time algorithms for recognition of tolerable and weakly-tolerable interval eigenvectors in max-min algebra are described. This research is a continuation of an earlier investigation concerning strongly-tolerable interval eigenvectors.

The more criteria a human decision involves, the more inconsistent the decision. This study experimentally examines the effect on the degree of pairwise comparison inconsistency by using the (im)possibility of selecting the criteria for the evaluation and the size of the decision-making problem. A total of 358 participants completed objective and subjective tasks. While the former was associated with one possible correct solution, there was no single correct solution for the latter. The design of the experiment enabled the acquisition of eight groups in which the degree of inconsistency was quantified using three inconsistency indices (the Consistency Index, the Consistency Ratio and the Euclidean distance) and these were analysed by the repeated measures ANOVA. The results show a significant dependence of the degree of inconsistency on the method of determining the criteria for pairwise evaluation. If participants are randomly given the criteria, then with more criteria, the overall inconsistency of the comparison decreases. If the participants can themselves choose the criteria for the comparison, then with more criteria, the overall inconsistency of the comparison increases. This statistical dependence exists only for males. For females, the dependence is the opposite, but it is not statistically significant.

Systems working in discrete time (discrete event systems, in short: DES)—based on binary operations: the maximum and the minimum—are studied in so-called max–min (fuzzy) algebra. The steady states of a DES correspond to eigenvectors of its transition matrix. In reality, the matrix (vector) entries are usually not exact numbers and they can instead be considered as values in some intervals. The aim of this paper is to investigate the eigenvectors for max–min matrices (vectors) with interval coefficients. This topic is closely related to the research of fuzzy DES in which the entries of state vectors and transition matrices are kept between 0 and 1, in order to describe uncertain and vague values. Such approach has many various applications, especially for decision-making support in biomedical research. On the other side, the interval data obtained as a result of impreciseness, or data errors, play important role in practise, and allow to model similar concepts. The interval approach in this paper is applied in combination with forall–exists quantification of the values. It is assumed that the set of indices is divided into two disjoint subsets: the E-indices correspond to those components of a DES, in which the existence of one entry in the assigned interval is only required, while the A-indices correspond to the universal quantifier, where all entries in the corresponding interval must be considered. In this paper, the properties of EA/AE-interval eigenvectors have been studied and characterized by equivalent conditions. Furthermore, numerical recognition algorithms working in polynomial time have been described. Finally, the results are illustrated by numerical examples.

The field of medical education is characterized by existing tensions associated with possible errors and their negative consequences which reduce learning effectiveness. Diagnostics-related mistakes and errors represent a specific issue. Unfortunately, it is not an easy task to find an ideal solution which would deal with improving the diagnostic process. Based on the script theory, this study outlines a proposal how to ameliorate diagnostic reasoning. As possibility to work with good teaching cases has been traditionally limited by time and chance, virtual patients have been used as a tool for enhancement of student learning experience. That is why this paper presents a proposal of a knowledge-based system for evaluation of diagnostic abilities of medical students. It is grounded in branch-oriented model that support evaluation of students' flow of ideas during the diagnostic process. Description of primary inputs, process model, evaluation procedure and a method for configuring a virtual patient case is provided. The system enables students to focus on the interpretation of the case and usage of relevant knowledge while information and given decision making choices like in real diagnostic cases are presented. By fostering the acquisition of "negative knowledge" about typical cognitive errors in the medical reasoning process, the system supports learners in avoiding future erroneous decisions and actions in similar situations.

Human decision making involving many alternatives is encumbered with inconsistent prioritization. Although inconsistency is assumed to grow with the number of comparisons, it is shown to be reduced by conscious awareness under certain conditions. This study experimentally investigated the effect of repeating a criteria ranking task on inconsistency scores as measured by four different inconsistency coefficients. A total of 107 participants were engaged in a selection task that comprised of ranking from 3 to 10 criteria and was repeated in three trials. Upon completing the first trial, the participants were informed about the inconsistency issues and could improve their ranking in another two trials. The inconsistency score was computed for each set of comparisons and the effect of repeating the selection task on inconsistency concerning the number of criteria was analyzed using the repeated measures ANOVA. The results reveal a significant change in the inconsistency as the task was repeated but the difference depended on the number of criteria. There exists a borderline in the problem size under which the rankings are associated with significantly lower inconsistency, while the rankings with the larger number of criteria were found to have significantly higher inconsistency.