AI-native decision systems — in which generative artificial intelligence generates, executes, and adapts decisions in real time — are shifting the centre of gravity of analytical effort. Whereas the principal challenge used to be the extraction of insight, it is now becoming the validation of AI outputs while action is still being taken upon them. Existing approaches address this problem only partially: explainable artificial intelligence and governance frameworks treat validation as an ex post layer; reinforcement learning reduces it to a single scalar reward; control theory and the broader cybernetic tradition formalise the closed loop but neglect its sociotechnical constraints; while the PRIME–INSPECT framework establishes a governance foundation, yet leaves real-time validation implicit. This paper proposes a validation-centric architecture that links the GAVA decision loop (Generate–Act–Validate– Adapt), introduced here, with the PRIME–INSPECT framework and elevates validation to a central analytical function comprising four subdimensions: statistical, operational, cognitive, and governance-related. The proposed architecture is empirically examined on a dual sample of IT professionals and top-management representatives, using descriptive statistics, multiple regression, mediation, moderation, and structural equation modelling. The results confirm the central role of trust, the negative effect of perceived risk, and the importance of top management support, while robustness checks separated by the IT and TMT subsamples further strengthen the structural model. Taken as a whole, the findings support the view that validation should be treated as a first-order organisational stage in AI-native decision systems.

Intelligent automation is a core component of Industry 4.0, enabling artificial intelligence (AI) systems to support or execute operational and managerial decisions in real time. In high-risk industrial environments such as mining and metallurgy, real-time decision-making improves efficiency but also raises critical challenges related to trust, explainability, human oversight, and institutional accountability. This study proposes PRIME–INSPECT, a two-layer socio-technical framework designed to support trustworthy AI-driven real-time decision-making. The PRIME (predict, regulate, interpret, mitigate, execute) layer formalizes the operational decision flow, embedding control mechanisms, uncertainty quantification, and explainability into the automation pipeline. The INSPECT (integrity, navigability, supervisory control, policy maturity, ethical compliance, collaboration, trust calibration) layer defines the organizational and governance conditions required for safe deployment. The framework is conceptually developed through a structured literature synthesis and supported by exploratory empirical grounding through stakeholder perceptions from IT and top management participants, alongside an illustrative industrial use case intended to demonstrate conceptual applicability rather than engineering performance validation. The findings highlight the importance of aligning operational AI processes with institutional safeguards to support calibrated trust and responsible automation. The empirical component is intended to provide conceptual and organizational grounding of framework dimensions rather than quantitative validation of predictive performance. PRIME–INSPECT provides a structured architecture for designing and governing AI-enabled real-time decision systems in high-risk industrial contexts.

The continuous growth of the world’s population and extending life expectancy, as well as frequent natural disasters and emergencies, increase the demand for health services every day. One of the crucial elements for addressing this problem is triage – a critical process that enables healthcare providers to efficiently identify patients’ needs in terms of medical treatments and resources. However, the complexity of implementing an appropriate triage process has led to the development of various systems, each offering a unique approach to address this challenge.This paper presents a comparative analysis of different patient triage systems, focusing on their key characteristics, identifying their common and unique elements, and providing an understanding of their advantages and limitations. Relying on the findings of the conducted analysis, the paper proposes a generic model of the triage process, designed around universal components that provide a foundation for standardizing the process while maintaining the flexibility to adapt to specific requirements. The proposed generic model further can be employed as a basis for exploring opportunities to enhance the patient triage process through the application of model-driven engineering concepts and techniques.

In modern medical circumstances, effective assessment of patients' conditions is recognized as crucial for quick decision-making, especially in critical situations. In these circumstances, the application of automated triage systems and their role in improving health care are considered key elements, with special focus being placed on the integration of technologies that enable a more accurate and faster assessment of the patient's condition. Based on the above, the paper analyzed various traditional methods of patient triage, as well as the potential for e-triage. Special importance is attached to reducing subjectivity in decision-making and improving the efficiency of emergency services. Challenges and advantages of implementing automated triage systems in real conditions were also discussed, with the aim of achieving optimal results. Considering the factors, a one of possible framework was proposed for the future development of advanced triage systems, which contribute to the improvement of the quality of health care provision.

The fluent API, also known as Internal DSL, is one of the concepts introduced primarily for the purpose of increasing readability and maintainability in the process of software development. It is most commonly used when there is a need to perform operations according to precisely defined rules that determine their possible orders. However, implementing a Fluent API by manual coding can divert focus from defining these rules toward technical implementation, increasing the risk of errors, unexpected behavior, and higher development costs. To address these challenges, a model-driven engineering (MDE) approach can be applied, enabling the visual design of the Fluent API model and its transformation into a code skeleton.This paper aims to present how the previously created graphical development tool, implemented as a Microsoft Visual Studio extension for modeling Fluent API, can be enhanced with model-to-text transformation in order to provide code generation of a fluent API structure. That objective is achieved by using the template-based code generation technique, implemented by enabling the execution of the appropriate T4 text templates. The proposed approach is validated by demonstrating a real-life fluent API example's code generation from its model, resulting in C# source files that contain classes, interfaces, and their corresponding methods.

The appearance of asymmetric loading in the low voltage power distribution network has a negative effect on the voltage profile and power quality. In order to successfully analyze the conditions of the low voltage power supply, this paper presents simulated and analyzed voltage disturbances along the distribution network radial lines for the occurrence of different three-phase power system loading. In the simulation, the influence of asymmetric loading, section length and character of loads on the measured values is presented. The effects of distributed or concentrated loads at individual points of the power lines in terms of the voltage conditions improving were specifically considered.

The fluent API is most often used when there is a need to implement a certain flow of execution of operations, which is achieved by the method chaining technique. Conventional methods of implementing fluent API such as manual coding require a lot of development time and increase the chances of errors. The lack of visual representation of the relationships between interfaces and methods makes it difficult to understand them. This paper presents the design and implementation of the model-based graphical development tool, which is built using Domain-Specific Language that relies on fluent API grammar rules. The proposed tool is implemented as a Microsoft Visual Studio extension using DSL Tools, and it can be used for graphical modeling of the fluent API structure. Such a graphical development tool is the first step towards the automated process of generating the code skeleton of the fluent API structure out of its domain model.

Nowadays, Internet communication and global data exchange rely on markup languages, which represent one of the most commonly used modern digital technologies. Due to the lack of a global standard, the conversion of content from one markup language to another is usually a complicated task. One of the most common solutions found in related research is based on the use of code-centric software engineering, which requires a significant investment of time and shifts the focus from the problem domain to the technical implementation. These draw-backs can be overcome by proposing a Model-Driven Engineering (MDE) approach, which observes a problem on a higher level of abstraction. Based on the previously created meta-models of markup languages, this research aims to show that it is possible to convert the content between different markup language models using the ATL Model-to-Model (M2M) transformation language. To verify this approach, a real-life example of the conversion of content from the Markdown language model into an HTML model using ATL transformation is provided. The developed transformation represents one of the necessary prerequisites for a comprehensive solution to the content conversion from one markup language to another.

When students enroll at universities, various datasets can be available to managers and teachers. Clustering techniques can be applied in order to divide the instances within those datasets into natural groups. In this paper, one clustering-based approach combined with attribute selection methods for identifying specific input dataset variables meaningful for the disjunction of distinct students' profiles has been proposed. Also, an analysis of the descriptive students' model obtained by the proposed methodology is performed.

New forms of communication are created as a result of the advancement of information and communication technologies, particularly the Internet and the WWW. These technologies introduce technological solutions as a response to ongoing difficulties. Relying on the predominant written form, besides its semantics, content on WWW must convey additional information like its structure, formatting, and relationships between its parts. Markup languages were born. Although markup languages addressed the issue at hand, they also raised several new issues, one of which is how to exchange material between disparate markup languages. In this paper, creating a meta-model of the two most commonly used markup languages, Markdown and HTML, is shown. The meta-model is created using the Eclipse Modeling Framework (EMF) ECore model, which relies on grammar obtained from markup languages. This meta-model simplifies comprehension of the relationship between markup language content and its model, which represents the first step towards the automatic transformation between models, i.e., the interchange of their content.