Abstract
The accelerating digitalization of the public and private sectors has made information technologies (IT) indispensable in modern life. As services shift to digital platforms and technologies expand across industries, the complexity of legal, regulatory, and technical requirement documentation is growing rapidly. This increase presents significant challenges in managing, gathering, and analyzing documents, as their dispersion across various repositories and formats hinders accessibility and efficient processing. This paper presents the development of an automated repository designed to streamline the collection, classification, and analysis of cybersecurity-related documents. By harnessing the capabilities of natural language processing (NLP) models-specifically Generative Pre-Trained Transformer (GPT) technologies-the system automates text ingestion, extraction, and summarization, providing users with visual tools and organized insights into large volumes of data. The repository facilitates the efficient management of evolving cybersecurity documentation, addressing issues of accessibility, complexity, and time constraints. This paper explores the potential applications of NLP in cybersecurity documentation management and highlights the advantages of integrating automated repositories equipped with visualization and search tools. By focusing on legal documents and technical guidelines from Portugal and the European Union (EU), this applied research seeks to enhance cybersecurity governance, streamline document retrieval, and deliver actionable insights to professionals. Ultimately, the goal is to develop a scalable, adaptable platform capable of extending beyond cybersecurity to serve other industries that rely on the effective management of complex documentation.

Abstract
In the current era of digital transformation, adopting circular business models that blend circularity principles with advanced digital technologies, is fundamental for sustainable industrial practices. This paper suggests a semantic model for a Digital Twin based on an Asset Administration Shell. It also explores the Digital Product Passport topic since this will be the final goal for the Digital Twin. The Digital Product Passport serves as a complete digital record of the product life cycle to improve traceability and circularity. The Asset Administration Shell provides a standardized digital representation of assets, facilitating interoperability and fluid data exchange. By taking advantage of a Digital Twin, industries can optimize performance and predict product needs. Moreover, it enriches the Digital Product Passport with updated and accurate data, facilitating traceability and efficient product management. The application of semantic models ensures a consistent interpretation of data across all platforms, increasing the reliability of digital interactions and interoperability. This article explains the potential of these technologies to promote a circular economy, focusing in the particular case of the Digital Product Passport. © 2025 The Author(s).

Abstract
Forest fires represent a significant and growing threat to natural ecosystems and human settlements, with their unpredictable behavior and capacity for rapid expansion over time, creating substantial challenges for effective prevention, control, and mitigation. This paper presents the development of a forest fire simulator designed to model and predict fire spread under varying environmental conditions. Such a simulator must consider how fire spreads in different locations and climate conditions, showing the final shape of the fire in a given period of time. Using the NetLogo agent-based modeling platform, a simulated forest environment was created in which trees function as autonomous agents interacting with one another and the environment. Identifying and understanding the risk factors that increase the likelihood of a fire occurring, as well as those that contribute to its spread and intensity, is essential for the development of an accurate forest fire simulator. Such a simulator can integrate the complex interactions among these variables to produce dynamic visualizations of fire progression, allowing users to evaluate different scenarios and make informed decisions for preventing, controlling and fighting forest fires. By incorporating key factors—such as vegetation density, temperature, humidity, topography, and wind direction—the system calculates the probability of fire propagation and generates visual representations of fire behavior over time. This tool allows users to forecast fire behavior and assess response strategies proactively, thereby improving the accuracy and efficiency of firefighting efforts. In addition, the simulator yields significant social benefits, especially for older adults residing in fire-prone areas, by supporting early warning systems, enabling prompt evacuations, and mitigating their susceptibility to fire-related risks through enhanced preparedness and coordinated response measures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.

Abstract
Mutation testing is an effective testing technique for improving how well a test suite can detect small changes to a program under test. This testing technique is seeing increased industry adoption. This paper aims to study the use of mutation testing in an educational setting and understand students' technical and conceptual challenges in applying mutation testing concepts. We report on two case studies of incorporating mutation testing into software engineering curricula. The Scaffolding Study explores the impact of using different mutation analysis tools directly or indirectly via a uniform interface provided by an educational infrastructure. We observe that scaffolding (indirect tool use) improved the consistency of student performance for those using the same mutation analysis tool on the same code as well as helping students perform more effective mutation testing. The Visualization Study explores the impact of different forms of output of a mutation analysis tool. Specifically, it assesses to what extent visualizations support students in reasoning about mutants and writing tests to detect them. We observe that like scaffolding, visualizations helped students perform more effective mutation testing, with lower-performing students seeing a boost in particular. We further explore challenges around automatic assessment of mutation testing exercises. For example, we observe that even with assignment scaffolding, 18-21% of student submissions required manual modifications to successfully execute.

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