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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Integrated Access and Backhaul (IAB) in cellular networks combines access and backhaul within a wireless infrastructure reducing reliance on fibre-based backhaul. This enables flexible and more cost-effective network expansion, especially in hard-to-reach areas. Positioning a mobile IAB node (MIAB) in a seaport environment, in order to ensure on-demand, resilient wireless connectivity, presents unique challenges due to the high density of User Equipments (UEs) and potential shadowing effects caused by obstacles. This paper addresses the problem of positioning MIABs within areas containing UEs, fixed IAB donors (FIABs), and obstacles. Our approach considers user associations and different types of scheduling, ensuring MIABs and FIABs meet the capacity requirements of a special team of served UEs, while not exceeding backhaul capacity. With a Genetic Algorithm solver, we achieve capacity improvement gains, by up to 200% for the 90th percentile, particularly during emergency capacity demands.

Abstract
The paper presents an application of the new control paradigm, which is based on process moments, to a model of a DC motor. The basis of the new control paradigm is that it eliminates the process transfer function within the closed loop, as it estimates the final steady-state value of the process output and compares it with the reference signal. As a result, the closed loop response is much more stable and generally without overshoots. This property makes it suitable for application to motor-driven processes where overshoots is undesirable. It was shown that the control method provides very stable closed-loop responses even when the actual motor and the model parameters differ. It was also shown that the proposed method can be applied to constrained systems as the anti-windup protection is implicitly embedded in the control solution. © 2025 IEEE.