Abstract
The impact of wildfires is increasing worldwide. The root causes of these effects are manifold, encompassing among others climate change and the accumulation of fuels and increasing settlements in wildland-urban interfaces (WUI). Reports and initiatives to better understand and govern these developments have been launched and call for more integrated approaches to wildfire risk management, including the use of targets or Key Performance Indicators (KPIs). However, despite some examples such as Portugal, wildfire risk management targets are still mainly lacking in Europe. This is surprising since they find wider application in the U.S. and are also more widely applied for flooding in Europe. This perspective hence takes a closer look at the use of targets in reducing disaster risk for different hazards worldwide and reflects about the opportunities and challenges for wildfire risk reduction targets for Europe. It concludes with some suggestions for the application of wildfire risk reduction targets for Europe.

Abstract
Rapidly evolving scientific and technological advances are introducing both exciting and disruptive educational tools. However, they also present new challenges in engaging and motivating students, particularly in courses with a strong mathematical foundation like control engineering. The first class of any course offers a prime opportunity to make a lasting impression that encourages active learning. This paper addresses the following question: How can the first feedback control class be transformed into a unique and memorable event that leaves a positive impact on students for the remainder of the course, and perhaps, ambitiously, for their lives? Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
Pose estimation by computer vision is essential in underwater robot navigation. Several works already use computer vision and ArUco markers for this purpose. The method is widely spread and developed. In terms of software, libraries have already been developed, for instance, the ArUco module in the OpenCV library. However, there is still a need to characterize the relationship between the performance of the system and the computer vision hardware itself, as well as the spatial arrangement of the markers. Another aspect to take into account is the environmental condition. This work seeks to relate these factors to the error resulting from the estimation of relative positions between cameras and markers.

Abstract
This study addresses the variability of gamma radiation measurements over the Atlantic Ocean. The analysis of back trajectories shows that the path of the air masses is the main factor determining gamma radiation levels over the ocean, rather than the distance to the coast. Different gamma values were recorded at different times in the same location as a result of the distinct origin of the corresponding air masses. Higher counts observed in the northeast Atlantic in winter compared with the spring values result from air masses coming from Europe and the African continent. In general, gamma radiation values over the ocean increase with increasing continental influence on the air mass above. A predictive classifica-tion model is developed showing that marine gamma observations can be used to classify marine boundary layer air masses according to the degree of continental influence.

Abstract
As electric vehicle (EV) adoption accelerates, residential buildings-particularly multi-dwelling structures-face increasing challenges to electrical infrastructure, notably due to conservative sizing practices of electrical feeders based on maximum simultaneous demand. Current sizing methods assume all EVs charge simultaneously at maximum capacity, resulting in unnecessarily oversized and costly electrical installations. This study proposes an optimized methodology to estimate accurate coincidence factors, leveraging simulations of EV user charging behaviors in multi-dwelling residential environments. Charging scenarios considering different fleet sizes (1 to 70 EVs) were simulated under two distinct premises of charging: minimization of current allocation to achieve the desired battery state-of-charge and maximization of instantaneous power delivery. Results demonstrate significant deviations from conventional assumptions, with estimated coincidence factors decreasing non-linearly as fleet size increases. Specifically, applying the derived coincidence factors can reduce feeder section requirements by up to 86%, substantially lowering material costs. A fuzzy logic inference model is further developed to refine these estimates based on fleet characteristics and optimization preferences, providing a practical tool for infrastructure planners. The results were compared against other studies and real-life data. Finally, the proposed methodology thus contributes to more efficient, cost-effective design strategies for EV charging infrastructures in residential buildings.

Abstract
This paper presents a method for position correction in collaborative robots, applied to a case study in an industrial environment. The case study is aligned with the GreenAuto project and aims to optimize industrial processes through the integration of various hardware elements. The case study focuses on tightening a specific number of nuts onto bolts located on a partition plate, referred to as “Cloison”, which is mounted on commercial vans produced by Stellantis, to secure the plate. The main challenge lies in deviations that may occur in the plate during its assembly process, leading to uncertainties in its fastening to the vehicles. To address this and optimize the process, a collaborative robot was integrated with a 3D vision system and a screwdriving system. By using the 3D vision system, it is possible to determine the bolts’ positions and adjust them within the robot’s frame of reference, enabling the screwdriving system to tighten the nuts accurately. Thus, the proposed method aims to integrate these different systems to tighten the nuts effectively, regardless of the deviations that may arise in the plate during assembly. © 2025 by the authors.