Abstract
The transition to the water circular economy (WCE) requires several stakeholders' awareness, articulation, and action involving complex governance concerns. As a participatory approach to identifying problems, designing solutions, and implementing strategic actions, the co-creation process should support stakeholder involvement to adjust existing institutional arrangements to foster the WCE. This article designs and applies a co-creation process to analyse the perception of key stakeholders about institutional challenges for water reuse and explore their contributions to innovate policy, planning, and governance for the implementation of new water reuse technology in Almendralejo (Spain), Lecce (Italy), Omis (Croatia), and Eilat (Israel). The findings indicate that implementing a new water loop encounters complex institutional and production-related obstacles, which different stakeholders address in varying ways. Moreover, the proposed solutions to the on-site issues identified emphasise the need for actions that foster engagement and collaboration, particularly to enhance awareness, training, and regulation. Addressing these challenges associated with adopting new water loops, even when technical, may depend on non-technical solutions regarding the institutional framework. The co-creation processes highlight the importance of focusing on institutional arrangements and stakeholder awareness while implementing new water loops to ensure and promote symbiotic territories that consider the policy, producers', and users' strategies.

Abstract
Neonatal seizures represent a critical medical issue that requires prompt diagnosis and treatment. Typically, at-risk newborns undergo a Magnetic Resonance Imaging (MRI) brain assessment followed by continuous seizure monitoring using multichannel EEG. Visual analysis of multichannel electroencephalogram (EEG) recordings remains the standard modality for seizure detection; however, it is limited by fatigue and delayed seizure identification. Advances in machine and deep learning have led to the development of powerful neonatal seizure detection algorithms that may help address these limitations. Nevertheless, their performance remains relatively low and often disregards the non-stationary attributes of EEG signals, especially when learned from weakly labeled EEG data. In this context, the present paper proposes a novel deep-learning approach for neonatal seizure detection. The method employs rigorous preprocessing to reduce noise and artifacts, along with a recently developed time-frequency distribution (TFD) derived from a separable compact support kernel to capture the fast spectral changes associated with neonatal seizures. The high-resolution TFD diagrams are then converted into RGB images and used as inputs to a pre-trained ResNet-18 model. This is followed by the training of an attention-based multiple-instance learning (MIL) mechanism. The purpose is to perform a spatial time-frequency analysis that can highlight which channels exhibit seizure activity, thereby reducing the time required for secondary evaluation by a doctor. Additionally, per-instance learning (PIL) is performed to further validate the robustness of our TFD and methodology. Tested on the Helsinki public dataset, the PIL model achieved an area under the curve (AUC) of 96.8%, while the MIL model attained an average AUC of 94.1%, surpassing similar attention-based methods.

Abstract
Road safety is a global issue, with road-related accidents being one of the biggest leading causes of death. Motorcyclists are especially susceptible to injuries and death when there is an accident, due to the inherent characteristics of motorcycles. Accident prevention is paramount. To improve motorcycle safety, this paper discusses and proposes a preliminary architecture of a system composed of various sensors, to assist and warn the rider of potentially dangerous situations such as front and back collision warnings, pedestrian collision warnings, and road monitoring.

Abstract
Engagement in the ongoing energy transition is particularly challenging for energy-poor citizens. As such, there is a pressing need for a better understanding of their experiences and for strategies that enable their engagement. In this study, we identify different groups of citizens based on their energy poverty issues and examine their engagement behaviors (seeking information, proactive managing, sharing feedback, helping others, and advocating). Using cluster analysis and multiple correspondence analysis, we analyzed a sample of 915 citizens from eight European cities participating in a Horizon2020 EU project (Alkmaar-NL, Bari-IT, Celje-SI, Evora-PT, Granada-ES, Hvidovre-DK, Ioannina-GR, & Uacute;jpest-HU). Several groups of citizens reported either multiple energy issues, a single issue (energy bills, insulation, cooling, heating), or no issues, and the statistical tests showed significant differences across these groups in terms of engagement in seeking information, helping, and advocating. Moreover, we identified that certain groups tend to have specific levels of engagement (high, medium, low) and that sharing feedback generally has a low level of engagement. Overall, this study provides empirical insights into how energy-poor citizens exercise agency through engagement behaviors and offers actionable insights for designing measures to mitigate energy poverty in complementarity with technical and economical solutions.

Abstract
Pedestrian safety is a pressing subject in urban areas. The disorderly sharing of streets and roads between pedestrians and vehicles leads to potentially serious accidents for pedestrians. This student project aims to tackle the issue by placing an interactive gaming device at traffic lights. SMASHY by Stempe Safety offers pedestrians an amusing and active way to discourage jaywalking. The multipurpose solution features a smashing game with buttons on one side and a screen displaying useful information on the other side. While the traffic light remains red for pedestrians, the module buttons light up and the players can start smashing the buttons as fast as possible, until the light turns green and consequently, the game ends. Ultimately, the modules are connected to an app where, if desired by the player, scores can be tracked and difficulty can vary based on user performance. Multiple modules can be placed around the city and the app will track player scores by location. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Abstract
CoffeeMush is an innovative and sustainable project developed as part of the European Project Semester (EPS) at ISEP in 2024. This student project aims to tackle waste management environmental problems by turning coffee waste into mushrooms, a valuable food source. CoffeeMush consists of a smart device providing optimal conditions for mushroom cultivation, complemented by a user-friendly Android application for remote monitoring and control. The design was guided by ethical, sustainability, market and technical considerations. The paper describes the theoretical background of the project, the technical design, and the prototype development and testing. The results show the feasibility of CoffeeMush as a practical and environmentally friendly solution for urban mushroom cultivation, and its impact on sustainable food production and waste reduction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.