Abstract
Water stress is a significant issue in many countries, including Portugal, which has seen a 20% reduction in water availability over the last 20 years, with a further 10-25% reduction expected by the end of the century. To address potable water consumption, this study aims to identify the optimal rainwater harvesting (RWH) system for a commercial building under various non-potable water use scenarios. This research involved qualitative and quantitative methods, utilizing the Rippl method for storage reservoir sizing and ETA 0701 version 11 guidelines. Various scenarios of non-potable water use were considered, including their budgets and economic feasibility. The best scenario was determined through cash flow analysis, considering the initial investment (RWH construction), income (water bill savings), and expenses (energy costs from hydraulic pumps), and evaluating the net present value (NPV), payback period (PB), and internal rate of return (IRR). The energy savings obtained were calculated by sizing a hybrid system with an RWH system and a photovoltaic (PV) system to supply the energy needs of each of the proposed scenarios and the water pump, making the system independent of the electricity grid. The results show that the best scenario resulted in energy savings of 92.11% for a 7-month period of regularization. These results also demonstrate the possibility for reducing potable water consumption in non-essential situations supported by renewable energy systems, thus helping to mitigate water stress while simultaneously reducing dependence on the grid.

Abstract
Mecanum wheeled mobile robots have become relevant due to their excellent maneuverability, enabling omnidirectional motion in constrained environments as a requirement in industrial automation, logistics, and service robotics. This paper addresses a low-level controller based on the H-Infinity (H-infinity) control method for a four-wheel Mecanum mobile robot. The proposed controller ensures stability and performance despite model uncertainties and external disturbances. The dynamic model of the robot was developed and introduced in MATLAB to generate the controller. Further, the controller's performance is validated and compared to a traditional PID controller using the SimTwo simulator, a realistic physics-based simulator with dynamics of rigid bodies incorporating non-linearities such as motor dynamics and friction effects. The preliminary simulation results show that the H-infinity reached a time-independent Euclidean error of 0.0091 m, compared to 0.0154 m error for the PID in trajectory tracking. Demonstrating that the H-infinity controller handles nonlinear dynamics and disturbances, ensuring precise trajectory tracking and improved system performance. This research validates the proposed approach for advanced control of Mecanum wheeled robots.

Abstract
Concurrent systems are often complex and difficult to design. Choreographic languages, such as Multiparty Session Types (MPST), allow the description of global protocols of interactions by capturing valid patterns of interactions between participants. Many variations of MPST exist, each one with its rather specific features and idiosyncrasies. Here we propose a tool-CoMPSeT-that provides clearer insights over different features in existing MPST. We select a representative set of MPST examples and provide mechanisms to combine different features and to animate and compare the semantics of concrete examples. CoMPSeT is open-source, compiled into JavaScript, and can be directly executed from any browser, becoming useful both for researchers who want to better understand the landscape of MPST and for teachers who want to explain global choreographies.

Abstract
ABSTRACTContextGenerative artificial intelligence (GenAI) tools have become increasingly prevalent in software development, offering assistance to various managerial and technical project activities. Notable examples of these tools include OpenAI's ChatGPT, GitHub Copilot, and Amazon CodeWhisperer.ObjectiveAlthough many recent publications have explored and evaluated the application of GenAI, a comprehensive understanding of the current development, applications, limitations, and open challenges remains unclear to many. Particularly, we do not have an overall picture of the current state of GenAI technology in practical software engineering usage scenarios.MethodWe conducted a literature review and focus groups for a duration of five months to develop a research agenda on GenAI for software engineering.ResultsWe identified 78 open research questions (RQs) in 11 areas of software engineering. Our results show that it is possible to explore the adoption of GenAI in partial automation and support decision-making in all software development activities. While the current literature is skewed toward software implementation, quality assurance and software maintenance, other areas, such as requirements engineering, software design, and software engineering education, would need further research attention. Common considerations when implementing GenAI include industry-level assessment, dependability and accuracy, data accessibility, transparency, and sustainability aspects associated with the technology.ConclusionsGenAI is bringing significant changes to the field of software engineering. Nevertheless, the state of research on the topic still remains immature. We believe that this research agenda holds significance and practical value for informing both researchers and practitioners about current applications and guiding future research.

Abstract
Higher education institutions face increasing challenges, necessitating advanced technological solutions. This systematic review analyzed 35 articles to map the application of Multi-Agent Systems (MAS), Agent-Based Modeling (ABM), and simulation in higher education. Findings show these AI techniques are widely used for simulating complex systems, curriculum design, academic planning, and personalized learning, with simulation also supporting virtual environments. Other AI and Machine Learning (ML) techniques, such as Learning Analytics and Reinforcement Learning, are also applied to enhance student performance, engagement, and self-regulation. Critical gaps identified include the limited adoption of emerging technologies like Generative AI and Large Language Models (LLMs), reliance on small and homogeneous samples, and a general lack of complex implementations. This review highlights these gaps, offering recommendations to guide researchers, educators, and policymakers in advancing AI integration towards smart higher education strategies.

Abstract
Virtual Reality Laboratories (VRLs) are essential progress towards educational solutions that would allow students to learn the sometimes complex experimental processes without spending physical resources. The resources used in some experiments often require significant procurement effort, significantly impacting the environment where an experiment would be conducted. The main focus of this research is to review the possibility of using VR solutions as educational tools and innovative approaches to scientific knowledge production. This paper systematically reviews relevant publications in this area while covering several existing virtual lab solutions. The analysis shows that VRL tools can be effectively used for educational purposes, allow access to lab resources for people with disabilities, and could be used to reach the desired learning outcomes. A gap exists between natural laboratories and VRLs, especially in the collaborative aspect of the laboratory exercises. However, there is significant ongoing research on this topic and a high potential for development. © 2025 The Authors