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
In this work, we present a tutorial on how to account for the computational time complexity overhead of signal processing in the Spectral Efficiency (SE) analysis of wireless waveforms. Our methodology is particularly relevant in scenarios where achieving higher SE entails a penalty in complexity, a common trade-off present in 6G candidate waveforms. We consider that SE derives from the bit rate, which is impacted by time-dependent overheads. Thus, neglecting the computational complexity overhead in the SE analysis grants an unfair advantage to more computationally complex waveforms, as they require larger computational resources to meet a signal processing runtime below the symbol period. We demonstrate our points with two case studies. In the first, we refer to IEEE 802.11a-compliant baseband processors from the literature to show that their runtime significantly impacts the SE perceived by upper layers. In the second case study, we show that waveforms considered less efficient in terms of SE can outperform their more computationally expensive counterparts, if provided with equivalent high-performance computational resources. Based on these cases, we believe our tutorial can address the comparative SE analysis of waveforms that operate under different computational resource constraints.

Abstract
This study aimed to investigate the effect of organizational culture on employee-perceived performance and whether this relationship is mediated by perceived organizational support and moderated by employee motivation. Three hundred individuals working in organizations located in Portugal and Angola participated in this study. This is a quantitative, exploratory, correlational, and cross-sectional study. The results indicate that only goal culture, rule culture, affective organizational support perception, and identified motivation have a positive and significant effect on perceived performance. Supportive culture and goal culture have a positive and significant effect on affective organizational support perception. All dimensions of organizational culture have a significant effect on cognitive organizational support perception, with the effects of the supportive culture and the goal culture being positive and significant, while the effects of the innovative culture and the rule culture are negative and significant. The perception of affective organizational support has a total mediating effect on the relationship between goal culture and perceived performance. Intrinsic motivation and identified motivation have a moderating effect on the relationship between all dimensions of organizational culture and perceived performance. This study is expected to help human resource managers understand the importance of the type of organizational culture that prevails in their organization to enhance employees' perception of organizational support and performance.