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.

Abstract
Hierarchical document classification is essential for structuring large-scale textual corpora in domains such as digital libraries and academic repositories. While recent advances in large language models (LLMs) have opened new possibilities for text classification, their applicability to hierarchical settings under real-world constraints remains underexplored. This study investigates both generative and discriminative transformer-based models, evaluating their effectiveness across multiple inference strategies: zero-shot baseline, local fine-tuning, and a global approach using category-specific models. Experiments on two real-world hierarchical datasets provide a comprehensive comparison of classification accuracy, F1-macro scores, and inference times. The results highlight that, although generative LLMs can deliver competitive (yet variable) performance at higher levels of the hierarchy, their high inference costs hinder their use in time-sensitive applications. In contrast, fine-tuned discriminative models-particularly BERT-based architectures-consistently offer a more favorable trade-off between performance and efficiency.

Abstract
Topological photonics, leveraging concepts from condensed matter physics, offers transformative potential in the design of robust optical systems. This study investigates the integration of topologically protected edge states into plasmonic nanostructures for enhanced optical sensing. We propose a toy model comprising two chains of metallic filaments forming a one-dimensional plasmonic crystal with diatomic-like unit cells, positioned on a waveguide. The system exhibits edge states localized at the boundaries and a central defect, supported by the Su-Schrieffer-Heeger (SSH) model. These edge states, characterized by significant electric field enhancement and topological robustness, are shown to overcome key limitations in traditional plasmonic sensors, including sensitivity to noise and fabrication inconsistencies. Through coupled mode theory, we demonstrate the potential for strong coupling between plasmonic and guided optical modes, offering pathways for improved interferometric sensing schemes. This work highlights the applicability of topological photonics in advancing optical sensors.