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
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. © 2025 Elsevier B.V., All rights reserved.

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.

Abstract
In recent years, the development of Decision Support Systems (DSS) has played an instrumental role in the advancement of offshore renewable energy projects, particularly within the blue energy sector. Notwithstanding the technological advancements that have been made, the acceleration of such projects continues to be impeded by significant obstacles related to stakeholder engagement, feasibility assessment, and policy compliance. The objective of this study is to propose a design for a DSS for accelerating the construction of blue offshore energy platforms. This is to address the aforementioned challenges by integrating insights from stakeholder feedback and innovation trends. A participatory action study was conducted through a workshop with a diverse group of experts (n=20), including policymakers, practitioners, researchers, and public entities involved in offshore energy projects. The evaluation facilitated the determination of the DSS's efficacy in addressing user requirements and the identification of areas for enhancement. This study proposes a model for integrating stakeholder insights into technological solutions for offshore energy installations, thus offers significant contributions to the domain of sustainable blue energy development.