Abstract

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
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.