Abstract
Unmanned Aerial Vehicles (UAVs) offer a promising solution for enhancing wireless connectivity and Quality of Service (QoS) in urban environments, acting as aerial Wi-Fi access points or cellular base stations to support vehicular users and Vehicle-to-Everything (V2X) applications. Their flexibility and rapid deployment capabilities make them suitable for addressing infrastructure gaps and traffic surges. However, optimizing UAV positions to maintain Line of Sight (LoS) links with ground User Equipment (UEs) remains challenging in obstacle-dense urban scenarios. Existing approaches rely on probabilistic blockage models or require dedicated infrastructure such as Reconfigurable Intelligent Surfaces. This paper proposes VTOPA, a Vision-Aided Traffic- and Obstacle-Aware Positioning Algorithm that complements these approaches by autonomously extracting environmental information-such as obstacle geometries and UE locations-via computer vision, enabling infrastructure-free deployment. The algorithm employs Particle Swarm Optimization to determine UAV positions that maximize aggregate throughput while prioritizing LoS connectivity and accounting for heterogeneous traffic demands. VTOPA is particularly suited for rapid deployment scenarios such as emergency response and temporary events. Evaluated through simulations in ns-3, VTOPA achieves up to 50% increase in aggregate throughput and 50% reduction in delay, outperforming state of the art benchmarks in obstacle-rich environments.

Abstract
Unmanned Aerial Vehicles (UAVs) are a promising solution for next-generation wireless networks due to their mobility, rapid deployment, and ability to provide Line-of-Sight (LoS) connectivity. However, deploying multiple UAVs in realt-ime to meet dynamic, non-uniform traffic demands remains a significant challenge, especially when aiming to optimize network throughput and resource utilization. In this paper, we propose the Efficient Multi-UAV Traffic-Aware Deployment (EMTAD) algorithm, a scalable algorithm that jointly minimizes UAV count and optimizes 3D positioning based on real-time user distribution and traffic demand. In contrast to prior works that assume static user patterns or fixed UAV counts, EMTAD dynamically adapts UAV deployment to maximize spectral efficiency and satisfy user-specific Quality of Service (QoS) requirements. Simulation results demonstrate that EMTAD reduces the number of UAVs required and achieves superior aggregate throughput compared to baseline approaches.

Abstract
Medical rescuers are the frontline for COVID-19 and their psychological experience and health are major concerns to our society and healthcare system. This study aims to understand how medical rescuers psychologically experienced this pandemic and explore the contributing variables to COVID-19 anxiety. Portuguese medical rescuers (n = 203) answered questions about their COVID-19 experience, the COVID-19 Anxiety Scale, Patient-Health Questionnaire, Perceived Stress Scale, Obsessive-Compulsive Inventory, and Well-Being Questionnaire. Rescuers presented low COVID-19 anxiety and low-moderate levels of fear. Most already faced or were facing changes in their job-related tasks, did not change household and did not feel stigma/discrimination. COVID-19 workplace security measures were considered moderately adequate and low anxiety, depression and obsessive-compulsive symptoms, low to moderate stress and moderate well-being were found. Only COVID-19 fear and security measures, anxiety, depression and obsessive-compulsive symptoms explained COVID-19 anxiety. Overall, findings showed these rescuers were psychologically well adjusted during the pandemic's initial stages. © 2023 Associacao Portuguesa de Psicologia. All rights reserved.

Abstract
Background: Trauma is a public health issue with a significant social and economic impact. However, national data on its characterization and the role of nursing in its management is still scarce. Objective: To assess the effectiveness of prehospital nursing interventions in stabilizing trauma victims provided by nurses of Immediate Life Support Ambulances in Portugal. Methodology: Observational, prospective, and descriptive-correlational study. Data were collected by nurses of the Immediate Life Support Ambulances in mainland Portugal, from 01/03/2019 to 30/04/2020, and the Azores, from 01/10/2019 to 30/04/2020. Trauma severity indices were assessed before and after the nursing interventions. Results: This study included 606 cases (79.4% blunt trauma; 40.8% road accidents) reported by 171 nurses. Nurses performed mostly interventions for hemodynamic support (88.9%) and non-pharma-cological pain control (90.6%) of trauma victims. The nursing interventions improved the Revised Trauma Score and the Shock Index (p<0.001). Conclusion: Prehospital nursing interventions improve trauma victims’ clinical status.

Abstract
Unmanned Aerial Vehicles (UAVs) increasingly enhance the Quality of Service (QoS) in wireless networks due to their flexibility and cost-effectiveness. However, optimizing UAV placement in dynamic, obstacle-prone environments remains a significant research challenge due to their complexity. Reinforcement Learning (RL) offers adaptability and robustness in such environments, proving effective for UAV positioning optimization. This paper introduces RLpos-3, a novel framework that integrates standard RL techniques and simulation libraries with Network Simulator 3 (ns-3) to facilitate the development and evaluation of UAV positioning algorithms. RLpos-3 serves as a supplementary tool for researchers, enabling the implementation, analysis, and benchmarking of UAV positioning strategies across diverse environmental conditions while meeting user traffic demands. To validate its effectiveness, we present use cases demonstrating RLpos-3's performance in optimizing UAV placement under realistic conditions, such as urban and obstacle-rich environments.

Abstract