Abstract

Abstract
The COVID-19 pandemic places unique challenges to medical rescuers’ occupational health. Thus, it is crucial to assess its direct and indirect impacts on key psychological outcomes and adaptation strategies. This study aims to analyse the impact of this pandemic on medical rescuers’ coping and emotion regulation strategies, and their levels of work-related psychological outcomes, such as burnout, trauma and post-traumatic growth. Additionally, it aims to analyse the contribution of coping and emotion regulation strategies, employed to manage the COVID-19 pandemic, on burnout, trauma and post-traumatic growth. A sample of 111 medical rescuers answered the Brief Cope, Emotion Regulation Questionnaire, Oldenburg Burnout Inventory, Impact of Event Scale-Revised and Post-Traumatic Growth Inventory. Medical rescuers resorted moderately to coping and emotion regulation strategies, since the beginning of COVID-19. They presented moderate burnout and post-traumatic growth and low trauma. Coping presented a higher weight on burnout, trauma and post-traumatic growth, than emotion regulation. Expressive suppression and dysfunctional coping predicted burnout and trauma, and problem and emotion-focused coping predicted post-traumatic growth. Dysfunctional coping mediated and, thus, exacerbated the effect of expressive suppression on burnout and on trauma. Practitioners should pay closer attention to professionals with higher burnout and trauma. Occupational practices should focus on reducing dysfunctional coping and expressive suppression and promoting problem-focused coping. © 2022, Instituto Superior de Psicologia Aplicada. All rights reserved.

Abstract
The growing demands of the Blue Economy are increasingly supported by sensing platforms, including as Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Vehicles (AUVs). Multimodal Underwater Wireless Networks (MUWNs), which may combine acoustic, radio-frequency, and optical wireless technologies, enhance underwater data transmission capabilities. Although Delay-Tolerant Networks (DTNs) address connectivity intermittency in such environments, not all data streams are delay-tolerant, and transmitting high-bandwidth DTN traffic over narrowband links can lead to significant inefficiencies. This paper presents QoS-MUWCom, a Quality of Service (QoS)-aware communication solution designed to manage both real-time and delay-tolerant traffic across dynamically selected multimodal interfaces. Experimental evaluations conducted in a freshwater tank demonstrate that QoS-MUWCom achieves near-zero packet loss for low-demand traffic even under link saturation, improves throughput for prioritized flows up to three times in mobility scenarios, and adapts to link availability and node mobility. The results confirm that QoS-MUWCom outperforms conventional multimodal strategies, contributing to more robust, resilient and efficient underwater communications.

Abstract
Underwater wireless communications face significant challenges due to propagation constraints, limiting the effectiveness of traditional radio and optical technologies. Long-range acoustic communications support distances up to a few kilometers, but suffer from low bandwidth, high error ratios, and multipath interference. Semantic communications, which focus on transmitting extracted semantic features rather than raw data, present a promising solution by significantly reducing the volume of data transmitted over the wireless link. This paper evaluates the resilience of SAGE, a semantic-oriented communications framework that combines semantic processing with Generative Artificial Intelligence (GenAI) to compress and transmit image data as textual descriptions over acoustic links. To assess robustness, we use a custom-tailored simulator that introduces character errors observed in underwater acoustic channels. Evaluation results show that SAGE can successfully reconstruct meaningful image content even under varying error conditions, highlighting its potential for robust and efficient underwater wireless communication in harsh environments.

Abstract
The increasing complexity of wireless technologies, such as Wi-Fi, presents significant challenges for Rate Adaptation (RA) due to the large configuration space of transmission parameters. While extensive research has been conducted on RA for low-mobility networks, existing solutions fail to adapt in Flying Networks (FNs), where high mobility and dynamic wireless conditions introduce additional uncertainty. We propose Linear Upper Confidence Bound for RA (LinRA), a novel Contextual Bandit-based approach that leverages real-Time link context to optimize transmission rates in predictable FNs, where future trajectories are known. Simulation results demonstrate that LinRA converges 5.2× faster than benchmarks and improves throughput by 80% in Non Line-of-Sight conditions, matching the performance of ideal algorithms. © 2025 Elsevier B.V., All rights reserved.

Abstract