Abstract
[No abstract available]

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
In the study, a new method for analyzing Electrocardiogram (ECG) signals is suggested, which is vital for detecting and treating heart diseases. The technique focuses on improving ECG signal classification, particularly in identifying different heart conditions like arrhythmias and myocardial infarctions. An enhanced version of the differential evolution (DE) algorithm integrated with neural networks is leveraged to classify these signals effectively. The process starts with preprocessing and extracting key features from ECG signals. These features are then processed by a multi-layer perceptron (MLP), a common neural network for ECG analysis. However, traditional MLP training methods have limitations, such as getting trapped in suboptimal solutions. To overcome this, an advanced DE algorithm is used, incorporating a partition-based strategy, opposition-based learning, and local search mechanisms. This improved DE algorithm optimizes the MLP by fine-tuning its weights and biases, using them as starting points for further refinement by the Gradient Descent with Momentum (GDM) local search algorithm. Extensive experiments demonstrate that this novel training approach yields better results than the traditional method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Predicting and controlling crowd dynamics in emergencies is one of the main objectives of simulated emergency exercises. However, during emergency exercises, there is often a lack of sense of danger by the actors involved and concerns about exposing real people to potentially dangerous environments. These problems impose limitations in running an emergency drill, harming the collection of valuable information for posterior analysis and decision-making. This work aims to mitigate these problems by using Agent Based Modelling (ABM) simulator to deepen the comprehension of human actions when exposed to a sudden variation in extensive crowded environmental conditions and how evacuation strategies affect evacuation performance. To assess the impact of the evacuation strategy employed, we propose a modified informed leader-flowing approach and compare it with common evacuation strategies in a simulated environment, replicating stadium benches with narrow corridors leading to different exit points. The objective is to determine the impact of each set of configurations and evacuation strategies and compare them against other established ones. Our experiments determined that agents following the crowd generally lead to a higher number of victims due to the rise of herding phenomena near the exits, which was significantly reduced when agents were guided towards the exit via knowing the exit beforehand or following leader agent with real-time information regarding exit location and exit current state, proving that relevant and controlled information in combination with Follow Leader strategies can be crucial in an emergency evacuation scenario with limited evacuation exit capabi and distribution. © The Author(s) 2024.

Abstract
This paper explores advancements in Video Anomaly Detection (VAD), combining theoretical insights with practical solutions to address model limitations. Through comprehensive experimental analysis, the study examines the role of feature representations, sampling strategies, and curriculum learning in enhancing VAD performance. Key findings include the impact of class imbalance on the Cross-Modal Awareness-Local Arousal (CMALA) architecture and the effectiveness of techniques like pseudo-curriculum learning in mitigating noisy classes, such as Car Accident. Novel strategies like the Sample-Batch Selection (SBS) dynamic segment selection and pre-trained image-text models, including Contrastive Language-Image Pre-training (CLIP) and ViTamin encoder, significantly improve anomaly detection. The research underscores the potential of multimodal VAD, highlighting the integration of audio and visual modalities and the development of multimodal fusion techniques. To support this evolution, the study proposes a Unified WorkStation 4 VAD (UWS4VAD) to streamline research workflows and introduces a new VAD benchmark incorporating multimodal data and textual information. The work envisions enhanced anomaly interpretation and performance by leveraging joint representation learning and Large Language Models (LLMs). The findings set the stage for future advancements, advocating for large-scale pre-training on audio-visual datasets and shifting toward a more integrated, multimodal approach to VADs. Source code of the project available at https://github.com/zuble/uws4vad