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 presents a solution for visualizing oil spills at sea by combining satellite data with virtual choreographies. The system enables dynamic, interactive visualization of oil slicks, reflecting their shape, movement, and interaction with environmental factors like currents and wind. High resolution geospatial data supports a multiplatform experience with aerial and underwater perspectives. This approach promotes independence, interoperability, and multiplatform compatibility in environmental disaster monitoring. The results validate virtual choreographies as effective tools for immersive exploration and analysis, offering structured data narratives beyond passive visualization especially valuable for mixed reality applications.

Abstract
Background/Objectives: Boccia is an attractive and growing adapted sport. For approximately 30 years, this parasport was played together by male and female athletes, a fact that recently changed, to our best knowledge, without scientific support. Hence, this study aimed to analyse the relationship between gender participation and performance in Boccia international-level events. Methods: For data collection, four specific international-level Boccia events between 2012 and 2018 were selected as partials were available in the official competition websites (2708 partials, which represent a total of 32,496 ball throws). Results: We found that partials won by male athletes systematically increased between 2012 and 2018 but tended to stabilize between 2017 and 2018, contrary to females, with a growing trend from 2016 onwards. No differences were observed, considering the players' gender and the type of partials (adjusted, balanced, and unbalanced) in the Boccia classes BC1, BC2, and BC3. In BC4 differences were found, but with little variance or low association level (Cramer's Phi coefficient of 0.114). Conclusions: The results emphasize that based on performance, both men and woman can play Boccia together. Although, if the focus of separating genders in Boccia is toward growing and effective female participation and equal success and reward opportunities, this study highlights as a good perspective aiming regular practice of physical activity, exercise, and sport in people with disabilities, promoting their quality of life.

Abstract

Abstract
This paper explores the potential of Large Language Models (LLM) to optimize various stages of the software development lifecycle, including requirements elicitation, architecture design, diagram creation, and implementation. The study is grounded in a real-world case, where development time and result quality are compared with and without LLM assistance. This research underscores the possibility of applying prompt patterns in LLM to support and enhance software development activities, focusing on a B2C digital commerce platform centered on fashion retail, designated LUNA. The methodology adopted is Design Science, which follows a practical and iterative approach. Requirements, design suggestions, and code samples are analyzed before and after the application of language models. The results indicate substantial advantages in the development process, such as improved task efficiency, faster identification of requirement gaps, and enhanced code readability. Nevertheless, challenges were observed in interpreting complex business logic. Future work should explore the integration of LLM with domain-specific ontologies and business rule engines to improve contextual accuracy in code and model generation. Additionally, refining prompt engineering strategies and combining LLM with interactive development environments could further enhance code quality, traceability, and explainability.

Abstract
Vehicle cybersecurity has emerged as a critical concern, driven by innovation in the automotive industry, e.g., autonomous, electric, or connected vehicles. Current efforts to address these challenges are constrained by the limited computational resources of vehicles and the reliance on connected infrastructures. This motivated the foundation of Vehicle Security Operations Centers (VSOCs) that extend IT-based Security Operations Centers (SOCs) to cover the entire automotive ecosystem, both the in-vehicle and off-vehicle scopes. Security Orchestration, Automation, and Response (SOAR) tools are considered key for implementing an effective cybersecurity solution. However, existing state-of-the-art solutions depend on infrastructure networks such as 4G, 5G, and WiFi, which often face scalability and congestion issues. To address these limitations, we propose a novel SOAR architecture EVSOAR that leverages the EV charging stations for connectivity and computing to enhance vehicle cybersecurity. Our EV-specific SOAR architecture enables real-time analysis and automated responses to cybersecurity threats closer to the EV, reducing cellular latency, bandwidth, and interference limitations. Our experimental results demonstrate a significant improvement in latency, stability, and scalability through the infrastructure and the capacity to deploy computationally intensive applications that are otherwise infeasible within the resource constraints of individual vehicles.