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
Brain-computer interfaces (BCIs) represent an emerging technology that facilitates direct communication between the brain and external devices. In recent years, numerous review articles have explored various aspects of BCIs, including their fundamental principles, technical advancements, and applications in specific domains. However, these reviews often focus on signal processing, hardware development, or limited applications such as motor rehabilitation or communication. This paper aims to offer a comprehensive review of recent electroencephalogram (EEG)-based BCI applications in the medical field across 8 critical areas, encompassing rehabilitation, daily communication, epilepsy, cerebral resuscitation, sleep, neurodegenerative diseases, anesthesiology, and emotion recognition. Moreover, the current challenges and future trends of BCIs were also discussed, including personal privacy and ethical concerns, network security vulnerabilities, safety issues, and biocompatibility.

Abstract
Subject-independent seizure detection algorithms are typically grounded in scalp electroencephalogram (EEG) databases, due to standardized channels and locations of EEG electrodes. Intracranial EEG (iEEG) has the characteristics of low noise and high temporal resolution compared with scalp EEG. However, it is still a big challenge for seizure detection using iEEG, because of the inconsistent number and locations of implanted electrodes in different patients, which results in a lack of unified algorithms. This study introduces an innovative approach for subject-independent seizure detection using iEEG, combining channel-wise mixup, transformer networks, and multi-task learning. Channel-wise mixup enhances data utilization by effectively leveraging information from different subjects, while multi-task learning improves the generalization of the model by concurrently optimizing both the seizure detection and the subject recognition tasks. 2983 files from two well-known epilepsy databases, i.e. SWEC-ETHZ and HUP were used in our study and the result showed that our approach surpasses currently existing methods. In terms of accuracy and generalization of seizure detection, our method achieved an area under the receiver operating characteristic curve (AUC) of 0.97 and 0.95 on the two databases respectively, which are significantly higher than the result of the currently existing methods. This study proposed anew method with great potential for surgery planning of epilepsy patients.

Abstract
[No abstract available]

Abstract
Accurate surgical planning is critical in mandibular reconstruction to restore the oncology patient's function and aesthetics. However, the use of physical three-dimensional (3D) models is often limited by time-consuming manual segmentation procedures or the high cost of commercial solutions. This work addresses the need for an accessible, quick, and low-cost pipeline to obtain a 3D printed model of the segmented mandible from a Computed Tomography (CT) scan. The automatic segmentation stage relied on the two-dimensional U-Net architecture, which was trained and validated with slices across two public datasets (PDDCA, HaN-Seg) and tested with the other two public datasets (TCIA RT, Austrian). The best model achieved an average dice similarity coefficient (DSC) of 0.912 ± 0.077 across all test sets. The segmentation output was reconstructed into a 3D volume, improved through a post-processing method (with morphological closing, upsample, smoothing, and mesh reduction), and 3D printed through fused deposition modelling. The assessment of a stomatologist confirmed overall high anatomical fidelity to the CT and clinical utility, even though further improvements in important fine anatomical elements were suggested. This solution contributes to a promising alternative to producing 3D personalised mandibles for surgical planning, reducing time and manual effort while improving the quality and accessibility. Future work may explore the use of 3D DL architectures and a broader evaluation of the 3D mandible models. © 2025 IEEE.

Abstract
Deep learning has been extensively applied to medical imaging tasks over the past years, achieving outstanding results. However, the obscure reasoning of the models and the lack of supportive evidence causes both clinicians and patients to distrust the models' predictions, hindering their adoption in clinical practice. In recent years, the research community has focused on developing explanations capable of revealing a model's reasoning. Among various types of explanations, example-based explanations emerged as particularly intuitive for medical practitioners. Despite the intuitiveness and wide development of example-based explanations, no work provides a comprehensive review of existing example-based explainability works in the medical image domain. In this work, we review works that provide example-based explanations for medical imaging tasks, reflecting on their strengths and limitations. We identify the absence of objective evaluation metrics, the lack of clinical validation and privacy concerns as the main issues that hinder the deployment of example-based explanations in clinical practice. Finally, we reflect on future directions contributing towards the deployment of example-based explainability in clinical practice.