Abstract
The aim of this work is to explore normalising flows to detect anomalous behaviours which is an essential task mainly for surveillance systems-related applications. To accomplish that, a series of ablation studies were performed by varying the parameters of the Spatio-Temporal Graph Normalising Flows (STG-NF) model [3] and combining it with attention mechanisms. Out of all these experiments, it was only possible to improve the state-of-the-art result for the UBnormal dataset by 3.4 percentual points (pp), for the Avenue by 4.7 pp and for the Avenue-HR by 3.2 pp. However, further research remains urgent to find a model that can give the best performance across different scenarios. The inaccuracies of the pose tracking and estimation algorithm seems to be the main factor limiting the models' performance. The code is available at https://github.com/AnaFilipaNogueira/Abnormal-Human-Behaviour-Detection- using-Normalising-Flows-and- Attention-Mechanisms.

Abstract
Maritime transport faces increasing decarbonisation requirements, placing new demands on port energy systems. Yet most existing studies analyse isolated components or short time horizons, limiting their usefulness for long-term planning. This work develops a holistic, least-cost optimisation model of the Port of Sines energy system using OSeMOSYS, integrating electricity and fuel consumption across port operations and fuel-management processes from 2020 to 2050.The study evaluates alternative technology pathways and policy measures, including carbon taxation, national emission-reduction targets, and the adoption of an innovative ocean-going vessel fleet. Results show that electrification, driven by onshore power supply and renewable expansion, is the most cost-effective decarbonisation route, while its performance depends on local generation capacity and the carbon intensity of the electricity mix. Policy mechanisms and fleet innovation further influence the timing and depth of emissions reductions. Overall, the model provides a replicable framework to support strategic port decarbonisation planning.

Abstract
The limited availability and high cost of acquiring real-world image data impacts the creation of high-quality datasets, hindering the development of robust machine learning models, particularly in complex visual domains. This paper investigates the feasibility of enhancing image classification performance by incorporating balanced synthetic data into existing datasets. Three distinct machine learning tasks—image classification, instance detection, and image segmentation—were explored across diverse image domains. Synthetic images were generated to complement real-world data, and various testing scenarios were conducted, adjusting the relative weights of real and synthetic samples. The results demonstrate that balanced datasets, comprising an equitable mix of real and synthetic images, consistently yielded the highest performance metrics across all tasks. It was also observed that even a small introduction of synthetic data can improve performance over real data alone. The 50–50 split showed to optimally balance the realism of real data and the variability of synthetic data. Real data ensures that the model learns accurate representations of objects, while synthetic data enriches the training process with additional variations, reducing overfitting to specific real-world examples. The proposed approach highlights the potential of strategically integrating synthetic data to improve model accuracy and robustness, particularly in scenarios where real-world data is limited or challenging to acquire. © 2025 Elsevier B.V., All rights reserved.

Abstract
The lack of open-source platforms capable of integrated operational modeling and multi-scenario decarbonization analysis, often hinders data-driven decision-making in the maritime sector. To address this gap, this paper presents an open-source, multi-agent, discrete-event simulator capable of accurately forecasting the energy consumption associated with the diverse assets and activities within a container terminal. The tool's modular architecture enables transparent evaluations of operational strategies and decarbonization alternatives by allowing users to systematically modify inputs or alter embedded energy modules. The tool's capabilities were validated through a case study of a medium-sized Portuguese container terminal. For this particular port, findings indicate that installing three onshore power supply (OPS) units and fully electrifying the internal truck fleet yields the most substantial emission reductions. However, these interventions result in a two-fold increase in daily electricity demand, potentially straining grid capacity. This finding underscores that the effectiveness of terminal electrification as a decarbonization strategy ultimately depends on a simultaneous transition to a decarbonized and secure energy supply.

Abstract
In the context of the diversity and complexity of laboratory processes, it is crucial to address the vulnerabilities associated with healthcare. Proper risk management becomes essential to ensure quality and safety in this environment. In this sense, the application of risk management tools and methodologies plays a crucial role in the identification, assessment and mitigation of potential risks present in laboratory processes performed, especially in a hospital environment. The present work addresses the theme of risk and safety management in a hospital environment, with the aim of promoting a safe environment for this community. The Healthcare Failure Mode and Effect Analysis methodology was applied to identify and mitigate the risks associated with medical equipment used in a medical genetics laboratory. The methodology included data collection, failure analysis, risk quantification, decision tree application and risk evaluation. Among the 19 failures analyzed none demonstrated a Risk Priority Number (RPN) greater than 8, suggesting that the equipment operates within acceptable risk thresholds. The results highlighted the importance of the safety of healthcare professionals and the proper functioning of equipment to ensure patient safety. The study contributed to the development of preventive and corrective actions, as well as providing future improvements and implementation of the methodology in other services of the hospital. © 2025 Elsevier B.V., All rights reserved.

Abstract
Container terminals are of pivotal importance to global trade, as they act as a bridge between maritime and land transport. However, inefficiencies in operations, such as long waiting times and high emissions, continue to challenge the industry. Current practices, including first-come-first-served (FCFS) berth allocation, often result in ships arriving too early and idling at anchorage, leading to increased fuel consumption and negative environmental impacts. Just-in-Time (JIT) strategies have been identified as a potentially effective approach to address these issues by aligning ship arrivals with berth availability, thus optimising speed and reducing emissions. In this work, we present a simulation-based decision-support tool to evaluate JIT strategies in container terminal operations. By analysing scenarios involving speed optimisation and resource investments, the tool provides insights into key performance metrics, including waiting times, emissions, and resource utilisation. A case study designed around a large Portuguese seaport was used to validate the approach, with significant reductions in emissions and operational inefficiencies. These findings highlight the potential of JIT operations to enhance sustainability and efficiency in the maritime sector. Copyright © 2025. Published by Elsevier B.V.