Maritime transportation plays a vital role in international trade and commerce, with ports serving as critical points of connection between land and sea transportation systems. The operational efficiency of port facilities is essential to ensure the uninterrupted flow of goods and services, making port safety a top priority for governments, authorities, and shipping companies. Due to the importance of Occupational Health and Safety (OHS) within port environments, it is crucial to develop a structured framework in order to collect and analyze port accidents data. Today there are several different national agencies, private organizations, and/or local regulatory bodies taking charge of these data over different areas, each with variations in how they document and classify the events; in addition these are frequently limited to only major disasters and/or summary statistics. This paper aims to create a general framework to collect and fuse open-source port accident data from different sources in a structured way and to analyze the safety conditions of port facilities by conducting a comparative evaluation based on design of experiment (DoE). Through this analysis, we identify common causes of accidents and injuries in port facilities, as well as any differences in safety conditions across regions, types of port facilities, and other relevant factors. This information can be used to inform policies and practices aimed at improving port safety, reducing accidents and injuries, and ultimately enhancing the efficiency and sustainability of maritime transportation systems. The motivation to develop this research relies on the necessity to define requirements for the development of innovative solutions to be developed by the authors using modeling and simulation (M&S) and XR (extended reality) in order to increase safety in these contexts.

Wastewater treatment plants (WWTPs) are critical infrastructures that account for a significant share of global electricity, with aeration alone often responsible for over half of the total demand. Reducing the energy intensity of blower operation is, therefore, essential for sustainable and resilient WWTP management. This study presents a modeling and simulation framework for optimizing parallel blower operation in grit chamber aeration system. The framework integrates a modular structure with a blower model, a distribution network model, and an optimization layer that work together to capture equipment performance, simulate hydraulic interactions, and determine energy-optimal operating strategies under process and safety constraints. Two optimization strategies are compared: a heuristic grid search and a Safe Bayesian Optimization (SBO) method. Both algorithms enforce vendor surge and overheat limits, network pressure constraints, and process requirements. Simulation campaigns under representative demand scenarios show that both approaches achieve feasible operating points, while SBO consistently demonstrates higher energy savings and substantially faster runtime. Overall, the findings highlight the potential of data-driven optimization for achieving efficient and safe blower control, with reduced computation time making progress for real-time supervisory optimization in WWTPs.

In capital-intensive industries, operational knowledge is often trapped in unstructured technical manuals, creating a barrier to efficient and reliable maintenance planning. This work addresses the need for an integrated system that can automate knowledge extraction and generate optimized, resilient, operational plans. A synergistic multi-agent framework is introduced that transforms unstructured documents into a structured knowledge base using a self-validating pipeline. This validated knowledge feeds a scheduling engine that combines multi-objective optimization with discrete-event simulation to generate robust, capacity-aware plans. The framework was validated on a complex maritime case study. The system successfully constructed a high-fidelity knowledge base from unstructured manuals and the scheduling engine produced a viable, capacity-aware operational plan for 118 interventions. The optimized plan respected all daily (6) and weekly (28) task limits, executing 64 tasks on their nominal date, bringing 8 forward, and deferring 46 by an average of only 2.0 days (95th percentile 4.8 days) to smooth the workload and avoid bottlenecks. An interactive user interface with a chatbot and planning calendar provides verifiable “plan-to-page” traceability, demonstrating a novel, end-to-end synthesis of document intelligence, agentic AI, and simulation to unlock strategic value from legacy documentation in high-stakes environments.

Health misinformation across digital platforms has emerged as a critical fast-growing challenge to global public health, undermining trust in science and contributing to vaccine hesitancy, treatment refusal and heightened health risks. In response, this study introduces Impact, a novel simulation framework that integrates agent-based modeling (ABM) with large language model (LLM) integration and retrieval-augmented generation (RAG) to evaluate and optimize health communication strategies in complex online environments. By modeling virtual populations characterized by demographic, psychosocial, and emotional attributes, embedded within network structures that replicate the dynamics of digital platforms, the framework captures how individuals perceive, interpret and propagate both factual and misleading health messages. Messages are enriched with evidence from authoritative medical sources and iteratively refined through sentiment analysis and comparative testing, allowing the proactive pre-evaluation of diverse communication framings. Results demonstrate that misinformation spreads more rapidly than factual content, but that corrective strategies, particularly empathetic and context-sensitive messages delivered through trusted peers, can mitigate polarization, enhance institutional trust and sustain long-term acceptance of evidence-based information. These findings underscore the importance of adaptive, data-driven approaches to health communication and highlight the potential of simulation-based methods to inform scalable interventions capable of strengthening resilience against misinformation in digitally connected societies.

Background: We focus on the inland rail forwarding of import containers from a marine terminal. Specifically, we present a discrete-event simulation study related to container-loading operations by train, evaluating different train departure policies within a predetermined schedule based on the capacity of the rail network. The aim is threefold: manage more containers shipped by train, improve terminal operational efficiency, and increase the rail share. Methods: The proposed procedures take full advantage of the digitization and visualization of data currently present in the terminal with the aim of improving the performance indices of interest, thereby increasing the terminal‘s operational efficiency. We evaluate the medium- and long-term impact of alternative strategies on container dwell times and the possible increase in the number of containers shipped by train. Results: The computational tests are performed with data from a terminal in the port of Genoa (Italy). The results show the relationships between train departure management policies and train departure distributions. The number of departing trains, cancelled trains, and trains departing with load percentages below 70% is studied. Average loads per train and estimated delays are also analyzed. Conclusions: It is noted that the results, which can be obtained with data from any terminal, are of great importance for optimizing operational management, offering practical solutions to improve efficiency and reduce container downtime.

The deployment of green hydrogen production is increasingly considered a strategic opportunity for energy-exporting countries. However, beyond technological and environmental aspects, large-scale industrial projects may generate complex and uncertain social and economic impacts at the regional level. This study investigates the potential social implications of introducing a green hydrogen production plant in the Department of Paysandú, Uruguay, using a System Dynamics modeling approach. It proposes an initial system model designed to establish a foundational Modeling and Simulation framework. The model explicitly represents feedback mechanisms linking public finance, education, labor competencies, productivity, and social behavior impact, allowing the exploration of long-term socio-economic trajectories under alternative institutional and policy conditions. It is used as an exploratory decision-support tool to assess conditional pathways, trade-offs, and risks. Results indicate that positive social outcomes, such as human capital accumulation and regional income growth, are possible but not automatic; they depend critically on governance capacity, fiscal sustainability, labor market coordination, and social acceptance, and may be attenuated or delayed under adverse scenarios. While this framework provides a strategic engineering lens on the social dimension, it represents a first step toward a comprehensive decision-making tool. The study analyzes a complex system by integrating energy, production, economic, social, and environmental aspects from strategic engineering lens and contributes to the literature by integrating social dimension and institutional constraints into a Modeling and Simulation framework applied to green hydrogen industrialization, offering insights into policy design under uncertainty in emerging energy-export contexts.