Search Results (435)

Ship collision accidents remain a critical concern in maritime safety because of their potential to cause operational disruption as well as environmental and economic damage in areas with dense shipping activity. Complex traffic interactions, differences in vessel characteristics, and dynamic environmental conditions make collision risk increasingly difficult to manage using traditional navigation measures alone. This paper presents a structured review of ship collision research, focusing on collision impacts, collision avoidance strategies, risk assessment methodologies, and safety index development. The review synthesizes reported collision cases and their environmental consequences, examines commonly used analytical frameworks including probabilistic, data-driven, and multicriteria approaches, and discusses recent developments in AIS-based analysis, sensor-based monitoring, and intelligent prediction techniques. The analysis identifies several methodological gaps in existing studies. Collision avoidance methods and risk assessment models are often developed independently, while their integration with safety index frameworks remains limited. In addition, safety index formulations differ considerably in terms of indicator selection and modeling approaches, which reduces comparability between studies conducted in different waterways. The findings highlight how different analytical approaches contribute to maritime safety evaluation at strategic, operational, and real-time levels and provide insights for developing more integrated safety assessment frameworks to support navigation risk monitoring in high-traffic maritime environments. Full article

Coastal socio-ecological systems are increasingly exposed to the combined pressures of climate change, land-use intensification, hydrological alterations and expanding infrastructure networks. These pressures interact across the land–catchment–lagoon–sea continuum, generating complex feedbacks that challenge traditional planning instruments, which remain sectoral and fragmented. The Mar Menor (SE Spain), a semi-enclosed Mediterranean lagoon affected by intensive agriculture, urbanisation, hydrological modifications and recurrent extreme climatic events, exemplifies this systemic vulnerability. Existing planning frameworks—local urban plans, regional territorial plans, river basin management plans, maritime spatial plans and lagoon-specific strategies—operate independently, each addressing only a fragment of the system and none integrating climate change as a structuring axis. This article introduces Integrated Spatial Planning (ISP) as a novel territorial–climatic framework designed to overcome these limitations. ISP integrates climate forcing, land uses, catchment processes, lagoon dynamics, marine conditions, critical infrastructures, intermodal and energy corridors and multilevel governance into a single analytical structure. A central component of the methodology is a four-zone multilevel zoning system that connects municipal, regional, basin, marine and EEZ planning domains within a unified territorial–climatic logic. The ISP matrix is applied to the Mar Menor to produce the first holistic diagnosis of the system. Results reveal strong land–sea–catchment interactions, high climatic exposure, vulnerable infrastructures and structural governance fragmentation. The matrix exposes systemic incompatibilities and vulnerabilities that remain invisible in sectoral planning instruments. The discussion demonstrates how ISP clarifies the roles and responsibilities of each governance level, supports multilevel coherence and integrates critical infrastructures and intermodal corridors into climate-resilient planning. ISP reframes climate change as the organising principle of territorial planning and provides a replicable, scalable methodology for coastal socio-ecological systems facing accelerating climate pressures. The Mar Menor case illustrates the urgent need for integrated territorial–climatic governance and positions ISP as a scientifically robust and operationally viable pathway for long-term adaptation and resilience. Full article

This paper proposes a dynamic obstacle-avoidance algorithm for unmanned surface vehicles (USVs) that combines a Fuzzy-enhanced Dynamic Window Approach (FDWA) with an Improved Bidirectional A*–Improved Grey Wolf Optimizer (IBA*–IGWO) framework. Firstly, the traditional dynamic window method (DWA) is improved by adopting an initial heading angle optimization strategy to reduce the heading deviation of unmanned vessels during cruising. Secondly, a fuzzy controller is introduced, which can adaptively adjust the weight coefficients in the cost function of the DWA algorithm based on the current position of the unmanned vessel, surrounding environmental information, etc., to improve obstacle avoidance ability and adaptability in different environments. Finally, using the global static cruise route provided by the IBA*–IGWO algorithm, key nodes are selected as local endpoints for the FDWA algorithm to ensure that the unmanned vessel can perform cruise tasks according to the optimal plan during navigation and make dynamic adjustments in case of emergencies. The simulation results demonstrate the feasibility of the proposed method in handling unknown and dynamic obstacles under the current grid-based experimental settings, while enabling the USV to return to the pre-planned global route after local obstacle avoidance. These results provide a basis for further development toward more robust and rule-aware autonomous navigation in realistic maritime environments. Full article

Building world-class ports requires not only scale expansion but also sustainable structural capability. However, the concept of port “hardcore strength” remains insufficiently clarified and operationalized in existing sustainability and port evaluation research. In this study, port hardcore strength is understood as an integrated capability framework covering infrastructure efficiency and logistics capability, connectivity and regional integration, maritime services and industrial clustering, strategic leadership and innovation capability, and sustainable governance and green port development. This study proposes a sustainability-oriented evaluation framework for assessing the “hardcore strength” of world-class ports through a multi-dimensional indicator system. Methodologically, the study integrates the EWM and CRITIC, and introduces Bland–Altman analysis to examine whether the EWM and CRITIC weight vectors exhibit an obvious systematic bias prior to game-theoretic integration. Using 18 representative global ports from 2019 to 2023 as a case study, the results show that the overall ranking structure remains broadly stable, with Singapore Port and Shanghai Port consistently ranking first and second, respectively, while some middle-ranked ports exhibit moderate positional changes. The findings suggest that differences in world-class port development are rooted not only in operational scale, but also in the coordination of multiple capability dimensions. The study enriches the understanding of world-class port evaluation from a sustainability-oriented perspective. Full article

Ports play a central role in global trade and act as key hubs for both maritime and land transport. Free ports, characterized by special customs regimes and fiscal advantages, represent a distinctive segment of this landscape. Despite their relevance, the literature on port gate management and on free ports has developed disconnected research streams, leaving the operational implications of special customs regimes largely unexplored. This study addresses this gap by investigating how gate procedures in free ports can be managed more efficiently, using the Port of Trieste as a case study. The analysis combines Business Process Model and Notation (BPMN) with discrete event simulation: BPMN served as the logical foundation for capturing the procedural complexity of free port gate operations, while simulation provided the quantitative framework for scenario evaluation. The model was calibrated on real gate access data and validated against observed vehicle volumes. Nine scenarios were evaluated, covering managerial, technological, infrastructural, and disruption-related interventions. The results show that no single measure produces significant improvements across all performance indicators and the integrated approaches consistently outperform standalone measures. Infrastructure interventions, while more costly, prove particularly valuable in improving port resilience under severe disruption conditions. Full article

This paper proposes a physics-informed hybrid digital CO₂ emission-control technology for maritime transport, designed for adaptive ship speed optimization along a predefined geographical route between two ports, discretized into quasi-stationary segments and evaluated under forecasted metocean conditions, subject to economic and regulatory constraints associated with maritime decarbonization. The framework integrates two exact optimization methods, Backtracking (BT) and Dynamic Programming (DP), with a reinforcement learning approach based on Proximal Policy Optimization (PPO), operating on a unified physical, economic, and regulatory modeling core. By reducing propulsion fuel demand, the system acts as an upstream CO₂ emission-control mechanism for ship propulsion. This operational stabilization of the engine load creates favourable boundary conditions for advanced combustion processes and reduces the volumetric flow of exhaust gas, thereby lowering the technical burden on potential post-combustion carbon capture systems. Segment-wise speed profiles are optimized subject to propulsion limits, Estimated Time of Arrival (ETA) feasibility, and regulatory constraints, including the Carbon Intensity Indicator (CII), the European Union Emissions Trading System (EU ETS) and FuelEU Maritime. The physics-based propulsion and energy model is validated using full-scale operational data from four real voyages of an oil/chemical tanker. A detailed case study on the Milazzo–Motril route demonstrates that adaptive speed optimization consistently outperforms conventional cruise operation. Exact optimization methods achieve voyage time reductions of approximately 10% and fuel and CO₂ emission reductions of about 9–10%. The reinforcement learning approach provides the best overall performance, reducing voyage time by approximately 15% and achieving fuel savings and CO₂ emission reductions of about 13%. At the route level, the Carbon Intensity Indicator is reduced by approximately 10% for the exact methods and by about 13% for PPO. Backtracking and Dynamic Programming converge to nearly identical globally optimal solutions within the discretized decision space, while PPO identifies solutions located on the most favourable region of the cost–time Pareto front. By benchmarking reinforcement learning against exact discrete solvers within a shared physics-informed structure, the proposed digital platform provides transparent validation of learning-based optimization and offers a scalable decision-support technology for pre-fixture evaluation of fixed-route voyages. The system enables quantitative assessment of CO₂ emissions, ETA feasibility, and regulatory exposure (CII, EU ETS, FuelEU Maritime penalties) prior to transport contracting, thereby supporting economically and environmentally informed operational decisions. Full article

Green ammonia is increasingly recognised as a sustainability enabler for decarbonising fertiliser production, energy storage, and maritime transport, but offshore wind-to-ammonia pathways remain subject to significant economic and operational uncertainty. This study evaluated the techno-economic and sustainability performance of integrating power-to-ammonia (PtA) with an operating offshore wind farm in Denmark under three supply-chain scenarios (SCs): SC1, a fully offshore PtA with vessel-based ammonia transport; SC2, a fully offshore PtA with pipeline export; and SC3, a hybrid offshore–onshore configuration. An hourly dispatch framework allocated wind electricity between grid export and ammonia production by comparing incremental operating margins, while accounting for minimum-load, ramping, storage, and logistics constraints. Hourly wind generation and DK1 electricity-price data for 2020–2025 are used to construct a deterministic base case and a 30-year block-bootstrap Monte Carlo analysis. Sensitivity analysis is performed by varying electrolyser rated power over 10–200 MW and ammonia selling price over 1400–3200 €/tNH3, with additional breakeven-price estimation and flexibility cases based on reduced minimum-load requirements and faster ramping. A screening-level climate indicator was additionally reported by estimating potential CO₂ emissions avoided if delivered green ammonia displaces conventional natural-gas-based ammonia. Results indicated that SC3 is the most favourable configuration under the adopted assumptions, while overall project viability remained highly sensitive to PtA sizing, ammonia market value, operational flexibility, and the assumed infrastructure cost structure. Full article

This paper develops the Mobilized Nostalgia Tourism conceptual model, positioning historic automotive events as dynamic, multisensory mobile heritage performances through which nostalgia is actively produced rather than merely recalled. Drawing on interdisciplinary scholarship across heritage studies, mobilities and performance perspectives, and destination branding, the model specifies how event design levers (sensory staging, narrative scripting, participation architecture, and digital mediation) can mobilize nostalgia as an affective mechanism, shaping visitor outcomes (authenticity, memorability, attachment, advocacy) and, under certain conditions, destination outcomes (brand meaning, dispersion effects, and cultural capital). The paper uses three illustrative cases—Mille Miglia (Italy), Goodwood Revival (England), and the Historic Acropolis Rally (Greece)—to demonstrate the model’s portability and to highlight variation in how mobilized nostalgia is staged and contested. By clarifying constructs, boundary conditions, and propositions, the paper provides an analytical vocabulary that supports comparative research and offers practical insight for designing heritage events that are emotionally resonant, culturally legitimate, and strategically coherent. The proposed model is widely applicable, extending beyond automotive events to vintage railway, aviation, maritime heritage tourism, and diverse cultural festivals. Furthermore, it translates the mechanism model into a practical design toolkit that can inform event organizers, destination managers, and policymakers as they develop affect-rich heritage experiences and manage trade-offs around authenticity, community legitimacy, and sustainability. Last, the paper outlines empirical pathways, including mixed-method approaches, for future validation of its conceptual propositions. Full article

To address the peak-fluctuating cooling load of ocean-going fishing vessels and the dependency of traditional refrigeration systems on fuel-driven power, this study proposes an exhaust waste-heat-driven ammonia water absorption–compression hybrid refrigeration system. The proposed system was thermodynamically analyzed and simulated based on the principles of heat and mass transfer. Considering the full-cycle cooling demand, an objective optimization model with the goal of minimizing the total operating cost was established and solved using the Northern Goshawk Optimization (NGO) algorithm. Using real data from a fishing company, a voyage cycle of Lu Huang Yuan Yu 105 was selected as a case study. Results showed that NGO outperformed the Genetic Algorithm and Particle Swarm Optimization, achieving the smallest cooling deficit and faster convergence. Compared with the independent compression refrigeration system, the hybrid system reduced the cooling deficit by 9.7%, improved cooling capacity by over 35% during voyage, 5% during fishing, and 2% during processing, while lowering fuel consumption by 10% and efficiently utilizing exhaust heat. Sensitivity analysis identified optimal ranges for ammonia concentration and circulation ratio and highlighted the significant influence of cooling water temperature on system performance. This study provides a valuable reference for the design and optimization of low-grade waste-heat-driven hybrid refrigeration systems in maritime applications. Full article

This paper explores the feasibility of employing small language models (SLMs) on edge devices powered by batteries in environments with limited/no internet connectivity. SLMs in fact offer significant advantages in such scenarios due to their lower resource requirements with respect to large language models. The use case in this study is maritime navigation—in particular, the documentation on Sailing Directions (Enroutd) of the World Port Index (WPI) provided by the National Geospatial-Intelligence Agency (NGA), which provides information that cannot be shown graphically on nautical charts and is not readily available elsewhere. In this environment, response immediacy is not critical, as users have sufficient time to query information while navigating and planning activities, making edge devices ideal for running these models. On the contrary, the response quality is fundamental. For this reason, given the constrained knowledge of SLMs in maritime contexts, we investigate the use of the retrieval-augmented generation (RAG) methodology, integrating external information from sailing directions. A comparative analysis is presented to evaluate the performance of various state-of-the-art SLMs, focusing on response quality, the effectiveness of the RAG component, and inference times. Full article

Shore power (SP) is a critical solution for decarbonizing maritime transport, yet its adoption is hindered by the “high investment, low utilization” paradox, driven by high initial costs and misaligned incentives between ports and ships. While government subsidies are essential, traditional static policy designs often fail to adapt to the complex, non-linear dynamics of technology diffusion. To address this, the study proposes a dynamic evaluation framework combining System Dynamics (SD) with Evolutionary Game Theory (EGT), embedding a Rolling Horizon Optimization algorithm. Using Shanghai Port as a case study, simulation results demonstrate that optimal subsidies are highly state-dependent. Specifically, effective promotion requires prioritizing ship-side incentives during the early start-up phase, followed by facilities subsidies supporting the coordinated evolution of both ships and berths, and finally a market-driven exit. Furthermore, the proposed dynamic strategy demonstrates superior robustness against oil price volatility and demand shocks compared to static policies, while strictly complying with fiscal budget caps. This framework provides a foundation for the adaptive management of green port infrastructure, facilitating the advancement of energy-saving and environmental protection initiatives within the maritime industry. Additionally, it contributes to the forecasting and evaluation of the policy outcomes of green technology adoption. Full article

For nations heavily dependent on fossil-fuel exports, hydrogen is emerging as a promising solution to reduce carbon emissions while preserving economic stability and promoting countries’ energy independence. This research study examines hydrogen potential as a renewable energy source to facilitate the transition toward a sustainable economy with a special focus on Middle East and North Africa (MENA) countries. The analysis delves into policy frameworks, technological advancements, and infrastructure adaptations to build a reliable green hydrogen supply chain for a scalable and bankable future. The role played by other renewable energies like solar and wind, together with the risk related to the high demand for water resources to achieve the green hydrogen transition, has also been assessed. Furthermore, key challenges have been highlighted, including the repurposing of the existing pipelines into the energy networks, public–private partnerships to secure investment, and legislation requirements to encourage the adoption of novel hydrogen applications. In order to do that, a SWOT-PESTEL analysis has been carried out to identify the main decarbonization strategies for achieving a replicable framework. Moreover, a multi-criteria decision analysis was performed, applying 11 indicators across supply-side (e.g., solar/wind potential, LCOE, and water stress), demand-pull/logistics (e.g., maritime connectivity, steel production, and LNG export capacity), and risk/regulation dimensions (e.g., governance effectiveness, regulatory quality, and fossil rent dependence). The Analytic Hierarchy Process (AHP) was used for weighting, the entropy method for weighting variability (hybrid 50/50 combined weights), min–max normalization for costs, 5% Winsorization for outliers, and TOPSIS for aggregation following OECD-JRC composite indicator guidelines. Results have been validated through a multiple scenario analysis (base, supply-led, and risk-aware) and sensitivity testing via Dirichlet bootstrapping (5000 iterations) with ±20% weight perturbations. Six countries of the MENA region have been studied. The multi-criteria decision analysis outcomes rank Egypt (composite score 0.518), Algeria (0.482), and Oman (0.479) as the most suitable countries for large-scale green hydrogen and ammonia production/export, while Saudi Arabia, Qatar, and Kuwait achieved lower supply scores in the base case due to higher perceived risks. Full article

This study numerically investigates the hydrothermal behaviour of a Jeffrey nanofluid with relevance to maritime thermal systems. The coupled nonlinear governing equations for momentum, heat, and mass transport are solved using a shooting technique that accounts for magnetohydrodynamic effects, Darcy porous-media resistance, viscous dissipation, and spatially varying internal heat generation. Variable thermophysical properties, including temperature-dependent viscosity and density, are also considered. The results reveal that porous resistance, fluid elasticity, and thermophysical variations significantly influence velocity, temperature, and concentration fields. The combined effects of porous drag and variable properties markedly alter the characteristics of heat and mass transfer. These findings provide insights into thermal and mass-transport performance, including skin friction, heat transfer, and concentration distributions, which are critical metrics for porous heat exchangers and nanofluid-based maritime coatings. Here, maritime relevance is represented via a generalised porous nanofluid model rather than a specific material. Among the key findings, increasing the slip velocity factor can reduce the surface skin-friction coefficient by approximately 48.7%, while the heat-transfer rate increases by nearly 27.1%, accompanied by a decrease of about 18.9% in the Sherwood number. Conversely, raising the density factor enhances the skin friction coefficient by roughly 103.8% and also augments the heat and mass transfer rates by about 61.3% and 106.1%, respectively. Likewise, at zero relaxation–retardation ratio, the flow reduces to the Newtonian case. Increasing this factor reduces the local Nusselt number by about 1.45%, indicating a slight weakening of heat transfer due to elastic effects. Furthermore, the reliability of the current numerical framework is established through a dual-validation approach, including an analytical assessment of limiting cases and a rigorous comparison with established data from the literature. Full article

The decarbonization of shipping and the transformation towards digitally assisted or automated ship operation require new methods to analyze, predict, and optimize energy demand during maneuvering. The SimPleShip-SigMa sub-project of Hochschule Wismar developed and validated a comprehensive simulation-based framework combining real-time capable fast-time simulation of ship motion, detailed thermodynamic engine modeling, and hybrid data exchange via Functional Mock-up Units (FMU/FMI). The approach enables consistent coupling between navigation-related and machinery-related simulations and supports energy-optimized decision-making on the bridge. Operational relevance and validation of use cases were supported through collaboration with Carnival Maritime GmbH, providing practical feedback on large passenger-ship operations. The study presents the architecture of the simulation environment, the implementation of energy- and emission-prediction models, and the result of validation runs and simulator-based trials. The developed method was applied to a virtual cruise-ship scenario representing a confined coastal environment similar to the Geiranger Fjord. The work builds upon earlier research on simulation-augmented maneuvering and extends it toward a modular digital-twin concept linking hydrodynamic and thermodynamic models. The paper concludes with an outlook on applying the system for crew training, on-board support, and gradual automation of sustainable ship operations. Full article

Ensuring strategic resilience in critical infrastructures supported with a machine learning approach requires moving beyond compliance checklists and post-incident analysis toward proactive, intelligence-based approaches. This study introduces the Forensic Resilience Operational Model (FROM), a systems thinking framework designed to embed forensic intelligence into the resilience cycle of complex socio-technical systems. To quantify this integration, the study investigates the determinants of the extent to which four operational pillars (forensic readiness, anomaly detection, governance and privacy safeguards, and structured intelligence integration) affect forensic resilience, using empirical survey data from 212 cybersecurity professionals across critical infrastructure sectors. We deploy Partial Least Squares Structural Equation Modelling (PLS-SEM) to investigate these relationships, and the results confirm that anomaly detection is the strongest contributor to forensic resilience, followed by structured intelligence integration and forensic readiness. Governance safeguards, while comparatively weaker, provide the necessary legitimacy and assurance of compliance. Supported with sector-specific case studies in the maritime, financial, and CERT domains, the findings highlight both the adaptability of the proposed FROM and the operational constraints encountered in real-world contexts. The study contributes to the field of systems-oriented strategic management by demonstrating that, when systematically embedded, forensic intelligence enhances adaptive capacity, supports predictive decision-making, and strengthens resilience in environments characterized by uncertainty and high complexity. Full article