Search Results (232)

Aging port infrastructure demands frequent and reliable inspections, yet the existing automated systems often require expensive industrial drones, posing significant adoption barriers for local governments with limited resources. To address this challenge, this study develops a low-cost, automated crack detection system for port quay walls utilizing orthophotos generated from a small general-purpose drone. The system employs the YOLOR (You Only Learn One Representation) object detection algorithm, enhanced by two novel image processing techniques—overlapping tiling and pseudo-altitude slicing—to overcome the resolution limitations of low-cost cameras. While official guidelines for port facilities designate 3 mm as an inspection threshold, our system is specifically designed to achieve a higher-resolution detection capability for cracks as narrow as 1 mm. This approach ensures reliable detection with a sufficient safety margin and enables the proactive monitoring of crack progression for preventive maintenance. The effectiveness of the proposed image processing techniques was validated, with an F₁ score-based analysis revealing key trade-offs between maximizing detection recall and achieving a balanced performance depending on the chosen simulated altitude. Furthermore, evaluation using real-world inspection data demonstrated that the proposed system achieves a detection performance comparable to that of a well-established commercial system, confirming its practical applicability. Crucially, by mapping the detected cracks to real-world coordinates on georeferenced orthophotos, the system provides a foundation for advanced, data-driven asset management, allowing for the quantitative tracking of deterioration over time. These results confirm that the proposed workflow is a practical and sustainable solution for infrastructure monitoring. Full article

In the ongoing transition to renewable energy sources, power converters have become indispensable. Their prevalence is increasing, enabling efficient energy conversion, enhancing reliability and stability, and optimizing power extraction from renewable sources. Multi-port dc/dc power converters are widely used because they offer advantages in managing multiple sources and loads. However, designing an automatic control system for these converters presents a challenge due to their complexity. Many configurations for multi-port dc/dc power converters have been proposed, featuring diverse combinations of controllers, modulation techniques, and topologies tailored to specific applications. The body of knowledge on these configurations has grown. Yet, papers have been published according to the authors’ areas of specialization, thus generating a scattered and unorganized body of knowledge and making it difficult to discern research trends and open challenges. Previous studies have attempted to organize knowledge about these configurations, but they have not established a systematic mapping process that follows a rigorous and objective methodology. This paper conducts a systematic mapping study on Automatic Control Systems of multi-port dc/dc power converters. Our study analyzed 122 papers from the 777 papers found around the topic to find and organize the body of knowledge on topology, controller, efficiency, number of elements, modulation technique, and practical applications. This systematic mapping provides a foundational framework for researchers, aiming to inspire further exploration and the development of innovative controller systems in multi-port dc/dc power converters. We found the application of machine learning techniques in dc/dc power converters constitutes an open challenge in these devices. Full article

Reliable coastal and offshore sediment transport data is a requirement for many engineering and environmental projects including port and harbour design, dredging and beach nourishment, sea shoreline protection, inland navigation, marine pollution monitoring, benthic habitat mapping, and offshore renewable energy (ORE). Novel sediment transport numerical modelling approaches allow engineers and scientists to investigate the physical interactions involved in these projects both in the near and far field. However, a lack of confidence in simulated sediment transport results is evident in many coastal and offshore studies, mainly due to limited access to validation datasets. This study addresses the need for cost-effective sediment validation datasets by investigating the applicability of four new suspended load validation techniques to a 2D model of the south-western Irish Sea. This involves integrating an estimated spatial time series of suspended solids concentration (${SSC}_{solids}$) derived from acoustic Doppler current profiler (ADCP) acoustic backscatter with several in situ water sample-based ${SSC}_{solids}$ datasets. Ultimately, a robust spatial time series of ADCP-based ${SSC}_{solids}$ was successfully calculated in this offshore, tidally dominated setting, where the correlation coefficient between estimated ${SSC}_{solids}$ and directly measured ${SSC}_{solids}$ is 0.87. Three out of the four assessed validation techniques are deemed advantageous in developing an accurate 2D suspended sediment transport model given the assumptions of the depth-integrated approach. These recommended techniques include (i) the validation of 2D modelled suspended sediment concentration (${SSC}_{sediment}$) using water sample-based ${SSC}_{solids}$, (ii) the validation of the flood–ebb characteristics of 2D modelled suspended load transport and ${SSC}_{sediment}$ using ADCP-based datasets, and (iii) the validation of the 2D modelled peak ${SSC}_{sediment}$ over a spring–neap cycle using the ADCP-based ${SSC}_{solids}$. Overall, the multi-disciplinary method of collecting in situ metocean and sediment dynamic data via acoustic instruments (ADCPs) is a cost-effective in situ data collection method for future ORE developments and other engineering and scientific projects. Full article

Urban expansion threatens sustainable development in densely populated countries like Bangladesh. This study aims to quantitatively identify and evaluate the key drivers influencing the spatial distribution of urban surfaces (SDUS) in Chattogram City, providing insights into urban growth patterns over 30 years. Using Landsat 5 and 9 imageries, the Normalized Difference Built-up Index (NDBI) was computed for 1993 and 2023 to map urban surface changes. A total of 16 geospatial variables representing potential drivers were analyzed. Four statistical and machine learning methods, including GeoDetector, Distributed Random Forest (DRF), global Geographically Weighted Random Forest (GWRF), and local GWRF, were employed to quantify individual and interactive influences on SDUS. The Geodetector analysis identified the central business district (CBD) as the most influential driver of urban surface distribution, with a q statistic of 0.22, followed by river proximity (q = 0.14) and administrative boundaries (q = 0.13). Across all models, CBD consistently ranked as a dominant factor. In the Distributed Random Forest (DRF) model, CBD showed the highest importance score (0.57), followed by coastlines (0.35) and rivers (0.35). The DRF model achieved the highest performance (R² = 0.612), outperforming the global GWRF (R² = 0.59) and local GWRF (R² = 0.529). Although variables like the proximity of administrative location and forests have low individual impacts, they show a stronger coupled influence. This industrial port-based economy expanded, facing challenges of uncontrolled urbanization, poor governance, and environmental issues. Promoting mixed land use planning, decentralizing urban governance, and improving coordination among implementing agencies may better resolve these issues. This work may help planners and policymakers in planning future cities and developing policies to promote sustainable urban growth. Full article

Interorganizational risk management (IRM) in Canadian ports faces significant challenges due to the interconnected nature of operations and the interdependence of safety, security, environmental, organizational, and technological risks. Existing siloed risk management frameworks often fail to capture these dynamic interrelations, underscoring the need for a more integrated, systemic approach. This study introduces a Port Risk Control Structure (PRCS) designed specifically for Canadian Port Authorities (CPAs), based on the Systems-Theoretic Accident Model and Processes (STAMP). The PRCS maps control actions, feedback loops, and stakeholder roles across international, national, and local levels to better reflect the layered nature of port governance. The model aims to clarify the roles of key actors, such as the International Maritime Organization, Transport Canada, and local port stakeholders, and is designed to facilitate more structured risk identification, communication, and coordination across organizational levels. Although the model has not yet been empirically validated, its design suggests strong potential for scalability and adaptability across diverse port contexts. This research contributes to IRM literature by illustrating how STAMP principles can be operationalized within port systems. Future research will focus on integrating a taxonomy of IRM challenges to refine control structures and feedback mechanisms in response to evolving risks. Full article

Environmental monitoring is essential to assess and, if possible, anticipate the consequences of various marine economic developments. This study describes progress in environmental monitoring by developing and applying a eutrophication index (EI) for marine protected areas (MPAs). The EI combines available data, such as biological oxygen demands, dissolved inorganic nitrogen and phosphorus, and chlorophyll-a, with the weighting factors calculated from principal component analysis to assess environmental quality. Its effectiveness was tested using nearly three decades of environmental data (since 1996) from the Nha Trang MPA in Vietnam. The EI revealed clear trends in environmental quality. In the period 1996–2006, environmental conditions deteriorated, negatively impacting aquaculture. In the later period, 2007–2024, improved environmental protection policies, technological developments, expanding tourism, and heightened public awareness contributed to a reversal of this trend. During the earlier period, the EI indicated poor environmental quality (Level V), while in the later years, it improved significantly, approaching Level II. This study also identified the spatial eutrophication patterns and helped to determine the causes of specific eutrophication levels. These included port development, aquaculture activities, and domestic waste discharge. These findings highlight the close relationship between environmental quality and economic activities in the bay. Overall, the new EI and its sensitivity maps enhance environmental monitoring capabilities. They provide valuable tools for decision-makers, aiding in the strategic planning of marine economic development, ecosystem protection, and sustainable resource use. The approach supports long-term environmental stewardship and more informed, adaptive management of coastal and marine areas. Full article

This study addresses the critical environmental challenge of increasing carbon emissions from Thailand’s freight transport sector, focusing on container movement in the strategic Lat Krabang ICD–Laem Chabang Port corridor. The research quantifies and compares CO₂ emissions between rail and road container transport modes to identify potential carbon reduction strategies. A comprehensive activity-based methodology was employed, incorporating fuel consumption testing across multiple load conditions, detailed transport activity mapping, and the application of locally relevant emission factors. The results demonstrate that rail transport produces 32.82 kgCO₂eq/TEU compared to 53.13 kgCO₂eq/TEU for road transport, representing a 38.23% emission advantage. Fuel consumption testing revealed a power relationship between train weight and fuel consumption (y = 0.1121x^0.5147, R² = 0.97), indicating improving efficiency with increased loading. Terminal operations contribute significantly to rail transport’s emission profile, accounting for 36% of total emissions. The current modal split presents substantial opportunities for emission reduction through increased rail utilization. This study identifies and evaluates practical carbon reduction strategies across operational, technological, and policy dimensions, with priority interventions including load factor optimization, terminal efficiency improvements, locomotive modernization, and differential road pricing. This research contributes empirical evidence to support sustainable freight transport development in Thailand while establishing a methodological framework applicable to emission assessments in similar corridors throughout developing economies. Full article

The rapid evolution of wireless communications has introduced new possibilities for the digital transformation of maritime operations. As 5G begins to take shape in selected nearshore and port environments, the forthcoming 6G promises to unlock transformative capabilities across the entire maritime domain, integrating Terrestrial/Non-Terrestrial Networks (TN/NTN) to form a space-air-ground-sea-underwater system. This paper presents a comprehensive review of how 6G-enabling technologies can be adapted to address the unique challenges of Maritime Communication Networks (MCNs). We begin by outlining a reference architecture for heterogeneous MCNs and reviewing the limitations of existing 5G deployments at sea. We then explore the key technical advancements introduced by 6G and map them to maritime use cases such as fleet coordination, just-in-time port logistics, and low-latency emergency response. Furthermore, the critical Artificial Intelligence/Machine Learning (AI/ML) concepts and algorithms are described to highlight their potential in optimizing maritime functionalities. Finally, we propose a set of resource optimization scenarios, including dynamic spectrum allocation, energy-efficient communications and edge offloading in MCNs, and discuss how AI/ML and learning-based methods can offer scalable, adaptive solutions. By bridging the gap between emerging 6G capabilities and practical maritime requirements, this paper highlights the role of intelligent, resilient, and globally connected networks in shaping the future of maritime communications. Full article

The common dolphin, Delphinus delphis, is the most abundant cetacean species along the western Iberian Peninsula and faces many anthropogenic threats, with bycatch being the most impactful. Its preferred prey, sardine (Sardina pilchardus), has shown fluctuating abundance over the past decade, potentially influencing dolphin distribution. This study provides the first insights into common dolphin distribution along the western Iberian coast, using sighting data from vessel research surveys (2005–2020) to identify hotspot areas while accounting for monthly and seasonal distributions overlapping with sardine abundance. Common dolphin hotspots were located along the central–western and southern Portuguese mainland coasts, coinciding with important fishing ports, oceanographic features, and sardine juvenile habitats. Furthermore, during 2013–2016, common dolphins were observed significantly farther from the coast, coinciding with a period of particularly low coastal sardine biomass. However, GAM analysis indicated that the relationship between sardine biomass and the distance of common dolphins was not significant. This study highlights the major common dolphin hotspots and presents the most comprehensive temporal and distribution maps of the common dolphin along the western Iberian coast, particularly in response to sardine availability. These results can be used by managers to inform conservation measures and for the sustainable management of the Portuguese sardine purse seine fishery fleet, which interacts the most with the species. Full article

Routing and application mapping are critical stages in the design of continuous-flow microfluidic biochips (CFMBs). The routing stage determines the channel network connecting components and ports, while application mapping schedules fluid transportation and wash operations based on the designed biochip architecture. Existing methods typically handle these stages separately: routing focuses solely on physical metrics without considering subsequent scheduling requirements, while application mapping adopts one-shot scheduling strategies that can lead to suboptimal solutions. This paper proposes an integrated path-driven methodology that jointly optimizes routing and application mapping. For routing, we develop a hybrid particle swarm optimization algorithm that incorporates conflict awareness and channel utilization strategies. For application mapping, we introduce an iterative approach that leverages historical scheduling information to progressively optimize fluidic-handling and wash operations. Experimental results on both real and synthetic benchmarks demonstrate significant improvements over state-of-the-art methods, achieving reductions of 22.05% in total channel length, 21.79% in intersections, 21.97% in total delay time, and 8.30% in biochemical reaction completion time. The proposed methodology provides an effective solution for the automated design of CFMBs with enhanced physical and operational efficiency. Full article

Synthetic aperture radar (SAR) is characterized by its all-weather monitoring capabilities and high-resolution imaging. It plays a crucial role in operations such as marine salvage and strategic deployments. However, existing vessel detection technologies face challenges such as occlusion and deformation of targets in multi-scale target detection and significant interference noise in complex scenarios like coastal areas and ports. To address these issues, this paper proposes an algorithm based on YOLOv8 for detecting ship targets in complex backgrounds using SAR images, named DFENet (Denoising and Feature Enhancement Network). First, we design a background suppression and target enhancement module (BSTEM), which aims to suppress noise interference in complex backgrounds. Second, we further propose a feature enhancement attention module (FEAM) to enhance the network’s ability to extract edge and contour features, as well as to improve its dynamic awareness of critical areas. Experiments conducted on public datasets demonstrate the effectiveness and superiority of DFENet. In particular, compared with the benchmark network, the detection accuracy of mAP75 on the SSDD and HRSID is improved by 2.3% and 2.9%, respectively. In summary, DFENet demonstrates excellent performance in scenarios with significant background interference or high demands for positioning accuracy, indicating strong potential for various applications. Full article

The accurate detection of small ships based on images or vision is critical for many scenarios, like maritime surveillance, port security, and navigation safety. However, achieving accurate detection for small ships is a challenge for cost-efficiency models; while the models could meet this requirement, they have unacceptable computation costs for real-time surveillance. We propose YOLO-LPSS, a novel model designed to significantly improve small ship detection accuracy with low computation cost. The characteristics of YOLO-LPSS are as follows: (1) Strengthening the backbone’s ability to extract and emphasize features relevant to small ship objects, particularly in semantic-rich layers. (2) A sophisticated, learnable method for up-sampling processes is employed, taking into account both deep image information and semantic information. (3) Introducing a post-processing mechanism in the final output of the resampling process to restore the missing local region features in the high-resolution feature map and capture the global-dependence features. The experimental results show that YOLO-LPSS outperforms the known YOLOv8 nano baseline and other works, and the number of parameters increases by only 0.33 M compared to the original YOLOv8n while achieving 0.796 and 0.831 AP^50:95 in classes consisting mainly of small ship targets (the bounding box of the target area is less than 5% of the image resolution), which is 3–5% higher than the vanilla model and recent SOTA models. Full article

This paper tackles the highly challenging problem of automatic berthing for autonomous surface vessels (ASVs), encompassing trajectory planning, trajectory tracking, and collision avoidance. Firstly, a novel A* algorithm integrated with a quasi-uniform B-spline and quadratic interpolation method (A*QB) is proposed for generating a smooth trajectory from the initial position to the berth, utilizing an offline-generated scaled map. Secondly, the optimal nonlinear model predictive control (NMPC)-based trajectory-tracking framework is established, incorporating the model’s uncertainty, the input saturation, and environmental disturbances, based on a 3-DOF model of a ship. Finally, considering the collision risks during port berthing, a COLREGs-based collision avoidance method is investigated. Consequently, a novel trajectory-tracking and COLREGs-based collision avoidance (TTCCA) scheme is proposed, ensuring that the ASV navigates along the desired trajectory, safely avoids both static and dynamic obstacles, and successfully reaches the berth. To validate the TTCCA approach, numerical simulations are conducted across four scenarios with comparisons to existing methods. The experimental results demonstrate the effectiveness and superiority of the proposed scheme. Full article

To address the high-precision measurement requirements for container pose on dual-trolley quayside crane-transfer platforms, this paper proposes a machine vision-based measurement method that resolves the challenges of multi-scale lockhole detection and precision demands caused by complex illumination and perspective deformation in port operational environments. A hardware system comprising fixed cameras and edge computing modules is established, integrated with an adaptive image-enhancement preprocessing algorithm to enhance feature robustness under complex illumination conditions. A multi-scale adaptive frequency object-detection framework is developed based on YOLO11, achieving improved detection accuracy for multi-scale lockhole keypoints in perspective-distortion scenarios (mAP@0.5 reaches 95.1%, 4.7% higher than baseline models) through dynamic balancing of high–low-frequency features and adaptive convolution kernel adjustments. An enhanced EPnP optimization algorithm incorporating lockhole coplanar constraints is proposed, establishing a 2D–3D coordinate transformation model that reduces pose-estimation errors to millimeter level (planar MAE-P = 0.024 m) and sub-angular level (MAE-$\theta$ = 0.11°). Experimental results demonstrate that the proposed method outperforms existing solutions in container pose-deviation-detection accuracy, efficiency, and stability, proving to be a feasible measurement approach. Full article

Container intermodal scheduling is critical for advancing low-carbon logistics within inland port systems. However, the scheduling process faces several challenges, including the complexity of coordinating transport modes and complying with carbon emission policies. To address these issues, this study proposes a multi-objective optimization model that simultaneously considers transportation cost, carbon emissions, and time efficiency under soft time window constraints. The model is solved using an improved grey wolf–Harris hawks hybrid algorithm (IGWOHHO). This algorithm enhances population diversity through Tent chaotic mapping, balances global exploration and local exploitation with adaptive weight adjustment, and improves solution quality by incorporating an elite retention strategy. Benchmark tests show that IGWOHHO outperforms several well-established metaheuristic algorithms in terms of convergence accuracy and robustness. A case study based on an intermodal transport network further demonstrates that adjusting the objective weights flexibly provides decision support under various scenarios, achieving a dynamic balance between cost, efficiency, and environmental impact. Additionally, the analysis reveals that appropriate carbon tax pricing can encourage the adoption of greener transport modes, promoting the sustainable development of multimodal logistics systems. Full article