Search Results (802)

While battery-powered propulsion represents a promising pathway for inland waterway freight, its widespread adoption is hindered by range anxiety and high investment costs. Strategic energy replenishment has emerged as a critical and cost-effective solution to extend voyage endurance and mitigate these barriers. This paper introduces a novel approach to optimize energy replenishment strategies for inland electric ships that considers the possibility of adopting multiple technologies (charging and battery swapping) and partial replenishment. The proposed approach not only identifies optimal replenishment ports but also determines the technology to employ and the corresponding amount of energy to replenish for each operation, aimed at minimizing total replenishment costs. This problem is formulated as a mixed-integer linear programming model. A case study of a 700-TEU electric container ship operating on two routes along the Yangtze River validates the effectiveness of the proposed approach. The methodology demonstrates superior performance over existing approaches by significantly reducing replenishment costs and improving solution feasibility, particularly in scenarios with tight schedules and limited technology availability. Furthermore, a sensitivity analysis examines the impacts of key parameters, offering valuable strategic insights for industry stakeholders. Full article

The increasing need to optimize efficiency in port container terminals has led to the transition of operations from manual to automated or semi-automated processes. Automation involves integrating or gradually adopting digital technologies and equipment that reduce human intervention, enhance productivity, safety and sustainability. This study investigates the impact of automation on port efficiency through a comparative analysis of 20 container ports in the wider Mediterranean region, using a two-stage modeling approach. In the first stage, Data Envelopment Analysis (DEA) is applied under constant and variable returns to scale to estimate port efficiency using infrastructure, equipment, and container throughput data. The second stage employs Tobit regression to assess the effect of automated operations or systems on port efficiency, including variables such as the automation index, TEUs per employee, TEUs per ship (call) and revenue. A key contribution of this study is the development of a methodological framework for qualitatively classifying and evaluating these ports based on their level of automation, the introduction of digital technologies or equipment, and investments in new technologies. The results indicate that automation alone does not necessarily lead to higher efficiency unless it is effectively integrated into operations accompanied by adequate staff training and supported by gradual investment strategies. By contrast, cargo intensity (TEUs per call), highlights the importance of vessel size and cargo concentration in improving port performance. Full article

The desire to improve the safety of navigation, especially in restricted and very busy areas like the straits, leads researchers to evaluate possible uses of Artificial Intelligence as an alternative to the traditional probabilistic methods. This is possible thanks to the large amount of available AIS data generated by ships in transit. In this work, a Machine Learning algorithm (Classification Decision Tree) was trained with eight features coming from AIS data of the Strait of Messina (Italy), with the aim of carrying out a two-class classification of the single AIS data to find anomalies in ship transits that could compromise navigation safety. Since anomalous events are relatively rare, compared to the large amount of information related to the normal navigation situations, the challenge of this work was to obtain an artificial dataset with the aim of simulating the possible anomalous navigation conditions for the Strait investigated, known the active risk mitigation means one. For this reason, the dataset containing abnormal events was obtained simulating different risk scenarios. A hyperparameters tuning with a Bayesian optimization approach and a 5-fold cross validation have been performed to improve the quality of the model and a large dataset has been tested. The accuracy of both validation and test phases is <99.5% and <95.9%, respectively. This can make it possible to identify anomalous navigation conditions in real time, in order to quickly classify possible conditions of risk. The method can be used as a Decision Support Tool by the authority in order to improve the capacity of the single operator to identify the possible risk situation inside the Strait of Messina. Full article

In recent years, deep learning (DL)-based synthetic aperture radar (SAR) ship detection has made significant strides. However, many existing DL-based SAR ship detection methods treat sea regions and land regions equally, failing to be fully aware of the differences between the two regions during training and testing. This oversight may prevent the network’s attention from fully concentrating on valuable regions (i.e., sea regions and ship regions), thereby adversely affecting overall detection accuracy. To address these issues, we propose the Sea–Land Aware Net (SLA-Net), which introduces a novel SLA Hierarchical Attention mechanism to gradually focus the network’s attention on sea and ship regions across different stages. Specifically, SLA-Net instantiates the SLA Hierarchical Attention mechanism through three components: the SLA Sea-Attention Backbone, which incorporates sea attention in the feature extraction stage; the SLA Ship-Attention FPN, which implements ship attention in the feature fusion stage; and the SLA Ship-Attention Detection Heads, which enforce ship attention in the detection refinement stage. Moreover, to tackle the lack of sea–land priors in the community working on DL-based SAR ship detection, we introduce the sea–land segmentation dataset for SSDD (SL-SSDD). Built upon the well-established SAR ship detection dataset (SSDD), it serves as a synergistic dataset for ship detection when used in conjunction with SSDD. Quantitative experimental results on SSDD and generalization results on HRSID and LS-SSDD demonstrate that SLA-Net achieves superior SAR ship detection performance compared to other methods. Furthermore, SL-SSDD, which contains sea–land segmentation information, can provide a new perspective for the community working on DL-based SAR ship detection. Full article

The port of Gaženica, managed by the Port Authority of Zadar, is open to public traffic of special economic interest to the Republic of Croatia. Situated outside Zadar’s city centre, with convenient access to the airport and A1 highway, this port presents significant opportunities for Zadar County’s economic growth. While also serving as a cargo and fishing port, as the second-largest passenger port in Croatia, the port of Gaženica prioritises the development of cruise ship traffic. The expansion of intermodal traffic is being facilitated through the development of a multipurpose terminal to accommodate general, roll-on/roll-off, and containerised cargo (full and empty containers). The rising number of passenger ships—particularly cruise ships—along with the increasing passenger, vehicle, and cargo traffic, poses a significant risk of pollution due to dust, noise, greenhouse gases, and other pollutants. Considering these risks, the use of alternative energy sources, decarbonisation of maritime transport, the separation of waste by type, and the proper handling and disposal of ship waste are of utmost importance. The aim of this study is to present and analyse the green transition process of the port of Gaženica through the results that have been achieved or are yet to be achieved through the implementation of green projects by the Port Authority of Zadar. For this purpose, a mixed-methods approach combining project analysis and the qualitative analysis of emissions data is used. It is important to highlight that the method of interviews with relevant representatives of institutions involved in the project was also used to gain insight into financial and infrastructural challenges, the accessibility of certain data, and potential improvements in implementation. The research results indicate that the port of Gaženica has completed four green projects, while another four are currently being implemented, with their completion expected by 2026. The research concludes that it is necessary to strengthen environmental awareness regarding proper waste disposal among all stakeholders in maritime transport, including the local community, businesses, and local authorities. The results demonstrate a need to focus on certification with the aim of strengthening the green transition process through involvement in the EcoPorts and Green Award certification schemes. It is also necessary to actively improve the public availability of data from the base station in the port of Gaženica to inform the public about environmental impacts in real time (24/7) while facilitating data collection for statistical reporting purposes. Full article

Power electronic devices for AC/DC and AC/AC conversion are, nowadays, widely distributed in electrified transportation and industrial applications, which can determine significant deviation in supply voltage waveform from the AC sinusoidal and promote insulation extrinsic aging mechanisms as partial discharges (PDs). PDs are one of the most harmful processes as they are able to cause accelerated extrinsic aging of electrical insulation systems and are the cause of premature failure in electrical asset components. PD phenomenology under pulse width modulated (PWM) voltage waveforms has been dealt with in recent years, also through some IEC/IEEE standards, but less work has been performed on PD harmfulness under AC distorted waveforms containing voltage harmonics and notches. On the other hand, these voltage waveforms can often be present in electrical assets containing conventional loads and power electronics loads/drives, such as for ships or industrial installations. The purpose of this paper is to provide a contribution to this lack of knowledge, focusing on PD sensing and phenomenology. It has been shown that PD patterns can change considerably with respect to those known under sinusoidal AC when harmonic voltages and/or notches are present in the supply waveform. This can impact PD typology identification, which is based on features related to PD pattern-based physics. The adaptation of identification AI algorithms used for AC sinusoidal voltage as well as distorted AC waveforms is discussed in this paper, showing that effective identification of the type of defects generating PD, and thus of their harmfulness, can still be achieved. Full article

Background: Schedule reliability in container liner services is essential for the efficiency of maritime and inland transport, terminal operations, and the overall supply chain. Disruptions to vessel schedules can trigger a series of disruptions at other points, generating additional operational costs for carriers, terminal operators, inland transport providers, and ultimately, for importers, exporters, and end consumers. Methods: The research paper combines literature reviews and shipping company data. A qualitative analysis contains specific causes of vessel delays and corrective actions used to realign schedules with the pro forma plan. The analysis was expanded to include transport of cargo in containers from origin to the final inland destination. Results: Disruption factors are identified and classified by their place of occurrence: (1) inland transport, (2) anchorage, (3) ports, and (4) navigation between ports. The research produced several new disruptive factors previously not identified and published. It has been confirmed that port congestion acts as the principal cause of delay in liner service. Conclusions: The findings indicate that while the number and complexity of disruptive factors are increasing due to global and regional dynamics, the range of recovery measures remains narrow. A deeper understanding of these causes enables more effective prevention, aiming to minimize supply chain disruptions and costs and increase the reliability of door-to-door container transport. Full article

To reduce fossil fuel dependency in shipping, adopting alternative fuels and innovative propulsion systems is essential. Solid Oxide Fuel Cells (SOFC), powered by hydrogen carriers, represent a promising solution. This study investigates a multi-fuel SOFC system for ocean-going vessels, capable of operating with ammonia, methanol, or hydrogen, thus enhancing bunkering flexibility. A thermodynamic model is developed to simulate the performance of a 3 kW small-scale system, subsequently scaling up to a 10 MW configuration to meet the power demand of a container ship used as the case study. Results show that methanol is the most efficient fueling option, reaching a system efficiency of 58% while ammonia and hydrogen reach slightly lower values of about 55% and 51%, respectively, due to higher auxiliary power consumption. To assess technical feasibility, two installation scenarios are considered for accommodating multiple fuel tanks. The first scenario seeks the optimal fuel share equivalent to the diesel tank’s chemical energy (17.6 GWh), minimizing mass increase. The second scenario optimizes the fuel share within the available tank volume (1646 m³), again, minimizing mass penalties. In both cases, feasibility results have highlighted that changes are needed in terms of cargo reduction, equal to 20.3%, or, alternatively, in terms of lower autonomy with an increase in refueling stops. These issues can be mitigated by the benefits of increased bunkering flexibility. Full article

This study evaluates the environmental impacts of shipbuilding materials through life cycle assessment (LCA) and assesses potential exposure under the EU Carbon Border Adjustment Mechanism (CBAM). Three representative vessel types, a pure car and truck carrier (PCTC), a bulk carrier, and a container ship, were analyzed across scenarios reflecting different steelmaking routes, recycling rates, and regional energy mixes. Results show that structural steel (AH36, EH36, DH36, A/B grades) overwhelmingly dominates embedded emissions, while aluminium and copper contribute secondarily but with high sensitivity to recycling and energy pathways. Coatings, polymers, and yard processes add smaller but non-negligible effects. Scenario-based CBAM cost estimates for 2026–2030 indicate rising liabilities, with container vessels facing the highest exposure, followed by bulk carriers and PCTCs. The findings highlight the strategic importance of steel sourcing, recycling strategies, and verifiable supply chain data for reducing embedded emissions and mitigating financial risks. While operational emissions still dominate the life cycle, the relative importance of construction-phase emissions will grow as shipping decarbonizes. Current EU-level discussions on extending CBAM to maritime services, together with recognition of domestic carbon pricing as a potential pathway to reduce liabilities, underscore regulatory uncertainty and emphasize the need for harmonized methods, transparent datasets, and digital integration to support decarbonization. Full article

The sustainability of a port is directly related to the time spent by ships in terminals and depends on the terminal, the technologies used in it, and external conditions. Currently used sustainable port terminal technologies allow a significant increase in the intensity of ship loading operations and, at the same time, shorten the time spent by ships at the quays. Since port construction processes take a lot of time, many ports have many quays every day that are not moored by ships. Ports try to attract passenger and cargo flows, but they are also not infinite. In individual port terminals, for example, container and Ro–Ro terminals, most of the time is spent on cargo processing inside the terminal, and only part of the time is spent on ship loading operations. Probabilistic assessment of ship mooring at quays allows an understanding of not only the optimal need for quays and modernization of their equipment, but at the same time for a more purposeful assessment of the possibilities of using quays, accepting diversification options and, therefore, optimizing the ports themselves as a sustainable port entity. The article presents a methodology for assessing berth occupancy focused on the development of a sustainable port based on probabilistic methods that would allow calculating potential berth occupancy. The developed methodology, compared to existing methodologies and models, allows for a more realistic assessment of the expected berth occupancy, using actual port and ship data. The presented theoretical and experimental research results confirm the suitability of the developed methodology for the development of a sustainable port and the possibilities of applying the developed methodology in any port, adapting it to specific port conditions. Full article

Accurate carbon emission estimation across all operational stages of container terminals is essential for advancing low-carbon development in the transportation sector and designing effective emission reduction pathways. This study develops a two-layer carbon accounting framework that integrates vessel berthing–waiting and terminal operations, tailored to the operational characteristics of Shanghai Port container terminals. The Ship Traffic Emission Assessment Model (STEAM) is applied to estimate emissions during berthing, while a bottom-up method is employed for mobile-mode container handling operations. Targeted mitigation strategies—such as shore power adoption, operational optimization, and “oil-to-electricity” or “oil-to-gas” transitions—are evaluated through comparative analysis. Results show that vessels generate substantial emissions during erthing, which can be significantly reduced (by over 60%) through shore power usage. In terminal operations, internal transport trucks have the highest emissions, followed by straddle carriers, container tractors, and forklifts; in stacking, tire cranes dominate emissions. Comprehensive comparisons indicate that “oil-to-electricity” can reduce total emissions by approximately 39%, while “oil-to-gas” can achieve reductions of about 73%. These findings provide technical and policy insights for supporting the green transformation of container terminals under the national dual-carbon strategy. Full article

Synthetic aperture radar (SAR) provides all-weather and all-day imaging capabilities and can penetrate clouds and fog, playing an important role in ship detection. However, small ships usually contain weak feature information in such images and are easily affected by noise, which makes detection challenging. In practical deployment, limited computing resources require lightweight models to improve real-time performance, yet achieving a lightweight design while maintaining high detection accuracy for small targets remains a key challenge in object detection. To address this issue, we propose a novel lightweight network for accurate small-ship recognition in SAR images, named S2M-Net. Specifically, the Space-to-Depth Convolution (SPD-Conv) module is introduced in the feature extraction stage to optimize convolutional structures, reducing computation and parameters while retaining rich feature information. The Mixed Local-Channel Attention (MLCA) module integrates local and channel attention mechanisms to enhance adaptation to complex backgrounds and improve small-target detection accuracy. The Multi-Scale Dilated Attention (MSDA) module employs multi-scale dilated convolutions to fuse features from different receptive fields, strengthening detection across ships of various sizes. The experimental results show that S2M-Net achieved mAP50 values of 0.989, 0.955, and 0.883 on the SSDD, HRSID, and SARDet-100k datasets, respectively. Compared with the baseline model, the F1 score increased by 1.13%, 2.71%, and 2.12%. Moreover, S2M-Net outperformed other state-of-the-art algorithms in FPS across all datasets, achieving a well-balanced trade-off between accuracy and efficiency. This work provides an effective solution for accurate ship detection in SAR images. Full article

The growth of greenhouse gas emissions, driven by the use of internal combustion engines (ICE), highlights the urgent need for sustainable solutions, particularly in the shipping sector. Non-invasive predictive maintenance using acoustic signal analysis has emerged as a promising strategy for fault diagnosis in ICEs. In this context, the present study proposes a hybrid Deep Learning (DL) model and provides a novel publicly available dataset containing real operational sound samples of ICEs, labeled across 12 distinct fault subclasses. The methodology encompassed dataset construction, signal preprocessing using log-mel spectrograms, and the evaluation of several Machine Learning (ML) and DL models. Among the evaluated architectures, the proposed hybrid model, BiGRUT (Bidirectional GRU + Transformer), achieved the best performance, with an accuracy of 97.3%. This architecture leverages the multi-attention capability of Transformers and the sequential memory strength of GRUs, enhancing robustness in complex fault scenarios such as combined and mechanical anomalies. The results demonstrate the superiority of DL models over traditional ML approaches in acoustic-based ICE fault detection. Furthermore, the dataset and hybrid model introduced in this study contribute toward the development of scalable real-time diagnostic systems for sustainable and intelligent maintenance in transportation systems. Full article

The use of ammonia-fueled solid oxide fuel cells (NH3-SOFC) in shipping has emerged as a key area of research for advancing zero-carbon transportation. This study integrates and analyzes a novel ship design powered by NH3-SOFCs to quantify its environmental impact across its entire life-cycle, from production to disposal. A 200 TEU ammonia-fueled container ship operating on the Yangtze River is used as the reference vessel. Comprehensive technical analysis and modeling of the ship’s construction, operation, and Decommissioning stages are conducted. By utilizing life-cycle assessment and the ReCiPe 2016 method for calculations, 19 environmental impact indicators were obtained, weighted, and normalized. Life-cycle characterization results reveal that ecosystem and human health impacts are predominantly influenced by the operation stage. Thus, focusing on environmental protection measures and technological innovations during operation is crucial to mitigate these impacts. Conversely, resource depletion is mainly driven by the construction stage, underscoring the need for optimized design, production processes, and the use of eco-friendly materials to reduce resource consumption. A comparative analysis between diesel-powered and ammonia-powered ships shows that while ammonia SOFC ships have a slightly higher environmental load in terms of metal consumption, diesel-powered ships exhibit higher overall environmental loads in other impact indicators. This demonstrates the superior environmental and social benefits of ammonia SOFC ships compared to traditional diesel power systems. Full article

Every year, more and more general and other types of cargo are transported by containers, and many ports, including small and medium-sized ones, are trying to join the container transportation processes. Port connectivity with container shipping is associated with easier and faster cargo processing and reduced environmental impact by optimizing ship arrivals and processing in small and medium-sized ports. Small and medium-sized ports are often limited by port infrastructure, especially suitable quays; therefore, it is very important to correctly assess the capabilities of such ports so that ships do not have to wait for entry and so that quays and other port infrastructure are optimally used. The research is relevant because small and medium-sized ports are increasingly involved in the activities of logistics chains and are becoming very important for the development of individual regions. The wider use of small and medium-sized ports in logistics chains is a new and original research direction. Optimal assessment of port or terminal and berth utilization is possible using the principles of probability theory. The article develops and presents a probabilistic method for assessment of port and terminal and ship mooring at their berths, using possible and actual time periods, based on the principles of transport process organization and linked to the capabilities of the port infrastructure and terminal superstructure. The conditional probability method was used to assess port and terminal capacity, as well as a method for assessing ship maneuverability under limited conditions. The developed probabilistic method for assessing port terminals and ship berthing at port quays can be used in any port or terminal, taking into account local conditions. Combined theoretical research and experimental results of the optimal use of small and medium-sized ports ensure sufficient research quality. Full article