Search Results (334)

High-speed switching chips are key components of network core devices in the high-performance computing paradigm, and their scheduling algorithm performance directly influences the throughput, latency, and fairness within the system. However, traditional scheduling algorithms often encounter issues such as high implementation complexity and high communication overhead when dealing with bursty traffic. To addressing the issue of bottlenecks in high-speed switching chip scheduling, we propose a low-complexity and high-performance scheduling algorithm called m-RGA, where m represents a priority mechanism. First, by monitoring the historical service time and load level of the VOQs at the port, the priority of the VOQs is dynamically adjusted to enhance the efficient matching and fair allocation of port resources. Additionally, we prove that an algorithm achieving a 2× speedup under a constant traffic model can simultaneously guarantee throughput and latency, making this algorithm theoretically as excellent as any maximum matching algorithm. Through simulation, we demonstrate that m-RGA outperforms Highest Rank First (HRF) arbitration in terms of latency under non-uniform and bursty traffic patterns. Full article

The rising frequency of network intrusions has significantly impacted critical infrastructures, leading to an increased focus on the detection of malicious network traffic in recent years. However, traditional port-based and classical machine learning-based malicious network traffic detection methods suffer from a dependence on expert experience and limited generalizability. In this paper, we propose a malicious traffic detection method based on an efficient federated learning framework of Bidirectional Encoder Representations from Transformers (BERT), called MT-FBERT. It offers two major advantages over most existing approaches. First, MT-FBERT pretrains BERT using two pre-training tasks along with an overall pre-training loss on large-scale unlabeled network traffic, allowing the model to automatically learn generalized traffic representations, which do not require human experience to extract the behavior features or label the malicious samples. Second, MT-FBERT finetunes BERT for malicious network traffic detection through an efficient federated learning framework, which both protects the data privacy of critical infrastructures and reduces resource consumption by dynamically identifying and updating only the most significant neurons in the global model. Evaluation experiments on public datasets demonstrated that MT-FBERT outperforms state-of-the-art baselines in malicious network traffic detection. Full article

Advanced Persistent Threat (APT) attacks pose a serious challenge to traditional detection methods. These methods often suffer from high false-alarm rates and limited accuracy due to the multi-stage and covert nature of APT attacks. In this paper, we propose TSE-APT, a time-series ensemble model that addresses these two limitations. It combines multiple machine-learning models, such as Random Forest (RF), Multi-Layer Perceptron (MLP), and Bidirectional Long Short-Term Memory Network (BiLSTM) models, to dynamically capture correlations between multiple stages of the attack process based on time-series features. It discovers hidden features through the integration of multiple machine-learning models to significantly improve the accuracy and robustness of APT detection. First, we extract a collection of dynamic time-series features such as traffic mean, flow duration, and flag frequency. We fuse them with static contextual features, including the port service matrix and protocol type distribution, to effectively capture the multi-stage behaviors of APT attacks. Then, we utilize an ensemble-learning model with a dynamic weight-allocation mechanism using a self-attention network to adaptively adjust the sub-model contribution. The experiments showed that using time-series feature fusion significantly enhanced the detection performance. The RF, MLP, and BiLSTM models achieved 96.7% accuracy, considerably enhancing recall and the false positive rate. The adaptive mechanism optimizes the model’s performance and reduces false-alarm rates. This study provides an analytical method for APT attack detection, considering both temporal dynamics and context static characteristics, and provides new ideas for security protection in complex networks. Full article

This study presents a data-driven analysis of navigator stress and workload levels in simulated ship encounters within restricted waters, leveraging real-world automatic identification system (AIS) data from Makassar Port, Indonesia. Six close-quarter scenarios were recreated to reflect critical encounter geometries, and 24 Indonesian seafarers were evaluated using heart rate variability (HRV), perceived stress scale (PSS), and task load index (NASA-TLX) workload assessments. The results indicate that crossing angles, particularly 135° port and starboard encounters, significantly influence physiological stress levels, with age being a moderating factor. Although no consistent relationship was found between workload and HRV metrics, the findings underscore key human factors that may impair navigational performance under cognitively demanding conditions. By integrating AIS-derived traffic data with simulation-based human performance monitoring, this study supports the development of intelligent maritime training frameworks and adaptive decision support systems. The research contributes to broader efforts toward enhancing navigational safety and situational awareness amid increasing automation and traffic densities at sea. Full article

Danube River represents a critical axis of ecological and economic importance for the countries along its course. From this perspective, this paper aims to assess the most significant characteristics of the river and of its main tributaries, as well as its impact on the environmental sustainability and socio-economic development. Navigation and the economic contribution of the Danube River are the key issues of this work, emphasizing its importance as an international transport artery that facilitates trade and tourism, and develops the energy industry through hydropower plants. The study includes an analysis of the volume of goods transported from 2019 to 2023, as well as an analysis of the goods traffic in the busiest port on the Danube. Furthermore, climate change affects the hydrological regime of the Danube, as well as the ecosystems, economy, and energy security of the riparian countries. Main impacts include changes in the hydrological regime, increased frequency of droughts and floods, reduced water quality, deterioration of biodiversity, and disruption of the economic activities dependent on the river, such as navigation, agriculture, and hydropower production. Thus, hydrological risks and challenges are investigated, focusing on the extreme events of the last two decades and the awareness of their repercussions. In this context, the national and international institutions responsible for monitoring and managing the Danube are presented, and their role in promoting a sustainable river policy is explored. Methods and technologies are shown to be essential tools for monitoring and prediction studies. The Danube includes an extensive network of hydrometric stations that help to prevent and manage the most significant risks. Finally, a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis of the development of the hydrological studies was conducted, highlighting the potential of the river. Full article

Background: The Port of Santa Marta, located on Colombia’s northern Caribbean coast, plays a vital role in the country’s maritime trade, particularly in the export of agricultural and perishable goods. This raises the question: how competitive is Santa Marta’s container terminal compared to national and regional ports, and what strategic factors shape its performance within the Colombia and Latin American maritime logistics system? Methods: This study evaluates the port’s competitiveness by applying Porter’s Extended Diamond Model. A mixed-methods ap-proach was employed, combining structured surveys and interviews with port stakeholders and operational data analysis. A competitiveness matrix was developed and examined using standardized residuals and L1 regression to identify critical performance gaps and strengths. Results: The analysis reveals several competitive advantages, including the port’s strategic location, natural deep-water access, and advanced infrastructure for refrigerated cargo. It also benefits from skilled labour and proximity to global shipping routes, such as the Panama Canal. Nonetheless, challenges remain in storage capacity, limited road connectivity, and insufficient public investment in hinterland infrastructure. Conclusions: While the Port of Santa Marta shows strong maritime capabilities and spe-cialized services, addressing its land-side and institutional constraints is essential for positioning it as a resilient, competitive logistics hub in the Latin American and Caribbean region. Full article

Due to increased container traffic, the problems of congestion at terminal gates generate serious air pollution and decrease terminal efficiency. To address this issue, many terminals are implementing a truck appointment system (TAS) based on several concepts. Our work addresses gate congestion at a container terminal. A conceptual model was developed to identify system components and interactions, analyzing container flow from both static and dynamic perspectives. A truck appointment system (TAS) was modeled to optimize waiting times using a non-stationary approach. Compared to existing methods, our TAS introduces a more adaptive scheduling mechanism that dynamically adjusts to fluctuating truck arrivals, reducing peak congestion and improving resource utilization. Unlike traditional static appointment systems, our approach helps reduce truckers’ dissatisfaction caused by the deviation between the preferred time and the assigned one, leading to smoother operations. Various genetic algorithms were tested, with a hybrid genetic–tabu search approach yielding better results by improving solution stability and reducing computational time. The model was applied and adapted to the Port of Casablanca using real-world data. The results clearly highlight a significant potential to enhance sustainability, with an annual reduction of 785 tons of CO₂ emissions from a total of 1281 tons. Regarding trucker dissatisfaction, measured by the percentage of trucks rescheduled from their preferred times, only 7.8% of arrivals were affected. This improvement, coupled with a 62% decrease in the maximum queue length, further promotes efficient and sustainable operations. Full article

In recent years, congestion on port motorways has become increasingly frequent, significantly constraining transportation efficiency and contributing to higher pollution emissions. This paper proposes a novel max-pressure-driven integrated control (IFC-MP) for port motorways, inspired by the max pressure (MP) concept, which continuously adjusts the weights of ramp metering (RM) and the variable speed limit (VSL) based on pressure feedback from the on-ramp and upstream, assigning greater control weight to the side with higher pressure. A queue management mechanism is incorporated to prevent on-ramp overflow. The effectiveness of IFC-MP is verified in SUMO, filling the gap where the previous integrated control methods for port motorways lacked micro-simulation validation. The results show that IFC-MP enhances bottleneck throughput by approximately 7% compared to the no-control case, optimizes the total time spent (TTS) by 26–27%, and improves total pollutant emissions (TPEs) by about 11%. Compared to strategies that use only RM and VSL control, or activate VSL control only after RM reaches its lower bound, the time–space distribution of speed under IFC-MP is more uniform, with smaller fluctuations in bottleneck occupancy. Additionally, IFC-MP maintains relatively stable performance under varying compliance levels. Overall, the IFC-MP is an effective method for alleviating congestion on port motorways, excelling in optimizing both traffic efficiency and pollutant emissions. Full article

Seaports, as major transportation hubs, generate significant air pollution due to intensive ship traffic, directly affecting local air quality. While emission inventories are commonly used to manage ship-based air pollution, they reflect only the emission-related aspect of a specified period and area, limiting the broader interpretability and comparability of the results. To overcome the mentioned challenges, this research presents the PrE-PARE model, which enables the prediction, analysis, and risk evaluation of ship-sourced air pollution in port areas. The model comprises three interconnected modules. The first is applied for quantifying emissions using detailed technical and movement datasets, which are combined into individual voyage trajectories to enable a high-resolution analysis of ship-based air pollutants. In the second module, the Multivariate Adaptive Regression Splines (MARS) machine learning method is adapted to predict emissions in varying operational scenarios. In the third module, novel metric methods are introduced, enabling a standardised efficiency comparison between ships. These methods are supported by a unique classification system to determine the emission risk in different periods, evaluate the intensity of various ship types, and rank individual ships based on their operational efficiency and emission optimisation potential. By combining new methods with technical and operational shipping data, the model provides a transparent, comparable, and adaptable system for emissions monitoring. The results demonstrate that the PrE-PARE model has the potential to improve strategic planning and air quality management in ports while remaining flexible enough to be applied in different contexts and future scenarios. Full article

Congestion on port motorways often leads to reduced capacity and traffic efficiency, while the growing prevalence of connected vehicles (CVs) offers new opportunities for improving traffic control. This paper proposes a hierarchical control method integrating ramp metering (RM) and variable speed limits (VSLs) explicitly designed for port motorway environments dominated by CVs. The method uses real-time CV data to reduce congestion through a hierarchical control framework in which the upper-level optimization determines system-wide parameters, and the lower-level execution translates them into local control commands. A microscopic simulation using SUMO in the Guoju area of the Chuanshan Port Motorway demonstrated that the proposed method increases traffic capacity by approximately 16% compared to the no-control scenario and improves traffic efficiency by 4.8% and 4.5% compared to PI-ALINEA and MTFC-FB, respectively. Further experiments in varying CV penetration rates (MPRs) from 60% to 100% revealed that while lower MPRs result in higher traffic fluctuations, the method remains effective and robust, particularly when MPRs exceed 80%. This highlights its ability to mitigate congestion and enhance the utilization of the existing infrastructure. Full article

This study presents a structured multi-agent system (MAS) architecture aimed at optimizing operational efficiency in roll-on/roll-off (Ro-Ro) terminal management through intelligent coordination and decentralized decision-making. The proposed framework enhances space allocation, route planning, traffic control, and boarding coordination, enabling real-time decision-making and adaptive operational strategies. Through structured MAS architecture, agents interact dynamically to optimize vehicle flow, reducing congestion and improving overall efficiency. The study evaluates the system’s potential benefits compared to traditional port management models, highlighting improvements in transit time reduction, resource utilization, and operational resilience. The findings suggest that MAS-based automation can enhance decision-making, sustainability, and integration with Industry 4.0 paradigms, driving the transition toward intelligent, efficient, and scalable port logistics. Full article

Ballast water has become a significant vector for the global spread of non-indigenous aquatic species. These species may cause severe ecological disruption and economic losses when introduced into new environments. Traditional monitoring techniques often lack the sensitivity and efficiency required for early monitoring, hindering timely and effective management. In this study, we used environmental DNA (eDNA) technology to assess fish diversity and identify non-indigenous fish species in ballast water samples collected from 14 international vessels entering Dongjiakou Port, China. Genetic evidence of five non-indigenous fish species was monitored, including two recognized invasive species (Lates calcarifer and Anguilla anguilla). Among all groups, samples from Group B (V2, V3, V6, V8) exhibited the highest diversity of non-indigenous species, suggesting regional differences in species composition that may reflect source port biodiversity. These findings highlight the utility of eDNA-based monitoring not only for early detection of potentially non-indigenous taxa but also for capturing biogeographic patterns associated with global maritime traffic. By demonstrating the effectiveness of this approach at an international port, this study contributes a scientific foundation for both local biodiversity conservation and broader ecological surveillance, offering valuable insights for the ongoing development of ballast water management strategies worldwide. Full article

The long-term sustainability of seaports depends on various operational factors, including infrastructure efficiency, digital innovation, environmental management, and regulatory compliance, among which maritime pilotage plays a crucial role in ensuring safe navigation and minimizing environmental, economic, and social risks. This research employed the PRISMA-ScR framework to evaluate the environmental, economic, and social impacts of pilotage on the sustainability of seaports. The findings demonstrate efficient navigation and spill avoidance, which reduce emissions, safeguard marine biodiversity, and maintain water quality. Economically, it reduces delays, optimizes operational expenses, and increases port competitiveness by increasing maritime traffic. Moreover, pilotage improves navigational safety, local professional skill development, and community interactions via ecological conservation and operational efficiency. It also indicates how environmental initiatives benefit the economy, increase port competitiveness, and promote job security and community happiness. The results also emphasize the significance of pilotage in sustainable seaport operations by quantifying pollution reductions, cost savings, and safety. The result also suggests that successful pilotage enhances ports’ viability and responsibility in global shipping networks while addressing environmental, economic, and social concerns. Full article

Ship collisions pose a significant threat to maritime safety, especially in congested precautionary areas with high vessel traffic density. Traditional collision risk assessment methods, such as distance to closest point of approach (DCPA) and time to closest point of approach (TCPA), often overlook environmental uncertainties and variations in human response. To address these limitations, this study proposes a novel approach for collision risk assessment using automatic identification system (AIS) data. AIS data from vessels in precautionary areas are resampled to synchronize their temporal frameworks, enabling the systematic identification of ship encounters. Each encounter is analyzed by evaluating critical parameters, including the minimum ship encounter distance (MSED), relative azimuth angles, and trajectories, within a customized ship domain model that incorporates vessel characteristics such as ship length and course. Key metrics, such as intrusion depth and time, are calculated based on vessels’ entry and exit points during each encounter. A set of collision risk indices, which integrates both intrusion depth and time, is introduced, with particular emphasis on intrusion depth due to its heightened sensitivity to proximity danger and constrained maneuvering space. An extensive analysis of vessel interactions in the precautionary area establishes a holistic collision risk index. A case study using AIS data from Ningbo–Zhoushan Port, involving a dataset of 1000 ship encounters, demonstrates the effectiveness of the proposed method. Specifically, the holistic collision risk in the No.2 precautionary area is 0.456, while the No.3 precautionary area shows a risk value of 0.443. These results confirm the effectiveness and feasibility of the proposed method for evaluating and classifying collision risks, offering a more precise and reliable framework for collision risk assessment in complex navigational environments. Full article

With the prosperity of the economy and the continuous expansion of the port area, container trucks have become the main means of transportation on port roads. Traditional traffic flow research mainly focuses on passenger cars. In view of the unique characteristics of container truck traffic flow and the lack of research on conflict-influencing factors for this traffic flow, this paper is committed to filling this research gap. This paper uses drones and YOLOv8 technology to construct a vehicle trajectory dataset in the container truck traffic flow scenario and extracts relevant features of container truck traffic flow from vehicle trajectory data from a macro perspective. For the trajectory data after denoising, the time to collision (TTC) indicator is used to identify conflict events, and then the synthetic minority oversampling technique (SMOTE) is used to obtain four datasets. Machine learning and related classification models are selected for conflict prediction. It is worth noting that the XGBoost model performs better than other models on the four datasets, with an accuracy of 0.86 and an AUC value of 0.933. The Shapley additive explanation (SHAP) theory is used to explain and analyze the model results and compare them with existing studies. The results show that in container truck traffic flow, traffic density is the most important factor affecting conflicts, and conflicts occur more frequently when the traffic density is between 50 and 70 vehicles/km, followed by lane change rate. In contrast, for general traffic flows, studies have shown that speed is the main factor affecting conflicts. Full article