Behavior Of Vessels Research Articles

Vessel Trajectory Prediction Based on AIS Data: Dual-Path Spatial–Temporal Attention Network with Multi-Attribute Information

With the rapid growth of the global shipping industry, the increasing number of vessels has brought significant challenges to navigation safety and management. Vessel trajectory prediction technology plays a crucial role in route optimization and collision avoidance. However, current prediction methods face limitations when dealing with complex vessel interactions and multi-dimensional attribute information. Most models rely solely on global modeling in the temporal dimension, considering spatial interactions only later, failing to capture dynamic changes in trajectory interactions at different time points. Additionally, these methods do not fully utilize the multi-attribute information in AIS data, and the simple concatenation of attributes limits the model’s potential. To address these issues, this paper proposes a dual spacial–temporal attention network with multi-attribute information (DualSTMA). This network models vessel behavior and interactions through two distinct paths, comprehensively considering individual vessel intentions and dynamic interactions. Moreover, we divide vessel attributes into dynamic and static categories, with dynamic attributes fused during feature preprocessing, and with static attributes being controlled through a gating mechanism during spatial interactions to regulate the importance of neighboring vessel features. Benchmark tests on real AIS data show that DualSTMA significantly outperforms existing methods in prediction accuracy. Ablation studies and visual analyses further validate the model’s reliability and advantages.

Observation and Analysis of In Vitro Digestibility of Different Breads Using a Human Gastric Digestion Simulator.

The digestion behavior of a food bolus comprising bread particles in the presence of gastric peristalsis remains poorly understood. This study systematically investigated the effect of bread type on in vitro gastric digestion behavior using a human gastric digestion simulator (GDS) that is capable of quantitatively simulating gastric peristalsis. A food bolus consisting of 60 g of bread (white bread, bagel, German bread, French bread, or croissant), 15 mL of a simulated salivary fluid, and 240 mL of a simulated gastric fluid was used for gastric digestion in vitro using the GDS for 3 h at 37 °C. Direct observation of the gastric digestion behavior in the GDS vessel demonstrated that the structure and composition of breads considerably influenced the physical digestion processes of bread particles. These processes include their fracture, rubbing, disintegration, swelling owing to the penetration of gastric fluid, and release of fat from their surface. Fluorescence microscopy enabled an improved understanding of the variations in the microstructure and major component distribution of the breads during the gastric digestion in vitro. The results showed how the different breads influenced gastric digestibility in vitro through quantitative gastric peristalsis. The GDS can also be applicable to studying gastric digestibility in vitro of other types of bread.

A Novel BEM for the Hydrodynamic Analysis of Twin-Hull Vessels with Application to Solar Ships

A novel Boundary Element Method (BEM) is presented for predicting the hydrodynamic behavior of twin-hull vessels, traveling at low speeds, aiming to quantify the benefits of integrating solar technology onboard. In particular, the power requirements of an electric 33 m long twin-hull ship are examined. The study discusses the placement of solar panels on deck and assesses their utilization in terms of real-time energy generation, aiming to extend the autonomy range while also reducing carbon emissions. The discussed methodology predicts the power needs by considering various operational variables, design specifications and hydrodynamic principles. In addition, it addresses the viability and possible advantages of integrating solar technology onboard and provides preliminary estimates regarding the extent to which solar energy may compensate for power needs, based on several factors, including the velocity, the prevailing sea state and the incident solar irradiance. The results provide useful information regarding the utilization of solar energy in the shipping sector, in addition to advancing sustainable maritime propulsion.

Onboard multivariate hazard assessment for UIKKU chemical tanker by Gaidai reliability method

The current study outlines a novel multimodal structural reliability methodology, suitable for naval dynamic systems, that are either numerically simulated, or directly physically measured. Cross-correlations between the system's principal/key dimensions/components, along with the high dimensionality of complex naval dynamic systems, are not easily addressed by existing reliability methods, that are mostly univariate or bivariate. The objective of this study had been to apply a novel reliability/hazard assessment methodology to UIKKU chemical tanker onboard dynamic measurements to demonstrate the efficiency of the proposed multimodal structural reliability methodology. Current investigation utilized onboard measured ice loadings, exerted on the UIKKU tanker hull and propulsion devices during its Arctic voyage. Onboard dynamic data recordings then were interpreted to gain knowledge about dynamic load levels. The hull of UIKKU had been instrumented so that ice-induced loads/stresses on vessel shell plating had been monitored at various key locations, both in longitudinal as well as in vertical directions, along the vessel’s hull. Longitudinal bending stresses at several locations on the vessel hull and vessel vertical accelerations had been measured to analyze global vessel behaviour when colliding e.g., moderately heavy ice ridges. In addition to UIKKU onboard measurements onboard, Russian partners had instrumented icebreaker vessel Capitan Dranitsyn to real-time measure strains/stresses within ice belt grillage, located at the vessel’s bow. Icebreaker vessel Capitan Dranitsyn acted as escort icebreaker during the whole Arctic voyage. Obtained results may be utilized to harmonize IACS Polar ship rules. In the current study, the onboard measuring system along with measured results, during the whole voyage have been briefly represented.

Fine spatio-temporal prediction of fishing time using big data

Overfishing, bycatch, and other anthropogenic threats may lead to the destruction of fragile habitats and substantial losses of marine life. Marine fishery resources can be protected by adjusting fishing intensity and establishing marine reserves. Currently, China adopts the closed fishing season management approach to protect traditional fishing grounds, where fine spatio-temporal prediction is essential to efficiently supervise the wide scope. Fishing vessel behaviors reflect fishers’ experience as well as the information provided by fish detection radar, while the fishery resource distribution is relevant to the marine environment. In this study, we identified fishing vessel behaviors (gillnets, trawls, purse seines, and abnormal behaviors) and qualitatively assessed and predicted fishing time of different fishing vessel behaviors to search for high intensity fishing operation areas by constructing a time-space prediction model. The model was based on big data of fishing vessel automatic identification systems and3 the marine environment, and was verified in the East China Sea. The prediction results generally corresponded with the distribution of traditional fishery resources in the East China Sea and the fishing efforts provided by the Global Fishing Watch. This model can provide an accurate and effective refined fishing vessel operation time prediction, and benefits fishing management and fishery resources protection.

Intelligent monitoring of marine vessel dynamics based on data mining

This study explores vessel navigation's universal characteristics and individual anomaly behavior from dispatch to destination. With the help of information technology, it introduces big data and machine learning technologies into the work of vessel traffic management and conducts statistical analysis and monitoring studies of vessel behavior from a data-driven perspective. The study was conducted by modeling vessel speed, position, course, and other navigational characteristics behavior through comprehensive statistical analysis of vessel AIS, radar, and VTS data. The study was based on the monitoring methods for anomalous behavior of vessel speed, position, and heading inconsistencies with the expected behavior identified by the model.

Analysis of rapid decompression failure in polymer liner of Type IV hydrogen storage vessels using a novel fluid–solid coupling model

Type IV vessels have been developed for hydrogen storage systems, but the rapid decompression failure during the decompression process can lead to the collapse of the liner, significantly reducing the lifespan of the vessels. This study aims to investigate nonlinear buckling behaviors and collapse mechanisms of polymer liner in Type IV hydrogen storage vessels. Considering the intrinsic coupling between hydrogen gas depletion and mechanical behavior of vessels, a fluid–solid coupling model was proposed using the fluid cavity techniques and HyperMesh. Results indicated that the pressure difference generated on the liner is the primary cause leading to the polymer liner collapse. The critical pressure difference significantly increases with the thickness of the liner, while it decreases nonlinearly with the increase in void defect size. Parametric sensitivity analysis highlighted the depth of initial void defect and the liner thickness as two significant influencing factors in the critical decompression rate.

Grid-Based Bayesian Bootstrap Approach for Real-Time Detection of Abnormal Vessel Behaviors From AIS Data in Maritime Logistics

Grid-Based Bayesian Bootstrap Approach for Real-Time Detection of Abnormal Vessel Behaviors From AIS Data in Maritime Logistics

Online Ornstein–Uhlenbeck based anomaly detection and behavior classification using AIS data in maritime

Detecting anomalies in maritime systems involves recognizing vessel patterns that are not in line with what is expected or planned prior to the voyage. These patterns are identified for a variety of reasons at different stages. However, the available approaches are mostly focused on batch processing, which assumes that all data are available at once. Furthermore, limited attention has been paid to the identification of vessel behavior in the maritime domain. To gain a deeper understanding of the behavior of the vessel, this paper proposes a framework for online vessel anomaly detection and behavior identification for streaming AIS data. The framework employs an Ornstein–Uhlenbeck (OU)-based algorithm for anomaly detection and a machine learning-based behavior identification algorithm for ship behavior classification. In particular, we use the XGBoost algorithm as a classification method to distinguish various behaviors of vessels within the maritime domain. The proposed framework does not require labeled data for anomaly detection, and shows robustness in the presence of missing information (a passage plan, which contains the parameters to be followed during the voyage). The numerical results demonstrate that the proposed approach can achieve reasonable values of the F1 score for the various activities of the vessels.

A Hybrid Framework for Maritime Surveillance: Detecting Illegal Activities through Vessel Behaviors and Expert Rules Fusion.

Maritime traffic is essential for global trade but faces significant challenges, including navigation safety, environmental protection, and the prevention of illicit activities. This work presents a framework for detecting illegal activities carried out by vessels, combining navigation behavior detection models with rules based on expert knowledge. Using synthetic and real datasets based on the Automatic Identification System (AIS), we structured our framework into five levels based on the Joint Directors of Laboratories (JDL) model, efficiently integrating data from multiple sources. Activities are classified into four categories: illegal fishing, suspicious activity, anomalous activity, and normal activity. To address the issue of a lack of labels and integrate data-driven detection with expert knowledge, we employed a stack ensemble model along with active learning. The results showed that the framework was highly effective, achieving 99% accuracy in detecting illegal fishing and 92% in detecting suspicious activities. Furthermore, it drastically reduced the need for manual checks by specialists, transforming experts' tacit knowledge into explicit knowledge through the models and allowing continuous updates of maritime domain rules. This work significantly contributes to maritime surveillance, offering a scalable and efficient solution for detecting illegal activities in the maritime domain.

Deep transfer learning for efficient and accurate prediction of composite pressure vessel behaviors

Composite pressure vessels are manufactured by winding carbon fiber-reinforced plastic, while its performance is determined by fabrication conditions. To properly design composite pressure vessels, it is essential to analyze their behavior according to the design parameters. However, analytical or numerical methods have limitations in terms of accuracy or cost-effectiveness. In this study, a method is proposed to accurately and efficiently predict the behavior of composite pressure vessels through deep transfer learning. A prediction model is built by pre-training on a sufficient amount of analytical data, and then fine-tuning on a limited amount of numerical data. The performance of the deep transfer learning-based prediction model was evaluated through various error assessments. Its computational cost was also compared with that of finite element analysis. Furthermore, the deep transfer learning-based prediction model was used to analyze the impact of design parameters on the behavior of composite pressure vessels and for design optimization.

From ports to routes: Extracting multi-scale shipping networks using massive AIS data

Maritime transportation is a critical component of global trade and commerce. To ensure maritime safety, fixed shipping routing has been established in many complex waters. However, there is currently a lack of comprehensive digital shipping networks in wide-range maritime areas. To better understand the navigational patterns, this paper proposes a data-driven extraction framework for multi-scale shipping networks, including port-, node-, and route-level shipping networks. It is essentially a hierarchical approach, which progresses from port to route. In particular, for the extraction of port-level shipping networks, the clustering in quest (CLIQUE) and alpha-shapes algorithms are employed to accurately extract the boundaries and spatial extents of individual ports. For the node-level shipping network extraction, an adaptive Douglas-Peucker algorithm is developed to identify crucial feature points, and CLIQUE clustering is further exploited to extract the network waypoints. A novel slice-based traffic flow fitting algorithm is finally introduced to extract the route-level shipping network. To verify the performance of shipping network extraction methods, comprehensive experiments are conducted using the massive Automatic Identification System (AIS) data in different water areas. The experimental results have demonstrated that our method was capable of extracting multi-scale shipping networks, revealing traffic characteristics and vessel behaviours. Overall, the method proposed herein is useful for shipping logistic analysis and provides a foundation for several potential maritime applications, including route planning, trajectory prediction, and others.

Study on Leakage and Diffusion Behavior of Liquid CO2 Vessel in CCES

Numerical simulations of the leakage and diffusion behavior of liquid CO2 vessels and security analyses were conducted in this paper, based on a CO2 compression energy storage system. With isentropic choking model, the leakage of vessels under specific conditions was numerically simulated. The influence of different wind speeds on leakage in near-zone field was studied. Meanwhile, the diffusion characteristics of CO2 under three different influencing factors were investigated with the UDM (Unified Dispersion Model) diffusion model, and the diffusion ranges of certain concentrations were detected in the far-zone field. The results show that the low-temperature zone of the 50 mm leak aperture can reach 0.74 m downwind, and basically does not change with wind speed. In the leakage direction, the maximum damage zone of high-speed flow can reach 7.70 m. For the far-zone field, the diffusion area and downwind distance of a dangerous concentration decrease with the increasing of wind speed, and the hazardous area of the low concentration is greatly affected. Based on specific conditions, the maximum diffusion area is 78.46 m2 at 1 m/s wind speed, and the dangerous range reaches 36.32 m downwind. The larger the leakage aperture, the faster the growth trend of the low concentration area under the same conditions. As the equivalent radius of the leakage aperture is less than 50 mm, the maximum diffusion area is proportional to the cubic of the leakage aperture radius. The higher the height of the leakage source, the smaller the concentration range at 1.5 m, which is the average human breathing height. The overall cloud moves upward, meaning that the ground risk decreases. When the leakage aperture is 50 mm and the wind speed is 1 m/s, the maximum cloud diffusion range is 857.35 m2 at the leakage height of 2 m, and the dangerous range reaches 109.53 m downwind, where the maximum concentration is 14.65%.

Instability in Computational Models of Vascular Smooth Muscle Cell Contraction

PurposeThrough their contractile and synthetic capacity, vascular smooth muscle cells (VSMCs) can regulate the stiffness and resistance of the circulation. To model the contraction of blood vessels, an active stress component can be added to the (passive) Cauchy stress tensor. Different constitutive formulations have been proposed to describe this active stress component. Notably, however, measuring biomechanical behaviour of contracted blood vessels ex vivo presents several experimental challenges, which complicate the acquisition of comprehensive datasets to inform complex active stress models. In this work, we examine formulations for use with limited experimental contraction data as well as those developed to capture more comprehensive datasets.MethodsFirst, we prove analytically that a subset of constitutive active stress formulations exhibits unstable behaviours (i.e., a non-unique diameter solution for a given pressure) in certain parameter ranges, particularly for large contractile deformations. Second, using experimental literature data, we present two case studies where these formulations are used to capture the contractile response of VSMCs in the presence of (1) limited and (2) extensive contraction data.ResultsWe show how limited contraction data complicates selecting an appropriate active stress model for vascular applications, potentially resulting in unrealistic modelled behaviours.ConclusionOur data provide a useful reference for selecting an active stress model which balances the trade-off between accuracy and available biomechanical information. Whilst complex physiologically motivated models’ superior accuracy is recommended whenever active biomechanics can be extensively characterised experimentally, a constant 2nd Piola-Kirchhoff active stress model balances well accuracy and applicability with sparse contractile data.

Assessing vessel transportation delays affected by tropical cyclones using AIS data and a bayesian network: A case study of veronica in northwestern Australia

Tropical cyclones frequently disrupt maritime transportation systems, impacting the normal operation of vessels and causing transportation delays. Analyzing the occurrence and degree of vessel transportation delays under disaster conditions is one of the critical aspects of maritime transportation management. This article, based on Automatic Identification System (AIS) data, examined the real behavioral trajectories of vessels and delay characteristics during tropical cyclones. Taking the example of Tropical Cyclone Veronica, which occurred in the waters off northwestern Australia in 2019, we conducted a comprehensive analysis. This involved numerical simulations of the entire disaster process and the cleaning and matching of trajectory data for all affected vessels in the area. We delved deeply into the relationship between vessel behavior characteristics and tropical cyclones, identified factors contributing to delays, and, based on our findings, constructed a Bayesian network inference model for vessel transportation delays under disaster conditions. This model uses information such as tropical cyclone intensity, vessel basic attributes, behavior choices, and port disaster avoidance measures as its primary nodes. The research results indicate that vessel rerouting, vessel loss of control, and waiting for port entry are the three major sources of vessel delays during disasters. Key influencing factors contributing to these consequences include port closure measures, the duration of vessels being adversely affected by strong winds, vessel tendencies toward loss of control, and risk preferences. To control the extent of vessel delays more effectively, it is crucial to prioritize these sensitive factors and strengthen monitoring and management efforts. This model is driven by real data, providing an objective reflection of real-world scenarios, and its effectiveness has been validated through sensitivity analysis. The research perspective and algorithmic framework presented in this paper offer a novel paradigm and fresh perspective for the study of maritime transportation disasters. The research findings have significant implications for bolstering the resilience of maritime transportation networks, enhancing our comprehension and anticipation of delays, and ensuring the continuity, security, and efficiency of the global supply chain for goods.

Construction of a large-scale maritime element semantic schema based on knowledge graph models for unmanned automated decision-making

In maritime logistics optimization, considerable research efforts are focused on the extraction of deep behavioral characteristics from comprehensive shipping data to discern patterns in maritime vessel behavior. The effective linkage of these characteristics with maritime infrastructure, such as berths, is critical for the enhancement of ship navigation systems. This endeavor is paramount not only as a research focus within maritime information science but also for the progression of intelligent maritime systems. Traditional methodologies have primarily emphasized the analysis of navigational paths of vessels without an extensive consideration of the geographical dynamics between ships and port infrastructure. However, the introduction of knowledge graphs has enabled the integration of disparate data sources, facilitating new insights that propel the development of intelligent maritime systems. This manuscript presents a novel framework using knowledge graph technology for profound analysis of maritime data. Utilizing automatic identification system (AIS) data alongside spatial information from port facilities, the framework forms semantic triplet connections among ships, anchorages, berths, and waterways. This enables the semantic modeling of maritime behaviors, offering precise identification of ships through their diverse semantic information. Moreover, by exploiting the semantic relations between ships and berths, a reverse semantic knowledge graph for berths is constructed, which is specifically tailored to ship type, size, and category. The manuscript critically evaluates a range of graph embedding techniques, dimensionality reduction methods, and classification strategies through experimental frameworks to determine the most efficacious methodologies. The findings reveal that the maritime knowledge graph significantly enhances the semantic understanding of unmanned maritime equipment, thereby improving decision-making capabilities. Additionally, it establishes a semantic foundation for the development of expansive maritime models, illustrating the potential of knowledge graph technology in advancing intelligent maritime systems.

Nonlinear Elasticity of Blood Vessels and Vascular Grafts.

The transplantation of vascular grafts has emerged as a prevailing approach to address vascular disorders. However, the development of small-diameter vascular grafts is still in progress, as they serve in a more complicated mechanical environment than their counterparts with larger diameters. The biocompatibility and functional characteristics of small-diameter vascular grafts have been well developed; however, mismatch in mechanical properties between the vascular grafts and native arteries has not been accomplished, which might facilitate the long-term patency of small-diameter vascular grafts. From a point of view in mechanics, mimicking the nonlinear elastic mechanical behavior exhibited by natural blood vessels might be the state-of-the-art in designing vascular grafts. This review centers on elucidating the nonlinear elastic behavior of natural blood vessels and vascular grafts. The biological functionality and limitations associated with as-reported vascular grafts are meticulously reviewed and the future trajectory for fabricating biomimetic small-diameter grafts is discussed. This review might provide a different insight from the traditional design and fabrication of artificial vascular grafts.

Digital Twin-Enabled Response Function Analysis: A Synthetic Approach to Ship's Propulsion System Assessment

Analyzing the behavior of vessels in actual sea conditions is crucial for conceptual system design, safety and energy efficiency considerations. However, the essential seakeeping problem is reduced to the analysis of wave-hull interactions often neglecting consideration of the propulsion. But in the meantime, the synthetic consideration of wave–propulsion interactions is a key for safety and energy efficiency. Furthermore, safety evaluation of a ship's design with reduced propulsion power in adverse seas is vital for risk management. Response functions are commonly used to estimate propulsion system responses in incoming seaways. This paper proposes a synthetic approach using digital twin technology for rapid response function estimation. It introduces a companion linearized state-space model linked with the digital twin, enabling immediate retrieval of coefficients for response function analysis at the desired operating point. This integrated methodology provides a comprehensive representation of ship propulsion behavior in wave environments, offering a comprehensive framework for system performance assessment.

A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

The disruptive role of LRG1 on the vasculature and perivascular microenvironment.

The establishment of new blood vessels, and their subsequent stabilization, is a critical process that facilitates tissue growth and organ development. Once established, vessels need to diversify to meet the specific needs of the local tissue and to maintain homeostasis. These processes are tightly regulated and fundamental to normal vessel and tissue function. The mechanisms that orchestrate angiogenesis and vessel maturation have been widely studied, with signaling crosstalk between endothelium and perivascular cells being identified as an essential component. In disease, however, new vessels develop abnormally, and existing vessels lose their specialization and function, which invariably contributes to disease progression. Despite considerable research into the vasculopathic mechanisms in disease, our knowledge remains incomplete. Accordingly, the identification of angiocrine and angiopathic molecules secreted by cells within the vascular microenvironment, and their effect on vessel behaviour, remains a major research objective. Over the last decade the secreted glycoprotein leucine-rich α-2 glycoprotein 1 (LRG1), has emerged as a significant vasculopathic molecule, stimulating defective angiogenesis, and destabilizing the existing vasculature mainly, but not uniquely, by altering both canonical and non-canonical TGF-β signaling in a highly cell and context dependent manner. Whilst LRG1 does not possess any overt homeostatic role in vessel development and maintenance, growing evidence provides a compelling case for LRG1 playing a pleiotropic role in disrupting the vasculature in many disease settings. Thus, LRG1 has now been reported to damage vessels in various disorders including cancer, diabetes, chronic kidney disease, ocular disease, and lung disease and the signaling processes that drive this dysfunction are being defined. Moreover, therapeutic targeting of LRG1 has been widely proposed to re-establish a quiescent endothelium and normalized vasculature. In this review, we consider the current status of our understanding of the role of LRG1 in vascular pathology, and its potential as a therapeutic target.