Search Results (554)

The Automatic Identification System (AIS) has become a key data source for ship behavior monitoring and maritime traffic management, widely used in trajectory prediction and anomaly detection. However, AIS data suffer from issues such as spatial sparsity, heterogeneous features, variable message formats, and irregular sampling intervals, while vessel trajectories are characterized by strong spatial–temporal dependencies. These factors pose significant challenges for efficient and accurate modeling. To address this issue, we propose a lightweight vessel trajectory prediction framework that integrates Spatial–Temporal Identity encoding with an MLP-Mixer architecture. The framework discretizes spatial and temporal features into structured IDs and uses dual MLP modules to model temporal dependencies and feature interactions without relying on convolution or attention mechanisms. Experiments on a large-scale real-world AIS dataset demonstrate that the proposed STID-Mixer achieves superior accuracy, training efficiency, and generalization capability compared to representative baseline models. The method offers a compact and deployable solution for large-scale maritime trajectory modeling. Full article

The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain. Full article

Detection of small vessels is of great significance for maritime safety assurance, abnormal vessel tracking, illegal fishing supervision, and combating smuggling. However, the radar reflection intensity of small vessels is low, making them difficult to detected with the radar’s constant false-alarm rate (CFAR) algorithm. To enhance the detection capability for small vessels, we propose an improved CFAR scheme. Specifically, we first compared traditional CFAR processing results of radar data with automatic identification system (AIS) data to identify some special targets. These special targets, which possessed AIS information, but remained undetected by radar, enabled an iTransformer model to generate more reasonable CFAR threshold adjustments. iTransformer adaptively lowered the threshold of the areas around these targets until they were detected by radar. This process made it easier to discover the small boats in the surrounding area. Experimental results showed that our method reduces the missed detection rate of small vessels by 73.4% and the false-alarm rate by 60.7% in simulated scenarios, significantly enhancing the CFAR detection capability. Overall, our study provides a new solution for ensuring maritime navigation safety and strengthening illegal supervision, while also offering new technical references for the field of radar detection. Full article

Vessel trajectory prediction is fundamental to maritime navigation, safety, and operational efficiency, particularly as the industry increasingly relies on digital solutions and real-time data analytics. This study addresses the challenge of forecasting vessel movements using historical Automatic Identification System (AIS) data, with a focus on understanding the temporal behavior of deep learning models under different input and prediction horizons. To this end, a robust data pre-processing pipeline was developed to ensure temporal consistency, filter anomalous records, and segment continuous vessel trajectories. Using a curated dataset from the eastern Mediterranean, three deep recurrent neural network architectures, namely LSTM, Bi-LSTM, and Bi-GRU, were evaluated for short- and long-term trajectory prediction. Empirical results demonstrate that Bi-LSTM consistently achieves higher accuracy across both horizons, with performance gradually degrading under extended forecast windows. The analysis also reveals key insights into the trade-offs between model complexity, horizon-specific robustness, and predictive stability. This work contributes to maritime informatics by offering a comparative evaluation of recurrent architectures and providing a structured and empirical foundation for selecting and deploying trajectory forecasting models in operational contexts. Full article

Background: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), including granulomatosis with polyangiitis (GPA), microscopic polyangiitis (MPA), and eosinophilic granulomatosis with polyangiitis (EGPA), represent a spectrum of systemic disorders characterized by necrotizing inflammation of small- to medium-sized vessels. Nailfold videocapillaroscopy (NVC) is a validated, non-invasive technique routinely employed in the assessment of microvascular involvement in systemic sclerosis and in the differential diagnosis of Raynaud’s phenomenon; its application in the context of AAV, particularly EGPA, has not been investigated yet. The present study aims to assess the presence and the possible pattern of microcirculatory abnormalities detected by NVC in EGPA patients, and to explore potential correlations between capillaroscopic findings and disease activity status. Methods: A total of 29 patients with EGPA (19 women and 10 men), aged between 51 and 73 years, and 29 age- and sex-matched healthy controls were retrospectively enrolled between October 2023 and April 2025, after providing informed consent and meeting the inclusion and exclusion criteria. NVC was conducted in both groups to assess various morphological parameters, and mean capillary density was also calculated. Results: This study observed the presence of capillaroscopic alterations in the EGPA group, including decreased capillary density (38%), neoangiogenesis (72%), rolling (100%), pericapillary stippling (66%), and inverted capillary apex (52%). Overall, when comparing healthy controls with EGPA patients, microcirculatory abnormalities were significantly more prevalent in the latter. Specifically, scores for neoangiogenesis, capillary rolling, pericapillary stippling, and inverted capillary apex showed p-values < 0.001. Conclusions: Our study demonstrates a higher prevalence of four nailfold videocapillaroscopic abnormalities in patients with EGPA compared to healthy controls. However, the identification of these capillaroscopic alterations as specific to EGPA requires further confirmation. Ongoing studies aim to explore the potential role of NVC as a diagnostic marker and to investigate its correlation with the clinical manifestations of EGPA. Full article

The presence of sea ice threatens low ice-class vessels’ navigation safety in the Arctic, and traditional Navigation Risk Assessment Models based on sea ice parameters have been widely used to guide safe passages for ships operating in ice regions. However, these models mainly rely on empirical coefficients, and the accuracy of these models in identifying sea ice navigation risk remains insufficiently validated. Therefore, under the binary classification framework, this study used Automatic Identification System (AIS) data along the Northeast Passage (NEP) as positive samples, manual interpretation non-navigable data as negative samples, a total of 10 machine learning (ML) models were employed to capture the complex relationships between ice conditions and navigation risk for Polar Class (PC) 6 and Open Water (OW) vessels. The results showed that compared to traditional Navigation Risk Assessment Models, most of the 10 ML models exhibited significantly improved classification accuracy, which was especially pronounced when classifying samples of PC6 vessel. This study also revealed that the navigability of the East Siberian Sea (ESS) and the Vilkitsky Strait along the NEP is relatively poor, particularly during the month when sea ice melts and reforms, requiring special attention. The navigation risk output by ML models is strongly determined by sea ice thickness. These findings offer valuable insights for enhancing the safety and efficiency of Arctic maritime transport. Full article

This study introduces an algorithm for quantifying oceanic wind speed and identifying sound sources in the local underwater soundscape. Utilizing low-complexity metrics like one-minute spectral kurtosis and power spectral density levels, the algorithm categorizes different soundscapes and estimates wind speed. It detects rain, vessels, fin and blue whales, as well as clicks and whistles from dolphins. Positioned as a foundational tool for implementing the Ocean Sound Essential Ocean Variable (EOV), it contributes to understanding long-term trends in climate change for sustainable ocean health and predicting threats through forecasts. The proposed soundscape classification algorithm, validated using extensive acoustic recordings (≥32 kHz) collected at various depths and latitudes, demonstrates high performance, achieving an average precision of $89\%$ and an average recall of $86.59\%$ through optimized parameter tuning via a genetic algorithm. Here, wind speed is determined using a cubic function with power spectral density (PSD) at 6 kHz and the MASLUW method, exhibiting strong agreement with satellite data below 15 m/s. Designed for compatibility with low-power electronics, the algorithm can be applied to both archival datasets and real-time data streams. It provides a straightforward metric for ocean monitoring and sound source identification. Full article

The increasing complexity of maritime traffic imposes growing demands on the safety and rationality of ship-collision-avoidance decisions. While most existing research focuses on simple encounter scenarios, autonomous collision-avoidance strategies that comply with the International Regulations for Preventing Collisions at Sea (COLREGs) in complex multi-ship environments remain insufficiently investigated. To address this gap, this study proposes a novel collision-avoidance framework that integrates a quantitative COLREGs analysis with a distributed stochastic search mechanism. The framework consists of three core components: encounter identification, safety assessment, and stage classification. A cost function is employed to balance safety, COLREGs compliance, and navigational efficiency, incorporating a distance-based weighting factor to modulate the influence of each target vessel. The use of a distributed stochastic search algorithm enables decentralized decision-making through localized information sharing and probabilistic updates. Extensive simulations conducted across a variety of scenarios demonstrate that the proposed method can rapidly generate effective collision-avoidance strategies that fully comply with COLREGs. Comprehensive evaluations in terms of safety, navigational efficiency, COLREGs adherence, and real-time computational performance further validate the method’s strong adaptability and its promising potential for practical application in complex multi-ship environments. Full article

Chronic thromboembolic pulmonary hypertension (CTEPH) is a rare, progressive form of pre-capillary pulmonary hypertension characterized by persistent, organized thromboemboli in the pulmonary vasculature, leading to vascular remodeling, elevated pulmonary artery pressures, right heart failure, and significant morbidity and mortality if untreated. Despite advances, CTEPH remains underdiagnosed due to nonspecific symptoms and overlapping features with other forms of pulmonary hypertension. Basic Methodology: This review synthesizes data from large international registries, epidemiologic studies, translational research, and multicenter clinical trials. Key methodologies include analysis of registry data to assess incidence and risk factors, histopathological examination of lung specimens, and molecular studies investigating endothelial dysfunction and inflammatory pathways. Diagnostic modalities and treatment outcomes are evaluated through observational studies and randomized controlled trials. Recent Advances and Affected Population: Research has elucidated that CTEPH arises from incomplete resolution of pulmonary emboli, with subsequent fibrotic transformation mediated by dysregulated TGF-β/TGFBI signaling, endothelial dysfunction, and chronic inflammation. Affected populations are typically older adults, often with prior venous thromboembolism, splenectomy, or prothrombotic conditions, though up to 25% have no history of acute PE. The disease burden is substantial, with delayed diagnosis contributing to worse outcomes and higher societal costs. Microvascular arteriopathy and PAH-like lesions in non-occluded vessels further complicate the clinical picture. Conclusions: CTEPH is now recognized as a treatable disease, with multimodal therapies—surgical endarterectomy, balloon pulmonary angioplasty, and targeted pharmacotherapy—significantly improving survival and quality of life. Ongoing research into molecular mechanisms and biomarker-driven diagnostics promises earlier identification and more personalized management. Multidisciplinary care and continued translational investigation are essential to further reduce mortality and optimize outcomes for this complex patient population. Full article

Retinal ischemia is implicated in ocular diseases involving aberrant neovessel proliferation that characterizes proliferative retinopathies. Their therapy still remains confined to the intravitreal administration of anti-vascular endothelial growth factor (VEGF) medication, which is limited by side effects and progressive reduction in efficacy. Mimicking neovascular diseases in rodents, although of great help for translating fundamental mechanistic findings and assessing therapeutic potential in humans, is limited by the rodent’s short life span, which prevents retinal vessel proliferation over time. However, the oxygen-induced retinopathy (OIR) model, which mimics retinopathy of prematurity, seems to meet some criteria that are common to proliferative retinopathies. The present review provides insight into preclinical models and their suitability to mimic proliferative retinopathies. Further considerations will be applied to emerging approaches and advanced methodologies for the management of proliferative retinopathies, leading to the identification of new therapeutic targets, including our contribution in the field. Major emphasis is given to the possibility of using systemic therapies either alone or in combination with intravitreal anti-VEGF administration to maximize clinical benefits by combining drugs with different modes of action. This review is concluded by an in-depth discussion on future advancements and a critical view of preclinical finding translatability. Despite the major effort of preclinical and clinical research to develop novel therapies, the blockade of VEGF activity still remains the only treatment for proliferative retinopathies for more than twenty years since its first therapeutic application. Full article

Intravascular lymphoma (IVL) is a rare, aggressive subtype of non-Hodgkin lymphoma (NHL) characterized by the selective proliferation of neoplastic lymphoid cells within small and medium-sized blood vessels, most frequently of B-cell origin (IVLBCL). Its protean clinical presentation, lack of pathognomonic findings, and absence of tumor masses or lymphadenopathies often lead to diagnostic delays and poor outcomes. IVLBCL can manifest in classic, hemophagocytic syndrome-associated (HPS), or cutaneous variants, with extremely variable organ involvement including the central nervous system (CNS), skin, lungs, and endocrine system. Diagnosis requires histopathologic identification of neoplastic intravascular lymphoid cells via targeted or random tissue biopsies. Tumor cells are highly atypical and display a non-GCB B-cell phenotype, often expressing CD20, MUM1, BCL2, and MYC; molecularly, they frequently harbor mutations in MYD88 and CD79B, defining a molecular profile shared with ABC-type DLBCL of immune-privileged sites. Therapeutic approaches are based on rituximab-containing chemotherapy regimens (R-CHOP), often supplemented with CNS-directed therapy due to the disease’s marked neurotropism. Emerging strategies include autologous stem cell transplantation (ASCT) and novel immunotherapeutic approaches, potentially exploiting the frequent expression of PD-L1 by tumor cells. A distinct but related entity, intravascular NK/T-cell lymphoma (IVNKTCL), is an exceedingly rare EBV-associated lymphoma, showing unique own histologic, immunophenotypic, and molecular features and an even poorer outcome. This review provides a comprehensive overview of the current understandings about clinicopathological, molecular, and therapeutic landscape of IVL, emphasizing the need for increased clinical awareness, standardized diagnostic protocols, and individualized treatment strategies for this aggressive yet intriguing malignancy. Full article

In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a series of auxiliary variables, and after linearly parameterizing the USV dynamic model, a parameter adaptive update law is developed based on Lyapunov’s second method to estimate unknown dynamic parameters in the USV dynamics model. This parameter adaptive update law enables online identification of all USV dynamic parameters during trajectory tracking while ensuring convergence of the estimation errors. Second, a radial basis function neural network (RBF-NN) is employed to approximate unmodeled dynamics in the USV system, and on this basis, a robust damping term is designed based on neural damping technology to compensate for environmental disturbances and unmodeled dynamics. Subsequently, a trajectory tracking controller with parameter adaptation law and robust damping term is proposed using Lyapunov theory and adaptive control techniques. In addition, finite-time auxiliary variables are also added to the controller to handle the actuator saturation problem. Signal delay compensators are designed to compensate for input signal delays in the control system, thereby enhancing controller reliability. The proposed controller ensures robustness in trajectory tracking under model uncertainties and time-varying environmental disturbances. Finally, the convergence of each signal of the closed-loop system is proved based on Lyapunov theory. And the effectiveness of the control system is verified by numerical simulation experiments. Full article

Blood–brain barrier (BBB) dysfunction is a hallmark of cerebral small vessel disease (cSVD). This study aimed to identify a mouse model that replicates BBB impairment and shares key cSVD risk factors. Transgenic db/db and LDLr^−/−.Leiden mice, both prone to obesity and hypertension, were compared to C57BL/6J controls. BBB leakage was assessed using DCE-MRI and sodium fluorescein (NaFl); cerebral blood flow (CBF) by MRI. Dyslipidemia and vascular inflammation were measured by plasma tests. Tight junction integrity, endothelial dysfunction (glucose transporter 1, GLUT-1) and neuroinflammation were evaluated with immunohistochemistry and PCR. Both transgenic models developed an obese phenotype with hyperinsulinemia, but only LDLr^−/−.Leiden mice showed human-like dyslipidemia. When fed a high-fat diet (HFD) or HFD plus cholesterol, LDLr^−/−.Leiden mice showed reduced CBF, endothelial dysfunction (lowered GLUT-1), elevated vascular inflammation (ICAM-1, VCAM-1, S-selectin), and BBB leakage, as evidenced by DCE-MRI and NaFl, together with reduced ZO-1 and claudin-5 expression. Contrastingly, db/db mice showed endothelial dysfunction without BBB leakage. Neuroinflammation (IBA-1, GFAP) was observed only in LDLr^−/−.Leiden groups, consistent with BBB disruption. These findings indicate that LDLr^−/−.Leiden mice, but not db/db mice, are a promising translational model for studying BBB dysfunction in cSVD, offering insights into disease mechanisms and a platform for therapeutic development. Full article

Breast cancer invasion and subsequent metastasis to distant tissues occur when cancer cells lose cell–cell contact, develop a migrating phenotype, and invade the basement membrane (BM) and the extracellular matrix (ECM) to penetrate blood and lymphatic vessels. The identification of the mechanisms which induce the development from a ductal carcinoma in situ (DCIS) to a minimally invasive breast carcinoma (MIBC) is an emerging area of research in understanding tumor invasion and metastatic potential. To investigate the progression from DCIS to MIBC, we analyzed peritumoral collagen architecture using correlative scanning electron microscopy (SEM) on histological sections from human biopsies. In DCIS, the peritumoral collagen organizes into concentric lamellae (‘circular fibers’) parallel to the ducts. Within each lamella, type I collagen fibrils align in parallel, while neighboring lamellae show orthogonal fiber orientation. The concentric lamellar arrangement of collagen may physically constrain cancer cell migration, explaining the lack of visible tumor cell invasion into the peritumoral ECM in DCIS. A lamellar dissociation or the development of small inter fiber gaps allowed isolated breast cancer cell invasion and exosomes infiltration in the DCIS microenvironment. The radially arranged fibers observed in the peri-tumoral microenvironment of MIBC biopsies develop from a bending of the circular fibers of DCIS and drive a collective cancer cell invasion associated with an intense immune cell infiltrate. Type I collagen fibrils represent the peri-tumoral nano-environment which can play a mechanical role in regulating the development from DCIS to MIBC. Collectively, it is plausible to suggest that the ECM effectors implicated in breast cancer progression released by the interplay between cancer, stromal, and/or immune cells, and degrading inter fiber/fibril hydrophilic ECM components of the peritumoral ECM, may serve as key players in promoting the dissociation of the concentric collagen lamellae. Full article

Rapid identification of vessel height within the navigable space beneath bridges is crucial for ensuring bridge safety. To prevent bridge collisions caused by vessels exceeding their height limits, this article introduces a real-time warning framework for excessive vessel height based on video spatial transformation. The specific contributions include the following: (1) A spatial transformation-based method for locating vessel coordinates in the channel using buoys as control points, employing laser scanning to obtain their world coordinates from a broad channel range, and mapping the pixel coordinates of the buoys from side channel images to the world coordinates of the channel space, thus achieving pixel-level positioning of the vessel’s waterline intersection in the channel. (2) For video images, a key point recognition network for vessels based on attention mechanisms is developed to obtain pixel coordinates of the vessel’s waterline and top, and to capture the posture and position of multiple vessels in real time. (3) Analyzing the posture of vessels traveling in various directions within the channel, the method accounts for the pixel distance of spatial transformation control points and vessel height to determine vessel positioning coordinates, solve for the vessel’s height above water, and combine with real-time waterline height to enable over-height vessel collision warnings for downstream channel bridges. The method has been deployed in actual navigational scenarios beneath bridges, with the average error in vessel height estimation controlled within 10 cm and an error rate below 0.8%. The proposed approach enables real-time automatic estimation of vessel height in terms of computational speed, making it more suitable for practical engineering applications that demand both real-time performance and system stability. The system exhibits outstanding performance in terms of accuracy, stability, and engineering applicability, providing essential technical support for intelligent bridge safety management. Full article