Search Results (888)

Black carbon (BC) is a short-lived climate pollutant with substantial warming and health impacts, yet its contribution from maritime activities in data-limited regions remains poorly constrained. This study conducts a methodological and uncertainty-focused evaluation of tier-based emission inventory approaches from the European Monitoring and Evaluation Programme/European Environment Agency (EMEP/EEA) Guidebook, examining fuel-based (Tier I) and activity-based (Tier III) methodologies using national fuel statistics, port call activity, vessel registry data, and an operational Philippine Coast Guard dataset. Monte Carlo uncertainty analysis, spatial mapping, and hotspot intensity analysis are applied to evaluate how each tier responds to data limitations and parameter uncertainty rather than to reconcile absolute emission magnitudes. Results indicate that Tier I provides scalability for national reporting but exhibits substantial uncertainty for gasoline-dominated segments due to reliance on particulate matter-based proxies, underscoring the role of Tier II as a targeted refinement option. Tier III applies an activity-based formulation using fuel consumption resolved by operational phase and phase-specific emission factors, consistent with EMEP/EEA Tier III guidance. These findings are integrated into a decision-oriented synthesis to support informed selection and combination of tiered emission approaches under data-limited maritime conditions aligned with national and international climate commitments. Full article

Rotating machinery are essential elements in industrial systems and strongly present aboard vessels and maritime platforms, whose unexpected failure can lead to significant economic and operational losses, both for the maritime industry and for industry in general. Condition Monitoring (CM), through the analysis of specific parameters, aims to assess equipment health and enable the early detection of deviations from normal operating conditions. Among existing techniques, vibration analysis stands out for its effectiveness. However, when applied to naval environments, it requires human resources and equipment that are not always prepared or available. Aligned with the principles of Industry 4.0, maintenance has been integrating technologies that enhance data collection and analysis, becoming more autonomous and intelligent. The integration of Machine Learning (ML) into CM offers an alternative to conventional approaches, enabling systems to learn real operating behavior and recognize fault patterns with high accuracy and reduced human intervention. Addressing a real industrial challenge, this paper proposes an automatic framework for fault detection and diagnosis using ML models. As a case study, vibration data from rotating machinery were analyzed, encompassing common faults such as unbalance, misalignment, and the combination of both. The obtained results highlight the potential of the proposed framework for CM in maritime environments, modernizing it with new trends and making it more autonomous, efficient, and less dependent on specialized knowledge. Full article

Object recognition in remote sensing images is essential for applications such as land resource monitoring, maritime vessel detection, and emergency disaster assessment. However, detection accuracy is often limited by complex backgrounds, densely distributed targets, and multiscale variations. To address these challenges, this study aims to improve the detection of small-scale and densely distributed objects in complex remote sensing scenes. An improved object detection network is proposed, called omnidirectional and adaptive YOLOv8 (OA-YOLOv8), based on the YOLOv8 architecture. Two targeted enhancements are introduced. First, an omnidirectional perception refinement (OPR) network is embedded into the backbone to strengthen multiscale feature representation through the incorporation of receptive-field convolution with a triplet attention mechanism. Second, an adaptive channel dynamic upsampling (ACDU) module is designed by combining DySample, the Haar wavelet transform, and a self-supervised equivariant attention mechanism (SEAM) to dynamically optimize channel information and preserve fine-grained features during upsampling. Experiments on the satellite imagery multi-vehicle dataset (SIMD) demonstrate that OA-YOLOv8 outperforms the original YOLOv8 by 4.6%, 6.7%, and 4.1% in terms of mAP@0.5, precision, and recall, respectively. Visualization results further confirm its superior performance in detecting small and dense targets, indicating strong potential for practical remote sensing applications. Full article

This paper presents the results of research on an innovative, integrated IACMS (Intelligent Integrated Automation, Control, and Monitoring System), developed for energy-efficient operation of auxiliary machinery in ship engine rooms. The system, validated both in the laboratory and during full-scale operation on the MF Skania Ro-Pax ferry, integrates process monitoring, diagnostics, predictive maintenance, and intelligent energy optimization within a unified control architecture. This approach enables a significant reduction in electricity consumption while maintaining thermal safety and operational reliability. Laboratory tests focused on a pump cooling system with PLC and frequency converter control, achieving a 90.5% reduction in energy consumption compared to conventional constant-speed operation. During full-scale validation, the IACMS managed the seawater pump via adaptive frequency control (30–60 Hz). Two consecutive voyages demonstrated energy savings of 84.6% and 86.0%, with a daily energy reduction of 0.84 MWh, resulting in a decrease of approximately 0.5 tons of CO₂ emissions per day. Additionally, an observed reduction of about 6–7% in daily generator-set energy was recorded during the analyzed period; this vessel-level value is indicative, as the generator supplies multiple onboard consumers. All trials confirmed stable cooling system temperatures, and comprehensive diagnostics revealed no negative impact of inverter control on the technical condition of equipment. The findings indicate that IACMS is a universal and scalable tool for improving energy efficiency and enabling predictive maintenance in ship engine room auxiliary systems. The system was positively validated in commercial operation and certified by the Polish Register of Shipping, confirming its technological maturity and readiness for widespread adoption in the maritime industry. The results pave the way for further deployments of intelligent energy management solutions in shipping, supporting maritime decarbonization goals. Full article

Ship monitoring using Synthetic Aperture Radar (SAR) data faces significant challenges in detecting small vessels due to low spatial resolution and speckle noise. While ESRGAN (Enhanced Super-Resolution Generative Adversarial Network) has shown promise for image super-resolution, it struggles with SAR imagery characteristics. This study proposes SA/SU-ESRGAN, which extends the SU-ESRGAN framework by incorporating a spatial attention mechanism loss function. SU-ESRGAN introduced semantic structural loss to accurately preserve ship shapes and contours; our enhancement adds spatial attention to focus reconstruction efforts on ship regions while suppressing background noise. Experimental results demonstrate that SA/SU-ESRGAN successfully detects small vessels that remain undetectable by SU-ESRGAN, achieving improved detection capabilities with a PSNR of approximately 26 dB (SSIM is around 0.5) and enhanced visual clarity in ship boundaries. The spatial attention mechanism effectively reduces noise influence, producing clearer super-resolution results suitable for maritime surveillance applications. Based on the HRSID dataset, a representative dataset for evaluating ship detection performance using SAR data, we evaluated ship detection performance using images in which the spatial resolution of the SAR data was artificially degraded using a smoothing filter. We found that with a 4 × 4 filter, all eight ships were detected without any problems, but with an 8 × 8 filter, only three of the eight ships were detected. When super-resolution was applied to this, six ships were detected. Full article

The recent growth of Blue Economy-related human activities has increased underwater noise pollution. Sound is a key factor in ensuring the well-being of marine animals as it allows them to communicate with each other and extract valuable information from the environment. Although the Marine Strategy Framework Directive requires monitoring programs to achieve good environmental status, there remains a significant deficit of information concerning three key domains: the characteristics of the underwater soundscape, its transformation due to anthropogenic activities, and the effects of noise on marine animals. This study aimed to evaluate the impact of anthropogenic activities on marine acoustic environments. Acoustic metrics and ecoacoustic indices were applied to characterise variability and assess daily, weekly, and seasonal patterns, as well as the effects of trawling restrictions. Three underwater soundscapes were compared in this study: two natural environments in the Mediterranean Sea and one artificial environment, a land-based fish farm tank. High anthropogenic noise levels were found, primarily due to fishing vessels near the selected locations. Similarly, the soundscape exhibited notable seasonal variations (annual and weekly), demonstrating a significant dependence on tourist activities. The results highlight the benefits of acoustic parameters as a tool for monitoring environmental conditions over time. Full article

Background/Objectives: Aneurysmal subarachnoid hemorrhage (aSAH) involves a sudden onset of a perfusion-pressure injury from the initial insult combined with a secondary injury phase produced by delayed cerebral ischemia, cerebrospinal fluid circulation disturbances, and generalized instability of the patient’s physiological state. The situation may be further complicated when there has been rupture of the aneurysm at the site of the carotid–posterior communicating (PCom) artery junction that occurs in conjunction with a fetal configuration of the posterior cerebral artery (fPCA), thereby making definitive treatment dependent on preserving the critical nature of the branches of the posterior circulation since the aneurysm’s neck plane coincides with the dominant posterior circulation conduit. Case Presentation: A 65-year-old female patient who was obese (Grade III BMI = 42), had chronic bronchial asthma, and arterial hypertension experienced a “thunderclap” type of headache in the right retro-orbital area followed by a syncopal episode and developed acute confusion with agitation. Upon admission to the hospital, her Glasgow Coma Scale (GCS) was 13, her FOUR score was 15, her Montreal Cognitive Assessment (MoCA) score was 12/30, her Hunt–Hess grade was 3, WFNS grade 2, and Fisher grade 4 SAH with intraventricular extension. Digital subtraction angiography (DSA) and three-dimensional rotational angiography revealed a posteriorly directed right carotid communicating aneurysm that had a relatively compact neck (approximately 2.5 mm) and sac size of approximately 7.7 × 6.6 mm, with the fPCA originating at the neck plane. Microsurgical treatment was performed with junction-preserving reconstruction with skull base refinement, temporary occlusion of the internal carotid artery for a few minutes, placement of clips reconstructing the carotid–PCom interface, and micro-Doppler verification of patent vessel. Postoperatively, the blood pressure was kept within the range of 110–130 mmHg with nimodipine and closely monitored. The neurological recovery was sequential (GCS of 15 by POD 2; MoCA of 22 by POD 5). By POD 5 CT scan, the clip remained positioned in a stable fashion without evidence of infarct, hemorrhage, or hydrocephalus; at three months she was neurologically intact (mRS 0; Barthel 100; MoCA 28/30), and CTA confirmed persistent exclusion of the aneurysm and preservation of fPCA flow. Conclusions: In cases where the ruptured aneurysm is located at the carotid communicating junction with the PCom artery in a configuration of the posterior cerebral artery that is described as fetal, clip treatment should be viewed as a form of branch-preserving junction reconstruction of the carotid–PCom junction supported by adherence to controlled postoperative physiology and close ppostoperativesurveillance. Full article

Despite their ecological and conservation significance, morphometric relations remain scarce for elasmobranch species in the Mediterranean. This study examined morphometric parameters of the eight elasmobranch species (one shark and seven batoids) presented in the Amvrakikos Gulf that has been designated as a National Park. A total of 1247 specimens were sampled between 2022 and 2025, caught by small-scale fishing vessels using trammel nets, gillnets or bottom longlines and collected through onboard surveys or landing sites monitoring. Linear regressions were applied to describe relations between total length and other body measures (disc length, disc width, fork length), and length measurements and body weight. Results showed strong relations across morphometric traits, with R² values exceeding 0.655 for most relations. Growth patterns varied: four species (Aetomylaeus bovinus, Dasyatis pastinaca, D. tortonesei, Mustelus mustelus) exhibited positive allometry, one species (D. marmorata) displayed negative allometry and Gymnura altavela showed near-isometric growth. Sexual dimorphism was generally absent, although significant differences were found between sex in disc width slopes for D. marmorata, Myliobatis aquila and Torpedo torpedo, and in length–weight relations for M. mustelus. These findings substantially fill regional data gaps, offering new baseline estimates for rare and threatened elasmobranchs. Full article

Vasculitides are a heterogeneous group of disorders characterized by inflammation of blood vessel walls, leading to tissue ischemia and organ injury. Traditional inflammatory markers such as the erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP) are widely used but lack diagnostic specificity. This has driven the search for more informative biomarkers across vasculitis subtypes. This review summarizes current evidence for validated and emerging biomarkers in large-, medium-, small-, and variable-vessel vasculitis, as well as single-organ vasculitis. Key analytes reflect systemic inflammation, such as serum amyloid A (SAA) and interleukin-6 (IL-6), as well as endothelial activation, complement pathways, neutrophil and macrophage activation, and organ-specific damage. Promising candidates include pentraxin-3 (PTX3) and matrix metalloproteinase-9 (MMP-9) in large-vessel vasculitis; N-terminal pro-B-type natriuretic peptide (NT-proBNP) and S100 proteins in Kawasaki disease; galactose-deficient immunoglobulin A1 (Gd-IgA1) and urinary angiotensinogen (AGT) in IgA vasculitis; and tissue inhibitor of metalloproteinases-1 (TIMP-1), S100 proteins, complement C3, and PTX3 in antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis. Although these biomarkers provide mechanistic insight, most lack disease-specificity, external validation, or standardized assays. Future progress will require multicenter studies, harmonized testing, and integrated biomarker panels combined with imaging modalities to improve diagnosis, activity assessment, and monitoring. Full article

Space conditions offer new insights into fundamental biological and molecular mechanisms. The study aimed to evaluate the enzymatic activity of proteinase K (PK) under extreme conditions relevant to space environments: simulated microgravity, hypergravity, and gamma radiation. PK activity was tested using azocasein (AZO) as a chromogenic substrate, with enzymatic reactions monitored spectrophotometrically at 450 nm. A rotating wall vessel (RWV) simulated microgravity, centrifugation at 1000× g (3303 rpm) generated hypergravity, and gamma radiation exposure used cesium-137 as the ionizing source. PK activity showed no remarkable changes under microgravity after 16 or 48 h; however, higher absorbance values after 96 h indicated enhanced AZO proteolysis compared to 1 g (Earth gravity) controls. In hypergravity, low PK concentrations exhibited slightly increased activity, while higher concentrations led to reduced activity. Meanwhile, gamma radiation caused a dose-dependent decline in PK activity; samples exposed to deep-space equivalent doses showed reduced substrate degradation. PK retained enzymatic activity under all tested conditions, though the type and duration of stress modulated its efficiency. The results suggest that enzyme-based systems may remain functional during space missions and, in some cases, exhibit enhanced activity. Nevertheless, their behavior must be evaluated in a context-dependent manner. These findings may be significant to advance biotechnology, diagnostics, and the development of enzyme systems for space applications. Full article

Doppler assessment of fetal cerebral circulation has become a cornerstone of modern fetal medicine. It is used to evaluate cerebral vascular malformations, brain anomalies, fetal growth restriction due to placental insufficiency, fetal anemia, and hemodynamic complications arising from placental vascular anastomoses in monochorionic pregnancies. Emerging research also explores the predictive value of Doppler parameters for perinatal outcomes and long-term neurodevelopment. To review the anatomy and physiology of fetal cerebral vessels accessible to Doppler evaluation, outline key technical aspects, and summarize current obstetric applications. A PubMed search identified 113 relevant publications, published between 1984 and 2025. Three book chapters by authors recognized internationally within the scientific community were included. A total of 116 publications were critically analyzed in this narrative review. Strong evidence supports the use of Doppler ultrasound in obstetrics, particularly for evaluating fetal cerebral hemodynamics, where it contributes to reducing fetal morbidity and mortality. Doppler assessment of fetal brain circulation is a valuable tool for evaluating brain vascular malformations, other structural abnormalities, and for assessing fetuses with growth restriction, anemia, and twin-to-twin transfusion syndrome. It allows targeted fetal monitoring and timely interventions, providing critical prognostic information and aiding parental counseling. Ongoing advances in Doppler technology and understanding of fetal brain physiology are likely to broaden its clinical uses, improving both perinatal outcomes and long-term neurological health. Full article

Traditional water quality monitoring methods are limited in providing timely chlorophyll-a (Chl-a) assessments in small inland reservoirs. This study presents a rapid Chl-a retrieval approach based on a cooperative unmanned aerial vehicle–uncrewed surface vessel (UAV–USV) framework that integrates UAV hyperspectral imaging, machine learning algorithms, and synchronized USV in situ sampling. We carried out a three-day cooperative monitoring campaign in the Longhu Reservoir of Fujian Province, during which high-frequency hyperspectral imagery and water samples were collected. An innovative median-based correction method was developed to suppress striping noise in UAV hyperspectral data, and a two-step band selection strategy combining correlation analysis and variance inflation factor screening was used to determine the input features for the subsequent inversion models. Four commonly used machine-learning-based inversion models were constructed and evaluated, with the random forest model achieving the highest accuracy and stability across both training and testing datasets. The generated Chl-a maps revealed overall good water quality, with localized higher concentrations in weakly hydrodynamic zones. Overall, the cooperative UAV–USV framework enables synchronized data acquisition, rapid processing, and fine-scale mapping, demonstrating strong potential for fast-response and emergency water-quality monitoring in small inland drinking-water reservoirs. Full article

The Automatic Identification System (AIS) frequently suffers from data loss and irregular report intervals in real maritime environments, compromising the reliability of downstream navigation, monitoring, and trajectory reconstruction tasks. To address these challenges, we propose BAARTR (Boundary-Aware Adaptive Regression for Kinematically Consistent Vessel Trajectory Reconstruction), a novel kinematically consistent interpolation framework. Operating solely on time, latitude, and longitude inputs, BAARTR explicitly enforces boundary velocities derived from raw AIS data. The framework adaptively selects a velocity-estimation strategy based on the AIS reporting gap: central differencing is applied for short intervals, while a hierarchical cubic velocity regression with a quadratic acceleration constraint is employed for long or irregular gaps to iteratively refine endpoint slopes. These boundary slopes are subsequently incorporated into a clamped quartic interpolation at a 1 s resolution, effectively suppressing overshoots and ensuring velocity continuity across segments. We evaluated BAARTR against Linear, Spline, Hermite, Bezier, Piecewise cubic hermite interpolating polynomial (PCHIP) and Modified akima (Makima) methods using real-world AIS data collected from the Mokpo Port channel, Republic of Korea (2023–2024), across three representative vessels. The experimental results demonstrate that BAARTR achieves superior reconstruction accuracy while maintaining strictly linear time complexity ($\mathit{O}\left(\mathit{N}\right)$). BAARTR consistently achieved the lowest median Root Mean Square Error (RMSE) and the narrowest Interquartile Ranges (IQR), producing visibly smoother and more kinematically plausible paths-especially in high-curvature turns where standard geometric interpolations tend to oscillate. Furthermore, sensitivity analysis shows stable performance with a modest training window (n ≈ 16) and minimal regression iterations (m = 2–3). By reducing reliance on large training datasets, BAARTR offers a lightweight, extensible foundation for post-processing in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic Service (VTS), as well as for accident reconstruction and multi-sensor fusion. Full article

Background: Tuberculous meningitis (TBM) is a severe central nervous system infection that can lead to cerebral vasculitis and infarction. This study aimed to evaluate changes in cerebral perfusion and vasculitis on magnetic resonance imaging (MRI) before and after anti-tuberculosis treatment, focusing on both infarcted and non-infarcted brain regions and comparing them with age-matched controls. Methods: Quantitative arterial spin labeling (ASL) perfusion and black-blood vessel wall MRI were performed at diagnosis and after 3–6 months of treatment in TBM patients and healthy controls. Regions of interest included infarcted areas, the contralateral normal brain, and TBM-affected regions without infarction. Cerebral blood flow (CBF), perfusion grading, and vasculitis were assessed and correlated with clinical stage and disease severity. Results: In total, 73 TBM patients and 26 controls were included. Among the patients, 26 (35.6%) had acute infarctions, mainly in the basal ganglia and corona radiata, and 65 (89.0%) exhibited vasculitis predominantly involving anterior circulation. Pretreatment MRI showed significantly reduced CBF in infarcted regions compared with contralateral brain and controls (p < 0.05), and both contralateral and non-infarcted TBM regions also showed lower CBF than controls (p < 0.05). After treatment, CBF increased significantly in non-infarcted regions (p < 0.05), and post-treatment perfusion grade correlated with TBM stage and vasculitis severity. Conclusions: TBM-related infarcts demonstrated marked hypoperfusion, while non-infarcted regions exhibited reversible ischemic changes. ASL and vessel wall imaging can quantitatively monitor treatment response and vascular inflammation, as well as predict late infarction in TBM patients. Full article

AI enhances aquatic resource management by automating species detection, optimizing feed, forecasting water quality, protecting species interactions, and strengthening the detection of illegal, unreported, and unregulated fishing activities. However, these advancements are inconsistently employed, subject to domain shifts, limited by the availability of labeled data, and poorly benchmarked across operational contexts. Recent developments in technology and applications in fisheries genetics and monitoring, precision aquaculture, management, and sensing infrastructure are summarized in this paper. We studied automated species recognition, genomic trait inference, environmental DNA metabarcoding, acoustic analysis, and trait-based population modeling in fisheries genetics and monitoring. We used digital-twin frameworks for supervised learning in feed optimization, reinforcement learning for water quality control, vision-based welfare monitoring, and harvest forecasting in aquaculture. We explored automatic identification system trajectory analysis for illicit fishing detection, global effort mapping, electronic bycatch monitoring, protected species tracking, and multi-sensor vessel surveillance in fisheries management. Acoustic echogram automation, convolutional neural network-based fish detection, edge-computing architectures, and marine-domain foundation models are foundational developments in sensing infrastructure. Implementation challenges include performance degradation across habitat and seasonal transitions, insufficient standardized multi-region datasets for rare and protected taxa, inadequate incorporation of model uncertainty into management decisions, and structural inequalities in data access and technology adoption among smallholder producers. Standardized multi-region benchmarks with rare-taxa coverage, calibrated uncertainty quantification in assessment and control systems, domain-robust energy-efficient algorithms, and privacy-preserving data partnerships are our priorities. These integrated priorities enable transition from experimental prototypes to a reliable, collaborative infrastructure for sustainable wild capture and farmed aquatic systems. Full article