Search Results (168)

The Baltic Sea region presents challenging environmental and operational conditions for search and rescue (SAR) and oil spill recovery activities, including strong winds, high waves, seasonal ice, and low water temperatures. The current Lithuanian search and rescue and oil pollution response capabilities, particularly the existing vessel “Šakiai”, are insufficient to meet modern operational and safety requirements. This study aims to determine the optimal concept and technical characteristics of a new vessel capable of operating effectively in Lithuanian maritime responsibility area. The research combines hydrometeorological data analysis, review of international regulatory frameworks, evaluation of equipment requirements, and bridge simulator modelling of two reference vessel concepts: patrol-type and supply-type. Additional oil spill dispersion modelling was performed using the simulation tool. Findings show that search and rescue tasks prioritize speed, while spill response operations require stability and maneuverability. Simulations indicate that patrol-type vessels reach search and rescue zones faster, while supply-type vessels provide superior station maintenance and equipment deployment in adverse conditions. The optimal vessel concept should be based on a supply-type hull with dynamic positioning, ≥15 kn speed, ≥113 t bollard pull, ≥6-day endurance and oil recovery arms with ≥40 m sweep width. Full article

In the Arctic region, the navigation and positioning accuracy of shipborne and autonomous underwater vehicle (AUV) integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) solutions is severely degraded due to poor satellite geometry, frequent ionospheric disturbances, non-Gaussian measurement noise, and strong multipath effects, as well as long-term INS-based dead-reckoning for AUVs when GNSS is unavailable underwater. In addition, the sparse ground-based augmentation infrastructure and the lack of reliable reference trajectories and dedicated test ranges in polar waters hinder the validation and performance assessment of existing marine navigation systems, further complicating the achievement of accurate and reliable navigation in this region. To improve the positioning accuracy of the GNSS/INS shipborne navigation system, this paper adopts a tightly coupled GNSS/INS navigation approach. To further enhance the accuracy and robustness of tightly coupled GNSS/INS positioning, this paper proposes an improved Adaptive Robust Extended Kalman Filter (IAREKF) algorithm to effectively suppress the effects of gross errors and non-Gaussian noise, thereby significantly enhancing the system’s robustness and positioning accuracy. First, the residuals and Mahalanobis distance are calculated using the Adaptive Robust Extended Kalman Filter (AREKF), and the chi-square test is used to assess the anomalies of the observations. Subsequently, the observation noise covariance matrix is dynamically adjusted to improve the filter’s anti-interference capability in the complex Arctic environment. However, the state estimation accuracy of AREKF is still affected by GNSS signal degradation, leading to a decrease in navigation and positioning accuracy. To further improve the robustness and positioning accuracy of the filter, this paper introduces a sliding window mechanism, which dynamically adjusts the observation noise covariance matrix using historical residual information, thereby effectively improving the system’s stability in harsh environments. Field experiments conducted on an Arctic survey vessel demonstrate that the proposed improved adaptive robust extended Kalman filter significantly enhances the robustness and accuracy of Arctic integrated navigation. In the Arctic voyages at latitudes 80.3° and 85.7°, compared to the Loosely coupled EKF, the proposed method reduced the horizontal root mean square error by 61.78% and 21.7%, respectively. Full article

This work investigated machine-learning algorithms for remote-control and autonomous operation of the Very-Small, Long-Life, Modular (VSLLIM) microreactor. This walk-away safe reactor can continuously generate 1.0–10 MW of thermal power for 92 and 5.6 full power years, respectively, is cooled by natural circulation of in-vessel liquid sodium, does not require on-site storage of either fresh or spent nuclear fuel, and offers redundant means of control and passive decay heat removal. The two ML algorithms investigated are Supervised Learning with Long Short-Term Memory networks (SL-LSTM) and Soft-Actor Critic with Feedforward Neural Networks (SAC-FNN). They are trained to manage the movement of the control rods in the reactor core during various transients including startup, shutdown, and to change the reactor steady state power up to 10 MW. The trained algorithms are incorporated into a Programmable Logic Controller (PLC) coupled to a digital twin dynamic model of the VSLLIM microreactor. Although the SL-LSTM algorithms demonstrate high prediction accuracy of up to 99.95%, they demonstrate inferior performance when incorporated into the PLC. Conversely, the PLC with SAC-FNN algorithm accurately adjusts the control rods positions during the reactor startup transients to within ±1.6% of target values. Full article

The phenomenon of seawater flow-freezing exists during ballast water injection and drainage in polar vessels, but the heat transfer and ice evolution behaviors under low-temperature flow conditions remain unclear. This study developed a computational model for ballast tank freezing using the volume of fluid (VOF) and enthalpy–porosity method, and constructed a scaled experimental platform for the simulation model validation. Based on this model, the flow-heat transfer and ice evolution process in the ballast tank are analyzed in detail, with a focus on the influence of injection velocity, pipe diameter, and position on seawater freezing characteristics. The results show that during low-temperature water injection, phase change occurs preferentially in the tank bottom region, with ice presenting as a slurry morphology; when injection velocity increases from 0.25 m/s to 3.5 m/s, the maximum ice-phase volume fraction increases by 48.9%, indicating faster flow accelerates phase-change freezing; compared to other diameters, DN150 piping exhibits the highest turbulent kinetic energy (0.054 m²/s²) and the maximum shear stress (12.49 Pa), demonstrating optimal freezing resistance; compared to bottom injection, sidewall injection intensifies heat transfer/icing near tank walls and increases ice-clogging risk around ports. This study reveals intrinsic mechanisms of dynamic ice-blockage evolution, providing theoretical basis for anti-clogging design in polar ship systems. Full article

The small waterplane area twin hull (SWATH) is a type of high-performance vessel known for its excellent seakeeping performance, remarkable maneuverability, and high lateral stability. These advantages have led to its growing application in scientific research ships. Since many research operations require a vessel to maintain a fixed position, Dynamic Positioning Systems (DPSs) are essential. To better support diverse scientific tasks, the R/V SHIYAN 1 was upgraded with an enhanced dynamic positioning system. A ship motion model was established after comprehensively accounting for environmental factors such as wind, waves, and currents. By automatically controlling three actuators, the system successfully achieved effective dynamic positioning. In comparative tests conducted under conditions of wind speed at 13.4 m/s, wave height at 3.2 m, and current at 0.2 m/s, the power system was able to maintain a positioning radius within 5 m. Analysis of data from three dynamic positioning experiments revealed that wave loads had the most significant impact on positioning accuracy, followed by wind loads, while ocean current loads had the least influence. This upgrade not only improves the vessel’s operational capability but also enhances its effectiveness in marine scientific exploration. Full article

Background/Objectives: We aimed to characterize the microvascular imaging (MI) to demonstrate in hepatic hemangiomas in routine practice and to quantify the impact of lesion depth on MI signal detectability, and—when present—describe the distribution of MI appearances. Methods: In this single-center, retrospective study from January 2021 to December 2023, we screened 91 patients with 121 focal hepatic lesions on ultrasound. Lesions without typical hemangioma enhancement on dynamic MRI or dynamic CT were excluded. Two radiologists independently assessed MI signals and patterns using the Jeon classification, blinded to clinical and CT/MRI data; inter-observer agreement was quantified with Cohen’s κ. Results: Of 121 screened lesions, 36 lacked typical enhancement and were excluded; 85 hemangiomas remained. A total of 13 were excluded for motion artifacts near the heart or pulsatile vessels, yielding 72 hemangiomas (61 patients) for analysis. No lesion showed flow on color or power Doppler. MI signals were detected in 68/72 hemangiomas (94.4%). Among signal-positive lesions (n = 68), the patterns were non-specific in 25.0% (17/68), nodular rim in 22.1% (15/68), strip rim in 17.6% (12/68), central dot-like in 16.2% (11/68), peripheral dot-like in 10.3% (7/68), and staining in 8.8% (6/68). Signal-negative lesions were deeper than signal-positive lesions (median depth: 85 mm vs. 41.5 mm; p < 0.05). The inter-observer agreement was very good (κ = 0.821, 95% CI 0.767–0.921). Conclusions: MI is a reproducible, contrast-free technique that demonstrates hemangioma vascularity with high detection rates, particularly in more superficial lesions. In this cohort, lesion depth rather than size was the primary determinant of MI signal detectability. MI should be considered complementary to CT/MRI and may be especially useful where contrast agents are unavailable or contraindicated. Full article

Offshore vessels are nowadays equipped with dynamic positioning systems, meaning they have additional thrusters dedicated to the station keeping of the unit. However, there is no rational criterion on the placement of these devices to increment station keeping capabilities. This is true both in case of a vessel retrofitting or for the design of a new unit. The present work proposes investigating a methodology for the optimal placement of thrusters along the hull of an offshore unit. This implies the adoption of a suitable optimisation algorithm capable of handling all the constraints of the optimisation problem. As the target is the optimal capability, the optimisation should handle multiple dynamic positioning capability calculations, meaning (in a quasi-static approach) that it is capable of solving multiple thrust allocation problems at each optimisation step. As thruster allocation is another optimisation problem, the process should handle two nested optimisations. Here, the global location problem is solved with a differential evolution algorithm, while the thrust allocation employs non-linear programming. As the capability calculations imply the adoption of a specific wind–wave correlation, the present work compares the effect of different correlations on the optimised location of the thrusters. The results presented on a reference Pipe Lay Crane Vessel highlight the differences in the final optimum as a function of the environmental modelling. Full article

Regulated proteolysis via autophagy is essential for cellular homeostasis, yet the specific role of autophagy-related gene 7 (ATG7) in corneal epithelial maintenance remains unclear. Using a conditional knockout mouse model (Atg7^f/f K14Cre^+/−), we investigated the impact of ATG7 deficiency on corneal epithelial autophagy, morphology, and vascular dynamics. Loss of ATG7 disrupted autophagosome formation, evidenced by increased LC3B expression but reduced LC3B-positive puncta and absence of autophagosomes ultrastructurally. Although gross corneal morphology was preserved, ATG7 deficiency led to thickened epithelium and increased peripheral lymphatic vessel sprouting, indicating a pro-inflammatory and pro-lymphangiogenic microenvironment. Proteomic analysis revealed upregulation of RAB8, TM9S3, and RETR3, suggesting activation of compensatory pathways such as exophagy, reticulophagy, and Golgiphagy. Inflammatory and angiogenic components were downregulated, suggesting a moderate loss of inhibitory capacity based on the lymphatic phenotypes observed. At the same time, while these two compensatory changes occur, other proteins that positively regulate lysosome formation are reduced, resulting in a phenotype linked to deficient autophagy. These findings demonstrate that ATG7-mediated autophagy maintains corneal epithelial homeostasis and immune privilege, with implications for understanding corneal inflammation and lymphangiogenesis in ocular surface diseases. Full article

Accurately predicting the reception area of the Automatic Identification System (AIS) is critical for ship tracking and anomaly detection, as errors in signal interpretation may lead to incorrect vessel localization and behavior analysis. However, traditional propagation models, whether they are deterministic, empirical, or semi-empirical, face limitations when applied to dynamic environments due to their reliance on detailed atmospheric and terrain inputs. Therefore, to address these challenges, we propose a data-driven approach based on graph neural networks (GNNs) to model AIS reception as a function of environmental and geographic variables. Specifically, inspired by attention mechanisms that power transformers in large language models, our framework employs the SAmple and aggreGatE (GraphSAGE) framework convolutions to aggregate neighborhood features, then combines layer outputs through Jumping Knowledge (JK) with Bidirectional Long Short-Term Memory (BiLSTM)-derived attention coefficients and integrates an attentional pooling module at the graph-level readout. Moreover, trained on real-world AIS data enriched with terrain and meteorological features, the model captures both local and long-range reception patterns. As a result, it outperforms classical baselines—including ITU-R P.2001 and XGBoost in F1-score and accuracy. Ultimately, this work illustrates the value of deep learning and AIS sensor networks for the detection of positioning anomalies in ship tracking and highlights the potential of data-driven approaches in modeling sensor reception. Full article

Bio-based lubricants are rapidly gaining prominence as sustainable alternatives to petroleum-derived counterparts, driven by their inherent biodegradability, low ecotoxicity, and strong alignment with global environmental and regulatory imperatives. Despite their promising tribological properties, their widespread adoption continues to confront significant challenges, particularly related to oxidative and thermal instability, cold-flow behavior, and cost competitiveness in demanding high-performance applications. This comprehensive review critically synthesizes the latest advancements in bio-based lubricant technology, spanning feedstock innovations, sophisticated chemical modification strategies, and the development of advanced additive systems. Notably, recent formulations demonstrate remarkable performance enhancements, achieving friction reductions of up to 40% and contributing to substantial CO₂ emission reductions, ranging from 30 to 60%, as evidenced by comparative life-cycle assessments and energy efficiency studies. Distinguishing this review from existing literature, this study offers a unique, holistic perspective by integrally analyzing global market trends, industrial adoption dynamics, and evolving regulatory frameworks, such as the European Union Eco-Label and the U.S. EPA Vessel General Permit, alongside technological advancements. This study critically assesses emerging methodologies for tribological evaluation and benchmark performance across diverse, critical sectors including automotive, industrial, and marine applications. By connecting in-depth technical innovations with crucial socio-economic and environmental considerations, this paper not only identifies key research gaps but also outlines a pragmatic roadmap for accelerating the mainstream adoption of bio-based lubricants, positioning them as an indispensable cornerstone of sustainable tribology. Full article

This paper addresses the path tracking problem of underactuated unmanned surface vessels (USVs) in the presence of unknown external disturbances. A fixed-time adaptive integral sliding mode control (AISMC) method, incorporating a nonlinear disturbance observer (NDO), is proposed. Initially, a three-degree-of-freedom dynamic model of the USV is developed, accounting for external disturbances and model uncertainties. Based on the vessel’s longitudinal and transverse dynamic position errors, a virtual control law is designed to ensure fixed-time convergence, thereby enhancing the position error convergence speed. Next, a fixed-time NDO is introduced to estimate real-time external perturbations, such as wind, waves, and currents. The observed disturbances are fed back into the control system for compensation, thereby improving the system’s disturbance rejection capability. Furthermore, a sliding mode surface is designed using a symbolic function to address the issue of sliding mode surface parameter selection, leading to the development of the adaptive integral sliding mode control strategy. Finally, compared with traditional SMC and PID, the proposed AISMC-NDO offers higher accuracy, faster convergence, and improved robustness in complex marine environments. Full article

To improve the maneuverability of the installation vessel and shorten the operational timeframe, a dynamic positioning (DP) semi-submersible vessel with a sophisticated control strategy is utilized to replace the conventional traction systems and towing tugs. In this paper, an improved fuzzy control law is developed for the standby and docking stage of the float-over installation. The control thrust is divided into a dynamic part and a static part. Both parts are computed based on a set of logical if–then-type statements based on the human sense of realism and expert experience. The dynamic part acts as the proportional–derivative control, while the static part contributes the integral action. The implementation of accumulation time enhances the static thrust’s effectiveness in positioning the vessel compared to traditional integral control methods. A model test is further conducted to validate the effectiveness of the proposed improved fuzzy control based on a DP barge. Wind, wave, and current are considered from different directions in the experiment to simulate the practical operation condition. Experimental results illustrate that the proposed improved fuzzy control law can accurately keep position and guide the vessel into the slot of the jacket. Finally, some conclusions are summarized for the characteristic of fender forces in different environmental load directions. Full article

Head and neck squamous cell carcinomas (HNSCCs) represent a diverse group of malignancies, both clinically and biologically, with human papillomavirus (HPV) infection playing a significant role. HPV-positive tumours generally tend to have a better prognosis and are driven by oncoproteins E6 and E7. In contrast, HPV-negative tumours typically have a worse prognosis and are often linked to mutations in tumour suppressor genes. HNSCCs exist within a complex environment known as the tumour microenvironment (TME). The TME includes tumour cells, cancer stem cells (CSCs), cancer-associated fibroblasts (CAFs), immune cells, extracellular matrix (ECM), blood vessels, and various signalling molecules. These components support tumour progression, invasion, metastasis, and resistance to treatment. Intercellular signalling within the TME—mediated by cytokines such as IL-6, TGF-b, and galectins—further promotes tumour growth and systemic effects like cachexia. Notably, the TME shares features with granulation tissue during wound healing, supporting the concept of cancer as a chronic, non-resolving wound. Effective therapy must target not only tumour cells but also the dynamic TME. Full article

Meeting the International Maritime Organization’s (IMO) 2050 targets for reducing greenhouse gas (GHG) emissions requires cost-effective solutions that minimize wind resistance without compromising safety, particularly for medium-sized multipurpose vessels (MPVs), which have been underrepresented in prior research. This study numerically evaluates 20 bow-mounted NaviScreen configurations using a coupled high-fidelity computational fluid dynamics (CFD) and finite element analysis (FEA) approach. Key design variables—including contact angle (35–50°), lower-edge height (1.2–2.0 m), and horn position (3.2–5.3 m)—were systematically varied. The sloped Type-15 shield reduced aerodynamic resistance by 17.1% in headwinds and 24.5% at a 30° yaw, lowering total hull resistance by up to 8.9%. Nonlinear FEA under combined dead weight, wind loads, and Korean Register (KR) green-water pressure revealed local buckling risks, which were mitigated by adding carling stiffeners and increasing plate thickness from 6 mm to 8 mm. The reinforced design satisfied KR yield limits, ABS buckling factors (>1.0), and NORSOK displacement criteria (L/100), confirming structural robustness. This dual-framework approach demonstrates the viability of NaviScreens as passive aerodynamic devices that enhance fuel efficiency and reduce GHG emissions, aligning with global efforts to address climate change by targeting not only CO2 but also other harmful emissions (e.g., NOx, SOx) regulated under MARPOL. The study delivers a validated CFD-FEA workflow to optimize performance and safety, offering shipbuilders a scalable solution for MPVs and related vessel classes to meet IMO’s GHG reduction goals. Full article

In maritime blurred image recognition, ship collision accidents frequently result from three primary blur types: (1) motion blur from vessel movement in complex sea conditions, (2) defocus blur due to water vapor refraction, and (3) scattering blur caused by sea fog interference. This paper proposes a dual-branch recognition method specifically designed for motion blur, which represents the most prevalent blur type in maritime scenarios. Conventional approaches exhibit constrained computational efficiency and limited adaptability across different modalities. To overcome these limitations, we propose a hybrid CNN–Transformer architecture: the CNN branch captures local blur characteristics, while the enhanced Transformer module models long-range dependencies via attention mechanisms. The CNN branch employs a lightweight ResNet variant, in which conventional residual blocks are substituted with Multi-Scale Gradient-Aware Residual Block (MSG-ARB). This architecture employs learnable gradient convolution for explicit local gradient feature extraction and utilizes gradient content gating to strengthen blur-sensitive region representation, significantly improving computational efficiency compared to conventional CNNs. The Transformer branch incorporates a Hierarchical Swin Transformer (HST) framework with Shifted Window-based Multi-head Self-Attention for global context modeling. The proposed method incorporates blur invariant Positional Encoding (PE) to enhance blur spectrum modeling capability, while employing DyT (Dynamic Tanh) module with learnable α parameters to replace traditional normalization layers. This architecture achieves a significant reduction in computational costs while preserving feature representation quality. Moreover, it efficiently computes long-range image dependencies using a compact 16 × 16 window configuration. The proposed feature fusion module synergistically integrates CNN-based local feature extraction with Transformer-enabled global representation learning, achieving comprehensive feature modeling across different scales. To evaluate the model’s performance and generalization ability, we conducted comprehensive experiments on four benchmark datasets: VAIS, GoPro, Mini-ImageNet, and Open Images V4. Experimental results show that our method achieves superior classification accuracy compared to state-of-the-art approaches, while simultaneously enhancing inference speed and reducing GPU memory consumption. Ablation studies confirm that the DyT module effectively suppresses outliers and improves computational efficiency, particularly when processing low-quality input data. Full article