Search Results (4,606)

Accurate Structural Health Monitoring of offshore wind turbines is critical for ensuring their long-term operational safety in harsh marine environments. Although displacement is a fundamental metric for assessing structural deformation and stress distribution, its direct measurement in open-ocean conditions is severely hindered by environmental interference and the absence of stable spatial references. Consequently, reconstructing displacement from structural acceleration through double integration is widely adopted, yet it suffers from severe baseline drift. Furthermore, existing drift-mitigation techniques often rely on empirical parameter selection and are limited to single-point reconstructions, failing to capture the full three-dimensional (3D) spatial attitude of the structure. To address these limitations, this paper proposes a novel 3D spatial attitude reconstruction framework based on advanced drift removal and spatial interpolation. First, an improved drift removal algorithm is developed to accurately eliminate baseline errors from acceleration signals, ensuring the physical fidelity of the reconstructed local displacements. Subsequently, cubic spline interpolation is utilized to extrapolate these discrete local measurements into a comprehensive full-field attitude profile of the entire turbine structure. The performance and robustness of the proposed method are systematically verified through numerical simulations and finite element analysis. Finally, its engineering applicability and accuracy are further validated via laboratory experiments and field measurements. The proposed framework effectively mitigates noise sensitivity and significantly enhances the accuracy of full-field attitude reconstruction, providing a reliable foundation for refined structural health assessments of OWTs. Full article

CD4⁺ T lymphocytes are important regulators of immune homeostasis, and their dysregulation is closely linked to a wide range of diseases. For this reason, their reliable detection remains a major challenge. Despite the fact that current methods are analytically robust, they rely mainly on laboratory infrastructure, limiting their flexibility and wider applicability. The present review analyzes established and emerging approaches for CD4⁺ T-cell detection, with a focus on their practical limitations regarding portability, flexibility and applicability. To the best of our knowledge, for the first time, the authors are examining the possibility of exploring marine magnetotactic bacteria (MTBs) as active biological elements in immune cell detection due to their intrinsic magnetic properties, biological organization, and surface biofunctionalization potential. Rather than offering an immediate technological solution, the use of MTBs serves as a challenging biological framework that could provide more adaptable and sensitive detection strategies. At the same time, the limitations of this concept are acknowledged, emphasizing the need for further experimental validation, considering that this strategy, although promising, remains an exploratory concept. Full article

Marine by-products represent a promising source of bioactive peptides. This study aimed to isolate and characterize a low-molecular-weight peptide fraction with antioxidant activity from Argentine shortfin squid carcass by-products, and to evaluate in vitro its cytocompatibility and protective effects against corticosterone (CORT)-induced oxidative injury in rat adrenal pheochromocytoma (PC12) cells and human astrocyte (hACs) cells. Argentine squid antioxidant peptide (PASN) was obtained by size-exclusion chromatography and fractionation-based screening. PASN exhibited the strongest overall free-radical-scavenging activity and consisted predominantly of components below 1 kDa (211.73–1013.48 Da). Spectroscopic analyses indicated that enzymatic hydrolysis transformed its structure from a rigid triple-helix conformation to a more flexible conformation dominated by β-turns (50.78%) and random coils (17.38%). In addition, thermogravimetric analysis confirmed its excellent thermal stability, with an onset decomposition temperature as high as 244.81 °C, supporting its potential applicability in high-temperature food-processing matrices. In vitro assays demonstrated that PASN exhibited high biocompatibility and promoted proliferation of both PC12 cells and hACs, while significantly improving cell viability under CORT challenge. PASN also reduced lactate dehydrogenase (LDH) leakage (hACs: 38.31%; PC12: 31.17%) in both cell models and restored total superoxide dismutase (T-SOD) activity (hACs: 69.46%, PC12: 66.40%). Immunofluorescence further revealed that PASN rescued the expression of brain-derived neurotrophic factor (BDNF) (hACs: 35.23%, PC12: 12.50%) and glutamate decarboxylase (GAD1/2) (hACs: 102.66%, PC12: 31.31%), key markers associated with synaptic plasticity and GABAergic sleep regulation. Collectively, PASN is a thermally stable squid-derived peptide fraction that exerts antioxidant and cytoprotective effects in neural cell models in vitro and represents a promising sustainable candidate for nutraceutical development. Full article

The development of highly efficient and stable photoanodes is essential for advancing photoelectrochemical cathodic protection towards practical applications. Herein, a novel ternary sulfide heterojunction was engineered through the construction of a three-dimensional interlocked architecture of ZnIn₂S₄ on SnIn₄S₈ nanosheets via a sequential hydrothermal synthesis. This unique three-dimensional interlocked configuration creates an intimate interface and continuous charge transfer highways, effectively addressing the slow electron movement and poor interfacial contact that plague conventional photoelectrodes. Spectroscopic and electrochemical analyses verified the formation of a Type-II band alignment, which drives the directional migration of photogenerated electrons from ZnIn₂S₄ to SnIn₄S₈ under an intrinsic built-in electric field. Upon coupling with 304 stainless steel, the ZnIn₂S₄/SnIn₄S₃ heterojunction exhibited outstanding photoelectrochemical cathodic protection performance. It delivered impressive photocurrent densities of 15.22, 19.76, and 72.27 μA·cm⁻² in 3.5 wt% NaCl, 0.1 M Na₂S₂O₃, and 0.1 M Na₂S/NaOH electrolytes, respectively, along with a prominent 720 mV cathodic potential shift in the Na₂S/NaOH system. Most importantly, its good activity and stability in the scavenger-free 3.5 wt% NaCl solution and natural seawater highlight the strong practical potential of this 3D interlocked photoanode for sustainable marine metal corrosion control. Through a strategic multi-electrolyte assessment, the underlying protection mechanisms were decoupled, revealing that the synergy between the heterojunction-induced charge separation enabled by the three-dimensional interlocked structure and electrolyte-specific hole scavenging is key to the enhanced performance. Full article

Battery Energy Storage Systems (BESSs) are increasingly deployed in grid-scale applications, electric mobility, and renewable integration, where safety, reliability, and longevity are critical. Thermal runaway remains one of the most severe failure modes in lithium-ion batteries, often triggered by complex interactions between electrochemical, thermal, and mechanical phenomena. This paper presents an extended hybrid Physics-Informed Neural Network (PINN) framework for thermal anomaly prediction and early detection of runaway precursors in BESS. The proposed architecture integrates governing physical laws, specifically the Bernardi heat generation equation and Fick’s diffusion law, within a deep learning pipeline composed of a physics module, a temporal Bi-LSTM, and an attention mechanism for explainability, which may represent an obstacle in the application of deep learning algorithms. Beyond the initial formulation, the extended version presented here provides a deeper theoretical background, an expanded methodological justification, a more comprehensive comparison with state-of-the-art approaches, and a detailed discussion on scalability, uncertainty, and deployment challenges. The results for synthetic yet physically consistent datasets represent a proof of concept of the PINN approach, which can achieve superior generalization, robustness to noise, and interpretability compared to purely data-driven baselines, achieving an accuracy above 90% and an AUC of 0.95. The framework contributes to proactive safety management in cyber-physical energy systems and establishes a foundation for real-time, physics-aware anomaly detection in safety-critical BESS applications, e.g., marine transportation contexts and port environments. Full article

A Ti-Al-Si composite coating was prepared on Ti65 titanium alloy using a two-step hot-dipping + pre-oxidation method to improve its tribological performance and high-temperature oxidation resistance. The second-step dipping time strongly affected the coating microstructure and wear behavior. The optimal coating, prepared with a dipping time of 5 min in each step, exhibited negligible wear after oxidation at 800 °C for 1000 h and 2500 h, with slight adhesive wear and oxidative wear as the dominant mechanisms. Longer dipping times led to mixed wear modes and reduced wear resistance. Under high-temperature corrosion conditions, the coating showed good long-term stability in water vapor, with its mass gain following a sub-parabolic law, Δm = 0.39·t^0.47, because the internal multilayered structure effectively blocked inward oxygen diffusion. However, in environments containing NaCl or 75 wt.% Na₂SO₄ + 25 wt.% NaCl, catastrophic hot corrosion occurred, regardless of the presence of water vapor, through a chlorine-driven oxidation–chlorination–reoxidation autocatalytic cycle. In the mixed salt environment, Na₂SO₄ decomposition supplied additional oxygen and alkaline species, accelerating the degradation and spallation of the Al₂O₃ and TiO₂ scales. Water vapor further intensified this cycle by generating HCl, which promoted rapid consumption of Al and Ti in the coating. This study reveals the wear behavior and hot corrosion failure mechanisms of Ti-Al-Si coatings under complex conditions, providing guidance for process optimization and applications in marine atmospheres. Full article

The Stewart–Gough Platform (SGP) is a spatial parallel manipulator offering high accuracy, rigidity, and adaptability, with applications spanning medical systems, marine engineering, agriculture, manufacturing, entertainment, aerospace, and architectural installations. This paper presents a comparative analytical and computational study of three SGP configurations: the regular SGP, with regular hexagonal base and top platforms; the Irregular-Parallel SGP, derived from the regular SGP by a novel graphical decomposition-and-modification procedure and characterized by similar symmetric hexagonal platforms with limbs preserved parallel; and the Irregular-Skewed SGP, in which the irregular hexagonal platforms of the Irregular-Parallel SGP are retained, but the limbs are connected in an inclined, alternating clockwise (or anticlockwise) topology. The Irregular–Skewed SGP is free from the constraint singularity that persists in the first two configurations and requires the shortest maximum actuator stroke. Static force analysis shows that the regular SGP and the Irregular–Parallel SGP both exhibit a rank-deficient rigidity matrix (rank = 3) across the geometric scaling range tested (radius ratios 1:2 to 1:10; inter-platform distances 100–1000 mm), whereas the Irregular-Skewed SGP achieves full rank (rank = 6) through inclined limb connectivity and is the only configuration capable of sustaining static equilibrium under the loading conditions examined. The forward kinematics of the Irregular-Parallel SGP is verified against a SolidWorks model: under a 9 mm uniform limb extension, the MATLAB and SolidWorks positions of node 7 agree to within 1.27 mm. The rotational workspace volume is equivalent across the three configurations, but the density of valid solution points within that workspace differs. The workspace within joint limits, alternating compression–tension force partition, and asymmetric stroke economy of the Irregular-Skewed SGP indicate applicability to kinetic facades and transformable interiors in architectural-robotics deployment. Full article

Meat, as an important source of animal protein, plays a central role in the human diet, and its processing operations critically influence the product quality. As an emerging non-thermal physical technology, ultrasound has demonstrated considerable application potential and distinct advantages in meat processing. This review systematically summarizes recent advances in the application of ultrasound for meat tenderization, marination, sterilization, fermentation, freezing, thawing, drying, and the extraction of bioactive compounds from meat by-products, with particular emphasis on its ability to enhance processing efficiency and final product quality. The underlying mechanisms of ultrasound action in meat systems are discussed in depth. Current evidence indicates that ultrasonication not only intensifies processing operations but also positively modulates the physicochemical and functional properties of meat products, including improved tenderness, water-holding capacity, and color stability, promoted flavor development, reduced cooking loss, and extended shelf life. This review aims to provide a theoretical foundation for the scientific research, practical application, and future development of ultrasound technology in meat processing, highlighting its potential to partially replace conventional methods and contribute to more sustainable food processing practices. Full article

Tropical estuaries are highly productive yet increasingly threatened by natural and anthropogenic pressures, necessitating robust ecological assessments for sustainable management. This study assesses the spatio-seasonal distribution of macrobenthic assemblages and evaluates the ecological health of the Sangu River estuary based on their bioindicator potential. Sediment samples for macrobenthos analysis were collected during three seasons (pre-monsoon, monsoon, and post-monsoon) from nine stations across three estuarine zones influenced by sedimentation, aquaculture, and terrestrial runoff. We employed microbenthic diversity indices, multivariate analyses, the AZTI’s Marine Biotic Index (AMBI), and multivariate-AMBI (M-AMBI) to evaluate the ecological health status of the study area. Our study recorded 13 taxa, dominated by Nereididae (40.90%), Mysidae (14.29%), and Capitellidae (10.20%). Macrobenthos diversity (Shannon diversity) ranged from 0.80 to 1.22, and abundance showed negative correlations with salinity (r = −0.29) and silt (r = −0.22), and a positive correlation with dissolved oxygen (r = 0.29). Analysis of Similarities (ANOSIM) indicated that seasonal variation was the primary driver of community structure (p < 0.001). AMBI classified most stations as having good to moderate ecological status, while M-AMBI indicated moderate disturbance across seasons, with elevated proportions of opportunistic taxa (EG V: 14.4–32%) reflecting persistent anthropogenic stress. This study provides the first empirical ecological baseline for the Sangu River estuary and highlights the applicability of family-level AMBI assessments in data-limited tropical estuarine systems. Full article

Foundation segmentation models exhibit strong generalization on natural images yet degrade substantially in underwater scenes due to color distortion, scattering, and low contrast, which collectively impair feature representation. Parameter-efficient fine-tuning strategies have been explored to adapt SAM to marine domains while preserving generalization, but degraded image quality still hampers feature extraction. Moreover, existing SAM-based underwater methods typically rely on ground-truth box prompts during inference. Since ground-truth boxes are inherently unavailable in real-world underwater scenarios, this dependence yields evaluation outcomes that fail to reflect actual deployment conditions, thereby limiting their practical applicability. To address these issues, Water-AutoSAM is introduced—a dual-domain enhanced auto-prompting framework tailored for underwater image segmentation. The auto-prompting mechanism decouples semantic and positional representations for generalized point generation, which are optimized via enhanced sharpness, correctness, and diversity losses under staged training. To counter the degrading effects typical of underwater imagery, a lightweight module designated SS-UIE is integrated as a frozen pre-enhancement stage. This module operates with spatial–frequency dual-branch processing and utilizes a fixed residual fusion coefficient to combine the two streams. Operating entirely without box prompts, Water-AutoSAM achieves competitive annotation-free performance, attaining 92.38% mIoU on SUIM and reducing the gap to the fully supervised upper bound to 2.08% on COD10K. Full article

Underwater image enhancement is essential for marine visual perception tasks. However, the highly heterogeneous optical degradations in real-world waters, the scarcity of paired training data, and the inherent dilemma for existing models in balancing long-range dependency modeling with computational overhead pose significant challenges. To address these issues, this paper proposes a prior-guided, self-supervised underwater image enhancement framework called Scene-Aware Retinex Mamba (SARM). This framework seamlessly integrates Retinex theoretical priors with state space models (SSMs) and operates without paired supervision by employing a prior-guided pseudo-labeling strategy to guide network optimization. Architecturally, SARM deeply couples the physical Retinex prior with SSM. Its core module integrates multi-color space features and leverages a 2D selective scan mechanism to achieve global context modeling with linear complexity $\mathcal{O}\left(HW\right)$, effectively removing complex color casts and suppressing non-uniform scattering noise. To further overcome the generalization bottlenecks in cross-domain underwater testing, this paper introduces a Scene-Aware Adapter (SAA), which facilitates dynamic loss scheduling and adaptive feature gating by quantifying scene-specific degradation characteristics. Comprehensive evaluations on multiple benchmark datasets, including UIEB, EUVP, and UCCS, demonstrate that SARM achieves state-of-the-art subjective and objective enhancement quality (e.g., yielding a URanker score of 2.491 and a CCF score of 35.76), while maintaining an ultra-fast inference speed of 136.52 FPS on the UIEB dataset. Furthermore, extended experiments reveal that SARM can significantly boost the performance of downstream vision tasks, validating its potential as a robust preprocessing module for various practical marine vision applications. Full article

An effective sensor network strategy is fundamental to structural health monitoring (SHM). Optimal sensor placement (OSP) for transmission towers remains insufficiently studied, primarily owing to the extensive number of candidate nodes and the complex structural responses of these structures under diverse environmental loads. Utilizing finite element analysis (FEA), this paper proposes a novel framework for the sensor placement of transmission towers. The maximum modal order of a Y-shaped transmission tower is determined using the Fisher Information Matrix (FIM), which characterizes its dynamic properties, while the Modal Assurance Criterion (MAC) is employed to identify the optimal number of sensors. The Marine Predators Algorithm (MPA) is then utilized to determine the optimal sensor configuration for the transmission tower based on four different fitness functions. The performance of these four fitness functions in sensor layout design is systematically compared. The results indicate that the MPA can efficiently generate optimal sensor configurations under a constraint on the maximum number of sensors. The choice of fitness function has a significant impact on the sensor placement results. The proposed MPA-based OSP method provides a reliable technical framework for the optimal design of SHM systems in power transmission engineering. Full article

Underwater object detection is important for ocean exploration and marine applications. However, underwater images are often degraded by absorption, scattering, and background interference, which weaken object contours, blur boundaries, and obscure fine texture details, thereby increasing the difficulty of detecting small objects and objects with large shape variations. To address these challenges, we propose WEC-UOD, an underwater object detector that improves structure-sensitive representation learning and multi-scale feature fusion within the detector, without relying on a separate image enhancement stage. In the backbone, the Wavelet–Edge Collaboration (WEC) module first uses wavelet-subband guidance to compensate for degraded structural and texture information and then applies edge-guided spatial correction to refine object boundaries and local geometry. In the neck, the Scale-Selective Fusion (SSF) module adaptively selects informative responses from branches with different receptive fields and further suppresses background interference through channel and spatial recalibration. Experiments on RUOD and DUO show that WEC-UOD achieves mAP@0.5 scores of 87.4% and 86.9%, respectively, consistently outperforming the YOLOv11s baseline. These results demonstrate the effectiveness of combining structural enhancement with selective multi-scale aggregation for underwater object detection. Full article

The study of bubble pulsation from underwater explosions is critical for applications in marine resource exploration, underwater demolition, and offshore engineering. However, the existing research methods have significant limitations: Laboratory experiments struggle to replicate the dynamic decompression during the process of bubble rising. Field experiments in seas or lakes find it difficult to systematically cover complex parameter ranges. Furthermore, theoretical calculations face the problems of accurately coupling the bubble pulsation with its buoyancy-driven ascent. Therefore, this paper proposes a novel method for calculating the bubble pulsation period of underwater explosions. This method accurately simulates the pulsation and buoyancy-driven ascent of an underwater explosion bubble. Based on the bubble’s energy attenuation characteristics, it establishes the relationship between the pulsation period, TNT equivalent, and ambient hydrostatic pressure. To verify the accuracy of the method, we conducted underwater explosion experiments in the South China Sea with varying TNT equivalents and detonation depths. Abundant bubble pulsation period data of underwater explosions were obtained spatially by deploying hydrophone arrays at various depths. The close agreement between the theoretical predictions and the experimental results confirms the accuracy of the proposed method. By matching the measured values of the first pulsation period and the ratio of the second pulsation period to the first against a database of theoretical curves, a combination of depth and charge equivalent that satisfies both values can be identified, thereby enabling the inversion of the explosion parameters. Full article

This paper analyzes the vibrational characteristics of a novel sandwich plate configuration composed of an auxetic honeycomb (AH) core and laminated functionally graded carbon nanotube-reinforced composite (FG-CNTRC) face sheets, hereafter referred to as the SD-AuCNT plate. Based on Reddy’s third-order shear deformation theory (SDT), which accurately accounts for transverse shear effects without requiring shear correction factors, the equations of motion are derived using Hamilton’s principle and subsequently solved using a pb-2 Ritz formulation combined with the Newmark time integration scheme for dynamic response analysis. By combining an auxetic core with negative Poisson’s ratio characteristics and laminated FG-CNTRC face sheets featuring tailored CNT distribution patterns and orientations, the hybrid SD-AuCNT plate can improve structural stiffness, energy absorption, and dynamic performance; however, it has not been thoroughly investigated in the existing literature. After verifying the accuracy of the proposed computational procedure, the effects of auxetic core geometry, CNT distribution patterns, thickness ratios, and boundary conditions on the natural frequencies and transient responses of the plate are comprehensively investigated. The results provide new insights into the dynamic behavior of advanced sandwich plates and offer practical guidance for the design of high-performance lightweight structures in aerospace, marine, defense, and other engineering applications. Full article