Search Results (4,523)

From an environmental perspective, the use of clam shells contributes positively to marine waste management and promotes more sustainable construction practices. This study aims to analyze the influence of clam shell-derived filler on the mechanical properties of cementitious mortars, evaluating its effect as a function of dosage and particle fineness, in order to determine its potential as a sustainable additive in construction applications. The shells were ground for 0.5, 1.0, and 1.5 h and incorporated at percentages ranging from 0.5% to 5.0% by mass of cement. Slump (reduced Abram’s cone) was performed in the fresh state for each specimen mixture, while flexural strength, and compressive strength tests were performed at 7, 14, and 28 days of curing. Microstructural characterization was also performed using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDX) analysis. In addition, particle size distribution parameters were determined to quantify the effect of grinding time on particle refinement and its relationship with mechanical performance. A multifactor ANOVA was conducted to evaluate the statistical significance of grinding time and filler dosage on compressive strength. The results showed that the combination of 0.5 h of grinding and 1.0% filler provided the best mechanical performance for both flexural and compressive strength, with values of 7.27 MPa and 26.16 MPa, respectively. Dosages higher than 2.0% tended to decrease strength, which is associated with saturation of non-cementing particles. EDX analysis showed adequate calcium distribution without generating chemical segregation. The results showed that the combination of 0.5 h of grinding and 1.0% filler provided the best mechanical performance for both flexural and compressive strength, with values of 7.27 MPa and 26.16 MPa, respectively. Dosages higher than 2.0% tended to decrease strength, which is associated with saturation effects and increased specific surface area. The statistical analysis confirmed that both grinding time and filler dosage significantly influence compressive strength, highlighting the importance of optimizing particle size distribution and filler content to achieve improved mechanical performance. Full article

The skin serves as the first line barrier of innate immunity, protecting the body from external influences and maintaining its homeostasis. Exogenous and endogenous stress factors alter the structure and functional properties of the skin. The search for compounds capable of counteracting these processes has allowed the identification of peptides as promising ingredients of products for medicinal and cosmetic applications. This review comprehensively examines the mechanisms of action and dermatological applications of two distinct classes of natural products—endogenous human peptides and those derived from marine organisms. Human peptides exhibit numerous biological functions, including antimicrobial and immunomodulatory ones, as well as promoting antioxidant protection and wound healing. Microbiome-associated peptides are an underestimated but powerful regulator of skin aging through immunomodulation, inflammation control, barrier function maintenance, and selection of the proper microbial community. Peptides from marine organisms exhibit significant structural diversity and a broad spectrum of biological activity, including regenerative effects and effects on antibiotic-resistant microorganisms. This review summarizes current data obtained from in vitro, ex vivo, and clinical studies demonstrating a broad potential of peptides for maintaining skin health. Both peptide classes represent powerful, targeted strategies for innovative dermatological interventions aimed at promoting skin rejuvenation, protection, and overall homeostasis. Full article

Copper-containing steel is widely used in ship plates and other marine engineering fields due to its excellent mechanical properties and good weldability. However, in hydrogen-containing media environments, ship plate steel is prone to hydrogen embrittlement during service. Existing research primarily focuses on steel grades with copper content below 3 wt.%, while the diffusion and trapping behavior of hydrogen in ultra-high-copper steel with copper content exceeding 3 wt.% remains unclear. Therefore, this study designed an ultra-high-copper-content steel with a copper content of 6.01% and investigated the diffusion behavior of hydrogen in the test steel under different hydrogen charging current densities through microstructure characterization, slow strain rate tensile testing, electrochemical hydrogen permeation, and internal friction tests. The results indicate that with an increase in hydrogen charging current density, accompanied by a slight degradation in mechanical properties, the irreversible hydrogen trap density increases by 50.7%. A large number of microstructures, such as phase boundaries, grain boundaries, and dislocations, have formed inside the material, which have reversible trapping effects on hydrogen, effectively suppressing the migration of hydrogen in the crystal structure and reducing the embrittlement phenomenon caused by hydrogen. This study expands the application potential of copper-containing steel in the field of ocean engineering, providing an important reference for the future development of high-strength, hydrogen embrittlement-resistant copper steel with ultra-high copper content. Full article

Titanium alloys exhibit exceptional strength-to-density ratios, high hardness, and outstanding resistance to elevated temperatures, making them indispensable structural materials in aerospace engineering, marine construction, and biomedical applications. In aerospace systems specifically, fatigue failure represents the predominant failure mode for titanium alloy components. This review systematically examines prevalent surface treatment techniques for titanium alloys—including shot peening, ultrasonic rolling treatment, hot isostatic pressing (HIP), physical vapor deposition (PVD), micro-arc oxidation (MAO), and thermal spray processes—and critically evaluates their respective effects on fatigue performance. The underlying mechanisms of each technique are concisely outlined, with emphasis on stress state evolution, near-surface microstructural refinement, and interfacial integrity. Building upon the characteristic surface-dominated fatigue fracture behavior of titanium alloys, this work focuses on how coating composition, architecture (e.g., graded, multilayer, or nanocomposite designs), and interfacial bonding strength govern fatigue resistance. A unified analysis is presented on the distinct yet complementary roles of substrate deformation strengthening (e.g., residual compression, grain refinement) and coating-mediated protection (e.g., barrier function, crack deflection, stress redistribution) during fatigue crack initiation and propagation. Key determinants of fatigue performance, including residual stress distribution, coating/substrate adhesion, thermal mismatch, and environmental degradation susceptibility, are rigorously assessed. Finally, emerging research frontiers are identified, including intelligent process–structure–property mapping, in situ monitoring of fatigue damage at coated interfaces, and design of multifunctional gradient coatings that synergistically enhance strength, wear resistance, and fatigue endurance of titanium alloy components. Full article

Reliable underwater acoustic (UWA) communication is fundamental to marine sensing applications, including environmental monitoring, underwater sensor networks, and autonomous platforms, yet remains severely challenged by multipath propagation, Doppler effects, and limited bandwidth. This paper investigates a memory-based multicarrier modulation framework in which controlled phase continuity is introduced at the symbol-mapping stage to enhance robustness against channel-induced distortions. Unlike conventional memoryless multicarrier schemes, the proposed approach embeds intentional phase memory at the transmitter and exploits it at the receiver, improving reliability in highly dispersive underwater environments. A comprehensive bit-error-rate (BER) evaluation is conducted using extensive simulations over realistic shallow-water acoustic channel models. The analysis examines rational modulation indices, pulse-shaping filters, roll-off factors, transmitter–receiver separation distances, and receiver structures. Both matched-filter and zero-forcing receivers are considered to assess trade-offs between interference mitigation and noise amplification. Results demonstrate consistent and significant BER improvements compared with conventional memoryless multicarrier systems. A modulation index of 7/16 achieves the minimum BER with matched-filter detection, while 3/10 yields optimal performance with zero-forcing detection. The Dirichlet pulse provides the most robust performance across operating conditions. These findings establish phase-memory-aware multicarrier design as a practical strategy for reliable underwater sensing and communication systems. Full article

This is the first systematic review evaluating Black Sea plankton as biosensor organisms for Biological Early Warning Systems (BEWS)—real-time monitoring approaches that detect sublethal behavioral or physiological responses to pollutants before irreversible ecosystem damage occurs. The systematic literature review was guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach, ensuring methodological transparency and applicability. A total of 140 publications from databases (Web of Science Core Collection, Scopus, PubMed, and Google Scholar databases) were included in the final analysis. We assess nine native planktonic taxa as candidates for automated video-based water quality monitoring, using a multi-criteria framework encompassing biological sensitivity, technical detectability, and practical feasibility. Three species emerge as the most suitable candidates: Aurelia aurita as a universal indicator (sensitive to copper, surfactants, petroleum, and microplastics; its large size enables standard video detection); Acartia tonsa for trace contamination (reproductive toxicity at metal concentrations 4–33× below regulatory standards); and Mnemiopsis leidyi for metal-specific discrimination (bioluminescent responses: 650% Zn, 430% Cu, and 350% Hg at 0.001 mg/L). Analysis of 140 publications reveals critical gaps: 33% of species lack toxicological data, 95% of studies test single toxicants despite natural mixture exposure, and microplastic methodology varies 1000-fold in particle size. Threshold analysis suggests planktonic sublethal stress at “safe” concentrations under current standards, suggesting inadequate protection of marine food webs. A complementary monitoring approach integrating these species with computer vision algorithms offers autonomous early-warning capability for Black Sea environmental management. Full article

Ultraviolet (UV) irradiation induces complex skin damage, including hyperpigmentation, oxidative stress, and alterations in proteins related to keratinocyte differentiation and epidermal barrier-associated status. This study investigated the multifunctional protective effects of Padina arborescens ethanolic extract (PAEE) against skin damage in melanocytes, keratinocytes, and zebrafish. In alpha-melanocyte-stimulating hormone (α-MSH)-stimulated B16F10 cells, PAEE effectively suppressed the protein kinase A (PKA)/cyclic adenosine monophosphate (cAMP) response element-binding protein (CREB) signaling pathway, which was associated with reduced expression of microphthalmia-associated transcription factor (MITF) and tyrosinase, leading to decreased melanin synthesis. PAEE also exhibited photoprotective properties by reducing reactive oxygen species (ROS), inhibiting interleukin-1 beta (IL-1β), and attenuating matrix metalloproteinase-1 (MMP-1) upregulation associated with UVB (ultraviolet B)-induced photodamage in HaCaT keratinocytes. Notably, PAEE restored the UVB-reduced expression of filaggrin and involucrin, representative markers of keratinocyte differentiation and epidermal barrier-associated status, in HaCaT keratinocytes. In zebrafish embryos, PAEE suppressed α-MSH-induced melanin accumulation and UVB-induced ROS generation at non-toxic concentrations. Taken together, these results suggest that PAEE exerts anti-melanogenic and photoprotective effects in cellular and zebrasfish models and may serve as a promising marine-derived ingredient for cosmeceutical applications targeting UVB-related skin damage. Full article

Estimating remaining useful life (RUL) and predicting bearing faults based on data-driven models have become central components of modern Prognostics and Health Management (PHM) systems. Although deep learning models have demonstrated strong performance under controlled and stationary operating conditions, their reliability in real-world industrial and marine environments is limited. In practice, operating conditions, sensor properties, and degradation mechanisms evolve continuously over time, leading to non-stationary and shifting data distributions that violate the assumptions of conventional static learning approaches. To address these challenges, two research areas have gained increasing attention: Domain Adaptation (DA), which aims to mitigate distribution discrepancies across operating conditions or machines, and Continual Learning (CL), which enables models to learn sequentially while mitigating catastrophic forgetting. However, existing studies often examine these paradigms in isolation, limiting their effectiveness in long-term deployments, where domain shifts and temporal evolution coexist. This paper presents a comprehensive and systematic review of data-driven methods for bearing fault prognosis and remaining useful life (RUL) prediction under evolving data distributions, adopting the framework of Domain-Adaptive Continual Learning (DACL). By jointly examining the DA and CL methods, this review analyses how these approaches have been individually and implicitly combined to cope with non-stationarity, knowledge retention, and limited label availability in practical PHM scenarios. We categorised existing methods, highlighted their underlying assumptions and limitations, and critically assessed their applicability to long-term, real-world monitoring systems. Furthermore, key open challenges, including scalability, robustness under sequential domain shifts, uncertainty handling, and plasticity–stability trade-offs, are identified, and research directions are outlined based on the identified limitations and practical deployment requirements of the proposed method. This review aims to establish a structured and critical reference framework for understanding the role of domain-adaptive CL in data-driven prognostics, clarifying current research trends, limitations, and open challenges in evolving data distributions. Full article

The resolution and accuracy of airborne/spaceborne SAR are continuously improving, making it an effective means for observing ocean dynamic processes and detecting marine targets. In contrast, utilizing its unique orbital angular momentum (OAM) mode, vortex radar does not require temporal accumulation to achieve azimuthal resolution, making it particularly suitable for observing moving sea surfaces. This capability enables stable and continuous monitoring of dynamic ocean scenes. This paper proposes a vortex radar imaging method based on three-dimensional sea surface scattering characteristics: first, a three-dimensional wind-driven sea surface geometric model is established based on the Elfouhaily sea spectrum, and its scattering characteristics under different incident angles, wind speeds, and wind directions are analyzed using the semi-deterministic facet-based two-scale method; then, two-dimensional range-azimuth imaging is achieved through coordinate transformation, echo modeling, pulse compression, and fast Fourier transform (FFT) in OAM mode domain, with the correctness of the imaging algorithm verified through multiple point target imaging results. Finally, simulation results of two-dimensional sea surface vortex imaging under different incident angles are presented, and the influence of wind speed and direction on sea surface vortex imaging is analyzed. The study shows that the vortex imaging system can effectively reflect wave fluctuations and wind direction characteristics, demonstrating the feasibility and potential of vortex radar imaging in oceanographic applications. Full article

The prediction of dynamic responses of offshore wind turbine foundations under wind-wave-current multi-field coupled loads is the cornerstone of safety in offshore wind power engineering. The currently widely adopted equivalent load application method, while computationally efficient, simplifies loads into concentrated forces applied at the pile top and tower top, neglecting fluid-structure dynamic interaction mechanisms, which leads to deviations in response predictions. To overcome this limitation, this paper proposes a high-precision bidirectional fluid-structure interaction numerical framework. The fluid domain employs computational fluid dynamics (CFD) to construct an air-seawater two-phase flow model, utilizing the standard k-ε turbulence model and nonlinear wave theory to accurately simulate complex marine environments. The solid domain establishes a wind turbine-stratified seabed system via the finite element method (FEM), describing soil-rock mechanical properties based on the Mohr-Coulomb constitutive model. Comparative studies indicate that the equivalent static method significantly underestimates the displacement response of pile foundations, particularly under the extreme shutdown conditions examined in this study. This value should be interpreted as a case-specific observation rather than a universal deviation, and the discrepancy may vary with sea state, wind speed, current velocity, and wind–wave misalignment, thereby leading to non-conservative estimates of stress distribution. In contrast, the fluid-structure interaction method can reveal key physical processes such as local flow acceleration and wake–interference effects around the tower and the parked rotor under shutdown conditions, and the nonlinear interaction and resistance-increasing mechanisms between waves and currents. This model provides a reliable tool for safety assessment and damage evolution analysis of wind turbine foundations under extreme marine conditions, promoting the transformation of offshore wind power structure design from empirical formulas to mechanism-driven approaches. Full article

Coral reef ecosystems are declining rapidly due to climate change, disease, and anthropogenic stressors, driving the expansion of land-based coral propagation for reef restoration. A major bottleneck in these efforts is the manual measurement of coral recruit tissue area from microscopy images, which requires 2–7 min per image and limits scalability. We present a hierarchical deep learning pipeline that automates this measurement by integrating YOLO-based detection with Segment Anything Model (SAM) segmentation. YOLO localizes recruits and classifies them by developmental stage; stage-specific fine-tuned SAM models then segment live tissue using bounding box and background point prompts to suppress segmentation leakage and improve boundary precision. Surface area is computed directly from the segmented masks using pixel size extracted from image metadata. The pipeline reduces processing time to approximately 3–5 s per image—a 24–140× speedup over manual tracing. Evaluated on 3668 microscopy images from two national coral research facilities, the system achieves a mean IoU exceeding 95% and an auto-acceptance rate (AAR) of 71.51%, where predicted-to-ground-truth area ratios fall within a ±5% tolerance of expert annotation, substantially reducing manual workload while maintaining measurement reliability across species, developmental stages, and imaging conditions. This workflow addresses a critical bottleneck in restoration research and demonstrates the broader applicability of AI-based image analysis in marine ecology. Full article

Anthropogenic marine debris (AMD) in shallow coastal benthic areas poses serious threats to ecosystems, human health, and the economy. Addressing this issue is hindered by limited data on AMD distribution and classification. We explored the use of semantic segmentation, specifically Pyramid Scene Parsing Network (PSPNet) and Deep Convolutional Neural Network for Semantic Image Segmentation, Version 3, (DeepLabV3) models, for automated AMD detection and classification. The performance was evaluated using mean intersection over union (mIoU), pixel accuracy, and frames per second (FPS). PSPNet achieved a higher mIoU (77.03%) than DeepLabV3 (75.98%), indicating better object identification. However, DeepLabV3 outperformed PSPNet in pixel accuracy (92.24% vs. 92.01%) and FPS (8.83 vs. 6.92), making it more appropriate for real-time applications. To enable real-time identification and classification of AMD, the models are deployed in a minicomputer with adequate processing power, significantly enhancing the models’ frame rate during real-time image processing. While both models are effective, DeepLabV3 is recommended for real-time AMD segmentation. The study contributes to improving AMD monitoring and management in coastal environments through AI-driven solutions. Full article

Microbially induced desaturation and precipitation (MIDP) is a promising eco-friendly technique for liquefaction mitigation. However, existing studies have primarily focused on silica sands under element-scale cyclic loading, and the dynamic response of MIDP-treated marine sand under seismic excitation remains poorly understood. In this study, the denitrifying bacterium Pseudomonas stutzeri was used to generate nitrogen gas in situ within typical liquefiable marine sand from the Haikou Jiangdong New Area, producing treated specimens with degrees of saturation ranging from approximately 99% to 80%. Shaking table tests were performed under Wenchuan earthquake motions with peak ground accelerations of 0.10–0.20 g. The results show that reducing the degree of saturation by approximately 18.9% decreases surface settlement by 77.6%, while the peak pore water pressure and lateral displacement are reduced by 21% and 15%, respectively. The acceleration response of the treated specimens also exhibits a notable attenuation effect. These findings provide preliminary comparative experimental evidence for the application of MIDP in the eco-friendly liquefaction mitigation of coastal marine sand foundations. Full article

Polyhydroxyalkanoates (PHAs) emerge as promising biodegradable polymers for sustainable applications, yet predicting their biodegradation behavior under different environmental conditions remains challenging. In this study, we propose a novel data-driven computational framework for predicting biodegradation-induced weight/mass loss in PHA-based materials. A comprehensive database of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV)-based formulations was manually curated by systematically collecting and harmonizing material descriptors, environmental parameters, and experimental biodegradation outcomes from laboratory- and large-scale studies conducted in soil, marine, freshwater, and compost environments. Multiple regression-based quantitative structure–activity relationship (QSAR) models were developed and rigorously validated, demonstrating high predictive performance and strong correlations between polymer structure, environmental conditions and degradation behavior. “Exposure time”, “degradation environment” and “hydroxybutyrate (HB) ratio” were identified as the most important features for weight loss. Finally, the predictive model was integrated into the Jaqpot computational platform, enabling open access and facilitating data-driven assessment and design of biodegradable polymer systems. Full article

In harsh marine environments, vessel energy storage systems (VESS) face elevated thermal runaway (TR) risk, yet practical early warning remains difficult because early voltage differences between TR high-risk cells and normal cells are often weak, warning thresholds vary across operating segments, and decisions relying on a single feature are prone to false or missed warnings. To overcome these difficulties, this study develops a four-part early warning strategy for TR high-risk cells in VESS. First, the original cell voltages are denoised through multiscale jump plus mode decomposition and Spearman correlation guided mode reconstruction to suppress irrelevant interference. Second, an improved Sigmoid nonlinear mapping is introduced to enhance subtle inter-cell voltage deviations and improve early separability. Third, sparse representation is used to construct a cell deviation score, and an adaptive threshold is employed to perform primary abnormal-cell screening under varying segment conditions. Finally, multidimensional mutual information value derived from voltage, temperature, and their rates of change is incorporated into a joint assessment methodology to further verify the abnormal state of flagged cells. Validation on 18 independent real operation cases comprising 2483 discharge segments shows that, across the evaluated TR high-risk cases, the shortest confirmed warning lead time achieved by the proposed strategy was 14 days. The proposed strategy also reduced false and missed warnings, outperformed the compared benchmark methods overall, and retained computational feasibility for onboard application in VESS. Full article