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Na 0.5 Bi 0.5 TiO 3 (NBT) ceramic is a potential dielectric material for the manufacture of high-performance dielectric ceramic capacitors due to its high polarization and environmentally friendly characteristics. In this work, (1– x)(0.70Na 0.5 Bi 0.5 TiO 3 –0.30SrTiO 3)– x Bi(Ni 0.5 Hf 0.5)O 3 (NBST– x BNH) ceramics have been prepared based on the strategy of refining grain size and inducing polar nano-regions (PNRs). As a result, the NBST–0.15BNH ceramic achieved large energy density and efficiency (W rec = 4.11 J/cm3, η = 82.1%) at a low electric field of 240 kV/cm. This performance is outstanding compared to existing reports at similar breakdown electric fields. The introduction of Sr2+ enabled the NBST to have a high relaxation property, and the introduction of BNH induced PNRs, which ensured the high energy storage efficiency of the ceramics. BNH doping refined grain size, which contributed to the large breakdown electric field strength of the ceramics. In addition, stability tests showed that the NBST–0.15BNH ceramic had excellent thermal and frequency stability. The ceramic also demonstrated a fast discharge rate (t 0.9 = 148 ns) in the charge-discharge test. [Display omitted] [ABSTRACT FROM AUTHOR]

The subsea jumper, a crucial pipeline connector facilitating liquid transport between underwater structures, experiences dynamic alterations in flow direction, rate, and phase volumes owing to its distinctive design. These complex flow characteristics can trigger flow-induced vibrations, potentially causing fatigue damage to the jumper. This paper explores the multiphase flow patterns (oil, gas, and water) in a rigid M-shaped subsea jumper under varied mixing velocities (2–4 m/s) and different volume fractions of each phase (0.1–0.7). It analyzes the factors influencing structural vibration response. The findings reveal diverse flow patterns within the jumper, each exhibiting distinct characteristics based on flow parameters. Concurrently, spatiotemporal oscillations from fluid dynamics impact each bend. Pressure on jumper walls gradually diminishes with changing flow direction. Furthermore, the study performs structural modal and frequency domain analyses of pulsating pressure to evaluate potential resonance conditions. The results of this paper contribute to a deeper understanding of the multiphase flow characteristics in jumpers, offering valuable insights to enhance the reliability of underwater oil and gas production. • The three-phase flow pattern evolution of oil, gas, and water in the M-shaped jumper was studied through simulation. • The flow characteristics of slug flow induced by multiphase flow in the jumper were investigated. • Explored the pressure drop characteristics of oil, gas, and water flow in the jumper at different flow rates. • Through one-way fluid-structure interaction, the jumper's vibration under multiphase flow was obtained. [ABSTRACT FROM AUTHOR]

• The collection of a substantial video dataset of the fibre rope in oceanographic winch winding process is built. The semantic segmentation algorithm was applied to fault detection in the fibre rope arrangement, and the performance of different models under various construction methods was validated for fault detection. • A model selection method was proposed. This method constructs evaluation indicators and assessment system to adapt models for varying engineering requirements. • Analysis scenarios were established for real-time monitoring during the maritime voyage of ships, for off-ship video analysis specifically targeting fault analysis, and for small detection devices used in embedded systems. • The training of the model underwent evaluation from various perspectives, affirming both the reliability and the practicality of the assessment approach. The quality of synthetic fibre rope arrangement is crucial for the orderly and efficient launch and recovery of marine exploration equipment in oceanographic winch systems. However, due to the unique properties of synthetic fibre ropes, faults are prone to occur during the rope winding process. This paper proposes a framework for fault detection method selection of oceanographic multi-layer winch fibre rope arrangement. An autonomous image dataset is used to train and evaluate the effectiveness and performance of various semantic segmentation models. Based on the performance metrics, a training model selection framework is proposed. The results of the fault detection method demonstrate that deep learning semantic segmentation algorithms perform well in the detection of rope winding arrangement faults. This evaluation framework provides guidance for the best model selection under various detection scenarios. It has made contribution to the fault detection and safe operation of oceanographic multi-layer winch fibre rope arrangement. [ABSTRACT FROM AUTHOR]

Detecting damages, especially micro-damages, early and accurately is crucial to the safety of drilling risers. This study presents a high-precision model for detecting micro-damages in drilling risers based on modal information using Machine Learning (ML) algorithms. The under-sampling technique is applied to ensure a balanced distribution of data. The modal information data set is used to analyze three machine learning models: Random Forest (RF), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN). The study investigates their applicability and interpretability in damage identification. SHapley Additive exPlanations (SHAP) analysis is utilized to unveil the decision-making process behind the models. The findings indicate that each model offers unique advantages, with the RF and BPNN models achieving a superior performance in terms of generalization ability. The study finally conducts feature optimization on the BPNN model based on SHAP values and develops a BPNN-4 model with enhanced stability and generalization capabilities. This research highlights the potential of applying interpretable ML techniques in structural health monitoring, offering a promising direction for future offshore engineering diagnostics. • Accuracy of damage identification in drilling risers is improved by using an interpretable feature optimization method. • The under-sampling method is employed to balance the data set and improve the efficiency of model training. • Three machine learning models, i.e., BPNN, ELM, and RF, are compared for damage identification of drilling risers. • Effectiveness of the machine learning algorithms is verified by combining statistical parameters and generalization analysis. [ABSTRACT FROM AUTHOR]

[Display omitted] • A biomimetic collagen implant was developed through self-assembly and covalent crosslinking. • Bionic collagen fibers demonstrated enhanced stability and durability. • The collagen implant exhibited superior biocompatibility and bioactivity. • The NACF implant promoted collagen regeneration and inhibited calcification. • The NACF implant outperformed traditional implants, showing promise for effective skin rejuvenation. Skin aging, a complex and inevitable biological process, considerably impacts an individual's physical appearance and overall well-being. While collagen-based implants hold potential for skin rejuvenation, their clinical application is limited by the poor durability of non-crosslinked implants and the negative impacts of crosslinking agents, such as cytotoxicity and calcification. Herein, we report a highly durable and biocompatible bionic collagen implant for superior skin rejuvenation with advanced anti-calcification characteristics and enhanced collagen regeneration. Utilizing biomimetic self-assembly and covalent crosslinking approach, we successfully created long-lasting and biomimetic collagen implants with densely-packed collagen fibers. Crosslinking of collagen fibers with a low concentration of N-hydroxysuccinimide activated suberic acid (NHS-SA) was highly efficient, resulting in complete crosslinking and thicker collagen fiber formation with a uniform network structure. Compared with glutaraldehyde-crosslinked collagen fibers, NHS-SA-crosslinked assembled collagen fibers (NACF) demonstrated considerably improved thermal stability and durability. Furthermore, the NACF implant exhibited superior biocompatibility, establishing NHS-SA crosslinked collagen fibers as a safer and more bioactive choice for implant applications. In mouse subcutaneous implant models, the NACF implant outperformed other collagen implants, demonstrating exceptional biocompatibility, effective collagen regeneration, and excellent anti-calcification potential. The NACF implant shows immense promise in collagen regeneration, offering considerable potential to advance skin rejuvenation therapies. [ABSTRACT FROM AUTHOR]

Late Pleistocene thick laminated mats of Ethmodiscus rex (E. rex) have been found on the seafloor of the western tropical Pacific. Exploring the causes of E. rex mats formation can improve our understanding of the role of diatom mats in the global carbon-silicon cycle. Nutrients, such as silicate, are a major factor contributing to the growth of diatoms. However, the origin of the nutrients necessary for E. rex to bloom remains unclear. Here we analysed the 14C ages and silicon isotope compositions (δ30Si) of E. rex mats from deep-sea sediment cores collected in the western Pacific to reveal the nutrient source of the diatom bloom. The results indicate that the age of the cores ranges from 11 ka to 19 ka. This is the first discovery of a last deglacial thick layer of E. rex mats in the western Pacific Hadal zone. Furthermore, the silicon isotope results show that, similar to previous studies in the western Pacific, all δ30Si of E. rex frustules are negative. This is in contrast to the North Atlantic upwelling region where all δ30Si are positive. According to the binary mixing model calculations, eolian dust were responsible for supplying 80%–90% of the silica during the last deglaciation, while upwelling waters accounted for only about 10%–20% of the silica. During Heinrich Stadial 1 (HS1) period, the strengthened East Asian Winter Monsoon (EAWM) transported a lot of eolian dust to the western Pacific, providing nutrients for diatom blooms. In addition, the enhanced stratification in the western Pacific since the late HS1 period was a significant factor that contributes to the blooms of E. rex when the supply of eolian dust material decreased. • First thick mats of Ethmodiscus rex were discovered in the western Pacific during the last deglaciation. • Ethmodiscus rex 's silicon isotope (δ30Si) suggested silicate source from Asian eolian. • Stratification in western Pacific promoted Ethmodiscus rex blooms since late HS1. [ABSTRACT FROM AUTHOR]

The underwater configuration analysis of a towing rope is crucial for estimating the rope tension and the state of the towed body. In the case where the rope inclination angle significantly increases, the partial hydrodynamic force coefficients of a rope segment should display apparent variations with the local inclination angle. However, constant drag coefficients ignoring the angle-dependent drag effect are widely used in numerical studies. To address the importance of the angle-dependent force coefficients for analyzing the underwater configuration of a towing rope, Large Eddy Simulations are carried out to obtain the drag forces of a cylindrical rope segment with varying inclination angles and Reynolds numbers. To model the rope deformation, a tensile experiment is also conducted to establish the nonlinear tension-strain relationship of the fibre rope. Parametric studies are performed to investigate the difference in the results calculated by both constant and angle-dependent hydrodynamic force coefficients. It was found that the application of constant drag coefficients leads to significant underestimation of the rope tension near the towed body, especially when the rope experiences large inclination angles. Therefore, the present study highlights the importance of using the angle-dependent hydrodynamic coefficients to predict the configuration and tension of towing ropes. • The underwater configuration of a towing rope is analyzed based on the drag coefficient validated by the inclination angle. • Large Eddy Simulations are carried out to obtain the drag coefficients at various inclination angles and Reynolds numbers. • Experiment is performed to establish the tension-elongation relationship of the synthetic fibre rope. • The rope tension predicted by the angle-dependent coefficients may be much larger than the one by constant coefficients. [ABSTRACT FROM AUTHOR]

The influences of jumper leakage on the M-type jumper's internal two-phase flow are investigated. The CFD simulation results are validated against the experimental data, demonstrating a high level of agreement. The effects of leak dimensions, different leak locations, gas-liquid ratio, and mixing speed are discussed in terms of the gas-liquid phase distribution, pressure gradient, and jumper vibrations. It is found that the flow patterns can be useful indicators for jumper leak detection, especially when the leak dimension is larger, the slug flow may be formed, producing more serious damage to the jumper. The flow pattern varies significantly with different leak locations. In addition, the flow pattern is affected by the volume percentage of water and faster mixing speeds. The pressure variations can be useful indicators. It is larger as the leak dimension increases and varies significantly in front of the leak location of the jumper. The vibrations can be useful indicators. The larger the leak dimension is, the more pronounced the vibration response is. If vibration sensors are used for leak detection, the downstream vibration signals of the jumper can be a key indicator. Faster mixing rates and water content will cause the jumper to vibrate more intensely. • The evolution of flow patterns near the jumper leakage port was investigated. • The pressure gradient changes of the jumper under different leakage conditions were studied. • The vibration response law of the jumper under different leakage conditions was presented. • The impact of different mixing speed or gas-liquid ratios on the jumper was discussed when a leak occurs. [ABSTRACT FROM AUTHOR]

Salmonella enterica is a zoonotic bacterium that not only causes serious economic losses to the livestock and poultry industries but also seriously endangers human health. Long-term indiscriminate use of antibiotics has led to drug resistance in Salmonella , and thus the identification of alternatives to antibiotics is crucial. In this study, the effects of puerarin on the S. enterica -infected chickens were investigated. A total of 360 chicks were randomly assigned as the control group (CON), the S. enterica group (S), and puerarin-treatment group (P). Chicks in the P group were fed the basal diet supplemented with 50 (P50), 100 (P100), 200 (P200), and 400 (P400) mg/kg puerarin, respectively. It was found that puerarin treatment markedly altered the serum activities of aspartate aminotransferase (AST), alanine transaminase (ALT), and superoxide dismutase (SOD), together with the malondialdehyde (MDA) and total antioxidant capacity (T-AOC) contents in the serum. The mRNA expression of IL-6, IL-1β, TNF-α, Bcl-2, and caspase-8 in the livers of S. enterica -infected chicks was increased after infection but significantly reduced after treatment with puerarin. Histologic analysis showed that puerarin effectively mitigated morphological damage in the liver caused by S. enterica. Proteomic analysis revealed that S. enterica infection led to metabolic disorders in the liver, resulting in oxidative stress, increased inflammation, and significantly elevated levels of hepatocellular carcinoma biomarkers. The findings of the filtered sequencing were verified by using quantitative PCR (qPCR). Treatment with 100 mg/mL puerarin thus effectively alleviated disordered liver metabolism, reduced inflammation and oxidative damage and significantly reduced the levels of hepatocellular carcinoma biomarkers in the liver. The results suggest that puerarin has the potential to replace antibiotics to control Salmonella infection in poultry and thus improve food safety. [ABSTRACT FROM AUTHOR]

Flow pattern identification is of great significance for the flow assurance of multiphase flow in offshore pipelines. For this purpose, a deep learning algorithm integrating convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is developed in this study to identify different flow patterns of two-phase flow in a Z-shaped pipeline. To verify the applicability and performance of the proposed algorithm, a series of experiments are conducted in the multiphase flow loop. Then, the flow pattern pictures, mass flow rates, and vibration signals are collected by the NI data acquisition system and subjected to denoising using the density-based spatial clustering of applications with noise (DBSCAN) algorithm combined with LSTM. Meanwhile, the Kendall rank correlation is employed for feature processing. Finally, the proposed deep learning algorithm achieves accuracies of 0.98077 with engineering parameters and 0.955 with time-domain parameters. In addition, a comparison analysis is conducted with four other common deep learning algorithms in terms of computation time, learning rate, accuracy, and different sample quantities. The results demonstrate that the proposed algorithm can not only improve the identification accuracy but can also achieve faster convergence speed and higher computational efficiency. • A DBSCAN-Kendall–CNN–SLTM model is proposed to characterize and identify different flow patterns in the offshore pipelines. • Experiments are conducted with the multiphase flow loop to verify the performance of the proposed deep learning model. • The performance of the proposed model is verified by comparing four other deep learning algorithms. [ABSTRACT FROM AUTHOR]