Search Results (9,608)

Tailoring the microstructural heterogeneity of metallic coatings is a promising strategy for enhancing their corrosion resistance; however, its systematic optimization remains underexplored. Here in, we present a one-step, scalable electrodeposition strategy to fabricate Ni coatings with tunable nanocrystalline heterostructures on Cu substrates by varying the current density from 1 mA/cm² to 50 mA/cm². The coating with a current density of 10 mA/cm², featuring a heterogeneous nanograin structure of coexisting small and large grains, exhibited optimal corrosion resistance in 3.5 wt.% NaCl solution, with a low self-corrosion current density of 4.48 µA/cm². Electrochemical impedance spectroscopy (EIS) and molecular dynamics (MD) simulations revealed that the heterostructure dispersed Cl⁻ adsorption sites and promoted passivation. High-resolution transmission electron microscopy (HRTEM) revealed that as the current density increased from 10 mA/cm² to 50 mA/cm², the corrosion product transitioned from a crystalline NiOOH structure to an amorphous structure, which correlated with a reduced corrosion resistance. The heterogeneous microstructure enhances durability, offering a cost-effective and alloy-free alternative for offshore applications. These findings provide a theoretical and experimental basis for designing advanced corrosion-resistant coatings. Full article

SiC matrix composites are widely used in high-temperature structural components of aircraft engines and nuclear reactor materials because of their excellent properties such as their high modulus, high strength, corrosion resistance, and high-temperature resistance. However, the bonding of SiCf/SiC composites poses significant challenges in practical engineering applications, primarily due to residual stresses, anisotropy in composite properties, and the demanding conditions required for high-performance joints. This work reviews various bonding technologies for SiC ceramics and SiC matrix composites. These include solid-state diffusion bonding, NITE phase bonding, direct bonding without filling materials, MAX phase bonding, glass ceramic bonding, polymer precursor bonding, metal brazing bonding, and Si-C reaction bonding. Key results, such as the highest bending strength of 439 MPa achieved with Si-C reaction bonding, are compared alongside the microstructural characteristics of different joints. Additionally, critical factors for successful bonding, such as physical mismatch and metallurgical incompatibility, are discussed in detail. Future research directions are proposed, emphasizing the optimization of bonding techniques and evaluation of joint performance in harsh environments. This review provides valuable insights into advancing bonding technologies for SiC composites in aerospace and nuclear applications. Full article

Ecological security is integral to national security strategies, making the construction of ecological security patterns essential for mitigating ecological risks. However, predictive research on ecological security patterns (ESPs) remains limited. This study integrates the Patch-generating Land Use Simulation (PLUS) model with ecological security pattern analysis to provide scientific insights into spatial governance and optimization in the Poyang Lake Ecological and Economic Zone (PLEEZ). First, the PLUS model simulated land use changes in 2030 under three scenarios: natural development (ND), economic development (ED), and ecological protection (EP). Based on these projections, ecological security patterns were constructed using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, the Morphological Spatial Pattern Analysis (MSPA) method, Conefor 2.6, the Minimum Cumulative Resistance (MCR) model, and resistance theory. The results indicate: (1) 19, 18, and 21 ecological source areas were identified under different scenarios, covering 6093.16 km², 5973.21 km², and 6702.56 km², respectively, with 9, 8, and 10 important source sites, primarily in the north. (2) 37, 35, and 43 ecological corridors were delineated, exhibiting a spiderweb-like distribution. (3) 94, 62, and 107 ecological pinch points and 116, 121, and 104 ecological barrier points were detected. The Ecological Node Aggregation Area was identified as a critical zone for targeted ecological protection and restoration. Finally, the ecological zoning management strategy of “Four Cores, Two Zones, and One Belt” was proposed. This study offers valuable insights for sustainable land use planning and ecological risk mitigation. Full article

Herein, Elaeagnus angustifolia L. was utilized as a raw material to extract bound polyphenols using an ultrasound-assisted complex enzyme method for the first time. The effects of enzyme ratio, ultrasonic time, liquid-to-solid ratio, and pH value on the bound polyphenol yield were investigated using single-factor experiments. The key parameters were subsequently optimized using the Box–Behnken design. The optimal conditions identified were as follows: enzyme ratio (α-amylase/cellulase = 5:1 mg/mg), ultrasonic time of 50 min, liquid-to-solid ratio of 12:1 mL/g, and pH value of 5. Under these conditions, the bound polyphenol yield was measured at 13.970 ± 0.3 mg/g. A total of 27 phenolic compounds were identified using ultrahigh-performance liquid chromatography–ion mobility quadrupole time-of-flight mass spectrometry (UPLC–IMS-QTOF-MS), including two coumarins, five lignins, 10 polyphenols, nine flavonoids, and one tannin, and specifically containing Angeloylgomisin Q, Yakuchinone A, Furosin, 6-Dehydrogingerdione, and 4′-Methylpinosylvin, and so on. Antioxidant activity was assessed using the 1,1-diphenyl-2-picryl-hydrazil (DPPH) and 2,2′-azino-bis-(3-ethylbenzthiazoline-6-sulfonate) (ABTS) methods, revealing significant antioxidant potential. This study introduced a novel extraction process for bound polyphenols from E. angustifolia L. and provided the first qualitative analysis of bound polyphenols in this species, establishing a scientific foundation for its development and application in the functional food, medicine, and cosmetics industries. Full article

The migration of radioactive waste in geological environments often exhibits anomalies, such as tailing and early arrival. Fractional derivative models (FADE) can provide a good description of these phenomena. However, developing models for solute transport in unsaturated media using fractional derivatives remains an unexplored area. This study developed a variably saturated fractional derivative model combined with different release scenarios, to capture the abnormal increase observed in monitoring wells at a field site. The model can comprehensively simulate the migration of nuclides in the unsaturated zone (impermeable layer)—saturated zone system. This study fully analyzed the penetration of pollutants through the unsaturated zone (retardation stage), and finally the rapid lateral and rapid diffusion of pollutants along the preferential flow channels in the saturated zone. Comparative simulations indicate that the spatial nonlocalities effect of fractured weathered rock affects solute transport much more than the temporal memory effect. Therefore, a spatial fractional derivative model was selected to simulate the super-diffusive behavior in the preferential flow pathways. The overall fitness of the proposed model is good (R² ≈ 1), but the modeling accuracy will be lower with the increased distance from the waste source. The spatial differences between simulated and observed concentrations reflect the model’s limitations in long-distance simulations. Although the model reproduced the overall temporal variation of solute migration, it does not explain all the variability and uncertainty of the specific sites. Based on the sensitivity analysis, the fractional derivative parameters of the unsaturated zone show higher sensitivity than those of the saturated zone. Finally, the advantages and limitations of the fractional derivative model in radionuclide contamination prediction and remediation are discussed. In conclusion, the proposed FADE model coupled with unsaturated and saturated flow conditions, has significant application prospects in simulating nuclide migration in complex geological and hydrological environments. Full article

Understanding the sensitivity of vehicle energy consumption to average speed variations is critical for accurately assessing the environmental impacts of urban transportation systems. While the energy consumption patterns of conventional vehicles (CVs) have been extensively studied, the response characteristics of electric vehicles (EVs) and their fundamental differences from CVs remain insufficiently explored. This knowledge gap may lead to misguided policy interventions—for instance, implementing congestion mitigation strategies that may paradoxically increase EV energy demand. To address this research gap, we developed an energy consumption model based on vehicle-specific power (VSP) distribution analysis, calibrated with over 25 million second-by-second driving records from Beijing. The proposed comparative framework systematically evaluates the sensitivity of EV and CV energy consumption across different speed regimes. The results indicated that EV energy use exhibits a distinctive parabolic trend, with high energy use at both low and high speeds, and a notable increase beyond approximately 70 km/h. A case study indicates that, during the pandemic lockdown, which led to a significant increase in average speed, CV energy use generally decreased, whereas EV energy consumption increased. This discrepancy is primarily attributed to differences in energy consumption rates rather than variations in driving behavior, as reflected in VSP distributions. Full article

The development of black tea quality is the outcome of the synergistic interaction between tea cultivars and the ecological environment of the production area, including factors such as climate, soil, and cultivation practices. Nevertheless, within a specific geographical region, systematic analysis of the environmental regulation mechanisms governing processing adaptability and quality formation among different cultivars remains insufficient. This study evaluated six Camellia sinensis cultivars from the Jingshan region of Hangzhou, China, integrating non-targeted metabolomics, sensory profiling, bioassays, and molecular docking to elucidate cultivar-specific quality attributes. Non-volatile metabolomics identified 84 metabolites linked to color and taste, including amino acids, catechins, flavonoid glycosides, and phenolic acids. Sensory and metabolite correlations revealed that amino acids enhanced brightness and imparted fresh-sweet flavors, while catechins contributed to bitterness and astringency. Specific metabolites, such as 4-hydroxybenzoyl glucose and feruloyl quinic acid, modulated color luminance. Volatile analysis identified 13 aroma-active compounds (OAV ≥ 1), with 1-octen-3-ol, phenylacetaldehyde, and linalool endowing JK with distinct floral-fruity notes. Molecular docking further demonstrated interactions between these volatiles and olfactory receptors (e.g., OR1A1 and OR2J2), providing mechanistic insights into aroma perception. These findings establish a robust link between cultivar-driven metabolic profiles in black tea, offering actionable criteria for cultivar selection and quality optimization in regional tea production. Full article

The combination of dynamic vision sensors (DVSs) and polarization can overcome the limitation of DVSs whereby they can only detect dynamic scenes, and it also has the ability to detect artificial targets and camouflaged targets, and is thus expected to become a new means of remote sensing detection. Remote sensing detection often requires the field-of-view (FOV) and width to be large enough to improve detection efficiency, but when large FOV polarization imaging is performed, the polarization state in the edge FOV and the center FOV will not be consistent, which does not meet the paraxial approximation condition, and the inconsistency increases as the angle between the incident light and the optical axis increases. This affects the accuracy of target detection, so in this paper, based on the characteristics of polarization imaging using a DVS, factors such as the polarizer rotation step, incident light polarization state, and incident angle are considered to establish a theoretical model of large FOV polarization imaging using DVSs. And the influence of the detection ability is analyzed for three types of incident conditions, namely linearly polarized light, natural light, and partially polarized light. The results show that when the rotation step is 5°, the highest false alarm rate for natural light incident in the edge FOV will be nearly 53%, and the highest false alarm rate for linearly polarized light incident will be nearly 32%. Full article

Yinhua Pinggan Granules (YPG) is a patented traditional Chinese medicine (TCM) compound prescription, with wide clinical application against cold, cough, and relevant diseases. However, the chemical profiles of YPG in vivo are still unknown, hindering further pharmacological and quality control (QC) researches. This study presents an ultra-high-performance liquid chromatography coupled with high-resolution orbitrap mass spectrometry (UHPLC-MS)-based method. Using the Compound Discoverer platform and a self-built ‘in-house’ compound database, the metabolic profiles and pharmacokinetics characters of YPG were investigated. Consequently, a total of 230 compounds (including 39 prototype components and 191 metabolites) were tentatively identified, in which the parent compounds were mainly flavonoids, alkaloids, and terpenoids, and the main metabolic pathways of metabolites include hydration, dehydration, and oxidation. The serum concentration of seven major representative compounds, including quinic acid, chlorogenic acid, amygdalin, 3′-methoxypuerarin, puerarin, glycyrrhizic acid, and polydatin, were also measured, to elucidate their pharmacokinetics behaviors in vivo. The pharmacokinetic study showed that the seven representative compounds were quantified in rat plasma within 5 min post-administration, with T_max of less than 2 h, followed by a gradual decline in concentration over a 10 h period. The method demonstrated excellent linearity (R² > 0.998), precision, and recovery (RSD < 15%). As the first systematic characterization of YPG’ s in vivo components and metabolites using UHPLC-MS, this study may contribute to comprehensively elucidate the metabolic profiles of the major components in YPG, and provide a critical foundation for further investigation on the QC and bioactivity research of YPG. Full article

The piggyBac+TET-on transposon induction system has a high efficiency in integrating exogenous genes in multiple cell types, can precisely integrate to reduce genomic damage, has a flexible gene expression regulation, and a strong genetic stability. When used in conjunction with somatic cell nuclear transfer experiments, it can precisely and effectively reveal the intrinsic mechanisms of early biological development. This study successfully reprogrammed black-boned sheep fibroblasts (SFs) into induced pluripotent stem cells (iPSCs) using the piggyBac+TET-on transposon system and investigated their impact on early embryonic development. Seven exogenous reprogramming factors (bovine OCT4, SOX2, KLF4, cMyc, porcine NANOG, Lin-28, and SV40 Large T) were delivered into SFs, successfully inducing iPSCs. A growth performance analysis revealed that iPSC clones exhibited a raised or flat morphology with clear edges, positive alkaline phosphatase staining, and normal karyotypes. The transcriptome analysis indicated a significant enrichment of iPSCs in oxidative phosphorylation and cell proliferation pathways, with an up-regulated expression of the ATP5B, SDHB, Bcl-2, CDK1, and Cyclin D1 genes and a down-regulated expression of BAX (p < 0.05). Somatic cell nuclear transfer experiments demonstrated that the cleavage rate (85% ± 2.12) and blastocyst rate (52% ± 2.11) of the iPSCs were significantly higher than those of the SFs (p < 0.05). The detection of trilineage marker genes confirmed that the expression levels of endoderm (DCN, NANOS3, FOXA2, FOXD3, SOX17), mesoderm (KDR, CD34, NFH), and ectoderm (NEUROD) markers in iPSCs were significantly higher than in SFs (p < 0.01). The findings demonstrate that black-boned sheep iPSCs possess pluripotency and the potential to differentiate into all three germ layers, revealing the mechanisms by which reprogrammed iPSCs influence early embryonic development and providing a critical foundation for research on sheep pluripotent stem cells. Full article

Methane is the second largest greenhouse gas. The detection of methane super-emitters and the quantification of their emission rates are necessary for the implementation of methane emission reduction policies to mitigate global warming. High-spectral-resolution satellites such as Gaofen-5 (GF-5), EMIT, GHGSat, and MethaneSAT have been successfully employed to detect and quantify methane point source leaks. In this study, a matched filter (MF) algorithm is improved using data from the EMIT instrument and applied to data from the Advanced Hyperspectral Imager (AHSI) onboard the Ziyuan-1 (ZY-1) satellite. Validation by comparison with EMIT′s L2 XCH₄ products shows the good performance of the improved MF algorithm, in spite of the lower spectral resolution of AHSI/ZY-1 in comparison with other point source imagers. The improved MF algorithm applied to AHSI/ZY-1 data was used to detect and quantify methane super-emitters in global methane hotspot regions. The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. Sixteen methane plumes were detected in global hotspot regions, originating from coal mines, oil and gas fields, and landfills, with emission rates ranging from 0.57 to 78.85 t/h. The largest plume was located at an offshore oil and gas field in the Gulf of Mexico, with instantaneous emissions nearly equal to the combined total of the other 15 plumes. The findings demonstrate that AHSI, despite its lower spectral resolution, can detect sources with emission rates as small as 571 kg/h and achieve faster retrieval speeds, showing significant potential for global methane monitoring. Additionally, this study highlights the need to focus on methane emissions from marine sources, alongside terrestrial sources, to efficiently implement reduction strategies. Full article

Moral hazard in health insurance arises when insured individuals are incentivized to over-utilize healthcare services, especially when they face low out-of-pocket costs. While existing literature primarily addresses moral hazard through qualitative studies, this paper introduces a quantitative approach by developing a mathematical model based on quasi-arbitrage conditions. The model optimizes health insurance design, focusing on the transition from Low-Deductible Health Plans (LDHPs) to High-Deductible Health Plans (HDHPs), and seeks to mitigate moral hazard by aligning the interests of both insurers and insured. Our analysis demonstrates how setting appropriate deductible levels and offering targeted premium reductions can encourage insured to adopt HDHPs while maintaining insurer profitability. The findings contribute to the theoretical framework of moral hazard mitigation in health insurance and offer actionable insights for policy design. Full article

As the frequency of cyberattacks rises, extracting actionable cyber threat intelligence (CTI) from diverse online sources has become critical for proactive threat detection and defense. Named entity recognition (NER) serves as a foundational task in CTI extraction, supporting downstream applications such as cybersecurity knowledge graph construction and attack attribution. However, existing NER methods face significant challenges in the cybersecurity domain, including the need to identify highly specialized entity types and adapt to rapidly evolving threats. These challenges are further exacerbated in few-shot scenarios with limited annotated data. In this work, we focus on few-shot NER for CTI extraction in general cyber environments. Our goal is to develop robust and adaptable methods that are not restricted to specific infrastructures (e.g., traditional IT systems), but instead can generalize across diverse cybersecurity contexts. Specifically, to address these issues, we propose CyberDualNER, a novel dual-stage framework for few-shot NER, which includes span detection and entity classification. In the first stage, we proposed a span detector that can utilize data from large-scale general domains to detect possible entity spans. Based on the detected spans, in the second stage, we propose a prompt-enhanced metric-based classifier. We use category descriptions to build prompt templates, extract category anchor representations, and classify entities based on similarity to span representations. By incorporating prior knowledge, we improve performance while reducing data dependency, which ensures generalizability in the face of emerging entities. Extensive experiments on real-world CTI datasets demonstrate the effectiveness of CyberDualNER, with significant performance improvements over baseline methods. Notably, the framework achieves robust results in scenarios with minimal annotated samples, highlighting its potential for practical applications in cybersecurity intelligence extraction. Full article

The safe and efficient design of dynamic submarine cables is critical for the reliability of floating offshore wind turbines, yet traditional time-domain simulation-based optimization approaches are computationally intensive and time consuming. To address this challenge, this study proposes a closed-loop optimization framework that couples machine learning with intelligent optimization algorithms for a dynamic cable configuration design. A high-fidelity surrogate model based on a backpropagation (BP) neural network was trained to accurately predict cable dynamic responses. Three optimization algorithms—Particle Swarm Optimization (PSO), Ivy Optimization (IVY), and Tornado Optimization (TOC)—were evaluated for their effectiveness in optimizing the arrangement of buoyancy and weight blocks. The TOC algorithm exhibited superior accuracy and convergence stability. Optimization results show an 18.3% reduction in maximum curvature while maintaining allowable effective tension limits. This approach significantly enhances optimization efficiency and provides a viable strategy for the intelligent design of dynamic cable systems. Future work will incorporate platform motions induced by wind turbine operation and explore multi-objective optimization schemes to further improve cable performance. Full article

Porcine infectious pleuropneumonia (PCP) caused by Actinobacillus pleuropneumoniae (APP) leads to severe economic losses in swine production. Commercial vaccines offer limited cross-protection for the 19 serotypes, while APP mRNA vaccines remain unexplored. This study evaluated eight candidate APP proteins (ApxI-IV, OlmA, TbpB, GalT, and GalU) using immunobioinformatics tools, and their immunogenicity and cross-protection were assessed in a mouse model. The results revealed that ApxI and ApxII excel due to their stability, strong antigenicity, non-sensitization, and high immune receptor affinity. Compared to the PBS group, both ApxI and ApxII induced higher serum IgG, IL-2, IL-4, and IFN-γ levels. Following challenge with the two most prevalent APP strains in Mainland China, APP 5b and APP 1, the survival rates for ApxI (71.4% and 62.5%) and ApxII (75% and 71.4%) were measured, with notably reduced lung lesions and neutrophil infiltration. These findings highlight ApxI and ApxII’s potential in mRNA vaccine development as a promising approach to overcome current vaccine limitations. Future research should focus on creating APP mRNA vaccines and testing their efficacy in swine. This study is the first to combine immunoinformatics with experimental validation for APP mRNA vaccine antigens, representing a novel contribution. Full article