Search Results (8,328)

Evapotranspiration (ET) links the water cycle with the energy balance and serves as a key driving process for ecosystem functioning and water resource management. Canopy conductance (Gc) plays a central role in regulating transpiration, but many models inadequately represent its regulatory mechanisms and show varying applicability across different land cover types. This study develops a remote-sensing ET estimation approach suitable for large scales and diverse land cover types and proposes an improved canopy conductance model for daily latent heat flux (LE) estimation. By integrating the canopy radiation transfer concept from the K95 model into the multiplicative Jarvis framework, an improved canopy conductance model is developed that includes limiting effects from photosynthetically active radiation (PAR), vapor pressure deficit (VPD), air temperature (T), and soil moisture (θ). Eighteen combinations of limiting functions are designed to evaluate structural performance differences. Using observations from 79 global flux sites during 2015–2023 and integrating multi-source datasets, including ERA5, MODIS, and SMAP, a two-stage parameter optimization was applied to determine the optimal limiting function combination for each land cover type. And nine sites from nine different land cover types were selected for independent spatial validation. Temporal validation within the optimization sites shows that, at the daily scale, the model achieves a Kling–Gupta efficiency (KGE) of 0.82, a correlation coefficient (R) of 0.82, and a Root Mean Square Error (RMSE) of 27.83 W/m², demonstrating strong temporal stability. Spatial validation over independent holdout sites achieved KGE = 0.84, R = 0.84, and RMSE = 22.53 W/m². At the 8-day scale, when evaluated over the holdout sites, the model achieves KGE = 0.87, R = 0.88, and RMSE = 18.74 W/m². Compared with the K95 and Jarvis models, KGE increases by about 34% and 15%, while RMSE decreases by about 38% and 12%, respectively. Relative to the MOD16 and PML-V2 products, KGE increases by about 32% and 16%, while RMSE decreases by about 33% and 17%, respectively. Comprehensive comparisons show that explicitly coupling canopy structure with multiple environmental constraints within the Jarvis framework, together with structure optimization across land cover types, can markedly improve large-scale remote-sensing ET retrieval accuracy while maintaining physical consistency and physiological rationality. This provides an effective pathway and parameterization scheme for producing ET products applicable across ecosystems. Full article

Hydrogenated amorphous silicon (a-Si:H) is a mature thin-film technology for large-area devices and thin-film sensors, and its low-temperature growth via Plasma-Enhanced Chemical Vapor Deposition (PECVD) makes it particularly suitable for biomedical flexible and wearable platforms. However, the reliable integration of a-Si:H sensors on polymer substrates requires a quantitative assessment of their electrical stability under mechanical stress, since bending-induced variations may affect sensor accuracy. In this work, we provide a quantitative, direction-dependent evaluation of the static-bending robustness of both single-doped a-Si:H layers and complete p-i-n junction stacks on polyimide (Kapton^®), thereby linking material-level strain sensitivity to device-level functionality. First, n- and p-doped a-Si:H layers were deposited on 50 µm thick Kapton^® and then structured as two-terminal thin-film resistors to enable resistivity extraction under bending conditions. Electrical measurements were performed on multiple samples, with the current path oriented either parallel (longitudinal) or perpendicular (transverse) to the bending axis, and resistance profiles were determined as a function of bending radius. While n-type layers exhibited limited and mostly gradual variations, p-type layers showed a stronger sensitivity to mechanical stress, with a critical-radius behavior under transverse bending and a more progressive evolution in the longitudinal one. This directional response identifies a practical bending condition under which doped layers, particularly p-type films, are more susceptible to strain-induced degradation. Subsequently, a linear array of a-Si:H p-i-n sensors was fabricated on Kapton^® substrates with two different thicknesses (25 and 50 µm thick) and characterized under identical bending conditions. Despite the increased strain sensitivity observed in the single-layers, the p-i-n diodes preserved their rectifying behavior down to the smallest radius tested. Indeed, across the investigated radii, the reverse current at −0.5 V remained consistent, confirming stable junction operation under bending. Only minor differences, related to substrate thickness, were observed in the reverse current and in the high-injection regime. Overall, these results demonstrate the mechanical robustness of stacked a-Si:H junctions on polyimide and support their use as sensors for wearable biosensing architectures. By establishing a quantitative, orientation-aware stability benchmark under static bending, this study supports the design of reliable a-Si:H flexible sensor platforms for curved and wearable surfaces. Full article

This study investigates the aerodynamic and structural behavior of a traditional horizontal-axis windmill equipped with a passively controlled fabric-sail rotor system, representative of the historic Lasithi Plateau windmills of Crete. The traditional windmill of the Lasithi Plateau, historically employed for water pumping to support irrigation and domestic water supply, constituted the conceptual basis for its further development into a wind energy system capable of electrical power generation. To this end, the structural and constructional characteristics of the traditional windmill are thoroughly investigated, with the objective of defining the technical specifications required for the design of a new product, namely a small-scale wind turbine incorporating a sail-based rotor configuration. First, the local meteorological conditions in the area are assessed using a long-term mesoscale to microclimatic approach. These parameters determine the operational and extreme working conditions of the windmill. Then emphasis is placed on understanding how important design features—such as the sail geometry, the supporting framework, and the passive aeroelastic deformation mechanism—govern the rotor’s performance and operational robustness. The sail’s ability to deform substantially plays a central role in regulating aerodynamic loading, serving as an inherent load-shedding mechanism that enhances survivability during high-wind events up to 40 m/s. The observed nonlinear trends in torque and thrust with increasing wind speed highlight the importance of aeroelastic effects in the functional design of fabric-sail rotors. Particular attention is given to the behavior of the woven polyester sail material, which enables large reversible deformations without mechanical failure, thereby preserving structural integrity and operational continuity. Overall, this study provides insight into the design principles and operational characteristics of flexible-sail windmills, illustrating how traditional configurations can inform the development of resilient, low-cost wind-driven systems. Full article

In this paper, the bond performance of tensile lap-spliced Glass and Basalt Fiber-Reinforced Polymer bars is investigated in high-strength concrete. Eighteen large-scale GFRP-reinforced concrete beams were fabricated and subjected to four-point loading. Key parameters explored included bar diameter and splice length for both GFRP and BFRP reinforcement. The results indicate that the flexural capacity of GFRP-reinforced beams was comparable to that of BFRP-reinforced beams, though BFRP bars exhibited marginally superior bond and strength with concrete. The bond strength of spliced FRP bars was directly proportional to the splice length. This study also determined that characteristics of development lengths necessitate splice lengths that exceed the bar diameter 40 times to mitigate bond stress. Critical splice lengths, derived from experimental findings, were compared with existing models and code-based equations, specifically, Guide for the Design and Construction of Structural Concrete Reinforced with Fiber-Reinforced Polymer Bars (ACI 440.1R-15) and Canadian standard that provides comprehensive guidelines for incorporating Fiber-Reinforced Polymer reinforcement in concrete structures (CSA S806-12). Both codes were conservative in splice length prediction for GFRP and BFRP bars, with ACI 440.1R-15 showing greater accuracy for BFRP bars with a larger diameter. A modification factor, based on hyperbolic functions, is proposed to enhance the accuracy of ACI 440.1R-15 in predicting splice lengths for various FRP bar diameters. Full article

Aircraft structures are highly susceptible to fatigue damage, particularly in thin-walled aluminum alloy components such as skin panels. Damage in the form of holes or material loss drastically reduces fatigue life and compromises structural safety, which makes effective repair strategies essential. This study presents an experimental investigation into the fatigue performance of EN AW-2024-T3 aluminum alloy plates with central openings subjected to uniform shear. Repair nodes were applied using two approaches: conventional riveted metal patches and adhesively bonded composite patches. Variants of patch geometry, thickness, and diameter were evaluated to determine their influence on load transfer, buckling response, and fatigue life. The results show that central holes significantly shorten fatigue life, with a 20 mm hole causing a 67% reduction and a 50 mm hole causing a 95% reduction when compared with undamaged plates. Riveted metal patches restored only part of the lost performance, as stress concentrators introduced by fastener holes initiated new fatigue cracks. In contrast, adhesively bonded composite patches provided a substantial improvement, extending fatigue life beyond that of the riveted solutions, improving buckling shape, and delaying crack initiation. Larger patches, particularly those combined with metallic inserts, proved most effective in restoring structural functionality. The findings confirm the effectiveness of bonded composite repairs as a lightweight and reliable method for extending fatigue life and enhancing the safety of damaged aircraft structures. The study highlights the importance of patch geometry and stiffness in the design of repair nodes. Full article

The ligand-driven self-assembly of metal clusters offers a powerful strategy for constructing discrete molecular architectures with tunable magnetic and structural properties. By judiciously selecting appropriate multidentate ligands, researchers can direct the formation of polynuclear metal assemblies with diverse nuclearities, geometries, and topologies. Coordination-driven processes commonly stabilize such assemblies where multidentate ligands operate as templates and linkers. These will also determine how the metal centers are arranged in space and how they connect to each other. These clusters can take on shapes that range from basic bridging dimers to more complicated icosahedral and cubane-type motifs. They often have excellent symmetry and strong frameworks. Magnetically, these clusters are a great place to study exchange interactions, spin frustration, and the behavior of single-molecule magnets (SMMs). The magnetic characteristics depend on things like the type of metal ions, the bridging ligands, the overall shape, and the local coordination environment. Interestingly, a large number of ligand-assembled clusters exhibit high spin ground states and slow magnetization relaxation, which makes them attractive options for quantum information storage and molecular spintronic devices. This review connects coordination chemistry, supramolecular design, and molecular magnetism of pyridine–amine–carboxylate frameworks, offering insights into fundamental magnetic phenomena and guiding the development of next-generation functional materials. Continued exploration of ligand frameworks and metal combinations holds the potential to yield novel clusters with enhanced or unprecedented magnetic characteristics. Full article

Backpropagation Neural Networks (BPNNs) are widely used in fault diagnosis and parameter prediction due to their simple structure and strong universal approximation capabilities. However, BPNNs suffer from slow convergence and susceptibility to poor local minima under basic gradient descent settings. To address these issues, this paper proposes a Balanced Grey Wolf Optimizer (BGWO) as an alternative to gradient descent for training BPNNs. This paper proposes a novel stochastic position update formula and a novel nonlinear convergence factor to balance the local exploitation and global exploration of the traditional Grey Wolf Optimizer. After exploration, the optimal convergence coefficient is determined. The test results on the six benchmark functions demonstrate that BGWO achieves better objective function values under fixed iteration settings. Based on BGWO, this paper constructs a training method for BPNN. Finally, three public datasets are used to test the BPNN trained with BGWO (BGWO-BPNN), the BPNN trained with Levenberg–Marquardt, and the traditional BPNN. The relative error and mean absolute percentage error of BPNNs’ prediction results are used for comparison. The Wilcoxon test is also performed. The test results show that, under the experimental settings of this paper, BGWO-BPNN achieves superior predictive performance. This demonstrates certain advantages of BGWO-BPNN. Full article

The present study extends the previous work which was concerned with the identification of damage in GFRP composite plates by damage detection algorithms such as the Normalized Curvature Damage Factor (NCDF), Strain Energy Difference (SED), and Damage Index (DI), using a novel damage (crack) modeling technique called the ‘Node-Releasing Technique’ (NRT) in Finite Element Analysis (FEA) for modeling and detecting perpendicular and slant partial-depth cracks in GFRP composite beams. This study explores the sensitivity of the damage modeling technique NRT in damage detection for composite beams using the NCDF algorithm, since it was concluded in the previous work that the NCDF performs better compared to the other methods when detecting both perpendicular and slant partial-depth cracks. This study also examines the variations in the Frequency Response Function (FRF) as another novel tool for identifying even small-scale damage. Most prior research in this domain has focused on variations in natural frequency, displacement mode shape, and damping as indicators for detecting and localizing structural damage through various experimental, theoretical, and computational approaches. However, these conventional parameters often lack the sensitivity required to detect small-scale damage and, still, there exists a gap in the use of the node-releasing technique in FEA to model the partial-depth perpendicular and slant crack damage in laminated composite structures, such as beam-like structures. To fill this gap, the present study attempts to use Curvature Mode Shapes (CMS)-based NCDF, obtained from numerical modal analysis, and variations in the Frequency Response Function (FRF), obtained through harmonic analysis, as more sensitive indicators for damage detection in laminated composite beams. FEA simulations are performed using the commercial FEA software package ANSYS 2021 R1 to obtain the first five flexural natural frequencies and the corresponding displacement mode shapes of both the intact and damaged composite beams. The curvature mode shapes are obtained from the displacement mode shapes data using the central difference approximation method to compute the NCDF. Simultaneously, GFRP composite beams were fabricated by the hand lay-up method, and Experimental Modal Analysis (EMA) was employed to substantiate the FE model and the validity of the numerical results. By combining both numerical and experimental methods, we proved that NCDF and FRF are reliable tools to determine and locate structural damage, even at a comparatively small scale. In general, the results indicate that NCDF is a stable and practically applicable parameter to locate cracks in laminated composite beams and provide meaningful information to be used as guidelines in applications of vibration-based structural health monitoring. Full article

A Group A Streptococcus (GAS, Streptococcus pyogenes) is an exclusively human pathogen whose virulence is driven by a diverse array of surface structures, secreted toxins, and immune evasion mechanisms. Central to its pathogenicity is the M protein, a surface-anchored molecule that inhibits phagocytosis by interfering with complement deposition and binding host factors such as fibrinogen. GAS also secretes a wide range of toxins and enzymes that damage tissues and disrupt host defences. Streptolysin O and streptolysin S are potent cytolysins that lyse immune cells and contribute to tissue necrosis. Pyrogenic exotoxins (such as SpeA and SpeC) act as superantigens, triggering massive, dysregulated T cell activation and cytokine release, an underlying mechanism in streptococcal toxic shock syndrome. Additional factors like DNases and streptokinase facilitate bacterial spread by breaking down host tissue and counteracting neutrophil extracellular traps (NETs). Immune evasion is further supported by the production of enzymes that interfere with complement functions, like the cleavage of chemokines and the targeting of antibodies. Together, these virulence determinants allow GAS to cause a wide spectrum of diseases, ranging from uncomplicated pharyngitis and impetigo to invasive conditions like necrotising fasciitis and sepsis. This review provides a timely overview of the important GAS virulence factors and an update on the current vaccine landscape. Full article

Optimizing nanofiber morphology is essential for promoting osteoblast elongation and supporting bone regeneration. This study aimed to develop a machine-learning framework capable of predicting optimal scaffold architectures directly from scanning electron microscopy (SEM) images and chemical composition. A four-module pipeline was implemented, combining tile-based SEM preprocessing, Cellpose-based cell morphology extraction with edge correction, ensemble machine-learning models, and an end-to-end convolutional neural network (CNN). Cellular quality was quantified using an elongation-weighted metric to emphasize morphological maturity over cell number. The analysis revealed consistent structure–function relationships across samples, with Sample_5 achieving the highest quality score at the 72 h time point. Ensemble models reached an R² of 0.400, while the end-to-end CNN achieved an R² of 0.750, indicating that raw SEM texture provides additional predictive information beyond handcrafted features. Feature-importance analysis identified nonlinear MgO effects and synergistic interactions between MgO and gold nanoparticles as key determinants of cell morphology. These findings demonstrate that the integrated workflow can reliably identify morphology–chemistry combinations favorable for osteoblast performance and provide a foundation for data-driven scaffold optimization. The approach supports rational design of nanofibrous biomaterials and may facilitate future development of intelligent scaffolds for bone regeneration applications. Full article

Forest cover condition is a key determinant of ecosystem functioning and ecological resilience, yet its spatial variability across large and environmentally heterogeneous regions remains insufficiently understood. Leaf area index (LAI) provides a continuous and physically meaningful indicator of forest canopy condition, reflecting variations in canopy density associated with climate and landscape structure. Here, we develop a spatially explicit and interpretable analytical framework to diagnose the dominant climatic and landscape controls on forest cover condition across mainland China during 2000–2020. By integrating machine-learning modelling with SHapley Additive exPlanations, GeoDetector interaction analysis, and nonlinear dependence diagnostics, we quantify the relative contributions and interactions of precipitation, temperature, topography, and forest landscape structure to spatial patterns in forest LAI. The results reveal pronounced spatial heterogeneity in forest cover control regimes. Precipitation dominates forest cover condition in humid regions but exhibits nonlinear saturation, whereas forest fragmentation strongly constrains canopy development and moderates climate-LAI relationships in arid and semi-arid forested landscapes. In high-elevation regions, topographic and thermal factors exert primary control. Overall, the findings demonstrate that forest cover condition reflects climate-conditioned and landscape-dependent control regimes, providing a transparent basis for large-scale forest cover assessment and ecological monitoring. Full article

This research investigates the potential of secondary lavender biomass (Lavandula officinalis) as a raw material for paper production within the context of the circular economy and its practical applications. Lavender stems, a by-product of essential oil extraction, were processed using the nitrate–alkali pulping method. The chemical composition of the raw material was analysed according to TAPPI standards, and the resulting pulp was characterised in terms of its mechanical and physical properties, including tensile strength and air permeability. Lavender stems contained 29.43% cellulose and 24.10% lignin, indicating moderate delignification efficiency under the applied conditions. The pulp yield was 24.2% with a Kappa number of 15.9. Of the prepared sheets, the paper with a weight of 80 g·m⁻² showed the best mechanical properties, with a breaking length of 1.71 km and a tensile strength index of 16.76 N·m·g⁻¹. In addition, lavender-based paper demonstrated measurable repellent activity against Tineola bisselliella, reducing insect presence by 70% compared to control samples, as determined by controlled laboratory exposure tests. This bioactivity is attributed to residual volatile compounds such as linalool and linalyl acetate, originating from lavender biomass. Overall, lavender secondary biomass represents a promising non-wood fibre for the production of biodegradable, functional paper materials that combine structural integrity with natural repellent properties. Full article

Informal food trading is a cornerstone of urban livelihoods and food security in Namibia, yet traders operate under fragile conditions marked by limited capital, policy exclusion, and exposure to shocks such as COVID-19. Despite this vulnerability, traders exhibit resilience through everyday forms of social innovation. This study investigates how adaptive pricing, customer credit, and digital communication and e-payment practices function as pathways of resilience among 470 informal food traders in Windhoek, using Structural Equation Modelling to assess gender-differentiated determinants and outcomes. The analysis reveals that women’s adoption of adaptive pricing and digital tools is driven primarily by education and startup capital, while men’s innovation practices are shaped by vendor type and access to financing. Social innovations mediate the effects of these structural factors on enterprise growth, demonstrating that innovation acts as a critical mechanism linking resources and resilience. The study concludes that enhancing informal traders’ resilience requires policies that strengthen human and financial capital, improve digital inclusion, and recognize gendered differences in access to opportunity. It recommends targeted support for women’s entrepreneurial training, affordable credit, and digital infrastructure to transform the informal food sector into a more equitable and sustainable component of Namibia’s urban economy. Full article

In this study, a double-lattice photonic crystal structure was designed to achieve deep ultraviolet lasing without the use of any Distributed Bragg Reflector (DBR), which is called a photonic-crystal surface-emitting laser (PCSEL). The plane wave expansion (PWE) method was used to study the influence of various structural parameters on the resonant wavelength. Utilizing the random forest algorithm, we determined that the importance of the lattice constant to the resonant wavelength is 95.24%. Furthermore, we realized the reverse design of double-lattice photonic crystals from the target wavelength to optimal structural parameters through a radial basis function (RBF) network algorithm. Comparative analysis of the extreme learning machine (ELM) and back propagation (BP) algorithms demonstrated that RBF-based performance was notably superior to the training outcomes of other algorithms. The mean absolute error (MAE) of the lattice constant of the test set in the training results was 0.7610 nm, root mean square error (RMSE) was $1.143\times {10}^{-3}$ nm, and mean absolute relative error (MARE) was $5.489\times {10}^{-3}$. We verified the reliability of the algorithm and designed 13 groups of photonic crystals with different epitaxial structures. The mean square error (MSE) was 0.6188 ${\mathrm{nm}}^2$ compared with that of the plane wave expansion method. This work demonstrates applicability across various wavebands and epitaxial structures in GaN-based devices, providing a novel approach for the rapid iteration of deep ultraviolet PCSELs. Full article

Previous studies on kafirins in sorghum (Sorghum bicolor Moench) have focused mainly on grain and sprouts grown under tropical and subtropical climate conditions, while data on the content and fractional composition of kafirins in sorghum sprouts cultivated in temperate climates are scarce. In particular, the influence of the northern growing conditions, characteristic of Central Europe, on sorghum storage proteins has not yet been described, despite the fact that sorghum is currently cultivated in Poland. This study aimed to determine the total kafirin content and the distribution of α-, β-, and γ-kafirin fractions in sprouts of white and red sorghum grown under temperate climate conditions in Poland. Six-day-old sprouts were freeze-dried and extracted using a Tris-HCl/SDS/β-mercaptoethanol buffer. Kafirin content was quantified using the Bradford assay, SDS-PAGE, and HPLC, with method validation performed for accuracy, precision, and linearity. Total kafirin content ranged from 5.5 to 7.0 g/100 g dry matter (DM), with α-kafirin as the predominant fraction (4.2–5.0 g/100 g DM), followed by β-kafirin (0.5–1.0 g/100 g DM) and γ-kafirin (0.2–0.6 g/100 g DM). Sprouts of red sorghum varieties showed significantly higher total kafirin levels and a greater proportion of the γ-fraction, which may be associated with differences in protein structural properties and could suggest potential bioactivity, as indicated by previous literature. However, no direct functional or bioactivity assays were performed in this study. Statistical analysis revealed significant differences among selected sorghum varieties in total kafirin content and the proportion of the γ fraction (p < 0.05), with α being the dominant fraction in all tested samples. These results provide, for the first time, detailed data on the kafirin composition of sorghum sprouts grown in a temperate climate and address a key gap in the literature concerning the effect of environmental conditions on sorghum storage proteins. The findings support further research on the use of sorghum sprouts as a raw material for functional foods, protein-enriched products, and animal feed under European growing conditions. Full article