Search Results (1,685)

Anthropogenic activities and environmental changes have exerted an increasingly high impact on the habitats of wild animals, especially endangered species. Researchers have paid attention to the effects of future climate change on wildlife habitats. However, the impact of climate change on the suitable habitats of Tragopan birds has rarely been reported. Here, we used the Maxent model to assess the influence of climate change on the geographical distribution of five Tragopan species. The results showed that the SSP585 scenario projected relatively favorable conditions, with the total area of suitable habitats expected to show an overall increasing trend over time. Centroid analysis revealed that the centroid gradually shifts toward lower latitudes and elevations due to climate warming. Environmental factor analysis showed that human-induced factors (particularly land use) are the main determinants affecting the habitat suitability of Tragopan birds. Notably, a comparison between dispersal velocity and biological velocity showed that despite the predicted gradual expansion of habitat area, Tragopan birds may be difficult to expand into the newly suitable habitat regions. We further emphasize that establishing ecological corridors and setting up new protected areas will have a more significant impact on conserving the Tragopan birds. Full article

During the machining processes, surfaces are often contaminated by cutting fluids, metallic debris, and residual films, which may compromise subsequent operations (e.g., coating, bonding, or precision assembly). In the present study, the effectiveness of several cleaning methods applied to machined metallic surfaces was experimentally evaluated. A set of commonly used industrial metals, including stainless steels, alloy steels, aluminum alloys, and brass, was machined under controlled conditions and subjected to various cleaning treatments, including solvent-based cleaning, ultrasonic washing, and aqueous detergent processes. Surface conditions were first assessed through optical microscopy, focusing on machining grooves as preferential sites for contaminant accumulation. Then, scanning electron microscopy (SEM) combined with energy dispersive X-ray spectroscopy (EDS) was employed to better identify residual contaminants. Optical observations highlighted the progressive removal of debris and lubricant residues, while SEM–EDS analyses revealed the presence of thin organic films and localized carbon-rich contaminants, even on apparently clean surfaces. Results show a consistent trend across all materials, with increasing cleaning effectiveness from solvent-based treatments to ultrasonic cleaning and specific aqueous detergent processes. Ultrasonic cleaning proved particularly effective in removing thin films and contaminants in complex geometries, whereas aqueous detergent treatment demonstrated superior performance in eliminating larger debris and achieving overall surface cleanliness. The findings, combining a broad experimental campaign across multiple materials, cleaning treatments, and characterization techniques, underline the importance of multi-scale characterization for a reliable assessment of cleaning efficiency and suggest that combined cleaning approaches may further enhance surface quality in demanding industrial applications. Full article

Phytolacca acinosa Roxb., a perennial herb native to East Asia, is increasingly naturalizing in Europe, yet its reproductive ecology in the secondary range remains poorly understood. This study evaluated seed productivity across central and edge populations in the secondary range, fruit and seed morphometrics, and germination responses to cold storage, acid scarification (simulating bird endozoochory), and light exposure. Fruit production per raceme was influenced by an interaction between insolation and range position: reduced insolation increased fruit set in central populations but decreased it at the range edge. Raceme number per shoot was lower in spontaneous plants compared to cultivated ones. Fresh seeds exhibited strong dormancy with no germination without scarification. Acid scarification significantly enhanced germination, particularly with light exposure, reaching up to 55%. Cold storage did not increase germination percentage but accelerated germination of scarified seeds under light, reducing median germination time from 24 to 21 days. Compared to the congeneric P. americana, P. acinosa shows more stringent dormancy requirements. We conclude that P. acinosa retains deep seed dormancy in its secondary range and relies on bird-mediated endozoochory for both dispersal and dormancy release. At the northern range edge, reduced plant vigor and lower raceme numbers are partially offset by increased flower production per raceme, though fruit set remains constrained. The species does not exhibit the simplified germination requirements often associated with successful invaders; instead, its invasion success appears driven by a bet-hedging strategy combining persistent seed banks with specific dormancy-breaking cues. Full article

Thisarticle develops and analyzes a fractional-order Leslie–Gower prey–predator–parasite system incorporating two discrete delays and nonlocal spatial diffusion. The model’s central novelty lies in the simultaneous integration of three biologically realistic features that have not previously been combined: (i) fractional-order memory effects via a Caputo derivative of order $\alpha \in (0,1]$, (ii) two distinct biological delays—an infection transmission delay ${\tau }_1$ and a predator handling delay ${\tau }_2$—and (iii) nonlocal spatial dispersal modeled through fractional Laplacian operators ${(-\Delta )}^{\gamma /2}$. This triple integration enables the model to capture long-range temporal memory, delayed biological responses, and nonlocal spatial interactions simultaneously, offering insights into dynamics that are challenging to capture with classical integer-order or single-delay formulations. The fractional Laplacian generalizes classical diffusion by allowing long-range dispersal events (Lévy flights), where individuals can occasionally move over large distances with heavy-tailed step-size distributions—a phenomenon observed in many animal movement patterns but absent from standard diffusion models. We provide rigorous proofs of solution existence, uniqueness, non-negativity, and boundedness in both temporal and spatiotemporal settings. Local asymptotic stability conditions are derived for all feasible equilibrium states via characteristic equation analysis. The coexistence equilibrium undergoes a Hopf bifurcation when either delay crosses a critical threshold, with fractional order $\alpha$ modulating the bifurcation point and post-bifurcation oscillation frequency. A Lyapunov functional demonstrates global asymptotic stability of the infection-free equilibrium under biologically interpretable conditions. Turing instability analysis reveals conditions for spontaneous pattern formation, with the fractional exponent $\gamma$ controlling pattern wavelength and correlation length. Numerical simulations validate theoretical predictions, including spatial patterns, traveling waves, and chaos. To bridge theory with potential applications, we outline a statistical framework for parameter estimation and uncertainty quantification, suggesting that $\beta$, $\alpha$, and ${\tau }_1$ may be priority targets for parameter estimation. Full article

Bacterial infections can delay wound healing and represent serious medical problems both in the hospital and community settings, especially skin wound infections caused by Staphylococcus aureus. In this work, a gelatin hydrogel modified with photo-cross-linkable methacrylamide groups at 10% concentration (GelMA10%), enriched with titanium dioxide nanoparticles (TiO₂NPs), and loaded with Neomycin sulphate was developed with the aim to realize a tissue for wound care with improved mechanical and antimicrobial properties. TiO₂ nanocomposite GelMA films with two concentrations of TiO₂NPs were characterized to assess physicochemical, structural and mechanical properties by scanning electron microscopy equipped with an energy-dispersive X-ray spectrometer (SEM/EDX), micro-computed tomography (micro-CT) and X-ray photoelectron spectroscopy (XPS). TiO₂ nanocomposite GelMA films showed a more compact structure, reduced pore sizes and a higher compressive modulus at the increasing concentration of TiO₂NPs. They were able to absorb and retain water for a prolonged time; however, no significant differences in the swelling degree at the increasing concentration of TiO₂NPs were observed. In vitro drug release and antibacterial activity against Staphylococcus aureus of TiO₂ nanocomposite GelMA film enriched with higher concentrations of TiO₂NPs, identified as a suitable candidate for wound healing, were investigated. Both GelMA10% and TiO₂ nanocomposite GelMA films loaded with drug exhibited a strong antibacterial action, whereas GelMA10% containing only TiO₂NPs did not show any antimicrobial properties. Full article

In this study, an electrochemical sensor based on magnesium zirconate (MgZrO₃) synthesized using a deep eutectic solvent (DES)-assisted approach was developed for the detection of dopamine. The structural and morphological properties of MgZrO₃ were characterized using X-ray diffraction, Fourier-transform infrared spectroscopy, field-emission scanning electron microscopy, energy-dispersive spectroscopy, and elemental mapping. The electrochemical performance of the MgZrO₃-modified glassy carbon electrode (GCE) was evaluated using cyclic voltammetry and differential pulse voltammetry. The MgZrO₃/GCE exhibited an enhanced redox response and a reduced oxidation potential for dopamine in phosphate-buffered solution (PBS, pH 7.0), indicating improved electrocatalytic activity compared to the bare electrode. This improvement is attributed to the material’s increased active surface area and facilitated charge transfer kinetics. Under optimized conditions, the sensor showed a linear response over a concentration range of 0.3–80 µM, with a detection limit of 127 nM and quantification limit of 423 nM. The MgZrO₃/GCE also demonstrated good selectivity in the presence of common interfering species and was successfully applied for dopamine detection in biological samples, with satisfactory recovery results. The findings presented here contribute to the growing body of knowledge in the field and open up new possibilities for the development of advanced electrochemical sensors for neurotransmitter detection in clinical and research settings related to Breast Cancer Treatment. Full article

Pig weighing is an essential procedure for monitoring growth and animal health; however, conventional methods are often labor-intensive, costly, and potentially stressful. In this context, this study proposes a non-invasive approach for estimating the body weight of pigs during the growing stage based on computer vision and the YOLOv11 algorithm, enabling automatic segmentation and individual identification in multi-animal environments. The study used RGB images of 10 group-housed pigs captured throughout the growing phase, in which automatic dorsal segmentation was combined with individual identification through numerical markings. From the generated binary masks, the segmented dorsal area was extracted and used as a predictor variable in Linear Regression and a Multilayer Perceptron (MLP) Artificial Neural Network. The YOLOv11 model showed consistent performance in the segmentation task, achieving test-set metrics of Precision = 0.849, Recall = 0.886, mAP@0.50 = 0.936, and mAP@0.50–0.95 = 0.819, demonstrating good generalization capability in scenarios with intense animal interaction. In the weight prediction stage, Linear Regression and the MLP achieved high coefficients of determination (R² = 0.96 and 0.95, respectively) with low errors (RMSE = 1.52 kg and 1.63 kg; MAE = 1.20 kg and 1.25 kg), indicating a strong correlation between segmented dorsal area and actual body weight. Class-wise analysis revealed superior performance for classes 7 and 9, with R² values up to 0.98 and RMSE below 1.1 kg, whereas class 8 showed greater error dispersion, associated with higher morphological variability and a smaller number of available samples. These results demonstrate that the direct use of morphometric information extracted from segmented masks in 2D images constitutes a robust, accurate, and low-cost approach for automatic pig body-weight estimation. Moreover, this study is among the few addressing this task specifically during the growing stage, highlighting its potential for future deployment in embedded systems and intelligent monitoring platforms for precision pig farming, although further evaluation of computational efficiency and real-time performance is still required. Full article

Hydrogeochemistry is a practical and low-impact tool for mineral exploration that relies primarily on groundwater as sampling media. It is particularly valuable for blind or deeply buried deposits where surface geochemical methods are ineffective, as groundwater acts as a natural integrator of geochemical signals from depth. This study presents a PRISMA 2020-compliant systematic review of hydrogeochemical exploration practices published between 1946 and 2025, synthesizing 118 empirically screened case studies from diverse geological and climatic settings. The review evaluates the geochemical processes governing aqueous dispersion halos, including sulphide oxidation, water–rock interaction, redox controls, and physicochemical speciation, and assesses how these processes influence pathfinder behaviour and anomaly expression. Quantitative synthesis highlights consistent patterns in hydrogeochemical footprints across major mineral systems and demonstrates the effectiveness of thermodynamically informed and multivariate interpretation strategies over simple concentration-based approaches. Emerging trends identified include the growing application of non-traditional stable isotope fractionation, nanoparticle geochemistry using single-particle ICP-MS, and integration of hydrogeochemical datasets with GIS, geophysics, and machine learning-based prospectivity modelling. Unlike recent narrative reviews, this study provides a fully reproducible, structured evaluation of the global evidence base and formalizes a standardized end-to-end workflow. Full article

To improve the predictive accuracy of wind-field distributions during fires in high-rise buildings, this study targets the shortcomings of traditional prediction methods, including insufficient information fusion and dispersed feature representations under high-rise fire conditions. An efficient attention mechanism, termed Adaptive Channel and Multi-Scale Spatial Fusion Attention Mechanism (CSFAM), is proposed, which endows the model with enhanced adaptive focusing and multi-scale integration capabilities. CSFAM can account for environmental features across multiple dimensions to enable high-spatial-resolution wind-field reconstruction, thereby improving robustness and prediction accuracy in complex environments. To validate the effectiveness of CSFAM for predicting wind fields under high-rise-fire conditions, CFD-based scenario modeling was employed to generate a dataset of 1050 CFD-derived wind-field distributions across diverse inflow-wind and fire-source scenarios, partitioned into training, testing, and validation sets according to the fire-source size. When applying the CSFAM-enhanced multi-layer perceptron (MLP), the wind-field predictions achieved a mean squared error (MSE) of 0.0004, a mean absolute error (MAE) of 0.0141, and an R² of 0.9766, outperforming state-of-the-art methods. The results demonstrate that CSFAM plays a significant role in markedly improving wind-speed prediction accuracy during high-rise-building fires, and enhances the model’s ability to identify and express vortex-like and other key aerodynamic features generated by the fire, thereby improving the capture of the complex nonlinear aerodynamic structures induced by fire. Full article

Milltailings deposition areas in abandoned mines are inherently unstable and spatially extensive and heterogeneous, making regional-scale field investigations challenging under intense rainfall. With the advancement of space–airborne remote sensing technologies, large-scale surface deformation monitoring has become feasible. In this study, a 22.02 km² abandoned mine in Lingqiu County, Shanxi Province, was selected as a case site; during the late-July 2023 extreme rainfall event, the site experienced large-scale surface displacements. Surface deformation was interpreted using Sentinel-1 SBAS-InSAR data, combined with differential digital elevation models (DEMs) derived from UAV surveys before and after heavy rainfall. A bivariate spatial autocorrelation analysis was conducted to evaluate the spatial relationship between differential DEMs and InSAR-derived deformation. The results indicate that: (1) SBAS-InSAR revealed significant spatial heterogeneity of ground deformation, with pronounced subsidence observed in the milltailings deposits; (2) the bivariate spatial autocorrelation analysis yielded a Moran’s I value of 0.2, suggesting a weak but positive spatial correlation between the DEM differences and InSAR results, with dispersed correlation patterns; (3) hotspot analysis highlighted notable clustering of deformation, with approximately 27.84% of the study area showing strong deformation responses, while 25.81% represented low–low clusters with limited deformation. Beyond tailings-deposit settings, this workflow is also applicable to the regional investigation of rainfall-responsive deformation and debris-flow-related terrain change on natural slopes under global change, providing technical support for surface investigations and offering insights for disaster early warning and ecological restoration in similar regions. Full article

While superdeterministic and fractal spacetime models offer compelling alternative perspectives on quantum foundations, the simulation and validation of effective wave dynamics in such non-differentiable, deterministic settings remain computationally and theoretically challenging. To address this, a framework built around the Fractional Nonlinear Klein–Gordon Equation (FNKGE), defined through the spectral fractional Laplacian, was developed. This equation was solved and benchmarked through a comparative study between Physics-Informed Neural Networks (PINNs) with Fourier features and Physics-Informed Graph Neural Networks (PI-GNNs). Additionally, detection patterns were simulated via deterministic agents, and theoretical links between fractal geometry, computational irreducibility, and deviations from statistical independence were formalized. Regarding the computational evaluation, superior accuracy was achieved by the PI-GNNs, yielding a mean relative error of $0.5\%$ ($\overline{ϵ}=0.005$), alongside faster convergence and a more well-conditioned Hessian spectrum compared to PINNs. Crucially, a continuous power-law decay ($S\left(k_y\right)\sim k_y^{-1.8}$) was revealed by the spectral analysis of the simulated detection patterns, confirming the emergence of classical optical caustics rather than discrete quantum-interference peaks. Furthermore, a modified dispersion relation that accurately predicts linear instability regimes was derived, and specific boundary artifacts in non-periodic domains were identified. Taken together, the FNKGE is validated by these results as a viable effective model for fractal wave phenomenology and as a robust benchmark for physics-informed learning architectures. Full article

Municipal solid waste (MSW) landfill site selection is a complex multi-criteria decision-making (MCDM) problem involving uncertainty and conflicting criteria. Although spherical fuzzy extensions of the Technique for Order Preference by Similarity to Ideal Solution (SF-TOPSIS) are widely used, existing studies rely on conventional distance measures that do not fully capture the geometric structure of spherical fuzzy sets. To address this limitation, this study proposes an enhanced SF-TOPSIS framework incorporating a novel spherical distance measure to improve consistency, discrimination capability, and structural compatibility. The framework integrates Spherical Fuzzy Weighted Arithmetic Mean (SWAM) and Spherical Fuzzy Weighted Geometric Mean (SWGM) operators and evaluates robustness using Spearman rank correlation. Additionally, a coefficient of variation (CV)-based analysis is conducted to examine the dispersion of closeness coefficients. The applicability of the approach is demonstrated through a landfill site selection case; however, the main contribution lies in a generalized distance-based formulation applicable to various MCDM problems. Results show that the proposed distance improves agreement between aggregation operators, increasing correlation values from 0.905 to 0.976, while producing a more stable distribution of closeness coefficients. Overall, the study advances spherical fuzzy MCDM by introducing a geometrically consistent distance formulation. Full article

The aim of this in vitro study was to evaluate the ability of the new strontium-containing composite, Stela (SDI, Victoria, Australia), to induce hydroxyapatite formation and promote remineralization of demineralized dentin, compared to SDR Flow+ (York, PA, USA). Twenty-four dentin slices (1 mm thick) were obtained from extracted wisdom teeth using a microtome and demineralized with 17% EDTA for 2 h. A layer of either Stela or SDR Flow+ was applied to each slice, allowed to set, and preserved in 0.1% thymol solution. Samples were analyzed at 1, 7, 14 and 28 days (n = 3 per group and time). Measurements were taken at baseline, after demineralization, and after application. Apatite formation was assessed using 'Fourier-transform infrared spectroscopy (FTIR), while changes in the Calcium/Phosphate (Ca/P) ratio were evaluated by Energy Dispersive Spectroscopy (EDX). Statistical comparisons were performed using the Wilcoxon test (p < 0.05). Both materials promoted carbonated hydroxyapatite formation and increases in calcium and phosphate. Stela exhibited an apatite peak (1420 cm⁻¹) as early as 24 h and significant increases in calcium and phosphate from day 7. SDR Flow+ reached its peak at 14 days and showed significant increases in the Ca/P ratio. By 28 days, both materials achieved comparable remineralization, confirming their effectiveness in treating demineralized dentine. Full article

Chicken manure and its potential to contaminate water systems through the dispersal of pathogenic bacteria are major concerns in environmental and public health. In this study, a metagenomic analysis was employed to systematically identify and compare bacterial assemblages in chicken manure (CM) and in a contaminated sample of chicken manure wastewater (CMW). Whole DNA was extracted from CM and CMW, followed by whole-genome shotgun sequencing; data analysis was done using online Galaxy software (ver. 26.0.1.dev1). Metagenomic analysis reveals a complex One Health challenge. Data showed that CM and CMW are different in their microbiota, as indicated by a distinct separation of beta diversity values and limited overlapping of species between sample types. In the current study, we found a greatly significant common functional set of adapted bacterial masses, including major pathogenic bacterial groups as well as opportunistic and environmental bacterial species, indicative of a direct contamination from CM and CMW. Notably, in both CM and CMW, a plethora of opportunistic, enteric, and environmental pathogens like Escherichia coli, Salmonella enterica, and Acinetobacter baumannii were found, coupled with an indication of a direct functional flow between both ecosystems as tangled reservoirs. Chicken manure samples showed differences in taxonomic composition and inferred functional profiles at the time of sampling: CM1 was pathogen-enriched, CM2 exhibited strong nitrogen-supportive metabolism, CM3 was dominated by fiber-degrading decomposers, and CM4 showed high methane-producing potential with environmental risk. Such findings underscore the raising of chickens as a potential source of harmful bacteria for the environment. It is important to note that this study represents a preliminary investigation with certain limitations, including the absence of biological replicates, lack of temporal sampling, and limited capacity to infer dynamic ecological interactions. Yet this metagenomic report is more about describing the taxonomy and functional potential of the bacteria, rather than discussing the actual ecological processes of these microorganisms in the environment. Future studies will be required to explore these aspects. Full article

The Technology Acceptance Model (TAM) has been widely used to explain e-banking and digital technology adoption. Existing literature supports the robustness of its core relationships, but the magnitude of the effects varies considerably across studies, raising questions about their stability and generalizability in new contexts. Existing meta-analysis studies focus primarily on pooled effect sizes, providing limited insight into the temporal stability of relationships, their sensitivity to individual studies, and the extent to which observed heterogeneity reflects contextual variation. This study contributes by reinterpreting heterogeneity not as a problem to be reduced, but as a feature that defines the limits of generalizability. We advance the TAM literature by moving beyond average effects and rethinking empirical evidence through the joint lens of robustness, stability, and dispersion. We conduct a random-effects meta-analysis on 44 effect sizes (correlation coefficients) coming from 43 research papers indexed in Web of Science and Scopus. In addition to pooled correlations, the analysis employed cumulative meta-analysis, leave-one-out influence diagnostics, prediction intervals, and publication bias assessments to evaluate the evolution, consistency, and variability of TAM relationships across contexts. The findings show that core TAM relationships are consistently positive and stable at the aggregate level yet display substantial variation across empirical settings. While some relationships remain robust across contexts, others exhibit prediction intervals that include zero, indicating that their strength and even direction may depend on contextual conditions. As prior TAM meta-analyses have not systematically incorporated prediction intervals, this study provides new evidence to the extent to which TAM relationships generalize beyond average effects. The results further show that although TAM offers a reliable structural framework, interventions and policies based on its core relationships must be context-sensitive, because relying on average effects alone may lead to ineffective or inconsistent adoption outcomes. Full article