Search Results (49,476)

Existing macroscopic finite element models for electron beam welding (EBW) typically assume isotropic material behavior, often failing to accurately predict residual stresses induced by strong crystallographic textures. To address this limitation, this study established a sequential dual-scale coupled numerical model bridging micro-texture to macro-mechanics by combining the crystal plasticity finite element method (CPFEM) with thermal-elastic-plastic theory. Representative volume elements (RVEs) incorporating α and β dual-phase characteristics were constructed based on electron backscatter diffraction (EBSD) data from the TA15 weld cross-section. Through simulated tensile and shear calculations on the RVEs, homogenized orthotropic stiffness matrices and Hill yield constitutive parameters were derived and mapped onto the macroscopic model. Simulation results indicate that the proposed model maintains the prediction error for molten pool morphology within 16.3%, while effectively correcting the stress overestimation inherent in isotropic models. Specifically, it adjusts the peak longitudinal residual stress at the weld center from 800 MPa to approximately 350 MPa, significantly reducing the anomalous “M-shaped” stress distribution. By successfully capturing shear stress components, this work provides a high-fidelity computational approach for predicting complex stress states in welded joints, offering critical insights for structural integrity assessment. Full article

Backgrounds: Roegneria ciliaris is a perennial tetraploid wild relative of wheat that is widely distributed in China. It can be used both as a forage crop and ecological grass (the grasses specifically bred for ecological restoration) due to its strong stress tolerance, early green-up, vigorous seedling growth in spring, and great palatability. Methods: It is necessary to select and validate appropriate reference genes (RGs) for gene expression normalization by qRT-PCR in order to decipher the stress tolerance mechanism of this grass species. Therefore, eight candidate RGs were identified from transcriptome data of R. ciliaris ‘Liao sheng’ in response to drought stress. The expression stability of these RGs was evaluated by five algorithms (∆Ct, geNorm, NormFinder, Bestkeeper and ReFinder) using samples from different tissues and drought stress. Results: The results showed that MDH and RPL19 were the most stable RGs among all samples, while GAPDH and TUBA presented the lowest expression stability. These representative RGs were further used to normalize the expression level of the pyrroline-5-carboxylate synthase (P5CS) and protein phosphatase 2C (PP2C) genes in different tissues and under drought stress. The results of P5CS and PP2C expression were consistent with transcriptome data. Conclusion: Our study provided the first systematic evaluation of the most stable RG selection for qRT-PCR normalization in R. ciliaris, which will promote further research on its tissue-specific gene expression and mechanism of drought tolerance. Full article

This paper proposes a Bayesian estimation method for spatial lagged panel quantile models. The proposed model simultaneously considers spatial lag effects of the dependent variable and the quantile regression framework, enabling effective capture of spatial dependence and conditional distribution heterogeneity. The research constructs a Bayesian estimation framework based on the asymmetric Laplace distribution by decomposing the random disturbance term into a combination of normal and exponential distributions, successfully developing a probabilistic model with both thick tail robustness and computational efficiency. On this basis, the study derives the full conditional posterior probability distributions of model parameters and designs a hybrid Markov Chain Monte Carlo (MCMC) sampling algorithm integrating Gibbs sampling and Metropolis–Hastings algorithm for parameter estimation. Numerical simulation experiments demonstrate that, compared with traditional estimation methods, the proposed Bayesian estimation approach exhibits superior estimation accuracy and robustness across different quantiles, with particularly pronounced advantages in small sample and heavy-tailed distribution scenarios. This methodology provides a more reliable theoretical tool for analyzing panel data with spatial dependencies. This method can not only accurately quantify the spatial spillover effect, but also identify the different effects of the same influencing factor at different emission levels, which provides a strong methodological support for formulating differentiated and precise emission reduction policies. Full article

Background: Surface chemistry and cleanliness are widely regarded as important factors influencing the host response to titanium dental implants. Despite advances in manufacturing and sterilization, trace residues may persist at the nanoscale even in commercially sterile devices. This study provides a preliminary evaluation of premium-grade titanium dental implants using time-of-flight secondary ion mass spectrometry (ToF-SIMS) to assess surface chemical uniformity and trace contaminant distribution. Method: Two commercially available titanium implants from Schütz Dental were analyzed under static and dynamic ToF-SIMS modes using Bi₃⁺ and Cs⁺ ion beams. Both positive and negative ion spectra were collected to identify elemental and molecular species. Chemical mapping and depth profiling were performed to evaluate contaminant distribution and surface depth composition. Results: In the two implants analyzed, the surfaces were dominated by TiO⁺ and TiO₂⁺ species, consistent with a native titanium oxide layer. In both analyzed implants, localized contaminants—including fluorine, chlorine, sulfur, CN groups, and organic residues—were detected within the outermost ~0.1 µm. These signals showed heterogeneous distribution along the thread-related regions within the analyzed ROIs, compatible with residues originating from machining, surface treatments, packaging, and/or sterilization steps. Conclusions: The present data support only the descriptive finding that trace contaminants were detected on the two analyzed implants. ToF-SIMS enabled nanoscale chemical mapping and depth profiling of these residues, supporting the feasibility of this approach for trace-level surface auditing and hypothesis generation. Any biological/clinical implications remain speculative and require dedicated in vitro/in vivo validation on larger sample sets. Full article

With the rapid growth of data transmission and visual encryption technologies, Visual Secret Sharing (VSS) has become an important technique for image-based information protection. However, many existing VSS schemes remain vulnerable to dishonest participants who attempt to recover secret images through unauthorized stacking or manipulation of shares. To address this issue, this paper proposes a dishonest-participant-resistant VSS scheme based on linearly polarized shares and Probability Distribution Trees (PDTs). The proposed method embeds both secret and fake images into polarized shares, such that any unauthorized stacking of ordinary shares produces a visually plausible fake image or random noise, while only stacking that includes the master share under a predefined optical ordering reveals the true secret image. Binary image binarization and probability-guided polarization assignment are employed to improve computational efficiency and increase uncertainty against adaptive attacks. In addition to visual inspection and contrast analysis, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and visual information fidelity (VIF) are used as complementary metrics to distinguish authorized reconstructions from unauthorized and partial ones. Experimental results show that authorized reconstructions achieve high visual fidelity and perceptual recognizability, whereas unauthorized and partial reconstructions yield significantly degraded or misleading outputs, demonstrating effective suppression of information leakage and strong resistance against dishonest behavior. Consequently, the proposed scheme enhances security and practical usability compared with existing polarization-based VSS approaches. Full article

This study presents a comparative flood frequency analysis of annual maximum discharges for major Romanian river basins, assessing the performance of the Halphen-A distribution relative to the Pearson Type III distribution, the reference model in Romanian hydrological practice. Four long-term discharge series from the Siret, Ialomița, and Danube rivers are analyzed, covering diverse hydroclimatic conditions. Distribution parameters are estimated using the method of moments and maximum likelihood estimation. Model performance is evaluated using RMSE and MAE, complemented by an analysis of extreme quantile behavior. The results show that both distributions fit the observed data well, with only minor differences in global error metrics. However, for high return periods (T > 100 years), Halphen-A exhibits smoother extrapolation and yields more stable extreme quantile estimates, particularly when estimated by MLE. Although Pearson III often achieves slightly lower metrics values, its upper tail is more constrained and sensitive to skewness and record length. The study concludes that classical goodness-of-fit measures alone are insufficient for selecting models for design floods and that Halphen-A provides a robust complementary alternative for extreme flood estimation. Full article

Familial partial lipodystrophy type 3 (FPLD3) is a rare autosomal dominant disorder caused by mutations in peroxisome proliferator-activated receptor gamma(PPARG), which encodes the key adipogenic transcription factor peroxisome proliferator-activated receptor gamma(PPARγ). Clinical diagnosis is challenging due to phenotypic overlap with common metabolic syndromes. We identified a novel PPARG variant in a Chinese family and performed comprehensive functional characterization to elucidate its pathogenic mechanism. The proband, a 15-year-old boy presenting with atypical fat distribution, severe insulin resistance, hypertriglyceridemia, and pancreatitis, underwent clinical evaluation and whole-exome sequencing. The identified variant was confirmed by Sanger sequencing. Its functional impact was assessed through in silico modeling, luciferase reporter assays, protein stability analysis (cycloheximide chase), and evaluation of mitochondrial function (JC-1 staining) and adipocyte gene expression in cellular models. A heterozygous PPARG c.634C>T (p.Arg212Trp, R212W) variant was identified and segregated with the phenotype. Functional studies revealed that the R212W mutant exhibits a partial loss of transcriptional activity (~40% of wild-type) while retaining ligand sensitivity. Crucially, we demonstrated that the mutant protein has significantly reduced stability due to accelerated degradation. In adipocyte models, R212W expression led to impaired mitochondrial membrane potential, depleted cellular ATP levels, and downregulated expression of key metabolic genes (glucose transporter 4[GLUT4], adiponectin[ADIPOQ], fatty acid binding protein 4[FABP4], lipoprotein lipase[LPL], perilipin 1[PLIN1]). These functional deficits were partially rescued by treatment with the PPARγ agonist rosiglitazone. We report a novel pathogenic PPARG R212W variant associated with FPLD3. Our data extend beyond a simple loss-of-function model by establishing a multi-faceted pathogenic mechanism involving protein destabilization, mitochondrial dysfunction, and cellular bioenergetic failure. The partial rescue by rosiglitazone suggests a potential therapeutic avenue. This study underscores the importance of integrating clinical phenotyping with deep functional analysis to diagnose and understand rare monogenic lipodystrophies. Full article

Background/Objectives: Cutaneous leishmaniasis (CL) remains a major public health and economic challenge in Morocco, where its transmission dynamics are increasingly influenced by climatic variability. This study aimed to evaluate the impact of meteorological factors on CL incidence in the province of Essaouira, a high-incidence region, to identify the environmental drivers behind recent epidemic trends. Methods: Epidemiological data (N = 834 cases) were collected from the Hygiene and Health Laboratory of Essaouira for the period between January 2014 and December 2023. Climatic variables were obtained from the Moroccan Directorate of National Meteorology. Data were analyzed at annual, seasonal, and monthly scales using the Spearman rank correlation in R 4.5.0 software to account for non-normal distributions and non-linear associations. Results: CL incidence remained stable from 2014 to 2021 before an unprecedented surge in cases during 2022–2023. Annual analysis indicated that warm and dry years pose a higher risk, with incidence positively correlated with temperatures and negatively associated with humidity and precipitation. Monthly results identified a biphasic regulatory mechanism: a winter hydric constraint phase with strong negative correlations with January rainfall and humidity (p < 0.05), followed by a summer thermal promotion phase where minimum temperature (T_min) emerged as the dominant driver (rho = 0.53), peaking in September (rho = 0.59). Conclusions: Our findings confirm the significant influence of climatic factors on CL incidence through complex seasonal dynamics. These results highlight the necessity of integrating high-resolution meteorological monitoring and predictive modeling into public health surveillance to anticipate future outbreaks in the context of increasing Mediterranean aridification. Full article

Groundwater, a vital resource for drinking water supply, must be managed sustainably to ensure its availability and quality. In France, the SISE-Eaux database on water intended for human consumption, archived by the Regional Health Agencies (ARS) since 1990, constitutes a rich source of information. This study focused on the groundwater of the Nouvelle-Aquitaine region, the largest administrative region in metropolitan France, covering 84,061 km² with 6 million inhabitants. It is based on a 22-year data extraction, resulting in a matrix of 121,649 observations and 51 physico-chemical and bacteriological parameters. Following logarithmic transformation of the data and fitting of variograms using the mean value of each parameter for each sampling point, the spatial distribution of numerous parameters across the region is presented. From this initial sparse matrix, a dense matrix of 23,319 samples (rows) and 15 key parameters (columns) was selected for a multivariate approach. A Principal Component Analysis (PCA) was used to condense the information and create summary maps capturing over 68% of the information contained in the dense matrix. The combined results of the multivariate analysis (dense matrix) and the distribution of individual parameters (sparse matrix) highlight the diversity of sources contributing to the spatial variability of groundwater, such as the role of lithology, the origin and pathways of fecal contamination, and the influence of redox processes. Neither the large size of the study area nor the high number of parameters proved to be an obstacle to the analysis. The understanding of ongoing processes and the factorial axis distribution maps, which enable the spatial representation of these mechanisms, can be used to facilitate groundwater monitoring and protection. Full article

Urban green spaces (UGSs) are vital ecological infrastructure supporting climate resilience, public health, and environmental equity. Despite UGS’s importance, the distribution of UGS in rapidly growing desert cities is wildly disproportionate, as evidenced by a recent study that links UGS availability with road hierarchy using the Road Buffer Framework. Using Landsat 8-derived UGS (overall accuracy = 0.885; Kappa = 0.853), OpenStreetMap Roads, and WorldPop Population Data, this study found that UGS availability per capita is very low across all road classifications (0.020–0.033 m²/person) and falls significantly short of the World Health Organization’s (WHO) recommendation of 9 m²/person. Primary roads only marginally improved based on distance from roadways (0.026–0.032 m²/person), and secondary roads are experiencing little to no change (0.025–0.026 m²/person). Further, Tertiary roads show the most significant loss, with only 0.022 m²/person available within the 0–300 m buffers containing the most people. In addition, urban green spaces are still significantly inequitable, as demonstrated by Gini coefficient results of >0.80, peaking at 0.895, indicating that UGS availability per capita is substantially below international benchmarks. Therefore, the findings highlight the need of incorporating roadside greening, small park areas, and greenways into our transportation planning efforts to support the UN’s Sustainable Development Goals (SDG) 3, 10, 11, and 13. Full article

Statistical distributions play a crucial role in analyzing real data with varying behavior. In this study, the exponentiated generalized Weibull-G family is discretized using the survival discretization method. Furthermore, a three-parameter discrete distribution, called the exponentiated generalized Weibull–Rayleigh distribution, is generated from this discretized family. This distribution is flexible in modeling various data types, as evidenced by the distinct structures of its probability mass function and hazard rate function. Some statistical properties of both the family and the proposed distribution are discussed. Three estimation approaches—the maximum likelihood, the minimum chi-square, and the method of moments—are used to estimate the distribution’s parameters and are evaluated across three simulation cases. Moreover, the effectiveness of the proposed distribution is evaluated using four datasets from medicine and education. Overall, the results demonstrated the superiority of the proposed distribution fitting the examined data relative to some existing discrete models. Full article

Coal mining is a major source of methane emissions globally, and monitoring these emissions has become a sustained area of interest in both scientific research and policy-making. Coal mine methane emissions typically manifest as discrete point sources, such as individual mines or ventilation shafts, and spatially concentrated area sources, such as mining clusters. In recent years, satellite remote sensing technology has become a key tool for monitoring and assessing methane emissions from coal mines. Notable progress has been made in quantifying emissions through point-source inversion using high-resolution satellite data, such as GF-5B/AHSI, and in estimating regional-scale area-source emissions using wide-swath instruments, such as S5P/TROPOMI. However, there remains a lack of systematic comparison between inversion results derived from these two types of satellite data with differing spatial resolutions. This study comprehensively analyzes the strengths and limitations of the GF-5B/AHSI and S5P/TROPOMI sensors for quantifying methane emissions. It conducts a spatiotemporal comparative analysis of point-source and area-source methane emission datasets from the coal-mining regions of Shanxi Province. The research aims to clarify the intrinsic relationship between remote-sensing data at different observational scales and to systematically evaluate how prior information on emission-source locations influences emission quantification results. The comparative analysis between TROPOMI grid-level emissions and GF-5B/AHSI point-source emissions indicates that TROPOMI-gridded emission data, owing to its longer time series, can more effectively characterize the annual-average methane emission levels in mining areas. Meanwhile, high-resolution observations from GF-5B/AHSI show distinct advantages in detecting small-scale plumes and attributing emissions to specific facilities. Although the regional-average emissions derived from TROPOMI are significantly higher than point-source emission rate estimates, their data ranges overlap within their uncertainty intervals, demonstrating substantial consistency between the monitoring results of the two methods. Furthermore, the study reveals that when key emission facilities, such as ventilation shafts, are located far from the core operational areas of mines, relying solely on point-source observations may not fully capture the spatial distribution pattern of methane emissions at the mine scale. Full article

Cyclonic extreme events have recently undergone an important boost over the Mediterranean Sea, a trend closely linked to ongoing strong climate variations. Several studies are explaining the combination of many different effects that increase the frequency of mesoscale vortices’ intensification, namely Mediterranean tropical-like cyclones (TLCs), until the stage of Medicanes. Among these effects, processes like sea–atmosphere energy exchanges, baroclinic instability, and the release of latent heat lead to the intensification of these systems into fully tropical-like structures. This study investigates the formation and development of Ianos, the most intense Mediterranean tropical-like cyclone recorded in recent years, which affected the Ionian Sea and surrounding regions in September 2020. Using satellite observations and remote sensing data, the study applies a dual approach to characterise the system evolution across the spatial and temporal scales. Firstly, proper orthogonal decomposition (POD) is exploited to assess temperature and pressure fluctuations derived from the geostationary database of Meteosat Second Generation (MSG-11)/SEVIRI. POD allows for the identification of dominant modes of variability and the quantification of energy distribution across different spatial structures during the cyclone’s lifecycle. The decomposition reveals that a small number of orthogonal modes capture a significant proportion of the total variance, highlighting the emergence and persistence of coherent structures associated with the cyclone’s core and peripheral convection. To support scale-dependent energy organisation and dissipation within Ianos, total-period and three-period analyses were carried out, in addition to early-stage intensification patterns and implications for meteorological scale assessments. From the study on the temperatures’ spatio-temporal evolution, a comparison in the POD spectra and of the structures during the peak of intensity was carried out between the Ianos TLC and the Faraji and Freddy tropical cyclones. Additional multi-sensor data from Suomi NPP and Sentinel-3 satellites were integrated to analyse the evolution of the same parameters, also taking into account an evaluation of the vertical temperature gradient, over a 4-day period encompassing the full life cycle of Ianos. The study of the daily evolution helps investigate the spatial trends around the warm core regions, identifying the pressure minima for a comparison with the BOLAM and ERA5 databases of the mean sea level pressure. Overall, this study demonstrates the value of combining dynamic decomposition methods with high-resolution satellite datasets to gain insight into the multiscale structure and convective energetics of Mediterranean tropical-like cyclones. Some significant patterns come out from the spatial organisation of deep convection that seem to be linked to the permanent structures of atmospheric fluctuations near the warm core centre. Full article

This study systematically investigates the role of Ni in Co₂SiO₄ in a bimetallic (Co²⁺,Ni²⁺)₂SiO₄ olivine-type system and the materials’ catalytic efficiency in a model Heck–Mizoroki coupling reaction. Thus, a series of olivines with varying (Co²⁺,Ni²⁺)₂SiO₄ compositions (0–100% Ni) was synthesised and characterised by ICP-OES, FTIR/Raman, P-XRD and XPS analysis. Ideal mixing of metals was achieved with (49:51) Co:Ni. Catalytic testing revealed distinct conversion vs. time profiles, with the (69:31) Co:Ni olivine exhibiting the best overall performance, combining good reactivity with near-perfect selectivity (>99%) and improved stability. Mechanistic pathways were probed through product scope analysis, reactant–product temporal profiling, leaching and radical scavenging experiments. Results suggest a radical-assisted Heck–Mizoroki mechanism. Spectroscopic data correlated Co²⁺ and Ni²⁺ incorporation with M1 and M2 site occupancy, where Ni²⁺ M2 sites enhanced reactant activation and intermediate stability and Co²⁺ in the M1 site enhanced product release, though also homocoupling in Co₂SiO₄. Minimal leaching was observed for all bimetallic catalysts. These findings highlight the tunability of bimetallic olivines for C–C coupling reactions via controlled cation distribution. Full article

Exploring future habitat quality changes in the Mid-Section of the Tianshan Mountains (MSTM) is crucial for regional biodiversity conservation. This study utilizes climate projection data from CMIP6 and integrates the SD-PLUS-InVEST analytical framework to simulate future LULC and habitat quality under three distinct future scenarios. Additionally, the XGBoost-SHAP model is applied to identify and interpret the key regulatory factors within the modeling framework that influence habitat quality spatial heterogeneity. The results show the following: (1) the projections under the three 2035 scenarios generally follow the development trend of 2020, with continued spread of dry land and construction land, but general reduction in the ecological land, reflecting an intensifying conflict between land development and ecological preservation. (2) Habitat quality varies significantly across scenarios, generally exhibiting a “U-shaped” distribution pattern characterized by larger areas of high and low quality and smaller areas of moderate quality. Within the SSP5–8.5 scenario, habitat quality is relatively poor, accompanied by pronounced spatial heterogeneity and imbalance. (3) NDVI is identified as the dominant factor influencing habitat quality spatial heterogeneity, followed by GDP, TEM, and DEM. Although the influence of these factors varies slightly across scenarios, their relative importance remains generally consistent, reflecting the structural stability and response coherence of the ecosystem. Full article