Search Results (5,071)

This paper explores how school culture and key organizational culture dimensions, power distance, humane orientation, performance orientation, and in-group collectivism, affect different types of conflict (task, relationship, and process) in primary schools in Serbia. It also examines how gender and teachers’ organizational commitment moderate these relationships. Data were obtained by surveying 380 respondents, all of whom were primary school teachers in Serbia. The participants were between 23 and 65 years old. Of the total sample, 19.47% were male, and 80.53% were female. The mean values ranged from 1.8046 to 4.9847, with standard deviations between 0.7699 and 1.4785. The research was conducted using a simple random sampling technique. Teachers were given questionnaires through Google Forms, which they completed online. Printed versions were also distributed and later entered into the database. The study was guided by two research questions and two hypotheses. Data analysis was performed using SPSS (Statistical Package for the Social Sciences). The findings reveal that the school culture dimension of teacher professionalism and goal setting (r = −0.297 **; β = −0.496) and the organizational culture dimension of humane orientation (r = −0.303 **; β = −0.198) have the most substantial negative effects on conflict, indicating their beneficial role in reducing it. In contrast, power distance shows a strong positive relationship with conflict, particularly with relationship conflict (r = 0.230 **; β = 0.201). Additionally, excessive emphasis on teacher collaboration and performance orientation appears to increase relationship conflict (β = 0.226; β = 0.261, respectively). Gender differences emerged: cultural dimensions were more effective in reducing conflict among women than men. Among male teachers, power distance had a stronger influence, significantly increasing task conflict (r = 0.303 **). The school culture and organizational culture dimensions significantly reduce the conflict dimensions in the case of high teacher commitment. The dimension power distance has a statistically significant and positive effect on conflicts when organizational commitment is high (r = 0.247 **). Therefore, school culture and organizational culture dimensions achieve stronger effects in committed women, while power distance achieves stronger effects in committed men. Full article

Background: We examined changes in hematological, biochemical, and hormonal biomarkers, along with endurance and explosive performance indices, in amateur soccer players over a 4-week preseason period. Methods: Thirteen players (age: 19.7 ± 2.0 years; body mass: 73.0 ± 6.8 kg; height: 180 ± 0.1 cm; body fat: 8.6 ± 3.5%) were monitored during a 4-week preseason program, which included 21 training days, three friendly matches, and four days of rest. Before and after this period, endurance capacity was evaluated using the Yo-Yo IR1 test, and leg power was assessed using the CMJ. Blood samples were collected for three consecutive days in week 1 and after week 4 to assess hematological and biochemical parameters. Internal load during all weeks was assessed with session RPE (sRPE). Results: There was a 25.5% increase in Yo-Yo IR1 distance (2123 ± 413 vs. 1560 ± 356 m, p = 0.002), with the estimated VO₂max and the speed associated with VO₂max (vVO₂max) improving by 8.7% (49.5 ± 3.0 to 54.2 ± 3.5 mL/kg/min, p = 0.002) and 5.3% (16.0 ± 0.7 to 16.9 ± 0.6 km/h, p = 0.002), respectively. In contrast, CMJ performance in weeks 2–4 declined by 13.4–21.0% relative to baseline, while sRPE peaked during week 3 (4011 ± 440 AU). Hematological variables were mostly stable except for small increases in MCV and MCH (1.5–1.8%, p < 0.001), while there were significant reductions in urea (12%), uric acid (6.2%), and erythropoietin (33%). Conclusions: A 4-week preseason program substantially improved aerobic capacity yet compromised leg power. Changes in biomarker profiles suggest that the training load maintained an appropriate balance between overload and recovery. These findings provide valuable guidance for coaches seeking to optimize training protocols while minimizing the risk of overtraining and preventing injuries during the competitive season. Full article

In forensic anthropology, estimating stature is an essential part of constructing the biological profile of unknown individuals. While long bones are typically used for this purpose, they are often missing or incomplete in forensic contexts. This study examined the relationship between cranial and mandibular measurements and estimated stature in a sample of 84 identified adult (aged over 18 years) Portuguese skeletons (43 females and 41 males) from two osteological collections. Stature was estimated using Mendonça’s regression model based on humeral length. Four cranial and mandibular measurements were obtained, and intra- and interobserver reliability was assessed. All variables showed statistically significant correlations with stature, although only the mandibular measurement—the distance between the mental symphysis and the mental foramen (SMFM)—and sex contributed significantly to the final regression model. The model explained 51.3% of the variance in stature. These findings suggest that SMFM, a stable mandibular marker, may serve as a useful supplementary indicator for stature estimation in cases where long bones are not available. Further studies with larger and more diverse samples are needed to validate these findings and evaluate their applicability across different populations. Full article

Migratory birds play a key role in the ecology of tick-borne pathogens, serving as both hosts for ticks and as potential carriers of a wide range of infectious agents that can affect wildlife, domestic animals, and humans. Their long-distance movements contribute to the dispersal of ticks and the pathogens they harbor, with potential implications for the emergence and spread of zoonotic disease. This study focuses on the prevalence of Rickettsia spp. and Babesia/Theileria spp. in ticks collected from migratory birds in Sardinia, Italy, during two consecutive migration seasons (April–May and October–November 2021), corresponding to the spring and autumn migratory periods. A total of 961 ticks, primarily Ixodes ricinus, was collected from various bird species. Molecular analyses using polymerase chain reaction (PCR) and sequencing enabled the detection and identification of multiple Rickettsia species, with R. helvetica, R. monacensis, and R. aeschlimannii being the most frequently identified. Protozoan pathogens, including B. venatorum and Theileria ovis, were also detected in the tick samples. These findings underscore the diversity of pathogens in bird-associated ticks and the role of migratory birds in the geographical spread of these diseases. These results also provide valuable insights into pathogen transmission dynamics and stress the importance of monitoring migratory birds to assess and mitigate the risks of zoonotic diseases. Further research is needed to clarify the ecological interactions among birds, ticks, and pathogens across different geographic regions. Full article

Mammals harbor diverse microbial communities across different body sites, which are crucial to physiological functions and host homeostasis. This study aimed to understand the structure and function of gut and lung microbiota of pregnant Pomona leaf-nosed bats using V3-V4 16S rRNA gene sequencing. Of the 350 bats captured using mist nets in Yunnan, nine pregnant Pomona leaf-nosed bats with similar body sizes were chosen. Gut and lung samples were aseptically collected from each bat following cervical dislocation and placed in sterile cryotubes before microbiota investigation. Microbial taxonomic annotation revealed that the phyla Firmicutes and Actinobacteriota were most abundant in the guts of pregnant bats, whereas Proteobacteria and Bacteroidota were abundant in the lungs. Family-level classification revealed that Bacillaceae, Enterobacteriaceae, and Streptococcaceae were more abundant in the guts, whereas Rhizobiaceae and Burkholderiaceae dominated the lungs. Several opportunistic and potentially pathogenic bacterial genera were present at the two body sites. Bacillus, Cronobacter, and Corynebacterium were abundant in the gut, whereas Bartonella, Burkholderia, and Mycoplasma dominated the lungs. Alpha diversity analysis (using Chao1 and Shannon indices) within sample groups examined read depth and species richness, whereas beta diversity using unweighted and weighted UniFrac distance metrics revealed distinct clustering patterns between the two groups. LEfSe analysis revealed significantly enriched bacterial taxa, indicating distinct microbial clusters within the two body sites. The two Random Forest classifiers (MDA and MDG) evaluated the importance of microbial features in the two groups. Comprehensive functional annotation provided insights into the microbiota roles in metabolic activities, human diseases, signal transduction, etc. This study contributes to our understanding of the microbiota structure and functional potential in pregnant wild bats, which may have implications for host physiology, immunity, and the emergence of diseases. Full article

Groundwater quality in arid and semi-arid regions of Morocco is under increasing pressure due to both anthropogenic influences and climatic variability. This study investigates the physicochemical and heavy metal characteristics of groundwater across four Moroccan regions (Tangier-Tetouan-Al Hoceima, Oriental, Souss-Massa, and Marrakech-Safi) known for being argan tree habitats. Thirteen groundwater samples were analyzed for twenty-five parameters, including major ions, nutrients, and trace metals. Elevated levels of ammonium, turbidity, electrical conductivity, and dissolved oxygen were observed in multiple samples, surpassing Moroccan water quality standards and indicating significant quality deterioration. Inductively Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) detected arsenic concentrations exceeding permissible limits in sample AW11 alongside widespread lead contamination in most samples except AW5 and AW9. Spatial patterns of contamination were characterized using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), K-means clustering, and GIS-based Inverse Distance Weighted (IDW) interpolation. These multivariate approaches revealed marked spatial heterogeneity and highlighted the dual influence of geogenic processes and anthropogenic activities on groundwater quality. To assess consumption suitability, a Water Quality Index (WQI) and Human Health Risk Assessment were applied. As a result, 31% of samples were rated “Fair” and 69% as “Good”, but with notable non-carcinogenic risks, particularly to children, attributable to nitrate, lead, and arsenic. The findings underscore the urgent need for systematic groundwater monitoring and management strategies to safeguard water resources in Morocco’s vulnerable dryland ecosystems, particularly in regions where groundwater sustains vital socio-ecological species such as argan forests. Full article

Deep learning-based landslide detection in optical remote sensing images has been extensively studied. However, several challenges remain. Over time, factors such as vegetation cover and surface weathering can weaken the distinct characteristics of landslides, leading to blurred boundaries and diminished texture features. Furthermore, obtaining landslide samples is challenging in regions with low landslide frequency. Expanding the acquisition range introduces greater variability in the optical characteristics of the samples. As a result, deep learning models often struggle to achieve accurate landslide identification in these regions. To address these challenges, we propose a multi-scale target attention YOLO model (MSTA-YOLO). First, we introduced a receptive field attention (RFA) module, which initially applies channel attention to emphasize the primary features and then simulates the human visual receptive field using convolutions of varying sizes. This design enhances the model’s feature extraction capability, particularly for complex and multi-scale features. Next, we incorporated the normalized Wasserstein distance (NWD) to refine the loss function, thereby enhancing the model’s learning capacity for detecting small-scale landslides. Finally, we streamlined the model by removing redundant structures, achieving a more efficient architecture compared to state-of-the-art YOLO models. Experimental results demonstrated that our proposed MSTA-YOLO outperformed other compared methods in landslide detection and is particularly suitable for wide-area landslide monitoring. Full article

This paper investigates the inverse reconstruction of temperature fields under both steady-state and transient heat conduction scenarios. The central contribution lies in the structured development and validation of the Gappy Clustering-based Proper Orthogonal Decomposition (Gappy C-POD) method—an approach that, despite its conceptual origin alongside the clustering-based dimensionality reduction method guided by POD structures (C-POD), had previously lacked an explicit algorithmic framework or experimental validation. To this end, the study constructs a comprehensive solution framework that integrates sparse sensor layout optimization with data-driven field reconstruction techniques. Numerical models incorporating multiple internal heat sources and heterogeneous boundary conditions are solved using the finite difference method. Multiple sensor layout strategies—including random selection, S-OPT, the Correlation Coefficient Filtering Method (CCFM), and uniform sampling—are evaluated in conjunction with database generation techniques such as Latin Hypercube sampling, Sobol sequences, and maximum–minimum distance sampling. The experimental results demonstrate that both Gappy POD and Gappy C-POD exhibit strong robustness in low-modal scenarios (1–5 modes), with Gappy C-POD—when combined with the CCFM and maximum distance sampling—achieving the best reconstruction stability. In contrast, while POD-MLP and POD-RBF perform well at higher modal numbers (>10), they show increased sensitivity to sensor configuration and sample size. This research not only introduces the first complete implementation of the Gappy C-POD methodology but also provides a systematic evaluation of reconstruction performance across diverse sensor placement strategies and reconstruction algorithms. The results offer novel methodological insights into the integration of data-driven modeling and sensor network design for solving inverse temperature field problems in complex thermal environments. Full article

Three-dimensional reconstruction programs are essential tools for understanding the behavior of asphalt mixtures. On the basis of accurate 3D models, it is convenient to identify the complex relationship between spatial structures and physical properties. In this work, we explore a low-cost and data-efficient way to create a collection of 3D asphalt mixture models. The core idea is to introduce a foundational segmentation program and stochastic modeling into the asphalt mixture reconstruction framework. First, our approach captures a 2D image to present spatial structures of the investigated sample. The integration of a smartphone camera and an image quilting method has been designed to understand fine-grained details and facilitate full coverage. Aiming at realizing high-quality segmentation, we propose the Segment Anything Model (SAM)-driven method to distinguish aggregate grains and asphalt binder. Second, Multiple-Point Statistics (MPS) is activated to build 3D models from 2D training images. To speed up the reconstruction step, we apply Nearest Neighbor Simulation (NNSIM) to improve pattern searching efficiency. Aiming at calculating 3D conditional probabilities, the probability aggregation framework is introduced into the asphalt mixture investigation. Third, our program focuses on the modeling evaluation procedure. Determination of a two-point correlation function, analysis of distance and a grain size distribution assessment are separately performed to check the reconstruction quality. The evaluation results indicate that our program not only preserves spatial patterns but also expresses uncertainty during the material production step. Full article

Differential privacy (DP) is an important framework to provide strong theoretical guarantees on the privacy and utility of released data. Since its introduction in 2006, DP has been applied to various data types and domains. More recently, the introduction of metric differential privacy has improved the applicability and interpretability of DP in cases where the data resides in more general metric spaces. In metric DP, indistinguishability of data points is modulated by their distance. In this work, we demonstrate how to extend metric differential privacy to datasets representing three-dimensional rotations in SO(3) through two mechanisms: a Laplace mechanism on SO(3), and a novel privacy mechanism based on the Bingham distribution. In contrast to other applications of metric DP to directional data, we demonstrate how to handle the antipodal symmetry inherent in SO(3) while transferring privacy from $S^3$ to SO(3). We show that the Laplace mechanism fulfills $ϵ\varphi$-privacy, where $\varphi$ is the geodesic metric on SO(3), and that the Bingham mechanism fulfills $\widetilde{ϵ}\varphi$-privacy with $\widetilde{ϵ}={\displaystyle \frac{\pi }{4}}ϵ$. Through a simulation study, we compare the distribution of samples from both mechanisms and argue about their respective privacy–utility tradeoffs. Full article

Federated Clustering (FL clustering) aims to discover latent knowledge in multi-source distributed data through clustering algorithms while preserving data privacy. Federated learning is categorized into horizontal and vertical federated learning based on data partitioning scenarios. Horizontal federated learning is applicable to scenarios with overlapping feature spaces but different sample IDs across parties. Vertical federated learning facilitates cross-institutional feature complementarity, which is particularly suited for scenarios with highly overlapping sample IDs yet significantly divergent features. As a classic clustering algorithm, k-means has seen extensive improvements and applications in horizontal federated learning. However, its application in vertical federated learning remains insufficiently explored, with room for enhancement in privacy protection and communication efficiency. Simultaneously, client feature imbalance may lead to biased clustering results. To improve communication efficiency, this paper introduces Product Quantization (PQ) to compress high-dimensional data into low-dimensional codes by generating local codebooks. Leveraging the inherent k-means algorithm within PQ, local training preserves data structures while overcoming privacy risks associated with traditional PQ methods that require server-side data reconstruction (which may leak data distributions). To enhance privacy without compromising performance, Multidimensional Scaling (MDS) maps codebook cluster centers into distance-preserving indices. Only these indices are uploaded to the server, eliminating the need for data reconstruction. The server executes k-means on the indices to minimize intra-group similarity and maximize inter-group divergence. This scheme retains original codebooks locally for strict privacy protection.The nested application of PQ and MDS significantly reduces communication volume and frequency while effectively alleviating clustering bias caused by client feature dimension imbalance. Validation on the MNIST dataset confirms that the approach maintains k-means clustering performance while meeting federated learning requirements for privacy and efficiency. Full article

Locational errors and sampling bias may produce unrepresentative species distribution models. To decompose the influence of errors, I modeled species distributions of 31 mammal species from georeferenced records and random samples from range maps, with potential sources of errors added or removed, using the random forests algorithm. Errors included the addition of (1) cities, (2) administrative centers, (3) records flagged as potential errors (e.g., outliers), and (4) urban records to range map samples; the removal of (5) flagged records and (6) urban records from georeferenced records; and the addition of (7) random points and (8) clustered points to georeferenced records. I also examined separation between thinned and unthinned (i.e., locally concentrated) records and ocean and land areas. Errors generally did not perturb species distributions, particularly if errors were located within species ranges. The greatest departure relative to unaltered models (mean niche overlap values of 0.96 out of 1) was due to the addition of administrative centers at a 13% error rate. Because locational errors overall do not occur in modern georeferenced records, outliers may provide important samples from undersampled areas. Delineating land from ocean coordinates may require a land layer at the highest available resolution and buffered to match the distance of locational uncertainty for georeferenced records. Predicted areas for species distributions increased along the spectrum of models from concentrated georeferenced records, thinned records, and random samples from range maps. Species distributions modeled with all georeferenced records will have the greatest sampling concentration (to differentiate from bias, because predictive modeling is not hypothesis testing), resulting in model locational precision, whereas species distribution models from random samples of range maps will have locational generality (rather than errors). The risk of removing samples of suitable conditions is the generation of unrepresentative models whereas the benefit of sample removal is slightly more generalized models, but which also may represent overpredictions. Full article

Acerola (Malpighia emarginata DC.) is a tropical fruit known for its high vitamin C (ascorbic acid) content. This study aimed to determine the optimal sample size (OSS) required to reliably estimate postharvest quality traits in acerola. A total of 50 red-ripe fruit from four cultivars (BRS Rubra, Cabocla, Costa Rica, and Junko) were evaluated individually for their physical (weight, diameter, length, color, and firmness) and chemical (soluble solids content [SSC], titratable acidity [TA], SSC/TA ratio, and vitamin C) attributes. Bootstrap resampling and nonlinear power models were used to model the relationships between sample sizes and the width of 95% confidence intervals (CI_95%). Three methods were applied to determine the maximum curvature point (MCP): general, perpendicular distance (PD), and linear response plateau (LRP). The PD and LRP methods led to consistent and conservative OSS estimates, which ranged from 12 to 28 fruit depending on the trait and cultivar. A sample size of 20 fruit was identified as a practical and reliable reference. Chemical traits showed greater variability and required larger samples. Cultivar comparisons indicated that ‘BRS Rubra’, ‘Cabocla’, and ‘Costa Rica’ are suitable for fresh consumption, while ‘Junko’ is ideal for vitamin C extraction. These results provide statistical support for experimental planning in acerola postharvest research. Full article

Individual identification of Holstein cattle is crucial for the intelligent management of farms. The existing closed-set identification models are inadequate for breeding scenarios where new individuals continually join, and they are highly sensitive to obstructions and alterations in the cattle’s appearance, such as back defacement. The current open-set identification methods exhibit low discriminatory stability for new individuals. These limitations significantly hinder the application and promotion of the model. To address these challenges, this paper proposes a prototype network-based incremental identification framework for Holstein cattle to achieve stable identification of new individuals under small sample conditions. Firstly, we design a feature extraction network, ResWTA, which integrates wavelet convolution with a spatial attention mechanism. This design enhances the model’s response to low-level features by adjusting the convolutional receptive field, thereby improving its feature extraction capabilities. Secondly, we construct a few-shot augmented prototype network to bolster the framework’s robustness for incremental identification. Lastly, we systematically evaluate the effects of various loss functions, prototype computation methods, and distance metrics on identification performance. The experimental results indicate that utilizing ResWTA as the feature extraction network achieves a top-1 accuracy of 97.43% and a top-5 accuracy of 99.54%. Furthermore, introducing the few-shot augmented prototype network enhances the top-1 accuracy by 4.77%. When combined with the Triplet loss function and the Manhattan distance metric, the identification accuracy of the framework can reach up to 94.33%. Notably, this combination reduces the incremental learning forgetfulness by 4.89% compared to the baseline model, while improving the average incremental accuracy by 2.4%. The proposed method not only facilitates incremental identification of Holstein cattle but also significantly bolsters the robustness of the identification process, thereby providing effective technical support for intelligent farm management. Full article

Soil environmental quality in arid oases is crucial for regional ecological security but faces multi-source heavy metal (HM) contamination risks. This study aimed to (1) characterize the spatial distribution of soil HMs (As, Cd, Cr, Cu, Hg, and Zn) in the Ka Shi gar oasis, Xinjiang, (2) quantify the driving effect of irrigation water, and (3) elucidate interactions between HMs, soil properties, and land use types. Using 591 soil and 12 irrigation water samples, spatial patterns were mapped via inverse distance weighting interpolation, with drivers and interactions analyzed through correlation and land use comparisons. Results revealed significant spatial heterogeneity in HMs with no consistent regional trend: As peaked in arable land (5.27–40.20 μg/g) influenced by parent material and agriculture, Cd posed high ecological risk in gardens (max 0.29 μg/g), and Zn reached exceptional levels (412.00 μg/g) in gardens linked to industry/fertilizers. Irrigation water impacts were HM-specific: water contributed to soil As enrichment, whereas high water Cr did not elevate soil Cr (indicating industrial dominance), and Cd/Cu showed no significant link. Interactions with soil properties were regulated by land use: in arable land, As correlated positively with EC/TN and negatively with pH; in gardens, HMs generally decreased with pH, enhancing mobility risk; in forests, SOM adsorption immobilized HMs; in construction land, Hg correlated with SOM/TP, suggesting industrial-organic synergy. This study advances understanding by demonstrating that HM enrichment arises from natural and anthropogenic factors, with the spatial heterogeneity of irrigation water’s driving effect critically regulated by land use type, providing a spatially explicit basis for targeted pollution control and sustainable oasis management. Full article