Search Results (328)

Microsegregation in cast materials is important to their solidification, solid-state transformation, microstructure and material properties. This work studies quantitatively the microsegregation of Si, Mn, Cu, Sn, and P in graphitic cast irons using an electron microprobe with wavelength dispersive spectrometry. The alloys contain [mass%] C: 3.86, Si: 2.59, Mn: 0.64, P: 0.03, S: 0.01, Sn: 0.098, Cu: 0.84, Mg: 0.065, include graphite morphologies ranging from ductile iron to compacted graphite iron and solidified with a solidification time of 10 min. Concentration maps are presented, showing that microsegregation patterns provide detailed information about the solidification chronology of the metal matrix. Sequencing the measurements into concentration profiles showed that, despite large differences in microstructure and cooling curve characteristics, the severity of microsegregation was similar in the studied materials. Scheil simulation of concentration profiles provided decent prediction of concentration profiles, given appropriate thermodynamic data. Numerical simulation of isothermal diffusion suggested that, for about 10 min of solidification time, diffusion in austenite mainly affected the last 10% of the matrix to freeze. Effective partition coefficients extracted from the concentration profiles varied slightly through solidification. The estimated mean effective partition coefficients for the first 90% of the alloy to freeze are ${\overline{k}}_{Si}^{\gamma /L}=1.124\pm 0.006$, ${\overline{k}}_{Mn}^{\gamma /L}=0.696\pm 0.008$, ${\overline{k}}_P^{\gamma /L}=0.15\pm 0.03$, ${\overline{k}}_{Sn}^{\gamma /L}=0.50\pm 0.02$, ${\overline{k}}_{Cu}^{\gamma /L}=1.35\pm 0.01$, where $\pm$ indicates standard deviation. Full article

Effectively integrating graph topology and node attributes, while assigning nodes with both semantic similarity and structural closeness to the same community, remains a key challenge in attributed graph community detection. To address this challenge, this study proposes TVHCL-CD, a two-view hierarchical contrastive learning-driven method for community detection. The proposed method constructs an attribute view and a modularity view from the node attribute matrix and the modularity matrix, respectively, to model attribute semantics and high-order community structure priors. Structure-aware two-view representations are then learned in parallel through dual-view graph attention encoders incorporating multi-order neighborhood priors. Furthermore, a structure-enhanced Graph Transformer fusion module is designed to achieve node-level adaptive fusion of the two-view representations by introducing a learnable adjacency bias into global self-attention and a view-aware gating mechanism into the feed-forward network. To align the optimization objective with community semantics, a hierarchical contrastive learning strategy is further developed. Specifically, view-level consistency contrastive learning constructs modularity-guided augmented views to improve representation robustness, while community-level semantic contrastive learning incorporates partial ground-truth labels to enhance intra-community compactness and inter-community separation. Finally, clustering is performed on the fused representations to obtain community partitions. Experimental results on eight real-world attributed graphs and the generated tree-like attributed graph Tree-2500 indicate that TVHCL-CD achieves competitive performance under the semi-supervised transductive setting, while ablation results support the contributions of its main components. Full article

Synthetic Aperture Radar (SAR) is widely used for marine oil spill surveillance due to its all-weather capabilities and sensitivity to sea surface roughness. However, oil slicks often appear as dark formations that can be confounded with visually similar “look-alikes”, making automated detection and boundary delineation challenging. This study proposes a two-stage deep learning framework for oil spill mapping in Sentinel-1 SAR imagery. First, a ConvNeXt-T classifier screens image patches for likely slick presence, reducing the search space for dense prediction. Second, spill boundaries are extracted with a domain-adapted Segment Anything Model (SAM) configured for prompt-free, single-shot segmentation. The input representation is enhanced by combining preprocessed Sentinel-1 VV backscatter with Gray-Level Co-occurrence Matrix (GLCM) texture measures (homogeneity and variance) to better separate oil from heterogeneous background sea at the segmentation level. Quantitative evaluation against established segmentation baselines demonstrates that our adapted SAM achieves the highest overall accuracy, reaching an F1-score of 0.86. This outperforms traditional models such as UNet and CBDNet (0.83), as well as DeepLabV3, SegNeXt, and OFCNet (all at 0.82). Furthermore, an analysis of the wind speed on the test set shows that wind speed affects detectability but does not by itself determine segmentation quality. The results indicate that combining transformer-based screening with efficient foundation-model adaptation can provide accurate and scalable oil spill mapping for operational SAR monitoring. Full article

In recent years, vision–language models (VLMs) have been introduced into remote sensing semantic segmentation to provide richer semantic representations through visual–textual alignment. However, most existing VLM-based segmentation methods focus on global semantic alignment while neglecting pixel-level local neighborhood features, which are crucial for reliably understanding remote sensing imagery with high spatial resolution, complex structures, and strong spatial continuity. To address this issue, we propose LoVLANet (Localized Vision–Language Attention Network), a novel vision–language segmentation framework that integrates language-driven global semantics with local spatial context. LoVLANet consists of a text encoder, a visual encoder, and a segmentation decoder. Specifically, the text encoder is inherited from RemoteCLIP to preserve domain-adapted vision–language alignment. The visual encoder is built upon a Vision Transformer (ViT). To enhance local dependency modeling, we propose a Neighborhood Key–Key Encoder. It leverages a Gaussian-weighted neighborhood matrix for spatial correlation and uses key–key similarity to emphasize intrinsic semantic similarity over query-driven features, thus, preserving spatial consistency. Finally, the segmentation decoder fuses multi-scale visual features and aligns the image–text representations to generate accurate pixel-level segmentation results. Experiments on RGB remote sensing benchmarks, including LoveDA and GID, show that LoVLANet achieves competitive segmentation performance under the adopted experimental settings, with improved mIoU and clearer boundary delineation in qualitative visualizations. These results suggest the effectiveness of explicitly modeling local neighborhood relationships in VLM-based segmentation for supervised remote sensing scene understanding. Full article

The Stewart–Gough Platform (SGP) is a spatial parallel manipulator offering high accuracy, rigidity, and adaptability, with applications spanning medical systems, marine engineering, agriculture, manufacturing, entertainment, aerospace, and architectural installations. This paper presents a comparative analytical and computational study of three SGP configurations: the regular SGP, with regular hexagonal base and top platforms; the Irregular-Parallel SGP, derived from the regular SGP by a novel graphical decomposition-and-modification procedure and characterized by similar symmetric hexagonal platforms with limbs preserved parallel; and the Irregular-Skewed SGP, in which the irregular hexagonal platforms of the Irregular-Parallel SGP are retained, but the limbs are connected in an inclined, alternating clockwise (or anticlockwise) topology. The Irregular–Skewed SGP is free from the constraint singularity that persists in the first two configurations and requires the shortest maximum actuator stroke. Static force analysis shows that the regular SGP and the Irregular–Parallel SGP both exhibit a rank-deficient rigidity matrix (rank = 3) across the geometric scaling range tested (radius ratios 1:2 to 1:10; inter-platform distances 100–1000 mm), whereas the Irregular-Skewed SGP achieves full rank (rank = 6) through inclined limb connectivity and is the only configuration capable of sustaining static equilibrium under the loading conditions examined. The forward kinematics of the Irregular-Parallel SGP is verified against a SolidWorks model: under a 9 mm uniform limb extension, the MATLAB and SolidWorks positions of node 7 agree to within 1.27 mm. The rotational workspace volume is equivalent across the three configurations, but the density of valid solution points within that workspace differs. The workspace within joint limits, alternating compression–tension force partition, and asymmetric stroke economy of the Irregular-Skewed SGP indicate applicability to kinetic facades and transformable interiors in architectural-robotics deployment. Full article

The solidification behavior and microstructural evolution of Al–Ge microparticles were investigated on Si, SiO₂, Al₂O₃, amorphous carbon, and ZrO₂ substrates. Micrometer-sized particles with a hypereutectic composition were produced by melting and resolidifying 40 nm thick Al–Ge films. Their size, wetting angle, crystal structure, and internal morphology were characterized by SEM and TEM techniques. We demonstrate that Al–Ge particles exhibited strongly substrate-dependent wetting, with contact angles ranging from 46° on SiO₂ to 123° on ZrO₂. Nevertheless, all particles developed a similar internal microstructure consisting of a fully interconnected, irregular Ge network within an Al matrix, indicating complete phase separation during solidification. The eutectic network was quantified by its ligament thickness. No correlation was found between ligament thickness and substrate type or contact angle, indicating that the coral-like internal Ge network forms independently of particle wetting. Instead, the ligament thickness increased with particle size and during post-solidification annealing. The network gradually coarsened up to 310 °C, followed by its complete breakdown and transformation into an equilibrium Janus morphology at 370 °C. These findings provide new insight into the solidification of irregular eutectic systems and suggest a route for tailoring three-dimensional internal microstructures in eutectic microparticles. Full article

To address the problems of subtle style differences, high inter-class similarity, and complex structural and texture features in woodblock New Year paintings, this paper proposes a style classification method for woodblock New Year paintings based on an improved ResNeXt-50. The method introduces SA-CBAM at the middle- and high-level feature stages. Through the synergistic effect of channel attention and edge-enhanced spatial attention, the model is guided to focus on key structural regions such as human contours. Furthermore, single-stage 2D-DWT is introduced to separate deep features into low-frequency global structural components and high-frequency local detail components, thereby enabling effective representation of overall composition information and fine-grained carving textures. The Gram matrix is introduced to conduct statistical modeling of the fusion features, so as to characterize the overall style distribution from the perspective of channel correlation. The model is trained and tested on a dataset of 4043 independent images across six categories, achieving an overall classification accuracy of 97.68%, which is significantly superior to mainstream models such as Vision Transformer. Ablation experiments further verify the complementary effects of each module in structural perception, frequency-domain feature representation, and style statistical modeling, demonstrating the effectiveness and application potential of the proposed method for digital preservation and fine-grained style recognition of woodblock New Year paintings. Full article

Facial expression recognition (FER) is a well-established task in computer vision, yet its application to non-photorealistic domains, such as anime and manga, remains largely underexplored. The stylized, exaggerated, and often non-proportional facial features of illustrated characters present unique challenges for deep learning models trained predominantly on realistic imagery. In this work, we construct a balanced dataset of 3000 manga and anime face images spanning six emotion categories (Angry, Embarrassed, Happy, Manic–Euphoric, Sad, Scared) and conduct a systematic comparison of two major deep learning paradigms: Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). Specifically, we evaluate ResNet-18, ResNet-50, ViT-B/16, and ViT-S/16 under four fine-tuning strategies: linear probing, partial fine-tuning, full fine-tuning, and progressive unfreezing, enabling a controlled comparison of both architectural families and transfer learning depth. Our results show that fine-tuning strategy significantly impacts performance: the best configuration (ViT-B/16 with progressive unfreezing) achieves 81.33% test accuracy (single run, seed 42), compared to 61.33% for the weakest linear probe baseline (ViT-S/16), a gap of 20.00 percentage points. To isolate architectural differences from strategy effects, we note that under full fine-tuning, the only strategy applied identically to all four models, ViT-S/16 (76.00%) outperforms ResNet-18 (74.44%) by 1.56 percentage points and ViT-B/16 (74.22%) by 1.78 percentage points, confirming a modest but consistent architectural advantage for Transformers once backbone adaptation is permitted. Vision Transformers benefit disproportionately from fine-tuning, and the relative ranking of architectures changes across fine-tuning regimes. Confusion matrix analysis reveals persistent cross-class confusion between visually similar emotions (e.g., Happy vs. Embarrassed), while the highly distinctive Manic–Euphoric category is consistently well recognized across all architectures. To the best of our knowledge, this is the first work to conduct a controlled multi-architecture, multi-strategy transfer learning benchmark specifically for FER in anime and manga, revealing findings that are not predictable from photographic FER literature and that carry direct practical implications for model selection in non-photorealistic visual recognition tasks. The anime and manga domain provides a uniquely controlled testbed for studying transfer learning under deliberate stylization, where the domain gap from realistic imagery is not an artifact of image degradation or environmental noise but a principled artistic choice with codified visual conventions; observing that fine-tuning depth dominates architectural choice in this domain suggests the same conclusion likely holds in other non-photorealistic transfer scenarios such as medical illustrations, architectural drawings, and synthetic training data. Full article

Understanding how the development of large-scale coal mining bases affects river hydrochemistry is a key scientific issue in the field of water environment research. In this study, the Qingyang–Binzhou section of the Jinghe River Basin was selected as the study area, and a total of 29 water samples were collected in April 2025 from the upper to lower reaches of the coal mining base. Hydrochemical analysis, ion ratio methods, and the positive matrix factorization (PMF) model were comprehensively applied to systematically characterize the hydrochemical features and identify the pollution sources in the river under the influence of large-scale coal mining activities. The results showed that the mean concentrations of Na⁺, SO₄²⁻, Cl⁻, and total dissolved solids (TDS) in the mainstream were as high as 414 mg/L, 728 mg/L, 226 mg/L, and 1636 mg/L, respectively, reflecting a significant impact of coal mining activities on river hydrochemistry. Four spatial variation patterns were observed along the river: the first pattern was characterized by “stable in the upper reaches, sharp increase in the middle reaches, and fluctuating increase in the lower reaches,” represented by Na⁺ and SO₄²⁻; the second pattern showed “stable in the upper reaches, slight decrease in the middle reaches, and fluctuating decrease in the lower reaches,” represented by pH; the third pattern exhibited “fluctuating in the upper reaches, sharp decrease in the middle reaches, and extremely low levels in the lower reaches,” represented by NO₃⁻; and the fourth pattern was dominated by irregular variations controlled by nitrogen transformation processes, represented by NH₄⁺ and NO₂⁻. Gibbs plots and ion ratio diagrams indicated that the hydrochemistry of sites unaffected by coal mine drainage was primarily controlled by rock weathering, whereas contaminated samples shifted toward the evaporation-concentration zone and extended beyond its typical range, reflecting an “anthropogenic salinization effect” induced by the input of mine water superimposed on the arid to semi-arid climatic background. The PMF model identified three main pollution sources: coal mining and mine water discharge (48.3%), domestic sewage (30.2%), and carbonate weathering (21.5%). This study reveals the significant modification mechanism of river hydrochemistry by large-scale coal mining base development, providing a scientific basis for targeted water pollution control in the Jinghe River Basin and for water environment management in similar mining areas. Full article

This study reports on the physico-chemical and in vitro biological characterization of chromium-doped hydroxyapatite (10CrHAp, Cr³⁺, Ca_10-xCr_x(PO₄)₆(OH)₂, x_Cr = 0.1) and chromium-doped hydroxyapatite in dextran matrix (10CrHAp-Dx) coatings, prepared for the first time via the spin coating technique. X-ray diffraction analysis and Rietveld refinement were used to characterize the materials. Fourier-transform infrared (FTIR) spectroscopy confirmed the presence of functional groups specific to hydroxyapatite. Scanning electron microscopy (SEM) observations revealed the presence of a conglomerate of nanoparticles distributed unevenly across the coatings surface. Atomic force microscopy (AFM) showed that both coatings presented continuous surfaces with uniform morphology. The in vitro biocompatibility of 10CrHAp and 10CrHAp-Dx coatings was evaluated using human osteoblast-like MG63 cell line and MTT assay. SEM and MM visualization assessed the cell adhesion and proliferation and morphological changes in the adhered cells. The antibacterial properties of the 10CrHAp and 10CrHAp-Dx coatings was assessed in vitro against two of the most common bacterial reference strains, Pseudomonas aeruginosa ATCC 27853 and Staphylococcus aureus ATCC 25923. Overall, the coatings achieved log reductions up to ~9.35, corresponding to a bacterial kill rate (for S. aureus) exceeding 99.99%, with 10CrHAp-Dx showing slightly superior performance. Similar behavior (log reductions of ~8.6 and ~8.9, respectively, indicating a sustained antibacterial effect and >99.99% bacterial elimination) was observed and for Pseudomonas aeruginosa. AFM was used to evaluate the bacterial cells interaction with the coating’s surfaces. The biological assays demonstrated that both coatings possess notable antibacterial activity, underscoring their potential in biomedical applications, particularly in the design of new antimicrobial devices. Full article

Endometriosis is a chronic inflammatory disease in which transforming growth factor-beta (TGF-β) has been implicated in immune dysregulation, extracellular matrix remodeling, and fibrosis. Data on baseline secretion of TGF-β isoforms by systemic immune cells remain limited. This pilot study quantified unstimulated secretion of TGF-β1, TGF-β2, and TGF-β3 by peripheral blood mononuclear cell (PBMC) cultures from women with and without endometriosis and explored stage-related patterns. In this prospective case–control study, PBMCs from 50 women with surgically confirmed endometriosis and 30 controls were cultured for 24 h without exogenous stimulation. Supernatant concentrations were measured using a multiplex bead-based immunoassay (Bio-Plex, Bio-Rad) and expressed as pg/mL; between-group and stage-related differences were assessed using non-parametric tests. Median 24 h secretion was similar between groups (TGF-β1: 103,816 vs. 114,700 pg/mL, p = 0.25; TGF-β2: 3735 vs. 3732 pg/mL, p = 0.32; TGF-β3: 3280 vs. 3284 pg/mL, p = 0.70). Within the endometriosis cohort, TGF-β2 was significantly higher in moderate/advanced disease (rASRM stages III–IV) than in minimal/mild disease (stages I–II), whereas TGF-β1 and TGF-β3 did not reach statistical significance for a stage-dependent pattern in this pilot cohort (p = 0.42 and p = 0.41, respectively; Kruskal–Wallis), and a type II error cannot be excluded given the small sample size per rASRM (revised American Society of Reproductive Medicine)stage (n = 11–14). These findings suggest that TGF-β dysregulation is compartmentalized to the peritoneal environment rather than systemically imprinted in circulating immune cells. The stage-dependent elevation of TGF-β2 supports its role in progressive fibrogenesis and as a candidate severity biomarker, warranting confirmation in larger, stimulus-augmented studies. Full article

In this paper, an efficient and accurate framework for nonlinear spacetime fractional diffusion equations is proposed. The methods are based on the spectral deferred correction technique, which employs a compact difference scheme as the preconditioner via the Picard integral collocation formulation. The nonlinear term is incorporated into the preconditioner in a way similar to linear systems without using Newtonian methods. The preconditioner is proven to be a stable operator, and the resulting spectral deferred correction method maintains an arbitrary order of accuracy and excellent stability. Due to the dense property of the central finite difference approximation of the fractional Laplacian ${(-\Delta )}^s$, a dual accelerated algorithm for the exact computation of the matrix–vector product is presented by introducing the discrete sine transform. The numerical results demonstrate that the proposed new methods are highly efficient and precise. Full article

Frequency response analysis (FRA) is recognized as an effective method in power transformer winding fault diagnosis. However, the traditional numerical index methods focus on the overall features of FRA curves, making it difficult to capture subtle deformations in transformer windings. Similarly, existing digital image processing methods rely on a single feature or a simple feature combination without adaptive fusion. These methods ignore differences in the data distributions of features, leading to feature mismatch, the loss of sensitive fault information, and lower diagnostic accuracy. To solve this problem, a novel adaptive multiple-image-feature fusion method for transformer winding fault diagnosis is proposed. First, a multi-dimensional feature space combining image pixel matrix similarity, morphological features, and image texture features is built to decode the difference in fault of FRA images. Second, the multiple kernel learning (MKL) framework is used to dynamically adjust the fusion weights of different kernels to make features compatible and remove redundant information. Finally, comparative and ablation experiments show that the proposed method outperforms the traditional methods in identifying different types and levels of faults. The method achieves over 99% accuracy in fault type identification across SVM, KNN, and RF classifiers. For radial deformation (RD) severity prediction, the accuracy of the proposed model is 93.37% with SVM and 94.85% with KNN, outperforming the full-feature concatenation method. These results confirm the method’s robustness and diagnostic precision. Full article

Hydrogels derived from biopolymers have attracted considerable interest in biomedical applications because of their biocompatibility and structural similarity to the extracellular matrix (ECM). Bacterial Cellulose (BC), despite being a promising biopolymer for hydrogel preparation, lacks antimicrobial properties itself. To address this drawback, we prepared Probiotic Bacterial Cellulose (PBC) in our laboratory, which has intrinsic antibacterial properties. No research was found on the preparation of a hydrogel using PBC and κ-carrageenan, which motivated us to develop a PBC/κ-carrageenan-based hydrogel. In the study, a novel biocomposite hydrogel system has been developed by integrating PBC with κ-carrageenan, yielding a multifunctional hydrogel with enhanced antibacterial properties and biocompatibility. The novel hydrogel has been evaluated for its structural, physicochemical, antibacterial, and biocompatible properties. Fourier transform infrared spectroscopy (FTIR) analysis confirmed the formation of intermolecular interactions between PBC and κ-carrageenan. Scanning electron microscopy (SEM) images revealed a porous internal morphology and the presence of probiotic bacteria within the hydrogel networks. Porosity analysis and swelling behaviour indicated an elevated water uptake capacity and structural stability. The composite hydrogel demonstrated promising antibacterial properties against pathogenic bacteria Escherichia coli (Gram-negative) and Staphylococcus aureus (Gram-positive) and exhibited favourable in vitro biocompatibility. The developed PBC/κ-carrageenan hydrogel exhibits a synergistic combination of porosity, swelling capacity, biocompatibility, and antibacterial activity, making it a potential candidate for healthcare applications viz. wound healing and other tissue engineering applications. Full article

The use of cellulosic reinforcement fillers, including cellulose and holocellulose, in the development of sustainable biopolymer composites has become increasingly essential and continues to attract significant attention in the composite industry. This study aimed to improve the structural and morphological characteristics of the polyhydroxybutyrate (PHB) matrix by incorporating cellulosic fillers—namely, α-cellulose and holocellulose produced via a green processing method—and to evaluate the effect of hemicellulose, present in holocellulose and exhibiting compatibilizing capability, on the overall performance of PHB-based blends. For this, the PHB matrix was first dissolved in chloroform, after which the cellulosic fillers were incorporated into the PHB–chloroform mixtures at 1 wt.% to provide the best homogeneous fiber dispersion. The PHB and cellulosic filler mixtures were blended at 500 rpm with a magnetic mixer for 30 min, and the resulting composite was cast onto a Teflon plate. Scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier transform infrared (FTIR) spectroscopy were used to characterize the morphological and structural analysis of the obtained biopolymer-based composites. Thermogravimetric analysis (TG-DTG) was used to determine the thermal properties. The results obtained confirmed the presence of cellulosic fillers in the PHB matrix using FTIR, XRD, and SEM. In contrast to holocellulose, α-cellulose in the PHB matrix was shown to create a more organized structure. Both α-cellulose and holocellulose reinforcements were found to have similar effects on the thermal properties of the PHB matrix. Compared with neat PHB, the amount of residual char was found to be more than 36-fold in the sample containing α-cellulose and more than 41-fold in the sample containing holocellulose. Full article