Search Results (500)

Accurate estimation of sparse underwater acoustic channels is challenging because of multipath delay spread, correlated inputs, and impulsive non-Gaussian noise. Existing KRSL-based algorithms still suffer from limited convergence speed and tracking capability in time-varying sparse scenarios. This paper proposes a VFF-PRKRSL algorithm, which jointly introduces an error-driven variable forgetting factor and a proportionate gain matrix into the recursive KRSL framework to achieve adaptive historical-information weighting and enhanced updating of dominant taps. Simulation results show that for Bellhop-generated underwater acoustic channels with sparsity levels of 0.125 and 0.4688, the proposed algorithm achieves NMSD values of $-38.4763$ dB and $-37.9417$ dB at the 2000th iteration, improving upon PRKRSL by approximately 5.31 dB and 5.29 dB, respectively.Under Cauchy noise, it reaches an NMSD of $-46.3042$ dB, about 5.95 dB better than PRKRSL. Ablation results indicate that the variable forgetting factor is the main source of the performance gain and is complementary to the proportionate update mechanism. These results demonstrate that VFF-PRKRSL outperforms existing methods in convergence speed, steady-state accuracy, and robustness against impulsive noise. Full article

Visual similarity analysis of ethnic brocades is valuable for image retrieval, style comparison, and digital archiving in cultural heritage informatics. However, although deep neural networks provide powerful visual representations, their encoded similarity structures are often difficult to interpret. This study presents an interpretable deep feature framework for analyzing inter-ethnic visual similarity in brocade images from ten minority groups in Southwest China. Four convolutional neural network backbones, including AlexNet, VGG-16, ResNet-18, and an SE-enhanced ResNet-18 (SResNet-18), were first evaluated to identify a reliable feature extractor. The best-performing model was then used to construct deep feature-based similarity and distance relationships among ethnic categories. To interpret this structure, five handcrafted descriptor types, namely color, texture, geometric, local-structure, and frequency-domain features, were compared with the deep feature similarity matrix using Spearman correlation analysis and weighted descriptor fusion. Experimental results showed that SResNet-18 achieved the best classification performance, with an accuracy of 95.15% and an F1-score of 95.14%. Among the handcrafted descriptors, color showed the strongest correspondence with the RGB-based deep similarity structure ($r=0.643$), followed by local-structure descriptors ($r=0.416$), whereas classical texture descriptors showed near-zero correspondence ($r=-0.063$). The optimal weighted fusion further improved the correlation to $r=0.731$. These findings suggest that the SResNet-18 feature space is more strongly associated with color composition and local motif organization than with the specific grayscale texture, global geometric, or frequency-domain descriptors used in this study. The proposed framework provides an interpretable approach for understanding deep visual similarity in cultural heritage images and offers methodological support for pattern-based retrieval, comparative style analysis, and digital documentation. Full article

Background/Objectives: Supramolecular hydrogels formed by low-molecular-weight gelators present a chemically heterogeneous transport environment whose molecular-scale dynamics remain poorly understood. This study aimed to investigate how drug physicochemistry governs transport within a liquid-crystalline C18ADPA hydrogel at the molecular scale. Methods: Pulsed-field gradient NMR spectroscopy was used to measure self-diffusion coefficients of five model drugs (5-fluorouracil, acetylcholine, paracetamol, prednisolone, and amphotericin B) spanning a broad range of size, polarity, and charge state, in both free solution and the hydrogel matrix at pH 5.37. Results: Observed drug diffusion coefficients deviated substantially from classical obstruction theory predictions, demonstrating that transport is governed by host–guest chemical affinity rather than molecular size. The three water-soluble drugs exhibited bimodal diffusion, with relative amplitudes providing a direct estimate of bound and free drug fractions. Prednisolone co-diffused with the gelator scaffold, consistent with hydrophobic bilayer partitioning, while amphotericin B diffused at rates consistent with the structured interfacial water layer. The gel pH (5.37) emerged as an active determinant of transport: drug charge states at this pH from permanent cation (acetylcholine) to near-zwitterion (amphotericin B) correlated directly with the observed transport behavior. The near-zwitterionic character of amphotericin B at pH 5.37, arising from its carboxyl pKa (~5.5), suggests a previously unreported electrostatic interfacial trapping mechanism. Conclusions: The liquid-crystalline bilayer architecture creates chemically distinct microdomains that selectively recruit drugs based on hydrophobicity, hydrogen-bonding capacity, and pH-dependent charge state, providing a molecular-scale framework for rational formulation design in supramolecular drug delivery. Full article

Existing multi-view clustering approaches based on matrix factorization often fail to jointly capture global high-order correlations and local view-specific characteristics, and they typically suffer from instability in generating final clustering labels. To overcome these limitations, this paper presents a multi-view subspace clustering method termed dual-tensor constrained multi-view subspace clustering (DTCMVSC). Specifically, for each view, we learn an independent latent representation matrix, a projection matrix, and a basis matrix. The latent representations and projection matrices are stacked into third-order tensors, upon which tensor nuclear norm regularization is imposed to simultaneously exploit consensus structures and complementary information across views. Additionally, a consensus regularization term and adaptive view weights are introduced to align the latent representations of different views toward a unified consensus subspace. The resulting optimization problem is efficiently solved under the ADMM framework, after which a similarity matrix is constructed from the consensus representation and spectral clustering is performed to obtain the final labels. Experimental evaluations on six benchmark datasets demonstrate the superiority of DTCMVSC. Specifically, it achieves an ACC of 86.10% on CMU and an NMI of 94.17% on ORL, surpassing even the lowest-performing state-of-the-art baselines by 63.08 and 18.53 percentage points, respectively. Full article

Coal-mine-gas-monitoring data exhibits missing phenomena due to the harsh underground operating environment. Accurate imputation of missing values in gas-monitoring sequences serves as a key data foundation for guaranteeing the continuity of gas data, enhancing the reliability of disaster early warning, and improving the accuracy of mine safety situation analysis and judgment. Aiming at the prevalent random and segmented missing issues in coal-mine-gas-monitoring time-series data, and the limitation that existing imputation methods struggle to accurately capture the nonlinear spatiotemporal correlations and long-range temporal dependencies of such data, this study proposes a missing data imputation method for coal mine gas time-series data based on the Spatio-Temporal Graph Attention Network—Echo State Network (ST-GAT-ESN). Firstly, this method extracts temporal features of the gas concentration sequence using a Gated Recurrent Unit (GRU). Subsequently, it models multiple monitoring points as graph nodes through a Graph Attention Network (GAT), constructs an adjacency matrix based on airflow propagation relationships, and adaptively learns the spatial dependency weights between monitoring points to realize the deep fusion of spatiotemporal features. Finally, it designs a dual-channel Echo State Network (ESN), synchronously inputs the spatiotemporal fusion features of the missing regions before and after, efficiently fits the nonlinear evolutionary trend of the data by virtue of the echo state property of the reservoir, and solves the output layer weights through ridge regression to achieve accurate imputation of missing values. Experimental results demonstrate that, compared with the single-ST-GAT-ESN, ESN, and ARIMA models, the proposed method achieves the optimal imputation performance in both random and segmented missing scenarios within the missing rate range of 5–50%. The three evaluation metrics—Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE)—are reduced by 30–80% compared with the benchmark models. Moreover, the imputation curve achieves the best fitting performance with the ground-truth curve at a 50% segmented missing rate. This study confirms that the ST-GAT-ESN model effectively enhances the adaptability and robustness to complex missing patterns via spatiotemporal collaborative modeling and a dual-channel fusion mechanism, providing a high-precision and highly stable technical solution for ensuring the integrity of coal-mine-gas-monitoring data, and also provides theoretical references and engineering insights for the missing-value processing of industrial time-series monitoring data. Full article

Background and Objective: In NONMEM, the residual error model is implemented in the $ERROR block, where the user defines the prediction equation, Y, and a scaling factor, W, used to compute the individual weighted residual. This residual is reported in the diagnostic output as IWRES and corresponds to the individual residual divided by W. The residual error variance entering the likelihood is determined solely by the EPS and SIGMA structure of Y, independently of W. Multiple coding approaches for W are encountered in the literature, but no systematic analysis has examined how these choices affect diagnostic residuals. The aim of this study was to characterize the impact of W coding on three commonly used residual diagnostics in NONMEM, namely, IWRES, WRES, and CWRESI, across additive, proportional, and combined residual error models. Methods: Three population pharmacokinetic datasets (500 subjects; 6000 observations each) were simulated from a one-compartment oral model under additive (σ_add = 0.5 mg/L), proportional (CV = 20%), and combined (σ_prop = 0.15, σ_add = 0.5 mg/L) residual error structures. The following nine estimation runs were performed in NONMEM 7.6 (FOCE-I), each differing only in the $ERROR coding of W: normalized SIGMA-based, non-normalized, and THETA-based variants. Diagnostic residuals were compared pairwise by examining observation-by-observation ratios, standard deviations, and Pearson correlations. Results: For additive and proportional models, non-normalized W coding produced IWRES compressed by a constant multiplicative factor equal to sqrt(SIGMA(1,1)), reducing SD(IWRES) from 0.933 to 0.269 for the proportional model, while leaving WRES and CWRESI entirely unaffected. THETA-based normalized codings produced IWRES equivalent to SIGMA-based normalized codings. For the combined model, all three coding variants produced similar IWRES, but CWRESI differed by up to 0.586 units between the two-EPS (VAR.1) and one-EPS parameterizations, reflecting differences in NONMEM’s internal variance–covariance matrix structure. The SD coding additionally produced 19 extreme IWRES values (range: −59 to +74) at low predicted concentrations, attributable to the linear approximation of the combined standard deviation. Conclusions: The coding of W in NONMEM substantially affects IWRES but not WRES or CWRESI for simple error models. Cross-run comparisons of IWRES are invalid when W is not consistently normalized. For the combined model, the two-EPS VAR.1 parameterization is recommended for population-level diagnostics. These findings provide a practical framework for consistent and interpretable residual error coding in NONMEM. Full article

Background/Objectives: Coronal Plane Alignment of the Knee (CPAK) classification enables individualized alignment assessment in total knee arthroplasty (TKA), yet manual evaluation is time-consuming and lacks preoperative-to-postoperative transition analysis. Methods: This retrospective, single-center study aimed to develop and validate a fully automated deep learning-based CPAK classification system using internal validation on a held-out test set (n = 92) and to investigate individual-level transition patterns and their association with short-term clinical outcomes using paired radiographic data from a large Chinese cohort. A total of 919 KOA patients undergoing TKA were analyzed. A keypoint detection model (HRNet-W32) was developed to automatically calculate the medial proximal tibial angle, lateral distal femoral angle, arithmetic hip-knee-ankle angle, and joint line obliquity, from which CPAK types were derived. Results: On the validation set (92 cases), the model achieved a Mean Radial Error of 1.22 ± 0.43 mm for keypoint detection; mean absolute errors for MPTA and LDFA were ≤0.74°, while for aHKA and JLO they were 0.91° and 1.12°, respectively, with intraclass correlation coefficients ≥0.96 compared to manual annotations. Automatic CPAK classification accuracy was 80.98% (kappa = 0.767). Transition matrix analysis showed that only 9.36% of all patients maintained their original type postoperatively, with most shifting to types IV, V, or VII. After inverse probability weighting, no significant differences in clinical outcomes were observed among transition groups (all adjusted p > 0.05). Conclusions: These results demonstrate that the proposed automated system enables efficient CPAK assessment, revealing substantial postoperative alignment transitions that were not associated with differential short-term outcomes, thereby supporting AI-assisted individualized alignment planning in TKA. Full article

The lack of graph information caused by ignoring the association between learners often affects the accuracy of graph-based learning. This paper proposes an approach called attention-based multi-topological graph convolution (A-MTGCN) to address this. It uses a graph neural network to predict academic tasks. The method involves an attention mechanism that assigns weights to different academic characteristics to reflect their effects on prediction. Additionally, the topology between learners is constructed from multiple perspectives to capture potential interactions and collaboration, forming a weighted learner association diagram. This reduces redundancy and information dispersion in the graph, while retaining the correlation features. The approach divides learners into four types. Experiments show the enhanced GCN performs well in learner node classification, with an accuracy of 92.53%, precision of 89.15%, recall of 92.27%, and F1-score of 87.83%. The evolution process of learners’ learning state is reflected by constructing learners’ state transition matrix. Full article

Receiver nonlinear distortion severely impacts modern wireless systems. Traditional power series polynomial models suffer from numerical instability in parameter estimation, especially at high orders or with memory. This paper investigates orthogonal memory polynomial models from the perspectives of memory depth, nonlinear order, input signal distribution, and temporal correlation of the input signal, focusing on effective methods for improving the condition number. Comprehensive analysis reveals that the condition number of the Gram matrix grows rapidly with polynomial order and memory depth for the conventional polynomial, while orthogonal polynomials remain well-conditioned due to their inherent orthogonality and normalization. Notably, orthogonal polynomials maintain robust performance even when the input distribution does not perfectly match the basis weight function. Experiments using OFDM and 3-carrier WCDMA signals confirm that orthogonal polynomials achieve condition numbers orders of magnitude lower than those of power series, along with superior fitting accuracy. Full article

Background/Objectives: Most brain magnetic resonance imaging (MRI) is performed in supine position, although posture may influence cerebrovascular signal characteristics through gravity-related physiological changes. However, posture-dependent vascular signal alterations on low-field MRI have not been sufficiently quantified. This study aimed to quantify posture-related internal carotid artery (ICA) signal alterations using low-field MRI by comparing seated and supine images with intensity-, noise-, and texture-based metrics. Methods: Nine healthy adults (20–69 years old; one female) underwent 0.25 T tilting MRI in supine and seated postures. 3D gradient echo T1-weighted images were obtained. The bilateral ICA regions of interest (ROI) and adjacent reference ROI were manually delineated. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), signal intensity ratio (SIR), gray-level co-occurrence matrix (GLCM) texture features (contrast, correlation, energy, and homogeneity) were extracted and compared between postures using Wilcoxon signed-rank tests. Results: Seated posture produced significantly higher ICA signal intensity metrics than the supine posture, with increased SNR (median 17.11 vs. 13.48), CNR (median 21.94 vs. 18.36), and SIR (median 10.84 vs. 9.54) (p = 0.004). GLCM texture analysis demonstrated a significant decrease in contrast in the seated position (median 62.01 vs. 145.92; p = 0.004), whereas correlation, energy, and homogeneity showed no significant between-posture differences. Conclusions: Low-field MRI was sensitive to posture-dependent ICA signal alterations. ICA-based metrics may provide quantitative markers of gravity-related cerebrovascular adaptation. Full article

Background/Objectives: Zebrafish fin regeneration serves as a classic model for investigating vertebrate tissue regeneration, yet the core regulatory networks and their crosstalk with the immune microenvironment remain incompletely characterized. This study aimed to identify hub genes, and elucidate the underlying molecular mechanisms and immune microenvironment dynamics during zebrafish fin regeneration. Methods: We integrated multiple bulk RNA-seq datasets of zebrafish fin regeneration from the GEO database, followed by data standardization with batch effect removal. Hub genes were screened via differential expression analysis, weighted gene co-expression network analysis (WGCNA), and predictive models constructed with 13 classic machine learning algorithms. Functional enrichment, time-ordered gene co-expression network (TO-GCN) method, immune infiltration analyses and RT-qPCR validation were further performed. Results: We identified upregulated differentially expressed genes, regeneration-correlated gene modules and their overlapping genes, including 82 candidate genes and 10 hub genes enriched in cytoskeleton remodeling, extracellular matrix organization, and focal adhesion. Temporal analysis uncovered hierarchical gene regulation and functional switching during regeneration. Hub gene expression was significantly correlated with the infiltration of B cells, M1/M2 macrophages and CD8+ T cells, revealing a stage-specific immune microenvironment. RT-qPCR validation showed high consistency with the multi-omics data. Conclusions: This study provides potential gene targets for understanding zebrafish fin regeneration, and offers a valuable reference for investigating the crosstalk between regulatory networks and the immune microenvironment in vertebrate tissue regeneration. Full article

Surface discharge-induced degradation poses a significant threat to the operational reliability of high-voltage insulation systems. This research investigates the dielectric endurance of polyester-based nanocomposites reinforced with seven distinct nano-additives: iron oxide (Fe₃O₄), copper oxide (CuO), titanium oxide (TiO₂), aluminum oxide (Al₂O₃), silicon dioxide (SiO₂), zinc borate (ZnB) and graphene oxide (GO). Specimens were fabricated at 0.5% and 0.75% weight concentrations and subjected to constant AC electrical stress of 4.5 kV at 50 Hz until failure using the first-plane tracking method. To accurately monitor the aging process, a sophisticated signal processing framework involving Hankel-matrix-enhanced Robust Principal Component Analysis (RPCA) was developed to extract high-frequency discharge features from captured leakage current signals. The degradation characteristics were quantified using various statistical metrics, including Kurtosis, RMS and Burst Discharge Index (BDI). Experimental findings demonstrate that the incorporation of nanoparticles significantly extends the time-to-failure compared to neat polyester, although the effectiveness is highly dependent on both additive type and concentration. At 0.5 wt.%, ZnB exhibited the superior performance in delaying carbonized track formation. However, at 0.75 wt.%, Al₂O₃ emerged as the most effective additive, achieving a maximum endurance time of 31.61 min. In contrast, certain additives like TiO₂ showed a performance decline at higher loadings, likely due to nanoparticle agglomeration. The Hankel-RPCA methodology successfully isolated discharge-specific signatures from background noise, establishing a strong correlation between signal features and material failure stages. This study confirms that the synergy between advanced nanomaterial modification and robust signal processing provides an effective diagnostic tool for monitoring insulation health, offering a vital pathway for the designing of high-performance dielectrics for real-world power system applications. Full article

Cultural ecosystem services (CES) provide non-material benefits that support human well-being and motivate ecosystem conservation, yet their subjectivity and spatial ambiguity complicate quantitative assessment and management. Taking the Changbai Mountain tourism area as a case, we adopted the ecosystem service matrix method to assess the CES supply score based on the natural system and human system. The service coverage density was obtained through accessibility, thereby quantifying the available supply index for each tourist source area. In addition, we quantified CES demand using a questionnaire survey. Demand for 10 CES types was measured via preference ranking and integrated with the entropy weight method; statistical analysis and GIS mapping were used to examine spatial patterns and influencing factors. Results show that: (1) The overall CES demand in the Changbai Mountain tourism area exhibits clear spatial differentiation, with higher demand in the central and eastern regions and lower demand in the northwest. High-demand areas are mainly concentrated in cities relatively close to the Changbai Mountain tourism area. (2) Among individual CES, recreation (r = 6.58), natural landscapes (r = 6.35), and aesthetic value (r = 6.19) receive the highest demand, and demand structure is significantly associated with occupation, education level, consumption level, and spatial distance. The results indicate that cultural services dominated by knowledge-based services are significantly positively correlated with educational level (r = 0.549, p < 0.001). (3) CES supply capacity shows strong seasonal fluctuations, and is frequently overloaded during peak seasons, leading to prominent supply–demand conflicts; with the exception of Shenyang, Dalian, Jilin and Anshan, the other 17 cities exhibit supply–demand imbalance. By integrating multiple CES types and multiple drivers, this study reveals spatial matching patterns of CES supply and demand in a complex mountain ecotourism region and provides evidence to support ecotourism management, service capacity improvement, and sustainable development. Full article

To investigate the characteristics and health risks of heavy metal (HM) contamination in soils of typical industrial sites during urban renewal, this study selected Pudong New District, Shanghai, as a case. Seven HMs (Cd, Pb, Cu, Zn, Ni, Hg, and As) were analyzed for their concentrations, ecological risks, spatial patterns, and potential sources. Inverse Distance Weighted (IDW) interpolation was used to assess spatial distribution, Random Forest (RF) regression to predict HM concentrations, and a two-dimensional Monte Carlo simulation to evaluate human health risks. The results showed that all HMs except As exceeded Shanghai background values in surface soils, with varying levels observed in subsoil and saturated layers. The Index of Geoaccumulation (Igeo) and Risk Index (RI) indicated low contamination and moderate ecological risk. Pearson correlation combined with Positive Matrix Factorization (PMF) identified four major sources: traffic emissions dominated by Cd and Zn, combustion-related sources dominated by Pb and Hg, industry-related inputs dominated by Cu and Ni, and a natural source dominated by As. The RF model demonstrated strong predictive accuracy for Cd, Pb, Hg, and As (R² = 0.80–0.94), and predicted values were consistent with observations. Monte Carlo results showed that non-carcinogenic risks for children and adults were within acceptable limits, while carcinogenic risks reached “notable” levels with probabilities of 62.06%, 55.65%, and 22.49% for children, adult females, and adult males, respectively. Cd and As were identified as key contributors. This work provides scientific support for soil pollution prevention and remediation during urban renewal. Full article

As the demand for sustainable and bio-based alternatives to petroleum-derived membranes grows, polysaccharides have emerged as promising candidates. In this study, we fabricated free-standing membranes from konjac glucomannan (KGM), a neutral polysaccharide, using a simple base-induced insolubilization process. Fourier transform infrared spectroscopy revealed that the deacetylation of KGM chains promotes extensive intermolecular hydrogen bonding, creating a robust and stable three-dimensional network without the need for chemical cross-linkers. The resulting KGM free-standing membranes exhibited excellent mechanical properties, characterized by high tensile strength in the dry state and remarkable flexibility when hydrated. Furthermore, the membranes demonstrated superior chemical resistance to organic solvents such as acetone and n-hexane. Transport studies showed that the membranes possess a highly dense structure with no detectable pressure-driven pure-water permeation up to 0.25 MPa. Solute permeation experiments using eight model molecules (molecular weight = 144–14,600 Da) indicated that transport behavior is consistent with diffusion through a hydrated polymer network. The effective diffusion coefficient D_eff showed a strong correlation with molecular weight M, following the relationship D_eff ∝ M^−1.7. Furthermore, the permeation behavior remained stable across a wide pH range (2–12), and, within the investigated range of monovalent solutes, D_eff was insensitive to solute charge, indicating that mass transport is dominated by size-based diffusion rather than electrostatic interactions. These findings suggest that KGM free-standing membranes enable reliable molecular fractionation based on size-dependent diffusion within a stable, neutral matrix, offering significant potential for sustainable separation technologies and biomedical applications. Full article