Search Results (1,398)

This study develops a scale-aware and reproducible framework for examining the local clustering intensity of 612 recognized traditional villages in southern Zhejiang, China. Equal-weight Gaussian leave-one-out kernel density estimation (LOO-KDE; h = 21.01 km) excluded each focal village’s direct self-contribution, while explicitly acknowledging shared-neighbour dependence among the derived outcomes. Village-scale models used the same X1–X12 predictor set after correlation and variance-inflation screening; county socioeconomic indicators were analyzed separately for the 27 represented county-level units. A Random Forest selected from 144 prespecified candidates achieved an out-of-bag R2 of 0.353 and a county-grouped pooled R2 of 0.115. Repeated spatial holdouts yielded weak transferability: mean R2 was −0.081 for coordinate K-means and 0.082 for shifted 50 km grids without buffers, and became negative for both designs with one- or two-bandwidth buffers. SHAP ranked elevation, topographic relief, and village household count as the leading village-scale predictive contributors. OLS explained 30.5% of outcome variance, whereas SLM and SEM results depended strongly on whether structural/filtered fits or covariate-only predictions were evaluated. GWR and MGWR reached R2 = 0.9743 and 0.9984, and residual Moran’s I remained significant for all models except MGWR (I = 0.002, p = 0.429). At the county scale, rural disposable income and tertiary-industry output were negatively correlated with mean LOO-KDE, but multivariable HC3 estimates were imprecise and ecologically interpreted. The results identify associations with the clustering of recognized villages, not causes of individual-village heritage persistence. Full article

The tunable structural and chemical properties of graphitic carbon nitride (gCN) provide a promising route toward tailored activity in photocatalytic applications. A primary challenge in optimizing gCN toward this end is its intrinsically heterogeneous structure, due in part to the many parameters employed in its synthesis. Variability in type and density of chemical and structural defects simultaneously change the electronic, photocatalytic, and optical properties of gCN. Here, we elucidate the complicated structure–function relationship in a series of three related gCN samples by correlating photochemical activity to a host of structural, chemical, and spectroscopic characterization techniques. Of the 22 physical properties measured, we find that transient absorption spectroscopy lifetimes are the only observable that trends with photochemical activity across the series. These results show that a key challenge to photocatalytic material optimization stems from covariant material properties that have competitive influences on photocatalytic activity, making the determination of a robust structure–function relationship challenging. Full article

Externally oriented corporate social responsibility (ESR), defined here as the community- and environment-facing domain of CSR, may shape how residents evaluate firms in environmentally sensitive industries. This study examines the predictive association between community-perceived ESR and corporate image using a stratified random sample of 384 adults from the Chimbote metropolitan area, Peru. A 21-item ESR instrument and a 20-item corporate-image instrument were analyzed using partial least squares structural equation modeling (PLS-SEM), with corporate image specified as a Type I reflective-reflective higher-order construct comprising reputation, trust, perceived responsibility, social commitment, and familiarity/recognition. ESR was positively associated with corporate image (β = 0.519, t = 13.934, p < 0.001, 95% CI [0.449, 0.595]), explaining 26.9% of its variance (R² = 0.269; f² = 0.368; Q² = 0.265). The five first-order image dimensions displayed substantial empirical overlap, supporting a higher-order representation in this sample. Common-method-bias diagnostics, confirmatory factor analysis, covariance-based SEM, and robust OLS estimates supported the stability of the association. The findings suggest that consistent, visible, and community-oriented responsibility practices are associated with more favorable corporate evaluations in a high-salience fishing context. Because the design is cross-sectional, the results should be interpreted as predictive associations rather than causal effects and should not be generalized beyond comparable settings without replication. Full article

Phased-array radar networks require real-time collaborative scheduling of search, tracking, and identification tasks under coupled resource, beam, time-window, and power constraints. Current decisions affect future residual power, task refresh intervals, and tracking uncertainty, making long-horizon scheduling particularly challenging. This paper proposes CS-Mamba, a long-horizon constraint-aware collaborative scheduling framework for homogeneous multiple phased-array radars. The scheduling problem is formulated as a finite-horizon constrained decision process with structured hybrid actions, where the discrete component represents radar–task matching and the continuous component represents transmit-power allocation. A Mamba-based temporal encoder is introduced to summarize long scheduling histories with linear sequence complexity. Based on the encoded representation, the scheduler predicts task priorities, constructs a masked radar–task bipartite graph, solves a constrained maximum-weight matching problem, and projects raw transmit powers onto the feasible power domain. In addition, an action-dependent radar model is incorporated to link transmit power, effective SNR, detection probability, measurement noise, and tracking covariance. The model is trained using behavioral cloning from constraint-aware heuristic trajectories followed by actor–critic fine-tuning. Experiments on the proposed MRSched-Bench show that CS-Mamba improves the normalized cost-effectiveness score from 0.62 to 0.78 compared with MAPPO in the Medium scenario, while reducing end-to-end decision latency from 24.5 ms to 12.8 ms per step. Additional ablation studies verify the contributions of temporal encoding, structured matching, feasible power projection, and two-stage training. Full article

Accurate measurement of the 6-DOF motion responses of marine floating structures is essential for structural safety assessment and operational decision-making. To address the critical issues of integration drift in inertial navigation systems, susceptibility of GNSS to sea-surface multipath effects, and deployment complexity of binocular vision systems, this paper proposed a 6-DOF motion measurement method for floating structures based on monocular vision and natural feature point tracking. This method eliminates the reliance on artificial cooperative targets and auxiliary sensors, instead utilizing the inherent surface textures of the floating structures as feature sources. Stable feature point tracking is achieved through multi-strategy cascaded detection and the pyramidal KLT optical flow algorithm. RANSAC geometric consistency verification is introduced to eliminate outlier matches, retaining only identical physical points between two consecutive frames for motion estimation. In-plane translations and $RZ$ angle are extracted from the similarity transformation, while $RX$ and $RY$ angles are estimated using principal component analysis of the covariance matrix of the feature point set. The depth-direction displacement is linearly mapped from variations in the scale factor. Subsequently, two series of physical model tests under different conditions were conducted to validate the measurement accuracy and robustness of the proposed method on different floating structures. The results demonstrate that the proposed method can accurately capture the motion attitudes of floating structures, maintaining a consistently high inlier ratio exceeding 80% in regular waves and averaging 85.2% in irregular waves, with a reprojection error of less than 0.05 pixels. The NRMSE for the primary motion directions are all below 10%, and the dominant frequency errors are essentially zero. It offers advantages such as low cost, easy deployment, and strong robustness, thereby providing valuable technical support for field monitoring of marine floating structures. Full article

This paper develops a model-driven analytical framework for portfolio optimization under a multidimensional Black–Scholes model with time-varying parameters, where both the drift and volatility functions evolve linearly over time. Within this framework, explicit closed-form expressions are derived for the covariance matrix of normalized asset prices and subsequently incorporated into the classical Markowitz mean–variance framework to obtain analytical representations of the global minimum-variance portfolio, the mean–variance efficient portfolio, and the corresponding efficient frontier. The proposed methodology establishes a direct connection between continuous-time stochastic asset-price modeling and portfolio optimization through a model-implied covariance structure. Its practical implementation is investigated through both numerical experiments and an empirical study using daily stock price data from 20 constituents of the $S\&P$ 500 index over the period 2020–2024. Monte Carlo simulations demonstrate the finite-sample sensitivity of portfolio optimization to covariance estimation, while the empirical analysis illustrates how the estimated model parameters, obtained using the maximum likelihood framework of Aït-Sahalia for discretely sampled diffusion processes, can be incorporated into the analytical covariance matrix for constructing efficient frontiers under realistic market conditions. Overall, the proposed framework provides an analytically tractable methodology that integrates continuous-time asset pricing models with classical mean–variance portfolio optimization, offering a coherent model-based covariance representation for portfolio selection under time-varying market environments. Full article

Saudi Arabia’s Vision 2030 and global pressure for sustainability are driving organizations to adopt Green Human Resource Management (GHRM), an approach that aligns human capital practices with environmental, social, and economic goals; however, evidence on how the four GHRM dimensions of Green Recruitment and Selection (GRS), Green Training and Development (GTD), Green Performance Management (GPM), and Green Compensation and Rewards (GCR) simultaneously affect the triple bottom line of organizational sustainability remains limited, particularly in Gulf Cooperation Council (GCC) contexts. This study examines how these four GHRM practices are associated with environmental (ENV), social (SOC), and economic (ECO) sustainability in Saudi organizations. Drawing on a cross-sectional survey of 350 valid employee responses from four Saudi regions, the data were analyzed using covariance-based Structural Equation Modeling (CB-SEM) with confirmatory factor analysis to assess the measurement model and test twelve hypotheses. The measurement model demonstrated strong reliability and validity, with composite reliability ranging from 0.866 to 0.893 and average variance extracted ranging from 0.684 to 0.741. All twelve hypotheses were supported (β = 0.163–0.241, p < 0.01), and the model explained 48.7%, 41.2%, and 35.5% of the variance in environmental, social, and economic sustainability, respectively, indicating moderate to substantial explanatory power. Green Training and Development was the strongest predictor of social and economic sustainability, whereas Green Recruitment and Selection was the strongest predictor of environmental sustainability; regional differences were not statistically significant. These findings indicate that GHRM functions as a unified set of practices that support organizational sustainability across its environmental, social, and economic dimensions, underscoring the strategic importance of green training and green incentives for Saudi organizations pursuing the goals of Vision 2030. By testing all four GHRM practices against all three sustainability dimensions within a single model in an under-researched GCC setting, the study extends the cross-cultural evidence base on GHRM and offers context-specific guidance for managers and policymakers; future research should adopt longitudinal and multi-source designs to examine the causal and mediating mechanisms underlying these associations. Full article

This paper presents an attitude filter that encodes both orientation and uncertainty in a single four-dimensional vector, requiring no covariance propagation or normalization constraints. The filter state is the natural parameter of the von Mises–Fisher (vMF) distribution on S³, whose exponential family structure reduces measurement updates to vector addition. Prediction is governed by a continuous-time ODE (Ordinary Differential Equation) that couples rotational kinematics with concentration decay. The QUEST-based construction of measurement natural parameters with a Fisher-information-matched concentration, an antipodal switching mechanism for the quaternion double cover, and a global exponential convergence analysis of the attitude error are described. The filter construction is left-invariant: it commutes with rotations of the reference frame, making the error dynamics trajectory-independent. The result is a filter with the computational simplicity of a complementary filter and the statistical grounding of Bayesian vMF fusion, operating entirely in unconstrained ℝ⁴ space. The filter is validated in simulation, on two recorded flights—including an evaluation against an EFIS attitude reference—and its computational cost is measured down to on-target microcontroller cycle counts. Gyroscope bias estimation is not included and is left to future work. Full article

Background: High tibial osteotomy (HTO) is commonly performed to manage medial compartment knee osteoarthritis by correcting mechanical alignment; however, the role of adjunctive regenerative therapies remains uncertain. Methods: This retrospective study compared leukocyte-rich platelet-rich plasma (LR-PRP) with leukocyte-poor PRP (LP-PRP) in patients undergoing HTO. Forty patients were allocated into three groups: HTO alone (n = 10), HTO with LR-PRP (n = 20), and HTO with LP-PRP (n = 10). Clinical outcomes were assessed preoperatively and at 12 months using the Visual Analog Scale, Oxford Knee Score, and Western Ontario and McMaster Universities Osteoarthritis Index. Cartilage repair appearance was evaluated through second-look arthroscopy using the ICRS grading and Koshino staging systems. Multivariable analysis of covariance (ANCOVA), adjusting for baseline imbalances, was employed to evaluate postoperative outcomes. Results: All groups demonstrated significant improvements in pain and function (p < 0.05), with no significant differences among groups. However, Group B exhibited a greater shift toward lower ICRS grades than Group A (p < 0.05), whereas no significant difference was found between Groups C and A. Arthroscopic findings revealed more complete defect coverage and improved structural integrity in the LR-PRP group. Conclusions: These findings demonstrate a clear discrepancy exists between clinical and structural outcomes; while HTO drives substantial and comparable short-term functional improvements across all cohorts, adjunctive LR-PRP is positively associated with a significantly enhanced arthroscopic cartilage repair appearance compared to LP-PRP or HTO alone. Further prospective studies are needed to validate these findings and elucidate the underlying biological mechanisms. Full article

Sexual dimorphism may be expressed through absolute dimensions, relative proportions, and shape, yet these components have not been jointly evaluated in Dorcadion parilis, a Turkish endemic member of a flightless and taxonomically challenging genus. We examined 85 specimens (35 females and 50 males) using traditional and geometric morphometrics, sex-controlled allometric models, and two-block partial least squares analysis. Males had longer pronota, whereas females had wider pronota and longer and wider elytra. After Mosimann adjustment, the clearest differences were relatively longer pronota in males and broader elytra in females, while elytral length no longer differed. Pronotal and elytral shapes also differed between sexes, with stronger and more widespread differentiation in the pronotum and more localized elytral differences. Initial size–shape associations were not retained as independent effects after accounting for sex, and there was no evidence of sex-specific allometric slopes. Significant residual covariation between pronotal and elytral shapes persisted after adjustment for sex and structure-specific size. These findings show that sexual dimorphism in D. parilis is multidimensional and structure-specific rather than a simple consequence of centroid size and highlight the value of geometric morphometrics for detecting sex-related shape variation not fully captured by linear measurements and proportions. Full article

Plant diversity is often expected to enhance soil organic carbon (SOC) storage through greater and more heterogeneous plant inputs, but its relationships with functionally distinct SOC fractions in natural forests remain uncertain. This study compiled published SOC fraction data from 341 surface soil observations in natural forests across China and spatially matched these records with gridded plant alpha diversity, forest age, climate, topographic and soil properties datasets to evaluate biotic and abiotic associations with SOC, particulate organic carbon (POC), mineral-associated organic carbon (MAOC) and MAOC/SOC. Linear regression, multiple regression, piecewise structural equation modelling and stratified analyses were used to evaluate whether plant diversity was associated with the absolute accumulation and relative stabilization of SOC fractions. Plant alpha diversity was negatively associated with ln[SOC], ln[POC] and ln[MAOC] at the national scale, whereas its bivariate relationship with MAOC/SOC was weak. After accounting for forest age and environmental covariates, plant alpha diversity remained negatively related to the absolute contents of SOC fractions while showing a positive association with MAOC/SOC. Forest age was positively associated with ln[SOC], ln[POC] and ln[MAOC], and POC was more strongly related to plant diversity and forest age than MAOC. In contrast, MAOC and MAOC/SOC were more strongly associated with mineral protection potential, soil pH and precipitation background. Structural equation models indicated that mineral protection potential and mean annual precipitation were associated with greater MAOC accumulation and SOC allocation to the mineral-associated fraction, whereas temperature and topography were linked to MAOC partly through indirect associations with soil physicochemical conditions. Stratified analyses showed that plant diversity associations varied among forest types and climatic backgrounds. Additional interaction models showed that mineral protection potential significantly moderated the associations between plant alpha diversity and ln[SOC], ln[POC] and ln[MAOC], with negative diversity associations weakening under higher mineral protection potential. These findings indicate that plant diversity associations with SOC fractions in natural forests cannot be interpreted as universally positive input relationships. Instead, their direction and strength depend on hydroclimatic context and soil mineral protection, especially for the absolute accumulation of SOC fractions. Full article

Stress-associated transcriptional programs are common in single-cell perturbation data, but they are often treated as technical or experimental nuisance signals. Whether rare high-stress populations arise stochastically after perturbation or reflect outcomes associated with pre-existing cellular heterogeneity remains unclear. Herein, we build a cross-dataset stress-program prediction framework spanning 146,321 single cells and 926 perturbation–cell-line tasks. Untreated baseline heterogeneity, together with perturbation identity, predicted future rare integrated-stress burden across held-out cell-line–drug pairs ($R^2=0.742$, Pearson $r=0.862$). Single-cell stress-program prediction generalized across leave-task-out, leave-cell-line-out and leave-perturbation-out splits; retained signal in leave-dataset-out evaluation; and collapsed to near-null performance under within-task label permutation. Independent validation datasets provided external support for the inferred stress axes: tunicamycin and thapsigargin activated unfolded protein response/integrated stress response (UPR/ISR) modules in bulk RNA sequencing (RNA-seq), thapsigargin expanded populations with high X-box binding protein 1 (XBP1) UPR or activating transcription factor 4 (ATF4) ISR activity in donor-paired single-cell data, and an independent protocol-stress dataset indicated heat shock/proteostasis structure beyond cell type and quality control (QC). The resulting resource summarizes perturbation responses as activator protein 1 (AP1) immediate, UPR/ATF4, heat shock, replication-coupled and low/mixed dominant stress-program patterns. These findings suggest that stress variation should not be viewed solely as a nuisance covariate but may represent a predictable and biologically structured dimension of perturbation response space. Full article

Reliable deformation reconstruction supports structural health monitoring when a target sensor is unavailable, unreliable, or requires independent verification. The proposed framework contributes an interpretable correlation-driven representation for virtual sensing rather than new X-means, Gaussian process, LSSVR, or MK-LSSVR algorithms. BIC-guided X-means adaptively identifies monitoring-point groups with statistically similar standardized deformation histories. A fixed-covariance Gaussian process equipped witha squared exponential kernel, calibrated exclusively from training-period measurements and used solely as a probabilistic representation of inter-point dependence without explicit residual extraction, transforms the associated-point measurements into the time-varying synergistic expectation ${\mu }_{\mathrm{s}\mathrm{y}\mathrm{n}}\left(t\right)$. This quantity is combined with conventional water pressure, temperature, and time-effect factors as the dynamic M3 input. The same kernel formulation defines fixed synergistic-variance and mutual-information diagnostics for configuration-level interpretation; these quantities are neither dynamic regressors nor evidence used to establish the reported performance conclusions. Controlled M1-M2-M3 comparisons under identical LSSVR and MK-LSSVR backbones isolate the effects of conventional factors, raw associated-point measurements, and the correlation-derived representation. The framework was evaluated at M-α/C2, M-μ/C1, and M-ρ/C3 using horizontal-displacement records from 20 monitoring points in a hydraulic concrete structure in Sichuan Province, China. For M-α, M3 + MK-LSSVR achieved an RMSE of 0.1016 mm, an MAE of 0.0388 mm, and an $R^2$ of 0.9890; relative to M2 + MK-LSSVR, RMSE and MAE decreased by 68.4% and 69.3%, respectively. The same configuration gave the lowest RMSE and MAE and the highest $R^2$ for M-μ and M-ρ, reproducing the within-target ranking in all three X-means groups. The framework is intended for virtual sensing, sensor-replacement support, data verification, and monitoring–consistency checking, not as a direct damage or safety indicator. Full article

Self-supervised graph collaborative filtering suffers from two geometric pathologies. In Dimensional Collapse, embeddings quietly collapse into a low-rank subspace, a well-documented but poorly solved problem. Semantic Collapse is more complex: stiff $L^2$-squared orthogonalization penalties that are supposed to push the embeddings apart end up ripping through the heavy-tailed community overlaps that contain the collaborative signal. Earlier studies attempted to mitigate sparsity by injecting static noise or structural perturbations, but such interventions did not pinpoint the root cause, i.e., the distortions in the global covariance geometry itself. In this paper, we propose a Huber-Contrastive Graph Convolutional Network (HCGCN) that combines a spatial message-passing backbone with an $O(1)$ contrastive augmentation overhead and a Relation-Aware Dual-View Gated Contrastive Network. The main novelty is a Huber Covariance Whitening module that imposes a geometry-aware threshold on the cross-correlation matrix of augmented views—below the threshold, the gradients follow an $L^2$ penalty (enforcing uniformity); above it, the penalty flattens to $L^1$ (protecting genuine semantic clusters from gradient explosion). This theoretically motivated dual-regime penalty actively preserves the macro-semantic topology of the graph while aggressively stamping out spurious noise correlations. The HCGCN is evaluated on the Yelp2018, Amazon-Book, and MovieLens datasets and performs significantly better than state-of-the-art baselines like LightGCN, SGL, SimGCL, and NESCL, especially under severe cold-start settings where covariance regulation proves most critical. Full article

Background/Objectives: Disaster preparedness is central to public health resilience. This study examined the association between disaster-related digital technology engagement and Health Belief Model-based preparedness beliefs among adults attending a primary care clinic in Türkiye. Methods: In this cross-sectional study, 538 participants completed a sociodemographic form, a researcher-developed 10-item Disaster and Technology Use Questionnaire, and the 31-item General Disaster Preparedness Belief Scale. The technology questionnaire used five-point Likert-type response categories and was treated as a multidomain questionnaire; its prespecified equal-weighted sum formed an operational composite index rather than a unidimensional scale score. Supplementary internal-structure analyses described item covariance and clustering. Spearman correlations, false-discovery-rate correction, and hierarchical multiple linear regression with HC3 robust standard errors were used, adjusting for age, gender, education, income, marital status, and occupation. Results: The technology composite showed α = 0.793 and ω = 0.799, and exploratory and confirmatory analyses identified two correlated empirical item domains with adequate model fit (CFI = 0.952, TLI = 0.936, RMSEA = 0.055). Technology engagement was independently associated with total preparedness beliefs (B = 0.888, 95% CI 0.672–1.103; standardized β = 0.397; p < 0.001) and increased explained variance by 13.3% beyond sociodemographic factors. Adjusted associations remained significant for perceived susceptibility, perceived benefits, perceived low barriers, cues to action, and self-efficacy, but not for perceived severity. Conclusions: Greater disaster-related digital technology engagement was associated with stronger preparedness beliefs, but causal direction cannot be inferred. Primary care initiatives may promote official alert tools, practical application use, and digital-information literacy while maintaining non-digital alternatives for people with limited digital access. Full article