Search Results (1,442)

Three-dimensional (3D) sonar overcomes the limitations of traditional measurement methods regarding imaging coverage and accuracy, making it indispensable for underwater structure monitoring. However, complex underwater environments often introduce significant noise into 3D sonar data, degrading monitoring performance. To address this, we propose a geometry-based filtering method. First, Total Least Squares (TLS) is employed to construct local spatial features, which guides a region-growing segmentation based on normal vector attributes. Subsequently, the resulting clusters are refined using these local geometric characteristics. Finally, statistical filtering is applied to eliminate residual outliers from a local to a global scale. Experimental results demonstrate that the proposed method achieves F1 scores of 78.65% and 84.49% in outlier removal, effectively suppressing noise while preserving structural integrity. Full article

This study examines the intercorrelations that exist between brand equity, expectation confirmation, and sustainable repurchase intentions within Saudi Arabia’s burgeoning e-commerce sector, emphasizing its cultural and digital transformation context aligned with Vision 2030. The main objectives are to identify how brand perceptions influence customer satisfaction, and to explore the applicability of integrated theoretical frameworks, namely Brand Equity Theory and Expectation-Confirmation Theory in explaining sustainable consumer behavior in an emerging market. Utilizing a quantitative research approach, data was collected through an online self-reported questionnaire distributed via social media platforms targeted at active e-commerce consumers in the Hail region. Convenience sampling combined with snowballing yielded a sample size of 361 respondents, ensuring broader demographic representation. Data analysis was conducted using structural equation modeling with partial least squares (SEM-PLS), a technique suited for theory exploration and handling complex variable relationships. The findings demonstrate that brand awareness and brand image significantly positively influence customer satisfaction, which in turn positively impacts repurchase intentions in e-commerce platforms. Similarly, expectations and perceived performance also have significant positive effects on satisfaction, which in turn positively impacts repurchase intentions in e-commerce platforms. All hypotheses were supported, with significant relationships observed between the variables, with the model demonstrating robust validity and fit, evidenced by acceptable SRMR, d_ULS, and d_G values. The study’s originality lies in its culturally contextualized application of these theories to a less studied yet vital emerging market, providing novel insights into how cultural nuances influence digital consumer loyalty. These outcomes contribute to both academic theory and practical strategies for e-commerce firms aiming to build sustainable, trust-based relationships within culturally diverse digital environments, offering a valuable blueprint for similar markets undergoing digital transformation. Full article

The cheese maturation process involves complex physicochemical and structural changes that directly influence its final quality and consumer acceptance. The development of non-destructive and rapid analytical techniques is therefore essential for monitoring these changes and optimizing quality control strategies. This study evaluated the potential of dielectric spectroscopy and near-infrared (NIR) spectroscopy as tools to predict properties associated with the quality of Swiss-type cheese during the maturation process. The cheese samples were matured for 60 days, and NIR profiles (900–1700 nm), dielectric profiles (40¹–10⁶ Hz) and physical characteristics (color and texture) were obtained every 15 days. Based on these data, models were developed to predict the maturation time (days) and physical properties using partial least squares regression (PLSR). The performance of the model was evaluated using the determination coefficient ($R^2$) and the root mean square error (RMSE). The results showed that dielectric spectroscopy provided a better fit for all the parameters evaluated ($R_{\mathrm{day}}^2=0.999$, $R_{L^{*}}^2=0.912$, $R_{a^{*}}^2=0.983$, $R_{b^{*}}^2=0.982$, and $R_{\mathrm{firmness}}^2=0.625$), with prediction errors of ${\mathrm{RMSE}}_{\mathrm{day}}=0.219$, ${\mathrm{RMSE}}_{L^{*}}=1.184$, ${\mathrm{RMSE}}_{a^{*}}=0.163$, ${\mathrm{RMSE}}_{b^{*}}=0.308$, and ${\mathrm{RMSE}}_{\mathrm{firmness}}=91.094$. In conclusion, dielectric spectroscopy combined with PLSR showed slightly superior performance to predict maturation time and physical changes in Swiss-type cheese. Full article

Background: Medication use in paediatric populations is inherently complex and carries a heightened risk of prescribing errors, particularly within primary health-care settings. Despite this concern, evidence describing paediatric prescribing errors in Saudi Arabia remains scarce. Hence, the present study aimed to evaluate the prevalence and patterns of prescribing errors in paediatric primary care and to characterize the pharmacist-led interventions undertaken to resolve these errors. Methods: A prospective, mixed-methods cross-sectional study was conducted over three months at a primary health-care centre. Paediatric outpatient prescriptions were systematically reviewed during routine practice by trained clinical pharmacists. All suspected errors were independently validated and classified for severity by a multidisciplinary expert panel. Descriptive statistics were used to summarise prescribing errors, and associations with patient and prescription characteristics were assessed using chi-square tests. Qualitative data were analysed using a descriptive thematic approach to explore mechanisms of error identification and the nature of corrective pharmacist interventions. Results: A total of 545 paediatric outpatient prescriptions were reviewed, of which 142 prescriptions (26.1%) contained at least one prescribing error. Across these prescriptions, a total of 145 individual prescribing errors were identified. Dose-related errors were the most common (68.3%), followed by inaccuracies in dosing frequency (11.0%) and inappropriate drug selection (9.0%). The occurrence of prescribing errors was significantly associated with patient weight (p = 0.016), the number of medications per prescription (p < 0.001), and the recorded diagnosis (p = 0.018). The majority of errors were intercepted prior to medication dispensing (93.0%), and no cases of patient harm were identified. Qualitative analysis revealed that errors were predominantly detected through cross-checking with authoritative drug references, recalculation of weight-based doses, and application of clinical judgement, and were most often resolved through direct communication with the prescribing clinician. Conclusions: Prescribing errors occur frequently in paediatric outpatient settings; however, most are preventable with appropriate safeguards. Pharmacists play a critical role in identifying and resolving these errors before they result in patient harm. Enhancing paediatric prescribing support systems and strengthening interprofessional collaboration may further advance medication safety within primary health-care services. Full article

Accurate thermal conductivity measurement requires temperature control systems to establish stable operating conditions within a limited time. In practical thermal conductivity performance testing systems, large thermal inertia, complex heat transfer paths, and input time delays arising from thermal propagation and sensor placement often degrade dynamic response and control accuracy. To overcome these limitations, a composite PI–RLS–MPC control strategy is proposed for thermal systems with inertia and time delay. A proportional–integral (PI) controller serves as the baseline stabilizing controller, while model predictive control (MPC) is utilized to optimize the control input by explicitly considering system delay and input constraints. To enhance robustness against model uncertainty and parameter variations, recursive least squares (RLS) is adopted for online parameter identification and adaptive PI tuning, and a steady-state parameter freezing mechanism is introduced to suppress unnecessary parameter updates after convergence. Simulation studies are performed on an identified thermal process model with a 20 s input time delay. The results indicate that the proposed strategy reduces overshoot, shortens settling time, and improves disturbance rejection compared with conventional controllers. Overall, the proposed PI–RLS–MPC approach provides a practical solution for improving temperature control performance in thermal conductivity testing systems. Full article

Despite concern over air pollution, fine-scale spatial and demographic disparities in exposure remain largely unquantified in Australian cities due to sparse monitoring and coarse models. In Greater Melbourne, this gap limits neighbourhood-level assessment of PM_2.5 exposure and associated environmental inequalities. To address this gap, we integrated 6-month averaged PM_2.5 observations (October 2023 to March 2024) from 5 regulatory monitoring stations and 13 low-cost sensors (LCSs) to develop a land use regression (LUR) model estimating concentrations at a 100 m resolution. These estimates were used to calculate population-weighted PM_2.5 exposure (PWE) at the mesh block level across Melbourne. To examine factors associated with spatial heterogeneity in PWE, we applied a hybrid modeling framework combining Spatially Explicit Random Forest (Spatial-RF) and Geographically Weighted Regression (GWR), incorporating physical, built-environment, and socio-demographic variables from the Synthesized Multi-Dimensional Environmental Exposure Database (SEED). The Spatial-RF model initially exhibited an R² of 0.56. After multicollinearity diagnostics using the Variance Inflation Factor (VIF), three key explanatory variables were selected for GWR modeling: the Normalized Difference Vegetation Index (NDVI), the Index of Education and Occupation (IEO), and the proportion of culturally and linguistically diverse populations (CALDP). The developed GWR model achieved higher model performance (R² = 0.65) than Spatial-RF and global Ordinary Least Squares (OLS) regression (R² = 0.38), revealing strong spatial non-stationarity. Results show that PWE generally ranged from 5 to 7 µg/m³, exceeding the 2021 WHO air quality guideline, with hotspots in the urban core and along major transport corridors. Elevated exposure occurred in both socioeconomically disadvantaged areas and residents in urban centers with higher socio-economic status, reflecting complex, spatially contingent exposure inequalities. These findings support fine-scale, equity-oriented air quality management. Full article

Postharvest Lentinula edodes (shiitake mushroom) undergoes rapid textural deterioration, which is primarily driven by complex cell wall remodeling. This study investigates the physiological and transcriptomic changes in L. edodes during storage at 4 °C for 8 days. Results showed that cellulose content significantly decreased, while chitin and β-glucan levels exhibited anomalous increases, accompanied by a surge in the activities of cellulase, chitinase, and β-1,3-glucanase. Concurrently, intensifying membrane lipid peroxidation and an imbalance in reactive oxygen species (ROS) homeostasis were observed. Transcriptomic analysis identified 2204 and 1808 differentially expressed genes (DEGs) at the middle (4 d) and late (8 d) storage stages, respectively. Partial Least Squares Regression (PLSR) identified a core module of nine key regulatory genes (VIP > 1.0), including β-glucanase, laccase, and catalase, which significantly contributed to the physiological shifts. The results suggest that an upstream ROS imbalance may contribute to the dysregulation of midstream laccases, potentially reducing the oxidative cross-linking of phenolic components and loosening the cell wall matrix. These alterations may increase the accessibility of structural polysaccharides to downstream cell wall-degrading enzymes, which could contribute to structural collapse, although functional validation is required to establish causality. These findings provide a gene-level framework for understanding postharvest edible fungi physiology. Full article

This research addresses a structural cybernetic anomaly within strategic management precipitated by the integration of artificial intelligence into the organizational core. Traditional paradigms, specifically the resource-based view and the dynamic capabilities framework, operate under closed-system, first-order cybernetic assumptions that fail to capture the dissipative nature of algorithmic agents. By conceptualizing the enterprise as a complex adaptive system operating far from thermodynamic equilibrium, this study introduces the theory of dynamic cognitive advantage. Grounded in second-order cybernetics, the framework posits that competitive differentiation emerges from the historical, recursive, structural coupling of human semantic intent and machine syntactic processing. This research formalizes this co-evolutionary dynamic utilizing coupled non-linear differential equations and time decay integrals. Furthermore, it operationalizes the central mechanism of this capability—the cognitive flywheel—and proposes a fractal governance architecture to mitigate systemic vulnerabilities such as automation bias. To transition these propositions into management science, a proposed mixed-methods empirical research agenda is presented. It outlines a future partial least squares–structural equation modeling (PLS-SEM) approach to test the mediating role of the cognitive flywheel and the moderating effect of fractal governance on organizational resilience. This research provides a mathematically formalized, empirically testable architecture for navigating the artificial intelligence economy. Full article

This study presents a newly conducted comprehensive investigation into the lateral torsional buckling (LTB) behavior of un-bonded pre-stressed (PS) thin-walled steel I-beams subjected to non-uniform bending moments, combining a numerical study with a machine learning (ML) approach and experimental validation. Despite extensive prior work, no exact analytical solution exists particularly for non-uniform bending or can be extremely complicated, as the resulting differential equations contain variable coefficients particularly under non-uniform bending due to the complexity of the PS system. To overcome this limitation, a numerical study using finite element (FE) analysis is first conducted with emphasis on the key geometric and pre-stressing parameters, including unbraced beam length, tendon eccentricity, deviators configurations, and pre-stressing force, to evaluate the LTB behavior. The FE modeling was then validated against experimental testing to ensure the accuracy and reliability of the FE solutions. Subsequently, a comprehensive dataset is generated using FE simulations to train the ML models aimed at predicting the LTB resistance of the PS system. This study presents three ML approaches, including support vector regression (SVR), random forest (RF) and least-square boosting (LSBoost), and their optimal hyperparameters are determined using Bayesian optimization (BO) to enhance the prediction performance. The results indicate that the LTB capacity predicted by the Bayesian-optimized ML models achieve high predictive accuracy and are in close agreement with numerical FE simulations, thereby highlighting their potential in capturing the complex, underlying non-linear interactions influencing the buckling behavior of the PS structural system. Accordingly, the proposed framework offers a robust and effective predictive tool for evaluating LTB resistance, particularly under non-uniform bending where exact analytical solutions are not available, and for supporting the design and assessment of PS steel structures. Full article

Against the backdrop of global warming and the urgent demand for sustainable development, blue–green spaces (BGSs) play a vital role in carbon reduction and sequestration, yet the multi-scale spatial mechanisms by which blue–green space patterns (BGSPs) regulate carbon storage (CS) remain unclear. Taking the Zhengzhou Metropolitan Area as the study area, this research clarifies the BGSP-CS correlations at both class and landscape levels and quantifies their spatial interaction mechanisms, providing scientific support for integrated BGS planning that aligns with sustainable development objectives. Using the InVEST model coupled with regional carbon density correction, the total CS of the area is estimated at 1112.27 × 10⁶ t. Spearman’s correlation analysis shows that at the class level, area–edge and shape complexity indicators (e.g., Landscape Shape Index, LSI: r = −0.427) are negatively correlated with CS, while connectivity indicators exert no significant effect. At the landscape level, Shannon’s Diversity Index (SHDI: r = −0.635) and area–edge indicators inhibit CS, whereas Shannon’s Evenness Index (SHEI: r = 0.602), Largest Patch Index (LPI: r = 0.618) and shape complexity indicators exert positive effects. A comparative analysis of three regression models reveals that the multi-scale geographically weighted regression (MGWR) model outperforms the ordinary least squares (OLS) and geographically weighted regression (GWR) models, with R² values of 0.505 (class level) and 0.484 (landscape level). It effectively captures the “west–strong and east–weak” spatial heterogeneity of BGSP impacts on CS. This study identifies key BGSP indicators regulating CS and their spatial mechanisms, providing scientific support for integrated BGS planning, regional carbon sink enhancement, the achievement of “dual carbon” goals, and the promotion of sustainable development in metropolitan areas. Future research may optimize model parameters through field surveys and explore the coupling mechanism between BGSPs, land surface temperature and CS to better align BGS management with sustainable development agendas. Full article

Polarimetric information is essential for scattering interpretation and target characterization in synthetic aperture radar (SAR) remote sensing, yet many resource-constrained platforms (e.g., small satellites and unmanned aerial vehicles (UAVs)) operate with limited polarization modes or even a single radio frequency (RF) chain, which limits full polarimetric scattering acquisition. To address this limitation, this paper proposes a single-channel framework for estimating the full polarization scattering matrix (PSM) enabled by time-varying polarization modulation. The transmit/receive polarization states are steered along predefined trajectories on the Poincaré sphere to generate time-varying polarization tags that are encoded into the received echoes through the target’s polarization-varying response. A compact observation model is then derived to relate the single-channel echoes, the known polarization tags, and the unknown PSM; based on this, the PSM is then estimated via a least squares formulation with a low-rank approximation. Simulation results demonstrate the robust reconstruction of the full polarimetric scattering matrix under diverse modulation trajectories. For arbitrarily chosen random point targets, when the signal-to-noise ratio (SNR) exceeds −20 dB, the polarimetric similarity coefficient approaches 1, and the estimation errors of Pauli power components converge toward zero. Furthermore, the method’s reliability is validated on distributed vegetation clutter. Quantitative metrics demonstrate near-perfect statistical consistency, with polarimetric entropy and alpha angle errors within 0.14%. Overall, the proposed approach provides a practical pathway to enhance the availability of full polarimetric scattering information under limited-observation conditions, confirming its feasibility for downstream analysis in complex natural scenes while maintaining a single radio frequency (RF) chain architecture augmented by a polarization modulator. Full article

This paper investigates M-PSK symbol detection in Orthogonal Frequency Division Multiplexing (OFDM) systems for wideband Vehicle-to-Vehicle (V2V) communications using lightweight convolutional neural networks. In doubly dispersive channels, Inter-Carrier Interference (ICI) degrades subcarrier orthogonality, rendering conventional equalization ineffective. Current ICI mitigation techniques face a trade-off between Bit-Error Rate (BER) performance and computational complexity, limiting their applicability in dynamic vehicular scenarios. To address this issue, a low-complexity MobileNetV3-based receiver is proposed, incorporating a signal-model-driven preprocessing stage that compensates for Doppler-induced phase distortions responsible for ICI. Simulation results show that the proposed receiver improves BER performance compared to conventional equalizers and recent neural-based schemes in the low-SNR regime (below 15 dB) while maintaining computational complexity close to linear least-squares detection. Full article

Soil heavy metal pollution caused by mineral resource extraction activities poses a serious threat to the ecological environment within and surrounding mining areas. As a highly concealed toxic heavy metal, arsenic (As) urgently requires the establishment of efficient pollution monitoring methods to achieve pollution prevention and control, as well as environmental remediation in mining areas. This study investigated the feasibility of hyperspectral remote sensing inversion for soil heavy metal arsenic based on ZY-1 02E hyperspectral satellite imagery, focusing on a mining area and its surrounding soils in Sanlisha’an, Wuxuan County, Guangxi. Full Constrained Least Squares (FCLS) was employed to separate mixed pixels and enhance soil spectral contributions in ZY-1 02E imagery, thereby mitigating vegetation interference. Six mathematical transformations, including RT, AT, FD, RTFD, ATFD, and SD, were applied to both the original and enhanced spectra to enhance spectral features. The correlations between the transformed spectra, as well as the original image spectra (S), and soil As concentration were analyzed; then the spectra strongly correlated with soil As concentration were selected to construct Ratio Spectral Index (RSI) and Normalized Difference Spectral Index (NDSI). Correlation matrices were calculated between RSI/NDSI indices and As concentration. Sensitive features were screened using an improved Successive Projection Algorithm (SPA). As concentration inversion was also performed with four models: traditional regression models, PLSR and MLR, and ensemble learning models (RF and XGBoost). In the soil contribution-enhanced spectral modeling results, the optimal transformation–index combination is ATFD-NDSI. The performance indicators of each model are as follows: MLR test set R² = 0.65, PLSR test set R² = 0.62, RF test set R² = 0.7, and XGBoost test set R² = 0.64. The results indicate that the ATFD-NDSI-RF ensemble model provides the best performance. By integrating multiple decision trees, RF effectively handles complex nonlinear relationships, thus enhancing the accuracy and generalization ability of predication. The analysis of NDSI–ATFD–RF inversion results based on sampling points indicates that model error correlates with the pollution intensity gradient, showing greater errors, especially in high-concentration areas, but still maintaining strong correlations (tailings reservoir: r = 0.92, forested areas: r = 0.96, and cropland: r = 0.83). The spatial distribution reveals that the inversion results are closely similar to the spatial distribution of IDW interpolation. Areas with high As concentrations are concentrated in the tailings reservoir and in the southeastern part of the study area. The correlation coefficient between the inversion results and IDW interpolation is 0.6, which further verifies that the inversion results effectively reproduce the spatial distribution trend of highly polluted areas. Full article

This article offers a structural reappraisal of central bank Monetary Policy Transparency (MPT) by explicitly incorporating two dimensions that have long remained peripheral in the literature: Accounting Information Transparency (AIT) and Financial Stability Transparency (FST). Building on a rigorous theoretical foundation, we develop two original transparency dimensions centered on AIT and FST, designed to extend a widely recognized monetary policy transparency index developed in the existing literature. This extension aims to capture, in an integrated manner, the institutional and macroprudential foundations that underpin the credibility, coherence, and effectiveness of modern monetary policy. The empirical analysis relies on a balanced panel of 25 countries over the period 2000–2019 and employs both Ordinary Least Squares (OLS) and the Generalized Method of Moments (GMM) to address potential endogeneity concerns and ensure the structural robustness of the estimations. The results provide strong evidence that both AIT and FST exert a positive, statistically significant, and economically meaningful effect on MPT. These findings substantially enrich the analytical framework of central bank transparency by demonstrating that high-quality financial reporting and transparent macroprudential communication constitute fundamental pillars of central banks’ credibility capital in an increasingly complex and globalized financial environment. Full article

The expansion of wind energy projects in Indigenous territories has intensified debates about the social legitimacy of corporate practices. In the Isthmus of Tehuantepec, Oaxaca, the main wind corridor in Mexico, wind farms coexist with deeply rooted Zapotec governance systems, creating a complex interface between corporate responsibility and community well-being. Based on a survey of 184 workers employed by wind companies in the region, this study examines the relationship between perceived Corporate Social Responsibility (CSR), in its ethical, legal, and philanthropic dimensions, and social and economic well-being. Using partial least squares structural equation modeling (PLS-SEM) and Importance–Performance Map Analysis (IPMA), we found that legal and philanthropic CSR significantly enhance both types of well-being, whereas ethical CSR only affects social well-being. These findings reflect the perspective of workers as hybrid actors, simultaneously employees and members of Zapotec communities, and should be interpreted in light of the study’s limitations: its focus on employed individuals, cross-sectional design, and reliance on self-reported perceptions. The results contribute to global debates on symbolic versus substantive CSR, distributive justice, and the risk of “green colonialism” in energy transitions. Full article