Search Results (13,056)

Leadership literature suggests that a transformational leadership style can reduce negative employee outcomes, even in challenging work environments such as the education sector, where teachers play a key role in social development. This study aimed to analyze the association between transformational leadership and job satisfaction: the role of gratitude toward the organization in the Peruvian context. A cross-sectional study with an explanatory design was conducted considering 457 men and women who declared themselves teachers, aged between 18 and 73 years (M = 38.63; SD = 10.61), recruited through non-probability convenience sampling. The theoretical model was evaluated using the Partial Least Squares method (PLS-SEM). An adequately fitting measurement model was obtained (α = between 0.893 and 0.969; CR = between 0.897 and 0.971; AVE = between 0.757 and 0.845), demonstrating that transformational leadership is positively associated with the components of gratitude toward the organization and job satisfaction, as well as the association of the components of gratitude toward the organization and job satisfaction. In turn, it was evident how gratitude toward the organization plays a mediating role in these relationships. In this sense, the study provides valuable information for Peruvian educational leaders seeking to improve indicators of satisfaction, gratitude, and leadership in their work environment. These findings enrich educational management, given that it is the first empirical study to demonstrate these links in a challenging sector of an emerging country, offering a solid foundation for the development of more humanized, effective, and sustainable management strategies. Full article

This study investigates how Environmental, Social, and Governance (ESG) performance is shaped across 31 countries in the European Union (EU) and the Association of Southeast Asian Nations (ASEAN) from 1990 to 2020. To explore these relationships, we employed the Continuously Updated Generalized Method of Moments (CUE-GMM) and the Limited Information Maximum Likelihood (LIML), with additional robustness checks using Instrumental Variables Two-Stage Least Squares (IV-2SLS), Panel-Corrected Standard Errors (PCSE), and Driscoll-Kraay regressions. The results highlight democratic governance as a consistent driver of ESG advancement. Military expenditure can also support sustainability by reinforcing institutional stability, particularly in developing and upper-middle-income countries. Economic factors such as foreign direct investment, industrialization, and human capital show context-dependent effects, whereas globalization and natural resource rents generally enhance ESG performance, and inflation tends to constrain it. Overall, the findings underscore the importance of tailored, context-specific sustainability policies, showing that effective ESG progress depends on the interaction between institutions, economic structures, and global integration. Full article

In response to the growing importance of vocational education for youth employability, this study examines students’ perceptions of dual vocational education and training (dVET) in Bulgaria, focusing on the following determinants of student loyalty (SL) and word-of-mouth communication (WOM) in the secondary education context: brand associations, brand relevance, brand image, image of dVET, service quality, and student satisfaction, based on previously validated scales adapted to the Bulgarian context. A structured questionnaire was administered to a target population of 608 students across nine vocational secondary schools in the Plovdiv region. A total of 507 usable surveys were collected from students in 11th and 12th grades who were actively participating in work-based learning. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the SmartPLS 4 software. The findings indicate that brand image is the strongest direct predictor of the image of dVET. Furthermore, student satisfaction stands out as the most influential antecedent of WOM. The indirect pathways from service quality to both SL and WOM, mediated by student satisfaction, underscore the pivotal role of satisfaction as a transmission mechanism. The study contributes to the limited empirical research on branding in dVET and offers insights for policymakers, school administrators, and employers seeking to improve the attractiveness of these pathways. Full article

This research provides a new systematic solution to the essential issue of self-noise interference in underwater acoustic sensing signals induced by unmanned surface vehicles (USVs) operating at sea. The self-noise pertains to the near-field interference noise generated by the growing diversity and volume of acoustic equipment utilized by USVs. The generating mechanism of self-noise is clarified, and a self-noise propagation model is developed to examine its three-dimensional coupling properties within spatiotemporal fluctuation environments in the time-frequency-space domain. On this premise, the YOLOv11 object identification framework is innovatively applied to the delay-Doppler (DD) feature maps of self-noise, thereby overcoming the constraints of traditional time-frequency spectral approaches in recognizing noise with delay spread and overlapping characteristics. A comprehensive comparison with traditional models like YOLOv8 and SSD reveals that the suggested delay-Doppler YOLO (DD-YOLO) algorithm attains an average accuracy of 87.0% in noise source identification. An enhanced denoising method, termed optimized time-frequency regularized overlapping group shrinkage (OTFROGS), is introduced, using structural sparsity alongside non-convex regularization techniques. Comparative experiments with traditional denoising methods, such as the normalized least mean square (NLMS) algorithm, wavelet threshold denoising (WTD), and the original time-frequency regularized overlapping group shrinkage (TFROGS), reveal that OTFROGS outperforms them in mitigating USV self-noise. This study offers a dependable technological approach for optimizing the performance of USV acoustic systems and proposes a theoretical framework and methodology applicable to different underwater acoustic sensing contexts. Full article

The confined space of coal mines characterized by curved tunnels with rough surfaces and a variety of deployed production equipment induces severe signal attenuation and interruption, which significantly degrades the accuracy of conventional channel estimation algorithms applied in coal mine wireless communication systems. To address these challenges, we propose a modified Bilinear Generalized Approximate Message Passing (mBiGAMP) algorithm enhanced by intelligent reflecting surface (IRS) technology to improve channel estimation accuracy in coal mine scenarios. Due to the presence of abundant coal-carrying belt conveyors, we establish a hybrid channel model integrating both fast-varying and quasi-static components to accurately model the unique propagation environment in coal mines. Specifically, the fast-varying channel captures the varying signal paths affected by moving conveyors, while the quasi-static channel represents stable direct links. Since this hybrid structure necessitates an augmented factor graph, we introduce two additional factor nodes and variable nodes to characterize the distinct message-passing behaviors and then rigorously derive the mBiGAMP algorithm. Simulation results demonstrate that the proposed mBiGAMP algorithm achieves superior channel estimation accuracy in dynamic conveyor-affected coal mine scenarios compared with other state-of-the-art methods, showing significant improvements in both separated and cascaded channel estimation. Specifically, when the NMSE is ${10}^{-3}$, the SNR of mBiGAMP is improved by approximately 5 dB, 6 dB, and 14 dB compared with the Dual-Structure Orthogonal Matching Pursuit (DS-OMP), Parallel Factor (PARAFAC), and Least Squares (LS) algorithms, respectively. We also verify the convergence behavior of the proposed mBiGAMP algorithm across the operational signal-to-noise ratios range. Furthermore, we investigate the impact of the number of pilots on the channel estimation performance, which reveals that the proposed mBiGAMP algorithm consumes fewer number of pilots to accurately recover channel state information than other methods while preserving estimation fidelity. Full article

Background: Trust in information sources is essential to enhance an individual’s understanding of the message and boost their willingness to change or act on specific health behavior, including vaccine uptake. This study explores the association between trust in information sources and parents’ knowledge, attitudes, and practices regarding their children’s 13-valent pneumococcal conjugate vaccine (PCV13) uptake across seven cities in the Yangtze River Delta (YRD) region in China. Methods: A cross-sectional web-based survey was conducted from May to June 2023. Adult parents (N = 1304) who had at least one child aged 24 months or less and lived in the YRD region were recruited. The Adjusted Ordinary Least Squares (OLSs) regression model was applied to estimate the association between participants’ level of trust in different information sources and their knowledge, attitudes, and practices of children’s PCV13 vaccination. Results: Information from the Disease Control and Prevention Center (CDC) source received the highest trust score. Age, gender, education, and annual household income were related to varied trust levels in specific sources. Trust in the health service provider source was significantly associated with a better command of PCV13 knowledge, acceptance of PCV13, and a higher likelihood of vaccination. Trust in online community sources was positively associated with vaccine uptake. Conclusions: The study participants highly trusted information from health service provider sources. These sources may be effective channels with potential to enhance parents’ vaccine knowledge and acceptance of PCV13. Public health workers could utilize trusted sources to disseminate the benefits of the PCV13 and encourage the uptake of the vaccine. Full article

Banks in the United States face persistent challenges from non-performing loans (NPLs), despite conducting thorough client evaluations before issuing loans. To mitigate the impact of NPLs and support both local and global growth, banks must adopt effective risk management strategies. This study investigates the effect of NPLs on bank growth and the moderating of bank size and Capital Adequacy Ratio (CAR) through the lens of the Resource-Based View (RBV) theory. A sample of 253 banks listed on the New York Stock Exchange from 2006 to 2023 was selected using specific inclusion criteria from the Thomson Reuters Eikon DataStream. To address cross-sectional dependence and endogeneity, advanced estimation techniques—Feasible Generalized Least Squares (FGLS), Driscoll and Kraay standard errors, and the Generalized Method of Moments (GMM)—were employed. The results show that NPLs have a significant negative impact on banks’ asset and income growth. Furthermore, bank size and capital adequacy ratio (CAR) negatively and significantly moderate this relationship. These findings underscore the need for banks to enhance credit risk management by strengthening loan approval processes and leveraging advanced analytics to assess borrower risk more accurately. Full article

Local scour around bridge piers is one of the primary causes of structural failure in bridges. Therefore, this study focuses on addressing the estimation of maximum scour depth (d_sm), which is essential for safe and resilient bridge design. Many studies in the last eight decades have included metadata collection and developed around 80 empirical formulas using various scour-affecting parameters of different ranges. To date, a total of 33 formulas have been comparatively analyzed and ranked based on their predictive accuracy. In this study, novel formulas using semi-empirical methods and gene expression programming (GEP) have been developed alongside an artificial neural network (ANN) model to accurately estimate d_sm using 768 observed data points collected from published work, along with eight newly conducted experimental data points in the laboratory. These new formulas/models are systematically compared with 74 empirical literature formulas for their predictive capability. The influential parameters for predicting d_sm are flow intensity, flow shallowness, sediment gradation, sediment coarseness, time, constriction ratio, and Froude number. Performances of the formulas are compared using different statistical metrics such as the coefficient of determination, Nash–Sutcliffe efficiency, mean bias error, and root-mean-squared error. The Gauss–Newton method is employed to solve the nonlinear least-squares problem to develop the semi-empirical formula that outperforms the literature formulas, except the formula from GEP, in terms of statistical performance metrics. However, the feed-forward ANN model outperformed the semi-empirical model during testing and validation phases, respectively, with higher CD (0.790 vs. 0.756), NSE (0.783 vs. 0.750), lower RMSE (0.289 vs. 0.301), and greater prediction accuracy (64.655% vs. 61.935%), providing approximately 15–18% greater accuracy with minimal errors and narrower uncertainty bands. Using user-friendly tools and a strong semi-empirical model, which requires no coding skills, can assist designers and engineers in making accurate predictions in practical bridge design and safety planning. Full article

Chlorophyll is crucial for crop photosynthesis and useful for monitoring crop growth and predicting yield. Its content can be indicated by SPAD meter readings. However, SPAD-based monitoring of rice is time- and labor-intensive, whereas remote sensing offers non-destructive, rapid, real-time solutions. Compared with mainstream unmanned aerial vehicle, emerging phenotyping robots can carry multiple sensors and acquire higher-resolution data. Nevertheless, the feasibility of estimating rice SPAD using multi-sensor data obtained by phenotyping robots remains unknown, and whether the integration of machine learning algorithms can improve the accuracy of rice SPAD monitoring also requires investigation. This study utilizes phenotyping robots to acquire multispectral and RGB images of rice across multiple growth stages, while simultaneously collecting SPAD values. Subsequently, four machine learning algorithms—random forest, partial least squares regression, extreme gradient boosting, and boosted regression trees—are employed to construct SPAD monitoring models with different features. The random forest model combining vegetation indices, color indices, and texture features achieved the highest accuracy (R² = 0.83, RMSE = 1.593). In summary, integrating phenotyping robot-derived multi-sensor data with machine learning enables high-precision, efficient, and non-destructive rice SPAD estimation, providing technical and theoretical support for rice phenotyping and precision cultivation. Full article

Small and medium-sized enterprises (SMEs) often face severe resource constraints and operational fragility during crises. However, little is known about how managerial resilience (MR) translates into performance through time-related psychological resources and innovation—two capabilities that are both scarce and critical under such conditions. Drawing on Temporal Motivation Theory (TMT), this study develops and tests a dual-mediation model in which employee temporal psychological capital (TPC) and employee innovative behavior (EIB) transmit the effects of MR on performance. As a core methodological innovation, we adopt a multi-method analytical strategy to provide robust and complementary evidence rather than a hierarchy of results: Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to examine sufficiency-based causal pathways and quantify the mediating mechanisms; Support Vector Machine (SVM) classification offers a non-parametric predictive validation of how MR and its mediators distinguish high- and low-performance cases; and Necessary Condition Analysis (NCA) identifies non-compensatory conditions that must be present for high performance to occur. These three methods address different research questions—sufficiency, classification robustness, and necessity—therefore serving as parallel, equally important components of the analysis. A total of 455 SME managers and employees were surveyed, and results show that MR significantly enhances all three dimensions of TPC (temporal control, temporal fit, time pressure resilience) and EIB (idea generation, idea promotion, idea realization), which in turn improve employee performance. SVM classification confirms that high MR, strong TPC, and active innovation align with high performance, while NCA reveals temporal control, idea generation, and idea realization as necessary bottleneck conditions. By integrating sufficiency–necessity logic with predictive classification, our findings suggest that SMEs should prioritize leadership resilience training to strengthen managers’ adaptive capacity, while simultaneously implementing time management interventions—such as temporal control workshops, workload balancing, and innovation pipeline support—to enhance employees’ ability to align tasks with organizational timelines, execute ideas effectively, and sustain performance during crises. Full article

In various research domains, researchers frequently encounter multiple datasets pertaining to the same subjects, with one dataset providing explanatory variables for the others. To address this structure, we introduce the Binary 3-way PARAFAC Partial Least Squares (Bin-3-Way-PARAFAC-PLS), a novel multiway regression method. This method is specifically engineered for scenarios involving a three-way real-valued explanatory data array and a matrix of binary response data. We detail the algorithm’s implementation and illustrate its practical application. Furthermore, we describe biplot representations to aid in result interpretation. The accompanying software necessary for implementing the method is also provided. Finally, the proposed method’s utility in real-world problem-solving is demonstrated through its application to a psychological dataset. Full article

With the rise in motor vehicles, driving safety is a major concern, and autonomous driving technology plays a key role in enhancing safety. Vision-based lane departure warning systems are essential for accurate navigation, focusing on lane line detection. This paper reviews the development of such systems and highlights the limitations of traditional image processing. To improve lane line detection, a dataset from Roboflow Universe will be used, incorporating techniques like priority pixels, least squares fitting for positioning, and a Kalman filter for tracking. YOLOv5 will be enhanced with a di-versified branch block (DBB) for better multi-scale feature extraction and an improved segmentation head inspired by YOLACT (You Only Look At CoefficienTs) for precise lane line segmentation. A multi-scale feature fusion mechanism with self-attention will be introduced to improve robustness. Experiments will demonstrate that the improved YOLOv5 outperforms other models in accuracy, recall, and mAP@0.5. Future work will focus on optimizing the model structure and enhancing the fusion mechanism for better performance. Full article

The quality control of fruit purée products such as mango and mangosteen is crucial for maintaining consumer satisfaction and meeting industry standards. Traditional destructive techniques for assessing key quality parameters like the soluble solid content (SSC) and titratable acidity (TA) are labor-intensive and time-consuming; prompting the need for rapid, nondestructive alternatives. This study investigated the use of deep learning (DL) models including Simple-CNN, AlexNet, EfficientNetB0, MobileNetV2, and ResNeXt for predicting SSC and TA in mango and mangosteen purée and compared their performance with the conventional chemometric method partial least squares regression (PLSR). Spectral data were preprocessed and evaluated using 10-fold cross-validation. For mango purée, the Simple-CNN model achieved the highest predictive accuracy for both SSC (coefficient of determination of cross-validation (${\mathrm{R}}_{\mathrm{CV}}^2$) = 0.914, root mean square error of cross-validation (RMSE_CV) = 0.688, the ratio of prediction to deviation of cross-validation (RPD_CV) = 3.367) and TA (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.762, RMSE_CV = 0.037, RPD_CV = 2.864), demonstrating a statistically significant improvement over PLSR. For the mangosteen purée, AlexNet exhibited the best SSC prediction performance (${\mathrm{R}}_{\mathrm{CV}}^2$ = 0.702, RMSE_CV = 0.471, RPD_CV = 1.666), though the RPD_CV values (<2.0) indicated limited applicability for precise quantification. TA prediction in mangosteen purée showed low variance in the reference values (standard deviation (SD) = 0.048), which may have restricted model performance. These results highlight the potential of DL for improving NIR-based quality evaluation of fruit purée, while also pointing to the need for further refinement to ensure interpretability, robustness, and practical deployment in industrial quality control. Full article

In complicated scenarios, due to the low precision of float solutions and poor reliability of fixed solutions, it is challenging to achieve a balance between accuracy and reliability of the integer least squares (ILS) estimation. To address this dilemma, the best integer equivariant (BIE) estimation, which makes a weighted sum of all possible candidates, has recently been attached great importance. The BIE solution approaches the float solution at a low ILS success rate, maintaining positioning reliability. As the success rate increases, it converges to the fixed solution, facilitating high-precision positioning. Furthermore, the posterior variance of BIE estimation provides the capability of reliability evaluation. However, in environments with a limited number or a deficient configuration of available satellites, there is a sharp decline in the strength of the GNSS precise positioning model. In this case, the exactness of weight allocation for integer candidates in BIE estimation will be severely compromised by unmodeled errors. When the ambiguity is incorrectly fixed, the wrongly determined optimal candidate is probably assigned an excessively high weight. Therefore, the BIE solution in a weak GNSS model always exhibits a significant positioning error consistent with the fixed solution. Moreover, the posterior variance of BIE estimation approximately resembles that of a fixed solution, losing error warning ability. Consequently, the BIE estimation may exhibit lower reliability compared to the ILS estimation employing a validation test with a loose acceptance threshold. To improve the positioning performance in weak GNSS models, a BIE ambiguity resolution (AR) method based on Tikhonov regularization is proposed in this paper. The method introduces Tikhonov regularization into the least squares (LS) estimation and the ILS ambiguity search, mitigating the serious impact of unmodeled errors on the BIE estimation under weak observation conditions. Meanwhile, the regularization factors are appropriately selected by utilizing an optimized approach established on the L-curve method. Simulation experiments and field tests have demonstrated that the method can significantly enhance the positioning accuracy and reliability in weak GNSS models. Compared to the traditional BIE estimation, the proposed method achieved accuracy improvements of 73.6% and 69.3% in the field tests with 10 km and 18 km baselines, respectively. Full article

This study explores how Instagram fashion influencers build brand loyalty through customer engagement within the framework of sustainable consumption. Grounded in the stimulus–organism–response (SOR) theory, influencer marketing is conceptualized as a stimulus that activates customer engagement (organism), which in turn enhances brand loyalty (response). A cross-sectional survey was conducted with 279 Instagram users in Palestine who actively follow fashion influencers, and the model was tested using partial least squares structural equation modeling (PLS-SEM). The findings confirm that social media influencer marketing (SMIM) significantly improves both engagement and loyalty. Customer engagement was found to be both a partial mediator and a significant moderator, such that highly engaged consumers exhibited stronger loyalty responses—suggesting intensified value alignment and emotional resonance in sustainability contexts. This study extends the prior literature by integrating the creation–consumption–contribution (C–C–C) model into the SOR framework and conceptualizing engagement as both a psychological state and a boundary condition. It contributes to sustainable consumption research by illustrating how participatory digital behaviors can foster ethical brand relationships, particularly in emerging economies. Practically, it offers strategic guidance for fashion brands and influencers to design campaigns that promote co-creation, authenticity, and eco-conscious narratives. It also emphasizes the importance of aligning influencer values with those of sustainability-minded consumers to foster long-term loyalty. By contextualizing the findings within the Palestinian market, the study highlights how cultural factors may shape engagement and sustainability perceptions, paving the way for future cross-cultural investigations. Full article