Search Results (2,177)

The seasonal monsoon reversal drives runoff and current variability along the East Asian coast, intensifying eutrophication from terrestrial nutrients. However, phytoplankton responses to these combined pressures remain poorly understood. This study analyzed their effects using partial least-squares path modeling (PLS-PM) and generalized additive models (GAMs), based on 2021 data from Shantou Bay in the Taiwan Strait, a region with complex currents and significant nutrient inputs. A total of 359 phytoplankton species were identified, with seasonal mean abundances ranging from 6.76 × 10⁶ to 57.36 × 10⁶ cells m⁻³. Ocean currents and riverine runoff drive the seasonal turnover of dominant species by modulating the temperature and salinity. In summer, the exceptionally high phytoplankton abundance in the southwestern Taiwan Strait is driven by nutrient-rich terrestrial inputs, upwelling-induced thermal inhibition, and thermocline stratification from upwelling and offshore warm waters. The phytoplankton abundance and distribution were strongly correlated with the seasonal current and runoff-driven water masses. The PLS-PM results confirm that phytoplankton dynamics are regulated by currents and terrestrial nutrient inputs altering the hydrological and chemical environments, highlighting temperature and salinity as dominant controlling factors in eutrophic coastal zones. Full article

Soil stoichiometric characteristics, as sensitive indicators of soil nutrient supply capacity and ecosystem stability, have emerged as a frontier research focus in biogeochemical cycling and ecological studies. However, the spatial variations of soil stoichiometric characteristics and driving factors in desert steppes remain unclear. Therefore, we investigated soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP) contents and their ratios (C:N, C:P and N:P) in desert steppes in the Ili River basin, China. Results showed that: (1) in the Ili River basin, the SOC, TN, and TP contents were 30.27, 0.77, and 0.79 g·kg⁻¹, respectively, while the soil stoichiometry ratios of C:N, C:P, and N:P were 47.33, 35.48, and 1.13, respectively. All indicators demonstrated moderate variability, while soil C:P showed strong variability. (2) Significant seasonal variations were observed in SOC, TN, TP and stoichiometric ratios (p < 0.05), and soil stoichiometric characteristics were positively correlated with elevation. (3) According to Bayesian linear regression models and partial least squares-partial maximum likelihood (PLS-PM) models, climate was the principal driver of soil C, N, and their stoichiometric ratios, with mean annual temperature (MAT) and minimum temperature (Tmin) being the most influential determinants. These findings provide preliminary insights into the spatiotemporal variation patterns of soil chemical characteristics in desert steppe ecosystems of the Ili River basin. This study contributes to a deeper understanding of nutrient cycling processes within desert steppe ecosystems and offers a degree of scientific support. Full article

The shift to circular agrosystems necessitates using new ideas like sustainable biochar, which provides many eco-beneficial attributes like enhancing soil fertility, storing atmospheric carbon dioxide, and retaining soil moisture. However, there is still a small number of farmers worldwide (particularly those located in low-income countries) adopting biochar. Accordingly, this research is focused primarily on determining how factors affecting behavior will influence the decision of wheat producers in Marvdasht County, in Iran’s Fars Province, to use biochar as a circular technology for farming. The study will focus on addressing issues related to environmental challenges (e.g., degradation of soil and drought) through the implementation of resource-efficient, sustainable agricultural technologies. The intent of this paper was to research the behavioral characteristics associated with wheat farmers who choose to use biochar in the city of Marvdasht, Fars Region, Iran, using a new Theory of Planned Behavior (TPB). The model is theoretically enriched through the inclusion of personal norms and connectedness to the land, allowing for a more comprehensive understanding of pro-environmental decision-making. Data was collected from a total of 386 wheat farmers through the use of a structured survey. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the software Smart-PLS 3.0. The results reveal that attitude (β = 0.342, p < 0.001) and personal norms (β = 0.278, p < 0.001) are the strongest predictors of behavioral intention, while perceived behavioral control showed a weaker but significant effect (β = 0.178, p = 0.049). Subjective norms do not have a significant direct effect (β = 0.115, p = 0.199) but significantly influence intention indirectly through personal norms (β = 0.100, p < 0.001). Furthermore, connectedness to the land strongly affects personal norms (β = 0.420, p < 0.001) and exerts a significant indirect effect on intention (β = 0.117, p < 0.001), highlighting the importance of emotional attachment to land. The findings are significant because they demonstrated that farmers’ biochar adoption decisions are shaped not only by rational evaluations but also by moral obligations and emotional relationships with land. This study makes significant theoretical contributions by extending TPB with moral and relational constructs and empirically demonstrating their mediating roles in agricultural innovation adoption. The novelty of this study lies in integrating personal norms and connectedness to the land into the TPB framework to explain biochar adoption behavior within the context of circular agriculture in a developing country. Practically, the findings provide evidence-based insights for designing policies that integrate cognitive, ethical, and emotional drivers to promote biochar adoption and advance circular agriculture. Specifically, policymakers and extension agencies should prioritize behavioral-level strategies such as awareness campaigns, farmer training programs, and community-based initiatives that strengthen positive attitudes, environmental responsibility, and farmers’ emotional connection to land in order to enhance biochar adoption. Full article

How does artificial intelligence (AI) adoption shape administrative efficiency and e-citizen integration in local governments, and what role does professional development play in mediating these relationships? Drawing on a survey of 500 municipal employees across Spanish municipalities, this study employs partial least squares structural equation modelling (PLS-SEM), with formal non-linearity testing via Warp3 algorithms, to test a theoretically grounded model. The conceptual framework integrates Digital Transformation Theory and Public Value Theory as primary explanatory lenses, while drawing on the Technology Acceptance Model (TAM) and Total Factor Productivity (TFP) logic as complementary background perspectives that contextualise rather than directly operationalise the micro-level findings. Structural results reveal that AI adoption exerts a strong direct (and statistically linear) effect on perceived administrative efficiency (β = 1.04, p < 0.001; the standardised coefficient exceeding 1.0 and R² > 1 are a legitimate WarpPLS warp-model fit index rather than evidence of model misspecification: the Warp3 warp functions inflate the variance of predicted efficiency and break the additive identity SST = SSM + SSE, with the high AI–PD collinearity (r ≈ 0.84) as the contributing mechanism (RSCR = 1.000, SSR = 1.000); a comparative re-estimation without the moderation term yields β = 0.87 and R² = 0.76; we adopt this parsimonious specification (β ≈ 0.87, R² = 0.76) as the substantively interpretable estimate, with predictive relevance confirmed by a high Stone–Geisser Q² = 0.685, indicating that the model fits and predicts well rather than overfitting, while simultaneously stimulating professional development (β = 0.84, p < 0.001, R² = 0.70). Professional development positively predicted both efficiency (β = 0.27, p < 0.001) and e-citizen integration (β = 0.26, p < 0.01). Efficiency is the primary driver of e-citizen integration (β = 0.54, p < 0.001, R² = 0.53). The proposed moderation of AI adoption by professional development on efficiency was not supported (β = −0.01, p = 0.44), suggesting additive rather than synergistic effects. Model fit was robust (GoF = 0.701; ARS = 0.749; APC = 0.495); convergent and discriminant validity were confirmed by composite reliability, average variance extracted, Fornell–Larcker, and HTMT criteria; and common method bias diagnostics (Harman’s single-factor test, full-collinearity AFVIF, and marker-variable analysis) indicated that systematic method variance was not a material threat. These findings offer micro-empirical evidence of the mechanisms linking AI adoption to citizen service outcomes via a professional development pathway and provide actionable recommendations for Spanish and European municipalities navigating AI-driven governance reform. Full article

Background: Digital transformation is viewed as a lever of supply chain resilience, yet the intermediate pathways through which digital maturity relates to resilience remain underspecified, particularly in emerging-market contexts. Drawing on the Resource-Based View and the Dynamic Capabilities Framework, this study examines whether four dynamic capabilities (visibility, flexibility, risk management, and collaboration) mediate the relationship between digital maturity and supply chain resilience. Methods: Using a cross-sectional survey of 250 Moroccan firms and partial least squares structural equation modeling (PLS-SEM), we estimate a multi-mediator model and decompose the total association using variance accounted for (VAF). Results: The findings indicate that digital maturity is positively associated with resilience both directly (β = 0.219, p < 0.01) and indirectly through the four mediators, with the four capabilities jointly accounting for 63.7% of the total association (R² = 0.523, SRMR = 0.027). Visibility (18.9%) and flexibility (15.9%) emerge as the strongest indirect channels. Conclusions: The study contributes by simultaneously testing four dynamic capabilities as mediators within a single specification, documenting evidence from an under-represented emerging-market context, and providing empirically grounded managerial recommendations and policy implications. Because the data are cross-sectional, all reported coefficients describe statistical associations. Full article

Aim: This study aimed to assess the health literacy (HL) levels of patients visiting the emergency department of a university hospital and identify related factors. Methods: This cross-sectional study aimed to assess the health literacy levels of patients visiting the emergency department of a university hospital, and to identify related factors. The re-quired sample size was at least 384 individuals, assuming an inadequate HL level of 50%, with 95% confidence interval and 5% margin of error. Data were collected through a two-part questionnaire designed by the researchers. The first part covered the patients’ socio-demographic characteristics and details regarding their emergency department visits. Meanwhile, the second part included the widely used Chew’s short questions to assess inadequate HL. The analysis was conducted using IBM SPSS version 27.0. Descriptive sta-tistics, including frequency, percentage, and mean, were used to summarize the charac-teristics of the study group. The Chi-square test was applied for data analysis. Results: The study group included 58% (n = 250) female and 42% (n = 181) male. Their ages ranged from 18 to 64 years, with a mean (SD) of 29.6 (10.8) and a median of 26.0. In terms of HL levels, 197 individuals (45.7%) had inadequate HL. The frequency of inadequate HL was higher in individuals over the age of 40 years and those with an education level of ≤8 years (p < 0.05 for each). A total of 39.2% (n = 169) of the patients had visited the emergency department multiple times for their current complaints, whereas 243 participants (56.4%) visited the emergency department for a different reason within the past six months. Conclusions: In our study, four out of ten individuals had inadequate HL, and the frequency of repeated emergency department visits was quite high. No statistically significant association was found between emergency department usage characteristics and health literacy levels in the present sample, highlighting the need for larger longitudinal studies with adjusted analyses. Full article

Although the consequences of intimate partner violence (IPV) for female victims have been widely documented, the psychological and academic correlates of perpetration remain underexplored. This study examines whether emotional distress statistically mediates the association between IPV perpetration and academic presenteeism among male university students. A cross-sectional survey was administered to 343 students from the Universidad Mayor de San Andrés in Bolivia. Using validated instruments and Partial Least Squares Structural Equation Modeling, we assessed direct and indirect associations. Findings indicate that 50.1% of students reported perpetrating at least one form of IPV since entering university, with stalking and psychological violence being most common. Perpetrators reported higher levels of emotional distress compared to non-perpetrators and exhibited higher academic presenteeism (reduced academic functioning despite physical attendance). The structural model indicated a significant indirect statistical effect of IPV perpetration on academic presenteeism through emotional distress (β = 0.137, p < 0.001), accounting for 36.2% of the total effect. These findings suggest that universities may consider perpetrator-focused components within broader prevention and support systems, integrating behavioral accountability with screening, referral, and academic support while recognizing that intervention effectiveness was not tested in this study. Full article

Given the diversity and richness of China’s grain varieties, traditional physicochemical quality testing methods for rice, while providing accurate results, suffer from drawbacks such as time-consuming procedures, high costs, substantial reagent consumption, cumbersome sample preparation, and reliance on destructive or semi-destructive techniques. This study aims to employ near-infrared spectroscopy technology to establish rapid and non-destructive predictive models for key quality indicators of japonica rice. The research analyzed 133 samples from 71 widely cultivated japonica rice varieties across five major production regions in China, utilizing spectral data within a wavelength range of 660–1080 nm. Predictive models for moisture, protein, amylose, and fatty acid values were constructed using three algorithms—partial least squares regression (PLSR), ridge regression (RR), and extremely randomized trees (ERT)—linear regression and the extreme randomization tree (ERT)—their optimal parameters were determined using a 10-fold cross-validation optimization method. Eighty percent of the total dataset served as the training set, while the remaining 20% formed the test set, yielding a final test set comprising 26 samples. Performance comparisons revealed that the PLSR and RR models demonstrated superior predictive performance: the coefficient of determination (R_p²) exceeded 0.9 for all four indicators, with the R² value for fatty acid prediction reaching as high as 0.99; the root mean square error (RMSEP) of the PLSR and RR models ranged between 0.0534% and 0.3360%, confirming their high predictive accuracy. Although all ERT models (except the protein model) achieved R_p² values exceeding 0.9, their overall performance was slightly inferior to the first two methods. The protein ERT model demonstrated relatively low performance, with an R_p² value of 0.6984 on the test set, which may be attributed to the limited sample size and weak protein spectral response signals. Although the samples covered five major production regions and 71 japonica rice varieties, their distribution was uneven (multiple varieties were represented by only one or a few samples). This study provides an efficient rapid quality assessment method for japonica rice; however, the generalization ability of the models requires further validation in future studies employing larger and more balanced sample sizes. Full article

Chronic kidney disease (CKD) is a rising public health concern in low- and middle-income countries (LMICs), with urban populations disproportionately affected. In Bangladesh, particularly in Dhaka, dialysis services have become essential for CKD management. This study investigates the determinants of revisit intention among adult attendants of dialysis patients in Dhaka, using partial least squares structural equation modeling. A cross-sectional survey was conducted across four major dialysis centers totaling 399 valid responses. A purposive sampling technique was employed to ensure the inclusion of respondents with relevant experience and engagement in dialysis service utilization. Among respondents, over half were male, 43% had primary to higher secondary education, and one-third reported household incomes between BDT 40,001 and 60,000. The largest age group was 45–49 years (32.3%), and nearly 60% selected the facility due to nearness. Reliability and validity metrics met recommended thresholds, and multivariate normality was not assumed (Mardia’s test, p < 0.05). The structural model revealed significant direct effects of cost (β = 0.167, p = 0.003), Perceived trust in healthcare providers (β = 0.252, p < 0.001), and Perceived patient satisfaction (β = 0.422, p < 0.001) on Perceived revisit intention. Dialysis Delivery Service and word of mouth influenced revisit behavior indirectly through Perceived patient satisfaction. All mediation paths were statistically significant and classified as complementary. To improve patient retention, the policymaker should prioritize affordability, perceived trust in healthcare providers, and overall service quality, which together enhance perceived patients’ satisfaction and revisit intention. Full article

Laser-induced breakdown spectroscopy (LIBS) offers rapid, in situ, and multi-element detection, and therefore shows strong potential for quality monitoring of metal powders in additive manufacturing. However, direct LIBS analysis of flowing metal powders is often affected by particle splashing, unstable laser–particle coupling, and plasma fluctuations, which reduce signal repeatability and detection reliability. To address these issues, this study developed an integrated measurement and classification framework for identifying cross-contamination in additive-manufacturing metal powders. A stable powder particle stream was generated through vibratory feeding and particle-flow focusing, while a hollow quartz tube with a side opening was introduced to provide cylindrical spatial confinement, thereby improving the stability of laser–particle interaction and enabling in situ spectral acquisition without pellet preparation. TC4 powder was used as the base material and AlSi10Mg powder as the contaminant, and samples with contamination levels of 0, 0.5, 1, 2, and 5 wt.% were prepared. Two independent batches of single-shot LIBS spectra were collected. To reduce the influence of strong spectral fluctuations, outlier spectra were removed using full-spectrum total-intensity quantile filtering, followed by asymmetric least-squares baseline correction and standard normal variate transformation. PCA combined with multiple machine-learning models was then applied for contamination identification. The results showed that LIBS spectra at different contamination levels exhibited distinguishable distributions in principal-component space, and the spectral differences between clean and contaminated powders became more pronounced with increasing contamination level. In binary classification, several models achieved high classification accuracy at medium and high contamination levels, while PCA-SVM-RBF showed the best performance at low concentrations. In five-class cross-validation, the 5 wt.% class exhibited the clearest decision boundary, whereas confusion remained among low and adjacent contamination levels, indicating that contamination-induced spectral responses followed a more continuous transition. These results demonstrate that the proposed spatially confined particle-flow LIBS framework combined with machine-learning classification can effectively achieve rapid identification of cross-contamination in additive-manufacturing metal powders and provides a feasible technical route for online powder quality monitoring. Full article

Measurement outliers remain a major source of performance degradation in nonlinear bearings-only target tracking, where a few corrupted observations can produce large innovations and even trigger filter divergence. This paper proposes a non-iterative robust square-root cubature Kalman filter (RSCKF) for bearings-only tracking with measurement outliers. Motivated by a TLS-type errors-in-variables interpretation of the pseudo-linear bearings-only measurement model, robustness is introduced through a closed-form equivalent weighting and rejection mechanism within the square-root cubature Kalman filtering framework. The proposed method preserves the derivative-free square-root filtering structure in implementation and avoids inner fixed-point or variational iterations. For the scalar bearing update considered in this paper, the weighting thresholds are determined from a normalized innovation statistic using prescribed confidence levels, so that moderate outliers are down-weighted, and extreme ones are rejected. Simulations under nominal, moderately contaminated, and severely contaminated measurement conditions show that the proposed RSCKF achieves accuracy comparable to the standard square-root cubature Kalman filter (SCKF) in the Gaussian case, while providing improved robustness and only a small computational overhead under the measurement outliers. Under the most severe contamination setting, where 15% of the bearings are corrupted by outliers with a standard deviation 30 times the nominal noise level, RSCKF limits the position-MSE increase to 8.4% relative to the nominal case and achieves the lowest position MSE among the seven compared filters, whereas the standard SCKF deteriorates by more than two orders of magnitude. It is also the only filter whose time-averaged ANEES remains within the consistency band used in the Monte Carlo evaluation, with a computation time close to that of the baseline CKF. Full article

Sustainability-oriented entrepreneurship is becoming more widely acknowledged as a mean of addressing social and environmental issues while promoting economic development, though little research has looked at the cognitive processes by which innovation-related thinking translates into sustainability entrepreneurial intention. The relationships between innovative mindset, entrepreneurial mindset, and sustainability entrepreneurial intention among university students are examined in this study. A mediation model is proposed in which innovative mindset positively influences entrepreneurial mindset (H1), entrepreneurial mindset positively influences sustainability entrepreneurial intention (H2), and entrepreneurial mindset mediates the relationship between innovative mindset and sustainability entrepreneurial intention (H3). In total, 163 university students in the United Arab Emirates provided the data, which was then analyzed using partial least squares structural equation modeling (PLS-SEM). All of the proposed hypotheses are supported by the results. These findings offer preliminary and partial support for a theoretically defined cognitive pathway connecting sustainability entrepreneurial intention, innovative mindset, and entrepreneurial mindset. In particular, the findings indicate a positive correlation between innovation-oriented cognitive abilities and entrepreneurial cognition, which is linked to sustainability-oriented intentions. The low explained variance in sustainability entrepreneurial intention, however, suggests that the model only partially explains the variables influencing SEI. As a result, this study advances a more complex, mechanism-based understanding of one potential cognitive pathway in sustainability entrepreneurship and emphasizes the need for more thorough models that include contextual, motivational, and sustainability-related predictors. Additionally, it provides cautious practical implications for entrepreneurship education, especially when it comes to combining learning that is focused on sustainability with the development of an innovative and entrepreneurial mindset. Full article

Vibriosis caused by V. harveyi led to high mortality and enormous economic losses in Chinese tongue sole aquaculture. However, the intestinal metabolic alterations associated with V. harveyi infection remain unclear. In this study, ultra-performance liquid chromatography–mass spectrometry (LC-MS)-based metabolomics was used to investigate the variations in intestinal metabolic phenotypes among control, susceptible, and resistant Chinese tongue sole after 7 days of V. harveyi infection. Histopathological examination revealed severe intestinal damages in susceptible fish, whereas resistant fish displayed only mild changes. Principle components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) revealed distinct separation of intestinal metabolites among three groups. A total of 2948 metabolites were identified, with 437 and 794 differential metabolites detected in the resistant and susceptible groups, respectively. The KEGG enrichment analysis revealed that resistant individuals primarily enriched amino acid metabolism and TCA cycle to support immunity and tissue repair, whereas susceptible individuals enriched sphingolipid and cGMP-PKG signaling pathways linked to inflammation and apoptosis, indicating divergent metabolic strategies during V. harveyi infection. Thirty-two potential metabolite biomarkers (area under the curve (AUC) = 1) were screened, which could effectively distinguish susceptible and resistant individuals. Correlation analysis further demonstrated strong interactions among these metabolite markers, host immune-related differentially expressed genes (DEGs), and intestinal microbes. Collectively, our findings reveal distinct intestinal histopathological changes and metabolic reprogramming in resistant and susceptible individuals following V. harveyi infection and identify a set of candidate biomarkers, providing a theoretical foundation for developing targeted prevention strategies and immune enhancement approaches against V. harveyi infection in Chinese tongue sole. Full article

This study aimed to examine the association between digital life balance and flourishing in a sample of adolescents with a particular focus on the mediating roles of self-esteem and life satisfaction in the relationship between the two variables. A cross-sectional survey was conducted with a sample of 338 Romanian adolescents (mean age = 16.17 years; 66% girls) who completed measures of digital life balance (Digital Life Balance Scale), self-esteem (Rosenberg Self-Esteem Scale), life satisfaction (Satisfaction with Life Scale), and flourishing (Flourishing Scale). Data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results showed that digital life balance was positively associated with flourishing both directly (β = 0.125) and indirectly through life satisfaction and self-esteem (β = 0.309). The total association was also significant (β = 0.434) (all p < 0.001). These findings suggest that digital life balance represents an important correlate of flourishing in adolescence. Full article

National forest parks play an important role in maintaining the integrity of ecosystems, the sustainability of biodiversity, and the improvement of ecological service functions. Aboveground carbon storage (AGC) is an important indicator for evaluating forest ecosystem functions. Qianjiangyuan–Baishanzu National Park, located in the southern part of Lishui City, serves as a typical representative of the mid-subtropical evergreen broad-leaved forest ecosystem. It is characterized by high biodiversity and serves as a concentration area for rare and endangered species. Therefore, assessing the spatiotemporal dynamics of forest AGC in the typical subtropical forests of Qianjiangyuan–Baishanzu National Park is of great scientific significance. Taking Qianjiangyuan–Baishanzu National Park as a case study, this research utilized three periods of Landsat satellite remote sensing data (2009, 2014, and 2019) alongside contemporaneous ground-based AGC survey samples. Feature variables were extracted and subsequently screened using the Boruta algorithm. There were three algorithms, including least squares (LS), support vector regression (SVR), and random forest (RF), constructed to estimate forest AGC. The optimal AGC estimation model was selected, and the spatiotemporal variation characteristics of forest AGC within the national park were systematically analyzed. Research has shown that (1) texture features are important parameters for constructing forest AGC estimation models, accounting for up to 71%, with the 7 × 7 window having the greatest impact. (2) All three models can achieve high accuracy in estimating the forest AGC and its spatial distribution in Qianjiangyuan Baishanzu National Park. Among them, the RF model has the highest overall accuracy in estimating forest AGC, with a training set R² of 0.938, RMSE of 5.550 Mg/ha, rRMSE of 12.517%, a test set R² of 0.954, RMSE of 4.653 Mg/ha, and rRMSE of 10.087%. (3) The distribution of forest AGC in Qianjiangyuan Baishanzu National Park shows significant spatial heterogeneity, with higher carbon storage in the central, southern, and southeastern regions, while the northern region has relatively lower carbon storage. From 2009 to 2019, the forest AGC in the Qianjiangyuan–Baishanzu National Park exhibited a steady annual increase, with AGC density rising from 37.64 Mg/ha to 66 Mg/ha and total AGC stock increasing from 2.16 Tg C to 4.36 Tg C. These findings provide precise data support and a scientific basis for decision-making regarding differentiated ecological carbon enhancement and functional zone management within the national park. Full article