Search Results (432)

The factor structure of the Utrecht Work Engagement Scale (UWES) has been debated, with studies alternately supporting unidimensional and three-factor solutions. This inconsistency may reflect a methodological limitation: conventional confirmatory factor analysis (CFA) cannot always separate general from dimension-specific variance, producing similar fit indices across competing models when a dominant general factor is present. We examined the dimensionality of the UWES-17 and UWES-9 in a sample of 755 Chilean university students, comparing unidimensional, three-factor, second-order, and bifactor models using weighted least squares mean and variance adjusted (WLSMV) estimation appropriate for ordinal data. Bifactor indices, explained common variance (ECV), percent of uncontaminated correlations (PUC), and hierarchical omega (ω_h), were computed to evaluate essential unidimensionality. Results indicated that a general engagement factor explained approximately 85% of common item variance in both versions (ECV ≈ 0.85; ω_h > 0.90), while specific factors for vigor, dedication, and absorption retained negligible reliable variance, particularly absorption (ω_h ≈ 0.00). Measurement invariance by sex was supported for the UWES-9 at the metric level, whereas classical UWES-17 solutions showed instability, including factor collapse and non-convergence of the second-order model. Taken together, findings suggest that the apparent multidimensionality of the UWES may be, at least partly, an artifact of conventional CFA modeling rather than a substantive property of the construct in this student sample. For applied monitoring of student well-being, the UWES-9 total score appears to be the most pragmatic and psychometrically defensible approach for assessing general academic engagement in this Chilean university sample, while institutional well-being monitoring would ideally be further supported by criterion-related, predictive, and sensitivity-to-change evidence. Full article

Background: Stroke remains a leading cause of mortality, disability, and health-system burden in Korea’s rapidly aging population. We aimed to describe national stroke incidence trends from 2011 to 2023, characterize ecological associations between stroke incidence and health-screening indicators, and generate model-based projections through 2028 to support health-system planning. Methods: This retrospective ecological longitudinal analysis used three publicly available aggregate national data sources: (1) NHIS annual aggregate statistics on crude and age-standardized stroke incidence, stroke case counts, first-onset vs. recurrent stroke, and case-fatality rates (2011–2023); (2) regional standardized health-awareness survey rates for stroke symptoms, myocardial infarction symptoms, blood pressure, and blood glucose (2017–2025); and (3) national cancer-screening outcome tallies for breast and cervical cancer (2010–2024). All analyses used pre-aggregated annual summary data; individual-level NHIS records were not used. Annual trends were modeled with ordinary least-squares linear regression (n = 13 annual observations). Pearson correlations were computed only for overlapping observation windows. Model-based projections are presented with 95% prediction intervals and are explicitly distinguished from observed NHIS values. This study is purely descriptive and ecological; no causal inference is made. Results: Crude stroke incidence increased from 199.2 to 221.1 per 100,000 (2011–2023; slope +2.32/year, R² = 0.83), whereas age-standardized incidence declined from 158.3 to 113.2 per 100,000 (slope −3.41/year, R² = 0.96), a pattern consistent with demographic aging as a contributing factor to growing absolute burden, though formal age-decomposition analysis would be required to confirm this inference. Total cases increased from 99,837 to 113,098; the 30-day case-fatality rate declined from 8.5% to 7.5%. Ecological correlations showed that blood glucose awareness was strongly negatively correlated with age-standardized incidence (r = −0.944, p = 0.001, n = 7), though these are ecological associations and must not be interpreted as individual-level causal relationships. Model-based projections estimate crude incidence near 230.7 (95%PI 219.2–242.2) and age-standardized incidence near 103.2 (95%PI 95.7–110.8) per 100,000 by 2026. Conclusions: Concurrent increase in crude burden and decline in age-standardized incidence reflects demographic aging as the primary driver of Korea’s stroke burden. Projections support integrated cardiovascular prevention, public health education, and age-sensitive service planning. All projections are short-horizon statistical extrapolations intended for policy scenario planning only and must not be interpreted as observed future NHIS outcomes. 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

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

Background/Objectives: U.S. hospitals increasingly rely on contract labor to address persistent workforce shortages; however, the downstream implications for patient experience performance remain underexplored. This study examines whether contract labor reliance is associated with subsequent patient experience outcomes. Methods: Using a national sample of non-federal U.S. short-term acute care hospitals (N = 2099), this observational study evaluates whether contract labor reliance in 2024 was associated with patient experience outcomes in 2025. Contract labor reliance was measured as contract labor expense divided by total salary expense. Primary outcomes included HCAHPS Summary Star Rating and Hospital Compare Overall Rating. Multivariable ordinary least squares regression models with regional and ownership fixed effects were estimated, and quadratic specifications were used to assess nonlinear associations. Ordered logit models were also estimated as robustness checks and yielded substantively similar directional results. Results: Higher contract labor reliance was significantly associated with lower subsequent patient experience ratings across both outcomes (HCAHPS: β = −5.288, p < 0.001; Hospital Compare: β = −6.463, p < 0.001). Positive quadratic terms indicated a convex relationship, suggesting that marginal negative associations diminish at higher levels of reliance. Quartile-based analyses demonstrated a monotonic decline in patient experience performance across increasing levels of contract labor reliance. Conclusions: Contract labor reliance is significantly associated with subsequent patient experience performance. These findings should be interpreted as associations rather than causal effects and suggest that workforce composition may represent an important structural factor associated with hospital quality performance. Full article

The proliferation of large-scale electric vehicle charging stations has made power quality issues increasingly prominent. While conventional unidirectional charging stations already present complex harmonic interactions, the development of vehicle-to-grid technology has introduced more complex harmonic coupling in bidirectional charging stations. To improve the accuracy of harmonic power flow analysis, this paper proposes a hybrid mechanism–data-driven dynamic harmonic coupling matrix model (DHCMM) for power quality assessment in bidirectional charging stations. A DHCMM-based harmonic power flow calculation process is further developed to evaluate the harmonic impact on power distribution networks following station integration. The proposed method is validated using field measurement data from an actual bidirectional charging station with tests covering typical charging, discharging, and dynamic transition scenarios. Results show that the DHCMM provides accurate harmonic modeling with both the total harmonic current distortion estimation error and the voltage fluctuation estimation error within 5%. The validated model is applied to an IEEE 33-bus distribution system. A comparison of the results reveals that the power quality impact of such stations extends beyond the point of connection to neighboring nodes, while the proposed DHCMM outperforms mechanism-based models including the static harmonic coupling matrix model and the Norton harmonic equivalent model as well as data-driven models such as the backpropagation neural network and least squares support vector machines. Full article

Subalpine grasslands (SGs) of the Loess Plateau in China play a crucial role in the global carbon cycle of terrestrial ecosystems. However, the distribution pattern of total carbon stores along an elevation gradient on the SG plants of the eastern plateau remains unclear. In this study, eight typical mountains with one well-developed SG being surveyed as plot for each mountain were selected along an elevation gradient from 1722 m to 2954 m on the east of the plateau. The vegetation area, hydrothermal factors, soil elements, and species composition were analyzed using methods of spatial analysis and a partial least squares structural equation model (PLS-SEM), and these were used to estimate the total carbon stores of different plant functional groups for the entire area of each SG. This study revealed the driving factors of the elevational pattern of plant carbon storage in the SGs. The entire plant carbon storage of the eight SGs was 35,880.98 Mg in total. In addition, the aboveground and belowground carbon storage values both exhibited U-shaped trends along the elevation gradient. Significant minimum values were observed at the mid-elevation regions, ranging from 2305 m to 2673 m. The plant carbon storage was predominantly allocated to the belowground portions (accounting for 72.3% of the total carbon storage), and this allocation strategy was more pronounced at both low- and high-elevation regions. The carbon storage proportion among the different plant functional groups was the largest for forbs (average in 2348.85 Mg, accounting for 52%), medium for sedges (average in 1982.81 Mg, accounting for 44%), and the smallest for grasses (average in 153.47 Mg, accounting for 4%). The plant species diversity promoted carbon accumulation in the sedges and forbs, while the soil total phosphorus exhibited an inhibitory effect. In the PLS-SEM, hydrothermal factors (total effect = −0.8107) and species diversity (total effect = 0.4969) were the primary drivers of the plant carbon storage elevational pattern in the SGs, while the soil properties (total effect = −0.3501) and biomass (total effect = 0.0697) effects did not reach statistical significances. Therefore, the plant carbon storage distribution pattern along the elevation gradient was driven by hydrothermal factors and species diversity on the SGs of the eastern plateau. The plants such as forbs and sedges might play more important roles in improving regional plant carbon storage in high-elevation grasslands, through interactions with hydrothermal factors. Full article

Fused deposition modeling (FDM) of polylactic acid (PLA) produces parts whose weak interlayer bonding and low as-printed crystallinity limit their tensile performance. This work used a Taguchi L9 orthogonal array with five replicates per cell (n = 5; N = 45 annealed specimens plus five non-annealed controls) to study how annealing temperature (70, 80, and 90 °C) and holding time (40, 60, and 80 min) change the tensile response of a commercial PLA grade (eSUN PLA+) printed on a desktop FDM machine. Differential scanning calorimetry (DSC) and X-ray diffraction (XRD) were used in parallel to measure total crystallinity, and XRD was deconvoluted to estimate the α′/α polymorph fractions; the DSC α′→α exothermic shoulder was used as an independent cross-check. Every annealed condition exceeded the non-annealed baseline ultimate tensile stress (UTS) of 39.75 ± 1.28 MPa. The optimum, 47.00 ± 0.97 MPa at 70 °C/60 min, gave an 18.2% gain. Total crystallinity rose from 8.6% (DSC baseline) to 41.8% (DSC, 90 °C/80 min), with DSC and XRD ranking the conditions consistently. ANOVA confirmed both temperature (30.0% contribution) and time (24.2%) as significant at α = 0.05. The new contribution is a combined strength–crystallinity–polymorph map for desktop FDM-printed PLA: the best-performing specimens are dominated by the disordered α′ form, while the stiffer but weaker high-temperature specimens shift toward α. A partial least squares regression on all 50 specimens supports the polymorph-composition role beyond what total crystallinity alone explains. The practical conclusion is that moderate annealing just above the glass transition gives the best balance of crystal content, polymorph character, and geometric stability for FDM-printed PLA. Full article

Destructive methods for monitoring stress responses remain a bottleneck in precision agriculture. This study presents a non-destructive methodological framework evaluating drought responses in 30 Opuntia ficus-indica plants over four months under five irrigation levels. Cladode traits (color, weight, and thickness) were measured alongside RGB imagery from a UAV and hyperspectral imaging (400–1000 nm). Partial least squares regression (PLSR) models showed high capability to model proline (R² = 0.91), chlorophyll a (R² = 0.97), and total chlorophyll (R² = 0.97) within the experimental dataset. Crucially, these models reflected continuous spectral–physiological variation across the irrigation gradient rather than discrete treatment separation, with key spectral regions identified at 530–600 nm and 550–750 nm. UAV-derived RGB imagery enabled the estimation of plant area and biomass (R² = 0.88). Under extreme drought, cladode thickness decreased by approximately 41%, accompanied by reduced biomass and increased soluble solids (°Brix). While no statistically significant differences were observed among irrigation treatments for biochemical variables, limiting treatment discrimination based on discrete classification, the hyperspectral data successfully captured the underlying continuous physiological variation. Consequently, this work demonstrates the methodological viability of integrating UAV structural phenotyping and hyperspectral analysis as a continuous monitoring tool rather than a rigid classification system. These findings provide a methodological baseline that highlights the need for continuous sensing in CAM plants, though further validation with independent datasets remains essential for wider operational application. Full article

Reduced muscle mass and strength are highly prevalent in liver transplant recipients (LTRs). Citrate, a key intermediate of the tricarboxylic acid cycle, may adversely relate to muscle health through disturbances in mitochondrial energy metabolism and metabolic flexibility. We aimed to investigate the association of plasma citrate levels with estimated muscle mass, strength, and physical performance in LTRs. We included stable LTRs from the TransplantLines Biobank and Cohort Study at least 1 year after transplantation. Muscle mass, strength, and physical performance were assessed using the 24 h urinary creatinine excretion rate divided by height squared (creatinine excretion rate index, CERI), handgrip strength, sit-to-stand (STS) and Timed Up and Go (TUG) tests. Associations of plasma citrate with muscle-related parameters were examined using Spearman correlation and linear regression analyses. A total of 501 LTRs were included (median age 59 [49–67] years; 58.7% men) at a median of 14.0 (7.4–22.5) years post-transplantation. Spearman correlation analyses showed that plasma citrate levels were inversely correlated with CERI (ρ= −0.226, p < 0.001) and handgrip strength (ρ= −0.211, p < 0.001). Additionally, plasma citrate levels were positively correlated with STS time (ρ = 0.170, p = 0.019) and TUG time (ρ = 0.203, p = 0.006). In linear regression analyses, higher plasma citrate levels were associated with lower CERI and lower handgrip strength and with longer STS and TUG time. In multivariable linear regression analyses, plasma citrate remained independently associated with CERI (fully adjusted standardized β= −0.14, p = 0.001) and handgrip strength (fully adjusted standardized β= −0.11, p = 0.010), whereas associations with physical performance measures were no longer significant after adjustment. CERI mediated 45.8% of the association between citrate and handgrip strength. In conclusion, higher plasma citrate levels are independently associated with lower estimated muscle mass and strength in LTRs. Circulating citrate may serve as a potential biomarker of post-transplant muscle impairment risk. Full article

In recent years, the integration of renewable energy sources and the widespread use of nonlinear loads have increased dynamic uncertainties in modern power systems, making real-time and synchronized monitoring essential. Accurate M-class synchrophasor estimation under these nonstationary and spectrally uncertain conditions remains a challenging problem due to dynamic variations, harmonics/interharmonics, out-of-band interference, and measurement noise. This study proposes a suitably constrained optimization-based framework for M-class synchrophasor estimation, in which a hybrid structure integrating an ESPRIT-based subspace method with the Adaptive Fitness Distance Balance Artificial Rabbit Optimization (ES-AFDB-ARO) algorithm is employed. In this framework, the optimization stage is guided by spectral information obtained via the subspace stage to narrow the search space and improve convergence stability. Performance is evaluated under IEEE C37.118 steady-state and dynamic conditions via Monte Carlo simulations, showing that total vector error, frequency error, and rate-of-change-of-frequency error values remain within standard limits. Comparative analyses at 60 dB and 40 dB SNR demonstrate that the ES-AFDB-ARO method exhibits improved and more stable performance than the widely used interpolated discrete Fourier transform, Taylor weighted least squares and Taylor–Kalman filter methods. The results show that the proposed framework offers a reliable solution for synchrophasor estimation under dynamic operating conditions. Full article

Rapid and cost-effective estimation of soil total nitrogen (TN) is essential for soil fertility assessment and nutrient management. However, the performance of laboratory visible–near-infrared (Vis–NIR) models is shaped not only by preprocessing and modeling strategy but also by sample preparation and the soil’s compositional background. In this study, TN prediction was evaluated using 376 topsoil samples from two contrasting datasets: Mollisols from the black-soil region of Northeast China and Ultisols from Qiyang County, Hunan Province, southern China. Spectra acquired over 350–2500 nm for three particle-size fractions were preprocessed using Savitzky–Golay smoothing combined with standard normal variate (SNV), first-derivative, or second-derivative transformations, and modeled using partial least squares regression (PLSR), support vector regression (SVR), and extreme gradient boosting (XGBoost). Model development used a 5 × 5 nested cross-validation followed by evaluation on a sample-grouped held-out test set. Among all combinations, XGBoost with first-derivative preprocessing on the 0.25 mm fraction produced the best performance, with test R² values of 0.91 for Mollisol and 0.78 for Ultisol. Shapley additive explanations (SHAP) and principal component analysis (PCA) consistently identified informative spectral regions at 430–480 and 1330–1450 nm for Mollisol and at 585–635, 820–900, and 2180–2240 nm for Ultisol. Prediction errors were larger in the sampled Ultisol dataset and increased with DCB-extractable Fe and mineral backgrounds. A second-stage log-domain residual correction incorporating ancillary soil properties further reduced the Ultisol RMSE from 0.30 to 0.27 g kg⁻¹. These findings support the 0.25 mm, first-derivative, XGBoost workflow as a robust laboratory Vis–NIR approach for TN prediction and indicate that composition-aware residual correction can improve prediction in oxide- and mineral-rich soils. Full article

The output voltage harmonics of electric vehicle (EV) charging converters directly affect grid power quality. This paper proposes a harmonic analysis method based on geometric algebra (GA), which employs a multivector representation of signals and least squares estimation to accurately extract fundamental, integer-order, and inter-harmonics. A coupling coefficient is defined to quantify the phase correlation between frequency components. Based on measured data, harmonic characteristics under four typical operating conditions are analyzed, and an adaptive PID controller is designed to dynamically adjust the virtual resistance for harmonic suppression. The results show that the GA method significantly reduces spectral leakage under non-integer-period sampling conditions, with amplitude estimation errors below ±2%. The total harmonic distortion (THD) decreases with increasing active power and increases with reactive power injection. The droop coefficient exhibits a non-monotonic effect, while reducing the proportional gain raises the THD. Adaptive control reduces the average THD by 14.0–28.5% with a total response time of less than 0.05 s. The coupling coefficient $C_{13}$ is strongly positively correlated with THD and negatively correlated with the maximum Lyapunov exponent computed using the Rosenstein small-data method (correlation coefficient −0.85), confirming the intrinsic relationship between coupling and stability. Compared with fast Fourier transform (FFT) and other methods, GA achieves higher accuracy under short data records and non-integer-period sampling. This paper provides a complete theoretical framework and engineering solution for harmonic suppression in charging converters. Full article

This paper investigates the prediction of urban air temperature (Tair) from satellite-derived land surface temperature (LST) in the complex urban and topographic environment of Yerevan, Armenia. Building on previous work that demonstrated the effectiveness of machine learning (ML) approaches for point-based Tair estimation using Partial Least-Squares Regression (PLSR) with multiple environmental variables, this study shifts the focus to the spatial distribution of Tair. Several prediction methods and input variable combinations are evaluated to generate gridded Tair maps, which are assessed for spatial consistency against expected patterns driven by land cover, elevation, local knowledge, and spot observations. In total, five predicting methods were used—one regression approach (PLSR) and four ML methods: random forest (RF), quantile regression forest (QRF), support vector machine (SVM), multilayer perception (MLP). RF and QRF deliver the best overall results, with RF achieving the highest testing R² (0.74) and lowest RMSE (0.56). Spatial patterns are similar for PLSR, RF and QRF, highlighting cooler northern high-altitude areas and warmer southern urban areas. Overall, the results confirm the reliability of the proposed Tair spatial mapping methods in complex urban environments. Full article