Search Results (259)

Background/Objectives: This study aimed to verify a prediction model identifying the relationships and pathways among factors associated with Korean nursing students’ provision of person-centered care to patients with dementia. Methods: This was a covariance structure analysis study to establish a hypothetical model of 313 Korean nursing students located in a metropolitan area. IBM SPSS version 18.0 (Chicago, IL, USA) and AMOS version 5.0 (Chicago, IL, USA) were used to analyze the data. Structural equation modeling analysis was applied to verify convergent and discriminant validity using higher-order factor analysis in the final model analysis. Results: The model fit indices of the research model were as follows: χ²/df = 1.83 (p < 0.001), GFI = 0.91, AGFI = 0.88, NFI = 0.91, CFI = 0.90, RMR = 0.04, and RMSEA = 0.05. The factors affecting person-centered care, nursing professionalism (γ = 0.45, p = 0.024), and empathy (γ = 0.21, p = 0.036) showed significant associations, whereas clinical practice adaptation (γ = 0.21, p = 0.013) and nursing professionalism (γ = 0.08, p = 0.004) had indirect effects. These factors explained 40% of the variance in person-centered care. Conclusions: This study is significant because it provides basic data for developing an educational program that can improve the person-centered care capacity of domestic nursing students by confirming that clinical practice adaptation, nursing professionalism, and empathy are important factors related to person-centered care. Full article

The spatial distribution of flammable tree species directly influences forest fuel structure and fire risk patterns. However, mixed pixels limit the ability of conventional classification methods to characterize continuous within-pixel variation in species composition, thereby constraining fine-scale forest mapping. To address this issue, this study developed a subpixel mapping framework for flammable tree species in Yajiang County, Sichuan Province, by integrating Sentinel-2 time-series data with a spectral mixing–unmixing strategy. Using 2019 Sentinel-2 time-series data and National Forest Inventory (NFI) data, temporal mixed samples with known abundance fractions were generated using a linear spectral mixing model. An XGBoost-based collaborative multi-regression framework was then applied to estimate the proportions of different tree-species endmembers within complex forest pixels. Quantitative evaluation using synthetic mixed samples showed that the model achieved stable unmixing performance across different random mixing scenarios. The best performance was obtained under the Mixed 2 scenario with a sample size of 250 K, reaching an R² of 0.821. The resulting maps revealed continuous spatial variation in the abundance and composition of flammable tree species. Mountain pine was the most widespread and dominant species, followed by spruce and mountain oak, whereas birch and fir mainly exhibited localized patchy distributions. An additional NFI-based categorical evaluation assessed the consistency of the final maps with real forest inventory records. The identification accuracies were 93.95% for pure stands and 91.22% for mixed stands, while the species classification accuracies were 87.28% for pure stands and 84.41% for dominant species in mixed stands. The proposed framework provides useful spatial information for regional forest fuel assessment and fire risk management. Full article

National Forests in the United States provide a broad range of goods and services, safeguard biological diversity, and contribute to the resilience of ecosystems, societies, and economies. Given differences in land use history and forest management approaches between National Forests and neighboring ownerships, we investigated whether they differ across a spectrum of forest health indicators, from biomass stocking to structural diversity to invasion by non-native plants. We used Nationwide Forest Inventory (NFI) plot data from within National Forest System (NFS) lands across the conterminous United States (~20,000 plots) and from within 25 km of NFS lands on other ownerships (~20,000 plots) to quantify differences in forest health indicators. Controlling for environment, geography and forest composition, we found, nationally and regionally, that NFS forest plots had significantly greater tree species and structural diversity and evenness, basal area and biomass per hectare, and seedling density than neighboring plots. They were also less invaded by non-native plants. Such forest health monitoring results are an initial step toward better understanding the status of forest health indicators for NFS forests. This is particularly important because many disturbance factors threaten the sustainability of National Forests and their capacity to provide socioeconomic and ecological benefits. Systematic monitoring of forest health across broad scales increases our understanding of how these disturbances are changing forest conditions and informs land management and policy decisions. Full article

Background/Objectives: To develop and validate a multidimensional risk assessment scale for identifying patients at risk of peripheral venous catheter (PVC)-associated phlebitis. Methods: This methodological study followed a two-phase design. In Phase 1 (scale development), an initial item pool of 39 candidate items was generated from a focused literature review and refined using the Lawshe technique with 20 expert raters. Data were collected from 729 hospitalized patients, who contributed 1008 PVCs between February and September 2021. Because the scale items are catheter-level, the PVC was the unit of analysis: 502 PVCs (from 380 patients) were used for exploratory factor analysis (EFA), and 506 PVCs (from 349 patients) for confirmatory factor analysis (CFA). In Phase 2 (clinical application), the finalized scale was administered to a separate, independent cohort of 208 patients between September and October 2021 alongside the Infusion Nurses Society (INS) Phlebitis Scale. Reliability was assessed using the Kuder–Richardson 20 (KR-20) coefficient, and discriminative performance was evaluated with Receiver Operating Characteristic (ROC) curve analysis. Results: EFA and CFA confirmed a three-factor structure comprising 14 items distributed across Individual, Chemical, and Mechanical risk domains. The instrument demonstrated strong internal consistency (KR-20 = 0.823) and excellent discriminative accuracy (AUC = 0.898), with an optimal cut-off of 20.5 (sensitivity 87%, specificity 91%). All CFA fit indices met the conventional acceptability thresholds (χ²/df = 3.249; GFI = 0.943; AGFI = 0.914; CFI = 0.942; NFI = 0.919; IFI = 0.943; TLI = 0.925; RMSEA = 0.067). In Phase 2, scale scores correlated significantly with the INS Phlebitis Scale (r = 0.794, p < 0.001). Conclusions: The Risk Assessment Scale for PVC-Associated Phlebitis is a valid and reliable instrument with strong psychometric properties. It enables early identification of high-risk patients and supports targeted preventive strategies in clinical practice. Full article

Accurate digital canopy height models (DCHMs) derived from historical aerial photographs are essential for reconstructing long-term forest structural dynamics; however, the influence of photogrammetric software on DCHM quality and reliability remains insufficiently evaluated. This study compared the performance of two structure-from-motion (SfM) photogrammetric platforms, Metashape and Pix4Dmatic, for processing old aerial photographs and generating DCHMs in Ishikawa prefecture. Software performance was assessed using image processing efficiency, geometric accuracy based on root mean square error (RMSE), and correlation between derived DCHMs and National Forest Inventory (NFI) measurements. The results revealed that Metashape required shorter image processing times for the digital surface model generation and produced denser point clouds with broader spatial coverage. By contrast, Pix4Dmatic achieved higher geometric accuracy, with RMSE values of 0.571 m, 0.870 m, and 2.120 m in the X, Y, and Z directions, respectively. The Metashape-derived DCHM showed a higher mean value (15.267 ± 5.882 m) than Pix4Dmatic (14.749 ± 5.834 m), but Pix4Dmatic-generated DCHMs showed a closer relationship (r = 0.880) with NFI data (15.322 ± 5.451 m). These findings demonstrate that photogrammetric software selection substantially influences three-dimensional reconstruction from old aerial imagery and affects the reliability of DCHM generation. This study provides practical guidance for selecting SfM software for forest structural analysis and long-term forest monitoring. Full article

Enterprise systems are widely adopted in organizations, yet user acceptance remains a major challenge due to the complex interplay of cognitive, social, motivational, and innovation-related factors. Existing technology acceptance models often provide fragmented explanations by focusing on limited determinants. This study proposes TAM 4, an exploratory framework integrating constructs from the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Hedonic-Motivation System Adoption Model (HMSAM), and Diffusion of Innovation (DOI). The study was conducted in the context of enterprise application usage and professional enterprise system training environments involving organizational users, trainees, and practitioners. Data were collected from 115 enterprise system users (trainees and practitioners). To consolidate overlapping indicators and strengthen construct definition, principal component analysis (PCA) was applied, yielding seven higher-order constructs that explain 81.642% of cumulative variance. The framework was validated using PLS-SEM with three scenario-based structural models (full mediation, partial mediation, and direct effects). The results show that Model 3 provides the best fit and predictive performance (SRMR = 0.048; NFI = 0.786), indicating that enterprise system adoption is better explained through a direct effect structure rather than a purely mediated TAM pathway. The novelty of this study lies in introducing TAM 4 as a PCA-driven multi-theory acceptance model and evaluating its explanatory robustness through multi-scenario model comparison, offering practical insights for improving enterprise system implementation strategies. Full article

Reliable land cover information is essential for scaling plot-based measurements in national forest inventories (NFIs). This study compared the precision of key forest indicators in the Bangladesh NFI using remote sensing (RS)-derived and field-assigned land cover data. Field data from 1781 plots, collected as part of the Bangladesh NFI (2015–2019), were integrated with a 2015 national land cover map produced from SPOT-6/7, Landsat, and Sentinel-2 imagery. The precision of forest indicator estimates was evaluated across land cover domains and ecological zones. Results show that, under an unchanged NFI field measurement and estimation framework, RS-derived land cover reduced the width of confidence intervals (i.e., improved statistical precision) of estimates for most biomass related indicators, including above- and below-ground biomass, tree volume, basal area, and carbon pools, by 15–20% on average, with some reductions exceeding 50%. Improvements were less consistent for regeneration-related indicators (saplings, seedlings). The insights from this study highlight the advantages of remote sensing-derived land cover for improving NFI indicator precision, while underscoring the continued need for advancing ontology-driven approaches with necessary strengthening of field crew capacity to ensure the consistent application of land cover standards. Full article

Background/Objectives: Whole-cell vaccines have demonstrated clinical potential in cancer treatment and recurrence prevention, yet their immunogenicity and dendritic cell (DC) activation remain suboptimal. This study aimed to evaluate whether a needle-free injector (NFI) could enhance the immunogenicity and antitumor efficacy of whole-cell tumor vaccines. Methods: Adaptive immune responses induced by NFI and traditional syringe injection (SYI) were compared following whole-cell vaccine administration. The morphology of vaccine fluid ejected by NFI and SYI was examined, and the effects on DC antigen uptake and activation were assessed. Antitumor efficacy was further evaluated in MC38 colon adenocarcinoma challenge models. Results: NFI administration elicited stronger antigen-specific adaptive immune responses than SYI. The high-velocity pressure generated by NFI resulted in fragmentation of whole-cell vaccine material, and this morphological alteration was associated with enhanced DC antigen uptake and activation. These immunological improvements corresponded with superior tumor suppression in MC38 models following NFI-delivered vaccination. Conclusions: NFI delivery enhances the immunogenicity and antitumor efficacy of whole-cell tumor vaccines. These findings suggest that needle-free injectors may serve as a simple and effective strategy to improve the performance of whole-cell cancer vaccines. Full article

Background: Social anxiety in adolescence is a prevalent mental health concern characterized by intense fear of negative evaluation in social situations. The Social Anxiety Questionnaire for Adolescents (CASO-N24) is a Spanish-language instrument requiring validation in Peruvian populations. Objective: This study aimed to validate the CASO-N24 in Peruvian adolescents aged 12–17 years, extending its application beyond the original 9–15-year range, and examine its psychometric properties including factorial structure, measurement invariance, nomological validity, and internal consistency. Methods: A stratified probability sample of 710 adolescents (352 males, 358 females; M = 14.82 years, SD = 1.45) from four northern Peruvian educational centers completed the CASO-N24 and ASQ-14. Exploratory and confirmatory factor analyses, multigroup invariance testing by age and gender, nomological validity assessment, and reliability estimation (Cronbach’s α and McDonald’s ω) were conducted using polychoric correlations and robust estimation methods. Results: The six-factor structure was replicated, explaining 47.13% of variance with factor loadings ranging 0.48–0.78. Model fit indices were excellent (GFI = 0.981, AGFI = 0.976, NFI = 0.971, SRMR = 0.046). Complete measurement invariance was achieved across age groups (12–15 vs. 16–17 years). Partial invariance by gender was observed, with differential item functioning identified in item 17. Nomological validity was confirmed through moderate-to-high correlations with ASQ-14 (males: r = 0.622; females: r = 0.604). Internal consistency was adequate (total scale ω = 0.95; subscales ω = 0.69–0.82). Conclusions: The CASO-N24 demonstrated robust psychometric properties for assessing social anxiety in Peruvian adolescents aged 12–17 years, supporting its multidimensional structure and utility for early detection in school settings while highlighting gender-specific response patterns warranting clinical consideration. Full article

Feeding intolerance (FI) is common in preterm infants and disrupts enteral nutrition. Because clinical signs of FI are nonspecific, objective biomarkers are needed. In this exploratory study, we evaluated whether electrogastrography (EGG) can distinguish infants with FI from those with no FI (NFI) based on their gastric response to feeding. For each infant, the first available weekly EGG recording (postnatal week 1 or, if unavailable, week 2), comprising two consecutive feeding cycles, was analyzed. Each recording included pre-, during-, and post-feed segments. Power spectral density (PSD) was computed over 0.5–9 cycles per minute (cpm) to derive baseline mean PSD (mPSD) and PSD ratios (PSDR) for during/pre- and post/pre-feeding (PSDR_Dur/Pre, PSDR_Post/Pre). Mean power ratios (mPR) were calculated across bradygastria, normogastria, and tachygastria frequency bands. Group differences were assessed using bootstrap resampling. Eighty-four infants were analyzed (75 NFI, 9 FI). Baseline mPSD values were comparable between the two groups. FI infants demonstrated lower PSDR_Dur/Pre values in the bradygastria and tachygastria bands, whereas normogastria responses were similar. No differences were observed in PSDR_Post/Pre. EGG detected attenuated gastric activity specifically during feeding and not after feeding in infants with FI, supporting its potential as a non-invasive physiologic marker that warrants further validation. Full article

This study examines the differences in sports learning among Generation Z based on digital literacy, using the Unified Theory of Acceptance and Use of Technology (UTAUT) and Media Richness Theory (MRT). As non-face-to-face sports learning—including online lectures, remote coaching, and virtual reality—rapidly expands, digital literacy has become a key factor influencing learning outcomes and equity. Data were collected from Generation Z adults engaged in sports learning through platforms including YouTube, social networking services, online lecture platforms, and mobile applications. Participants were classified into low (n = 87)-, medium (n = 80)-, and high (n = 70)-digital-literacy groups. A 32-item questionnaire adapted from prior studies assessed digital literacy (4 items), four UTAUT constructs (performance expectancy, effort expectancy, social influence, and facilitating conditions; 16 items), and three media richness dimensions (multiple channels, immediacy of feedback, and personalness; 12 items). Confirmatory factor analysis demonstrated acceptable model fit (χ² = 779.013, df = 436, p < 0.001, NFI = 0.914, IFI = 0.960, TLI = 0.954, CFI = 0.960, SRMR = 0.037, RMSEA = 0.058), reliability (all ω and α > 0.70), and convergent/discriminant validity (all AVE > 0.50; C.R. > 0.70). Group comparisons indicated that higher digital literacy was linked to higher scores in technology acceptance and media richness perceptions (F = 40.364–64.150, p < 0.001, ηp² = 0.257–0.354) These findings indicate that intra-generational differences in digital literacy shape technology use and media experience in sports learning, highlighting the need to enhance media richness and systematically develop learners’ digital literacy to improve digital sports education’s effectiveness and equity. But causal inferences are limited by the cross-sectional design. Full article

Forestry projects have long faced the inherent tension between stringent conservation objectives and the enhancement of human well-being, making it increasingly important to assess the sustainable livelihoods of participating households. The Natural Forest Management Project in the Northeast and Inner Mongolia state-owned forest region (NSFR) aims to transform high-quality ecological products and services into inclusive public benefits while providing reasonable compensation for ecological conservation and restoration efforts. This approach seeks to achieve synergies among ecological protection, economic development, and livelihood improvement. Drawing on six consecutive years (2017–2022) of longitudinal micro-level household survey data, this study quantifies the sustainable livelihood levels of households participating in natural forest management. A Natural Forest Involvement (NFI) index was constructed to measure their degree of participation. Furthermore, the well-being effects of frontline participants in natural forest management activities were investigated. The findings indicate that the overall sustainable livelihood capital of these households shows a steady upward trend across NSFR, significant disparities exist among different areas. Moreover, approximately half of forestry worker households are deeply embedded in the natural forest management system, and this engagement pattern negatively affects households’ sustainable livelihood capital. These results not only enrich the empirical literature on forestry project effectiveness but also offer relevant insights for forestry project design and policy formulation in other developing countries. Full article

National forest inventory (NFI) data are often collected over a 5-year or 10-year period, meaning some are already outdated by the time the complete results are available. This study assesses changes in growing stock volume (GSV, m³/ha) using hybrid estimation supported by Sentinel-2 metrics. It focuses on constructing a model for estimating the change in GSV using NFI plot data and bitemporal remotely sensed auxiliary data, where such data are available for both points in time ($t_1\mathrm{a}\mathrm{n}\mathrm{d}{t}_2$), and unitemporal data for which remotely sensed auxiliary data are available only for $t_2$. A machine-learning approach based on the random forests (RFs) algorithm was used to predict plot-level GSV change. The original data for $t_2$ and $t_3$ were first used to evaluate the accuracy of the change prediction at the plot level, after which the predicted changes were applied to update the plot-level GSV to predict plot-level GSV at $t_3$, which was then assessed against the observed plot-level GSV at $t_3$. Predicted change was assessed with the Mean Average Annual Volume Change (MAAVC) method, representing the average annual change in GSV over a given period. The results indicate that at the plot level, the bitemporal model produced GSV change estimates with low accuracy (R² = 0.26, RMSE = 4.06 m³/ha, and MAE = 3.26 m³/ha), while the unitemporal model achieved R² = 0.40, RMSE = 3.64 m³/ha, and MAE = 2.65 m³/ha when predicting the $t_1-t_2$ GSV change. Using the predicted change to predict plot-level GSV at $t_3$, the MAAVC based on field data yielded R² = 0.91 and RMSE = 45.11 m³/ha, while the RS unitemporal yielded R² = 0.73 and RMSE = 83.79 m³/ha, and the bitemporal yielded R² = 0.72 and RMSE = 83.61 m³/ha. Mean population GSV at $t_3$, estimated from the RF models, was 254.61 and 255.19 m³/ha for the unitemporal and bitemporal models, respectively. Monte Carlo simulations with a novel stopping criterion were then used to estimate total standard errors, which were 10.48 and 10.40 m³/ha for the unitemporal and bitemporal models, respectively, incorporating both model prediction uncertainty and sampling variability. A test of significance revealed a significant effect of the proposed method on the estimated mean population GSV at $t_3$ (p < 0.001). Conclusively, MAAVC and spatiotemporal RS methods provide a robust framework for predicting GSV at $t_3$ using Estonian NFI and Sentinel-2 data. Full article

Background/Objectives: Understanding the determinants of physical activity (PA) and health outcomes requires integrating environmental and motivational perspectives. Grounded in Self-Determination Theory (SDT), this study tested a sequential model in which perceived sport infrastructure service quality enhances exercise motivation, which subsequently increases PA and leads to better self-rated health (SRT). Methods: A total of 546 recreational adult exercisers completed validated questionnaires assessing sport infrastructure service quality (SQAS), exercise motivation (MPAM-R), PA (IPAQ), and self-rated health. Structural equation modelling (SEM) was used to examine the hypothesised relationships among variables. Results: The proposed sequential model was supported. Perceived service quality positively predicted exercise motivation (β = 0.255, p < 0.001), motivation significantly predicted PA (β = 0.266, p < 0.001), and PA was positively associated with self-rated health (β = 0.115, p < 0.005). Model fit indices indicated a good and acceptable fit to the data (CFI = 0.947, TLI = 0.935, NFI = 0.914, GFI = 0.931, RMSEA = 0.072, SRMR = 0.067, χ²/df = 3.85). Conclusions: The findings underscore the importance of high-quality exercise infrastructure as a key environmental factor that supports motivational engagement and promotes healthier behaviour patterns. Interventions aimed at increasing PA and improving perceived health should address both environmental quality and motivational processes. Full article

Accurate and non-destructive assessment of plant nutritional status remains a key challenge in precision agriculture, particularly under dynamic physiological conditions such as dehydration. Therefore, this study focused on developing an integrated nutritional assessment framework for avocado (Persea americana Mill.) leaves across progressive dehydration stages using spectral analysis. A novel nutritional function index (NFI) was innovatively constructed using an entropy-weighted multi-criteria decision-making approach. This unified assessment metric integrated critical physiological indicators, such as moisture content, nitrogen content, and chlorophyll content estimated from soil and plant analyzer development (SPAD) readings. To enhance the prediction accuracy and interpretability of NFI, innovative vegetation indices (VIs) specifically tailored to NFI were systematically constructed using exhaustive wavelength-combination screening. Optimal wavelengths identified from short-wave infrared regions (1446, 1455, 1465, 1865, and 1937 nm) were employed to build physiologically meaningful VIs, which were highly sensitive to moisture and biochemical constituents. Feature wavelengths selected via the successive projections algorithm and competitive adaptive reweighted sampling further reduced spectral redundancy and improved modeling efficiency. Both feature-level and algorithm-level data fusion methods effectively combined VIs and selected feature wavelengths, significantly enhancing prediction performance. The stacking algorithm demonstrated robust performance, achieving the highest predictive accuracy (R2V = 0.986, RMSEV = 0.032) for NFI estimation. This fusion-based modeling approach outperformed conventional single-model schemes in terms of accuracy and robustness. Unlike previous studies that focused on isolated spectral predictors, this work introduces an integrative framework combining entropy-weighted feature synthesis and multiscale fusion learning. The developed strategy offers a powerful tool for real-time plant health monitoring and supports precision agricultural decision-making. Full article