Search Results (3,852)

With increasing demand for wellness tourism, identifying the key factors influencing emotional perceptions is essential for optimizing destination planning and management. Although Anhui Province has experienced rapid growth in wellness tourism destinations in recent years, scientific understanding of tourists’ emotional perceptions and their driving mechanisms has lagged behind this rapid expansion, a gap that can be addressed by integrating big data with spatial analysis to provide a scientific perspective for optimizing destination planning and informing regional wellness tourism policy. To address this gap, this study conducts a sentiment analysis of wellness bases in Anhui Province using user-generated content (UGC) data. Sentiment scores were quantified via SnowNLP, while kernel density, time-series, and multivariate statistical analyses were applied to examine spatial distributions, temporal dynamics of sentiments and review volumes, and emotional driving factors. The results indicate a spatial pattern of higher density in the south, lower density in the north, and dual-core agglomeration, closely linked to natural resource endowments. Temporally, sentiment scores rise in spring and summer and decline in winter, while review volumes peak in spring and autumn. Overall regression analyses reveal a significant positive effect of green coverage and a negative effect of accommodation prices. In the typological analysis, sentiment scores of Forest Wellness Bases (FWBs) relate to green coverage and negative ions, while Hydrological Wellness Bases (HWBs), Traditional Chinese Medicine Wellness Bases (TCMWBs), and Wellness Towns (WTs) are driven by the combined effects of facility services, locational price, and ecological environment. These findings provide a scientific basis for the sustainable development and differentiated management of wellness tourism destinations. Full article

Industrial/chemical processes usually involve significant time delays. The responses of systems/processes with long time delays can feature high overshoot and oscillation due to phase lag. Moreover, parameter variations and external disturbances make controlling such systems more difficult. The Smith Predictor (SP) Controller structure and the Coefficient Diagram Method (CDM) are commonly used in the literature to ensure robust control performance. This study introduces a novel design approach combining the strengths of SP and CDM. This method proposes using a second CDM-based controller for disturbance rejection, while using a CDM controller for setpoint tracking. The approach was tested on three high-order time-delay plant models, accounting for parameter variations and disturbance effects. Results show that this method can achieve low overshoot, quick rise time, and short settling time in set-point tracking. Furthermore, it delivers robust control performance under conditions of parameter changes and external disturbances. Full article

This study systematically examines the impact of radical urbanization on the quality of economic growth using city-level data from 290 major prefecture-level cities in China during 2003–2019. A comprehensive indicator system for economic growth quality is constructed using PCA, capturing multiple dimensions of efficiency, stability, and sustainability. Nighttime light data obtained from the NOAA is extracted and calibrated, and the ratio of urban built-up area to nighttime light intensity is employed to measure the degree of radical urbanization. Empirical results reveal a divergence between economic quantity and quality effects: while radical urbanization promotes economic expansion, it significantly inhibits the quality of economic growth. To address potential endogeneity concerns, the change in FDI relative to changes in built-up area is used to capture FDI flow direction, with its one-period lag serving as an instrumental variable. Mechanism analysis, based on an interaction-based identification framework, shows that radical urbanization suppresses growth quality primarily through two transmission channels: reduced fiscal output efficiency and declining land use efficiency. Further analysis indicates that radical urbanization crowds out science and education expenditures, weakening fiscal effectiveness and reinforcing the identified transmission mechanisms. These findings provide objective evidence for evaluating urbanization strategies and offer policy insights for promoting quality-oriented and sustainable urban development. Full article

While prior research shows that subordinates’ attributions can amplify or buffer the negative effects of abusive supervision on performance outcomes, it remains unclear whether similar moderating effects extend to subordinate well-being. Drawing on attribution theory and conservation of resources (COR) theory, this study investigates whether performance-promotion and injury-initiation attributions moderate the relationship between abusive supervision and emotional exhaustion. Applying a time-lagged research design, we surveyed full-time employees (N = 224) within a single Chinese transportation company and tested the proposed hypotheses using structural equation modeling (SEM). Contrary to the expectations and prior evidence, the moderating effect of injury-initiation attribution between abusive supervision and emotional exhaustion is nonsignificant. Moreover, performance-promotion attribution significantly moderates this relationship, in the opposite direction to the expectations: It exacerbates (rather than buffers) the positive association between abusive supervision and emotional exhaustion. These findings complicate the assumption that performance-promotion attributions are protective whereas injury-initiation attributions are destructive, instead suggesting a different pattern of attributional effects. The study advances the understanding of abusive supervision attributions and provides implications for management practice. Full article

Background/Objectives: Successful interception of moving targets requires combining predictive control, which anticipates future target states, and reactive control, which compensates for ongoing sensory discrepancies. How these components evolve over time and are distributed across gaze and manual behavior remains unclear. We aimed to explore the time-resolved dynamics of predictive control during continuous interception and to dissociate eye and hand contributions. Methods: Human participants intercepted a moving target in a two-dimensional arena using a joystick while eye movements were recorded. Target speed was systematically varied, and visual information was selectively reduced by occluding either the target or the user-controlled cursor. Predictive control was assessed using two complementary metrics: a geometric strategy index capturing moment-to-moment spatial lead or lag relative to target motion, applied separately to gaze and manual trajectories, and root mean square error (RMSE) computed relative to current and forward-shifted target positions to quantify predictive alignment. Results: Successful interception was characterized by structured, speed-dependent transitions between predictive and reactive control rather than a fixed strategy. Predictive alignment emerged early and was dynamically reweighted as temporal constraints increased. Gaze and manual behavior showed complementary but partially dissociable predictive signatures. Occluding the target decreased predictive alignment, whereas occluding the user-controlled cursor had comparatively minor effects, indicating strong reliance on internal state estimation rather than continuous visual feedback of the effector. Conclusions: Predictive and reactive control are continuously and dynamically reweighted during interception. Their interaction unfolds within single trials and depends on target dynamics and sensory availability. These findings provide quantitative evidence for time-resolved coordination between anticipatory and feedback-driven control mechanisms in goal-directed behavior. Full article

Forecasting monthly precipitation in mountainous terrain poses challenges that push conventional deep learning approaches to their limits: convective processes operate locally while orographic effects span entire drainage basins. We compare three architecture families on precipitation prediction across the Colombian Andes: ConvLSTM (convolutional recurrent), FNO-ConvLSTM (spectral–temporal), and GNN-TAT (graph attention LSTM). Using CHIRPS v2.0 and SRTM topography for Boyacá department (61 × 65 grid, 3965 nodes), we evaluate 39 configurations across feature bundles (BASIC, KCE elevation clusters, and PAFC autocorrelation lags) and horizons from 1 to 12 months. GNN-TAT matches ConvLSTM accuracy ($R^2$: 0.628 vs. 0.642; RMSE: 82.29 vs. 79.40 mm) with 95% fewer parameters (∼98K vs. 2.1M). Across configurations, GNN-TAT produces a lower mean RMSE (92.12 vs. 112.02 mm; $p=0.015$) and a 74.7% lower variance. The explicit graph structure, with edges weighted by elevation similarity, appears to reduce sensitivity to hyperparameter choices. Pure FNO struggles with precipitation’s spatial discontinuities ($R^2=0.206$), though adding a ConvLSTM decoder recovers much of the lost skill ($R^2=0.582$). Elevation clustering improves GNN-TAT significantly ($p=0.036$) but not ConvLSTM, suggesting that feature design should match the spatial encoding paradigm. ConvLSTM achieves peak accuracy on local patterns; GNN-TAT provides robust predictions with interpretable spatial reasoning. These complementary strengths motivate stacking ensembles that combine grid-based and graph-based representations. Full article

Soccer goalkeeper diving saves demand precise inter-segmental coordination to intercept shots under uncertainty, yet preparatory postures and kinematic adaptations between declared (D) and undeclared (ND) conditions remain underexplored in youth athletes. This study analyzed lower-limb kinematics and Continuous Relative Phase (CRP) in 10 elite youth male goalkeepers (14.3 ± 0.3 years) performing dives in different conditions using inertial sensors (Xsens MVN Awinda, 60 Hz) on a natural grass pitch. Data were time-normalized across the dive cycle and analyzed using Statistical Parametric Mapping 1D ANOVA to compare kinematic and coordination differences between conditions and preferred side. ND high dives showed significantly shorter total duration (1.02 ± 0.13 s vs. 1.09 ± 0.12 s) and take-off (0.19 ± 0.05 s vs. 0.21 ± 0.05 s) compared to the D condition. Pronounced laterality emerged in hip internal/external rotation (ipsilateral: 0–100%), with CRP alterations only in the ipsilateral ankle-hip/knee during preferred-side low dives (13–74%, p < 0.001), indicating tighter segmental coupling and reduced phase lag between joints from mid-stance to push-off. D condition appeared to favor mediolateral CoM shifts for reach optimization, while ND emphasized anteroposterior readiness. These findings highlight CRP’s sensitivity to coordination under uncertainty and reveal laterality effects in preferred-side low dives. Full article

Anticipation allows individuals to prepare actions by predicting upcoming events, yet its influence on motor learning and its practical relevance for rehabilitation remain unclear. This study investigates how anticipation mechanisms shape motor learning and skill acquisition in a virtual leader–follower task and explores their potential for adaptive training. Forty-nine healthy adults performed a joystick-controlled tracking task in virtual reality, following a dynamic leader that was always visible (Control), became invisible at regular intervals (Deterministic Anticipation), or disappeared randomly (Stochastic Anticipation) to elicit anticipatory behavior. Kinematic and kinetic metrics and time-series analysis were used to evaluate synchrony, smoothness, and coordination. Performance improved from baseline to retention, with no distinct differences in final performance between the groups. However, slope-based analyses found that anticipation-based training accelerated learning, especially in the novice subgroup (baseline score < 35), with marked improvements in metrics such as score pause duration, temporal lag, and spatial error. Although participants reached similar final performance levels across protocols, the rate and pattern of learning differed across training protocols. Anticipation accelerates early-stage improvements, with the strongest effects observed in novice participants. The paradigm provides a high-resolution framework for adaptive motor training and assessment. Full article

As artificial intelligence becomes increasingly embedded in hospitality and tourism services, it is reshaping employees’ innovative work behavior. Grounded in the Job Demands-Resources perspective, this study examines how AI self-efficacy affects innovative work behavior and proposes a moderated mediation model to investigate the mediating role of work engagement and the boundary condition of schedule idiosyncratic deals. Using a three-wave time-lagged design, the study collected data from 300 employees working in the hospitality and tourism industry in Korea. The findings show that AI self-efficacy positively predicts innovative work behavior both directly and indirectly through increased work engagement. Furthermore, this mediating process is strengthened by higher levels of schedule i-deals, confirming a positive moderating effect. Theoretically, this study extends human-AI collaboration research by broadening the explanatory scope of the Job Demands-Resources model in the AI context. Practically, organizations undergoing digital transformation should provide training that strengthens employees’ confidence in using AI and grant greater autonomy over work schedules. Such practices help create a supportive environment that enables AI self-efficacy to translate into work engagement and ultimately innovative work behavior. Full article

Financial crises repeatedly reveal organizations that appear internally aligned while failing to recognize accumulating tail risks. This paper argues that cohesion is observationally ambiguous. It can arise from information integration, in which heterogeneous inputs are debated and synthesized, or from exclusion, in which variance is removed through conformity pressure, gatekeeping, and intolerance of dissent. This distinction is formalized using a signal aggregation model in which an organization maintains an anchor belief and achieves agreement through two exclusion channels: report shrinkage toward the anchor and a tolerance rule that discards reports deviating beyond a threshold. Relative to a full inclusion benchmark, exclusion based cohesion jointly produces state contingent bias that is small in normal regimes but grows sharply under displacement, illusory precision in which observed disagreement falls as tail regime estimation error rises, effective concentration of decision inputs below the nominal participant count, and, when the anchor updates from filtered aggregates, dynamic lock in with delayed regime recognition and abrupt correction. External inputs that bypass internal filtering shorten recognition delays. The model yields testable governance diagnostics linking latent fragility to observable patterns in recorded dissent, anonymous to formal voting gaps, scenario set diversity, pipeline and method concentration, and anchor lag. The central implication is that governance systems should treat low internal conflict and unanimity as potentially diagnostic of variance depletion and should monitor whether heterogeneity is integrated or excluded before stress reveals the difference. Full article

Tipping points of vegetation transitions represent the thresholds beyond which ecosystems can no longer maintain their stable states. Approaching these critical points may result in declined resilience or irreversible vegetation transitions. Detecting and predicting tipping points remains notably challenging, yet it is essential for guiding the preservation and restoration of terrestrial ecosystems. In this study, lag-1 temporal autocorrelation (AC1) derived from the Kernel Normalized Difference Vegetation Index (kNDVI) was utilized as an early warning signal to monitor resilience dynamics. We developed a new tipping-point detection method by combining land-cover changes, time series segmentations and temporal–spatial filters. We revealed a widespread resilience decline in China, with the dominant transition type as shrub encroachment. Then, two machine learning models coupled with temporal cross-validation were employed to predict the probabilities of abrupt shifts in the near future. The results showed that Random Forest models (accuracy > 70%) demonstrated robustness across lead times. High probabilities of transitions in 2024 were concentrated along the 400 mm annual isohyet, mainly affected by decreased water availability, lower soil acidity and degraded vegetation functions. Our study provides an effective methodology to pinpoint hotspots of vegetation vulnerability and to support the conservation of ecosystems for a sustainable future. Full article

Current sustainability discourse promotes sustainable yard practices as a means for residents to contribute to urban environmental health and soil conservation. Social–ecological research suggests that yard practices are shaped by multiscale social drivers, including social contagion, whereby visible expressions of individuality in front yard design are copied by nearby neighbors. This study evaluated residential areas within the Río Piedras Watershed (RPWS) in the San Juan metropolitan area to assess evidence of social contagion in front yard configuration and vegetation structure, and to examine whether these variables were associated with socio-demographic and economic characteristics when spatial effects were considered. A total of 6858 front yards across six highly urbanized sites were analyzed using Google Earth Street View imagery. Housing lot sizes were quantified, and yards were classified into eight landscape configurations based on green and gray cover elements. Woody vegetation structures, including trees, shrubs, and palms, were also quantified to generate estimates of functional diversity and a front yard quality index. Significant differences in yard characteristics were observed among sites. Spatial analyses revealed significant clustering at distances of 65–80 m, particularly for front yard configuration, while clustering of woody vegetation density was weaker. Local clustering patterns and the distribution of outliers varied across sites. Spatial lag models indicated that lot area positively influenced yard configuration and quality, and the density and diversity of woody vegetation. While socio-economic variables were not significant predictors of yard quality, their effects cannot be discarded. Overall, results are consistent with social contagion processes but also highlight neighborhood design as a key driver of clustering, alongside widespread conversion of green to paved front yards, with implications for soil and green infrastructure loss as well as environmental and human health in the RPWS. Full article

The global transport sector stands at a critical juncture where economic growth imperatives intersect with urgent environmental sustainability challenges. This paper investigates the impact of air freight transport, digitalisation, energy consumption, economic growth, and regulatory quality on CO₂ emissions in Gulf Cooperation Council (GCC) countries. Despite the region’s strategic importance in global air freight networks and rapid digital transformation, empirical evidence on how these factors collectively influence environmental sustainability remains limited. GCC countries provide a unique context for examining the digitalisation–transport–environment nexus. Using panel data from six GCC member states spanning 1999–2022, this study employs a second-generation autoregressive distributed lag (CS-ARDL) model to analyse short- and long-run relationships while accounting for cross-sectional dependence and heterogeneity. The empirical model designates CO₂ emissions as the dependent variable, while the digitalisation indicator, air freight transport, and energy consumption serve as principal explanatory variables. The empirical findings indicate that energy consumption and economic growth are significant drivers of CO₂ emissions in GCC countries, while digitalisation is associated with lower emissions. Regulatory quality exhibits a weaker but non-negligible negative influence. Moreover, air freight transport does not display a significant long-run effect on emission in the GCC context. These findings are robust across multiple panel estimators. The research provides evidence-based guidance for GCC national vision programmes, green aviation initiatives, and digital transformation strategies, contributing to a sustainable development discourse in resource-rich economies. Full article

Background: Glofitamab is a bispecific antibody engaging CD3 on T-cells and CD20 on B-cells. Glofitamab is approved for the treatment of relapsed, refractory diffuse large B-cell lymphoma (R/R DLBCL). Lymphocyte-activation gene 3 (LAG3) and T-lymphocyte-associated protein 4 (CTLA4) are immune checkpoint receptors with inhibitory effects on T-cell activity. There are several common germline variants of both receptor genes. Methods: Here, we evaluate clinical outcomes in R/R DLBCL patients treated with glofitamab according to the single-nucleotide polymorphisms LAG3 rs870849 and CTLA4 rs231775. Results: While there was no apparent association of CTLA4 genotype with glofitamab treatment outcomes, significant differences emerged in LAG3 rs870849 carriers with extended progression-free and overall survival in homozygous LAG3 T455, intermediate PFS and OS in heterozygous LAG3 I455T, and short PFS and OS in homozygous LAG3 I455 carriers. Conclusions: LAG3 rs870849 may be a prognostic response marker in R/R DLBCL treated with glofitamab. Full article

The integration of intangible cultural heritage (ICH) and tourism development (TD) is regarded as a crucial national strategy for China’s sustainable development, as their synergistic relationship is considered pivotal for regional progress. A coupling coordination evaluation system was constructed. Kernel density estimation, entropy method, coupling coordination degree (CCD) and relative development degree (RDD) models, and a tobit model were employed to examine the spatiotemporal characteristics and influencing factors of ICH–TD integration in Sichuan Province. Key findings are as follows: (1) Sichuan is endowed with abundant ICH resources characterized by high heritage value and diverse typologies. However, the distribution is skewed toward traditional skills, exhibiting notable regional disparities. ICH demonstrates a “single-core, belt-shaped and multi-cluster” pattern, which is centered on Chengdu, extends along a north–south high-density belt, and forms several secondary high-density clusters. (2) Temporally, the CCD demonstrates a sustained upward trend, whereas the RDD transitions from ICH-lagged to TD-lagged. Spatially, the number of high coordinated cities increases annually, expanding radially from regional centers, while central-eastern regions consistently outperform the west. (3) Regarding influencing factors, comprehensive economic strength, distribution of industrial structure, overall level of urbanization, and transportation accessibility exert significant positive effects on the CCD, with comprehensive economic strength demonstrating the strongest influence. This study contributes to the theoretical understanding of ICH–TD synergy and provides policy-relevant guidance for integration. Full article