Search Results (51)

In the last twenty years, as global uncertainties increased, tourism demand has become more fragile and harder to predict. This study aims to examine the impact of global uncertainty on tourism demand in G20 countries from 2001 to 2023 and to compare the forecasting performance of machine learning algorithms with traditional econometric approaches. The analysis uses international tourist arrivals as the dependent variable; per capita income, the real effective exchange rate (REER), the Global Uncertainty Index (WUI), and lagged demand are included in the model. The dynamic panel (System GMM) estimation confirms strong habit persistence in tourism demand, with the coefficient on lagged arrivals being highly significant at 0.8672 ($p<0.01$) for the full G20 sample, 0.8812 ($p<0.01$) for advanced economies, and 0.8524 ($p<0.01$) for emerging markets. The negative and statistically significant WUI coefficient across the full sample ($-0.4568$, $p<0.01$), advanced economies ($-0.4683$, $p<0.01$), and emerging economies ($-0.4452$, $p<0.01$) demonstrates that global uncertainty systematically suppresses tourism demand. Furthermore, income elasticities ($\ln GDP$) exert a positive and statistically significant impact across both advanced ($0.2145$, $p<0.01$) and emerging nations ($0.3412$, $p<0.01$), whereas real exchange rate appreciation ($\ln REER$) significantly deters demand in emerging markets ($-0.1145$, $p<0.05$). The negative and significant WUI coefficient (−0.45 to −0.47) in both developed and developing countries indicates that uncertainty systematically suppresses tourism demand. Machine learning models, especially during periods of high volatility, produced more successful results; the hybrid model achieved an average MAPE of 3.98% for the 2016–2023 period, demonstrating high accuracy. The findings highlight the importance of flexible and data-driven approaches during periods of uncertainty; they suggest that policymakers should focus on reducing uncertainty shocks and increasing demand resilience. Full article

The construction of high-fidelity virtual factories relies heavily on the accurate reconstruction of historical production timelines. While traditional Manufacturing Execution Systems (MESs) provide idealized, static schedules, they inherently struggle to capture the “stochastic variability” and “undefined events” caused by machine-specific behaviors on the industrial shop floor. To bridge the gap between top-down scheduling and bottom-up physical reality, this study proposes the Power-Aware State Segmentation for Solder Paste Printers (PAS-SPP) algorithm. Utilizing non-intrusive, high-frequency continuous power features captured via an Industrial Internet of Things (IIoT) architecture with PA310 meters, the algorithm employs a synergistic combination of amplitude thresholding (${\theta }_{high}$) and temporal constraints (${\tau }_{blank}$, ${\tau }_{min}$) to actively filter transient electrical noise and accurately bound macroscopic operational blocks. This robust filtering thereby avoids the accuracy degradation commonly caused by noise interference in the analysis processes of traditional machine learning models. Consequently, the mechanism effectively decouples operational states into a virtual Solder Paste Printer (vSPP) behavioral meta-model integrated with a Finite State Machine (FSM). Empirical validation across distinct production cases demonstrates that the proposed model not only accurately extracts standard 33–35 s cycle times but also reveals critical hidden characteristics, such as 68 s automated cleanings and dynamically adjusted “3-to-1” print-to-clean ratios. Furthermore, a comprehensive comparative analysis was conducted against static MES logs, Naive Power Thresholding (NPT), and a Gaussian Hidden Markov Model (GMM-HMM). Evaluated under identical manufacturing-process conditions, the results reveal that PAS-SPP effectively mitigates the cascading misalignments in static schedules and avoids the severe over-segmentation limitations inherent in point-by-point probabilistic decoding, thereby achieving highly accurate state decoupling. Finally, this study systematically defines the method’s applicability boundaries across diverse DT domains, confirming its indispensable role as a non-intrusive, broadly applicable event-triggering foundation for the broader smart manufacturing ecosystem. Full article

This study examines the relationship between financial flexibility and firm performance, and the moderating role of corporate-governance mechanisms, using a large panel of Chinese A-share non-financial listed companies over 2017–2024. Financial flexibility reflects a firm’s capacity to access and deploy financial resources under uncertainty and is increasingly viewed as central to corporate resilience and value creation. Employing panel regressions with firm and year fixed effects, this study finds that financial flexibility is positively and significantly associated with Tobin’s Q and return on assets as measures of firm performance. Further analysis shows that this relationship is contingent on governance structures: ownership concentration and CEO duality weaken the positive association, while board independence is associated with a marginally significant strengthening of it. Marginal-effect analyses indicate that governance mechanisms systematically condition the value of financial flexibility. A dynamic system-GMM specification qualifies these findings: once persistence and reverse causality are modeled, the unconditional flexibility coefficient turns negative, underscoring that the fixed-effects estimates should be read as associations whose sign and magnitude depend on governance and on how endogeneity is treated. These findings contribute to the literature by integrating financial flexibility and corporate governance in a single analytical framework, highlighting governance as a key boundary condition for the effective use of financial slack. The results carry implications for managers, investors, and policymakers, emphasizing balanced ownership structures, leadership separation, and independent boards in enhancing the performance benefits of financial flexibility in emerging markets. Full article

Background/Objectives: Children with autism spectrum disorder (ASD) often exhibit differences in attention regulation and visual behavior. However, many ASD eye-tracking datasets lack reliable moment-to-moment behavioral or emotional annotations, limiting the direct application of supervised learning approaches. To address this challenge, this study proposes an interpretable gaze-based unsupervised framework for discovering behavioral pseudo-zones from unlabeled ASD eye-tracking data. Methods: Raw gaze recordings from ASD participants were segmented into fixed temporal windows and represented using interpretable gaze features, including gaze dispersion, fixation duration, tracking quality, motion ratio, pupil size, and gaze velocity measures. Multiple clustering models and alternative temporal window sizes were systematically compared, including K-means, Gaussian Mixture Modeling (GMM), Agglomerative Clustering, and HDBSCAN. Results: Among the evaluated configurations, the combination of 1000 ms windows with K-means clustering (k = 4) was retained as the final exploratory configuration. Although alternative solutions achieved slightly stronger internal validation metrics, the selected configuration provided a more interpretable four-zone structure while maintaining acceptable clustering quality. The final retained solution produced four interpretable behavioral pseudo-zones with statistically significant differences across all extracted gaze features according to the Kruskal–Wallis test (p < 0.05). A PCA projection further supported the exploratory structure of the discovered pseudo-zones, with the first two principal components explaining 72.3% of the total variance. Conclusions: The findings demonstrate that unlabeled ASD gaze data can be organized into interpretable behavioral pseudo-zones using an unsupervised and transparent feature-based framework. This work contributes a data-driven and interpretable framework for future gaze-based behavioral analysis and autism-related AI research. Full article

Achieving the coordinated development of the digital economy (DE), the tourism industry (TI) and the ecological environment (EE) is of great significance for regional sustainable development. This paper constructs a comprehensive evaluation index system for the digital economy–tourism industry–ecological environment (DTE) complex system. Indicator weights are determined via the entropy method, and the comprehensive development levels of the three subsystems in the Yangtze River Delta (YRD) region from 2010 to 2023 are systematically assessed. Based on this, the coupling coordination degree model is applied to measure the coordination of the DTE system, and the obstacle degree model is employed to identify the key factors restricting its coupling coordinated development. The results show the following: (1) From 2010 to 2023, the overall level of comprehensive development of the DE and EE in the YRD showed an upward trend, while the TI declined significantly during 2020–2022 due to the COVID-19 pandemic. (2) In terms of temporal evolution, the coupling coordination degree rose from 0.434 to 0.676 between 2010 and 2019, steadily improving from near disorder to primary coordination; although there were fluctuations between 2020 and 2023, it remained stable at a primary coordination level. Spatially, the region exhibited a “higher in the east, lower in the west” pattern. (3) From 2010 to 2019, the primary bottleneck in coordinated development stemmed from the DE subsystem; after 2020, the degree of constraints in the TI rose rapidly, creating a dual-system constraint pattern where the DE and the TI coexist. This study provides theoretical insights and practical recommendations for fostering positive DTE interactions in the YRD and offers valuable experience for other regions. This study has limitations regarding its research scale and indicator system, and it does not account for external influencing factors. Future research could adopt municipal or county-level analyses, apply causal inference methods such as panel Granger causality and system GMM, refine the evaluation index system, integrate internal and external factors, and thoroughly analyze the underlying mechanisms governing interactions within the DTE system. Full article

The development of China’s National Modern Agricultural Industrial Parks (NMAIPs) has provided valuable knowledge to guide regional agricultural structural adjustment. To systematically analyze and scale up the successful practices of crop–livestock spatial layouts, this study examines 335 NMAIPs established between 2017 and 2024. Based on seven natural environmental variables, a deep clustering model (VAE-GMM) was applied to classify the parks into representative environmental types, establishing a standardized spatial reference frame. Crucially, the study introduces a spatial discrepancy (Gap) metric—calculated as the difference between model-predicted theoretical suitability and actual occurrence frequencies—to evaluate industrial expansion potential. Results reveal the parks form five distinct environmental types with clear regional patterns. The LightGBM prediction (micro-average AUC = 0.75, macro-average AUC = 0.63; range: 0.37–0.86) effectively captures natural constraints. Discrepancy analysis exposes a structural divergence between environmental suitability and actual agricultural allocation. Quantifying this divergence highlights suitable yet underrepresented industries, offering a pathway for sustainable resource management. By treating existing parks as reference baselines, this AI-driven forecasting framework provides transferable decision support for preliminary ecological screening and early-stage option identification in newly established parks. Full article

Manipulation-detection models calibrated in developed markets are routinely applied to frontier economies without validation, yet the institutional conditions that make such tools function as pricing signals are rarely present in those settings. This study provides the first systematic test of the Beneish M-Score and Dechow F-Score as return predictors in Vietnam, a frontier market navigating staged International Financial Reporting Standards (IFRS) convergence. Apparent negative associations between manipulation scores and excess returns under System Generalized Method of Moments (System GMM) do not survive panel fixed effects, Fama–MacBeth, or between-firm estimation. Persistent second-order serial correlation confirms that the GMM signal reflects frontier-market return momentum rather than manipulation pricing. By contrast, Big Four audit quality generates a robust cross-sectional return premium, establishing audit credibility as the operative governance channel where regulatory enforcement is absent. Survival analysis further shows that high-risk firms face substantially elevated exit hazards, demonstrating that reporting risk shapes long-run viability even where short-run pricing is absent. These findings constitute an IFRS paradox: Vietnam has adopted the institutional form of international reporting standards while lacking the informational infrastructure to support detection models that function as reliable pricing signals. Governance infrastructure, not standards convergence, is the operative condition for market discipline in frontier settings. Full article

Exchange rate volatility has intensified in recent decades, yet its systematic implications for banking-sector stability remain contested. This study investigates whether exchange rate volatility constitutes a meaningful source of financial fragility using a global panel of 103 countries over the period 2000–2021. Financial stability is proxied by the banking-sector Z-score, while exchange rate volatility is estimated using a EGARCH-based framework to capture time-varying uncertainty. To address cross-sectional dependence, heterogeneity, and endogeneity, the analysis employs Driscoll–Kraay fixed effects, two-step system GMM, and quantile regressions. The results reveal that exchange rate volatility exerts a statistically and economically significant negative effect on banking stability, reducing Z-scores across countries and income groups. The findings remain robust across alternative specifications and estimators. Bank-level fundamentals—capitalisation, liquidity, and credit—enhance stability, whereas higher non-performing loans and risk exposure amplify fragility. Macroeconomic conditions also matter, with stronger growth, institutional quality and external balances supporting resilience, while inflation, economic policy uncertainty and expansionary government spending weaken stability. By integrating time-varying volatility modelling with dynamic panel techniques in a large cross-country setting, this study provides new global evidence that exchange rate volatility is not merely a macroeconomic fluctuation but a structural source of banking-sector risk. The findings carry important implications for macroprudential policy, foreign-exchange management, and coordinated monetary–fiscal responses aimed at safeguarding financial stability in open economies. Full article

To address the problems of unreasonable task allocation and low target search efficiency in the collaborative search of multiple unmanned undersea vehicles (UUVs) in complex marine environments, this paper proposes a search-information-driven collaborative task planning method for multi-UUV systems, and constructs a systematic and integrated multi-UUV collaborative task planning framework. Considering the spatial characteristics of the complex underwater environment and sonar detection rules, an underwater task environment grid model and an active sonar instantaneous detection model are constructed as the environmental and detection foundation of the framework. Within the framework, the Gaussian Mixture Model (GMM) is adopted to realize dynamic division of task regions, and reasonable resource allocation among multiple UUVs is achieved by defining scientific area allocation indicators. A search information map consisting of target probability distribution and environmental uncertainty is established, and a receding horizon planning framework is introduced to balance short-term detection effectiveness and long-term search value. Furthermore, a motion-coded Grey Wolf Optimization (GWO) algorithm is proposed to generate continuous UUV paths, which avoids path discontinuity caused by discrete grids and ensures the convergence efficiency of the algorithm. Simulation results verify that compared with traditional methods, the proposed method improves the total probability benefit by 19.87% and the number of discovered targets by 18.29%, demonstrating better search performance and environmental adaptability. Full article

Urban shrinkage has become a critical constraint on China’s pursuit of high-quality economic development. As a core driver of new-quality productive forces, digital technological innovation warrants systematic examination for its role in mitigating urban shrinkage. Given the current lack of research on multidimensional measures of urban shrinkage and the mechanisms through which digital technologies influence this phenomenon, this study utilizes panel data from 269 prefecture-level and higher cities in China from 2014 to 2022. By employing two-way fixed-effects models, mediation models, and threshold regression models, the study systematically examines the impact, mechanisms, and nonlinear characteristics of digital technology innovation on urban shrinkage. The empirical results demonstrate that digital technological innovation has a significant mitigating effect on urban shrinkage; this conclusion holds even after conducting a series of robustness tests, including replacing the core explanatory variable, accounting for lag effects, using SYS-GMM estimation, and adjusting the sample range. Heterogeneity analysis indicates that the mitigating effect is more pronounced in shrinking cities, peripheral cities, resource-based cities, and cities with lower educational attainment. Mechanism analysis reveals that agricultural-related innovation acts as a mediating channel, whereas rural entrepreneurship exhibits a “partial masking effect” in the relationship between digital technological innovation and urban shrinkage. Moderation analysis further shows that higher levels of ecological environmental resilience amplify the inhibitory effect of digital technological innovation. Finally, threshold regression results identify a significant double-threshold effect, with the mitigating impact of digital technological innovation emerging only after exceeding the first threshold value of 5.690. Based on these findings, this study recommends implementing differentiated digital-technology-driven innovation strategies, with agriculture-related innovation serving as a strategic entry point to stimulate regional innovation and entrepreneurial vitality. At the same time, strengthening ecological resilience should be promoted to support coordinated green and digital transformation. These findings provide empirical evidence for the formulation of differentiated urban digital transformation policies aimed at mitigating urban shrinkage. Full article

Height anomaly, defined as the separation between the quasi-geoid and the reference ellipsoid, is fundamental to quasi-geoid refinement. While the Goddard Mars Model-3 (GMM-3) developed by NASA’s Goddard Space Flight Center (GSFC) and the JPL Mars gravity field MRO120D (JGMRO_120D) model developed by NASA’s Jet Propulsion Laboratory (JPL) stand as two representative Martian gravity field models, the systematic differences between them and their associated physical implications remain insufficiently quantified. This study establishes a validated computational framework for Martian height anomaly determination using updated physical parameters and spherical harmonic expansions. Validation against terrestrial datasets confirms high reliability (standard deviation: 0.0695 m relative to International Centre for Global Earth Models (ICGEM)), ensuring confidence in subsequent analysis. Our analysis reveals three critical findings: (1) Systematic latitudinal biases between GMM-3 and JGMRO_120D exhibit a monotonic gradient from −1.3 m near the equator to +3.9 m at the North Pole, suggesting differential parameterization of polar mass loading or tidal models between the two centers. (2) Polar clustering of uncertainties and outliers exceeding the 95th percentile (>7 m) concentrate non-randomly at latitudes >60°, which is attributed to sparse satellite tracking and seasonal ice cap modeling limitations. (3) There is error amplification in lowland terrains, where relative errors exceed 60% in flat regions (near-zero anomalies), posing critical risks for precision landing missions. While global consistency between models is high (R² = 0.9999), the identified discrepancies provide new constraints on Mars’s geophysical models and essential guidance for future gravity field improvements and mission planning. Full article

Agro-ecosystem approaches are increasingly promoted as integrated solutions for sustainable land use, climate mitigation, and food security, yet concerns remain that market-based instruments may systematically exclude resource-poor smallholder farmers. Using microdata from 8894 households participating in Kenya’s long-running International Small Group and Tree Planting Program, this study examines how institutional and organizational arrangements shape access to agricultural carbon markets and associated sustainable land management practices. We document a participation paradox: farmers in the lowest income quartile exhibit significantly higher adoption than the wealthiest quartile (92.4% vs. 86.3%), challenging conventional resource-based targeting assumptions. Three distinct agro-ecosystem participation pathways are inferred using a Gaussian Mixture Model (GMM) estimated over a feature set of organizational, financial-access, and farm/household characteristics (income, farm size, financial access, crop diversity, livestock holdings, education, organizational membership, and leadership position). A Mainstream pathway (60.2%) reflects resource-driven adoption; an Innovative pathway (32.4%) is associated with high participation among low-income farmers through organizational membership, leadership, and collective action; and a Constrained pathway (7.5%) captures persistent exclusion. Organizational membership is strongly associated with high-adoption pathways, universally present among Mainstream and Innovative farmers and absent among Constrained farmers; readers should note that membership is partly definitional in the clustering procedure, so this association reflects the pathway construction as well as empirical patterns. Leadership roles are associated with substantially increased access to non-monetary benefit streams (OR = 2.13), including training, seedlings, and community infrastructure. These alternative compensation mechanisms are spatially clustered and strongly associated with enrollment, suggesting localized institutional capacity effects. Importantly, the Innovative pathway is associated with superior agro-ecosystem outcomes, including higher tree densities and a greater uptake of conservation farming practices, suggesting possible complementarities between inclusion and ecological performance. Women are overrepresented within this pathway, highlighting the equity potential of organizational channels. Overall, the findings suggest that strengthening local organizational infrastructure can simultaneously enhance land-use sustainability, climate mitigation, and livelihood inclusion. Given the cross-sectional observational design, all findings should be interpreted as associations rather than causal effects; the results offer actionable insights for designing agro-ecosystem programs that integrate governance, social equity, and ecological resilience in support of long-term food security. Full article

The aim of this study is to investigate and demonstrate the role of correction functions and optimisation-based refinement algorithms in enhancing the performance of machine learning models, particularly in predictive anomaly detection tasks applied in industrial environments. The performance of machine learning models is highly dependent on the quality of data preprocessing, model architecture, and post-processing methodology. In many practical applications—particularly in time-series forecasting and anomaly detection—the conventional training pipeline alone is insufficient, because model uncertainty, structural bias and the handling of rare events require specialised post hoc calibration and refinement mechanisms. This study provides a systematic overview of the role of correction functions (e.g., Principal Component Analysis (PCA), Squared Prediction Error (SPE)/Q-statistics, Hotelling’s T², Bayesian calibration) and adaptive improvement algorithms (e.g., Genetic Algorithms (GA), Particle Swarm Optimisation (PSO), Simulated Annealing (SA), Gaussian Mixture Model (GMM) and ensemble-based techniques) in enhancing the performance of machine learning pipelines. The models were trained on a real industrial dataset compiled from power network analytics and harmonic-injection-based loading conditions. Model validation and equipment-level testing were performed using a large-scale harmonic measurement dataset collected over a five-year period. The reliability of the approach was confirmed by comparing predicted state transitions with actual fault occurrences, demonstrating its practical applicability and suitability for integration into predictive maintenance frameworks. The analysis demonstrates that correction functions introduce deterministic transformations in the data or error space, whereas improvement algorithms apply adaptive optimisation to fine-tune model parameters or decision boundaries. The combined use of these approaches significantly reduces overfitting, improves predictive accuracy and lowers false alarm rates. This work introduces the concept of an Organically Adaptive Predictive (OAP) ML model. The proposed model presents organic adaptivity, continuously adjusting its predictive behaviour in response to dynamic variations in network loading and harmonic spectrum composition. The introduced terminology characterises the organically emergent nature of the adaptive learning mechanism. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

Background/Objectives: Chrononutrition—the temporal organization of food intake relative to circadian rhythms—has emerged as an important factor in cardiometabolic health. While meal timing is typically analyzed as an isolated variable, limited research has examined integrated meal timing patterns, and no study has systematically compared clustering approaches for their identification. This cross-sectional study compared four clustering techniques—traditional (K-means, Hierarchical) and non-traditional (Gaussian Mixture Models (GMM), Spectral)—to identify meal timing patterns from habitual breakfast, lunch, and dinner times. Methods: The sample included 388 Mexican university students (72.8% female). Patterns were characterized using sociodemographic, anthropometric, food intake quality, and chronotype data. Clustering method concordance was assessed via Adjusted Rand Index (ARI). Results: We identified five patterns (Early, Early–Intermediate, Late–Intermediate, Late, and Late with early breakfast). No differences were observed in BMI, waist circumference, or age among clusters. Chronotype aligned with patterns (morning types overrepresented in early clusters). Food intake quality differed significantly, with more early eaters showing healthy intake than late eaters. Concordance across clustering methods was moderate (mean ARI = 0.376), with the highest agreement between the traditional and non-traditional techniques (Hierarchical–Spectral = 0.485 and K-means-GMM = 0.408). Conclusions: These findings suggest that, while traditional and non-traditional clustering techniques did not identify identical patterns, they identified similar core structures, supporting complementary pattern detection across algorithmic families. These results highlight the importance of comparing multiple methods and transparently reporting clustering approaches in chrononutrition research. Future studies should generate meal timing patterns in university students from other contexts and investigate whether these patterns are associated with eating patterns and cardiometabolic outcomes. Full article