Search Results (544)

This study analyzes the spatial distribution of restaurant services in Tanta, Egypt, using a multi-scalar framework that integrates spatial autocorrelation, kernel density estimation, diversity measures, and spatial econometric modeling. It is theoretically grounded in Central Place Theory (CPT) and Central Flow Theory (CFT) to examine how urban hierarchy and mobility dynamics jointly shape food service geography in a mid-sized Global South city. The findings reveal significant spatial inequalities, with nearly half of all restaurants concentrated in a limited number of central neighborhoods, while peripheral areas remain underserved. Spatial regression analysis indicates that these patterns are not adequately explained by population distribution, as total population and density variables showed non-significant effects in the OLS model. Instead, clustering is more strongly associated with accessibility and infrastructure. The transition from OLS to the Spatial Error Model (SEM) significantly improved the explanatory power (R² increased from 0.369 to 0.534), with a highly significant Lambda coefficient (λ = 0.69, p < 0.00001) confirming that unobserved spatial processes and mobility flows are the primary drivers of restaurant concentration. Correlation results further indicate that road density (Coefficient = 2.10, p < 0.01) and educational facilities have significant positive relationships with restaurant density, whereas most demographic indicators show weak effects. Furthermore, a significant negative interaction between population and road density (−2.63, p = 0.014) underscores that mobility corridors can override traditional residential thresholds, providing empirical support for CFT. Diversity analysis highlights clear intra-urban disparities, with high-diversity clusters located along major accessibility axes. Kernel density results point to a hybrid spatial structure, where traditional urban cores coexist with emerging secondary nodes. Overall, the study demonstrates that restaurant distribution in Tanta is better explained through a hybrid CPT–CFT framework, where accessibility and mobility flows outweigh population thresholds. These findings challenge traditional models and emphasize the need for dynamic, accessibility-oriented planning approaches to address spatial inequalities in urban services. Full article

Tourism drives economic growth but also intensifies environmental pressure at travel destinations, particularly by exacerbating local challenges in waste management. Rather than merely testing the theoretical validity of the norm activation model (NAM), this study utilizes its key constructs—specifically moral and accountability variables—as a strategic framework to examine the psychological drivers of waste reduction in the urban context of Vientiane, Laos. Data from 382 domestic tourists were analyzed using ordinary least squares regression. Ascription of responsibility (AR) (β = 0.219, p < 0.001) was the strongest predictor of intention, followed by personal norm (PN) (β = 0.173, p < 0.01) and actual waste management behavior (β = 0.160, p < 0.01). Notably, environmental knowledge and awareness of consequences—factors often emphasized in traditional environmental campaigns—had no significant influence. The findings demonstrate that, in addressing urban waste challenges in developing regions, fostering internalized moral sentiments (AR and PN) is far more effective than mere pro-environmental education. This study concludes that sustainable waste management may benefit from operationalized interventions that activate personal accountability rather than relying solely on general environmental awareness. Full article

Under the open-innovation paradigm, firms’ management of innovation output has surpassed traditional approaches such as confidentiality and patenting, evolving toward mechanisms such as strategic knowledge disclosure (SKD). As firms become increasingly embedded in global open-innovation networks, reconciling the tension between the need for innovation-knowledge disclosure and the reality of external-relationship embedding has emerged as a research agenda. Grounded in open-innovation theory, this study uses a panel of A-share manufacturing companies spanning 2009–2021 to examine how customer dependence (CD) affects suppliers’ SKD. Employing fixed-effects negative binomial panel regression, as well as robustness checks, we find that stronger CD significantly weakens suppliers’ SKD. Mechanism analysis shows that this effect operates through the channel of research and development (R&D) investment. Suppliers with high CD are more likely to reduce R&D investment, thereby suppressing their SKD. We further find that knowledge accumulation positively moderates the relationship between CD and suppliers’ SKD, while market competitiveness negatively moderates it. By constructing a theoretical framework for suppliers’ SKD under CD, this study enriches our understanding of the mechanisms and boundary conditions of firms’ SKD in terms of supply-chain relationships. The findings offer actionable insights to help suppliers embedded in supply-chain business partnerships formulate SKD. Full article

This study integrates multidimensional computational approaches—network toxicology, machine learning, molecular docking, and molecular dynamics simulation—to systematically elucidate the toxic mechanism by which the environmental pollutant diisononyl cyclohexane-1,2-dicarboxylate (DINCH) contributes to atherosclerosis. By jointly mining multiple databases, we obtained 246 targets common to DINCH and atherosclerosis. LASSO regression and support vector machine–recursive feature elimination (SVM-RFE) then identified 8 significantly upregulated core targets (CSF1R, CD36, CCL3, CCR2, ADAM8, TLR1, CTSS, and MMP1). Functional enrichment analysis showed that these core targets were significantly associated with key signaling pathways, including lipid and atherosclerosis, the PPAR signaling pathway, the PI3K–Akt signaling pathway, and the AGE–RAGE signaling pathway in diabetic complications. Differential gene analysis confirmed that these genes were significantly upregulated in diseased tissues, and receiver operating characteristic (ROC) analysis demonstrated excellent diagnostic performance (AUC = 0.87–0.96). Immune cell infiltration analysis further revealed a strong association between the core targets and immune cell populations, notably macrophages and T cells. Molecular docking and molecular dynamics simulations showed that DINCH had high affinity for the core targets, and its binding to CCR2 was the most stable (binding free energy = −7.6 kcal/mol). The final AOP framework systematically presented the cascade by which DINCH may contribute to atherosclerosis through metabolic disruption and immune activation. This study provides new mechanistic insights into the development of DINCH-induced atherosclerosis and offers a theoretical basis for health risk assessment of environmental pollutants. Full article

Functionally graded materials (FGMs) effectively alleviate residual stress induced by physical property mismatch at dissimilar material interfaces through a graded transition in composition or structure. Among these, the matching of the coefficient of thermal expansion (CTE) is a core indicator for ensuring the service reliability of the joint. Traditional composition design relies on empirical trial-and-error, which makes it difficult to efficiently identify the optimal path in a high-dimensional composition space. This study proposes a data-driven, machine learning-assisted composition design method. Based on a high-precision dataset covering 15 elements and 747 CTE data points, six typical regression models were systematically evaluated. The results show that the random forest (RF) model achieves the best performance, with a coefficient of determination (R²) of 0.929 and a root mean square error (RMSE) of 0.658 on the test set. Using the SHapley Additive exPlanations (SHAP) method, the lattice constant (c), Young’s modulus (YM), and temperature (T) were identified as the key physical descriptors governing the thermal expansion behavior. Experimental validation shows that the CTE prediction deviation of the model for the high-performance Fe-based alloy Norem02 in the range of 20–300 °C is only 0.89%. Based on this framework, the composition of the 316L/Norem02 transition layer was successfully designed in this study. This effectively reduced the interfacial thermal expansion mismatch. Consequently, it provides a reliable theoretical basis for the rational design of dissimilar material interfaces under extreme service conditions. Full article

Cybersecurity awareness has become a critical concern in the context of rapid digital transformation and the growing sophistication of cyber threats. While previous research has identified password security, browser security, and social media activities as key factors influencing cybersecurity awareness, the role of digital literacy remains underexplored, particularly in multicultural environments. This study examines the factors affecting cybersecurity awareness among adults in the United Arab Emirates (UAE), extending the existing theoretical framework by introducing digital literacy as a novel variable. Data were collected from 150 respondents through a structured questionnaire and analyzed using Cronbach’s Alpha, exploratory factor analysis, Pearson correlation analysis, simple and multiple linear regression, the Mann–Whitney U test, and the Kruskal–Wallis test. Results indicate that all four predictors—password security (β = 0.317), browser security (β = 0.149), social media activities (β = 0.209), and digital literacy (β = 0.256)—significantly predict cybersecurity awareness, with the combined model explaining 53.6% of variance (R² = 0.536). Digital literacy showed the strongest correlation with cybersecurity awareness (r = 0.614, p = 0.000). Demographic analyses revealed significant differences across age groups and digital literacy levels, with younger respondents and those with higher digital literacy consistently demonstrating higher levels of cybersecurity awareness. These findings highlight the importance of integrating digital literacy into cybersecurity education programs, particularly in multicultural contexts. From a theoretical perspective, this study extends the existing cybersecurity awareness framework by introducing digital literacy as a novel predictor variable and validates its significance in a unique multicultural environment. From a practical perspective, the findings provide empirically grounded guidelines for the development of culturally adapted cybersecurity education programs, with particular emphasis on age-differentiated approaches and the potential role of younger generations as drivers of cybersecurity awareness in the UAE and similar multicultural contexts. Full article

We propose a new nonparametric estimator of the conditional mode in a regression framework where the covariates are functional in nature. The estimator is constructed through a quantile regression approach, which provides a robust alternative to classical density-based procedures. It is well documented that employing the $L^1$-structure in quantile regression, the estimation procedure improves robustness properties, particularly resistance to outliers and heavy-tailed error distributions. This feature makes the $L^1$ estimation of the conditional mode more stable and reliable in complex and high-variability functional data settings. The main objective of this paper is to establish strong consistency, with explicit convergence rates, for the associated kernel estimators, uniformly over a range of bandwidth parameters. The latter is developed under general regularity conditions involving the concentration distribution of the functional regressor, smoothness assumptions on the structural components of the model, and entropy conditions ensuring adequate control of the functional class complexity. Uniformity in bandwidth is essential both from a theoretical and practical issues, as it guarantees stability of the estimator under data-driven smoothing parameter selection. Beyond its theoretical contribution, this paper has direct implications for applied statistics. Specifically, it provides mathematical support for the automatic bandwidth selection procedures in the high-dimensional data context. Furthermore, the main theoretical novelty is highlighted through simulation experiments and applications to real data. Full article

With the proliferation of digital platforms, virtual brand communities have become important contexts for examining how individual perceptions shape discretionary behaviors in online environments. However, the mechanisms through which user experience translates into value co-creation behaviors remain underexplored. Drawing on relationship marketing theory and a behavioral perspective, this study develops and tests a theoretical model linking user experience to value co-creation citizenship behaviors through distinct dimensions of quality of relationship-satisfaction, trust, and commitment. Using a two-wave survey with 549 matched responses, we employ multiple regressions and bootstrapping analyses to assess mediation and moderation effects. The findings indicate that different dimensions of user experience have differential impacts on satisfaction, trust, and commitment, which in turn promote value co-creation citizenship behaviors, supporting their roles as central psychological mechanisms. Specifically, affective and behavioral experiences exert significant positive impacts on value co-creation citizenship behaviors, mediated by all three dimensions (satisfaction, trust, and commitment), whereas the influences of sensory and intellectual experiences are only mediated by two dimensions (satisfaction and trust) of the quality of relationship. In addition, perceived community support strengthens the relationship between satisfaction and value co-creation citizenship behaviors, while it exerts no significant moderating effects on the impact of trust or commitment on value co-creation citizenship behaviors. By situating value co-creation within a behavioral framework, this study contributes to the literature by exploring the mechanism through which user experience influences voluntary, citizenship-like behaviors in digital communities from a relational perspective, and by identifying boundary conditions under which these effects are amplified. Full article

Shipboard boarding systems operating under complex sea conditions are subject to vessel motion coupling, wave induced disturbances, strong nonlinearity, and engineering constraints, which make accurate end pose tracking difficult. Existing mechanism-based approaches often suffer from modeling inaccuracies and high online computational burden, whereas purely data driven methods usually provide limited interpretability for safety critical marine applications. To address these limitations, this paper proposes a data driven predictive control method for shipboard boarding systems based on a Fuzzy Neural Broad Learning System. An interpretable Linear Regression Decision Tree is first constructed to represent the plant through state space partition and local linear approximation. On this basis, a Fuzzy Neural Broad Learning predictor is developed to capture disturbance-induced uncertainty and parameter variation with fast analytical training and incremental updating capability. The predictor is then embedded into a constrained model-predictive control framework in which actuator saturation, input rate limits, and output safety constraints are handled explicitly, and closed-loop boundedness is analyzed theoretically. Simulation results on a MATLAB R2024a-based and Simulink-based coupled platform show that, for the translational outputs of the gangway end effector, the testing root mean square error ranges from 1.33 × 10⁻³ to 1.74 × 10⁻³, with corresponding coefficients of determination ranging from 0.820 to 0.912. In comparative closed-loop simulations against proportional integral derivative control, fuzzy control, and learning-based control under identical operating conditions, the proposed method achieves the lowest integral of squared error and integral of absolute error, reaching 3.40 × 10⁻⁷ and 4.28 × 10⁻⁴, respectively. Compared with the best value among the three baseline controllers, the proposed method reduces the integral of squared error by approximately 42.6% and the integral of absolute error by approximately 34.4%. Although its maximum deviation is not the smallest among all compared controllers, it remains within the same order of magnitude as the advanced baselines. In addition, the average and maximum per-step computation times are 1.61 × 10⁻⁴ s and 3.75 × 10⁻³ s, respectively, both of which are far below the adopted sampling period of 0.05 s. These results indicate that the proposed framework improves cumulative tracking accuracy while maintaining feasible online computational performance. Full article

Non-built-up green areas are essential for preserving the ecological functions of cities and fostering sustainable growth. Focusing on Shanghai, we developed a comprehensive framework of driving forces that integrates socioeconomic, natural, policy, and financial indicators. To assess the spatial-temporal changes in regional green space configurations and their underlying mechanisms between 2000 and 2020, we utilized stepwise regression alongside Geographically Weighted Regression (GWR) techniques. The results show that regional green space exhibited a clear stage-dependent evolution, with the total area decreasing from 580.56 km² in 2000 to 506.43 km² in 2005 and then increasing continuously to 905.70 km² in 2020. Forest land consistently expanded and became the dominant land type, while wetland showed a “decrease–increase” pattern and grassland experienced an early decline followed by partial recovery. The primary elements driving these changes underwent substantial transformations over the study period. During the initial phase, socioeconomic variables, particularly real estate investments (β = −0.296), demonstrated pronounced adverse impacts. Conversely, post-2005, financial allocations for landscaping and policy interventions emerged as the main favorable drivers (β = 0.598). Furthermore, environmental aspects like NDVI and waterway density provided a continuous positive influence on green space enlargement. Certain socioeconomic indicators, notably population density, transitioned from exerting adverse impacts to having beneficial effects during the latter periods. The primary drivers demonstrated considerable spatial variation; socioeconomic impacts were largely localized in regions undergoing urban growth, whereas environmental and policy variables exerted broader and more consistent influences. Overall, these outcomes highlight a shift from a socioeconomic-dominated evolutionary process to one governed by a synergy of multiple factors. This offers a theoretical foundation for refining urban ecological strategies and harmonizing city expansion with ecological conservation. Full article

The evolution from Industry 4.0 to Industry 6.0 represents a paradigm shift—moving from automation toward an integrated model that incorporates intelligentization, sustainability, and human-centric resilience. While numerous conceptual frameworks have been put forward, empirical research remains scarce, primarily because of the absence of standardized indicators derived from verifiable corporate disclosures. To fill this research gap, the present study develops three quantifiable indices—Intelligence (INT), Sustainability (SUS), and Resilience & Human-centric (RES)—by extracting data from the annual reports and ESG disclosures of 100 Chinese A-share manufacturing enterprises (covering 2022–2024). Fixed-effects panel regression models are employed to assess the impact of these indices on financial performance (ROA, ROE, EPS), market valuation (Tobin’s Q), and sustainability outcomes (ESG ratings). Our findings reveal that INT is the most significant predictor of profitability, with statistically significant positive effects on ROA and ROE—effects that are particularly pronounced among high-tech enterprises. This supports the view that digital capabilities serve as strategic assets. SUS also demonstrates a positive influence on performance, especially in non-high-tech enterprises, where eco-efficiency, regulatory compliance, and ESG-linked financing help offset technological disadvantages. RES contributes to operational and financial stability by enhancing human capital, safety protocols, and organizational practices that reduce performance volatility. Collectively, these results indicate that different types of enterprises follow distinct yet converging pathways toward Industry 6.0: high-tech enterprises capitalize on intelligence to generate innovation rents, while non-high-tech enterprises increasingly rely on sustainability and resilience as strategies to build legitimacy. This study makes significant contributions in three aspects: Methodologically, it differs from previous research that relies on questionnaires and interviews. Instead, it quantifies Industry 6.0 through auditable large-sample key indicators, enhancing the objectivity and operability of the indicators. Empirically, it provides the first empirical evidence on the development path of Industry 6.0 based on data from Chinese manufacturing enterprises. In practical terms, it offers clear references for enterprises and policymakers on the core indicators and their construction framework that should be prioritized during the transformation to Industry 6.0. By linking the index derived from enterprise disclosures with quantifiable performance results, this study effectively bridges the gap between theoretical conceptions and practical applications. It further emphasizes that Industry 6.0 is not merely a technological upgrade but a systematic transformation driven by digitalization, sustainability, and resilience aimed at enhancing enterprise performance and achieving sustainable industrial development. Full article

Understanding the emotional drivers of consumer choice is critical for effective food packaging design. This study proposes a novel ‘Emotion–AI Framework’ to decode consumer responses to ten processed fish product labels using the circumplex model of emotion. Explicit emotional responses and purchase intentions were collected from 100 participants, and unsupervised machine learning (K-Means clustering) successfully classified consumers into three distinct segments (Enthusiasts, Passives, and Rejectors) strictly based on their multidimensional emotional profiles. Furthermore, a supervised Random Forest regression model, coupled with permutation feature importance, revealed that aggregated emotional states (specifically the low-arousal/pleasant and high-arousal/unpleasant quadrants) are the dominant drivers of purchase intention. Crucially, these emotional states significantly outperformed the direct impact of physical label attributes. The findings demonstrate that integrating theoretical emotional models with predictive machine learning provides robust, data-driven insights for the food industry, enabling the optimization of product labels to evoke targeted affective states and maximize consumer acceptance. Full article

To overcome the limitations of traditional yield estimation methods—which are often subjective, costly, and difficult to implement at scale—this study developed a high-precision, interpretable model for predicting walnut yield by integrating multi-source remote sensing technology with interpretable machine learning. To provide a theoretical foundation for precise water and fertilizer management as well as intelligent production in walnut orchards. By employing interpretable machine learning and a multi-stage integration strategy, the model achieves not only high-precision yield estimation but also elucidates the influence pathways of water–fertilizer coupling on yield formation at a mechanistic level. This advancement offers reliable technical support and a decision-making framework for the precise management of orchards. This study focused on the Xinjiang ‘Wen 185’ walnut, employing field experiments with varying water and fertilizer gradients. A UAV equipped with a multispectral sensor was utilized to capture canopy images, from which vegetation indices and texture features were extracted. This process resulted in a comprehensive dataset that integrated remotely sensed features with management practices. Various machine learning algorithms, including random forest, support vector machine, partial least squares regression, and ridge regression, were applied. An innovative stacked integration model for growth stages was proposed, and the SHAP framework was incorporated to analyze feature contributions and enhance model interpretability. In this study, texture features—particularly those derived from the red-edge band—showed higher predictive importance than traditional vegetation indices. This suggests that they may be more sensitive to canopy structural heterogeneity under the tested conditions. Among the models, random forest showed numerically higher values in terms of R² and RPD compared to the other individual models under the present dataset, achieving a validation R² of 0.670 and an RPD of 1.836. The proposed growth stage stacking ensemble (GSSE) model further enhanced prediction accuracy, achieving validation R² of 0.789, an RMSE of 0.494, and an RPD of 2.296. Additionally, the results revealed that texture may have a potential ability to captured canopy heterogeneity as the primary mechanism underlying yield variation, and the integration of multi-stage spectral information was associated with higher estimation accuracy in this dataset in improving estimation accuracy, with the oil conversion stage contributing up to 60% to the final prediction. Full article

This paper develops a tractable theoretical framework to study how network participation shapes the boom–bust dynamics of non-fungible token (NFT) prices. We model NFT pricing under network effects and heterogeneous consumers, and show that prices and participation are jointly determined in equilibrium. The model implies a critical participation threshold that separates expansion from contraction regimes: above this threshold, positive feedback between participation and valuation generates self-reinforcing growth, while below it, weakening network benefits lead to contraction. We provide empirical evidence using data from the aggregate NFT market and prominent collections including Bored Ape Yacht Club (BAYC) and CryptoPunks. Reduced-form regressions show a positive association between prices and network participation, with stronger effects at the collection level than in the aggregate market. Threshold estimation further provides evidence consistent with regime-dependent dynamics, with clearer tipping behaviour in well-defined NFT communities than in the aggregate market. These findings suggest that NFT valuation is closely tied to network structure and participation dynamics. More broadly, this paper contributes a unified framework that links participation, price formation, and threshold behaviour in NFT markets. Full article

The employment and labor market landscape has undergone significant transformations globally, including the three Central European countries examined in this study. Over the past decades, organizations in this region have transitioned from a state of full employment to labor shortages, raising the question: What factors have driven these changes? Our study aims to present a theoretical framework highlighting key macro-level factors, such as demographic trends, economic development, labor market dynamics, the impact of the COVID-19 pandemic, and the role of robotization and artificial intelligence. Based on two empirical studies conducted in 2019 and 2022 among Czech, Hungarian, and Slovak organizations, we analyzed the extent and causes of labor shortages, as well as the labor market effects of robotization. Using descriptive and non-parametric statistical methods, including frequency analysis and Mann–Whitney U tests, the study examined key trends and compared the two periods to identify significant shifts. The analytical approach of this study primarily aims to compare perceptions across occupational groups and between the two survey waves (2019 and 2022). Because most variables were measured on ordinal Likert-type scales and the datasets represent independent cross-sectional samples rather than a panel dataset, non-parametric methods were considered the most appropriate. More advanced causal modeling techniques, such as regression or factor analysis, were not applied because the objective of the research was exploratory and comparative rather than to establish causal relationships between variables. The findings reveal significant shifts in the perceived causes of labor shortages across occupational groups in the surveyed Central European organizations. In particular, increasing labor shortages were observed in specific job categories, alongside changes in the relative importance of the underlying drivers of labor shortages. While adopting robotization and artificial intelligence has been positively received, demographic decline and emigration remain critical challenges. The study provides practical insights for policymakers and corporate leaders regarding labor market challenges, workforce planning, and the potential role of robotization and artificial intelligence in addressing labor shortages. Although the research is based on a non-representative sample, it offers valuable insights into the Central European region’s employment and labor market trends. Future research could examine whether, in hard-to-fill positions, robotization and AI primarily provide indirect support by augmenting and reallocating human work, or whether they may serve as direct substitutes. Full article