Search Results (19,950)

Educational equality is essential for achieving social justice and sustainable development. Accurately predicting the trend of educational inequality is important for improving education systems and ensuring equitable resource allocation. In this paper, the Educational Gini (E-Gini) index is calculated based on the population aged 6 and above in China from 2002 to 2024, quantifying educational inequality. To forecast the future trend in the E-Gini index, a hybrid prediction framework based on the grey prediction model (GM(1,1)) and Cuckoo search-support vector regression (CS-SVR) model is proposed. This framework incorporates three influencing factors, including government budget spending on education, per capita consumption expenditure on education, and the Consumer Price Index (CPI) for education. The results show that the E-Gini of China generally declines from 2002 to 2024 with fluctuations. The proposed approach predicts the E-Gini value of 2024 as 0.220130, while the actual value is 0.2206, corresponding to an absolute error of 0.000470 and a relative error of 0.213%. In the benchmark comparison, the proposed model outperforms the linear trend model, the univariate GM(1,1), the naive persistence model, ARIMA, and the standard SVR model. The comparative analysis demonstrates that the proposed framework effectively captures the inherent patterns of educational inequality and reveals its trends. The proposed framework serves as a valuable tool for forecasting trends in educational inequality and informing policy decisions. Full article

Hungarian teachers’ reform priorities remain insufficiently mapped, despite their central role in shaping feasible, evidence-based educational change. In a cross-sectional study with 1254 kindergarten, primary, and secondary teachers across Hungary (May 2025), we elicited and analyzed open-ended written responses in which participants identified their top three required reforms. Responses were segmented and coded into 18 mutually exclusive categories via a validated codebook, and prevalence was calculated using respondent-normalized weights. We then examined demographic, well-being, and personality correlates of reform priorities using χ² tests, Mann–Whitney tests, and multivariable logistic models with Benjamini–Hochberg false discovery correction. Teachers most frequently prioritized competency development and pedagogical reform, followed by curriculum flexibility and system governance. Reform priorities were not random: female teachers were substantially more likely to prioritize inclusion and SEN support, while male teachers more often prioritized governance and depoliticization; older age predicted governance priorities. Lower educational system satisfaction robustly predicted prioritizing curriculum reform, autonomy, and governance restructuring, and anxiety and depression were positively related to curriculum concerns. Conscientiousness predicted prioritizing salary and material recognition. The results indicate that teachers’ reform demands function as systematic, psychologically grounded signals that can guide more targeted, teacher-centerd educational policy in Hungary. 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

One of the engineering applications inspired by nature is bio-inspired wings. The aerodynamic properties and autorotation characteristics of samara wing models have been studied extensively using both experimental and numerical methods. However, the three-dimensional flow behavior and angle of attack interaction around a natural samara wing are not yet fully understood. This study investigates the flow behavior around a samara wing model, with the aim of underlying physics and qualitatively analyzing the flow field, as well as the aerodynamic forces and stresses. Since the samara wing and the flow around it are three-dimensional, the difficulty of experimental investigation was taken into account, and the numerical analysis was performed using Computational Fluid Dynamics techniques. The results obtained from the numerical solution of the governing equations for three-dimensional turbulent flow were verified with experimental data. The calculations were performed by varying the angle of attack of the model wing between 0 and 50 degrees at 10-degree intervals. Depending on the angle of attack, the velocity field around the wing, surface pressure, and stress distributions, vortex structures formed on the wing and streamlines were analyzed, and the results were presented. This study and its results on this model may lead to the development and optimization of the model and its use in turbines or air vehicles. Full article

This study investigates the determinants of GDP per capita across 26 European Union member states over the period of 2006–2024, with a particular focus on entrepreneurship, institutional quality, and macroeconomic factors. Given the presence of long-run income differences across EU countries, the analysis explicitly accounts for structural heterogeneity in economic development and institutional capacity. To ensure robust estimation in the presence of cross-sectional dependence and slope heterogeneity, the study employs advanced panel econometric techniques, including tests for cross-sectional dependence, unit roots, and cointegration. Long-run relationships and short-run dynamics are estimated using the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) model, complemented by robustness checks based on the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) estimators. In addition, the Method of Moments Quantile Regression (MMQR) is applied to capture heterogeneity across different points of the income distribution, thereby reflecting long-run income disparities among EU member states. The empirical results confirm the existence of a stable long-run equilibrium relationship among the variables. The baseline CS-ARDL estimates indicate that institutional quality, entrepreneurial activity, trade openness, and government expenditure exert positive and statistically significant effects on GDP per capita, while financial development exhibits a negative effect and foreign direct investment remains insignificant. In the short run, entrepreneurship and trade openness contribute positively to GDP per capita, whereas government expenditure and credit expansion generate contractionary effects. The robustness analysis using AMG and CCEMG estimators largely supports these findings, as the direction of the coefficients remains consistent across alternative specifications, although some variation in statistical significance is observed due to differences in the treatment of cross-sectional dependence and unobserved common factors. The MMQR results further reveal substantial heterogeneity across the income distribution, indicating that the effects of key determinants vary depending on countries’ long-run income levels. In particular, trade openness and institutional quality exert stronger positive effects in lower-income quantiles, while the adverse effects of excessive financial development are more pronounced in higher-income quantiles. Overall, the findings underscore the importance of promoting productive entrepreneurship, strengthening institutional frameworks, facilitating trade integration, and ensuring efficient financial intermediation to enhance GDP per capita within the European Union. The results also highlight the need for differentiated policy approaches that explicitly account for long-run income heterogeneity, structural differences, and varying institutional capacities across EU member states. Full article

Against the backdrop of global industrial sustainable transition and the advancement of UN Sustainable Development Goals (SDGs), higher education―a core carrier of sustainable human capital supply―plays a pivotal role in adjusting majors to meet labor market demands, resolving education–industry structural mismatch, and boosting regional sustainable development. From the perspective of “higher education supporting industrial sustainable transition,” this study explores how government Policy Mix Intensity enhances universities’ Major–Industry Alignment and its transmission mechanism, aiming to reveal higher education governance’s sustainable development path. Using panel data from 30 Chinese provinces (2012–2023), we constructed a PMI quantitative index and conducted empirical analysis via a two-way fixed-effects model. The results show the following: (1) high-intensity policy mixes significantly improve alignment, overcoming university organizational inertia and laying an institutional foundation for sustainable education–industry synergy; (2) Policy Mix Intensity acts through three pathways―optimizing capital allocation, deepening industry–education integration, and enhancing dynamic responsiveness―forming a “sustainable factor allocation—sustainable industry-education alignment” logic; (3) policy efficacy is more pronounced in highly marketized Eastern regions and via regulatory tools, reflecting the moderating effect of regional sustainable endowments and policy tool types. This study provides empirical evidence for the “policy mix intensity–sustainable efficacy” transformation mechanism, offers theoretical references and empirical insights from China for the global collaborative realization of SDG4, SDG8, and SDG9 through higher education policy optimization, and proposes that policy design should shift toward factor integration-based sustainable comprehensive governance. Full article

Industrial chains in agriculture are currently fragmented and do not support developing resource-based competitive advantages. This is true under the Big Food Framework’s strategic orientation. This research seeks to develop a new analytical framework for evaluating pathways to the integration of agricultural industrial chains and their impact on the performance of companies engaged in food processing in Xinjiang. A mixed-method approach, employing both an exploratory and sequential design, will be used to do this. The primary method of data collection for this study is the case study method, along with the questionnaire method involving 145 agricultural enterprises. From these data, structural equation modeling (SEM) will be used to test the paths of causation among cognitive managers of firms who have implemented the BFF. Evidence will be presented to demonstrate the relationship among three types of integration (vertical, horizontal, and lateral) in the agricultural industrial chain, dynamic capabilities, and company performance. Additionally, network topology and optimization simulations will be conducted to determine how effectively structures are organized in training the respective companies. Important findings revealed in this research include the following: The managerial cognition constructs offered by BFFs play a key role in enhancing the depth and structural balance of industry chain integration. There were complementary performance effects found, and they are related to vertical integration achieving operational efficiency and financial efficiency; horizontal integration improving market competitiveness and brand competitiveness; and lateral integration facilitating innovative growth. Dynamic capabilities are a significant mediating mechanism linking institutional support and digital capability with the depth of integration across different modes of integration. The findings from network optimization suggest that there is a positive effect of balanced connectivity across the different dimensions of integration on overall system efficiency and reduced structural inefficiencies. Based on these findings, the authors recommend that organizations establish governance mechanisms that facilitate coordinated connectivity; strengthen adaptive capabilities within the firm; and promote balanced integration across industrial networks. Future researchers should consider applying these findings to conducting longitudinal studies on network evolution; integrating sustainability measures as part of their analysis; and conducting comparative validation studies across regions or industry systems. Full article

Urban agglomerations have become the dominant spatial platform of urbanization, regional coordination, and economic transformation in China. Yet whether the expansion of agglomeration scale at the urban-agglomeration level alleviates or intensifies energy use remains insufficiently understood. Extending the scale of analysis from individual cities to integrated urban agglomerations, this study investigates 64 cities in four major Chinese urban agglomerations, including Beijing–Tianjin–Hebei, the Yangtze River Delta, the Pearl River Delta, and Chengdu–Chongqing, over the period 2006–2023. Using panel data models, this study examines the impact of the scale agglomeration within urban agglomeration on urban energy intensity. The results show that the overall agglomeration scale generated by urban agglomeration formation significantly suppresses energy intensity while indicating a robust energy-saving effect: every 10% increase in agglomeration scale is associated with a decline of approximately 0.0893 million tons of standard coal per CNY 100 million of GDP. This finding remains stable after addressing endogeneity concerns and performing a series of robustness checks. Mechanism analyses further suggest that this effect operates primarily through talent agglomeration, technological progress, and public transportation expansion. In addition, the energy-saving effect is more pronounced in smaller cities, cities with lower administrative rank, cities with weaker factor mobility, and cities characterized by poorer air quality but stronger public environmental attention. These findings contribute to the literature on urban agglomeration and green development by showing that the agglomeration scale within urban agglomerations can generate inclusive energy-efficiency gains, especially for relatively disadvantaged cities, thereby offering important implications for spatial governance and low-carbon transition in rapidly urbanizing economies. Full article

Environmental, Social, and Governance (ESG) text classification is important for applications in sustainable finance. However, it remains a challenging task due to domain terminology and regulatory constraints. While transformer-based models achieve strong predictive performance, they often lead to high energy costs and provide limited interpretability. To address these limitations, we introduce ESG-Graph, a lightweight and interpretable graph-based framework for modeling ESG disclosures. In our approach, each sentence is represented as a token-level dependency graph augmented with virtual nodes initialized from a European Sustainability Reporting Standards (ESRS)-based taxonomy, enabling the addition of new ESG concepts without retraining. A multi-layer Graph Attention Network is used instead of transformer encoders, allowing grammatical structure and domain semantics to be modeled jointly. Experiments on three ESG benchmark datasets show that ESG-Graph achieves performance comparable to efficient transformer baselines while consuming up to 60× less energy and using 10× fewer parameters. Additional attribution and ablation studies suggest the method’s policy alignment, interpretability, and robustness. Full article

Internet advertising, while enabling unprecedented commercial reach, has become a pervasive vehicle for deceptive practices that inflict measurable harm on consumers. This study empirically investigates the structural relationships between internet advertising falsity and consumer harm by integrating analyses of the mediating role of consumer cognitive processes and the moderating role of consumer vulnerability within a unified structural framework. Survey data were collected from 600 adult consumers with online purchase experience in the Republic of Korea—an advanced digital economy characterized by exceptionally high mobile-commerce penetration, mature e-commerce infrastructure, and evolving digital consumer protection regulation—and analyzed using structural equation modeling (SEM) with AMOS 24.0, supplemented by Hayes’ PROCESS macro Model 59 for conditional process analysis. All 13 hypotheses were supported, although path magnitudes varied substantially across falsity dimensions and mediator pathways—with direct effects ranging from β = 0.156 (false scarcity) to β = 0.224 (performance exaggeration), and indirect effects dominated by the risk assessment distortion pathway. Among the four sub-dimensions of advertising falsity—factual misrepresentation, performance exaggeration, price deception, and false scarcity—performance exaggeration exerted the strongest direct effect on consumer harm. The three cognitive mediators—perceived advertising credibility, risk assessment distortion, and purchase decision pressure—all demonstrated significant partial mediation, with risk assessment distortion emerging as the most powerful indirect pathway. All four consumer vulnerability dimensions—digital literacy level, demographic vulnerability, prior victimization experience, and impulsive buying tendency—significantly moderated the falsity–harm relationship, with low-digital-literacy consumers experiencing approximately 1.7 times the adverse effect of high-literacy counterparts. Moderated mediation analysis revealed that the conditional indirect effect for the high-vulnerability group was approximately 2.3 times that of the low-vulnerability group, confirming that the cognitive harm mechanism intensifies systematically for vulnerable consumers. These findings advance consumer vulnerability theory in the digital context and offer evidence-based implications for consumer protection policy, platform governance, and digital literacy education. Full article

School leadership practices may influence teachers’ motivation and professional engagement, which, in turn, may affect overall school performance. This study explores how secondary school teachers and deputy head teachers perceive head teachers’ leadership practices and how these practices are understood to influence teacher motivation and professional engagement. Drawing on a qualitative design, data were collected through semi-structured interviews with 12 teachers and six deputy head teachers from six government secondary schools in Kabwe District, Zambia. A qualitative approach enabled an in-depth exploration of leadership perceptions across participants from multiple school contexts. Data were analysed using thematic analysis to identify patterns in leadership practices described by participants. The findings indicate that participants frequently described leadership practices aligned with delegation, mentorship, and open communication, shaped by contextual and organisational factors. However, these practices were not consistently experienced across all school contexts. Participants also described the presence of democratic and autocratic leadership practices. Participants perceived participatory and supportive leadership practices as contributing to their motivation and professional engagement. However, participants from several schools reported that autocratic leadership practices continued to shape decision-making, largely due to contextual, institutional, and workload-related constraints. The study highlights the importance of understanding leadership as contextually negotiated and relationally enacted. It contributes to African educational leadership research by demonstrating how leadership practices are experienced and interpreted within specific school contexts and emphasising the value of examining leadership beyond a single theoretical model. The implications of these findings for school leadership practice, policy development, and international educational leadership research are discussed. Full article

Multispectral LiDAR can simultaneously obtain distance and spectral information and shows great potential for underwater detection. However, absorption and scattering caused by suspended particles in water lead to energy attenuation and multiple scattering, which affect echo intensity and ranging accuracy, while the propagation characteristics under multi-wavelength conditions remain insufficiently studied. In this study, a simplified underwater propagation simulation model for multispectral LiDAR is established based on the equivalent spherical-particle assumption, combining Mie scattering theory with a semi-analytical Monte Carlo method. The effects of particle size on echo intensity and ranging error are analyzed under fixed concentration conditions. Based on this model, a detection-threshold-constrained optimal wavelength selection criterion is formulated. Multi-distance analysis (3, 5, 8, and 15 m) confirms that the preferred wavelength is primarily governed by particle size and remains stable across depths. The results show that the optimal detection wavelength shifts with particle size, being about 560 nm for fine particles and gradually moving toward the 400–480 nm blue–green band for larger particles. Experimental validation shows that the simulation-based ranging correction reduces RMSE by 9.4–25.9% (average 18.1%) and MAE by 11.8–29.7% (average 22.0%) across five experimental distances. The results provide a preliminary reference for wavelength selection in multispectral LiDAR systems under simplified conditions. Full article

Water-level monitoring in rice paddies supports sustainable farming, responsible water management, and greenhouse gas emission mitigation. SAR-based remote sensing is an effective alternative for estimating water levels, especially in regions where optical observations are limited. This study evaluates ten ALOS-2/PALSAR-2 L-band SAR-derived polarimetric parameters for their contribution and effectiveness in water-level estimation across rice-growing phases using random forest regression in the Subang District, which is one of the largest rice-yield areas in West Java, Indonesia. Overall, L-band polarimetric information is clearly related to water-level dynamics throughout the rice-growing cycle, confirming its strong potential for quantitative water-level retrieval. The highest estimation accuracy was achieved by integrating all polarimetric parameter groups (MAE = 1.37 cm, RMSE = 1.79 cm, R² = 0.52, r = 0.73), indicating that no single group can adequately represent the complex scattering mechanisms governing water-level variability across an entire cropping season. Variable importance analysis shows a relatively uniform contribution (7.63–12.90%), suggesting synergies across parameters in water-level estimation. Phase-specific evaluation further reveals that Phase 2, corresponding to the vegetative-to-generative transition, is the optimal temporal window for L-band SAR-based water-level retrieval due to enhanced double-bounce scattering and reduced signal saturation. While Phase 2 data maximizes physical sensitivity and correlation, whole-phase modeling provides greater robustness and lower absolute errors, making it more suitable for L-band SAR-based operational water-level monitoring applications. Full article

Numerical simulation of sequential fracturing in horizontal wells for shale gas and oil extraction requires careful consideration of mechanical interactions between proppant and fracture surfaces—a challenge that remains largely unresolved. This study proposes a novel phase-field model featuring a strain-based formulation and a width-dependent proppant reaction force. Unlike previous studies, we integrate an empirical propped force solution, adapted from established work to account for rock properties and proppant support, to capture nonlinear fracture closure. Results show that reaction stress models significantly dictate propped geometry. The model’s fracture length, width, and closure predictions are validated against theoretical solutions. We conducted a sensitivity analysis to evaluate how fracture deflection angles and widths vary with dimensionless fracture spacing, in situ stress contrast, and proppant strength. Numerical results show that proppants induce pronounced morphological asymmetry and distinct geometric discrepancies. Specifically, the heterogeneous support provided by proppants and the resulting stress redistribution alter fracture propagation paths, leading to an 8% reduction in fracture length and a marked difference in fracture orientation of approximately 80° between supported and unsupported fractures, highlighting the important role of proppants in governing fracture geometry. Both dimensionless fracture spacing and in situ stress contrast strongly influence fracture deflection, with proppant strength also contributing. The propped-force formulation is further extended to nonplanar fractures, enabling application to sequential fracturing with multiple fractures. These results highlight fracture propagation mechanisms and demonstrate the robustness of the proposed phase-field model. Full article

In the aerospace industry, thin-web gears are preferred for achieving high power-density transmission. However, thin-webbed structures always lead to out-of-plane resonance during the transmission process, which commonly happens in helical gears, manifesting as severe vibration at a specific rotational speed. To address this, a shaft–web–ring dynamic model is proposed. The shaft, gear web, and gear ring are modelled based on the Timoshenko straight beam, Mindlin plate, and Timoshenko bent beam theory. Simultaneously, the potential energy caused by the time-varying meshing stiffness is coupled to the gear ring. The kinetic and potential energies of each discretized finite element of the components are derived based on elastic deformation theory, and the governing equations of each element are obtained using Hamilton’s principle. The model is verified through a modal experiment. The comparison with traditional rotor-gear models has demonstrated the significance of gear body flexibility in helical gears with thin webs. The effects of the web thickness and helix angle on dynamic response are studied, revealing that gear web elasticity and an appropriately high helix angle can effectively reduce vibrations at the support bearing, prevent excessive vibrations, and contribute to vibration and noise reduction in the transmission system. Full article