Search Results (2,694)

Against the backdrop of uneven educational development and structural constraints in rural Mainland China, teacher agency is critical for driving professional growth and instructional improvement. Rural educators face distinct challenges—limited resources, isolated work contexts, and systemic pressures—that shape their capacity to enact change. While scholarship has documented the roles of contextual resources and individual beliefs in shaping teacher agency, less is known about the mediating mechanisms linking job resources and self-efficacy to agency within China’s rural educational landscape. This study examines how perceived job resources (teaching resources, administrative support, colleague support, parental support) and teaching self-efficacy collectively shape rural teachers’ agency, to inform policy and practice for strengthening their professional capacity. Drawing on a quantitative survey of 625 rural teachers, we employ a two-stage analytical approach: first, descriptive statistics, t-tests, ANOVA, and Pearson correlations to map baseline variable relationships; second, Hayes’ PROCESS macro (Model 4) with bootstrapping to test the mediating role of teaching self-efficacy between job resources and teacher agency. Findings reveal the following: (1) Rural teachers report moderate agency (M = 3.53/5), indicating room for growth; (2) All four job resource dimensions significantly and positively predict agency (β = 0.099–0.163); (3) Teaching self-efficacy is a robust predictor of agency (β = 0.785–0.822, p < 0.001) after controlling for resources; (4) Self-efficacy partially mediates the links between each job resource and agency, with indirect effects ranging from 0.269 (teaching resources) to 0.451 (colleague support), highlighting its central role in translating contextual resources into agentic action. We conclude that fostering rural teacher agency requires a holistic approach addressing both external job resources and internal self-efficacy. Policymakers and administrators should prioritize investments in teaching resources, collaborative support structures, and professional development to build educators’ confidence and competence. Limitations include self-report bias, cross-sectional design constraints on causal inference, and limited generalizability. Future research should use longitudinal designs and broader samples to deepen understandings of agency in structurally constrained educational settings. Full article

In response to the significant challenges posed by ice accumulation and contamination from various fluids in complex operating conditions for metallic materials, this study utilises picosecond laser precision machining to develop a ‘slippery surface’ featuring a micro-channel network structure. The core innovation of this study lies in the use of laser-machined micrometre-scale array textures to overcome the limitations of traditional isolated pores. These globally interconnected micro-channels serve as highly efficient reservoirs and dynamic transport channels for lubricants, significantly enhancing the interfacial capillary locking force of the lubricant. Experimental results demonstrate that this unique network geometry endows the surface with exceptional fluid replenishment and self-healing properties, enabling it to exhibit outstanding broad-spectrum hydrophobicity towards various fluids—including water, crude oil and ethanol (surface tension range: 17.9–72.0 mN m⁻¹)—with sliding angles consistently below 12°, whilst effectively slowing the dehydration and solidification processes of biological fluids. At a low temperature of −15 °C, the surface achieved an ice formation delay of up to 286 s, with an ice adhesion strength of only 33.9 kPa, ensuring that accumulated ice could be spontaneously detached under minimal external force. Furthermore, the micro-channel network structure serves as a key protective mechanism against mechanical wear, maintaining robust slippery properties even after three hours of high-pressure water jet scouring (Weber number of 300). This reliable interface, achieved through structural management, provides an efficient and scalable platform for addressing the all-weather anti-icing and antifouling requirements of outdoor infrastructure. Full article

A self-powered graphene-enhanced hydrogel sensor (SGHS) with high contact-pressure sensitivity and mechanical robustness was developed for precise dynamic biomechanical and material contact sensing. The device generates transient electrical signals via contact electrification and electrostatic induction during contact–separation events, eliminating the need for any external power supply. The optimized SGHS achieves a maximum peak power density of 0.23 mW·m⁻², with contact-pressure sensitivities of 0.6 kPa⁻¹ and 0.26 kPa⁻¹ in the pressure ranges of 0.25–5 kPa and 5–25 kPa, respectively, which is competitive with or exceeds that of other externally powered and self-powered flexible dynamic stress sensors in the low-pressure range. Comprehensive analyses reveal that the pressure response originates from the enhanced piezodielectric effect in the graphene hydrogel layer under compression. The SGHS exhibits excellent mechanical durability, maintaining stable output after 10,000 loading–unloading cycles. Moreover, the pulse intensity, width, and waveform of its self-generated output provide distinctive features for identifying the type and surface characteristics of contacting objects. These results highlight SGHS as a promising candidate for next-generation intelligent, self-powered, and flexible dynamic sensing systems. Full article

Background: Conventional analyses of physiological recovery often rely on discrete metrics that assume independence across time points, thereby ignoring intrinsic temporal continuity and masking substantial interindividual heterogeneity. This proof-of-concept study assesses the efficacy of Functional Data Analysis (FDA) as a promising framework for characterizing individual response dynamics following a functional threshold power (FTP) test. Methods: Physiological time-series data (including blood lactate, heart rate, blood pressure, and glucose levels) collected from 21 trained cyclists (10 professionals, 11 amateurs) were represented as functional objects using FDataGrid on the original sampling grid (0, 3, 5, 10, 20 min), without basis expansion or smoothing. We conducted unsupervised functional clustering (K-means; Fuzzy K-means) and supervised classification (Maximum Depth with Modified Band Depth, K-Nearest Neighbors, Nearest Centroid, functional QDA with parametric Gaussian covariance). Model performance was estimated via Repeated Stratified 5-Fold Cross-Validation with 10 repetitions (50 folds), reporting accuracy, balanced accuracy (mean ± SD), 95% CIs, permutation p-values, and sensitivity/specificity from aggregated confusion matrices. Results: Lactate (CL) and diastolic blood pressure (DBP) provided useful and statistically significant discrimination across several classifiers (e.g., KNN, Nearest Centroid, functional QDA), whereas heart rate showed modest discriminative value and glucose intermediate performance. Unsupervised analyses revealed distinct lactate recovery profiles and graded membership for hemodynamic/metabolic variables, supporting the value of FDA for resolving heterogeneity beyond group-average trends. Conclusions: FDA offers a feasible and informative approach for classifying recovery phenotypes while preserving temporal structure. Findings are promising but should be interpreted with caution due to the small sample size, sparse time points, and the need for external validation in larger, independent cohorts before translation into routine decision-making. Full article

Corporate “greenwash” constrains high-quality economic development in China, and its identification and governance constitute a critical step in building a green market and advancing ecological civilization. However, existing studies have primarily focused on the green governance effects of green finance policies, while paying limited attention to whether such policies may induce corporate “greenwash”. Using panel data on A-share listed firms in China from 2011 to 2023, this study exploits the Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment and employs a Double Machine Learning model to identify the impact of green finance reform policies on corporate “greenwash” and its underlying mechanisms. The results show that the pilot policy induces corporate “greenwash”, but this effect exhibits significant temporal characteristics and does not persist in the long run. Heterogeneity analysis further indicates that the aggravating effect is more pronounced among non-state-owned enterprises, non-heavily polluting firms, and large-scale firms. Mechanism analysis reveals that the pilot policy promotes corporate “greenwash” by intensifying external competitive pressure and internal performance pressure, while such behavior can be mitigated through optimizing firms’ internal strategic decision-making and external capital structure. Based on these findings, this study proposes policy recommendations in three aspects, namely establishing a dynamic policy adjustment mechanism, improving the competitive environment, and strengthening corporate governance, thereby providing a policy basis for mitigating corporate “greenwash”. Full article

This study examines the asymmetric responses of stock market indices in Türkiye and Kazakhstan to oil price shocks during the 2010–2025 period. Using the Nonlinear Autoregressive Distributed Lag (NARDL) model, the study decomposes the nonlinear effects of oil price fluctuations on financial markets. Empirical findings reveal that in Türkiye, a net oil importer, the stock market exhibits a dual-sensitivity: while exchange rate dynamics (2.34) remain the dominant driver, oil price increases (−0.12) exert a direct and statistically significant negative pressure. In contrast, Kazakhstan, a net oil exporter, shows a high vulnerability to oil price decreases (−1.05) at the 1% significance level, confirming a strong asymmetric structure (p = 0.0122). Furthermore, the error correction speed is significantly higher in Türkiye (28%) than in Kazakhstan (4%), indicating divergent market efficiency and recovery mechanisms. These results demonstrate that financial market reactions to external shocks differ fundamentally based on energy trade structures. The findings suggest that oil-importing countries must prioritize exchange rate stability, while oil-exporting nations must develop specific policy buffers against the persistent downside risks of global energy cycles. Full article

This study aims to investigate the static performance of water-lubricated rubber bearings (WLRBs) with axial grooves. To achieve this objective, an analytical approach is employed that combines a modified Reynolds equation, accounting for surface groove effects and rubber deformation, with a Winkler model and finite element analysis of pressure distribution. By developing a fluid–structure interaction model that incorporates rubber liner deformation, this research reveals the interaction between WLRB geometry and steady-state performance parameters. The investigation evaluates the influence of geometric characteristics, including groove shape, number, and size, on the performance of elastomeric liner WLRBs, while assessing optimal groove depths under various conditions. The study analyzes five distinct groove geometries, including semi-cylindrical, rectangular prism, and three pyramidal types with different apex positions, in a six-groove bearing configuration, presenting their qualitative effects on the behavior of the examined bearings. The key findings indicate that increasing groove size or quantity reduces maximum pressure and load-carrying capacity while elevating friction coefficients. As groove count rises, supporting surfaces diminish, causing pressure distribution to intensify and minimum film thickness to decrease under a specified external load. A notable result reveals that when groove depth exceeds film thickness, performance becomes geometry-independent; however, shallower grooves exhibit significant geometric effects. Additionally, the study identifies groove ends as critical functional zones where film thickness reduction substantially enhances pressure distribution and static performance. Comparative analysis shows that longitudinal grooves with triangular cross sections outperform semi-circular and rectangular variants, with the backward triangular configuration demonstrating superior characteristics due to optimal end-film properties. In conclusion, this research provides a detailed understanding of how groove geometry influences the static performance of WLRBs, highlighting the importance of groove design, particularly at the groove ends, in optimizing bearing functionality. The findings offer valuable insights for the design and selection of groove configurations in water-lubricated rubber bearing applications. Full article

The European Union is increasingly confronted with a convergence of sustainability, democratic, and security-related challenges that affect the conditions for long-term transformation. While sustainable development and democratic resilience are often discussed separately, their interdependence has become more visible in the context of geopolitical instability, geoeconomic competition, hybrid threats, and growing societal polarization. This article examines the relationship between sustainable development and democratic resilience in the European Union and analyses how external pressures shape both agendas. The study employs a qualitative, concept-driven research design that combines the analysis of EU strategic and policy documents, a structured review of relevant scholarly literature, and triangulation with selected sustainability and governance indicators. The findings suggest that the implementation of sustainable development goals depends not only on regulatory and economic capacity, but also on social cohesion, public trust, and the resilience of democratic institutions, which together shape the legitimacy, continuity, and political feasibility of long-term transformative policies. At the same time, energy dependence, supply-chain vulnerabilities, technological dependencies, and information threats increasingly constrain the EU’s sustainability agenda. In response, the article proposes the concept of Sustainable Democratic Security as an analytical framework linking sustainability governance, democratic resilience, and strategic-security capacity. The article contributes to the literature by conceptualising these dimensions as mutually conditioning components of a common governance framework and by outlining their implications for integrated EU policymaking under conditions of geopolitical and geoeconomic pressure. Full article

The global fashion industry, particularly in production-intensive markets like China, faces pressing environmental challenges due to low utilization rates of post-consumer textiles, positioning the upcycling of secondhand clothing as a pivotal issue for sustainable transformation. Based on the Theory of Planned Behavior (TPB), this study examines the drivers of corporate participation in secondhand clothing upcycling at the organizational level, with a focus on how corporate attitudes, subjective norms, and perceived behavioral control shape behavioral intention and actual practice. Using data from a survey of 408 Chinese apparel firms and analyzing it via structural equation modeling, the findings reveal that attitudes, subjective norms, and perceived behavioral control each exert a significant positive influence on upcycling intention, which in turn strongly predicts actual upcycling behavior; the model demonstrates good overall fit. Beyond confirming the explanatory relevance of TPB in organizational sustainability decision-making, this study also develops a practical “value–pressure–capability” framework through sub-dimensional analysis. According to this framework, corporate upcycling engagement is motivated by perceived economic and brand value, moderated by external pressures from industry norms and policy signals, and enabled through technological innovation and resource support that lower implementation barriers. The research offers an evidence-based theoretical and practical pathway for policymakers and industry stakeholders seeking to advance the circular fashion economy, with implications for similarly structured economies in East Asia. Full article

The breeding blanket is a key component of tokamaks, primarily responsible for extracting heat from fusion reactions and for tritium breeding, which is essential to ensure a fusion reactor’s fuel self-sufficiency. Recent technological advancements have led to the development of Dual-Cooled Lead–Lithium (DCLL) breeding blankets, which employ a liquid metal (specifically a Lead–Lithium eutectic alloy) as a heat transfer medium and tritium breeder, while helium gas is used to cool the structural components of the reactor. The interaction between the moving electrically conducting fluid and the strong magnetic field in the tokamak environment leads to magnetohydrodynamic (MHD) effects. The latter are characterized by the induction of eddy currents within the fluid and resulting Lorentz forces generated by their interaction with the magnetic field, which cause additional pressure losses and reduce heat transfer efficiency. This work investigates the pressure drop experienced by a Lead–Lithium flow within a rectangular section conduit under the action of an external, uniform magnetic field of different intensities. An analytical model was developed to estimate the total MHD-induced pressure losses along the channel for different values of the external magnetic field intensity and then benchmarked against relative computational fluid dynamics (CFD) simulations carried out using COMSOL Multiphysics. This comparison allowed the validation of the analytical predictions as well as a better understanding of the influence of the applied magnetic field intensity on the overall pressure drop. Therefore, the aim of the analytical model is to provide analytical tools for reasonably accurate estimations of MHD pressure losses suitable for future preliminary design purposes. Full article

The safe release of external stores from aircraft is a complex aerodynamic problem governed by strong interactions between the store and the carrier. During separation, the store is subjected to rapidly varying pressure fields, strong aerodynamic interference, and inertial effects that collectively determine the trajectory and stability of the body in the critical milliseconds following release. This study presents a numerical investigation of the separation of an external store from the high-wing configuration aircraft. Both gravitational and impulse-based release mechanisms are examined across multiple suspension stations and a wide range of flight conditions. Computational fluid dynamics (CFD) methods were employed using a density-based, compressible solver with SST k–ω turbulence modeling, combined with a fully coupled six-degree-of-freedom (6DOF) solver and dynamic mesh deformation techniques. The study considers a wide range of Mach numbers from 0.6 to 0.9 and angles-of-attack between −2° and 4°, and three different suspension stations located at the inner wing pylon, outer wing pylon, and fuselage centerline. These conditions strongly influence the aerodynamic environment around the store and therefore affect its initial motion after release and flight path. The impulse ejection forces used in the analysis come from experimental data and are applied through a user-defined function (UDF) at each time step, allowing the simulation to reproduce the ejection event as realistically as possible. Numerical results confirm that the flight paths of external store are highly non-symmetrical, requiring the employment of complex computational models for their successful resolution, and that they gravely depend on the operating conditions, carrier geometry as well as the suspension location. Full article

Three-edge bearing (TEB) tests and a crack-width-dependent load-carrying model were used to assess the combined effects of recycled glass fiber-reinforced polymer (rGFRP) short fibers and glass fiber-reinforced polymer (GFRP) bars in concrete pipes. Using the force method, a circumferential statically indeterminate ring analysis was formulated to obtain internal forces at critical sections and the neutral-axis position. Fiber distribution was simulated by means of Monte Carlo sampling, and single-filament pull-out tests were fitted to relate embedded length to pull-out force, enabling calculation of the fiber-bridging contribution at cracked sections. Ten specimen types with different bar/fiber schemes were tested under external pressure to validate the model. Predicted cracking and ultimate loads agreed with measurements, with most errors within ±20%. Adding 1% (vol.) rGFRP fibers increased the cracking load by 11.81% and the ultimate load by 0.45%. Without fibers, replacing steel bars with equal-area GFRP bars increased the cracking load by 1.35% but reduced the ultimate load by 35.45%. For all specimens, the load–maximum crack-width relation was strongly linear (R² > 0.93). The proposed approach and dataset support engineering use of recycled GFRP materials for crack control and load-carrying design of concrete pipes. Full article

DOC coupled with SCR represents a key technological approach for reducing gaseous pollutant emissions from diesel engines. Based on engine bench testing using a light-duty diesel engine as a prototype, this study investigates the impact of DOC coupled with SCR at different catalyst loadings on diesel engine emission characteristics. Results indicate that higher DOC loadings lead to greater exhaust backpressure losses, with a maximum pressure difference reaching 4.3 kPa. The temperature difference across the DOC was minimally affected by catalyst loading. Higher DOC loading enhanced catalytic activity toward CO and THC. At medium-to-low loads, this effect was pronounced, while at high loads, the influence of catalyst loading diminished. Higher DOC loading enhances NO oxidation capacity. Under external characteristic conditions, elevated engine exhaust temperatures maximize post-DOC NO₂ formation, increasing post-DOC NO₂ production by over 100%. These findings provide useful guidance for optimizing diesel aftertreatment systems to achieve a better balance between pollutant reduction, energy consumption, and environmental sustainability, thereby supporting the sustainable development of cleaner diesel engine technologies. Full article

Hypertension remains a leading modifiable risk factor for cardiovascular morbidity and mortality. Nonetheless, blood pressure control rates remain suboptimal despite established treatment guidelines and effective pharmacologic therapies. In parallel, artificial intelligence (AI) has rapidly expanded within cardiovascular medicine, demonstrating promising capabilities in disease detection, risk prediction, and clinical decision support. However, most AI applications in hypertension have focused primarily on algorithmic performance rather than real-world implementation or measurable improvements in patient outcomes. This review examines artificial intelligence-driven hypertension management through the lens of quality improvement and prevention of end-organ damage. We summarize current applications of machine learning, deep learning, natural language processing, and imaging analytics in hypertension detection and risk stratification, and critically evaluate their integration into clinical workflows. Particular emphasis is placed on therapeutic inertia, primary care-centered implementation, and the use of AI to support continuous quality improvement frameworks. Beyond blood pressure reduction alone, we explore the potential of AI to identify patients at risk for hypertensive heart disease, heart failure, aortic pathology, renal dysfunction, and cerebrovascular events. We discuss implementation challenges, including external validation, algorithmic bias, workflow integration, and regulatory considerations, which must be addressed to ensure safe and equitable deployment. Artificial intelligence offers the opportunity to transform hypertension management from reactive blood pressure control to proactive organ protection. Critically, AI-driven quality improvement interventions must be evaluated against established non-AI strategies, including pharmacist-led management and team-based care, which provide the benchmarks for demonstrating added clinical value. Achieving this shift will require embedding predictive analytics within structured, outcome-oriented systems of care and rigorously evaluating their impact on cardiovascular morbidity and mortality. Full article

In marine renewable energy applications, offshore steel pipelines are subjected to complex combined loads during installation and operation, leading to significant plastic deformation and potential catastrophic fracture. To accurately characterize pipeline fracture failure, this study develops an enhanced failure assessment framework based on the Modified Mohr–Coulomb (MMC) criterion, integrating experimental parameter evaluation with numerical simulation for in-service offshore pipelines. The key parameters of the MMC model were determined directly from in-service pipeline samples to account for operational degradation. First, the plastic parameters were obtained by fitting the Swift hardening law to uniaxial tensile tests. Fracture parameters were then calibrated using a suite of five notched tensile specimens. Mesh sensitivity was analyzed using CT experiments to establish a suitable mesh size for the MMC-based damage model, enabling precise characterization of crack evolution from initiation to final tearing. Unlike prior applications, this framework is employed to investigate the response of X80 pipelines under combined tension, bending, and external pressure loading. Three-dimensional finite element models were developed to systematically analyze the stress–strain response, moment–curvature behavior, and evolution of hoop stress distribution. Results show that while the failure stress remains relatively stable under varying external pressure, both the critical strain and critical curvature increase markedly with pressure, by up to 20.9%. They also reveal a pronounced hierarchy in the influence of crack geometry on the failure behavior. Crack depth dominates failure sensitivity, affecting critical strain and pressure response far more than crack width or length. The reduction in failure stress for deep cracks under 12 MPa external pressure is over three times greater than for shallow cracks. In contrast, variations in crack length exert the most negligible influence on failure characteristics, with observed discrepancies of less than 6%. Overall, this research provides a high-precision failure prediction framework for in-service pipelines by quantitatively analyzing failure behavior under combined loads. It effectively characterizes failure evolution paths that differ from design conditions and dynamically tracks the residual fracture resistance after time-dependent degradation, offering a fundamental reference for the reliability assessment of pipelines in complex marine environments. Full article