Search Results (1,027)

This paper presents an integrated study on the design, simulation, manufacturing, and experimental testing of a three-blade tritoroidal propeller compared to a conventional two-blade configuration for small UAVs. The aerodynamic analysis was performed in ANSYS Fluent 2022 R1 using the k–ω SST turbulence model at 6000 rpm, while structural integrity was assessed through FEM simulations in ANSYS Mechanical 2022 R1. Both propellers were fabricated via SLA additive manufacturing using Rigid 4000 resin and evaluated on an RCbenchmark 1585 test stand. The CFD results revealed smoother flow attachment and reduced tip vortex intensity for the tritoroidal geometry, while FEM analyses confirmed lower deformation and a more uniform stress distribution. Experimental tests showed that the tritoroidal propeller produces thrust comparable to the conventional one (within 1%) but at a 58% higher torque, resulting in slightly lower efficiency. However, vibration amplitude decreased by up to 70%, and the SPL was reduced by 0.1–6.2 dB at low and moderate speeds. These results validate the tritoroidal concept as a structurally robust and acoustically efficient alternative, with strong potential for optimization in low-noise UAV propulsion systems. Full article

Accurate estimation of internal temperature is essential for safe operation and state estimation of lithium-ion batteries, yet it usually cannot be measured directly and requires physically grounded electro-thermal models. High fidelity 3D simulations capture geometry-dependent heat transfer behavior but are too computationally intensive for real-time use, whereas common lumped models cannot represent internal gradients. This work presents an integrated geometry-resolved workflow that combines detailed 3D finite volume thermal modeling with systematic reduction to a compact multi-node thermal network and its coupling with an equivalent circuit electrical model. A realistic 3D model of the Panasonic NCR18650B cell was reconstructed from computed tomography data and literature parameters and validated against published axial and radial thermal conductivity measurements. The automated reduction yields a five-node thermal network preserving radial temperature distribution, which was coupled with five parallel Battery Table-Based blocks in MATLAB/Simulink R2024b to capture spatially distributed heat generation. Experimental validation under dynamic loading is performed using measured surface temperature and terminal voltage, showing strong agreement (surface temperature MAE ≈ 0.43 °C, terminal voltage MAE ≈ 16 mV). The resulting model enables physically informed estimation of internal thermal behavior, is interpretable, computationally efficient, and suitable for digital twin development. Full article

Magnesium (Mg²⁺) ions require sensitive and selective detection due to their low concentrations and coexistence with similar ions in matrices. This study developed a potentiometric ISE using a new modified polyurethane membrane. The membrane’s negative surface charge facilitates selective interaction with Mg²⁺ ion. Optimal performance was obtained at 0.0061% (w/w) κ-carrageenan and 0.0006% (w/w) D2EHPA. The ISE exhibited a near-Nernstian response of 29.49 ± 0.01 mV/decade across a 10⁻⁹–10⁻⁴ M concentration range (R² = 0.992), with a detection limit of 1.25 × 10⁻¹⁰ M and a response time of 200 s. It remained stable in the pH range 6–8 for one month and demonstrated high selectivity over K⁺, Na⁺, and Ca²⁺ ($K_{ij}$ < 1). The repeatability and reproducibility tests yielded standard deviations of 0.15 and 0.39, while recovery rates confirmed analytical reliability. The water contact angle analysis showed a reduction from ~80° to ~69° after membrane conditioning, indicating increased hydrophilicity and improved interfacial for ion diffusion. FTIR analysis confirmed successful modification by reduced O–H peak intensity, while XRD verified the amorphous structure. SEM revealed a dense top layer with concave morphology, favorable for minimizing leakage and ensuring efficient ion transport within the sensing system. Full article

Against the backdrop of China’s economic transformation and the transition towards sustainable industrial systems, optimizing ownership structures to enhance the resilience and bargaining power of manufacturing suppliers has become crucial for building sustainable supply chains. This study empirically examines the impact of ownership concentration on supplier bargaining power using data from manufacturing companies listed on the Shanghai and Shenzhen A-share markets from 2008 to 2022, integrating insights from principal-agent theory and industrial dynamics within a sustainability-oriented framework. The findings reveal: (1) Ownership concentration significantly strengthens the bargaining power of supplier enterprises, contributing to more stable and equitable supply chain relationships. (2) R&D investment plays a partial mediating role between ownership concentration and supplier bargaining power, suggesting that innovation efforts—often aligned with green and sustainable technologies—can reshape dependency dynamics. (3) Industry competitiveness negatively moderates the relationship between ownership concentration and supplier bargaining power, indicating that intense competition may undermine the governance advantages of concentrated ownership in sustainable value creation. (4) Heterogeneity analysis shows that the positive effect of ownership concentration is more pronounced in central and western regions, state-owned enterprises, and large firms, highlighting contextual factors in achieving sustainable supply chain governance. Full article

As global sustainable development progresses and the United Nations Sustainable Development Goals (SDGs) gain increasing prominence, pharmaceutical manufacturing firms face mounting challenges in implementing environmental, social, and governance (ESG) practices; these include high environmental compliance costs, limited drug accessibility, and governance inefficiencies. Patient capital, characterized by long investment horizons and high tolerance for risk, is well aligned with the long-term nature of ESG-oriented activities in this industry. Using a sample of pharmaceutical manufacturing companies listed on the Shanghai and Shenzhen A-share markets from 2015 to 2024, this study systematically examines the impact of patient capital on corporate ESG performance and explores the underlying mechanisms. The empirical results show that patient capital significantly improves ESG performance among pharmaceutical manufacturing firms. These findings remain robust across a series of robustness checks, including alternative variable measurements, sample adjustments, propensity score matching, instrumental variable estimation, and changes in the sample period. Further analysis reveals that patient capital enhances ESG performance through two primary channels: alleviating financing constraints and increasing R&D investment intensity. By focusing on the pharmaceutical manufacturing industry, this study extends the literature on patient capital to a highly regulated and socially sensitive sector, providing empirical evidence on how long-term, value-oriented capital can support sustainable development and improve ESG performance in industries with strong public welfare attributes. Full article

Whether the New Energy Vehicle Promotion Policy (NEVPP) enhances supply chain resilience is pivotal to China’s green transition and global industrial security. Using data on A-share listed automobile manufacturers from 2012 to 2024, this study employs a multi-period difference-in-differences approach to identify the policy’s impact. Results show that NEVPP significantly strengthens supply chain resilience, and the findings remain robust across alternative specifications. Mechanism analysis reveals that the policy raises managerial attention, eases financing constraints, and stimulates technological innovation, thereby enhancing resilience through managerial, financial, and technological channels. Heterogeneity analysis by ownership, geography, R&D intensity, analyst coverage, and institutional ownership shows that the effect is stronger for state-owned enterprises, firms in central and western regions, low-R&D firms, those without analyst coverage, those with high analyst attention, and firms with low institutional ownership. This study provides firm-level evidence on the economic consequences of NEVPP, advances understanding of industrial policy and corporate resilience, and offers policy implications for supporting the global energy transition and safeguarding supply chain stability. Full article

The vulnerability of reservoirs in Mediterranean regions to eutrophication is attributable to two key factors: strong seasonal hydrological variability and intensive agricultural activity. The present study evaluated the trophic state of 47 reservoirs in the Ebro Basin in Spain using two complementary approaches: the Organisation for Economic Co-operation and Development (OECD) classification system and the criteria set out in Royal Decree (RD) 47/2022. Chlorophyll-a, total phosphorus and transparency data were monitored from 2023 to 2024. While most of reservoirs were classified as oligotrophic to mesotrophic under the OECD thresholds, the RD 47/2022 identified 87% as being at risk of eutrophication. A significant variation in transparency was observed among the different reservoir types (p < 0.05), with high-altitude systems showing higher levels of water transparency. However, chlorophyll-a and total phosphorus had a significant spatial variability, exhibiting only modest correlations. Chlorophyll-a was weakly but significantly correlated to transparency (r = −0.21), while total phosphorus was not significantly associated with either variable, suggesting a decoupling between nutrient availability and phytoplankton biomass. The observed discrepancy between the two classification frameworks is indicative of divergent conceptual approaches (ecological condition versus management risk). It underscores the requirement for integrated monitoring that incorporates chemical, biological and catchment-scale indicators. These findings offer new insight into the trophic dynamics of Mediterranean reservoirs and highlights the importance of adapting regulatory assessment methods to region-specific climatic and hydrological contexts. Full article

Against the backdrop of global climate governance and China’s “dual carbon” goals, carbon emissions trading (CET) has become a core policy instrument for promoting low-carbon transformation. However, it remains unclear whether CET policies can effectively improve corporate carbon performance and, more importantly, through which micro-level mechanisms such effects operate within firms. To address these gaps, this study applies a difference-in-differences (DID) approach to examine the impact of CET policy on corporate carbon performance and its transmission pathways. The results show that CET policy significantly enhances corporate carbon performance. Heterogeneity analysis further reveals that this positive effect is more pronounced in regions with lower environmental governance intensity, and that the policy’s effectiveness strengthens over time. Mechanism tests indicate that financing constraints and R&D investment serve as chain mediators: CET policy alleviates financing constraints, stimulates R&D investment, and thereby improves carbon performance. Moreover, the moderating effect analysis shows that executives’ green backgrounds reinforce the policy’s effectiveness by further easing financing constraints and mitigating their negative impact on R&D investment. Overall, these findings deepen the micro-level understanding of market-based environmental regulation and provide policy implications for optimizing CET policy design, improving resource allocation efficiency, and fostering low-carbon transformation and sustainable competitive advantages for enterprises. Full article

Green innovation is important for environmental sustainability and long-term ecological balance. Using 1129 observations of Chinese listed firms spanning 2014–2024, combined with text mining method to quantify data assets, this paper empirically examines the impact of customer data assets on suppliers’ green innovation. Our model is integrated with fixed effects for both industry and year. We find that there is a significant improvement in suppliers’ green innovation when customers have more data assets, with a one-notch improvement in the customer data assets of a customer firm. This results in an overall 0.06 increase in supplier green innovation output. Specifically, the spillover effect is more pronounced when there is a shorter geographic distance between suppliers and customers, as well as higher customer concentration. After conducting a variety of endogeneity tests, our results are robust. The mechanism analysis shows that customer data assets facilitate supplier digital transformation and improve supplier operational capacity. The heterogeneity analysis also reveals stronger effects when (1) customers are located in eastern regions, (2) customers belong to technology-intensive industries, (3) suppliers are state-owned enterprises (SOEs), and (4) suppliers face lower financial constraints. Further analysis suggests that customers with more data assets also increase suppliers’ R&D investment and improve green innovation quality. Our research contributes to understanding the spillover effect of customer data assets along the supply chain. Full article

Background: Integrated medical information systems process large volumes of sensitive clinical data and are exposed to persistent cyber threats. Artificial intelligence (AI) is increasingly used for anomaly detection and incident response, yet its systemic effect on the dynamics of security indicators is not fully quantified. Aim: To develop and numerically study a nonlinear dynamical model describing the joint evolution of system vulnerability, threat activity, compromise level, AI detection quality, and response resources in a medical data protection context. Method: A five-dimensional system of ordinary differential equations was formulated for variables $V$, $T$, $C$, $D$, $R$. Parameters characterize appearance and elimination of vulnerabilities, attack intensity, AI learning and degradation, and resource consumption. The corresponding Cauchy problem $V\left(0\right)=0.5$, $T\left(0\right)=0.6$, $C\left(0\right)=0.1$, $D\left(0\right)=0.4$, $R\left(0\right)=0.8$ was solved on $\left[0,200\right]$ numerically using a fourth-order Runge–Kutta method. Results: Numerical modelling showed convergence to a favourable steady regime. On the interval t ∈ [195, 200] the mean values were $V=0.0073$, $T=0.3044$, $C=7.7·{10}^{-5}$, $D=0.575$, $R=19.99$. Thus, the initial 10% compromise is reduced by more than 99.9%, while AI detection quality stabilizes at around 0.58, and response capacity increases 25-fold. Conclusions: The model quantitatively confirms that the integration of AI detection and a managed response capacity enables the system to reach a stable state with virtually zero compromised medical data even with non-zero threat activity. Full article

Virtual synchronous machine (VSG) technology provides a robust framework for integrating electric vehicle energy storage into modern microgrids. Nonetheless, conventional VSG control often suffers from intense interaction between active and reactive power flows, which can trigger persistent steady-state errors, power fluctuations, and potential system collapse. This research addresses these challenges by developing a 5th-order electromagnetic dynamic model tailored for a two-stage cascaded bridge inverter. By synthesizing a 3rd-order power regulation loop with a 2nd-order output stage, the proposed model captures stability boundaries across an extensive parameter spectrum. Unlike traditional 3rd-order “quasi-steady-state” approaches—which overlook essential dynamics under weak-damping or low-inertia conditions—this study utilizes the 5th-order model to derive an adaptive dynamic virtual impedance decoupling technique. This strategy facilitates real-time compensation of the cross-coupling between active and reactive channels, significantly boosting the inverter’s damping ratio. Quantitative analysis confirms that this approach curtails overshoot by 85.6% and accelerates the stabilization process by 42%, markedly enhancing the overall dynamic performance of the grid-connected system. Full article

To overcome the limitation of the Paris law in capturing stress-ratio (R) effects, a modification of the Goodman model is introduced to account for the nonlinear variation of the fatigue limit with mean stress in this study. Based on the modified formulation, an equivalent crack driving force model incorporating R-effects is subsequently derived for fatigue-crack growth (FCG). The model unifies the stress-intensity factor ranges at different values of R into an equivalent value at R = 0 without introducing fitting parameters other than the Paris constants, relying solely on basic material properties (fatigue limit and tensile strength). This feature facilitates practical application and avoids extensive experimental calibration. Validation using FCG test results of Q345qD steel and 23 datasets show that the model outperforms classical models, achieving a goodness of fit up to 0.98 and demonstrating strong robustness and practical value for FCG prediction and residual-life assessment in engineering structures. Full article

Pavement macrotexture, quantified by mean texture depth (MTD) and mean profile depth (MPD), is a critical parameter for road safety and performance. The traditional sand patch test is labor-intensive and slow, creating a bottleneck for modern pavement management systems. Accurately translating the rich point cloud data into reliable MTD values using the 3D scanning method remains a challenge, with current methods often relying on oversimplified correlations. This research addresses this gap by developing and validating a novel machine learning framework to predict MTD and MPD directly from high-resolution 3D laser scans. A comprehensive dataset of 127 pavement samples was created, combining traditional sand patch measurements with detailed 3D point clouds. From these point clouds, 27 distinct surface features spanning statistical, spatial, spectral, and geometric domains were developed. Six machine learning algorithms, consisting of Random Forest, Gradient Boosting, Support Vector Regression, k-Nearest Neighbor, Artificial Neural Networks, and Linear Regression, were implemented. The results demonstrate that the ensemble-based Random Forest model achieved superior performance, predicting MTD with an R² of 0.941 and a mean absolute error (MAE) of 0.067 mm, representing a 56% improvement in accuracy over traditional digital correlation methods. Model interpretation via SHAP analysis identified root mean square height (Sq) and surface skewness (Ssk) as the most influential features. Full article

This study aimed to assess the effect of a downhill-running (DR) bout on muscle damage biomarkers. It also examined whether training background and gait kinematics may influence DR-induced muscle damage and strength loss. Thirty-six experienced trail runners (25 men, 11 women), participants of a 106 km ultra-trail, performed a 5 km DR bout at 15% decline and at an intensity equivalent to their first ventilatory threshold. Muscle damage biomarkers (creatine kinase, lactate dehydrogenase, and myoglobin) were analyzed before and 30 min after the DR protocol, and also before and after the UT race. Isometric strength was assessed before and after DR, and gait parameters were recorded during DR. All muscle damage biomarkers increased following DR (d = 0.19 to 1.85). Lactate dehydrogenase concentrations after the race and DR were associated (r = 0.64). Athletes who habitually performed downhill repetitions showed reduced creatine kinase (182 ± 73 U/L vs. 290 ± 192 U/L; p < 0.05; d = 0.64) and greater squat strength retention (4 ± 10% vs. −9.1 ± 16.8%; p <0.05; d = 0.87). Ankle plantar flexion and squat strength retention were inversely correlated with vertical oscillation (r = −0.44) and step length (r = −0.37), respectively. In summary, lactate dehydrogenase response to a short DR bout could indicate an athlete’s readiness to handle ultra-trail-induced muscle damage, although further research is needed to confirm it. In addition, despite the exploratory nature of the study, regularly performing downhill intervals and adopting a more terrestrial gait pattern appear to soften strength loss and muscle damage response to DR. Full article

This study investigates how economic development interacts with sustainability performance in the European Union, focusing on the structural and technological factors that shape progress in the green transition. Using Eurostat data for 27 EU member states over the period 2015–2023, the analysis employs panel econometric models (Pooled Ordinary Least Squares, Fixed Effects, and Random Effects) to explore how circular economy performance, innovation capacity, human capital, and renewable energy use influence environmental and economic outcomes across member states. The results show that R&D intensity and skilled human resources are key drivers of sustainability. Higher levels of circular material use and resource productivity contribute to long-term competitiveness. In contrast, uneven progress in renewable energy deployment points to persistent regional disparities and possible structural constraints that limit convergence. Northern and Western Europe record the strongest advances in innovation and environmental efficiency, whereas Southern and Eastern regions remain affected by industrial legacies and lower absorptive capacity. The findings highlight that, in the short term, renewable energy expansion may involve adjustment costs and potential trade-offs with economic competitiveness in less technologically developed economies. This study provides new comparative evidence on the differentiated pathways of the green transition across the EU. Policy implications suggest the need to reinforce R&D investment, expand circular manufacturing, and support an inclusive technological transition consistent with the European Green Deal and the United Nations 2030 Agenda. Full article