Search Results (1,790)

Industrial digitalization reshapes production processes and can potentially improve carbon productivity by optimizing factor allocation and energy efficiency. Using panel data for 30 Chinese provinces from 2012 to 2022, this study constructs a comprehensive industrial digitalization index with four dimensions and 13 indicators using the entropy method and examines its impact on carbon productivity (GDP per unit of CO₂ emissions). We employ the Dagum Gini coefficient and kernel density estimation to describe regional disparities and their evolution, a dynamic panel threshold model to test the nonlinear role of industrial transformation and upgrading, and a spatial Durbin model to identify spatial spillover effects. The results indicate that industrial digitalization has risen nationwide but remains uneven; industrial digitalization significantly enhances carbon productivity, with stronger effects in the eastern and western regions and in plain areas; the effect exhibits a double-threshold pattern with respect to industrial transformation and upgrading, implying a U-shaped relationship; and industrial digitalization generates positive spatial spillovers. These findings suggest that policy should coordinate digital infrastructure investment with industrial upgrading and regional collaboration to accelerate low-carbon, high-efficiency growth. Full article

This study investigates the multi-scale tribo–thermo–viscoelastic performance of a sustainable bio-based FormuLITE epoxy reinforced with single and hybrid carbon nanofillers (0.1 wt.% total loading) under dry sliding up to 50 N. Pin-on-disk tests at 10, 30, and 50 N showed a consistent reduction in contact pressure and wear volume in the order: neat epoxy > 0.1 CNT > 0.1 GNP > 0.1 ND > 0.1 CNT/GNP > 0.1 CNT/ND > 0.1 GNP/ND. At 50 N and 1500 m sliding distance, neat epoxy exhibited a wear volume of 13.43 mm³ and contact pressure of 13.4 N/cm², while the GNP/ND hybrid reduced wear to 4.86 mm³ and contact pressure to 6.2 N/cm², corresponding to reductions of 64% and 54%, respectively. The accelerating wear coefficient decreased from 2.9 × 10⁻⁶ to 8.5 × 10⁻⁷, confirming slower damage accumulation in hybrid systems. Time-dependent contact pressure analysis revealed reduced asymptotic intensity and suppressed mid-cycle pressure spikes, indicating enhanced tribolayer stability. Effective surface hardness increased from 0.18 GPa (neat epoxy) to 0.30 GPa (GNP/ND), while normalized wear decreased from 1.00 to 0.36. Enhanced damping behavior and improved thermal conductivity in hybrid systems promoted stress redistribution and minimized flash-temperature localization. An interfacial energy-partition framework calibrated to experimental wear data quantitatively linked effective driving pressure, tribofilm stabilization, and surface hardness to material removal. The results demonstrate that wear mitigation in sustainable bio-epoxy systems is governed by coupled mechanical, viscoelastic, and thermal energy redistribution, with GNP/ND hybrids providing the most stable tribological interface under severe sliding. The findings contribute to the development of durable and sustainable bio-epoxy composite systems for engineering applications, supporting broader goals of responsible material utilization and sustainable industrial innovation aligned with the United Nations Sustainable Development Goals (SDG 9 and SDG 12). Full article

Urban areas in arid environments are increasingly affected by the urban heat island (UHI) effect, which intensifies thermal stress, disrupts ecological balance, and poses challenges for sustainable urban development. Understanding and predicting spatiotemporal variations in land surface temperature (LST) and land use dynamics is therefore critical for effective urban planning. This study develops a predictive framework for Riyadh, Saudi Arabia, using long-term Landsat time series data (1993–2023) and deep learning models to evaluate urban thermal patterns via the Urban Thermal Field Variation Index (UTFVI). Artificial Neural Networks (ANNs) with six hidden layers for LST and seven for UTFVI forecast future trends up to 2043. The results indicate that urban areas expanded by 521.62 km², increasing from 8.73% to 19.56% between 1993 and 2023, and are projected to reach 1509.40 km² (25.28%) by 2043, while vegetation coverage declined from 0.771% to 0.674%. The highest average summer LST increased from 56.73 °C in 1993 to 59.89 °C in 2023 and is predicted to rise to 60.79 °C by 2033 and 61.52 °C by 2043. Winter temperatures exhibited a comparable upward trend, rising from 30.75 °C to 32.33 °C in 2023 and projected to reach 34.48 °C by 2043. UTFVI analysis revealed a substantial expansion of weak thermal field zones, which covered 2778 km² in 2023 and are expected to reach 3018.44 km² (57%) by winter 2043, accompanied by a marked contraction of strong thermal field areas. The ANN models achieved a high predictive performance, with RMSE values of 0.759 (summer) and 0.789 (winter) for UTFVI and correlation coefficients of 0.91 and 0.89, respectively. Projections further indicate that, by 2043, approximately 39.31% of the study area will experience summer temperatures between 48 °C and 53 °C, compared to 5.59% in 2023. These findings highlight the accelerating interaction between urban growth and thermal intensification in arid cities. The proposed modeling framework provides a robust decision-support tool for urban planners and policymakers to mitigate UHI impacts and promote climate-resilient and sustainable urban development. Full article

Inspired by the concept of symmetry in functional representation, complex nonlinear relationships can be decomposed into combinations of lower-dimensional functions, providing an interpretable framework for modeling high-dimensional systems. With the continuous growth of road traffic volume in China and the rapid acceleration of urbanization, traffic safety issues have become increasingly prominent. To address the limitations of traditional traffic accident prediction models—including insufficient spatial information representation, weak nonlinear fitting capability, and poor interpretability—this study proposes an improved Kolmogorov–Arnold Networks (KANs) model. Specifically, a spatial embedding module, a multi-scale spline mechanism, and a residual connection structure are incorporated into the original KAN framework to enhance its ability to capture spatial heterogeneity and complex nonlinear relationships in traffic accident data. Experimental results demonstrate that the improved KAN model achieves a 2.38% increase in the coefficient of determination, while reducing the mean absolute deviation and mean squared prediction error by 24.89% and 34.69%, respectively, indicating a significant improvement in both prediction accuracy and model stability. Furthermore, the proposed model enhances interpretability by visualizing variable relationships through spline functions, enabling intuitive analysis of nonlinear effects. Overall, the improved KAN model exhibits strong capability in modeling spatially non-stationary and nonlinear structures, making it a promising tool for macroscopic traffic safety modeling with substantial application potential and practical value. Full article

Carryover slag (COS) entrained from the basic oxygen furnace (BOF) during tapping is highly oxidizing and affects secondary steelmaking by increasing deoxidizer consumption, refractory wear, P reversion, and decreasing steel cleanliness. A kinetic COS amount estimation model was developed by using the effective equilibrium reaction zone (EERZ) method. The amount of COS was determined by iteratively adjusting the carryover slag coefficient (CSC) until predicted steel and slag compositions approached industrial measurements. Validation with four industrial heats confirmed that the model effectively predicts COS under both complete and incomplete deoxidation conditions. Further simulation results show that increasing the CSC from 2 to 4 kg per tonne of steel leads to 9.3 ppm P reversion. The calculations also confirmed that larger COS amounts accelerate refractory wear due to the higher input of readily reducible components, particularly FeO and MnO. Full article

This study investigates the dynamic behavior of the suction-side lubrication gap between bushing and gear in external gear pumps (EGPs), with emphasis on how surface texturing and bushing micromotion influence the effective stiffness and damping of the oil film. A three-dimensional CFD model of a lubrication gap between bushing and gear is developed to resolve the coupled sliding–squeezing hydrodynamics arising under realistic suction-side operating conditions. Steady-state simulations are used to determine the nonlinear static force–gap height relationship and extract the hydrodynamic stiffness, while transient simulations with harmonic perturbations are post-processed to evaluate the damping coefficient through acceleration-based filtering. The results show that both stiffness and damping increase sharply as the gap height decreases due to the strong confinement of the lubricant in the small-clearance region. Increasing the textured area slightly enlarges the effective gap height and reduces the hydrodynamic load capacity, leading to lower stiffness and damping values; this behavior highlights that the choice of an appropriate texturing configuration is a critical design parameter. Overall, the study provides a comprehensive dynamic characterization of textured bushing–gear lubrication films in EGP and offers quantitative data for developing lumped parameter models of EGP with textured bushings. Full article

This study investigated the effects of microwave irradiation on the particle size distribution, rheological properties, fluorescent characteristics, and sensory characteristics of wine. Wine samples were treated under varying microwave power (100–500 W), temperature (20–60 °C), and time (1–5 min). Results indicated that microwave treatment modified the particle size distribution, especially the proportion of particles in the range of 0.3–0.5 μm, which increased with microwave power, temperature, and time. Rheological analysis indicated that the behaviour followed the Power-law model, with all samples exhibiting expansion fluid properties (n > 1). Fitting with the Casson model revealed that microwave treatment increased the yield stress (τ₀) and viscosity coefficient (K), with optimal improvements observed at 300 W, 30 °C, and 3 min (τ₀ = 0.7769 Pa, K = 2.9367 × 10⁻³ Pa s^0.5). These changes contributed to enhanced leg phenomenon and thickening effect. Furthermore, microwave treatment elevated the fluorescence intensity of wine, indicating accelerated formation of fluorescent substances. Sensory evaluation demonstrated that microwave treatment, particularly at 400 W, 40 °C, and 3 min, significantly improved colour, clarity, and mouthfeel while reducing astringency and bitterness. In conclusion, microwave treatment effectively modifies the sensory characteristics of wine, offering a viable technological approach to accelerate wine ageing and supporting its potential application in winemaking. Full article

Amid frequent external shocks and deepening market linkages, the information-transmission structure of financial markets is more prone to phase-specific abrupt changes, creating a need for real-time monitoring methods. This study develops an online framework to track financial information-flow networks and to provide early warnings of structural changes under exogenous shocks. Methodologically, information-flow networks are constructed from return spillovers using the Diebold–Yilmaz framework. An Exponential Random Graph Model is then employed to quantify how exogenous variables affect edge formation. Statistical process control methods, namely the Multivariate Cumulative Sum (MCUSUM) and Multivariate Exponentially Weighted Moving Average (MEWMA), are introduced to online monitoring of exogenous-effect coefficients. The simulation study uses simulated data to assess whether the two charts are properly calibrated and sensitive to alarms. The empirical study uses Shanghai Stock Exchange (SSE) 180 constituent stocks and exogenous variables—7-day Fixing Repo Rate (FR007), M2 growth rate (M2), the China Economic Policy Uncertainty Index (CEPU), and the Global Economic Policy Uncertainty Index (GEPU) over 2011–2025. The results indicate that both charts achieve the target in-control average run length, and detection accelerates with shock magnitude; FR007 is generally negative, M2 is positive, and uncertainty measures vary strongly over time; monitoring reveals shock clustering and long-term drift, implying both shock amplification and structural drift in the information-flow network. Practically, the framework provides an implementable warning tool for tracking shock amplification, supporting timely risk management. Full article

The hyperloop transportation system is a promising ultra-high-speed mobility solution operating in a reduced-pressure environment, where pod performance is governed by the coupled behaviour of structural integrity, aerodynamics, and electromagnetic propulsion. This paper presents the design, numerical analysis, and performance evaluation of a lightweight hyperloop pod equipped with a linear induction motor (LIM)-based propulsion and electromagnetic stabilisation system. The pod chassis was fabricated using Carbon Fibre-Reinforced Polymer (CFRP) and Aluminium 6061-T6, achieving a significant weight reduction while maintaining structural safety. Finite Element Analysis reveals a maximum von Mises stress of 82 MPa, which is well below the material yield strength, and a maximum deformation of 0.64 mm under worst-case loading conditions. Modal analysis indicates the first natural frequency at 47.6 Hz, ensuring sufficient separation from operational excitation frequencies. Computational Fluid Dynamics analysis conducted inside a rectangular tube shows a drag coefficient reduction of approximately 18% compared to a baseline blunt design, with stable velocity distribution and no flow choking at operating speeds. The optimised nose geometry enables rapid acceleration, achieving 25 km/h within 1.1 s in prototype testing. The LIM analysis demonstrates a peak thrust of 1.85 kN at an optimal slip range of 6–8%, with operating currents between 35 and 55A and power consumption of 18–25 kW. Thermal analysis confirms a maximum stator temperature of 78 °C, remaining within safe operating limits. The integrated numerical and experimental results confirm the feasibility, efficiency, and stability of the proposed hyperloop pod design. Full article

To effectively enhance the seismic performance of traditional Chinese timber structures, this study proposes a reinforcement method utilizing friction dampers. Based on the working mechanism of friction dampers and the extended discrete element theory, an analytical model for timber structures equipped with these dampers was developed and validated through shake table tests. Subsequently, dynamic analyses were conducted to systematically evaluate the enhanced seismic energy dissipation capacity of the ancient timber structures by the reinforcement of friction dampers. The friction coefficient (μ), bolt pre-tension strain (ε), and action distance (l) were selected as key parameters. A multi-objective optimization function was constructed using the weighted sum method, enabling a multi-objective parameter optimization analysis for the friction dampers to identify the optimal parameter combination under specific conditions. The results indicate that the established extended discrete element model effectively simulates the dynamic characteristics of the structure. The installation of friction dampers significantly enhanced the structure’s energy dissipation capacity and substantially reduced the peak displacement. However, due to the initial stiffness introduced by the dampers, the lateral stiffness of the column frame increased markedly, leading to a significant amplification of the acceleration response, with a maximum increase in peak acceleration reaching 77%. The multi-objective optimization analysis revealed that with weighting coefficients λ_a = λ_b = 0.5, the optimal damper parameter combination is μ = 0.36, ε = 102 με, and l = 268 mm. Under these conditions, the structural displacement response decreased by 38.5%, while the acceleration response increased by 93.7%. It is noted that the derived optimal design solutions are pertinent to the specific structural typology and ground motions considered. Full article

Dental resin composites are routinely exposed to chemically aggressive beverages that may compromise long-term functional performance. This study investigated the structure–property–tribology relationships of four restorative composites (Filtek Z250, Filtek Z550, Herculite, and Herculite Ultra) subjected to cyclic immersion in beverages with different pH values. A total of 120 cylindrical specimens (7 mm diameter, 2 mm thickness; n = 5 per material per condition) were fabricated and exposed to mineral water, tea, coffee, Coca-Cola^®, Cola Light^®, and red wine for 28 days under cyclic conditions. Microhardness, surface roughness (Ra), steady-state coefficient of friction (COF), and mass variation were evaluated. All composites exhibited significant microhardness reduction after acidic exposure (p < 0.05), with the greatest decrease observed for Herculite Ultra in red wine (−47.4%) and Coca-Cola^® (−35.3%). Filtek Z250 demonstrated the highest baseline hardness and the lowest degradation susceptibility. Surface roughness changes were formulation-dependent, with Herculite Ultra showing pronounced roughening (ΔRa up to +0.074 µm), whereas Filtek Z550 exhibited erosion-driven smoothing (ΔRa down to −0.068 µm). Tribological behaviour was primarily governed by matrix softening rather than roughness alterations, with softened systems displaying unstable frictional responses (COF range: 0.127–0.697; p < 0.05). The results indicate that polymer matrix stability plays a more critical role in long-term functional performance than surface roughness or mass variation alone. Clinically, frequent exposure to acidic and solvent-containing beverages may accelerate mechanical and tribological degradation of susceptible composite formulations. Full article

To evaluate the seismic performance of single-degree-of-freedom (SDOF) systems under mainshock–aftershock (MS–AS) seismic excitation, nonlinear time-history analyses were conducted on SDOF systems with various parameter combinations, using 50 sets of real MS–AS sequences and 150 sets of artificial sequences generated by repetition, random, and attenuation methods. The results indicate that the ground motion characteristics of MS–AS sequences generated by the repetition, random, and attenuation methods differ from those of real MS–AS sequences, with the repetition and random methods tending to overestimate the peak ground motion parameters and acceleration response spectra of MS–AS sequences, and the attenuation method potentially underestimating them, while all three methods for generating MS–AS sequences are prone to overestimating the ground motion duration of MS–AS sequences. Residual deformation is influenced by relative yield strength coefficient (η), aftershock relative intensity (χ), post-yield stiffness ratio (r), natural vibration period (T) and the hysteresis model under MS–AS seismic excitation, and residual deformation exhibits a positive dependence on aftershock intensity (χ) and a negative dependence on post-yield stiffness ratio (r), while the relationship between residual deformation and relative yield strength coefficient (η) is influenced by the natural vibration period (T), showing a positive correlation in the short-period range and a negative correlation in the mid-to-long period range. A log-normal distribution can be adopted to describe the probability distribution of the ratio of residual deformation to peak elastic-plastic deformation subjected to MS–AS seismic excitation with different parameters. Finally, a probabilistic prediction model for residual deformation under MS–AS seismic excitation was proposed which can effectively predict residual deformation under MS–AS seismic excitation. Full article

Resource-based cities in the Yellow River Basin are important pillars of national energy security and regional coordinated development, and their green transformation is closely related to the overall strategy of ecological protection and high-quality development in the basin. This study takes 34 resource-based cities within the basin as the research objects and employs a combination of methods, including the Super Slacks-Based Measure (SBM) model, the Malmquist–Luenberger index, the standard deviational ellipse, the Dagum Gini coefficient, and the geographical detector, to systematically analyze the spatio-temporal evolution characteristics and driving mechanisms of green development efficiency from 2012 to 2022. The results indicate that: (1) green development efficiency shows an overall upward trend, forming a pattern of leading performance in the lower reaches, lagging development in the middle reaches, and accelerated catching-up in the upper reaches, with efficiency improvements jointly driven by technical efficiency enhancement and technological progress; (2) the gravity center of efficiency shifts southwestward overall, and interregional disparities constitute the main source of overall differences; (3) economic development level, science and technology investment, fiscal expenditure, and energy intensity are the key driving factors, with significantly strengthened interactions among multiple factors. From the dual perspectives of basin location and the urban life cycle, this study constructs a multidimensional analytical framework that provides a reference for categorized regulation and coordinated regional governance of resource-based cities. Full article

The Particle swarm optimization (PSO) algorithm has strong universality and fast convergence speed, but when solving complex multimodal optimization problems, it is prone to fall into local optimum due to insufficient population diversity. To address this issue, this paper proposes a dual-population hybrid particle swarm optimization algorithm based on Hooke’s law competition mechanism (HLCM-DHPSO). This algorithm integrates the differential evolution algorithm into the PSO framework, and the two subpopulation sizes dynamically compete for computing resources according to the adaptive mechanism of Hooke’s law. When the algorithm stagnates, HLCM-DHPSO can automatically trace back to historical archives and adjust the inertia weight based on excellent experience data. Meanwhile, HLCM-DHPSO adaptively adjusts the acceleration coefficient through the Sine function to enhance the algorithm’s ability to escape from local optimum. To verify the effectiveness of the HLCM-DHPSO algorithm, it is compared with eight advanced optimization algorithms on the CEC2017 benchmark test set. The experimental results show that HLCM-DHPSO significantly outperforms the comparison algorithms in terms of solution performance, especially in handling high-dimensional and multi-peak complex functions, demonstrating superior global search and optimization capabilities. Full article

The digital divide between large enterprises and SMEs (Small and Medium-sized Enterprises) within industrial clusters poses a significant challenge to achieving collective digital transformation, exacerbated by the quasi-public goods, attributes of digital inclusion ecosystems, and the prevalence of free-riding behavior. This paper investigates whether platform enterprises, as core actors occupying structural holes in cluster networks, can foster the co-construction of a digitally inclusive ecosystem. We developed a complex network public goods game model, incorporating performance feedback into a modified Fermi learning to capture firms’ adaptive decision-making based on historical and social aspirations. The model simulates strategic interactions on both small-world and scale-free networks, characteristic of industrial clusters. Numerical simulations reveal that: (1) The core driver of co-construction is the investment return coefficient; (2) Performance feedback amplifies individual rationality, accelerating the formation or collapse of cooperation depending on the investment return coefficient; (3) Platform empowerment—specifically, selectively connecting and incentivizing cooperative firms—effectively promotes ecosystem co-construction, with this strategy proving most impactful when investment returns are moderate. Furthermore, while this selective empowerment strategy benefits the cluster overall, its effect on the platform’s own revenue is network-dependent, showing a more pronounced decline in small-world structures. This study provides a novel analytical framework for understanding strategic interactions in digital inclusion and offers practical insights for policymakers and platform leaders in orchestrating collaborative digital transformation. Full article