Search Results (9,014)

Hybrid polymeric woven composites (HPWCs) are increasingly important in automotive, aerospace, and renewable energy structures where low weight, impact tolerance, damage containment, and superior mechanical properties are required. By combining dissimilar fibres within woven architectures, HPWCs can achieve a more favourable balance of stiffness, strength, and energy absorption than single-fibre woven systems; however, experimental evidence and predictive modelling remain insufficiently integrated, particularly under dynamic and post-impact loading. This systematically searched critical review provides an HPWC-focused synthesis that links architecture-driven mechanical behaviour, damage development, and multiscale numerical simulation within a single framework. The effects of reinforcement architecture, fibre pairing, and matrix selection on tensile, flexural, compressive, interlaminar, strain rate-dependent, and impact responses are examined, with particular emphasis on barely visible impact damage and post-impact residual strength. Macroscale, mesoscale, and microscale finite element strategies are critically compared in terms of predictive fidelity, computational cost, and suitability for design-orientated assessment. The main contribution of this review lies in integrating experimental characterisation with modelling limitations, validation requirements, and industrial relevance, thereby clarifying where current approaches are effective and where critical gaps remain. Practical implications for lightweight structural design, impact-resistant components, and future validation-driven research are highlighted. Full article

Nowadays, Global Value Chains (GVCs) play a vital role in job creation, income generation, knowledge diffusion, and productivity growth. However, significant disparities exist across countries in terms of their integration into GVCs, and Tunisia is no exception to this pattern. In this regard, the question about factors that influence GVCs’ participation is yet to be discussed, to formulate and implement appropriate strategies and reforms. Thus, using firm-level data from the 2025 World Bank Enterprise Survey, this paper examines the role of female leadership and innovation in determining Tunisian firms’ participation in GVCs. Participation in GVCs is captured by a dummy variable indicating the firm’s export and import status. Estimation results from the logit model show that female representation in decision-making positions significantly increases the likelihood of firms’ participation in GVCs. The results also highlight the importance of process innovation in GVC participation, while product innovation appears to have no significant effect. Notably, when firms combine both types of innovation, their likelihood of joining GVCs increases further. Regarding control variables, firm size appears to be an important determinant, as larger firms display a greater tendency to participate in GVCs. The findings further indicate that firm certification and foreign equity participation significantly promote integration into GVCs, while corruption constitutes a major constraint on the integration of Tunisian firms. From a policy perspective, these findings highlight the need to rethink industrial policies, with a stronger focus on process innovation as a key lever of productive sector modernization. Achieving this transformation also requires the development of an inclusive policy ecosystem that supports meaningful and sustainable progress in female’s leadership representation. Full article

Modern agriculture must balance productivity with sustainability. In this context, unmanned aerial vehicles (UAVs) offer flexible, cost-effective tools for crop and soil monitoring in precision agriculture. This study aimed to evaluate the potential of UAV-borne RGB imagery, combined with vegetation indices and machine learning, to estimate surface soil properties and crop physiological traits in peanut (Arachis hypogaea) cultivation. A factorial field experiment with four varieties, two planting densities, and two tillage systems was monitored using high-resolution RGB orthomosaics acquired at key phenological stages. From these images, 17 RGB-based indices were computed and related to soil variables and crop traits using Spearman correlation and two regression algorithms: Random Forest (RF) and k-Nearest Neighbors (KNN). RF models outperformed KNN, with the Red Chromatic Coordinate (RCC) index achieving an R² of 0.87 for predicting soil organic matter content. Indices such as visible NDVI and the Green Vegetation Index also provided robust estimates of canopy condition and leaf chlorophyll. Overall, the results demonstrate that UAV RGB imagery, processed through simple vegetation indices and RF models, constitutes an effective, low-cost approach for monitoring key agronomic parameters in peanut farming. Full article

To improve the low-temperature crack resistance of asphalt pavement, this paper investigates the effects of fiber length, content, and type on the flexural and tensile properties of asphalt mastic. Firstly, a numerical program was developed in MATLAB to establish a three-dimensional finite element model of asphalt mastic with an uneven fiber distribution in ABAQUS. Then, the Burgers model selected for simulation was obtained through the asphalt low-temperature bending beam rheological test (BBR). Constructing a three-point bending virtual test of asphalt mastic using a three-dimensional fiber model and systematically analyzing the influence of fiber parameters on bending and tensile properties. The accuracy of the three-dimensional fiber model was verified through BBR experiments. The finite element simulation results show that the addition of fibers can significantly improve the tensile performance of asphalt mastic; increasing the fiber content or length can reduce the peak stress at the bottom of the mid-span and delay cracking. The higher the fiber elastic modulus, the smaller the vertical displacement of the specimen. The model established in this article can effectively elucidate the mechanism of fiber reinforcement, providing a theoretical basis for optimizing fiber parameters and improving the crack-resistance performance of asphalt pavement. Full article

Hydrogels, particularly those based on polymer networks, exhibit complex mechanical behaviors arising from the interplay between network architecture, molecular interactions, and external stimuli. In particular, their viscoelasticity, energy dissipation, and nonlinear mechanical responses arise from the dynamic nature of crosslinking and multiscale relaxation processes. This review provides a comprehensive overview of hydrogel mechanics from a multiscale perspective, covering viscoelastic behavior, relaxation dynamics, energy dissipation mechanisms, nonlinear deformation, and fracture properties. We summarize recent advances in experimental characterization, including bulk rheology and single-molecule force spectroscopy, and discuss how molecular-level interactions, bond kinetics and mechanochemical processes contribute to macroscopic mechanical performance. In addition, theoretical models and constitutive frameworks describing transient and dynamic polymer networks are critically evaluated to bridge microscopic dynamics with bulk responses. Emerging strategies that integrate dynamic bonding and force-responsive elements are also discussed in the context of tailoring mechanical adaptability and functionality. Finally, we outline current challenges and future directions toward the rational design of hydrogels with tunable viscoelasticity, enhanced mechanical robustness, and programmable mechanical functions. Full article

Entrepreneurs represent a key piece in the generation of jobs and contribution to the economy through the performance of their businesses. Taking into account that literacy and financial inclusion constitute a business facilitator for the development of businesses, this study was based on analyzing the three variables, aiming to identify whether inclusion and financial literacy influence business performance. Through a non-experimental, quantitative study based on structural equations, a sample of 469 entrepreneurs from Peru, Bolivia, and Colombia was studied. The hypotheses were supported by observing the positive effect of one component of financial literacy (Cash Forecasting) and three components of financial inclusion (Access, Barriers, and Use) on Business Performance. However, the proposed model shows that the direct effect of two components (Bookkeeping and Financial Education) of financial literacy is not statistically significant. Therefore, these factors are vital tools that can help Latin American entrepreneurs make informed financial decisions, manage resources effectively, and build solid and sustainable businesses. Full article

In accordance with the national strategy of “carbon peaking by 2030 and carbon neutrality by 2060” and Hunan Province’s target of achieving carbon peaking in the construction sector by 2030, this study uses carbon emission data from Hunan’s construction sector for the period 2005–2022 as a research sample to conduct research on carbon emission accounting, analysis of influencing factors, and peak prediction. The carbon emission coefficient method was employed to calculate industry-wide carbon emissions. Using the STIRPAT model combined with ridge regression, we identified and quantified the driving factors of carbon emissions. A CNN-LSTM-Attention hybrid deep learning model was constructed, and three development scenarios—high-carbon, baseline, and low-carbon—were established to simulate the evolution of carbon emissions in Hunan’s construction industry from 2023 to 2040. The results indicate that carbon emissions from Hunan’s construction industry showed an overall upward trend during the study period, with indirect emissions constituting the primary component. Through variable optimization, the core positive drivers and negative restraints of carbon emissions in the construction industry were identified. The constructed hybrid model demonstrated excellent fitting performance, with prediction accuracy significantly higher than that of traditional machine learning and single deep learning models. Carbon emission trends varied significantly across different development scenarios, with the low-carbon development scenario identified as the optimal path for achieving the industry’s carbon peak target. These findings provide a theoretical basis and data support for the low-carbon transition of Hunan Province’s construction sector, as well as for the formulation and optimization of carbon peaking implementation plans. Full article

The residual mechanical properties after fire exposure form the basis for evaluating the structural performance of aluminium alloy components subjected to fire without collapse. This research investigated the impact of low cooling rates on the residual mechanical properties of 6063-T5 aluminium alloy after various cooling methods were utilized. A total of 48 tensile specimens were subjected to controlled elevated temperatures (ETs) ranging from 200 to 500 °C for 30 min soaking, followed by two cooling regimes: cooling in air (CIA) and cooling in furnace (CIF). For both CIA and CIF conditions, an increase in ETs led to a gradual increase in ductility, particularly elongation at fracture. Moreover, the effects of ETs on the fracture performance were discussed. Key mechanical parameters—namely nominal yield strength, ultimate tensile strength, elastic modulus, and strain at ultimate strength—were quantified across ETs and cooling methods, which were compared among different aluminium alloys. Empirical predictive equations were developed to capture the temperature-dependent degradation trends of mechanical properties, and a plasticity Ramberg–Osgood model was proposed and validated against test data. The metallographic microstructure of 6063-T5 aluminium alloy after different ETs revealed that the evolution of precipitate was the primary contributor to strength degradation. Finally, finite element simulations of aluminium plate girders after various ETs were conducted, which incorporated the proposed constitutive model and replicated the degradation trends observed in tensile tests. These findings provide a reliable foundation for implementing the proposed model into finite element simulations and structural assessment tools for post-fire aluminium alloy structures. Full article

This study constitutes the second part of a comprehensive investigation of the persistence of weighted average cost of capital (WACC) rates despite declining risk-free interest rates. While theory suggests that WACC should reflect lower risk-free interest rates and decline with falling government bond yields, empirical evidence reveals minimal adjustment in the reported WACC figures. Disclosed WACC of DAX40 companies remain between 7% and 8% as the yield of a ten-year German government bond fell from 4.1% to −0.2%. After the quantitative risk analysis (part I) systematically lacks market-based and fundamental explanations—demonstrating that neither systematic risk, overall market risk, earnings risk nor leverage increased sufficiently to justify this stability—this article addresses the resulting explanatory gap through qualitative inquiry. Employing a grounded theory methodology, we investigate the causes and consequences of persistent WACC through systematic analysis of 18 problem-centered semi-structured expert interviews (22 respondents comprising corporate finance executives, investment bankers, strategy consultants, auditors). The investigation reveals that behavioral economics (risk aversion, opportunism, subjectivity), organizational constraints (strategic path dependency, implementation complexity, financial criterion rigidity), and model-theoretic discretion (parameter averaging, analyst influence, supplementary risk adjustments) substantially shape practical WACC determination—factors that quantitative risk analysis cannot capture. Practitioners employ disclosed WACC strategically to reconcile investor return requirements with long-term operational stability, avoid audit friction, and hedge geopolitical–monetary risks—consequences that generate capital opportunity costs offsetting traditional value-maximization objectives. Combined quantitative and qualitative evidence yields actionable insights for value-based capital cost methodologies that are aligned with organizational and market realities. Full article

In the United States, access to healthcare is shaped not only by patient need but also by payer policies that determine which providers are reimbursable, how care is sequenced, and what constitutes a legitimate entry point into the system. These gatekeeping functions, while valuable for supporting clinical prioritization, risk stratification, and continuity of care, can also unintentionally reinforce structural inequities and credential hierarchies that delay or limit timely and equitable care, particularly for historically marginalized populations. While reform efforts often focus on expanding benefits or provider networks, fewer address the underlying design of access itself or the rules that govern how patients enter care. It is argued in this paper that a more equitable and efficient healthcare system requires multi-entry care models, in which nurses, behavioral health clinicians, pharmacists, and community health workers may serve as condition-appropriate, reimbursable first points of contact within coordinated care teams. Drawing on evidence from Medicare, Medicaid, the Veterans Health Administration, and commercial payers, these models may support cost containment, improve care coordination, facilitate appropriate utilization, and promote earlier patient engagement. While findings from these models are not uniform across all settings, evidence suggests that outcomes are highly dependent on implementation context, system design, and supporting infrastructure. When implemented with appropriate safeguards (such as interoperable health records, team-based care requirements, and coordinated referral tracking), multi-entry systems can preserve continuity while expanding access. Payers are uniquely positioned to lead this transformation by aligning reimbursement policy with patient needs, supporting team-based care infrastructure, and embedding accountability into access pathways, thereby creating a system that can be more responsive, inclusive, and sustainable. Full article

In recent years, the homogenization and fragmentation of agricultural landscapes have intensified, leading to a decline in epigaeic arthropods. Landscape heterogeneity is a core factor regulating biodiversity, encompassing two key dimensions: composition heterogeneity and spatial configuration heterogeneity. Both landscape composition and spatial configuration heterogeneity influence the distribution of epigaeic arthropods through independent and joint effects. However, quantitative evidence addressing their relative and combined influences remains limited. This study was conducted across 30 independent landscape units (1 km × 1 km) in Changtu County. Pitfall traps were deployed across different habitat types, with three traps per habitat. The proportion of semi-natural habitats was used as an indicator of landscape compositional heterogeneity, while multiple landscape metrics were used to characterize spatial configuration heterogeneity. The effects of landscape heterogeneity on epigaeic arthropods were evaluated using two response variables: activity density (mean number of individuals captured per trap) and diversity (effective number of species). Variance partitioning analysis (VPA) and Bioenv analysis were applied to explore their individual and joint effects on epigaeic arthropods. The results showed that higher landscape composition heterogeneity was associated with greater activity density of epigaeic arthropods, but no significant correlation was found with arthropod diversity. In terms of landscape spatial configuration, patch density (PD) and landscape division index (DIVISION) constituted the optimal model explaining the activity density of epigaeic arthropods, highlighting the importance of patch structure within landscapes. Furthermore, spatial configurational heterogeneity showed a stronger independent contribution than compositional heterogeneity, although their joint effect accounted for the largest proportion of explained variation. These findings provide a theoretical basis for landscape optimization and biodiversity conservation in intensive agricultural regions of Northeast China. Full article

The detection of anomalies and the prediction of corrosion defects in oil and gas pipelines constitute critical tasks for ensuring industrial safety and improving operational reliability. This study addresses the problem of regression-based prediction of corrosion defect levels (CR—corrosion defect) using operational process parameters. Machine learning methods, including Decision Tree, Random Forest, LightGBM, and CatBoost, were employed to develop predictive models. Data preprocessing was performed, including feature standardization and hyperparameter tuning using KFold cross-validation. Model performance was primarily evaluated using the Root Mean Square Error (RMSE) on both training and test datasets, as this metric is more sensitive to large prediction errors, which is particularly important in the context of corrosion defect analysis. Additionally, Mean Squared Error (MSE), Mean Absolute Error (MAE), and the coefficient of determination (R²) were used to provide a comprehensive assessment of model accuracy and robustness. Experimental results demonstrate that the CatBoost model achieved the best performance, yielding the lowest RMSE on the test dataset (0.02040) with a close value on the training dataset (0.01682), indicating strong generalization capability. Furthermore, this model outperformed the others in terms of MSE, MAE, and R² on the test dataset (MSE = 0.000418, MAE = 0.006319, R² = 0.695544). The obtained results confirm the effectiveness of ensemble methods and gradient boosting algorithms for regression modeling of corrosion defect development processes in pipeline systems. Full article

Urban emergency shelters constitute essential spatial elements within the framework of urban disaster prevention and mitigation. Addressing the shortcomings of existing evaluation methods, which often overlook the relationship between shelters and their served populations, this study utilizes Xi’an as a case study to develop a resilience assessment model that integrates supporting facilities, operational efficiency, and safety performance. To link this model to the served population, the research incorporates the service population pressure index and employs the Gini coefficient alongside the Lorenz curve to assess the congruence between shelter resilience and population distribution. Moreover, the introduction of the intervention priority index and population vulnerability index facilitates a comprehensive determination of shelter intervention priorities. The results reveal that emergency shelters in Xi’an display a spatial pattern characterized by a “single core with multiple centers,” with higher resilience levels, service pressures, and intervention priorities concentrated in the central urban area and lower values observed in peripheral zones. Additionally, a significant spatial mismatch is identified between shelter resilience and population service demands. Despite relying on static population data and not accounting for the effects of population migration, the evaluation framework presented in this study offers a transferable methodological reference for the comprehensive evaluation of shelters in densely populated urban areas, contributing to sustainable urban development. Full article

During service in engineering fields, the performance of carbon black (CB)/silica-filled rubber suffers degradation because of the influence of aging. In the process of reproducing the mechanical behavior of CB/silica-filled rubber, many constitutive models have been proposed. However, the model selection strategy taking the aging effect into consideration is still unclear, especially the classical model selection strategy. In this work, the effects of thermo-oxidative and ultraviolet aging on the nonlinear viscoelasticity of CB/silica -filled rubber were investigated using dynamic mechanical analysis tests. It was found that aging conditions had a great effect on the nonlinear viscoelasticity of CB/silica -filled rubber. Meanwhile, the degradation mechanisms were discussed on the basis of the existing works. To accurately reproduce the nonlinear viscoelasticity degradation, classical models, such as the Kraus model and Maier–Göritz model, were used to describe the experimental data. In the reproducing process, fitting correlation coefficients and root mean square error were used to verify the reliability of classical models. Comparingsimulation results and experimental ones, it was found that the Maier–Göritz model was more reliable under all aging conditions. This work will contribute to a model selection strategy and a deeper understanding of the degradation mechanism. Full article

The paper deals with the asymptotic behavior of a widely used correlation characteristic in large quantum systems. The correlation is quantum entanglement, the characteristic is entanglement entropy, and the system is an ideal gas of lattice fermions. If the one-body Hamiltonian of fermions is an ergodic finite difference operator with an exponentially decaying spectral projection, then the large-block form of the entanglement entropy is the so-called area law. However, the only class of one-body Hamiltonians for which this spectral condition was verified consists of discrete Schrödinger operators with random potential. In this paper, we prove the area law for several classes of Schrödinger operators whose potentials are ergodic but not random. We begin with quasiperiodic and limit-periodic operators and then move to a highly non-trivial case of potentials generated by subshifts of finite type. These arose in the theory of dynamical systems when studying chaotic phenomena. The corresponding asymptotic study requires involved spectral analysis, which therefore constitutes the bulk of the paper. Specifically, we prove uniform localisation of the eigenfunctions for the Maryland model and exponential decay of the eigenfunction correlator for various models. We believe these properties are of significant independent interest. Full article