Search Results (220)

Gradient stiffness structures are increasingly recognized for their excellent energy absorption capabilities, particularly under challenging loading conditions. Most studies focus on varying the thickness of the structure in order to produce gradient stiffness. This work introduces an innovative approach to design honeycomb architectures with controlled gradient stiffness along the out-of-plane direction achieved by materials’ microstructure variations. The gradient is achieved by combining three types of thermoplastic polyurethane (TPU) materials: porous TPU, plain TPU, and carbon fiber (CF)-reinforced TPU. By varying the material distribution across the honeycomb layers, a smooth transition in stiffness is formed, improving both mechanical resilience and energy dissipation. To fabricate these structures, a dual-head 3D printer was employed with one head printed processed TPU with a chemical blowing agent to produce porous and plain sections, while the other printed a CF-reinforced TPU. By alternating between the two print heads and modifying the processing temperatures, honeycombs with up to three distinct stiffness zones were produced. Compression testing under out-of-plane loading revealed clear plateau and densification regions in the stress–strain curves. Pure CF-reinforced honeycombs absorbed the most energy at stress levels above ~4.5 MPa, while porous TPU honeycombs were more effective under stress levels below ~1 MPa. Importantly, the gradient stiffness honeycombs achieved a balanced energy absorption profile across a broader range of stress levels, offering enhanced performance and adaptability for applications like protective equipment, packaging, and automotive structures. Full article

This study focuses on the energy absorption characteristics of hierarchical origami honeycomb structures. By combining experimental and numerical simulation methods, it deeply explores their mechanical properties and energy absorption potential. This research emphasizes analyzing the influence of geometric parameters (including wall thickness, folding angle, multi-layer structure design, etc.) on bearing capacity, stiffness, and energy absorption efficiency and reveals the advantages of hierarchical design in regulating gradient stiffness. The results show that the energy absorption capacity and performance of the material can be significantly improved through the reasonable optimization of geometric parameters. This research provides important theoretical support for the design of high-efficiency energy-absorbing materials and innovative solutions for energy absorption problems in related engineering applications. Full article

Breaking the maize yield plateau necessitates the development of density-tolerant varieties, for which recurrent selection is a key breeding strategy. However, a systematic understanding of how the interaction between selection density (parental screening environment) and evaluation density (variety testing environment) modulates genetic gain remains a critical knowledge gap. This study aimed to systematically elucidate this interaction and its impact on genetic gain and combining ability. We established two F2 base populations from distinct heterotic groups: Zheng 58 × LH196 (Stiff Stalk, SS) and Chang 7-2 × MBUB (Non-Stiff Stalk, NSS). Through bulk selection, we advanced populations for three cycles (C0, C2, C4) under three selection densities: low (60,000 plants/ha), medium (90,000 plants/ha), and high (120,000 plants/ha). Hybrids were generated using a double tester design and evaluated in multi-environment trials at Shijiazhuang in Hebei province and Xinxiang in Henan province in 2023 across matching density gradients. We employed analysis of variance (ANOVA) and general combining-ability (GCA) estimates to assess the genetic gains for yield and combining ability across 14 parental materials and 28 hybrids. Our results demonstrate that density compatibility between selection and evaluation environments is paramount. Genetic gain decreased by 0.89–26.52% with a density discrepancy of >30,000 plants/ha and plummeted by 19.71–77.44% when the discrepancy exceeded 60,000 plants/ha, underscoring the necessity of aligning selection density with the target environment. Under matched densities, population yield increased significantly with escalating density, with the high-density selection regime showing a maximum yield improvement of 53.78% from C0 to C4. Materials selected under high density exhibited superior performance and significantly higher combining ability (averaging a 238.35% increase) and genetic gain (averaging a 263.39% improvement) in medium-to-high-density environments, confirming strong positive selection pressure. Conversely, materials from low-density selection processes were better adapted to environments of ≤60,000 plants/ha. This study provides a crucial theoretical and practical foundation for establishing density-optimized recurrent breeding systems to directionally enhance genetic gain in maize. Full article

Pulmonary fibrosis (PF) is a progressive and fatal lung disease characterized by irreversible alveolar destruction and pathological extracellular matrix (ECM) deposition. Currently approved agents (pirfenidone and nintedanib) slow functional decline but do not reverse established fibrosis or restore functional alveoli. Multifunctional bioscaffolds present a promising therapeutic strategy through targeted modulation of critical cellular processes, including proliferation, migration, and differentiation. This review synthesizes recent advances in scaffold-based interventions for PF, with a focus on their dual mechano-epigenetic regulatory functions. We delineate how scaffold properties (elastic modulus, stiffness gradients, dynamic mechanical cues) direct cell fate decisions via mechanotransduction pathways, exemplified by focal adhesion–cytoskeleton coupling. Critically, we highlight how pathological mechanical inputs establish and perpetuate self-reinforcing epigenetic barriers to regeneration through aberrant chromatin states. Furthermore, we examine scaffolds as platforms for precision epigenetic drug delivery, particularly controlled release of inhibitors targeting DNA methyltransferases (DNMTi) and histone deacetylases (HDACi) to disrupt this mechano-reinforced barrier. Evidence from PF murine models and ex vivo lung slice cultures demonstrate scaffold-mediated remodeling of the fibrotic niche, with key studies reporting substantial reductions in collagen deposition and significant increases in alveolar epithelial cell markers following intervention. These quantitative outcomes highlight enhanced alveolar epithelial plasticity and upregulating antifibrotic gene networks. Emerging integration of stimuli-responsive biomaterials, CRISPR/dCas9-based epigenetic editors, and AI-driven design to enhance scaffold functionality is discussed. Collectively, multifunctional bioscaffolds hold significant potential for clinical translation by uniquely co-targeting mechanotransduction and epigenetic reprogramming. Future work will need to resolve persistent challenges, including the erasure of pathological mechanical memory and precise spatiotemporal control of epigenetic modifiers in vivo, to unlock their full therapeutic potential. Full article

This study introduces a novel numerical approach to analyze the axisymmetric bending behavior of functionally graded (FG) graphene platelet (GPL)-reinforced annular sandwich nanoplates featuring a porous core. The nanostructures are exposed to coupled magnetic and hygrothermal environments. The porosity distribution and GPL weight fraction are modeled as nonlinear functions through the thickness, capturing realistic gradation effects. The governing equations are derived using the virtual displacement principle, taking into account the Lorentz force and the interaction with an elastic foundation. To address the size-dependent behavior and thickness-stretching effects, the model employs the nonlocal strain gradient theory (NSGT) integrated with a modified version of Shimpi’s quasi-3D higher-order shear deformation theory (Q3HSDT). The differential quadrature method (DQM) is applied to obtain numerical solutions for the displacement and stress fields. A detailed parametric study is conducted to investigate the influence of various physical and geometric parameters, including the nonlocal parameter, strain gradient length scale, magnetic field strength, thermal effects, foundation stiffness, core thickness, and radius-to-thickness ratio. The findings support the development of smart, lightweight, and thermally adaptive nano-electromechanical systems (NEMS) and provide valuable insights into the mechanical performance of FG-GPL sandwich nanoplates. These findings have potential applications in transducers, nanosensors, and stealth technologies designed for ultrasound and radar detection. Full article

This study presents a novel framework for uncertainty propagation in power-law, Bingham, and Casson fluids through rectangular ducts under stochastic viscosity (Case I) and pressure gradient conditions (Case II). Using the computationally efficient Stochastic Finite Difference Method with Homogeneous Chaos (SFDHC), validated via comparison with quasi-Monte Carlo simulations, we demonstrate significantly lower computational costs across varying Coefficients of Variation (COVs). For viscosity uncertainty (Case I), results show a 0.54–2.8% increase in mean maximum velocity with standard deviations reaching 75.3–82.5% of the COV, where the power-law model exhibits the greatest sensitivity (velocity variations spanning 71.2–177.3% of the mean at COV = 20%). Pressure gradient uncertainty (Case II) preserves mean velocities but produces narrower and symmetric distributions. We systematically evaluate the effects of aspect ratio, yield stress, and flow behavior index on the stochastic velocity response of each fluid. Moreover, our analysis pioneers a performance hierarchy: Herschel–Bulkley fluids show the highest mean and standard deviation of maximum velocity, followed by power-law, Robertson–Stiff, Bingham, and Casson models. A key finding is the extreme fluctuation of the Robertson–Stiff model, which exhibits the most drastic deviations, reaching up to 177% of the average velocity. The significance of fluid-specific stochastic analysis in duct system design is underscored by these results. This is especially critical for non-Newtonian flows, where system performance and reliability are greatly impacted by uncertainties in viscosity and pressure gradient, which reflect actual operational variations. Full article

This study investigates the free vibration behaviors of functionally graded (FGM) plates with a porous structure, resting on a Kerr-type elastic foundation, while accounting for thermal effects and complex material property distributions. Within the framework of higher-order shear deformation theory (HSDT), two novel shape functions are introduced to accurately model transverse shear deformation across the plate thickness without employing shear correction factors. These functions are constructed to satisfy shear stress boundary conditions and capture nonlinear effects induced by material gradation and porosity. A variational formulation is developed to describe the dynamic response of FGM plates in a thermo-mechanical environment, incorporating temperature-dependent material properties and three porosity distributions: uniform, linear, and trigonometric. Numerical solutions are obtained using in-house MATLAB codes, allowing complete control over the formulation and interpretation of the results. The model is validated through detailed comparisons with existing literature, demonstrating high accuracy. The findings reveal that the porosity distribution pattern and gradient intensity significantly influence natural frequencies and mode shapes. The trigonometric porosity distribution exhibits favorable dynamic performance due to preserved stiffness in the surface regions. Additionally, the Kerr-type elastic foundation enables fine tuning of the dynamic response, depending on its specific parameters. The proposed approach provides a reliable and efficient tool for analyzing FGM structures under complex loading conditions and lays the groundwork for future extensions involving nonlinear, time-dependent, and multiphysics analyses. Full article

Friction contact regulation has been widely acknowledged, yet research on micro-textured meshing interfaces appears to have reached an impasse. Conventional wisdom holds that the similarity of micro-element configurations is the key factor contributing to textured interface issues. The traditional perception is transcended, and a novel method for presetting the optimal parameters of gradientized micro-textured interface elements is proposed. The study has analyzed the Interface Enriched Lubrication (IEL) performance and meshing Anti-Scuffing Load-Bearing Capacity (ASLBC) of periodic symmetrical and continuously gradient micro-elements. By actively regulating IEL behavior through geometric constraint effects, dynamic micro-cavity lubrication storage units are formed, thereby extending the retention time of medium film layers. The textured edges induce micro-vortices, delaying scuffing failures induced by load-bearing. Validation analyses demonstrate that optimal micro-element configurations can distribute contact stress to achieve stress homogenization, with the maximum contact stress reduced by 21%. The localized hydrodynamic effect of micro-textured elements increases interfacial meshing stiffness by 5.32% while decreasing friction torque by 27.3%. This investigation reveals a synergistic mechanism between IEL performance and meshing ASLBC under heavy loads conditions. The findings confirm that gradient-based micro-textured element configuration presetting offers an effective solution to reconcile the inherent trade-off between lubrication and load-bearing performance in heavy loads applications. Full article

Addressing safety, environmental, and economic challenges associated with aging urban underground pipeline infrastructure, this study develops an integrated multi-objective optimization framework for sustainable trenchless spiral wound lining (SWL) rehabilitation. The framework integrates machine learning (ML)-driven predictive modeling with structural performance enhancement technologies to advance urban infrastructure management. To enhance the mechanical performance of SWL liners, a multi-objective structural optimization was conducted to systematically examine the impact of strip profile cross-sectional parameters on ring stiffness (S_p), material consumption (V), and total strip profile height (H). ANSYS finite element analysis was employed to conduct numerical simulations of ring stiffness tests for various liner structures, and S_p was calculated based on the resultant loading force (F). Random Forest (RF), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost) were evaluated for predicting F and V. The results demonstrated that the SVR model achieved high accuracy in predicting F (R² = 0.9873), while the XGBoost model exhibited excellent performance in predicting V (R² = 0.97). Using the Non-dominated Sorting Genetic Algorithm II (NSGA-II), multi-objective optimization of the SWL liner was performed, yielding an optimized liner that showed a 24.46% improvement in S_p with only a 1.82% increase in V. The established predictive formula for SWL liner S_p increments (R² = 0.9874) provides an efficient tool for structural optimization, offering important technical support and a theoretical foundation for sustainable urban pipeline infrastructure management. Full article

Objective: This study investigated associations between physical activity (PA) during late adolescence and emerging adulthood and bone strength in emerging adulthood by utilizing advanced finite element analysis of computed tomography (CT/FEA) technology beyond the traditional dual-energy X-ray absorptiometry (DXA) method. Methods: This study included 266 participants (152 females) from the Iowa Bone Development Study. PA volume (average acceleration) and intensity (intensity gradient) metrics were calculated from ActiGraph accelerometer data collected at ages 17, 19, 21, and 23 years. Compressive modulus and compressive stiffness of the tibia were estimated at age 23 via CT/FEA of the tibia. Sex-specific linear regression models were used to evaluate associations between PA metrics and bone outcomes, adjusting for age, height, weight, musculoskeletal fitness, and calcium intake. Results: Intensity gradient averaged over 17–23 years of age was positively associated with compressive stiffness at age 23 years in both females and males (p < 0.01). Intensity gradient was positively associated with compressive modulus in females (p < 0.01), but not in males. No significant associations were found between average acceleration and either compressive stiffness or modulus in either sex (p > 0.05). Conclusions: Using a state-of-the-art CT/FEA method, this study suggests that high-intensity PA during late adolescence and emerging adulthood improves bone strength. Full article

This study develops a thermo-mechanical damage (TMD) model for predicting damage evolution in heterogeneous rock materials after heat treatment. The TMD model employs a Weibull distribution to characterize the spatial heterogeneity of the mechanical properties of rock materials and develops a framework that incorporates thermal effects into a nonlocal gradient damage model, thereby overcoming the mesh dependency issue inherent in homogeneous local damage models. The model is validated by numerical simulations of a notched cruciform specimen subjected to combined mechanical and thermal loading, confirming its capability in thermo-mechanical coupled scenarios. Sensitivity analysis shows increased material heterogeneity promotes localized, X-shaped shear-dominated failure patterns, while lower heterogeneity produces more diffuse, network-like damage distributions. Furthermore, the results demonstrate that thermal loading induces micro-damage that progressively spreads throughout the specimen, resulting in a significant reduction in both overall stiffness and critical strength; this effect becomes increasingly pronounced at higher heating temperatures. These findings demonstrate the model’s ability to predict the mechanical behavior of heterogeneous rock materials under thermal loading, offering valuable insights for safety assessments in high-temperature geotechnical engineering applications. Full article

Inspired by natural gradient structures observed in biological systems such as lobster exoskeletons and bamboo, this study proposes a biomimetic strategy for developing advanced automotive materials that achieve an optimal balance between strength and ductility. Against this backdrop, the present work systematically reviews the design principles underlying natural gradient structures and examines the advantages and limitations of current additive manufacturing—specifically selective laser melting (AM-SLM)—as well as conventional forming and machining processes, in fabricating nature-inspired architectures. The research systematically explores hierarchical gradient designs which endow materials with superior mechanical properties, including enhanced strength, stiffness, and energy absorption capabilities. Two primary strengthening mechanisms—hetero-deformation-induced (HDI) hardening and precipitation hardening—were employed to overcome the conventional strength–ductility trade-off. Gradient-structured materials were fabricated using selective laser melting, and microstructural analyses demonstrated that controlled interface zones and tailored precipitation distribution critically influence property improvements. Based on these findings, an integrated material design strategy combining nature-inspired gradient architectures with post-processing treatments is presented, providing a versatile methodology to meet the specific performance requirements of automotive components. Overall, this work offers new insights for developing next-generation lightweight structural materials with exceptional ductility and damage tolerance and establishes a framework for translating bioinspired concepts into practical engineering solutions. Full article

Inspired by the self-organizing optimization mechanisms in nature, the leaf venation of the royal water lily exhibits a hierarchically branched fractal network that combines excellent mechanical performance with lightweight characteristics. In this study, a structural bionic approach was adopted to systematically investigate the venation architecture through macroscopic morphological observation, experimental testing, 3D scanning-based reverse reconstruction, and finite element simulation. The influence of key fractal geometric parameters under vertical loading on the mechanical behavior and energy absorption capacity was analyzed. The results demonstrate that the leaf venation of the royal water lily exhibits a core-to-margin gradient fractal pattern, with vein thickness linearly decreasing along the radial direction. At each hierarchical bifurcation, the vein width is reduced to 65–75% of the preceding level, while the bifurcation angle progressively increases with branching order. During leaf development, the fractal dimension initially decreases and then increases, indicating a coordinated functional adaptation between the stiff central trunk and the compliant peripheral branches. The veins primarily follow curved trajectories and form a multidirectional interwoven network, effectively extending the energy dissipation path. Finite element simulations reveal that the fractal venation structure of the royal water lily exhibits pronounced nonlinear stiffness behavior. A smaller bifurcation angle and higher fractal branching level contribute to enhanced specific energy absorption and average load-bearing capacity. Moreover, a moderate branching length ratio enables a favorable balance between yield stiffness, ultimate strength, and energy dissipation. These findings highlight the synergistic optimization between energy absorption characteristics and fractal geometry, offering both theoretical insights and bioinspired strategies for the design of impact-resistant structures. Full article

Coal gangue concrete (CGC), a recycled cementitious material derived from industrial solid waste, presents both opportunities and challenges for structural applications due to its heterogeneous composition and variable mechanical behavior. This study develops an ensemble learning framework—incorporating XGBoost, LightGBM, and CatBoost—to predict four key constitutive parameters based on experimental data. The predicted parameters are subsequently incorporated into an ABAQUS finite element model to simulate the compressive–bending response of CGC columns, with simulation results aligning well with experimental observations in terms of failure mode, load development, and deformation characteristics. To enhance model interpretability, a hybrid approach is adopted, combining permutation-based global feature importance analysis with SHAP (SHapley Additive exPlanations)-derived local explanations. This joint framework captures both the overall influence of each feature and its context-dependent effects, revealing a three-stage stiffness evolution pattern—brittle, quasi-ductile, and re-brittle—governed by gangue replacement levels and consistent with micromechanical mechanisms and numerical responses. Coupled feature interactions, such as between gangue content and crush index, are shown to exacerbate stiffness loss through interfacial weakening and pore development. This integrated approach delivers both predictive accuracy and mechanistic transparency, providing a reference for developing physically interpretable, data-driven constitutive models and offering guidance for tailoring CGC toward ductile, energy-absorbing structural materials in seismic and sustainability-focused engineering. Full article

This study investigates the mechanical performance and failure mechanisms of large-span, cast-in situ hollow-core floor slabs with square-box core molds under vertical loading. A combination of in situ tests and refined numerical simulations was used to investigate the slab’s behavior. An 8 m × 8 m hollow slab from the Xinluzhou Industrial Park in Hefei, China, was subjected to five-stage cyclic loading up to 9.0 kN/m² using a distributed water tank system. Real-time strain monitoring showed that the slab remained within the elastic range, exhibiting a linear strain-load relationship and bidirectional bending stiffness, with less than 5% deviation between the X and Y directions. Finite element analysis, incorporating a concrete plastic damage model and a bilinear steel model, replicated the experimental stress distribution, with errors of less than 6.9% for reinforcement and 8.8% for concrete. The simulation predicted an ultimate load-bearing capacity of 27.2 kN/m², with initial failure indicated by diagonal cracks at the column capital edges, followed by flexural cracks at the slab mid-span. These findings clarify the bidirectional bending behavior and stress redistribution, characterized by “banded gradient” and “island-shaped” stress zones. This study provides valuable insights and design optimization strategies to improve the structural performance and safety of hollow-core floor slabs in high-rise buildings. Full article