Search Results (626)

Background: The Sandwich technique is a commonly adopted method for reinforcing the dissected aortic wall during acute Type A aortic dissection (ATAAD) repair, using either felt or bovine pericardial strips. However, complications such as anastomotic bleeding, distal anastomotic new entry (DANE) and persistent false lumen (PFL) remain major challenges. This study evaluated and compared the sealing efficacy of felt versus pericardium in a human cadaver model. Methods: ATAAD was simulated in 20 fresh human cadavers. Repairs were performed using the sandwich technique with either felt (n = 10) or pericardium (n = 10), followed by end-to-end prosthetic graft anastomosis. Procedure time was recorded. Following the repair, the aortas were perfused at 160/90 mmHg using a glycerol-water solution to assess fluid leakage (mL), DANE and PFL. Results: Median leakage was significantly lower in the pericardium group (67.5 mL [IQR 40–198.8]) compared to the felt group (315 mL [IQR 285–445], p = 0.002). Procedure times were comparable between groups. DANE occurred in 20% (pericardium) and 30% (felt) of cases, while PFL was observed in 30% of cases in both groups; differences were not statistically significant. Conclusions: The superior sealing properties of pericardium in this study suggest a promising approach for reducing leakage in ATAAD repair. While rates of DANE and PFL were comparable, the advantage of pericardium was confined to leakage reduction. These findings highlight the need for further research to determine whether this experimental benefit translates into improved clinical outcomes. Full article

Reinforced concrete walls (RC walls) are widely used in transportation, building structures, and civil air defence engineering. RC walls are vulnerable to low-velocity impact, such as the fall of components caused by earthquakes or explosions, for example, and the impact from road objects, such as vehicles, during their service life. When subjected to instantaneous high-energy impact, RC walls at key positions are prone to severe damage, which can further lead to structural collapse. Therefore, it is necessary to consider improving the impact resistance of key RC walls in a structure. Using a porous honeycomb structure with excellent energy absorption performance to provide impact protection for key RC walls is an effective way to reduce the damage of RC walls and thereby enhance the impact resistance of a structure. Therefore, based on the author’s previous series of experimental and numerical studies on the impact resistance of RC walls, as well as the high-mass pendulum impact experimental study on the honeycomb sandwich panel composite RC wall (HSP-RC wall), this paper adopts a multi-scale modelling method in micro-mechanics and macro-mechanics to establish a pendulum impact finite element model (FEM) for the HSP-RC wall. The representative volume element (RVE) and periodic boundary condition (PBC) are used to calculate the elastic property parameters of the honeycomb, which guide the establishment of the FEMs for the HSP-RC wall. The FEMs can avoid the computational difficulty caused by refined simulation, analyse the impact damage of the HSP-RC walls more accurately, quantify the impact protection effect of the honeycomb sandwich panel, and thus facilitate the parametric analysis of the impact resistance of HSP-RC walls with different honeycomb panel structural parameters in subsequent studies. Full article

Tensegrity structures, by definition composed of compressed members suspended in a network of tensile cables, are characterised by a high strength-to-weight ratio and the ability to undergo reversible deformations. Their application as cores of sandwich panels represents an innovative approach to lightweight design, enabling the regulation of mechanical properties while reducing material consumption. This study presents a finite element modelling procedure that combines analytical determination of prestress using singular value decomposition with implementation in the ABAQUS™ 2019 software. Geometry generation and prestress definitions were automated with Python 3 scripts, while algebraic analysis of individual modules was performed in Wolfram Mathematica. Two models were investigated: M1, composed of four identical modules, and M2, composed of four modules arranged in two mirrored pairs. Model M1 exhibited a linear elastic response with a constant global stiffness of 13.9 kN/mm, stable regardless of the prestress level. Model M2 showed nonlinear hardening behaviour with variable stiffness ranging from 0.135 to 1.1 kN/mm and required prestress to ensure static stability. Eigenvalue analysis confirmed the full stability of M1 and the increase in stability of M2 upon the introduction of prestress. The proposed method enables precise control of prestress distribution, which is crucial for the stability and stiffness of tensegrity structures. The M2 configuration, due to its sensitivity to prestress and variable stiffness, is particularly promising as an adaptive sandwich panel core in morphing structures, adaptive building systems, and deployable constructions. Full article

Paulownia wood, as a fast-growing natural material, exhibits inherently low axial compressive strength. To improve the axial structural performance of Paulownia wood, wood-cored glass fiber-reinforced polymer (GFRP) sandwich Paulownia wood columns were developed in this study. Nevertheless, the behavior of such columns remained largely unexplored—particularly under elevated temperatures and upon subsequent cooling. Consequently, an experimental program was conducted to characterize the influences of GFRP wrapping layers, steel hoop end confinement, high temperature, post-cooling strength recovery, and chamfer radius on the axial compressive performance of the columns. End crushing occurred in the absence of steel hoops, whereas mid-height fracture dominated when end confinement was provided. As the temperature rose from room temperature to 100 °C and 200 °C, the load-bearing capacity of the columns decreased by 38.26% and 54.05%, respectively, due to the softening of the GFRP composites. After cooling back to room temperature, the post-high-temperature specimens recovered approximately 95% of their original capacity, confirming that no significant thermal decomposition had been initiated. The load-bearing capacity also increased significantly with the number of GFRP layers, as the additional thickness provided both higher axial load capacity and enhanced lateral confinement of the wood core. Relative to a 4.76 mm chamfer, a 9.52 mm radius increased axial capacity by 14.07% by mitigating stress concentration. A theoretical model accounting for lateral confinement was successfully developed to predict the axial load-bearing capacity of the wood-cored GFRP sandwich columns. Full article

Electric vehicle (EV) battery thermal management systems have gradually improved owing to the increasing power demand of EVs. This study aims to optimize the channel geometry of sandwich double-sided cold plates for EV battery cooling under 100% state of charge and 2C-rate charging conditions. For precise and accurate optimization, the conventional one-dimensional analysis model of the sandwich double-sided cold plate was converted into a three-dimensional computational fluid dynamics (CFD) model. Non-dimensional parameters were selected as the main variables of the channel geometry, and nine additional channel shapes were derived based on them. Battery modules with the derived channel shapes were subjected to CFD analysis in the Reynolds number range of 500 to 20,000. The goodness factor was calculated from these correlations, and optimization was performed using the Taguchi method. The results revealed that the wetted area of the channel had a greater impact on battery cooling than the number of channels. This study proposed more generalized design guidelines by employing non-dimensionalized parameters across a wide range of Reynolds numbers. The rectangular channel-based correlations developed in this study showed improved prediction accuracy compared to conventional annular pipe-based correlations and are expected to be applicable to various battery thermal management system designs in the future. Full article

A straightforward ex vivo approach has been developed and refined to enable high-resolution imaging and quantitative assessment of motile cilia function in mouse airway epithelial tissue, allowing critical insights into cilia motility and cilia generated flow using different mouse models or following different sample treatments. In this method, freshly excised mouse trachea is cut longitudinally through the trachealis muscle which is then sandwiched between glass coverslips within a thin silicon gasket. By orienting the tissue along its longitudinal axis, the natural curling of the trachealis muscle helps maintain the sample in a configuration optimal for imaging along the full tracheal length. High-speed video microscopy, utilizing differential interference contrast (DIC) optics and a fast digital camera capturing at >200 frames per second is then used to record ciliary motion. This enables detailed measurement of both cilia beat frequency (CBF) and waveform characteristics. The application of 1 µm microspheres to the bathing media during imaging allows for additional analysis of fluid flow generated by ciliary activity. The entire procedure typically takes around 40 min to complete per animal: ~30 min for tissue harvest and sample mounting, then ~10 min for imaging samples and acquiring data. Full article

The cores of honeycomb panels are usually made of hexagonal cells. Due to their structure, they create anticlastic surfaces that are difficult to use in furniture design. Synclastic surfaces in lightweight sandwich panels are typically associated with auxetic cores characterized by a negative Poisson’s ratio. This study aimed to transform the hexagonal cell cores into cells with a negative or positive Poisson’s ratio (NPR, PPR), enabling these cores to form synclastic surfaces. New core structures for synclastic furniture sandwich honeycomb panels were modeled numerically and experimentally. It has been demonstrated that reentrant cells with NPR create synclastic surfaces, and new shapes of core cells, created by transforming hexagonal cells with PPR, also enable the formation of synclastic surfaces. Cores’ synclasticity was assessed in two orthogonal planes using physical models and Finite Element Analysis (FEA). A new and original discovery is the demonstration that not only auxetic but also modified hexagonal cells with Poisson’s ratios of ${\nu }_{xy}$ = 0.545 and ${\nu }_{yx}$ = 0.512, respectively, exhibit excellent synclastic properties. The agreement between FEA and experiment was very high. The results show that not only NPR but also cell topology provides a practical route to the synclastic formation of cores without the use of auxetic materials. Full article

A fundamental limitation of maximum likelihood and Bayesian methods under model misspecification is that the asymptotic covariance matrix of the pseudo-true parameter vector ${\mathsf{\theta }}_{*}$ is not the inverse of the Fisher information, but rather the sandwich covariance matrix ${\displaystyle \frac{1}{n}}{\mathbf{A}}_{*}^{-1}{\mathbf{B}}_{*}^{\phantom{-1}}{\mathbf{A}}_{*}^{-1}$, where ${\mathbf{A}}_{*}$ and ${\mathbf{B}}_{*}$ are the sensitivity and variability matrices, respectively, evaluated at ${\mathsf{\theta }}_{*}$ for training data record ${\omega }_1,\dots ,{\omega }_n$. This paper makes three contributions. First, we review existing approaches to robust posterior sampling, including the open-faced sandwich adjustment and magnitude- and curvature-adjusted Markov chain Monte Carlo (MCMC) simulation. Second, we introduce a new sandwich-adjusted MCMC method. Unlike existing approaches that rely on arbitrary matrix square roots, eigendecompositions or a single scaling factor applied uniformly across the parameter space, our method employs a parameter-dependent learning rate $\lambda \left(\mathsf{\theta }\right)$ that enables direction-specific tempering of the likelihood. This allows the sampler to capture directional asymmetries in the sandwich distribution, particularly under model misspecification or in small-sample regimes, and yields credible regions that remain valid when standard Bayesian inference underestimates uncertainty. Third, we propose information-theoretic diagnostics for quantifying model misspecification, including a strictly proper divergence score and scalar summaries based on the Frobenius norm, Earth mover’s distance, and the Herfindahl index. These principled diagnostics complement residual-based metrics for model evaluation by directly assessing the degree of misalignment between the sensitivity and variability matrices, ${\mathbf{A}}_{*}$ and ${\mathbf{B}}_{*}$. Applications to two parametric distributions and a rainfall-runoff case study with the Xinanjiang watershed model show that conventional Bayesian methods systematically underestimate uncertainty, while the proposed method yields asymptotically valid and robust uncertainty estimates. Together, these findings advocate for sandwich-based adjustments in Bayesian practice and workflows. 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

Sandwich panels are widely used in industrial roofing due to their lightweight and thermal insulation properties; however, their structural fire resistance remains insufficiently understood. This study presents a data-driven approach to predict the mid-span deformation of glass wool-cored sandwich roof panels subjected to ISO 834-5 standard fire tests. A total of 39 full-scale furnace tests were conducted, yielding 1519 data points that were utilized to develop deep learning models. Feature selection identified nine key predictors: elapsed time, panel orientation, and seven unexposed-surface temperatures. Three deep learning architectures—convolutional neural network (CNN), multilayer perceptron (MLP), and long short-term memory (LSTM)—were trained and evaluated through rigorous 5-fold cross-validation and independent external testing. Among them, the CNN approach consistently achieved the highest accuracy, with an average cross-validation performance of $R^2=0.91(\mathrm{mean}\mathrm{absolute}\mathrm{error}\left(\mathrm{MAE}\right)=4.40;\mathrm{root}\mathrm{mean}\mathrm{square}\mathrm{error}\left(\mathrm{RMSE}\right)=6.42)$, and achieved $R^2=0.76(\mathrm{MAE}=6.52,\mathrm{RMSE}=8.62)$ on the external test set. These results highlight the robustness of CNN in capturing spatially ordered thermal–structural interactions while also demonstrating the limitations of MLP and LSTM regarding the same experimental data. The findings provide a foundation for integrating machine learning into performance-based fire safety engineering and suggest that data-driven prediction can complement traditional fire-resistance assessments of sandwich roofing systems. Full article

The effects of boundary conditions on the vibration characteristics of a sandwich plate with viscoelastic periodic cores were examined. The tangential, vertical, transverse, and torsional springs were utilized to restrict the sandwich plate’s edge in order to model a general boundary condition, bringing the benefit that the conventional free, clamped, and simply supported boundary conditions became special cases in the proposed model as these spring constants took extreme values. A theoretical model was established to calculate the forced response and band structure of the periodic sandwich plate, providing computational support for evaluating its vibration characteristics. The correctness of the theoretical model was also validated by the finite element method. The results show that the boundary spring stiffness has a significant effect on the band-gap frequencies and band-gap width of the periodic sandwich plate. Increasing the boundary spring stiffness contributes to achieving broader band gaps. In addition, the band-gap frequencies and band-gap width are more sensitive to transverse spring stiffness than the tangential, vertical, and torsional spring stiffnesses. Therefore, changing transverse spring stiffness is more effective for adjusting the band gap property. This study may provide helpful guidance on vibration and noise reduction design in engineering. Full article

Maximal Extractable Value (MEV) presents a significant challenge to the fairness and efficiency of decentralized finance (DeFi). This paper provides a game-theoretic analysis of the strategic interactions within the MEV supply chain, involving searchers, builders, and validators. A three-stage game of incomplete information is developed to model these interactions. The analysis derives the Perfect Bayesian Nash Equilibria for primary MEV attack vectors, such as sandwich attacks, and formally characterizes attacker behavior. The research demonstrates that the competitive dynamics of the current MEV market are best described as Bertrand-style competition, which compels rational actors to engage in aggressive extraction that reduces overall system welfare in a prisoner’s dilemma-like outcome. To address these issues, the paper proposes and evaluates mechanism design solutions, including commit–reveal schemes and threshold encryption. The potential of these solutions to mitigate harmful MEV is quantified. Theoretical models are validated against on-chain data from the Ethereum blockchain, showing a close alignment between theoretical predictions and empirically observed market behavior. Full article

This study examines the dynamic response of autoclaved aerated concrete (AAC) under solar radiation and ambient temperature coupling. A comparative analysis is conducted between traditional sintered bricks (brick), AAC, and autoclaved aerated concrete sandwich insulated wall panels (ATIM), using three thermal engineering models. The experimental group focuses on the south wall, with differentiated designs: Model A (brick), Model B (AAC), and Model C (ATIM). Temperature data collectors assess heat transfer and internal temperature regulation in winter. The results show that the AAC sandwich system significantly reduces thermal fluctuations, with a 26% and 14.8% attenuation in temperature amplitude compared to brick and AAC. The thermal inertia index of the AAC sandwich structure system is 51.5% and 14.58% higher than that of traditional brick walls and AAC walls, respectively. The heat consumption index of ATIM is, on average, 14% lower than that of AAC and 74.5% lower than that of the brick system. The study confirms that the AAC sandwich rock wool wall structure enhances temperature stability and energy efficiency, supporting green building and low-carbon energy-saving goals. Full article

Wrinkling is a localized buckling phenomenon that significantly compromises the structural integrity of lightweight sandwich structures. The objective of this study was to validate the experimental design of a sandwich beam to observe the initiation of wrinkling under compression and, more specifically, under shear stresses. The specimen under consideration consists of glass fibre–epoxy skins with polymethacrylimide (PMI) ROHACELL^® foam cores. The experimental tests were monitored using Digital Image Correlation (DIC) techniques, in conjunction with displacement and force sensors. A linear buckling simulation was performed using Finite Element Analysis (FEA) in ABAQUS and was compared with both the experimental test results and analytical predictions. The simulations demonstrated a good correlation with both the experimental data and analytical models for compression wrinkling. In the case of shear wrinkling, the numerical analysis significantly overestimated the wrinkling load in comparison to the experimental results. Full article