Search Results (141)

The mechanical response of cellular structures is governed not only by relative density and average cell geometry but also by the spatial arrangement of cells. However, the manner in which arrangement-dependent effects evolve with increasing cell number has not been systematically clarified. In this study, the compressive behavior of closed-cell structures with varying cell numbers was investigated using finite element analysis under dynamically equilibrated compression conditions while maintaining constant relative density and identical material parameters. Cellular models were generated using hierarchical Poisson disk sampling combined with Voronoi tessellation. The number of cells was increased through three distinct approaches: mirror replication of a reference structure, enlargement of the overall specimen size, and refinement of cell size under fixed external dimensions. To characterize arrangement-dependent effects, two distinct features of the compressive response were introduced: averaging, defined as a reduction in variability across responses from different initial cell arrangements, and smoothing, defined as the suppression of abrupt stress fluctuations within an individual response. Quantitative metrics were employed to evaluate both effects. Averaging was observed in plate-type models compressed in the z-direction and in fixed-size models, whereas mirror-connected models retained strong arrangement dependence despite large cell numbers. Smoothing occurred predominantly in plate-type models compressed in the z-direction and was strongly correlated with the number of cell layers aligned along the compression direction rather than with total cell number alone. The simulations were conducted in a dynamically equilibrated regime in which internal stress equilibrium was achieved during deformation. These results demonstrate that compressive behavior is governed not only by cell number but also by structural arrangement and directional cell-layer alignment, providing mechanistic insight into the transition from arrangement-dependent variability to stable macroscopic response under dynamic compression. Full article

With the rapid development of high-end equipment manufacturing, the number and size of complex surfaces continue to increase. Five-axis machining has become the dominant machining method. Effective prediction of cutting force and stability is of great significance for improving machining efficiency and quality. However, due to the complex and time-varying cutting geometry in five-axis machining of complex surfaces, low prediction efficiency has become a key issue restricting the research and engineering application of cutting force and stability. To address this issue, this study introduces the concept of dimensional compression and establishes an efficient prediction model for cutting force and stability. Each tool position along the tool path is discretized into inclined plane milling based on finite difference, thereby simplifying the research object. The tool twist angle and feed deflection angle are defined to describe the spatial relationship in five-axis machining. Using these two angles as new basis variables, a compressed space is constructed, and a mapping relationship between tool position and spatial point sets is established, further reducing the dimensionality of the research object. The cutting edge contact interval is determined using the spatial constraint method. Based on the full discretization method, the cutting force and stability of inclined plane milling are predicted, and the results are uniformly stored in the compressed space to form a sample point library. Consequently, the prediction process of complex surface five-axis machining is transformed into a process of sample point retrieval, significantly improving computational efficiency. Cutting force and vibration experiments in five-axis machining of complex surfaces are conducted. The results show that the predicted results are in good agreement with the experimental measurements, validating the accuracy of the proposed model and demonstrating its capability to guide practical machining. Full article

Forests constitute a large part of the global vegetation biomass, and various ecological metrics such as biodiversity and carbon stock can be determined by scanning them using LiDAR. LiDAR data is, however, inherently uncertain due to the finite beamwidth, and this uncertainty is propagated to any metrics derived from it. This study presents a methodology to propagate this uncertainty to tree metrics derived from quantitative structure models (QSMs), such as volume. First, the point cloud uncertainty is quantified using the laser beamwidth and an initial geometry estimate to create the so-called fuzzy cloud. This fuzzy cloud is then sampled iteratively using the Monte Carlo method until the variance estimate has converged. As a case study, we applied this method to three trees of varying size and present a selection of metrics for the trees as a whole, different branch orders and distributions along their heights. We show that the number of scanning locations has a large effect on both the volume and its uncertainty. We attained convergence at a 5% variance threshold within 30 iterations. Full article

This paper develops a unified asymptotic theory for conditional single-index U-statistics and the associated conditional U-processes in the setting of locally stationary functional time series subject to missing-at-random response mechanisms. The proposed framework addresses, within a single nonparametric inferential architecture, three major sources of complexity in modern functional data analysis: infinite-dimensional covariates, smoothly time-varying stochastic dynamics, and incomplete response observations. The methodology is based on a class of kernel-type estimators combining temporal localization, functional single-index smoothing, and inverse-propensity correction. Temporal localization captures the gradual evolution of the underlying regression structure, the single-index projection provides an effective dimension-reduction mechanism for functional covariates, and the propensity adjustment restores the target conditional functional under the MAR sampling scheme. The principal contribution of the paper is the establishment of weak convergence, in a suitable space of bounded functions, for the resulting propensity-adjusted conditional U-process indexed by a general class of measurable kernels. Under absolute regularity conditions, local stationarity assumptions, small-ball probability requirements, entropy restrictions of VC type, and uniform consistency of the propensity-score estimator, the normalized process is shown to converge weakly to a tight centered Gaussian process. The limiting covariance structure explicitly reflects the interaction between temporal smoothing, functional concentration, dependence, and the random loss of responses. In parallel, uniform convergence rates are derived for the associated conditional single-index U-statistic estimators, thereby quantifying the respective contributions of smoothing bias, stochastic fluctuation, local-stationarity approximation error, and missingness-induced variance inflation. A substantial part of the analysis is devoted to the technical difficulties created by the simultaneous presence of dependence, nonstationarity, functional covariates, and incomplete observations. The proofs combine Hoeffding-type decompositions adapted to weighted incomplete data, blocking and coupling arguments for absolutely regular triangular arrays, refined entropy bounds for kernel-indexed function classes, and small-ball probability techniques for functional covariates. The MAR mechanism is incorporated via inverse-propensity weighting, and its effects on the effective sample size, asymptotic variance, and bias structure are made explicit. The theory also provides a rigorous foundation for bandwidth selection through blocked, propensity-adjusted cross-validation and clarifies its relation to the corresponding oracle risk. The proposed framework encompasses a broad class of statistical learning and inference problems involving pairwise or higher-order functionals of functional time series. In particular, it applies to conditional Kendall-type functionals, discrimination problems, metric learning with incomplete labels, and conditional independence testing under local stationarity. A simulation study illustrates the finite-sample behavior of the proposed estimators and supports the theoretical findings across varying regimes of temporal nonstationarity, serial dependence, functional concentration, and response missingness. Overall, the results provide a mathematically rigorous and methodologically flexible foundation for inference from evolving functional data when dependence, infinite dimensionality, and incomplete observation are present simultaneously. Full article

Automotive roof racks are important lightweight accessories for vehicles, and their extrusion performance is affected by the coupled effects of material hot deformation behavior, die flow resistance and billet surface layer transport. In this study, Al-0.9Mg-0.6Si alloy samples were subjected to hot compression tests at 350–500 °C and strain rates of 0.01–10 s⁻¹. The corrected true stress–true strain data were used to establish and validate an Arrhenius-type constitutive model, which was then implemented in HyperXtrude to simulate the hot extrusion of an automotive roof rack profile. The hot working map showed that the main rheological instability region was located at high strain rates, and the preferred processing window was 437–500 °C and 0.01–0.6 s⁻¹. EBSD analysis showed that hot compression refined the microstructure relative to the initial average grain size of 173.147 μm, and the most uniform grain size distribution was obtained at 500 °C and 0.1 s⁻¹. The ODF results indicated strengthened {111}<121> and <110>//TD texture components after compression. The finite-element results showed that the standard deviation of outlet velocity (SDV), used here as an index of outlet flow uniformity, increased with ram speed, billet preheating temperature and die preheating temperature, but decreased with increasing container temperature. Finally, grain size and texture measurements from butt discard samples were compared with simulated surface layer flow paths, supporting the predicted difference between simple axial flow and complex recirculating flow near the die. Full article

Poisson mixed-effects models are essential for analyzing repeated count data, relying on latent random effects to account for unobserved heterogeneity and longitudinal dependence. However, the validity of likelihood-based inference in these models is highly sensitive to the specification of both the fixed-effects structure and the distributional assumptions of the random effects. While diagnostics based on the information matrix equality (IME) provide a theoretical framework for detecting misspecification, their high dimensionality and reliance on second-order derivatives often result in numerical instability and poor finite-sample performance in nonlinear settings. Here we introduce the Contrast of Information by Volume (CIV) test, a low-dimensional information-based diagnostic test for Poisson generalized linear mixed models (GLMMs). By integrating the scalar CIV statistics with novel graphical diagnostics, our approach facilitates the interpretation of specification errors in the random-effects structure. We derive the asymptotic behaviour of the CIV statistics under local misspecification and evaluate their properties through Monte Carlo simulations. To ensure robust inference in moderate samples, a parametric bootstrap procedure is employed for size calibration. Simulation results demonstrate that the CIV diagnostics maintain accurate Type I error control and achieve competitive power against common misspecification, including heteroskedasticity, correlation, and heavy-tailed random-effect distributions. Compared to traditional IME diagnostics, estimator-comparison tests, and GMM-based procedures, the CIV approach offers a superior balance between finite-sample stability and detection power. Finally, an empirical application illustrates the utility of the CIV framework in diagnosing latent misspecification and guiding the selection of random-effects covariance structures in applied research. Full article

So far, space exploration has attracted increasing scientific interest due to the growth of missions promoted by private investment, such as SpaceX, Boeing, Blue Origin, and the recent attention generated by astronomical phenomena such as 3I/ATLAS. However, access to space experimentation remains limited and expensive. For this reason, new approaches to simulate space conditions on Earth are being developed to broaden research opportunities bio-inspired by plant responses to phototropism and geotropism. In this context, Betta Aerospace has continued the development of a microgravity simulation system consisting of a 3-axis clinostat powered by a single motor, continuous external electrical supply, and, in this project, a continuous external liquid supply. The proposed pioneer system was designed as a flexible platform manufactured through reinforced 3D printing, with an approximate size of 30 cm, an estimated payload of 30 kg, and a 24 V supply. Its main goal is to study the effects of simulated microgravity on aquatic organisms while enabling longer observation times in a controlled freshwater environment. Candidate biological samples include Ulva lactuca, Pyropia, Spirulina/Arthrospira, and Chlorella. Preliminary motion tests confirmed continuous operation at 10 rpm. In addition, a simplified static finite element analysis under a 294 N load yielded a maximum von Mises stress of 5.45 × 10⁷ Pa and a maximum displacement of 1.73 mm. Full article

We develop a design-aware framework that combines penalized prediction and causal inference for finite populations observed through complex survey designs. The framework integrates survey-weighted pseudo-likelihoods, ${\mathsf{\ell }}_1$-penalized estimation, Neyman-orthogonal moment functions, and a bootstrap procedure that resamples primary sampling units within strata. Methodologically, the contribution is an explicit pipeline that supports design-based inference while separating predictive associations from structurally adjusted effects in high-dimensional, clustered data. We illustrate the framework using data from the Chilean National Health Survey (ENS) 2016–2017 to study the relationship between chronic kidney disease (CKD) and high cardiovascular (CV) risk. In the ENS adult population, the survey-weighted prevalence of CKD was 3.1% (95% CI: 2.4–3.8), and the prevalence of high CV risk was 23.9% (95% CI: 21.5–26.3). High CV risk was markedly more frequent among individuals with CKD than among those without CKD (90.9% versus 21.5%). Predictive and associational analyses combined survey-weighted penalized logistic regression (LASSO) with refitted unpenalized models. In conventional survey-weighted logistic regressions, CKD showed a strong association with high CV risk (odds ratio = 5.66; 95% CI: 2.71–11.82; $p<0.001$), and effect sizes remained stable after LASSO-based variable selection. To assess causal relevance under confounding and potential endogeneity, we implemented two endogeneity-aware estimators: two-stage residual inclusion (2SRI) and double/debiased machine learning (DML). The DML estimator, defined as the primary causal estimand, reports an orthogonalized estimate of the average treatment effect of CKD on the probability of high CV risk. After adjustment for age and major cardiometabolic comorbidities, the DML estimate was attenuated and statistically non-significant (average treatment effect = $-0.094$; 95% CI: $[-0.409,0.220]$). The 2SRI approach yielded unstable estimates with wide confidence intervals, consistent with the limited effective sample size of CKD cases ($n_{CKD}\approx 190$ in a sample with n ≈ 6233) and weak identification conditions under low-prevalence settings. Simulation experiments under ENS-like complex sampling suggest that naive predictive associations may overestimate the structural contribution of CKD under confounding, whereas orthogonalized estimators yield more conservative estimates when identification holds. The causal interpretation relies on a conditional mean independence assumption given observed covariates and survey design, while control-function specifications are treated as diagnostic sensitivity analyses due to the absence of credible exclusion-based instruments. Overall, the results demonstrate a fundamental divergence between predictive relevance and causal importance in finite-population settings, underscoring the need for design-aware and endogeneity-robust methods in statistical modeling. Full article

We introduce a minimal relational network model in which superconducting-like behavior emerges as a collective phase of constrained connectivity and phase coherence, without assuming microscopic electrons, phonons, or material-specific interactions. The model is formulated as a concrete instantiation of a previously introduced axiomatic relational–informational framework for emergent geometry and effective spacetime, in which geometry and effective forces arise from constrained information flow rather than from a background manifold. Mathematically, this construction is realized on a finite weighted graph with binary edge-activation variables and compact vertex phase variables, sampled through a Gibbs ensemble generated by an additive informational action. The system is represented as a finite weighted graph with weighted edges encoding transport or informational costs, augmented by dynamically activated low-cost channels and compact phase degrees of freedom defined at vertices. The effective edge costs induce a weighted shortest-path metric, providing an operational notion of emergent relational geometry. Using Monte Carlo simulations on two-dimensional periodic lattices, we show that the same informational action supports three distinct emergent regimes: a normal resistive phase, a fragile low-temperature-like superconducting phase characterized by noise-sensitive coherence, and a noise-robust high-temperature-like superconducting phase in which global phase coherence persists under substantial fluctuations. These regimes are identified using purely relational observables with direct graph-theoretic and statistical-mechanical interpretation, including percolation of low-cost channels, phase correlation functions, an operational phase stiffness (helicity modulus), and a geometric diagnostic based on relational ball growth. In particular, we extract an effective geometric dimension from the scaling of low-cost accessibility balls, using a ball-growth relation of the form $⟨B\left(r\right)⟩ ~ r^{d_{eff}}$, revealing a clear monotonic hierarchy between normal, fragile superconducting, and noise-robust superconducting—like regimes. This demonstrates that superconducting-like behaviour in the present framework corresponds not only to percolation and phase alignment, but also to a qualitative reorganization of relational geometry. Robustness is tested via finite-size comparison between 8 × 8, 12 × 12 and 16 × 16 lattice realizations. Within this framework, normal and superconducting-like behavior arise from the same underlying relational mechanism and differ only in the structural stability of connectivity, coherence, and geometric accessibility under fluctuations. The aim of this work is structural rather than material-specific: we do not reproduce detailed experimental phase diagrams or microscopic pairing mechanisms, but identify minimal relational conditions under which low-dissipation, phase-coherent transport can emerge as a generic organizational regime of constrained relational systems. Full article

Structural Equation Modeling (SEM) is a key framework for analyzing complex economic relationships involving latent variables, mediation effects, and endogeneity, yet the choice between frequentist and Bayesian estimation remains theoretically and practically contested, especially in settings with non-stationary data and small samples. This study provides a formal comparison of the two approaches by formulating SEM as a probabilistic graphical model and deriving the corresponding estimation procedures, identifiability conditions, and uncertainty measures. We examine asymptotic properties of frequentist estimators and posterior consistency in Bayesian SEM, with particular attention to integrated time-series SEM applications such as shadow economy estimation. The analysis shows that while both approaches converge under large-sample conditions, important differences arise in finite samples. Bayesian methods exhibit more stable point estimates through coherent uncertainty quantification, particularly when prior information regularizes an otherwise ill-conditioned likelihood. Under model misspecification, Bayesian posteriors concentrate around the pseudo-true parameter defined by the Kullback-Leibler projection, providing a probabilistic representation of misspecification uncertainty through posterior spread—an advantage over frequentist inference, which typically conditions on the maintained model as exact. These findings carry direct implications for empirical economic modeling under realistic data constraints. In settings where sample sizes are small, identification is weak, and model uncertainty is substantial, conditions that routinely characterize macroeconomic research, the choice of inferential framework is not a matter of philosophical preference but a determinant of whether policy-relevant conclusions can be credibly defended. Bayesian SEM offers a principled and transparent path forward in precisely these conditions. Full article

In marine renewable energy applications, offshore steel pipelines are subjected to complex combined loads during installation and operation, leading to significant plastic deformation and potential catastrophic fracture. To accurately characterize pipeline fracture failure, this study develops an enhanced failure assessment framework based on the Modified Mohr–Coulomb (MMC) criterion, integrating experimental parameter evaluation with numerical simulation for in-service offshore pipelines. The key parameters of the MMC model were determined directly from in-service pipeline samples to account for operational degradation. First, the plastic parameters were obtained by fitting the Swift hardening law to uniaxial tensile tests. Fracture parameters were then calibrated using a suite of five notched tensile specimens. Mesh sensitivity was analyzed using CT experiments to establish a suitable mesh size for the MMC-based damage model, enabling precise characterization of crack evolution from initiation to final tearing. Unlike prior applications, this framework is employed to investigate the response of X80 pipelines under combined tension, bending, and external pressure loading. Three-dimensional finite element models were developed to systematically analyze the stress–strain response, moment–curvature behavior, and evolution of hoop stress distribution. Results show that while the failure stress remains relatively stable under varying external pressure, both the critical strain and critical curvature increase markedly with pressure, by up to 20.9%. They also reveal a pronounced hierarchy in the influence of crack geometry on the failure behavior. Crack depth dominates failure sensitivity, affecting critical strain and pressure response far more than crack width or length. The reduction in failure stress for deep cracks under 12 MPa external pressure is over three times greater than for shallow cracks. In contrast, variations in crack length exert the most negligible influence on failure characteristics, with observed discrepancies of less than 6%. Overall, this research provides a high-precision failure prediction framework for in-service pipelines by quantitatively analyzing failure behavior under combined loads. It effectively characterizes failure evolution paths that differ from design conditions and dynamically tracks the residual fracture resistance after time-dependent degradation, offering a fundamental reference for the reliability assessment of pipelines in complex marine environments. Full article

This work examines the simulation-based torsion properties of TPMS structures. Although TPMS structures are gaining more interest in research and potential practical applications, their torsion properties are not widely studied. In this work, sheet-based Diamond, Gyroid, and Primitive TPMS structures are analyzed numerically using the finite element method. The samples have a diameter of 20 mm and a length of 40 mm. Relative densities are 30%, 50%, and 70%, while unit cell sizes are 10 mm, 15 mm, and 20 mm. Cell geometry did not significantly affect the properties for samples with a 10 mm unit cell size. For other unit cell sizes, the shear modulus and shear yield stress were 1.5–4 times higher for the Primitive structure than for other geometries. With increasing relative density, the shear modulus and shear yield stress increased by 1.5–2 times for the Diamond and Gyroid structures, as well as for the Primitive structure with a 10 mm unit cell size. The Primitive structure with 15 mm and 20 mm unit cell sizes showed a decrease in properties with increasing relative density. Regarding the effect of unit cell size, the shear modulus and shear yield stress showed insignificant differences for the Diamond and Gyroid structures, while the Primitive structure showed dependence on unit cell size. Samples with a 15 mm unit cell size had 1.5–2 times higher shear modulus and 1.5–3 times higher shear yield stress than samples with a 10 mm unit cell size. Samples with a 20 mm unit cell size exhibited slightly lower shear modulus and shear yield stress than those with 15 mm unit cells. Full article

Continuous-variable quantum communication (CVQC) relies on finite-window estimation of phase space moments, making receiver decisions sensitive to finite measurement resolution, calibration uncertainty, and confidence-calibrated tolerances. This paper develops a receiver-centric threat modeling framework for structured (including adversarial) physical-layer disturbances under finite-sample inference. We introduce an operational taxonomy, reconnaissance, exploratory, and denial-of-service, defined by statistical visibility relative to acceptance regions rather than by assumed physical mechanisms. Using an effective estimator space Gaussian model ${\widehat{\mathbf{r}}}^{\prime }=G\widehat{\mathbf{r}}+\mathbf{\xi }$ with additive covariance N, we show how distinct mechanisms can be observationally equivalent within finite tolerances and we propose a protocol-agnostic scalar severity coordinate $\Delta E$ based on the covariance trace. We derive ${\chi }^2$-based missed-detection expressions and a soft detectability boundary scaling as $1/\sqrt{n}$, and we corroborate the predicted $P_{\mathrm{miss}}\left(\nu \right)$ behavior via Monte Carlo simulations across representative block sizes. The resulting framework clarifies the delimitation from conventional CV-QKD excess noise parameterization and provides a structured basis for monitoring-layer design and comparative threat-taxonomy mapping. Full article

This study used finite element simulation and theoretical analysis to predict the crack distribution patterns that may occur during the shrinkage cracking process of rectangular timber beams. Based on the predictions, experimental specimens with six typical crack distribution patterns (I–VI) were designed. Subsequently, a four-point bending test method was employed to conduct large-sample size fracture tests on a total of 1200 small-sized Pinus sylvestris var. mongolica specimens, quantifying the effects of the crack depth, location, and distribution patterns on the specimens’ load-bearing capacity. The results indicate that when multiple cracks exist in a timber beam, their collective effect is not a simple superposition of individual cracks but a spatial distribution coupling effect. Both the depth and location of the cracks play crucial roles in their interaction. This study introduces three coefficients for evaluating the influence of cracks on timber beams, namely the load-bearing capacity coefficient (R), the decline ratio of load-bearing capacity (D), and the comprehensive crack-influence coefficient (β), which can effectively quantitatively evaluate crack damage effects. The framework established in this study, which links shrinkage crack characteristics with the load-bearing capacity of timber beams, along with the experimental data provided, can serve as a reference for the safety evaluation and scientific maintenance of historical timber components and modern timber structures with shrinkage cracks. Full article

Multicollinearity and outliers are common challenges in multiple linear regression, often adversely affecting the properties of least squares estimators. To address these issues, several robust estimators have been developed to handle multicollinearity and outliers individually or simultaneously. More recently, the robust Stein estimator (RSE) was introduced, which integrates shrinkage and robustness to effectively mitigate the impact of both multicollinearity and outliers. Despite its theoretical advantages, the finite-sample performance of this approach under multicollinearity and outliers remains underexplored. First, outliers in the y direction have been the main focus of earlier research on the RSE, not considering that leverage points could substantially impact regression results. Second, this study addresses the gap by considering outliers in the y direction and leverage points, providing a more thorough assessment of the RSE robustness. Finally, to extend the limited existing benchmark, we compare and evaluate the RSE performance with a wide range of robust and classical estimators. This extends existing benchmarking, which is limited in the current literature. Several Monte Carlo (MC) simulations were conducted, considering both normal and heavy-tailed error distributions, with sample sizes, multicollinearity levels, and outlier proportions varied. Performance was evaluated using bootstrap estimates of root mean squared error (RMSE) and bias. The MC simulation results indicated that the RSE outperformed other estimators under several scenarios where both multicollinearity and outliers are present. Finally, real data studies confirm the MC simulation results. Full article