Search Results (202)

Fiber-reinforced composites are widely used in aerospace and other fields due to their excellent specific strength, specific stiffness, and corrosion resistance, and further study of their failure criteria is essential to improve the accuracy and reliability of failure behavior prediction under complex loads. There are still some limitations in the current composite failure criterion research, mainly reflected in the lack of promotion of three-dimensional stress state, lack of sufficient consideration of multi-modal coupling effects, and the applicability of the criteria under multiaxial stress and complex loading conditions, which limit the wider application of composites in the leading-edge fields to a certain degree. In this work, a generalized Mohr failure envelope function approach is adopted to obtain the stress on the failure surface as a power series form of independent variable, and the unknown coefficients are determined according to the damage conditions, to extend the Chang–Chang criterion to the three-dimensional stress state, and to consider the coupling effect between the fiber and matrix failure modes. The modified Chang–Chang criterion significantly enhances the failure prediction accuracy of composite materials under complex stress states, especially in the range of multi-axial loading and small off-axis angles, which provides a more reliable theoretical basis and practical guidance for the safe design and performance optimization of composite structures in aerospace and other engineering fields. Full article

Background: Implementing effective interventions for specific cognitive symptoms is critical to reducing the disease burden of dementia. Previous studies have identified associations between overall cognitive function and dietary patterns in older adults with multimorbidity. However, the relationship between specific cognitive symptoms and different foods remains largely unknown. Methods: We included 3443 older adults with multimorbidity, aged 65 years or older, from the Chinese Longitudinal Health Longevity Survey (CLHLS, 2017–2018). We used the Chinese version of the Mini-Mental State Examination (MMSE) to assess cognitive function and selected 13 common foods to evaluate food consumption. Network analysis was used to identify central symptoms and bridge symptoms between the food consumption and cognitive symptom networks. Finally, the stability of the networks was examined using the case-dropping bootstrap procedure. Results: Network analysis revealed that B6 (mushrooms or algae), B4 (dairy products), and B5 (nut products) were the most influential in the food–cognition network model, and A5 (language ability), A1 (orientation ability), and B5 (nut products) were considered bridging symptoms in the food–cognition network. Bootstrap analysis showed that the 95% confidence interval of the edge weights in the network is narrow, indicating that this study accurately assesses the edge weights. The correlation stability coefficient of the centrality of the expected influence and bridge strength is 0.75, indicating that the network has good stability. Conclusions: Central symptoms as well as bridge symptoms play a key role in food and cognitive networks. Timely systematic and multilevel interventions targeting central symptoms and bridge symptoms may help to delay the risk of dementia in older adults with multimorbidity. Full article

The spatial distribution of precipitation is one of the major unknowns in hydrological modeling since meteorological stations do not adequately cover the territory, and their records are often short. In addition, regulations are increasingly restricting the amount of wastewater that can be discharged each year. Therefore, understanding the annual behavior of rainfall events is becoming increasingly important. This paper presents Rainfall Frequency Analysis (RainFA), a software package that applies a methodology for data curation and frequency analysis of precipitation series based on the evaluation of the L-moments for regionalization and cluster classification. This methodology is tested in the city of Palma (Spain), identifying a single homogeneous cluster integrated by 7 (out of 11) stations, with homogeneity values less than 0.6 for precipitation values greater than or equal to 0.4 mm. In the evaluation of the prediction capacity, the selected cluster of 7 stations performed in the first quartile of the 120 possible combinations of 7 stations, both for the detection of the occurrence of rainfall—in terms of Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI) and Bias Score (BS) statistics—and for the accuracy of rainfall—according to Root Mean Square Error (RMSE), Nash–Sutcliffe Efficiency coefficient (NSE) and Percent Bias (PBIAS). The cluster was also excellent for predicting different rainfall ranges, resulting in the best combination for both light—i.e., [1, 5) mm—and moderate—i.e., [5, 20) mm—rainfall prediction. The Generalized Pareto gave the best probability distribution function for the selected region, and it was used to simulate daily rainfall and system discharges over annual periods using Monte Carlo techniques. The derived discharge values were consistent with observations for 2023, with an average discharge of about 700,000 m³ of wastewater. RainFA is an easy-to-use and open-source software programmed using Python that can be applied anywhere in the world. Full article

When performing Bayesian modeling on functional data, the assumption of normality is often made on the model error and thus the results may be sensitive to outliers and/or heavy tailed data. An important and good choice for solving such problems is quantile regression. Therefore, this paper introduces the quantile regression into the partial functional linear spatial autoregressive model (PFLSAM) based on the asymmetric Laplace distribution for the errors. Then, the idea of the functional principal component analysis, and the hybrid MCMC algorithm combining Gibbs sampling and the Metropolis–Hastings algorithm are developed to generate posterior samples from the full posterior distributions to obtain Bayesian estimation of unknown parameters and functional coefficients in the model. Finally, some simulation studies show that the proposed Bayesian estimation method is feasible and effective. Full article

In realistic hyperspectral image (HSI) cross-scene classification tasks, it is ideal to obtain target domain samples during the training phase. Therefore, a model needs to be trained on one or more source domains (SD) and achieve robust domain generalization (DG) performance on an unknown target domain (TD). Popular DG strategies constrain the model’s predictive behavior in synthetic space through deep, nonlinear source expansion, and an HSI generation model is usually adopted to enrich the diversity of training samples. However, recent studies have shown that the activation functions of neurons in a network exhibit asymmetry for different categories, which results in the learning of task-irrelevant features while attempting to learn task-related features (called “feature contamination”). For example, even if some intrinsic features of HSIs (lighting conditions, atmospheric environment, etc.) are irrelevant to the label, the neural network still tends to learn them, resulting in features that make the classification related to these spurious components. To alleviate this problem, this study replaces the common nonlinear generative network with a specific linear projection transformation, to reduce the number of neurons activated nonlinearly during training and alleviate the learning of contaminated features. Specifically, this study proposes a dimensionally decoupled spatial spectral linear extrapolation (SSLE) strategy to achieve sample augmentation. Inspired by the weakening effect of water vapor absorption and Rayleigh scattering on band reflectivity, we simulate a common spectral drift based on Markov random fields to achieve linear spectral augmentation. Further considering the common co-occurrence phenomenon of patch images in space, we design spatial weights combined with label determinism of the center pixel to construct linear spatial enhancement. Finally, to ensure the cognitive unity of the high-level features of the discriminator in the sample space, we use inter-class contrastive learning to align the back-end feature representation. Extensive experiments were conducted on four datasets, an ablation study showed the effectiveness of the proposed modules, and a comparative analysis with advanced DG algorithms showed the superiority of our model in the face of various spectral and category shifts. In particular, on the Houston18/Shanghai datasets, its overall accuracy was 0.51%/0.83% higher than the best results of the other methods, and its Kappa coefficient was 0.78%/2.07% higher, respectively. Full article

A kriging regression model is a popular and effective type of surrogate model in computer experiments. A significant challenge arises when the mean function of the model includes polynomial terms with unknown coefficients, leading to identifiability problems and potentially unreliable results. To overcome this problem, Plumlee and Joseph (2018) introduced an orthogonal kriging model. Variable selection for kriging models has been widely considered by researchers in computer experiments. In this paper, we introduce a new method for combining orthogonal kriging with penalized variable selection. Furthermore, an efficient algorithm is given to select the correct mean function. The simulation results and an example study with real data show that the proposed method is superior to others in variable recognition rate and prediction accuracy. Full article

This paper presents a novel methodology for estimating space- and time-dependent source terms in heat and mass diffusion problems. The approach combines classical integral transform techniques (CITTs) with the least squares optimization method, enabling an efficient reconstruction of source terms. The method employs a double expansion framework, using both spatial eigenfunction and temporal expansions. The new presented idea assumes that the source term can be expressed as a spatial expansion in eigenfunctions of the eigenvalue problem, and then each transient function associated with each term of spatial expansion is rewritten as an additional expansion, where the unknown coefficients approximating the transformed source enable the direct use of the solution in the objective function. This, in turn, results in a linear optimization problem that can be quickly minimized. Numerical experiments, including one-dimensional and two-dimensional scenarios, demonstrate the accuracy of the proposed method in the presence of noisy data. The results highlight the method’s robustness and computational efficiency, even with minimal temporal expansion terms. Full article

Background/Objectives: Muscle power, estimated from the sit-to-stand (STS) test, is an important indicator of physical function (PF) in aging adults. Therefore, its assessment may be implemented into future clinical practice. The agreement between different STS power assessments is unknown, and the associations between methods and PF outcomes have not been compared. Methods: A total of 49 aging adults (mean age = 60.9 ± 10.9; 67% female) participated in this cross-sectional study. STS power from a validated equation (EQ) and a linear position transducer (LPT) were estimated. Handgrip strength (HGS), timed up-and-go (TUG), usual gait speed (UGS), fast gait speed (FGS), the 400-m walk test (400MWT), and self-reported total, basic lower-body, and advanced lower-body PF were assessed. The agreement of STS power methods was assessed with an intraclass correlation coefficient (ICC) and a Bland–Altman plot. Multiple linear regression evaluated the associations between STS power and PF outcomes. Results: EQ and LPT STS power demonstrated only moderate agreement (ICC = 0.69). EQ STS power was independently associated with TUG (β = −0.45), UGS (β = 0.37), FGS (β = 0.48), 400MWT (β = −0.55), self-reported total (β = 0.30), basic lower-body (β = 0.30), and advanced lower-body PF (β = 0.30), but not HGS (β = 0.14). LPT STS power was independently associated with HGS (β = 0.44), FGS (β = 0.40), 400MWT (β = −0.51), self-reported total (β = 0.31), basic lower-body (β = 0.29), and advanced lower-body PF (β = 0.32), but neither TUG (β = −0.26) nor UGS (β = 0.28). Conclusions: EQ and LPT STS power demonstrate limited agreement, and EQ STS power may be a superior indicator of PF in aging adults. Future research should examine the feasibility of implementing STS power tests in clinical settings to screen and refer patients with low muscle power to effective therapeutic interventions. Full article

Background/Objectives: Deoxynivalenol (DON), known as vomitoxin, is one of the most common mycotoxins produced by Fusarium graminearum, with high detection rates in feed worldwide. Ferroptosis is a novel mode of cell death characterized by lipid peroxidation and the accumulation of reactive oxygen species. Although it has been demonstrated that DON can induce ferroptosis in the liver, the specific mechanisms and pathways are still unknown. The aim of this experiment was to investigate that DON can induce iron metabolism disorders in the livers of mice, thereby triggering ferroptosis and causing toxic damage to the liver. Methods: Male C57 mice were treated with DON at a 5 mg/kg BW concentration as an in vivo model. After sampling, organ coefficient monitoring, liver function test, histopathological analysis, liver Fe²⁺ content test, and oxidative stress-related indexes were performed. The mRNA and protein expression of Nrf2 and its downstream genes were also detected using a series of methods including quantitative real-time PCR, immunofluorescence double-labeling, and Western blotting analysis. Results: DON can cause damage to the liver of a mouse. Specifically, we found that mouse livers in the DON group exhibited pathological damage in cell necrosis, inflammatory infiltration, cytoplasmic vacuolization, elevated relative liver weight, and significant changes in liver function indexes. Meanwhile, the substantial reduction in the levels of glutathione (GSH), catalase (CAT), superoxide dismutase (SOD), and total antioxidant capacity (T-AOC) in the DON group indicated that DON also caused oxidative stress in the liver. Notably, DON exposure increased the levels of Fe²⁺ and Malondialdehyde (MDA) in the liver, which provides strong evidence for the occurrence of iron metabolism and ferroptosis disorders. Most importantly, mRNA and protein expression of Nrf2, an important pathway for iron metabolism and ferroptosis, along with its downstream genes, heme oxygenase (HO-1), quinone oxidoreductase (NQO1), glutathione peroxidase (GPX4), and solute carrier gene (SLC7a11), were significantly inhibited in the DON group. Conclusions: Based on our results, the Nrf2 pathway is closely associated with DON-induced iron metabolism disorders and ferroptosis in mouse livers, suggesting that maintaining hepatic iron homeostasis and activating the Nrf2 pathway may be a potential target for mitigating DON hepatotoxicity in the future. Full article

This research proposes a method for reducing the dimension of the coefficient vector for Crank–Nicolson mixed finite element (CNMFE) solutions to solve the fourth-order variable coefficient parabolic equation. Initially, the CNMFE schemes and corresponding matrix schemes for the equation are established, followed by a thorough discussion of the uniqueness, stability, and error estimates for the CNMFE solutions. Next, a matrix-form reduced-dimension CNMFE (RDCNMFE) method is developed utilizing proper orthogonal decomposition (POD) technology, with an in-depth discussion of the uniqueness, stability, and error estimates of the RDCNMFE solutions. The reduced-dimension method employs identical basis functions, unlike standard CNMFE methods. It significantly reduces the number of unknowns in the computations, thereby effectively decreasing computational time, while there is no loss of accuracy. Finally, numerical experiments are performed for both fourth-order and time-fractional fourth-order parabolic equations. The proposed method demonstrates its effectiveness not only for the fourth-order parabolic equations but also for time-fractional fourth-order parabolic equations, which further validate the universal applicability of the POD-based RDCNMFE method. Under a spatial discretization grid $40\times 40$, the traditional CNMFE method requires $2\times {41}^2$ degrees of freedom at each time step, while the RDCNMFE method reduces the degrees of freedom to $2\times 6$ through POD technology. The numerical results show that the RDCNMFE method is nearly 10 times faster than the traditional method. This clearly demonstrates the significant advantage of the RDCNMFE method in saving computational resources. Full article

We obtain universal relations for fluid spheres without rotation made of dark energy assuming the extended Chaplygin gas equation-of-state. After integrating the relevant differential equations, we make a fit to obtain the unknown coefficients of the functions (a) normalized moment of inertia versus dimensionless deformability and (b) normalized moment of inertia versus factor of compactness. We find that the form of the functions does not depend on the details of the underlying equation-of-state. Full article

In this paper, we investigate the thermal friction sliding contact of a functionally graded piezoelectric material (FGPM)-coated half-plane subjected to a rigid conductive cylindrical punch. This study considers the effect of the thermal convection term in heat conduction. The thermo-electro-elastic material parameters of the coating vary exponentially along its thickness direction. Utilizing thermoelastic theory and Fourier integral transforms, the problem is formulated into Cauchy singular integral equations of the first and second kinds with surface stress, contact width, and electric displacement as the unknown variables. The numerical solutions for the contact stress, electric displacement, and temperature field of the graded coating surface are obtained using the least-squares method and iterative techniques. It can be observed that the thermo-electro-elastic contact behavior of the coating surface undergoes significant changes as the graded index varies from −0.5 to 0.5, the friction coefficient ranges from 0.1 to 0.5, and the sliding velocity changes from 0.01 m/s to 0.05 m/s. The results indicate that adjusting the graded index of the coating, the sliding speed of the punch, and the friction coefficient can improve the thermo-electro-elastic contact damage of the material’s surface. Full article

Micro-motion feature extraction is of great significance for target recognition. However, traditional methods mostly focus on single target and struggle to correctly separate the severely overlapping micro-motion curves of multiple targets. In this paper, a rapid micro-motion feature extraction algorithm of multiple space targets based on inverse radon transform (IRT) with a modified model is proposed. First, the high-resolution range profile (HRRP) generated from echo is subject to binarization to improve the unstable estimation caused by noise. Then, the micro-motion period in a complicated multi-target scenario is obtained by a period estimation method based on the autocorrelation coefficients of binarized HRRP. To further improve the extraction accuracy, the IRT model of the micro-range curve is modified from the sine function to second-order sine function. By searching for the remaining unknown parameters in the model in conjunction with the period, the precise micro-range curves are quickly separated. Each time the curves of a target are extracted, they are removed, and the next extraction is carried out until all the targets have been searched. Finally, simulation and experimental results indicate that the proposed algorithm can not only correctly separate the micro-motion feature curves of multiple space targets under low signal-to-noise ratio (SNR) conditions but also significantly outperforms the original IRT in terms of extraction speed. Full article

The Mecanum-wheeled robot has four special wheels. It can control four wheels independently and has seven turning axes. The robot can translate in all directions and travel in curves without changing its direction by means of the control commands for turning ratio, speed, and direction of travel. However, no model has been proposed that can accurately simulate the output of the actual machine for the three types of inputs, even when the characteristics of the motor and motor driver are unknown. In this study, we synthesized and simplified transfer functions and estimated the undetermined coefficients that minimize the sum of squared errors to construct a model of the robot that can output the position and posture equivalent to those of the actual robot for the input commands for turning ratio, speed, and the direction of travel. We modeled a Mecanum-wheeled robot using the proposed modeling method and parameter determination method and compared the outputs of the real robot to the step and ramp inputs. The results showed that the errors between the two outputs were very small and accurate enough to simulate AI learning, such as reinforcement learning, using the model of the robot. Full article

The dynamic stress analysis of a strip plate with elliptical holes under the action of an incident SH wave was performed using a complex function method and a successive mirror method. Firstly, a complex plane coordinate system of elliptic holes was established by using the complex variable function method and integral transformation method. The elliptic hole wave field and stress were established using the wave function expansion method. Then, the relation between the argument angle of any point on the edge of the ellipse hole and the angle between the vertical line and the coordinate axis was established. Using boundary conditions to solve the unknown coefficients in the equation, finally, the integral equation was simplified to a linear equation by means of the effective truncation method, and the steady-state response of dynamic stress under different parameters was analyzed. In addition, by comparing the finite element solution with the numerical solution, the accuracy of the results was effectively verified. The results show that the studied geometric model can provide solid theoretical support for the inspection of plate and shell structures, which is of great significance in practical engineering. Full article