Search Results (79)

The T-tail configuration, while offering advantages for large transport aircraft, is susceptible to peculiar aerodynamic phenomena such as deep stall and flutter, necessitating high-fidelity dynamic scaling for wind tunnel testing. In order to address the issue of similarity in the dynamic characteristics of scaled T-tail models, we propose a comprehensive optimization design method for dynamic scaled equivalent models of T-tail structures with rear fuselages. The development of an elastic-scaled model is accomplished through the integration of the least squares method with a genetic sensitivity hybrid algorithm. In this framework, the objective function is defined as minimizing a weighted sum of the frequency errors and the modal shape discrepancies ($1-$ Modal Assurance Criterion) for the first five modes, subject to lower and upper bound constraints on the design variables (e.g., beam cross-sectional dimensions). The findings indicate that the application of finite element modelling in conjunction with multi-objective optimization results in the scaled model that closely aligns with the dynamic characteristics of the actual aircraft structure. Specifically, the frequency error of the optimized model is maintained below 2%, while the modal confidence level exceeds 95%. A ground vibration test (GVT) was conducted on a fabricated scaled model, with all frequency errors below 3%, successfully validating the optimization approach. This GVT-validated high-fidelity model establishes a reliable foundation for subsequent wind tunnel tests, such as flutter and buffet experiments, the results of which are vital for validating the full-scale aircraft’s aeroelastic model and informing critical flight safety assessments. The T-tail elastic model design methodology presented in this study serves as a valuable reference for the analysis of T-tail characteristics and the design of wind tunnel models. Furthermore, it provides insights applicable to multidisciplinary optimisation and the design of wind tunnel models for other similar elastic scaled-down configurations. Full article

Maximizing the weighted sum-rate (WSR) in downlink multi-user multiple-input multipleoutput (MU-MIMO) systems remains computationally challenging due to the prohibitive complexity of classical weighted minimum mean square error (WMMSE) algorithms. In this article, we propose a novel low-complexity WMMSE (LC-WMMSE) precoding method specifically designed for massive MU-MIMO downlink systems. Our algorithm introduces a hybrid switching approach that adaptively blends standard WMMSE updates with computationally simpler approximations derived via the Woodbury matrix identity, coupled with an adaptive damping mechanism to ensure robust and stable convergence. Simulation results demonstrate that the proposed LC-WMMSE method achieves WSR performance comparable to classical WMMSE but with significantly reduced computational complexity, making it particularly suitable for practical implementation for massive MUMIMO systems. Full article

Incomplete absorption correction procedures in single-crystal diffraction experiments leave a characteristic trace—a “fingerprint”—in the residuals. Specifically, weak intensities are systematically overestimated, contributing disproportionately and sometimes even dominantly to the chi-square sum in least squares refinements. An analysis of six published crystal structures spanning a wide range of absorption coefficients reveals a consistent positive shift of the weighted residuals, which were significant for crystals with $>5.02$ mm⁻¹. This shift is all the stronger the greater the absorption coefficient and is accompanied by a proportionally increasing fraction of positive excess residuals. The simultaneous increase in the mean value of the residuals and the fraction of positive excess residuals proves that the shift is not caused by strong reflections or isolated outliers, but rather by the systematic overestimation of many weak intensities. Diagnostic plots and statistical metrics are presented for additional published data sets, supporting the generality of the findings. These findings can support the development of improved methods for absorption correction, which lead to physically meaningful thermal motion parameters even with strong absorption. Full article

Addressing the need for intrusion detection and localization in critical areas, this study develops a method for outdoor ground vibration source localization utilizing subterranean-deployed MEMS accelerometers. First, the Particle Swarm Optimization (PSO) algorithm is employed to minimize the Geometric Dilution of Precision (GDOP), thereby determining the optimal configuration of the sensor array. The acquired signals are then filtered, and a novel time delay estimation algorithm, termed the Sliding Window Derivative (SWD) algorithm, is proposed. This method utilizes a sliding window to compute the sum of squared differences between adjacent sampling points within the window, generating a time-windowed energy change signal. The derivative of this signal yields a rate-of-change curve, highlighting abrupt signal transitions. The SWD algorithm, in conjunction with the STA/LTA–AIC algorithm, precisely identifies the first arrival point of the vibration signal, determining its time of arrival at each of the four sensors. Finally, an improved two-step weighted least squares method based on Time Difference of Arrival (TDOA) is used to calculate the position of the vibration source. Experimental results demonstrate an average positional error of 0.095 m and an average directional error of 0.935 degrees, validating the efficacy of the proposed method in achieving high-precision localization in outdoor environments. Full article

To address the challenges of unstable motion and insufficient detection accuracy in robotic scanning trajectories, particularly under high curvature and irregular shape conditions during ultrasonic testing of complex free-form surface workpieces, this paper proposes a G³ continuous trajectory planning and optimization method based on quintic B-spline curves. First, the scanning trajectory of the robot is represented by a parametric curve, with explicit expressions for position, velocity, acceleration, and jerk derived in the form of quintic B-splines. These expressions ensure continuity in position, velocity, acceleration, and jerk (C³/G³ continuity), thus maintaining high-order geometric continuity and motion stability of the trajectory. Second, to achieve the dual optimization objectives of trajectory smoothness and surface fitting, this paper constructs a composite objective function that incorporates both the integral of acceleration squared and the surface fitting error. The smoothness index is weighted by the sum of the square integrals of the second and third derivatives of the trajectory, thereby suppressing high-order oscillations, while the fitting index is based on the mean square error between the robot end-effector path and the target surface. Finally, a numerical optimization algorithm is utilized to solve the objective function, resulting in an optimal scanning trajectory that ensures both motion stability and fitting accuracy, while maintaining G³ continuity. Simulation and experimental results demonstrate that this method effectively mitigates trajectory mutations and oscillations, enabling efficient and high-precision automatic ultrasonic testing, and provides a reliable trajectory planning strategy for online non-destructive testing of complex curved workpieces. Full article

In complicated scenarios, due to the low precision of float solutions and poor reliability of fixed solutions, it is challenging to achieve a balance between accuracy and reliability of the integer least squares (ILS) estimation. To address this dilemma, the best integer equivariant (BIE) estimation, which makes a weighted sum of all possible candidates, has recently been attached great importance. The BIE solution approaches the float solution at a low ILS success rate, maintaining positioning reliability. As the success rate increases, it converges to the fixed solution, facilitating high-precision positioning. Furthermore, the posterior variance of BIE estimation provides the capability of reliability evaluation. However, in environments with a limited number or a deficient configuration of available satellites, there is a sharp decline in the strength of the GNSS precise positioning model. In this case, the exactness of weight allocation for integer candidates in BIE estimation will be severely compromised by unmodeled errors. When the ambiguity is incorrectly fixed, the wrongly determined optimal candidate is probably assigned an excessively high weight. Therefore, the BIE solution in a weak GNSS model always exhibits a significant positioning error consistent with the fixed solution. Moreover, the posterior variance of BIE estimation approximately resembles that of a fixed solution, losing error warning ability. Consequently, the BIE estimation may exhibit lower reliability compared to the ILS estimation employing a validation test with a loose acceptance threshold. To improve the positioning performance in weak GNSS models, a BIE ambiguity resolution (AR) method based on Tikhonov regularization is proposed in this paper. The method introduces Tikhonov regularization into the least squares (LS) estimation and the ILS ambiguity search, mitigating the serious impact of unmodeled errors on the BIE estimation under weak observation conditions. Meanwhile, the regularization factors are appropriately selected by utilizing an optimized approach established on the L-curve method. Simulation experiments and field tests have demonstrated that the method can significantly enhance the positioning accuracy and reliability in weak GNSS models. Compared to the traditional BIE estimation, the proposed method achieved accuracy improvements of 73.6% and 69.3% in the field tests with 10 km and 18 km baselines, respectively. Full article

To address the decline in measurement accuracy of magnetometers due to process errors and environmental interference, as well as the insufficient robustness of traditional calibration algorithms under strong interference conditions, this paper proposes an ellipsoid fitting algorithm based on Dynamic Adaptive Elite Particle Swarm Optimization (DAEPSO). The proposed algorithm integrates three enhancement mechanisms: dynamic stratified elite guidance, adaptive inertia weight adjustment, and inferior particle relearning via Lévy flight, aiming to improve convergence speed, solution accuracy, and noise resistance. First, a magnetometer calibration model is established. Second, the DAEPSO algorithm is employed to fit the ellipsoid parameters. Finally, error calibration is performed based on the optimized ellipsoid parameters. Our simulation experiments demonstrate that compared with the traditional Least Squares Method (LSM) the proposed method reduces the standard deviation of the total magnetic field intensity by 54.73%, effectively improving calibration precision in the presence of outliers. Furthermore, when compared to PSO, TSLPSO, MPSO, and AWPSO, the sum of the absolute distances from the simulation data to the fitted ellipsoidal surface decreases by 53.60%, 41.96%, 53.01%, and 27.40%, respectively. The results from 60 independent experiments show that DAEPSO achieves lower median errors and smaller interquartile ranges than comparative algorithms. In summary, the DAEPSO-based ellipsoid fitting algorithm exhibits high fitting accuracy and strong robustness in environments with intense interference noise, providing reliable theoretical support for practical engineering applications. Full article

Background/Objectives: The relationship between OSA and adult hypertension has been extensively studied; however, it remains understudied in pediatric patients without OSA. The aim of this study is to identify factors associated with pediatric hypertension without OSA, through an IRB-approved retrospective chart review of patients who underwent polysomnography at Nemours Children’s Hospital, DE/NJ between January 2020 and July 2023. Methods: Eligibility criteria included children 8–17 years, completed PSG, and clinic visit blood pressure (BP). Anthropometrics, demographics, social determinants, and medical history were obtained from electronic medical records. Hypertension was defined as the average systolic and/or diastolic BP that is ≥95th percentile for gender, age, and height based on AAP Clinical Practice Guidelines. All variables were checked for normality. Chi-square tests for categorical data and Wilcoxon rank sum tests for continuous data were used to test significance between non-OSA non-hypertensives (NH) and hypertensives (H). p < 0.05 is considered significant. Results: Of 285 charts evaluated, 137 were classified as non-OSA. Patient information, including parents in household, smoking exposure, and food allergies, were statistically significant (p < 0.05) in hypertensive pediatric patients without OSA. Hypertension was significantly correlated (p < 0.05) with birth weight, BMI, daytime heart rate, systolic BP, and diastolic BP. Statistically significant differences (p < 0.05) were found in mental illnesses, neurological disease, and respiratory disease. Among polysomnography parameters, only nighttime heart rate was found to be statistically significant. Conclusions: The data suggests that in pediatric patients without OSA, there are multiple factors and co-morbidities associated with hypertension. These factors and co-morbidities warrant additional follow up in clinical practice to mitigate the risks of hypertension in pediatric patients. Full article

Energy resources are abundant in high-altitude regions, and the construction of power transmission and transformation projects has important value. However, harsh natural environments can increase project costs. To address the issue of insufficient accuracy caused by the impact of extreme weather factors on cost predictions for power transmission and transformation projects in high-altitude regions, this paper first constructs a four-dimensional influencing factor system covering climate and environment, engineering scale, material consumption, and technological economy. On this basis, a hybrid deep-learning model combining an improved whale optimization algorithm (IWOA) and a convolutional neural network (CNN) is then proposed. The model improves the training accuracy of CNNs and avoids falling into local optima through the use of an SGDM optimizer, the L2 regularization method, and the Bayesian optimization method. Nonlinear convergence factors and adaptive weights are introduced to enhance the WOA’s ability to optimize the CNN’s learning rate. The case analysis results show that, compared with the comparison model, the proposed IWOA-CNN model exhibits a better convergence performance and fitting effect in the training set and a better prediction effect on the test set. Its mean absolute percentage error is as low as 1.51%, which is 10.1% lower than the optimal comparison model. The root mean square error is reduced to 5.07, and the sum of squared errors is reduced by 72.4%, demonstrating high prediction accuracy. The comparative analysis of scenarios further confirms the crucial role of climate environment; that is, the prediction accuracy of models containing a climate dimension is improved by 51.6% compared to models without such a climate dimension, indicating that the nonlinear impact of low temperatures, frozen soil, and other characteristics of high-altitude regions on costs cannot be ignored. The research results of this paper enrich the method system and application scenarios for the cost prediction for power transmission and transformation projects and provide theoretical reference for engineering predictions in other complex geographical environments. Full article

To address the challenges in forecasting crude oil and hot-rolled coil futures prices, the aim is to transcend the constraints of conventional approaches. This involves effectively predicting short-term price fluctuations, developing quantitative trading strategies, and modeling time series data. The goal is to enhance prediction accuracy and stability, thereby supporting decision-making and risk management in financial markets. A novel approach, the multi-dimensional fusion feature-enhanced (MDFFE) prediction method has been devised. Additionally, a data augmentation framework leveraging multi-dimensional feature engineering has been established. The technical indicators, volatility indicators, time features, and cross-variety linkage features are integrated to build a prediction system, and the lag feature design is used to prevent data leakage. In addition, a deep fusion model is constructed, which combines the temporal feature extraction ability of the convolution neural network with the nonlinear mapping advantage of an extreme gradient boosting tree. With the help of a three-layer convolution neural network structure and adaptive weight fusion strategy, an end-to-end prediction framework is constructed. Experimental results demonstrate that the MDFFE model excels in various metrics, including mean absolute error, root mean square error, mean absolute percentage error, coefficient of determination, and sum of squared errors. The mean absolute error reaches as low as 0.0068, while the coefficient of determination can be as high as 0.9970. In addition, the significance and stability of the model performance were verified by statistical methods such as a paired t-test and ANOVA analysis of variance. This MDFFE algorithm offers a robust and practical approach for predicting commodity futures prices. It holds significant theoretical and practical value in financial market forecasting, enhancing prediction accuracy and mitigating forecast volatility. Full article

When performing linear fitting on datasets containing outliers, common algorithms may face problems like inadequate fitting accuracy. We propose a linear fitting algorithm based on Locality-Sensitive Hashing (LSH) and Random Sample Consensus (RANSAC). Our algorithm combines the efficient similarity search capabilities of the LSH algorithm with the robust fitting mechanism of RANSAC. With proper hash functions designed, similar data points are mapped to the same hash bucket, thereby enabling the efficient identification and removal of outliers. RANSAC is then used to fit the model parameters of the processed dataset. The optimal parameters for the linear model are obtained after multiple iterative processes. This algorithm significantly reduces the influence of outliers on the dataset, resulting in improved fitting accuracy and enhanced robustness. Experimental results demonstrate that the proposed improved RANSAC linear fitting algorithm outperforms the Weighted Least Squares, traditional RANSAC, and Maximum Likelihood Estimation methods, achieving a reduction in the sum of squared residuals by 29%, 16%, and 8%, respectively. Full article

This paper presents a simplified and computationally feasible multivariate extension. A correlation matrix is constructed using pairwise Spearman’s footrule correlation coefficients, and these coefficients are shown to jointly converge to a multivariate normal distribution. A global test statistic based on the Frobenius norm of this matrix asymptotically follows a weighted sum of chi-square distributions. Simulation studies and two real-world applications (a sensory analysis of French Jura wines and the characterization of plant leaf specimens) demonstrate the practical utility of the proposed method, bridging the gap between theoretical rigor and practical implementation in multivariate nonparametric inference. Full article

This study seeks to optimize the shape of a wave absorber with an inclined porous plate using an artificial neural network (ANN) model to improve the operating efficiency and experimental accuracy of a square wave basin. As our numerical tool, we employed the dual boundary element method (DBEM) to avoid the rank deficiency problem occurring at the degenerate plate boundary with zero thickness. A quadratic velocity model incorporating a CFD-based drag coefficient was employed to account for energy dissipation across the porous plate. The developed DBEM tool was validated through comparisons with self-conducted experiments in a two-dimensional wave flume. The input features such as the inclined angle and plate length affect the performance of the wave absorber. These features have been optimized to minimize the averaged reflection coefficient and the installation space (spatial footprint) with the application of a trained ANN model. The dataset used for training the ANN model was created using the DBEM model. The trained model was subsequently utilized to predict the averaged reflection coefficient using a larger dataset, aiding in the determination of the optimal wave absorber design. In the optimization process of minimizing both reflected waves and spatial footprint, the weighting factors are assigned according to their relative importance to each other, using the weighted sum model (WSM) within the multi-criteria decision-making framework. It was found that the optimal design parameters of the non-dimensional plate length ($l/h$) and inclined angle ($\theta$) are $1.46$ and $5.34°$ when performing with a weighting factor ratio (80%: 20%) between reflection and spatial footprint. Full article

Background: Thrombospondin-1 (TSP-1) is an extracellular glycoprotein that mediates the differentiation of pulmonary endothelial cells and specialized stem cells into alveolar epithelial lineage-specific cells during the repair phase after lung injury. Since bronchopulmonary dysplasia (BPD) involves the inhibition of lung development with altered lung structure and vasculature, differential expression of the THBS-1 gene may impact lung development and pulmonary endothelial cell repair and have an important role in BPD. Methods: This prospective single-center cohort study included ELBW infants with and without BPD. DNA from buccal swabs underwent RT-PCR with TaqMan probes, and TSP-1 protein was measured in tracheal aspirates. Statistical analyses used Chi-square tests, Fisher’s exact tests, Wilcoxon Rank Sum tests, and t-tests (p < 0.05). Results: ELBW infants with BPD had significantly lower gestational ages and birth weights compared to those without BPD [25 (24,26) and 27 (25,28) weeks; median (IQR); p = 0.008] and [712 (155) and 820 (153) grams; mean (SD); p = 0.002], respectively. There were significant differences in the haplotype distributions of THBS1 variants rs2664139/rs1478604 (p = 0.006) and THBS1 variants rs1478605/rs1478604 (p = 0.008) between no-BPD and BPD groups. There were also significant differences in airway TSP-1 protein levels between moderate and severe BPD patients [(p = 0.02) (no BPD: 527 (114–1755); moderate BPD: 312 (262–641); and severe BPD 211: (117–352) ng/dL; median (IQR)]. Conclusions: Although no individual variants differed, two THBS1 haplotypes and early TSP-1 airway expression varied by BPD severity, suggesting a role for TSP-1 in lung development and BPD pathogenesis in ELBW infants. Full article

Accurate area estimation of specific land cover/use types in administrative or natural units is crucial for various applications. However, land cover areas derived directly from classification maps of remote sensing via pixel counting often exhibit non-negligible bias. Thus, various design-based area estimators (e.g., bias-adjusted estimator, model-assisted difference estimator, model-assisted ratio estimator derived from confusion matrix), which combine the information of ground truth samples and the classification map, have been applied to provide more accurate area estimates and the uncertainty inference. These estimators work well for estimating areas in a region with sufficient ground truth samples, whereas they encounter challenges when estimating areas in multiple subregions where the samples are limited within each subregion. To overcome this limitation, we propose a novel Bias-Adjusted Estimator based on the Synthetic Confusion Matrix (BAESCM) for estimating land cover areas in subregions by downscaling the global sample information to the subregion scale. First, several clusters were generated from remote sensing data through the K-means method (with the number of clusters being much smaller than the number of subregions). Then, the cluster confusion matrix is estimated based on the samples in each cluster. Assuming that the classification error distribution within each cluster remains consistent across different subregions, the confusion matrix of the subregion can be synthesized by a weighted sum of the cluster confusion matrices, with the weights of the cluster abundances in the subregion. Finally, the classification bias at the subregion scale can be estimated based on the synthetic confusion matrix, and the area counted from the classification map is corrected accordingly. Moreover, we introduced a semi-empirical method for inferring the confidence intervals of the estimated areas, considering both the sampling variance due to sampling randomness and the downscaling variance due to the heterogeneity in classification error distribution within the cluster. We tested our method through simulated experiments for county-level area estimation of soybean crops in Nebraska State, USA. The results show that the root mean square errors (RMSEs) of the subregion area estimates using BAESCM are reduced by 21–64% compared to estimates based on pixel counting from the classification map. Additionally, the true coverages of the confidence intervals estimated by our method approximately matched their nominal coverages. Compared with traditional design-based estimators, the proposed BAESCM achieves better estimation accuracy of subregion areas when the sample size is limited. Therefore, the proposed method is particularly recommended for studies regarding subregion land cover areas in the case of inadequate ground truth samples. Full article