Simulation of wave propagation in plate structures by using new spectral element with piezoelectric coupling

This paper presents an efficient approach to simulate Lamb wave propagations in thin plate structures by using new time domain spectral plate elements. A novel approach is proposed to incorporate the coupling of piezoelectric transducers within the two-dimensional plate element. The diagonal mass matrix is obtained by using a simple method with less computational effort. Detailed formulations are given. The benchmark problem of a thin aluminum plate with two surface-mounted piezoelectric transducers was investigated in detail. Comparisons are made with results obtained by using ABAQUS to verify the developed spectral plate element. It is shown that the proposed element can efficiently simulate the propagation of Lamb wave generated by a piezoelectric actuator and picked up by a piezoelectric sensor

Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding scheme

BACKGROUND: Gene expression profiling has become a useful biological resource in recent years, and it plays an important role in a broad range of areas in biology. The raw gene expression data, usually in the form of large matrix, may contain missing values. The downstream analysis methods that postulate complete matrix input are thus not applicable. Several methods have been developed to solve this problem, such as K nearest neighbor impute method, Bayesian principal components analysis impute method, etc. In this paper, we introduce a novel imputing approach based on the Support Vector Regression (SVR) method. The proposed approach utilizes an orthogonal coding input scheme, which makes use of multi-missing values in one row of a certain gene expression profile and imputes the missing value into a much higher dimensional space, to obtain better performance. RESULTS: A comparative study of our method with the previously developed methods has been presented for the estimation of the missing values on six gene expression data sets. Among the three different input-vector coding schemes we tried, the orthogonal input coding scheme obtains the best estimation results with the minimum Normalized Root Mean Squared Error (NRMSE). The results also demonstrate that the SVR method has powerful estimation ability on different kinds of data sets with relatively small NRMSE. CONCLUSION: The SVR impute method shows better performance than, or at least comparable with, the previously developed methods in present research. The outstanding estimation ability of this impute method is partly due to the use of the most missing value information by incorporating orthogonal input coding scheme. In addition, the solid theoretical foundation of SVR method also helps in estimation of performance together with orthogonal input coding scheme. The promising estimation ability demonstrated in the results section suggests that the proposed approach provides a proper solution to the missing value estimation problem. The source code of the SVR method is available from for non-commercial use

A Review on Remanufacturing Reverse Logistics Network Design and Model Optimization

Remanufacturing has gained great recognition in recent years due to its economic and environmental benefits and effectiveness in the value retention of waste products. Many studies on reverse logistics have considered remanufacturing as a key node for network optimization, but few literature reviews have explicitly mentioned remanufacturing as a main feature in their analysis. The aim of this review is to bridge this gap. In total, 125 papers on remanufacturing reverse logistics network design have been reviewed and conclusions have been drawn from four aspects: (1) in terms of network structure, the functional nodes of new hybrid facilities and the network structure combined with the remanufacturing technologies of products are the key points in the research. (2) In the mathematical model, the multi-objective function considered from different aspects, the uncertainty of recovery time and recovery channel in addition to quantity and quality, and the selection of appropriate algorithms are worth studying. (3) While considering product types, the research of a reverse logistics network of some products is urgently needed but inadequate, such as medical and furniture products. (4) As for cutting-edge technologies, the application of new technologies, such as intelligent remanufacturing technology and big data, will have a huge impact on the remanufacturing of a reverse logistics network and needs to be considered in our research

Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric Chest X-ray images

Model initialization techniques are vital for improving the performance and reliability of deep learning models in medical computer vision applications. While much literature exists on non-medical images, the impacts on medical images, particularly chest X-rays (CXRs) are less understood. Addressing this gap, our study explores three deep model initialization techniques: Cold-start, Warm-start, and Shrink and Perturb start, focusing on adult and pediatric populations. We specifically focus on scenarios with periodically arriving data for training, thereby embracing the real-world scenarios of ongoing data influx and the need for model updates. We evaluate these models for generalizability against external adult and pediatric CXR datasets. We also propose novel ensemble methods: F-score-weighted Sequential Least-Squares Quadratic Programming (F-SLSQP) and Attention-Guided Ensembles with Learnable Fuzzy Softmax to aggregate weight parameters from multiple models to capitalize on their collective knowledge and complementary representations. We perform statistical significance tests with 95% confidence intervals and p-values to analyze model performance. Our evaluations indicate models initialized with ImageNet-pre-trained weights demonstrate superior generalizability over randomly initialized counterparts, contradicting some findings for non-medical images. Notably, ImageNet-pretrained models exhibit consistent performance during internal and external testing across different training scenarios. Weight-level ensembles of these models show significantly higher recall (p<0.05) during testing compared to individual models. Thus, our study accentuates the benefits of ImageNet-pretrained weight initialization, especially when used with weight-level ensembles, for creating robust and generalizable deep learning solutions.Comment: 40 pages, 8 tables, 7 figures, 3 supplementary figures and 4 supplementary table

Chinese international students in the United States: The interplay of students’ acculturative stress, academic standing, and quality of life

Background: Acculturation could cause grave health consequences in international students. However, there is a shortage of research into how acculturative stress might affect international students’ quality of life in light of their academic standing and experience. The lack of research is particularly pronounced among Chinese international students, representing the largest body of international students studying in the United States (U.S.). Thus, to bridge the research gap, this study aims to examine the interplay between international students’ acculturative stress, academic standing, and quality of life among a nationally representative sample of Chinese international students studying in the United States. Methods: An online survey that gauges Chinese international students’ levels of acculturative stress, academic standing, and quality of life was developed. Over 350 higher education institutions across the United States were approached, including public universities, private universities, and community colleges, among which approximately 220 institutions responded positively and supported survey distribution. A total of 751 students completed the survey. Multiple regression analyses were carried out to examine the associations between students’ acculturative stress, academic standing, and quality of life. Results: Findings reveal that acculturative stress negatively affects all four domains of Chinese international students’ quality of life, irrespective of their academic standing. Data analyses also show that compared to master’s and doctoral students, undergraduates experience the highest levels of acculturative stress. Furthermore, a significant difference emerged among undergraduate and doctoral international students’ acculturative stress levels, but not among undergraduate and master’s students, or master’s and doctoral students. Conclusion: Our study found that, compared to master’s and doctoral students, undergraduates had more significant acculturative stress associated with lower levels of quality of life. This finding highlights the potentially positive role of academic experience – while acculturative stress deteriorates international students’ quality of life, students’ academic standing and experience could be the protective factor in the equation. Future research could further examine how universities and colleges can capitalize on their academic apparatuses and resources to improve international students’ academic performance and students’ acculturation experience and quality of life