AMGNET: multi-scale graph neural networks for flow field prediction

Solving partial differential equations of complex physical systems is a computationally expensive task, especially in Computational Fluid Dynamics(CFD). This drives the application of deep learning methods in solving physical systems. There exist a few deep learning models that are very successful in predicting flow fields of complex physical models, yet most of these still exhibit large errors compared to simulation. Here we introduce AMGNET, a multi-scale graph neural network model based on Encoder-Process-Decoder structure for flow field prediction. Our model employs message passing of graph neural networks at different mesh graph scales. Our method has significantly lower prediction errors than the GCN baseline on several complex fluid prediction tasks, such as airfoil flow and cylinder flow. Our results show that multi-scale representation learning at the graph level is more effective in improving the prediction accuracy of flow field

Fusion algorithm of visible and infrared image based on anisotropic diffusion and image enhancement (capitalize only the first word in a title (or heading), the first word in a subtitle (or subheading), and any proper nouns).

Aiming at the situation that the existing visible and infrared images fusion algorithms only focus on highlighting infrared targets and neglect the performance of image details, and cannot take into account the characteristics of infrared and visible images, this paper proposes an image enhancement fusion algorithm combining Karhunen-Loeve transform and Laplacian pyramid fusion. The detail layer of the source image is obtained by anisotropic diffusion to get more abundant texture information. The infrared images adopt adaptive histogram partition and brightness correction enhancement algorithm to highlight thermal radiation targets. A novel power function enhancement algorithm that simulates illumination is proposed for visible images to improve the contrast of visible images and facilitate human observation. In order to improve the fusion quality of images, the source image and the enhanced images are transformed by Karhunen-Loeve to form new visible and infrared images. Laplacian pyramid fusion is performed on the new visible and infrared images, and superimposed with the detail layer images to obtain the fusion result. Experimental results show that the method in this paper is superior to several representative image fusion algorithms in subjective visual effects on public data sets. In terms of objective evaluation, the fusion result performed well on the 8 evaluation indicators, and its own quality was high

Incremental values of AOPP, IL-6, and GDF15 for identifying arteriosclerosis in patients with obstructive sleep apnea

Abstract Background The objective of this study was to determine the independent and incremental values of advanced oxidative protein product (AOPP), interleukin 6 (IL-6), and growth differentiation factor 15 (GDF15) in identifying arteriosclerosis in patients with obstructive sleep apnea (OSA). Methods A total of 104 individuals diagnosed with OSA by polysomnography were recruited in our study. Arteriosclerosis was defined by measuring the ultrafast pulse wave velocity of the carotid artery. Peripheral venous blood samples were collected to analyze the levels of AOPP, IL-6, and GDF15 utilizing commercially available enzyme-linked immunosorbent assays. Results Compared to OSA patients without arteriosclerosis, those with arteriosclerosis exhibited significantly higher levels of AOPP, IL-6, and GDF15. GDF15 remained significantly associated with arteriosclerosis even after accounting for clinical factors such as age, gender, body mass index, systolic blood pressure, fasting blood glucose, smoking, and the apnea–hypoxia index (AHI). GDF15 demonstrated the largest area under the curve (AUC) for identifying arteriosclerosis in OSA patients (AUC, 0.85 [0.77–0.94]). The logistic regression model, combining clinical factors and AHI, was enhanced by the inclusion of AOPP and IL-6 (Chi-square = 25.06), and even further improved when GDF15 was added (Chi-square = 50.74). The integrated discrimination index increased by 0.06 to 0.16 when GDF15 was added to the models including clinical factors, AOPP, and IL-6. Conclusions This study verified the independent and incremental value of GDF15 in identifying arteriosclerosis in OSA patients, surpassing clinical risk factors and other serum biomarkers such as AOPP and IL-6

Additional file 1 of Incremental values of AOPP, IL-6, and GDF15 for identifying arteriosclerosis in patients with obstructive sleep apnea

Additional file 1. Additional figures and tables