Marine centrifugal pumps are mostly used on board ship, for transferring liquid from one point to another. Based on the combination of orthogonal testing and numerical simulation, this paper optimizes the structure of a drainage trough for a typical low-specific speed centrifugal pump, determines the priority of the various geometric factors of the drainage trough on the pump performance, and obtains the optimal impeller drainage trough scheme. The influence of drainage tank structure on the internal flow of a low-specific speed centrifugal pump is also analyzed. First, based on the experimental validation of the initial model, it is determined that the numerical simulation method used in this paper is highly accurate in predicting the performance of low-specific speed centrifugal pumps. Secondly, based on the three factors and four levels of the impeller drainage trough in the orthogonal test, the orthogonal test plan is determined and the orthogonal test results are analyzed. This work found that slit diameter and slit width have a large impact on the performance of low-specific speed centrifugal pumps, while long and short vane lap lengths have less impact. Finally, we compared the internal flow distribution between the initial model and the optimized model, and found that the slit structure could effectively reduce the pressure difference between the suction side and the pressure side of the blade. By weakening the large-scale vortex in the flow path and reducing the hydraulic losses, the drainage trough impellers obtained based on orthogonal tests can significantly improve the hydraulic efficiency of low-specific speed centrifugal pumps.

Accurate segmentation of retinal vessels is an essential prerequisite for the subsequent analysis of fundus images. Recently, a number of methods based on deep learning have been proposed and shown to demonstrate promising segmentation performance, especially U-Net and its variants. However, tiny vessels and low-contrast vessels are hard to detect due to the issues of a loss of spatial details caused by consecutive down-sample operations and inadequate fusion of multi-level features caused by vanilla skip connections. To address these issues and enhance the segmentation precision of retinal vessels, we propose a novel high-resolution network with strip attention. Instead of the U-Net-shaped architecture, the proposed network follows an HRNet-shaped architecture as the basic network, learning high-resolution representations throughout the training process. In addition, a strip attention module including a horizontal attention mechanism and a vertical attention mechanism is designed to obtain long-range dependencies in the horizontal and vertical directions by calculating the similarity between each pixel and all pixels in the same row and the same column, respectively. For effective multi-layer feature fusion, we incorporate the strip attention module into the basic network to dynamically guide adjacent hierarchical features. Experimental results on the DRIVE and STARE datasets show that the proposed method can extract more tiny vessels and low-contrast vessels compared with existing mainstream methods, achieving accuracies of 96.16% and 97.08% and sensitivities of 82.68% and 89.36%, respectively. The proposed method has the potential to aid in the analysis of fundus images.

Analyzing heart sound signals presents a novel approach for early diagnosis of pediatric congenital heart disease. The existing segmentation algorithms have limitations in accurately distinguishing the first (S1) and second (S2) heart sounds, limiting the diagnostic utility of cardiac cycle data for pediatric pathology assessment. This study proposes a time bidirectional long short-term memory network (TBLSTM) based on multi-scale analysis to segment pediatric heart sound signals according to different cardiac cycles. Mel frequency cepstral coefficients and dynamic characteristics of the heart sound fragments were extracted and input into random forest for multi-classification of congenital heart disease. The segmentation model achieved an overall F1 score of 94.15% on the verification set, with specific F1 scores of 90.25% for S1 and 86.04% for S2. In a situation where the number of cardiac cycles in the heart sound fragments was set to six, the results for multi-classification achieved stabilization. The performance metrics for this configuration were as follows: accuracy of 94.43%, sensitivity of 95.58%, and an F1 score of 94.51%. Furthermore, the segmentation model demonstrates robustness in accurately segmenting pediatric heart sound signals across different heart rates and in the presence of noise. Notably, the number of cardiac cycles in heart sound fragments directly impacts the multi-classification of these heart sound signals.