In Silico Syndrome Prediction for Coronary Artery Disease in Traditional Chinese Medicine

Coronary artery disease (CAD) is the leading causes of deaths in the world. The differentiation of syndrome (ZHENG) is the criterion of diagnosis and therapeutic in TCM. Therefore, syndrome prediction in silico can be improving the performance of treatment. In this paper, we present a Bayesian network framework to construct a high-confidence syndrome predictor based on the optimum subset, that is, collected by Support Vector Machine (SVM) feature selection. Syndrome of CAD can be divided into asthenia and sthenia syndromes. According to the hierarchical characteristics of syndrome, we firstly label every case three types of syndrome (asthenia, sthenia, or both) to solve several syndromes with some patients. On basis of the three syndromes' classes, we design SVM feature selection to achieve the optimum symptom subset and compare this subset with Markov blanket feature select using ROC. Using this subset, the six predictors of CAD's syndrome are constructed by the Bayesian network technique. We also design Naïve Bayes, C4.5 Logistic, Radial basis function (RBF) network compared with Bayesian network. In a conclusion, the Bayesian network method based on the optimum symptoms shows a practical method to predict six syndromes of CAD in TCM

Preparation and Characterization of Esterified Bamboo Flour by an In Situ Solid Phase Method

Bamboo plastic composites have become a hot research topic and a key focus of research. However, many strong, polar, hydrophilic hydroxyl groups in bamboo flour (BF) results in poor interfacial compatibility between BF and hydrophobic polymers. Maleic anhydride-esterified (MAH-e-BF) and lactic acid-esterified bamboo flour (LA-e-BF) were prepared while using an in situ solid-phase esterification method with BF as the raw material and maleic anhydride or lactic acid as the esterifying agent. Fourier transform infrared spectroscopy results confirmed that BF esterification with maleic anhydride and lactic acid was successful, with the esterification degrees of MAH-e-BF and LA-e-BF at 21.04 ± 0.23% and 14.28 ± 0.17%, respectively. Esterified BF was characterized by scanning electron microscopy, contact angle testing, X-ray diffractometry, and thermogravimetric analysis. The results demonstrated that esterified BF surfaces were covered with graft polymer and the surface roughness and bonding degree of MAH-e-BF clearly larger than those of LA-e-BF. The hydrophobicity of esterified BF was significantly higher than BF and the hydrophobicity of MAH-e-BF was better than LA-e-BF. The crystalline structure of esterified BF showed some damage, while MAH-e-BF exhibited a greater decrease in crystallinity than LA-e-BF. Overall, the esterification reaction improved BF thermoplasticity, with the thermoplasticity of MAH-e-BF appearing to be better than LA-e-BF

A Lightweight Object Detection Method in Aerial Images Based on Dense Feature Fusion Path Aggregation Network

In recent years, significant progress has been obtained in object detection using Convolutional Neural Networks (CNNs). However, owing to the particularity of Remote Sensing Images (RSIs), common object detection methods are not well suited for RSIs. Aiming at the difficulties in RSIs, this paper proposes an object detection method based on the Dense Feature Fusion Path Aggregation Network (DFF-PANet). Firstly, for better improving the detection performance of small and medium-sized instances, we propose Feature Reuse Module (FRM), which can integrate semantic and location information contained in feature maps; this module can reuse feature maps in the backbone to enhance the detection capability of small and medium-sized instances. After that, we design the DFF-PANet, which can help feature information extracted from the backbone to be fused more efficiently, and thus cope with the problem of external interference factors. We performed experiments on the Dataset of Object deTection in Aerial images (DOTA) dataset and the HRSC2016 dataset; the accuracy reached 71.5% mAP, which exceeds most object detectors of one-stage and two-stages at present. Meanwhile, the size of our model is only 9.2 M, which satisfies the requirement of being lightweight. The experimental results demonstrate that our method not only has better detection accuracy but also maintains high efficiency in RSIs

Mechanism Analysis and Experimental Validation of Employing Superconducting Magnetic Energy Storage to Enhance Power System Stability

This paper investigates the mechanism analysis and the experimental validation of employing superconducting magnetic energy storage (SMES) to enhance power system stability. The models of the SMES device and the single-machine infinite-bus (SMIB) system with SMES are deduced. Based on the model of the SMIB system with SMES, the action mechanism of SMES on a generator is analyzed. The analysis takes the impact of SMES location and the system operating point into consideration, as well. Based on the mechanism analysis, the P-controller and Q-controller are designed utilizing the phase compensation method to improve the damping of the SMIB system. The influence of factors, such as SMES location, transmission system reactance, the dynamic characteristics of SMES and the system operating point, on the damping improvement of SMES, is investigated through root locus analysis. The simulation results of the SMIB test system verify the analysis conclusions and controller design method. The laboratory results of the 150-kJ/100-kW high-temperature SMES (HT-SMES) device validate that the SMES device can effectively enhance the damping, as well as the transient stability of the power system

A Lightweight Object Detection Method in Aerial Images Based on Dense Feature Fusion Path Aggregation Network

In recent years, significant progress has been obtained in object detection using Convolutional Neural Networks (CNNs). However, owing to the particularity of Remote Sensing Images (RSIs), common object detection methods are not well suited for RSIs. Aiming at the difficulties in RSIs, this paper proposes an object detection method based on the Dense Feature Fusion Path Aggregation Network (DFF-PANet). Firstly, for better improving the detection performance of small and medium-sized instances, we propose Feature Reuse Module (FRM), which can integrate semantic and location information contained in feature maps; this module can reuse feature maps in the backbone to enhance the detection capability of small and medium-sized instances. After that, we design the DFF-PANet, which can help feature information extracted from the backbone to be fused more efficiently, and thus cope with the problem of external interference factors. We performed experiments on the Dataset of Object deTection in Aerial images (DOTA) dataset and the HRSC2016 dataset; the accuracy reached 71.5% mAP, which exceeds most object detectors of one-stage and two-stages at present. Meanwhile, the size of our model is only 9.2 M, which satisfies the requirement of being lightweight. The experimental results demonstrate that our method not only has better detection accuracy but also maintains high efficiency in RSIs

Feature Enhancement-Based Ship Target Detection Method in Optical Remote Sensing Images

Ship targets in ORSIs (Optical Remote Sensing Images) have the characteristics of various scales, and most of them are medium and small-scale targets. When the existing target detection algorithms are applied to ship target detection in ORSIs, the detection accuracy is low. There are two main reasons for the above problems, one is the mismatch of the receptive fields, and the other is the lack of feature information. For resolving the problem that multi-scale ship targets are difficult to detect, this paper proposes a ship target detection algorithm based on feature enhancement. Firstly, EIRM (Elastic Inception Residual Module) is proposed for feature enhancement, which can capture feature information of different dimensions and provide receptive fields of different scales for mid- and low-level feature maps. Secondly, the SandGlass-L block is proposed by replacing the ReLu6 activation function of the SandGlass block with the Leaky ReLu activation function. Leaky ReLu solves the problem of 0 output when ReLu6 has negative input, so the SandGlass-L block can retain more feature information. Finally, based on SandGlass-L, SGLPANet (SandGlass-L Path Aggregation Network) is proposed to alleviate the problem of information loss caused by dimension transformation and retain more feature information. The backbone network of the algorithm in this paper is CSPDarknet53, and the SPP module and EIRM act after the backbone network. The neck network is SGLPANet. Experiments on the NWPU VHR-10 dataset show that the algorithm in this paper can well solve the problem of low detection accuracy caused by mismatched receptive fields and missing feature information. It not only improves the accuracy of ship target detection, but also achieves good results when extended to other categories. At the same time, the extended experiments on the LEVIR dataset show that the algorithm also has certain applicability on different datasets

Ultimate Bearing Capacity of Bottom Sealing Concrete in Underwater Deep Foundation Pit: Theoretical Calculation and Numerical Analysis

The cofferdam method is generally applied in the construction of underwater pier foundation in bridge engineering, and the pouring of bottom sealing concrete is one of the important links in the construction of the cofferdam. The bottom sealing concrete can prevent water seepage and balance the main body of the cofferdam, and its structural size and construction quality have a great influence on the above functions. Under the condition of large water level difference, it is difficult to determine the reasonable thickness of the bottom sealing concrete. There are few related studies in this field, and there is a lack of systematic summary of calculation theory. This work theoretically deduces the approximate solution of ultimate bending moment and ultimate stress of the bottom sealing concrete, introduces two different calculation methods, systematically summarizes the calculation methods of three kinds of ultimate stress, analyzes the calculation methods of ultimate bonding force, and uses ANSYS finite element software to simulate a specific bottom sealing concrete model, and compares it with the theoretical calculation results. The maximum stress obtained by the approximate solution is closer to the actual monitoring data than the traditional method, and the calculation method of the bonding force can be used to make a rough estimate

Feature Enhancement-Based Ship Target Detection Method in Optical Remote Sensing Images

Ship targets in ORSIs (Optical Remote Sensing Images) have the characteristics of various scales, and most of them are medium and small-scale targets. When the existing target detection algorithms are applied to ship target detection in ORSIs, the detection accuracy is low. There are two main reasons for the above problems, one is the mismatch of the receptive fields, and the other is the lack of feature information. For resolving the problem that multi-scale ship targets are difficult to detect, this paper proposes a ship target detection algorithm based on feature enhancement. Firstly, EIRM (Elastic Inception Residual Module) is proposed for feature enhancement, which can capture feature information of different dimensions and provide receptive fields of different scales for mid- and low-level feature maps. Secondly, the SandGlass-L block is proposed by replacing the ReLu6 activation function of the SandGlass block with the Leaky ReLu activation function. Leaky ReLu solves the problem of 0 output when ReLu6 has negative input, so the SandGlass-L block can retain more feature information. Finally, based on SandGlass-L, SGLPANet (SandGlass-L Path Aggregation Network) is proposed to alleviate the problem of information loss caused by dimension transformation and retain more feature information. The backbone network of the algorithm in this paper is CSPDarknet53, and the SPP module and EIRM act after the backbone network. The neck network is SGLPANet. Experiments on the NWPU VHR-10 dataset show that the algorithm in this paper can well solve the problem of low detection accuracy caused by mismatched receptive fields and missing feature information. It not only improves the accuracy of ship target detection, but also achieves good results when extended to other categories. At the same time, the extended experiments on the LEVIR dataset show that the algorithm also has certain applicability on different datasets

Supplementary document for Structural Design of an Improved SPIDER Optical System Based on Multimode Interference Coupler - 6615999.pdf

Regarding the derivation of formulas (9) and (10