Heterogeneity of abdominal obesity in patients with cardiovascular diseases

Aim: To assess the content of visceral adipose tissue (VAT) in patients with abdominal obesity and its relationship with metabolic disorders.Material and methods. Patients with abdominal obesity (n=107) were included in the study. All participants had an assessment of anthropometric parameters (height, weight), calculation of body mass index (BMI), proportion of total adipose tissue and VAT (bioimpedance analyzer), high-density lipoprotein cholesterol (HDL-c) levels, triglycerides, fasting blood glucose, epicardial thickness adipose tissue (two-dimensional echocardiography).Results. The median share of VAT (bioimpedance method) was 13%. Patients with abdominal obesity are divided by VAT into 2 groups: ≥14% or ≤13%. Patients with VAT≥14% had significantly higher levels of triglycerides (1.76 [1.27; 2.38] mmol / L) and glucose (6.33 [5.78; 7.87] mmol / L), and below HDL-c levels (0.95 [0.85; 1.21] mmol / L) compared with patients with VAT≤13% (1.32 [1.02; 1.50], 5.59 [5, 11; 6.16] and 1.31 [1.07; 1.58] mmol / L, respectively; p<0.001 for all three comparisons). A significant correlation was found between VAT and triglyceride, glucose and HDL-c levels (r=0.40; r=0.40; r=-0.31, respectively; p<0.001).Conclusion. Persons with abdominal obesity are heterogeneous in the proportion of VAT. The proportion of VAT above the median is associated with metabolic disorders that are significant for the development and progression of atherosclerosis. An increase in BMI in obese individuals is not associated with an increase in VAT and an increase in the severity of metabolic disorders.

Research on Evaluation Model of Hospital Informatization Level Based on Decision Tree Algorithm

In order to improve the weight calculation accuracy of hospital informatization level evaluation and shorten the evaluation time, a research method of hospital informatization level evaluation model based on the decision tree algorithm is proposed. Using the decision tree algorithm combining fuzzy theory and ID3, the decision tree is constructed to analyze the hospital information data. By means of questionnaire survey, expert experience, mathematical statistics, and in-depth interview, information facilities construction, information resources construction, information scientific research application, management information, and information guarantee are selected as the nodes of the decision tree to evaluate the hospital information level. Construct the structural equation model, standardize the data, extract the weight of each evaluation index, and complete the evaluation of hospital informatization level. The experimental results show that the weight calculation results of this method are basically consistent with the actual results, and the evaluation efficiency is improved.

Research on maintenance method of relay protection state based on combination weight and gray relation

Relatively independent evaluation parameters are selected from many parameters through pedigree clustering.Learning the analytic hierarchy process (ahp) and entropy weight method can determine the weight, and at the same time to understand the error of the analytic hierarchy process (ahp) and entropy weight method is large, so the combination of the subjective and objective weight obtained by the two methods, using the improved entropy weight-ahp method to determine the weight. The improved weight calculation method has a clear hierarchical structure, which not only considers the influence of subjective and objective factors, but also makes full use of the weight information in the hierarchical structure. Considering the uncertainty of information, gray relation is adopted to deal with the data, so as to make maintenance rules.

Using Ensemble and TOPSIS with AHP for Classification and Selection of Web Services

Due to the increasing number of Web Services with the same functionality, selecting a Web Service that best serves the needs of the Web Client has become a tremendously challenging task. Present approaches use non-functional parameters of the Web Services but they do not consider any preprocessing of the set of functionally Similar Web Services. The lack of preprocessing results in increased use of computational resources due to unnecessary processing of Web Services that have a very low to no chance of satisfying the consumer’s requirements. In this paper, we propose an Ensemble classification method for preprocessing and a Web Service Selection method based on the Quality of Service (QoS) parameters. Once the most eligible Web Services are enumerated through classification, they are ranked using the Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) method with Analytic Hierarchy Process (AHP) used for weight calculation. A prototype of the method is developed, and experiments are conducted on a real-world Web Services dataset. Results demonstrate the feasibility of the proposed method.

Modeling and Analysis of Football Players’ Specific Physical Ability Based on Training Evaluation Index

In order to accurately and efficiently evaluate the effect of special physical training for football players, modeling and analysis of evaluation indexes of special physical training for football players were mainly carried out. This paper analyzes the construction principle of the evaluation index system of football players’ special physical training, determines the evaluation primary index system, and constructs the evaluation index system of football players’ special physical training through comparison and screening. On this basis, the analytic hierarchy process is used to calculate the weight of the evaluation index, according to the weight calculation results of the evaluation index modeling using multilevel fuzzy comprehensive evaluation so as to get the evaluation results of the special physical training of football players. The experimental results show that the evaluation results of football players’ special physical training are consistent with the expert evaluation results, and the evaluation time is short, which can realize the accurate and efficient evaluation of football players’ special physical training effect.

Analysis of Internet Marketing Forecast Model Based on Parallel K-Means Algorithm

Based on the parallel K-means algorithm, this article conducts in-depth research on the related issues of marketing node detection under the Internet, including designing a new Internet marketing node detector and a location summary network based on FCN (Full Convolutional Network) to input the preprocessing of the node and verify its performance under the data sets. At the same time, to solve the problem of insufficient data sets of Internet marketing nodes, the Internet data sets are artificially generated and used for detector training. First, the multiclass K-means algorithm is changed to two categories suitable for Internet marketing node detection: marketing nodes and background categories. Secondly, the weights in the K-means algorithm are mostly only applicable to target detection tasks. Therefore, when processing Internet marketing node detection tasks, the K-means algorithm is used to regress the training set and calculate 5 weights. During the simulation experiment, the weight calculation formula is used to calculate the weight of the feature term. The basic idea is that if a feature word appears more often in this document but less frequently in other nodes, the word will be assigned higher. At the same time, this article focuses on k. Some shortcomings of the mean clustering algorithm have been specifically improved. By standardizing the data participating in the clustering, the data participating in the clustering is transformed from an irregular distribution to a cluster-like distribution, thereby facilitating the clustering process. The density is introduced to determine the initial center of the cluster, and the purity metric is introduced to determine the appropriate density radius of the cluster center, to achieve the most effective reduction of the support vector machine training samples.

Enterprise Credit Risk Management Using Multicriteria Decision-Making

The purpose of this study is to reduce the rate of multicriteria decision-making (MCDA) errors in credit risk management and to weaken the influence of different attitudes of enterprise managers on the final decision when facing credit risk. First, several solutions that are suitable for present enterprise credit risk management are proposed according to the research of enterprise risk management in the world. Moreover, the criteria and matrix are established according to the general practice of the expert method. A decision-making method of enterprise credit risk management with trapezoidal fuzzy number as the criteria of credit risk management is proposed based on the prospect theory; then, the weight is calculated based on G1 weight calculation, G2 weight calculation method, and the method of maximizing deviation; finally, the prospect values of the alternatives calculated by each method are adopted to sort and compare the proposed solutions. Considering the difference of risk degree of managers in the face of credit risk management, the ranking results of enterprise credit risk management solutions based on three weight calculation methods are compared. The results show that as long as the quantitative value of the risk attitude of the enterprise credit risk manager meets a certain range, the final choice of credit risk management scheme ranking is consistent. This exploration provides a new research direction for enterprise credit risk management, which has reference significance.

An Improved Artificial Potential Field Escape Method with Weight Adjustment

Aiming at the problems of obstacle avoidance and bullet avoidance during the patrol swarm, this paper analyzed the defects of the classical artificial potential field, proposed an adjustable escape method, which establish the relationship between the adjustment coefficient and the distance. This method avoid too large or too small escape force that get the bullet into new local shock problem near the target. Then given the weight calculation and parameter selection method, restricted the escape motion by kinematics according to the constraints in the actual motion. This improved method can effecting solve the problem of avoidance in dynamic and complex environment.

Marine Distributed Radar Signal Identification and Classification Based on Deep Learning

Distributed radar is applied extensively in marine environment monitoring. In the early days, the radar signals are identified inefficiently by operators. It is promising to replace manual radar signal identification with machine learning technique. However, the existing deep learning neural networks for radar signal identification consume a long time, owing to autonomous learning. Besides, the training of such networks requires lots of reliable time-frequency features of radar signals. This paper mainly analyzes the identification and classification of marine distributed radar signals with an improved deep neural network. Firstly, the time frequency features were extracted from signals based on short-time Fourier transform (STFT) theory. Then, a target detection algorithm was proposed, which weighs and fuses the heterogenous marine distributed radar signals, and four methods were provided for weight calculation. After that, the frequency-domain priori model feature assistive training was introduced to train the traditional deep convolutional neural network (DCNN), producing a CNN with feature splicing operation. The features of time- and frequency-domain signals were combined, laying the basis for radar signal classification. Our model was proved effective through experiments.

Actual Birth Weight Versus Sonographic Estimation of Fetal Weight

Objective: To assess the correlation of fetal weight at full-term pregnancy by ultrasound method and its authenticity with actual birth weight in obstetrics department of Creek general hospital of Karachi. Methodology: A Prospective cross-sectional study was conducted at Obstetrics and Gynecology department of Creek General hospital affiliated with United Medical and Dental College Karachi from October 2020 to June 2021. Non-Probability Sampling technique was employed. Estimated sample size was n=114, however to increase the precision of study 163 samples were collected. All the pregnant singleton women were enrolled in this study who were examined for fetal weight calculation sonographically at 37th to 40th weeks of gestation. Post-natal neonatal weight was recorded. Results: The mean fetal weight estimated by ultrasound in our survey was 2.9 kg, while mean, actual birth weight was 2.89 Kg. Conclusion: Our study found positive association between the actual birth weight and the estimated fetal weight. Keywords: Fetus, Correlation, Mean, Ultrasound, Birth weight