No abstract

We demonstrate an automatic recognition strategy for terahertz (THz) pulsed signals of breast invasive ductal carcinoma (IDC) based on a wavelet entropy feature extraction and a machine learning classifier. The wavelet packet transform was implemented into the complexity analysis of the transmission THz signal from a breast tissue sample. A novel index of energy to Shannon entropy ratio (ESER) was proposed to distinguish different tissues. Furthermore, the principal component analysis (PCA) method and machine learning classifier were further adopted and optimized for automatic classification of the THz signal from breast IDC sample. The areas under the receiver operating characteristic curves are all larger than 0.89 for the three adopted classifiers. The best breast IDC recognition performance is with the precision, sensitivity and specificity of 92.85%, 89.66% and 96.67%, respectively. The results demonstrate the effectiveness of the ESER index together with the machine learning classifier for automatically identifying different breast tissues.

Aim: To explore the relationships among self-efficacy, information literacy, social support and career success of clinical nurses and identify factors influencing clinical nurses' career success in northwestern China. Background: Understanding the influencing factors of career success is important for the professional development of nurses and the improvement of clinical nursing quality. Many influencing factors of career success have been identified, but there is no large-scale research on the relationships among self-efficacy, information literacy, social support and career success of clinical nurses based on Kaleidoscope Career Model. Studies examining the association of the four factors remain limited. Methods: A total of 3011 clinical nurses from 30 hospitals in northwestern China were selected in the cross-sectional survey, and the response rate was 94.71%. The clinical nurses completed the online self-report questionnaires including self-efficacy, information literacy, social support rating scale and career success scale. The data were analysed by SPSS23.0 statistical software using t test, analysis of variance, Pearson's correlation and multiple linear regression. Structural equation model (SEM) was used to analyse the influencing factors of career success using Mplus 8.3. Results:The career success of clinical nurses in northwestern China was at a medium level. The linear multivariate regression analysis showed that self-efficacy (β = .513), social support (β = .230), information support (β = .106), information consciousness (β = À.097), information knowledge (β = .067), information ethics (β = À.053), hospital grade (β = .118), marital status (β = À.071) and age (β = À.037) entered Chao Wu and Lin-yuan Zhang contributed equally to this work.

No abstract

This research explores the application of an image transform technique, the Hough transform (ht), to measuring fiber orientation distribution in nonwoven fabrics. In this paper, various forms of the ht for straight lines and the implementing algorithms are presented, the factors that may influence the accuracy of ht results are discussed, and the way to determine fiber orientation distribution from ht data for a nonwoven is explained. The usefulness of the ht method is tested using simulated nonwoven images with known dominant fiber orientations, and nonwoven fabrics differing in fiber content, weight, and other characteristics. Fiber orientation distributions generated by the ht method are also compared with those obtained from the zero-span tensile testing method. The principal advantages of using the ht are that it is robust in suppressing image noise and is able to deal with fibers containing gaps and breaks. Measurements with the ht method show good correlation with those obtained from other methods. The ht appears to be an effective and efficient image analysis technique for extracting information about fiber orientation from images of nonwoven fabrics.

The rapid development and application of AI in intelligent transportation systems has widely impacted daily life. The application of an intelligent visual aid for traffic sign information recognition can provide assistance and even control vehicles to ensure safe driving. The field of autonomous driving is booming, and great progress has been made. Many traffic sign recognition algorithms based on convolutional neural networks (CNNs) have been proposed because of the fast execution and high recognition rate of CNNs. However, this work addresses a challenging question in the autonomous driving field: how can traffic signs be recognized in real time and accurately? The proposed method designs an improved VGG convolutional neural network and has significantly superior performance compared with existing schemes. First, some redundant convolutional layers are removed efficiently from the VGG-16 network, and the number of parameters is greatly reduced to further optimize the overall architecture and accelerate calculation. Furthermore, the BN (batch normalization) layer and GAP (global average pooling) layer are added to the network to improve the accuracy without increasing the number of parameters. The proposed method needs only 1.15 M when using the improved VGG-16 network. Finally, extensive experiments on the German Traffic Sign Recognition Benchmark (GTSRB) Dataset are performed to evaluate our proposed scheme. Compared with traditional methods, our scheme significantly improves recognition accuracy while maintaining good real-time performance.

No abstract

This study reports a PC software, used in a Windows-based environment, which was developed based on the first order reaction of Biological Oxygen Demand (BOD) and a modified Streeter and Phelps equation, in order to simulate and determine the variations of Dissolved Oxygen (DO) and of the BOD along with the studied river reaches. The software considers many impacts of environmental factors, such as the different type of discharges (concentrated or punctual source, tributary contribution, distributed source), nitrogenous BOD, BOD sedimentation, photosynthetic production and benthic demand of oxygen, and so on. The software has been used to model the DO profile along one river, with the aim to improve the water quality through suitable engineering measure