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Rolling bearings are the core components of rotating machinery. Their health directly affects the performance, stability and life of rotating machinery. To prevent possible damage, it is necessary to detect the condition of rolling bearings for fault diagnosis. With the rapid development of intelligent fault diagnosis technology, various deep learning methods have been applied in fault diagnosis in recent years. Convolution neural networks (CNN) have shown high performance in feature extraction. However, the pooling operation of CNN can lead to the loss of much valuable information and the relationship between the whole and the part may be ignored. In this study, we proposed CNNEPDNN, a novel bearing fault diagnosis model based on ensemble deep neural network (DNN) and CNN. We firstly trained CNNEPDNN model. Each of its local networks was trained with different training datasets. The CNN used vibration sensor signals as the input, whereas the DNN used nine time-domain statistical features from bearing vibration sensor signals as the input. Each local network of CNNEPDNN extracted different features from its own trained dataset, thus we fused features with different discrimination for fault recognition. CNNEPDNN was tested under 10 fault conditions based on the bearing data from Bearing Data Center of Case Western Reserve University (CWRU). To evaluate the proposed model, four aspects were analyzed: convergence speed of training loss function, test accuracy, F-Score and the feature clustering result by t-distributed stochastic neighbor embedding (t-SNE) visualization. The training loss function of the proposed model converged more quickly than the local models under different loads. The test accuracy of the proposed model is better than that of CNN, DNN and BPNN. The F-Score value of the model is higher than that of CNN model, and the feature clustering effect of the proposed model was better than that of CNN.

In view of the limitations of existing rotating machine fault diagnosis methods in single-scale signal analysis, a fault diagnosis method based on multi-scale permutation entropy (MPE) and multi-channel fusion convolutional neural networks (MCFCNN) is proposed. First, MPE quantitatively analyzes the vibration signals of rotating machine at different scales, and obtains permutation entropy (PE) to construct feature vector sets. Then, considering the structure and spatial information between different sensor measurement points, MCFCNN constructs multiple channels in the input layer according to the number of sensors, and each channel corresponds to the MPE feature sets of different monitored points. MCFCNN uses convolutional kernels to learn the features of each channel in an unsupervised way, and fuses the features of each channel into a new feature map. At last, multi-layer perceptron is applied to fuse multi-channel features and identify faults. Through the health monitoring experiment of planetary gearbox and rolling bearing, and compared with single channel convolutional neural networks (CNN) and existing CNN based fusion methods, the proposed method based on MPE and MCFCNN model can diagnose faults with high accuracy, stability, and speed.

Poly(ethylene glycol) (PEG) has been widely used for decades as a "gold standard" in bioconjugation, nanomedicine, and antifouling. Although being extensively studied since 1859, PEG remains mysterious, as can be exemplified by the facts that PEG is the only polyether showing excellent water solubility, and the molecular structure of PEG is surprisingly simple if the fantastic properties are considered. Since PEG is usually used in an aqueous medium, the interactions between PEG and water should be the key to understanding the mechanism. Here, we find that by capturing hydronium ions (H 3 O + ) in water, PEG changes from a neutral polymer to a supra-polyelectrolyte, which is a new category of polymer that becomes a polyelectrolyte when an external ion is dynamically bonded to the polymer via intermolecular interactions. This conclusion is supported by multiple experimental methods from the ensemble to single-molecule level. This finding casts new light on the relationship between the simple structure and fantastic functions of PEG. With known species of polymers and ions, numerous novel supra-polyelectrolytes can be prepared, which may present exciting properties in water.

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A scientifically sound integrated assessment modeling (IAM) system capable of providing optimized cost-benefit analysis is essential in effective air quality management and control strategy development. Yet scenario optimization for large-scale applications is limited by the computational expense of optimization over many control factors. In this study, a multi-pollutant cost-benefit optimization system based on a genetic algorithm (GA) in machine learning has been developed to provide cost-effective air quality control strategies for large-scale applications (e.g., solution spaces of ~10 35 ). The method is demonstrated by providing optimal cost-benefit control pathways to attain air quality goals for fine particulate matter (PM 2.5 ) and ozone (O 3 ) over the Pearl River Delta (PRD) region of China. The GA is found to be > 99% more efficient than the commonly used grid searching method while providing the same combination of optimized multipollutant control strategies. The GA method can therefore address air quality management problems that are intractable using the grid searching method. The annual attainment goals for PM 2.5 (< 35 μg m −3 ) and O 3 (< 80 ppb) can be achieved simultaneously over the PRD region and surrounding areas by reducing NO x (22%), volatile organic compounds (VOCs, 12%), and primary PM (30%) emissions. However, to attain stricter PM 2.5 goals, SO 2 reductions (> 9%) are

The mechanisms leading to squamous cell carcinoma of head and neck (SCCHN) metastasis are not fully understood. Although evidence shows that the chemokine receptor 7 (CCR7) and its ligand CCL19 may regulate tumor dissemination, their role is not clearly defined in SCCHN. Matrix metalloproteinases break consisting of tissue barrier to the surrounding tissue invasion and metastasis by destroying the balance of matrix degradation of the basement membrane of tumor cells and extracellular matrix (ECM). We used chemotaxis and migration assays, western blotting, gelatin zymography, actin polymerization assay, immunofluorescence staining and immunohistochemical analysis to explore whether MMP-9 can be activated by CCL19 (CCR7's ligand) and its role in SCCHN. The experiments were performed in the metastatic SCCHN cell line PCI-37B after pre-incubation of the cells with CCL19 and SB-3CT (inhibitor of MMP-9). Our results demonstrated that CCR7 favors PCI-37B cell chemotaxis and migration, upregulation of MMP-9 protein and motivates the activity of MMP-9 protein, induces reorganization of the actin cytoskeleton and upregulation of MMP-9 protein. SB-3CT can block all these effects. Collectively, our data indicated that CCR7 regulates cell chemotaxis and migration via MMP-9 in metastatic SCCHN, and these results provide a basis for new strategies in preventing metastases of SCCHN.

BackgroundChina has witnessed a remarkable increase in sexually transmitted infections (STIs) and HIV. The study is to assess the prevalence of HIV, HBV, HCV and syphilis and related risk factors among drug users in mandatory detoxification center Qingyuan, Guangdong, China.MethodA cross-sectional study on drug use behaviors, sex behaviors, and presence of antibodies to HIV, HCV, Treponema pallidum, and surface antigen of HBV (HBsAg) was conducted among drug users recruited from 3 detoxification centers in Qingyuan, Guangdong, China. Risk factors for each of four infections were analyzed with logistic regression model.ResultsA total of 740 subjects were recruited, the median age was 31 years old (range 24-38). The seroprevalence rates of HIV, HBsAg, HCV and syphilis were 4.6%, 19.3%, 71.6% and 12.6%, respectively. Risk factors for HIV were intravenous drug use and co-infection with syphilis. Having a regular sexual partner who was a drug user was considered to be a risk factor for HBV. Intravenous drug use was a risk factor for HCV. However, the consistent use of condoms with commercial sex partners was protective for HCV infection. Compared to drug users living in urban area, those living in rural areas were more likely to be infected with syphilis, and there was an association between commercial sex and syphilis.ConclusionThe prevalence of HIV, HBV, HCV and syphilis were high among drug users in detoxification centers in Qingyuan, thus, risk reduction programs for the drug user population is urgently required.