Multiple Solutions With Constant Sign of a Dirichlet Problem for a Class of Elliptic Systems With Variable Exponent Growth

We present here, in the system setting, a new set of growth conditions under which we manage to use a novel method to verify the Cerami compactness condition. By localization argument, decomposition technique and variational methods, we are able to show the existence of multiple solutions with constant sign for the problem without the well-known Ambrosetti--Rabinowitz type growth condition. More precisely, we manage to show that the problem admits four, six and infinitely many solutions respectively

The Effect of Nanoparticle Surface State on Trap Level Distribution of Polyimide/Aluminum Nitride-montmorillonite Nanocomposite Films

The electrical properties of polyimide (PI) nanocomposites, which are widely used in microelectronic industry and electrical engineering fields, strongly depend on the surface state of nanoparticles. To explore this dependence, the aluminum nitride (AIN) nanoparticles were treated by γ-aminopropyltriethoxysilane coupling agent, while PI/MMT, PI/AlN, and PI/AlN-MMT nanocomposite films doped by 5 wt% of treated and untreated AlN nanoparticles were prepared by the in-situ polymerization process. The SEM and TEM results indicate that the untreated AlN nanoparticles are prone to accumulation in the polymer matrix, while those treated by the coupling agent are readily combined with the polyimide matrix, and their compatibility and dispersion exhibit a significant improvement. The trap level distributions of nanocomposite films were studied by the isothermal discharge current (IDC) method based on the charge decay theory linking IDC with the trap level density (TLD). The TLD and number of trapped charges of PI/AlN and PI/AlN-MMT films doped by treated AlN nanoparticles are found to be much higher than those of untreated ones. The TLD of the PI/AlN (treated) film is 6.490×1023 eV·m-3, which is about 2.27 times higher than that of pure PI film in the range of 0.9~1.1 eV, while the maximum TLD=9.370×1023 eV·m-3 is observed in the PI/AlN (treated)-MMT film

An Automatic Detection Method of Nanocomposite Film Element Based on GLCM and Adaboost M1

An automatic detection model adopting pattern recognition technology is proposed in this paper; it can realize the measurement to the element of nanocomposite film. The features of gray level cooccurrence matrix (GLCM) can be extracted from different types of surface morphology images of film; after that, the dimension reduction of film can be handled by principal component analysis (PCA). So it is possible to identify the element of film according to the Adaboost M1 algorithm of a strong classifier with ten decision tree classifiers. The experimental result shows that this model is superior to the ones of SVM (support vector machine), NN and BayesNet. The method proposed can be widely applied to the automatic detection of not only nanocomposite film element but also other nanocomposite material elements

An Ensemble Learning for Predicting Breakdown Field Strength of Polyimide Nanocomposite Films

Using the method of Stochastic Gradient Boosting, ten SMO-SVR are constructed into a strong prediction model (SGBS model) that is efficient in predicting the breakdown field strength. Adopting the method of in situ polymerization, thirty-two samples of nanocomposite films with different percentage compositions, components, and thicknesses are prepared. Then, the breakdown field strength is tested by using voltage test equipment. From the test results, the correlation coefficient (CC), the mean absolute error (MAE), the root mean squared error (RMSE), the relative absolute error (RAE), and the root relative squared error (RRSE) are 0.9664, 14.2598, 19.684, 22.26%, and 25.01% with SGBS model. The result indicates that the predicted values fit well with the measured ones. Comparisons between models such as linear regression, BP, GRNN, SVR, and SMO-SVR have also been made under the same conditions. They show that CC of the SGBS model is higher than those of other models. Nevertheless, the MAE, RMSE, RAE, and RRSE of the SGBS model are lower than those of other models. This demonstrates that the SGBS model is better than other models in predicting the breakdown field strength of polyimide nanocomposite films

Discharge Estimation Using Integrated Satellite Data and Hybrid Model in the Midstream Yangtze River

Remotely sensing data have advantages in filling spatiotemporal gaps of in situ observation networks, showing potential application for monitoring floods in data-sparse regions. By using the water level retrievals of Jason-2/3 altimetry satellites, this study estimates discharge at a 10-day timescale for the virtual station (VS) 012 and 077 across the midstream Yangtze River Basin during 2009–2016 based on the developed Manning formula. Moreover, we calibrate a hybrid model combined with Gravity Recovery and Climate Experiment (GRACE) data, by coupling the GR6J hydrological model with a machine learning model to simulate discharge. To physically capture the flood processes, the random forest (RF) model is employed to downscale the 10-day discharge into a daily scale. The results show that: (1) discharge estimates from the developed Manning formula show good accuracy for the VS012 and VS077 based on the improved Multi-subwaveform Multi-weight Threshold Retracker; (2) the combination of the GR6J and the LSTM models substantially improves the performance of the discharge estimates solely from either the GR6J or LSTM models; (3) RF-downscaled daily discharge demonstrates a general consistency with in situ data, where NSE/KGE between them are as high as 0.69/0.83. Our approach, based on multi-source remotely sensing data and machine learning techniques, may benefit flood monitoring in poorly gauged areas

The periodontal infection may be a contributing factor to the development of gastric cancer

Introduction: Self-reported tooth loss is highly prevalent in patients with gastric cancer, the second most common malignancy worldwide. Periodontal disease is characterized by loss of the periodontal ligament and alveolar bone, and is a major cause of tooth loss. The theories have been confirmed that chronic systemic inflammation and increased exposure to carcinogenic nitrosamines can increase the risk of cancer, and periodontal pathogens could induce the chronic inflammation. Poor oral hygiene and periodontal diseases may contribute to greater nitrosamine production. The Hypothesis: We hypothesize that periodontal diseases might be an important risk factor for gastric cancer. Major pathogens of periodontal diseases may play a more direct role through local inflammatory responses and carcinogenic transformations in the development of gastric cancer. Evaluation of the Hypothesis: It is possible that periodontal disease may be a marker of a type of immune function that has implications for tumor growth and progression in stomach. If periodontal bacteria indeed play an important role in the development of gastric cancer, the patients should be treated not only focused on the stomach disease itself but also the periodontal problems

Triglyceride-glucose index is a risk factor for breast cancer in China: a cross-sectional study

Abstract Background This research delved into the association between the risk of the Chinese population suffering from breast cancer (BC) and the triglyceride-glucose (TyG) index. Methods A total of 2,111 sufferers with benign breast disease (BBD) and 477 sufferers with BC were enrolled, and their TyG index was measured. Participants with varying TyG index values were categorized into quartiles. Logistic regression analysis was employed to assess the relationship between the TyG index and BC risk. The diagnostic performance of the TyG index for different stages of BC was measured using the receiver operating characteristic (ROC) curve. Results The TyG index of BC sufferers exceeded that of BBD (P < 0.001). A continuous increase in the risk of BC was found to be positively correlated with an ever-increasing TyG index. In the unadjusted model, the risk of getting BC mounted with quartiles of the TyG index growing (P < 0.001). In a logistic regression analysis that included all confounders, the highest quartile of the TyG index was strongly linked to BC risk [1.43 (1.01, 2.02), P < 0.05]. Moreover, with the adjustment of potential confounders, a high TyG index was found to result in a 2.53-fold higher risk of being diagnosed with advanced BC. Conclusions The risen TyG index was positively correlated to the heightening risk of BC and had the potential to serve as a promising biomarker for BC. Individuals with a high TyG index ought to be mindful of the heightened risk of BC onset and progression

Anti-Bioadhesion on Hierarchically Structured,Superhydrophobic Surfaces

We prepared hierarchically structured, superhydrophobic surfaces, with single-, dual-, and triple-scale roughness, via a layer-by-layer (LbL) particle deposition approach. The dual-/triple-scale structured, superhydrophobic surfaces exhibited significantly reduced protein adsorption (up to a 90% decrease). Furthermore, platelet adhesion and activation was completely suppressed on the triple-scale structured surface