Further Study On U(1) Gauge Invariance Restoration

To further investigate the applicability of the projection scheme for eliminating the unphysical divergence $s/m_e^2$ due to U(1) gauge invariance violation, we study the process $e^-+W^+\to e^-+\bar t+b$ which possesses advantages of simplicity and clearness. Our study indicates that the projection scheme can indeed eliminate the unphysical divergence $s/m_e^2$ caused by the U(1) gauge invariance violation and the scheme can apply to very high energy region.Comment: Latex, 13 pages, 4 EPS fiure

Diagnostic value of two dimensional shear wave elastography combined with texture analysis in early liver fibrosis.

BACKGROUND: Staging diagnosis of liver fibrosis is a prerequisite for timely diagnosis and therapy in patients with chronic hepatitis B. In recent years, ultrasound elastography has become an important method for clinical noninvasive assessment of liver fibrosis stage, but its diagnostic value for early liver fibrosis still needs to be further improved. In this study, the texture analysis was carried out on the basis of two dimensional shear wave elastography (2D-SWE), and the feasibility of 2D-SWE plus texture analysis in the diagnosis of early liver fibrosis was discussed. AIM: To assess the diagnostic value of 2D-SWE combined with textural analysis in liver fibrosis staging. METHODS: This study recruited 46 patients with chronic hepatitis B. Patients underwent 2D-SWE and texture analysis; Young\u27s modulus values and textural patterns were obtained, respectively. Textural pattern was analyzed with regard to contrast, correlation, angular second moment (ASM), and homogeneity. Pathological results of biopsy specimens were the gold standard; comparison and assessment of the diagnosis efficiency were conducted for 2D-SWE, texture analysis and their combination. RESULTS: 2D-SWE displayed diagnosis efficiency in early fibrosis, significant fibrosis, severe fibrosis, and early cirrhosis (AUC \u3e 0.7, P \u3c 0.05) with respective AUC values of 0.823 (0.678-0.921), 0.808 (0.662-0.911), 0.920 (0.798-0.980), and 0.855 (0.716-0.943). Contrast and homogeneity displayed independent diagnosis efficiency in liver fibrosis stage (AUC \u3e 0.7, P \u3c 0.05), whereas correlation and ASM showed limited values. AUC of contrast and homogeneity were respectively 0.906 (0.779-0.973), 0.835 (0.693-0.930), 0.807 (0.660-0.910) and 0.925 (0.805-0.983), 0.789 (0.639-0.897), 0.736 (0.582-0.858), 0.705 (0.549-0.883) and 0.798 (0.650-0.904) in four liver fibrosis stages, which exhibited equivalence to 2D-SWE in diagnostic efficiency (P \u3e 0.05). Combined diagnosis (PRE) displayed diagnostic efficiency (AUC \u3e 0.7, P \u3c 0.01) for all fibrosis stages with respective AUC of 0.952 (0.841-0.994), 0.896 (0.766-0.967), 0.978 (0.881-0.999), 0.947 (0.835-0.992). The combined diagnosis showed higher diagnosis efficiency over 2D-SWE in early liver fibrosis (P \u3c 0.05), whereas no significant differences were observed in other comparisons (P \u3e 0.05). CONCLUSION: Texture analysis was capable of diagnosing liver fibrosis stage, combined diagnosis had obvious advantages in early liver fibrosis, liver fibrosis stage might be related to the hepatic tissue hardness distribution

Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the highfrequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction

Short-Term Coalmine Gas Concentration Prediction Based on Wavelet Transform and Extreme Learning Machine

It is well known that coalmine gas concentration forecasting is very significant to ensure the safety of mining. Owing to the high-frequency, nonstationary fluctuations and chaotic properties of the gas concentration time series, a gas concentration forecasting model utilizing the original raw data often leads to an inability to provide satisfying forecast results. A hybrid forecasting model that integrates wavelet transform and extreme learning machine (ELM) termed as WELM (wavelet based ELM) for coalmine gas concentration is proposed. Firstly, the proposed model employs Mallat algorithm to decompose and reconstruct the gas concentration time series to isolate the low-frequency and high-frequency information. Then, ELM model is built for the prediction of each component. At last, these predicted values are superimposed to obtain the predicted values of the original sequence. This method makes an effective separation of the feature information of gas concentration time series and takes full advantage of multi-ELM prediction models with different parameters to achieve divide and rule. Comparative studies with existing prediction models indicate that the proposed model is very promising and can be implemented in one-step or multistep ahead prediction

Characterisation of volatile components of red and sparkling wines from a new wine grape cultivar 'Meili' (Vitis vinifera L.)

'Meili' (Vitis vinifera L.) is a new wine grape cultivar from China. Volatile profiles of red and sparkling wines made from 'Meili' grapes were analysed using stir bar sorptive extraction-gas chromatography-mass spectrometry in this study. Fiftyfive volatile compounds were quantified in both wines, and quantitative differences for most of the volatile compounds between 'Meili' wines were observed. 'Meili' sparkling wine had a greater content of esters, fatty acids and shikimic acid derivatives than 'Meili' red wine, although 'Meili' red wine had higher concentrations of alcohols, terpenoids and C13-norisoprenoids. On the basis of odour activity values, ethyl acetate, ethyl butanoate, ethyl hexanoate, ethyl octanoate, isoamyl acetate, ethyl 2-methylpropanoate, ethyl 3-methylbutanoate, octanoic acid, isoamyl alcohol, 2-phenyl ethanol, linalool, β-damascenone and β-ionone were considered as important aroma compounds in 'Meili' wines. For these compounds, 'Meili' sparkling wine had higher content of ethyl acetate, ethyl butanoate, ethyl hexanoate, ethyl octanoate and isoamyl acetate than 'Meili' red wine, while 'Meili' red wine had higher levels of isoamyl alcohol, 2-phenylethanol, linalool, β-damascenone and β-ionone. The concentration differences of aroma compounds due to the differential vinification procedures suggested the differences in sensory characteristics of the two types of wines. In particular, 'Meili' red wine had more rose aroma than 'Meili' sparkling wine.