乌蒙风情——观蒋若滨先生作品有感

丁酉初秋,"情系乌蒙——蒋若滨国画作品展"在六盘水市美术馆开幕。简短的开幕与致辞,少许的观者与学生参与。在当下美术展览如火如荼的时代,似乎展览的频次与规模也成为衡量艺术作品的一个硬指标。相对于其他一些大型展览,这种地方性的小型展览也就不足为奇,甚至其影响力也可以忽略不计。但就是这样一种地方性的展览,让笔者有机会对蒋老师作品有了更为全面的认识与触动。

Bile-acid-induced cell injury and protection

Several studies have characterized the cellular and molecular mechanisms of hepatocyte injury caused by the retention of hydrophobic bile acids （BAs） in cholestatic diseases. BAs may disrupt cell membranes through their detergent action on lipid components and can promote the generation of reactive oxygen species that, in turn, oxidatively modify lipids, proteins, and nucleic acids, and eventually cause hepatocyte necrosis and apoptosis. Several pathways are involved in triggering hepatocyte apoptosis. Toxic BAs can activate hepatocyte death receptors directly and induce oxidative damage, thereby causing mitochondrial dysfunction, and induce endoplasmic reticulum stress. When these compounds are taken up and accumulate inside biliary cells, they can also cause apoptosis. Regarding extrahepatic tissues, the accumulation of BAs in the systemic circulation may contribute to endothelial injury in the kidney and lungs. In gastrointestinal cells, BAs may behave as cancer promoters through an indirect mechanism involving oxidative stress and DNA damage, as well as acting as selection agents for apoptosis-resistant cells. The accumulation of BAs may have also deleterious effects on placental and fetal cells. However, other BAs, such as ursodeoxycholic acid, have been shown to modulate BA-induced injury in hepatocytes. The major beneficial effects of treatment with ursodeoxycholic acid are protection against cytotoxicity due to more toxic BAs; the stimulation of hepatobiliary secretion; antioxidant activity, due in part to an enhancement in glutathione levels; and the inhibition of liver cell apoptosis. Other natural BAs or their derivatives, such as cholyI-N- methylglycine or pharmacological properties. cholylsarcosine, interest owing have also aroused to their protective

Accelerated Recursive Feature Elimination Based on Support Vector Machine for Key Variable Identification

Key variable identification for classifications is related to many trouble-shooting problems in process indus-tries. Recursive feature elimination based on support vector machine (SVM-RFE) has been proposed recently in applica-tion for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diag-nosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee East-man process (TEP) simulator is used to evaluate the effectiveness of the key variable selection using accelerated SVM-RFE (A-SVM-RFE). A-SVM-RFE integrates computational rate and algorithm effectiveness into a consistent framework. It not only can correctly identify the key variables, but also has very good computational rate. In comparison with contribution charts combined with principal component aralysis (PCA) and other two SVM-RFE algorithms, A-SVM-RFE performs better. It is more fitting for industrial application.

A data-driven threshold for wavelet sliding window denoising in mechanical fault detection

Wavelet denoising is an effective approach to extract fault features from strong background noise.It has been widely used in mechanical fault detection and shown excellent performance.However,traditional thresholds are not suitable for nonstationary signal denoising because they set universal thresholds for different wavelet coefficients.Therefore,a data-driven threshold strategy is proposed in this paper.First,the signal is decomposed into different subbands by wavelet transformation.Then a data-driven threshold is derived by estimating the noise power spectral density in different subbands.Since the data-driven threshold is dependent on the noise estimation and adapted to data,it is more robust and accurate for denoising than traditional thresholds.Meanwhile,sliding window method is adopted to set a flexible local threshold.When this method was applied to simulation signal and an inner race fault diagnostic case of dedusting fan bearing,the proposed method has good result and provides valuable advantages over traditional methods in the fault detection of rotating machines.

Tunable Q-factor wavelet transform denoising with neighboring coefficients and its application to rotating machinery fault diagnosis

Fault diagnosis of rotating machinery is of great importance to the high quality products and long-term safe operation.However,the useful weak features are usually corrupted by strong background noise,thus increasing the difficulty of the feature extraction.Thereby,a novel denoising method based on the tunable Q-factor wavelet transform(TQWT)using neighboring coefficients is proposed in this article.The emerging TQWT possesses excellent properties compared with the conventional constant-Q wavelet transforms,which can tune Q-factor according to the oscillatory behavior of the signal.Meanwhile,neighboring coefficients denoising is adopted to avoid the overkill of conventional term-by-term thresholding techniques.Because of having the combined advantages of the two methods,the presented denoising method is more practical and effective than other methods.The proposed method is applied to a simulated signal,a rolling element bearing with an outer race defect from antenna transmission chain and a gearbox fault detection case.The processing results demonstrate that the proposed method can successfully identify the fault features,showing that this method is more effective than the conventional wavelet thresholding denoising methods,term-by-term TQWT denoising schemes and spectral kurtosis.