On the determination of the point of fracture initiation by the load separation criterion in J-testing of ductile polymers

The application of an experimental approach based on the load separation criterion for the determination of the point of fracture initiation in a fracture test on a ductile polymer was critically examined. To this aim, the fracture process outlined by the application of this method was related to that described by the visual analysis of the fracture surfaces obtained in fracture tests on nominally identical specimens, in which different levels of crack extension were produced. The material examined was an acrylonitrile-butadiene-styrene (ABS) resin, and the fracture tests were performed at low loading rate on single-edge notched in bending specimens.The results demonstrated that this load separation criterion based methodology is a promising approach for the determination of the point of fracture initiation, and for material fracture resistance, J_Ic, evaluation. The method also has experimental simplicity and a high degree of repeatability.     

氢键准则在DMSO水溶液中应用的评价

利用分子动力学模拟方法, 分别采用几何准则和能量准则分析了不同浓度下的二甲基亚砜(DMSO)水溶液的氢键统计和动力学等特性. 结果显示, 两种氢键准则可以很好地反映出溶液的氢键性质随浓度的变化趋势. 通过分析比较发现, 由于几何准则不能有效地排除具有弱对势能的分子对, 因此其统计的氢键数量要大于能量准则的结果.此外, 能量准则对于分子间相对取向的区分存在不足, 进而引起氢键寿命的计算结果偏大.因此,为使氢键分析更加准确, 本文建议使用几何-能量混合型氢键准则.     

A blow‐up criterion to the 2D full compressible magnetohydrodynamic equations

We establish a new regularity criterion for the 2D full compressible magnetohydrodynamic system in a bounded domain. Copyright © 2014 John Wiley & Sons, Ltd.     

A logarithmic regularity criterion for the 3D generalized MHD system

We prove a logarithmic regularity criterion for the 3D generalized magnetohydrodynamics (MHD) system with diffusion terms ?Δu and (?Δ)^βb, with . Copyright © 2015 John Wiley & Sons, Ltd.     

Additive hazards regression with random effects for clustered failure times

Additive hazards model with random effects is proposed for modelling the correlated failure time data when focus is on comparing the failure times within clusters and on estimating the correlation between failure times from the same cluster, as well as the marginal regression parameters. Our model features that, when marginalized over the random effect variable, it still enjoys the structure of the additive hazards model. We develop the estimating equations for inferring the regression parameters. The proposed estimators are shown to be consistent and asymptotically normal under appropriate regularity conditions. Furthermore, the estimator of the baseline hazards function is proposed and its asymptotic properties are also established. We propose a class of diagnostic methods to assess the overall fitting adequacy of the additive hazards model with random effects. We conduct simulation studies to evaluate the finite sample behaviors of the proposed estimators in various scenarios. Analysis of the Diabetic Retinopathy Study is provided as an illustration for the proposed method.     

Parametric inference for multiple repairable systems under dependent competing risks

The focus of this article is on the analysis of repairable systems that are subject to multiple sources of recurrence. The event of interest at the system level is assumed to be caused by the earliest occurrence of a source, thereby conforming to a series system competing risks framework. Parametric inference is carried out under the power law process model that has found significant attention in industrial applications. Dependence among the cause‐specific recurrent processes is induced via a shared frailty structure. The theoretical inference results are implemented to a warranty database for a fleet of automobiles, for which the warranty repair is triggered by the failure of one of many components. Extensive finite‐sample simulation is carried out to supplement the asymptotic findings. Copyright © 2014 John Wiley & Sons, Ltd.     

Determination of 12 potential nephrotoxicity biomarkers in rat serum and urine by liquid chromatography with mass spectrometry and its application to renal failure induced by Semen Strychni

In previous nephrotoxicity metabonomic studies, several potential biomarkers were found and evaluated. To investigate the relationship between the nephrotoxicity biomarkers and the therapeutic role of Radix Glycyrrhizae extract on Semen Strychni‐induced renal failure, 12 typical biomarkers are selected and a simple LC–MS method has been developed and validated. Citric acid, guanidinosuccinic acid, taurine, guanidinoacetic acid, uric acid, creatinine, hippuric acid, xanthurenic acid, kynurenic acid, 3‐indoxyl sulfate, indole‐3‐acetic acid, and phenaceturic acid were separated by a Phenomenex Luna C₁₈ column and a methanol/water (5 mM ammonium acetate) gradient program with a runtime of 20 min. The prepared calibration curves showed good linearity with regression coefficients all above 0.9913. The absolute recoveries of analytes from serum and urine were all more than 70.4%. With the developed method, analytes were successfully determined in serum and urine samples within 52 days. Results showed that guanidinosuccinic acid, guanidinoacetic acid, 3‐indoxyl sulfate, and indole‐3‐acetic acid (only in urine) were more sensitive than the conventional renal function markers in evaluating the therapeutic role of Radix Glycyrrhizae extract on Semen Strychni‐induced renal failure. The method could be further used in predicting and monitoring renal failure cause by other reasons in the following researches.     

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications.     

Heart Failure and PAHs,OHPAHs, and Trace Elements Levels in Human Serum: Results from a Preliminary Pilot Study in Greek Population and the Possible Impact of Air Pollution

Cardiovascular diseases (CVDs) have been associated with environmental pollutants. The scope of this study is to assess any potential relation of polycyclic aromatic hydrocarbons (PAHs), their hydroxylated derivatives, and trace elements with heart failure via their direct determination in human serum of Greek citizens residing in different areas. Therefore, we analyzed 131 samples including cases (heart failure patients) and controls (healthy donors), and the respective demographic data were collected. Significantly higher concentrations (p < 0.05) were observed in cases’ serum regarding most of the examined PAHs and their derivatives with phenanthrene, fluorene, and fluoranthene being the most abundant (median of >50 μg L⁻¹). Among the examined trace elements, As, Cd, Cu, Hg, Ni, and Pb were measured at statistically higher concentrations (p < 0.05) in cases’ samples, with only Cr being significantly higher in controls. The potential impact of environmental factors such as smoking and area of residence has been evaluated. Specific PAHs and trace elements could be possibly related with heart failure development. Atmospheric degradation and smoking habit appeared to have a significant impact on the analytes’ serum concentrations. PCA–logistic regression analysis could possibly reveal common mechanisms among the analytes enhancing the hypothesis that they may pose a significant risk for CVD development.