A new Monte Carlo model for predicting the mechanical properties of fiber yarns

Understanding the complicated failure mechanisms of hierarchical composites such as fiber yarns is essential for advanced materials design. In this study, we developed a new Monte Carlo model for predicting the mechanical properties of fiber yarns that includes statistical variation in fiber strength. Furthermore, a statistical shear load transfer law based on the shear lag analysis was derived and implemented to simulate the interactions between adjacent fibers and provide a more accurate tensile stress distribution along the overlap distance. Simulations on two types of yarns, made from different raw materials and based on distinct processing approaches, predict yarn strength values that compare favorably with experimental measurements. Furthermore, the model identified very distinct dominant failure mechanisms for the two materials, providing important insights into design features that can improve yarn strength.     

A statistical approach to characterize the viscoelastic creep compliances of a vinyl ester polymer

The objective of this study was to develop a model to predict the viscoelastic material functions of a vinyl ester (VE) polymer with variations in its experimentally obtained material properties under combined isothermal and mechanical loading. Short-term tensile creep experiments were conducted at three temperatures below the glass transition temperature of the VE polymer, with 10 replicates for each test configuration. The measured creep strain versus time responses were used to determine the creep compliances using the generalized viscoelastic constitutive equation with a Prony series representation. The variation in the creep compliances of a VE polymer was described by formulating the probability density functions (PDFs) and the corresponding cumulative distribution functions (CDFs) of the creep compliances using a two-parameter Weibull distribution. Both Weibull scale and shape parameters of the creep compliance distributions were shown to be time and temperature dependent. Two-dimensional quadratic Lagrange interpolation functions were used to characterize the Weibull parameters to obtain the PDFs and, subsequently, the CDFs of the creep compliances for the complete design temperature range during steady state creep. At each test temperature, creep compliance curves were obtained for constant CDF values and compared with the experimental data. The predicted creep compliances of the selected VE polymer in the design space are in good agreement with the experimental data for all three test temperatures.     

小样本定数截尾场合下威布尔和对数正态分布的拟合检验

本文给出了小样本定数截尾场合下两参数威布尔分布和对数正态分布的拟合检验方法,该方法还适用于一般的位置-刻度参数族分布,论文还通过实际算例说明方法的可行性。     

纤维树脂界面剪切强度及纤维强度分布参数的实验测定

界面剪切强度及纤维强度分布参数是复合材料设计及应用研究中的重要参数，本文采用单纤维段埋入法测定了上述两个参数。通过实验研究说明了单纤维段埋入法是一种测定界面剪切强度及纤维强度分布参数的有效的实验方法；同时证实了基体的力学性能对界面剪切强度有着重要的影响。全部实验在自制的实验系统上完成     

Optimal Randomness for Stochastic Configuration Network (SCN) with Heavy-Tailed Distributions

Stochastic Configuration Network (SCN) has a powerful capability for regression and classification analysis. Traditionally, it is quite challenging to correctly determine an appropriate architecture for a neural network so that the trained model can achieve excellent performance for both learning and generalization. Compared with the known randomized learning algorithms for single hidden layer feed-forward neural networks, such as Randomized Radial Basis Function (RBF) Networks and Random Vector Functional-link (RVFL), the SCN randomly assigns the input weights and biases of the hidden nodes in a supervisory mechanism. Since the parameters in the hidden layers are randomly generated in uniform distribution, hypothetically, there is optimal randomness. Heavy-tailed distribution has shown optimal randomness in an unknown environment for finding some targets. Therefore, in this research, the authors used heavy-tailed distributions to randomly initialize weights and biases to see if the new SCN models can achieve better performance than the original SCN. Heavy-tailed distributions, such as Lévy distribution, Cauchy distribution, and Weibull distribution, have been used. Since some mixed distributions show heavy-tailed properties, the mixed Gaussian and Laplace distributions were also studied in this research work. Experimental results showed improved performance for SCN with heavy-tailed distributions. For the regression model, SCN-Lévy, SCN-Mixture, SCN-Cauchy, and SCN-Weibull used less hidden nodes to achieve similar performance with SCN. For the classification model, SCN-Mixture, SCN-Lévy, and SCN-Cauchy have higher test accuracy of 91.5%, 91.7% and 92.4%, respectively. Both are higher than the test accuracy of the original SCN.     

Discussion of “Virtual age,is it real?”

In this note, some point of views on virtual ages are presented in terms of the discussion paper written by Finkelstein and Cha, which include generalized stochastic order‐based virtual ages, system‐level virtual ages, virtual ages in Weibull distribution and repair degrees with virtual ages. Finally, some possible future researches on virtual ages are described.     

Stochastic comparison of aggregate claim amounts between two heterogeneous portfolios and its applications

The aggregate claim amount in a particular time period is a quantity of fundamental importance for proper management of an insurance company and also for pricing of insurance coverages. In this paper, we show that the proportional hazard rates (PHR) model, which includes some well-known distributions such as exponential, Weibull and Pareto distributions, can be used as the aggregate claim amount distribution. We also present some conditions for the use of exponentiated Weibull distribution as the claim amount distribution. The results established here complete and extend the well-known result of Khaledi and Ahmadi (2008).     

西湖风景区游客逗留时间决定因素研究

随着近年来城际交通条件的改善,游客在旅游目的地的逗留时间普遍缩短,极大地影响了一个旅游城市旅游产业的赢利能力;但另一方面,热点景区的游客众多,又需要对游客的逗留时间进行控制,以利于景区环境和文物保护.本研究以杭州西湖风景区为例,运用生存分析中的Cox及Weibull模型,分析游客逗留时间的决定因素.调研数据来源于2009年夏天在西湖风景区调查的243份问卷,对象是完成西湖游览行程的来杭游客.研究发现:①家庭收入的增长、个体出行、亲友结伴、住宿开支减少等因素均能够显著增加在西湖景区的逗留时间,而随团出行、多次来杭旅行、住宿星级酒店等因素则会促使逗留时间减少.②景区的游览设施及服务质量、住宿接待的服务质量、西湖景区的整体改造、自然及文化吸引力、夜间景观及活动项目吸引力、景区门票制度的改变这几项对于增加游客逗留时间具有重要作用.③杭州整体旅游形象塑造、景区主管部门的宣传推广、亲友及网络口碑等几项对于增加游客逗留时间的效果并不明显.全文最后对西湖风景区的相关管理部门和旅游企业提出建议,以寻求景区发展面临不同影响因素时可采取的相应对策.     

威布尔分布参数的最高后验概率密度区间估计算法

研究了在先验分布为伽玛分布下,威布尔分布未知参数θ的Bayes区间估计方法,并给出参数的最高后验概率密度区间——HPD区间估计的条件极值解法.最后给出例子,说明该方法的优越性.     

Manothermosonication (MTS) treatment of apple-carrot juice blend for inactivation of Escherichia coli 0157:H7

This study was performed to evaluate the responses of Escherichia coli 0157:H7 inoculated in an apple-carrot blended juice to manothermosonication (MTS) treatments. The MTS treatments were conducted in a continuous-flow MTS system. The juice samples were exposed to ultrasound treatment at combinations of three temperatures (60, 50 and 40 °C) and three pressure levels (100, 200, and 300 kPa) for five residence times (15, 30, 45, 60, and 75 s). The results showed that higher treatment temperature (i.e. 60 °C) and hydrostatic pressure in the MTS system significantly enhanced the microbial reduction. A FDA mandated 5-log CFU/ml reduction of E. coli 0157:H7 for juice processing was achieved in 30 s for MTS treatment at 60 °C, in comparison to 60 s at 50 °C. The Weilbull and Log-logistic models provided the best fitting of the inactivation data for the MTS treatments. Extensive damage of E. coli 0157:H7 cells treated with MTS was observed on micro-images of scanning electron microscopy and transmission electron microscopy.