The Modified Pulak and Al-Sultan's Model for Determining the Optimum Process Parameters

Economic selection of process parameters has been an important topic in modern statistical process control. The optimum process parameters setting have a major effect on the expected profit/cost per item. There are some concerns on the problem of setting process parameters. Boucher and Jafari (1991 Boucher , T. O. , Jafari , M. A. ( 1991 ). The optimum target value for single filling operations with quality sampling plans . J. Qual. Technol. 23 : 44 – 47 . [CSA] [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) first considered the attribute single sampling plan applied in the selection of process target. Pulak and Al-Sultan (1996 Pulak , M. F. S. , Al-Sultan , K. S. ( 1996 ). The optimum targeting for a single filling operation with rectifying inspection . Omega 24 : 727 – 733 . [CSA] [CROSSREF] [Crossref], [Web of Science ®] , [Google Scholar]) extended Boucher and Jafari's model and presented the rectifying inspection plan for determining the optimum process mean. In this article, we further propose a modified Pulak and Al-Sultan model for determining the optimum process mean and standard deviation under the rectifying inspection plan with the average outgoing quality limit (AOQL) protection. Taguchi's (1986 Taguchi , G. ( 1986 ). Introduction to Quality Engineering . Asian Productivity Organization . [Google Scholar]) symmetric quadratic quality loss function is adopted for evaluating the product quality. By solving the modified model, we can obtain the optimum process parameters with the maximum expected profit per item and the specified quality level can be reached.     

Short run spc based upon the second order dynamic linear model for trend detection

Recently, several new applications of control chart procedures for short production runs have been introduced. Bothe (1989) and Burr (1989) proposed the use of control chart statistics which are obtained by scaling the quality characteristic by target values or process estimates of a location and scale parameter. The performance of these control charts can be significantly affected by the use of incorrect scaling parameters, resulting in either an excessive "false alarm rate," or insensitivity to the detection of moderate shifts in the process. To correct for these deficiencies, Quesenberry (1990, 1991) has developed the Q-Chart which is formed from running process estimates of the sample mean and variance. For the case where both the process mean and variance are unknown, the Q-chaxt statistic is formed from the standard inverse Z-transformation of a t-statistic. Q-charts do not perform correctly, however, in the presence of special cause disturbances at process startup. This has recently been supported by results published by Del Castillo and Montgomery (1992), who recommend the use of an alternative control chart procedure which is based upon a first-order adaptive Kalman filter model Consistent with the recommendations by Castillo and Montgomery, we propose an alternative short run control chart procedure which is based upon the second order dynamic linear model (DLM). The control chart is shown to be useful for the early detection of unwanted process trends. Model and control chart parameters are updated sequentially in a Bayesian estimation framework, providing the greatest degree of flexibility in the level of prior information which is incorporated into the model. The result is a weighted moving average control chart statistic which can be used to provide running estimates of process capability. The average run length performance of the control chart is compared to the optimal performance of the exponentially weighted moving average (EWMA) chart, as reported by Gan (1991). Using a simulation approach, the second order DLM control chart is shown to provide better overall performance than the EWMA for short production run applications     

EVVMA and cusum control charts in the presence of correlation

The statistical properties of control charts are usually evaluated under the assumption that the observations from the process are independent. For many processes however, observations which are closely spaced in time will be correlated. This paper considers EWMA and CUSUM control charts for the process mean when the observations are from an AR(1) process with additional random error. This simple model may be a reasonable model for many processes encountered in practice. The ARL and steady state ARL of the EWMA and CUSUM charts are evaluated numerically using an integral equation approach and a Markov chain approach. The numerical results show that correlation can have a significant effect on the properties of these charts. Tables are given to aid in the design of these charts when the observations follow the assumed model.     

Efficiency and Validity Analyses of Two-Stage Estimation Procedures and Derived Testing Procedures in Quantitative Linear Models with AR(1) Errors

Abstract

In a quantitative linear model with errors following a stationary Gaussian, first-order autoregressive or AR(1) process, Generalized Least Squares (GLS) on raw data and Ordinary Least Squares (OLS) on prewhitened data are efficient methods of estimation of the slope parameters when the autocorrelation parameter of the error AR(1) process, ρ, is known. In practice, ρ is generally unknown. In the so-called two-stage estimation procedures, ρ is then estimated first before using the estimate of ρ to transform the data and estimate the slope parameters by OLS on the transformed data. Different estimators of ρ have been considered in previous studies. In this article, we study nine two-stage estimation procedures for their efficiency in estimating the slope parameters. Six of them (i.e., three noniterative, three iterative) are based on three estimators of ρ that have been considered previously. Two more (i.e., one noniterative, one iterative) are based on a new estimator of ρ that we propose: it is provided by the sample autocorrelation coefficient of the OLS residuals at lag 1, denoted r(1). Lastly, REstricted Maximum Likelihood (REML) represents a different type of two-stage estimation procedure whose efficiency has not been compared to the others yet. We also study the validity of the testing procedures derived from GLS and the nine two-stage estimation procedures. Efficiency and validity are analyzed in a Monte Carlo study. Three types of explanatory variable x in a simple quantitative linear model with AR(1) errors are considered in the time domain: Case 1, x is fixed; Case 2, x is purely random; and Case 3, x follows an AR(1) process with the same autocorrelation parameter value as the error AR(1) process. In a preliminary step, the number of inadmissible estimates and the efficiency of the different estimators of ρ are compared empirically, whereas their approximate expected value in finite samples and their asymptotic variance are derived theoretically. Thereafter, the efficiency of the estimation procedures and the validity of the derived testing procedures are discussed in terms of the sample size and the magnitude and sign of ρ. The noniterative two-stage estimation procedure based on the new estimator of ρ is shown to be more efficient for moderate values of ρ at small sample sizes. With the exception of small sample sizes, REML and its derived F-test perform the best overall. The asymptotic equivalence of two-stage estimation procedures, besides REML, is observed empirically. Differences related to the nature, fixed or random (uncorrelated or autocorrelated), of the explanatory variable are also discussed.     

Bayes Prediction for a Heteroscedastic Regression Superpopulation Model Using Balanced Loss Function

We consider Prais–Houthakker heteroscedastic normal regression model having variance of the dependent variable same as square of its expectation. Bayes predictors for the regression coefficient and the mean of a finite population are derived using Zellner's balanced loss function. Bayes predictive expected losses are obtained and compared with those of classical predictors and Bayes predictors under squared error loss function to examine their loss robustness.     

High-order extensions of the Double Chain Markov Model

The Double Chain Markov Model is a fully Markovian model for the representation of time-series in random environments. In this article, we show that it can handle transitions of high-order between both a set of observations and a set of hidden states. In order to reduce the number of parameters, each transition matrix can be replaced by a Mixture Transition Distribution model. We provide a complete derivation of the algorithms needed to compute the model. Three applications, the analysis of a sequence of DNA, the song of the wood pewee, and the behavior of young monkeys show that this model is of great interest for the representation of data that can be decomposed into a finite set of patterns.     

OBE理念下对高校微课教学价值的反思

作为教育信息化时代新技术与教育改革融合发展的共生产物,微课在教育领域备受关注。高校微课的快速发展对教师教学活动层面产生了一些影响,但对教学水平和人才培养质量的提升并没有产生实质性的助推力,且存在实践成效不够显著、系统的价值思考不够深入、对学生学习成效的关注不够充分、理论支撑体系不够成熟等不足之处。因此,在成果导向（Outcome-Based Education,OBE）理念下反思高校微课的目标定位、价值取向、教学设计和效果评价原则,应“以生为本”“以学为中心”,以提升学生综合能力为目标,以促进学生成功发展为取向,坚持成果导向反向设计原则及多元化、可迁移性的评价原则。     

医药企业负面网络舆情演化及应对策略研究

探讨因媒体不当报道引发医药企业网络舆情危机的演变规律,为企业找寻适宜的对策,有助于提高医药企业危机应对能力。文章以“深圳康泰公司乙肝疫苗信任危机”为例,构建了医药企业负面网络舆情演化和响应策略的系统动力学模型,并采用Vensim PLE软件进行仿真。结果表明:医药企业负面网络舆情经过酝酿、爆发、衰退三个阶段,且呈现爆发时间短、热度高、负面情绪强的特点;医药企业应根据舆情演化阶段特征采取不同应对策略,三个阶段对应有效应急策略依次是提升舆情监测能力、端正担责态度、提高信息公开度和沟通能力、注意形象修复和注重人文关怀。其中,提高信息公开度和沟通能力对负面网络舆情热度的峰值均有明显的削弱效果,整体提升企业舆情应对能力是改善舆情、挽救医药行业信任危机的有力措施。     

湖南省旅游生态安全动态评价及障碍因素分析

旅游生态安全是实现旅游地高质量发展、推进生态文明建设的重要环节。文章基于DPSIR模型构建湖南省旅游生态安全评价指标体系,运用熵权TOPSIS法对湖南省2011—2020年旅游生态安全进行动态评价,采用ArcGIS软件分析其空间演变特征,同时借助障碍度模型,揭示影响旅游生态安全的障碍因子。研究表明:(1)研究期内,湖南省旅游生态安全系数呈“小幅度下降,大幅度上升”的波浪形演化趋势。空间上呈现出由西北和东南向中心逐渐变好的分布格局。(2)游客密度、生活垃圾无害化处理率、工业固体废物综合利用率、GDP增长率、旅游经济密度等是主要障碍因子。     

差错管理氛围对员工建言行为的影响研究——基于链式中介作用模型

基于社会认知理论和社会交换理论,以320名具有5年以上工作经验的员工为调查对象,探讨了差错管理氛围如何促进员工建言行为的产生以及组织支持感和心理安全感在差错管理氛围与员工建言之间的链式中介作用。研究表明:差错管理氛围对员工建言行为具有显著的正向影响;组织支持感和心理安全感在差错管理氛围和员工建言行为之间起中介作用;组织支持感和心理安全感在差错管理氛围和员工建言行为之间起链式中介作用。因此,企业要努力营造积极的差错管理氛围,从而提高员工的组织支持感和心理安全感,促进员工建言行为的产生。