In this paper, a new methodology is proposed to detect the magnitudes of shift in the mean of some correlated quality characteristics in multivariate-MULTISTAGE production PROCESSES. The proposed methodology is able to consider not only the cascade property of the production stages, but also, the inherent correlation structure of the variables in one stage. It is also able to visualize the movements of the process mean during monitoring time. The proposed method consists of two parts; a self-organizing map (SOM) neural network to detect the mean shifts and a fuzzy inference system to judge the exact magnitudes of the mean shifts. In-control average run lengths, out-of-control average run lengths, and average run lengths to detect the correct class of the proposed method, are investigated by a simulation study of a 2-variate 3-stage process. The results of the simulation are encouraging.

A review of the literature on cause selecting charts (CSCs) in MULTISTAGE PROCESSES is given, with a concentration on developments which have occurred since 1993. Model based control charts and multiple cause selecting charts (MCSCs) are reviewed. Several articles based on normally and non-normally distributed outgoing quality characteristics are analyzed and important issues such as economic design, auto correlated PROCESSES and adaptive design parameters of cause selecting charts are discussed. The results reveal that because selecting charts outperform traditional Shewhart charts for individuals when the process steps are dependent, in view of the relationship between input and output quality characteristics. A new method for modeling and simulating a MULTISTAGE process is proposed which can prove to be more reasonable in real practice. Finally, various directions for future research are given.

If the quality of a process is described using a linear functional relationship betweenthe response variable and independent variables, such a relationship is called the profile. Today, with the development of manufacturing technologies, MULTISTAGE PROCESSES have found a special position in manufacturing companies and industriesIn the present paper, we consider a MULTISTAGE process with AR(1) auto-correlatedsimple linear profile in each stage and address the effect of both auto-correlationand cascade property on the efficiency of common monitoring procedures. Toeliminate the effect of auto-correlation, we used a transformation method as a remedial measure at first. Then, an approach based on the U statistic is applied toeliminate the cascade effect. Next, a modified T 2 control chart is proposed to monitor the process in the second stage. The performance of the proposed controchart is evaluated in terms of the average run length criterion. The simulationstudies show that the proposed control chart perform satisfactorily.

Nowadays, most products are the results of MULTISTAGE PROCESSES with cascade property. In each step of MULTISTAGE PROCESSES, several variables with reliability characteristic may affect the performance. These reliability variables have some unique properties such as censoring and following the family of location-scale and log-location-scale distributions including Weibull, Lognormal and Log-logistic. The purpose of this paper is to monitor left-censored reliability data with the aid of advanced statistical process control (SPC) techniques in line with statistical modeling approaches. To this end, data modeling has been studied using survival analysis regression models. In fact, the accelerated failure time (AFT) regression model has been employed to establish the relationship between quality variables. Then, two monitoring schemes including a cumulative sum (CUSUM) and an exponentially weighted moving average (EWMA) control charts have been proposed by constructing likelihood function and conditional expected values (CEVs) respectively. It should be noted that the transformation of Weibull distribution to standard smallest extreme value distribution has been done to effectively remove the cascade property in the discussed process. Subsequently, the performance of the proposed control schemes has been examined and investigated in terms of average run length (ARL) criterion under various censoring levels of low, medium and high (20%, 50% and 80%). Finally, the performance of the superior (the cumulative sum) control chart has been investigated in a real case study in Taktab Zarif company located in Kashan, Iran. The quality variable of interest is actually the tensile strength of thread which is a, ected by the proportion (weight) of raw materials. Moreover, the values corresponding to tensile strength of threads are recorded just in case that they are beyond the speci, ed level. In other words, we encounter some data which are left-censored and thus remedial action should be considered to alleviate the mentioned obstacle for optimal MULTISTAGE process monitoring.

In most modern manufacturing systems, products are often the output of some MULTISTAGE PROCESSES. In these PROCESSES, the stages are dependent on each other, where the output quality of each stage depends also on the output quality of the previous stages. This property is called the cascade property. Although there are many studies in MULTISTAGE process monitoring, there are fewer works on profile monitoring in MULTISTAGE PROCESSES, especially on the variability monitoring of a MULTISTAGE profile in Phase-I for which no research is found in the literature. In this paper, a new methodology is proposed to monitor the standard deviation involved in a simple linear profile designed in Phase I to monitor MULTISTAGE PROCESSES with the cascade property. To this aim, an autoregressive correlation model between the stages is considered first.Then, the effect of the cascade property on the performances of three types ofT2 control charts in Phase I with shifts in standard deviation is investigated. As we show that this effect is significant, aU statistic is next used to remove the cascade effect, based on which the investigated control charts are modified. Simulation studies reveal good performances of the modified control charts.

Purpose: Students are presented with a variety of case formulas to promote the development of clinical reasoning.Methods: 120 third-year medical students at our medical university were randomly assigned to 18 small groups. After each ICM (Introduction to clinical Medicine) course, they were given a paper case. A few days later small group sessions were formed and students asked tutors some questions to have more history and physical examination data and discussed and developed a differential diagnosis list. Then, all small groups met in a large group session for further discussion. After that, back to small groups, students ordered preclinical tests and they were given the results by tutor. At this stage, each group tried to further narrow the differential diagnosis list and to determine the most probable diagnosis. Finally, in the large group session a consensus diagnosis was reached after reflective observation. A questionnaire was used before and after intervention. The small group tutor rated student's abilities at the end of first day and last day.Results: The attitude of students about the positive effects of MPCD increased significantly from the first day to the last day (P<0.05). The progress checklist of the student's abilities showed a statistically significant improvement post intervention.Conclusion: Our study suggests that using MPCD method is an effective way to increase clinical reasoning skills of medical students.