جستجوی مقالات مرتبط با کلیدواژه "کنترل فرایند آماری" در نشریات گروه "صنایع"

Both in the academic environment and in the realm of operation, it has been shown that there is a close relationship between product quality and maintenance of equipment. Thus, coordination of the decisions associated with maintenance and quality can lead to decrease in operational costs of a production system and improve the overall productivity. To coordinate the decisions related to maintenance and statistical process control, this thesis develops integrated mathematical models. The system performance is compared in two states: (1) while there is a coordination between the decisions of maintenance and SPC, (2) while the decisions of maintenance and SPC are conducted separately, and indeed, there is no coordination between SPC and maintenance. To determine the system performance in state 1, an integrated model of SPC and maintenance is developed. Optimization of the integrated model determines the value of ECT in state 1. To determine the system performance in the uncoordinated state,i.e., state 2, the following steps are performed: (1) a stand-alone model of maintenance is proposed, (2) a stand-alone model of SPC is developed, (3) according to the results of optimization of step 1 and 2, the value of ECT in the uncoordinated state is computed. Finally, comparison of the values of ECT in the coordinated state and uncoordinated state clarifies that, due to the performance of the integrated model, how much saving in operational cost is obtained. For the systems investigated in the thesis, using factorial designs, the effect of the system parameters on the decision variables and the objective function is analyzed. Also, it is investigated that, with respect to the operational costs, what circumstances are more suitable for implementing the integrated models of SPC and maintenance. According to the results of the study, integration of the decisions associated with maintenance and SPC leads to a better performance. The amount of saving obtained from the coordination is significant for the different production systems.

We introduce a method for the statistical design of a depth-based control chart, using the percentile-based approach. The proposed control chart is affine invariant and is asymptotically distribution-free. Generally, the performance of a control chart is evaluated with the average run length metric. The average run length metric has a geometric distribution skewed to the right with a large standard deviation and may not be a proper measure for evaluating the control chart. Therefore, we use the statistical design method of control charts with the PL approach, which is an improvement and development on classical statistical design. By employing constraints on average run length, the length of in-control and out-of-control performances are guaranteed with predetermined probabilities and we can ensure that the in-control run length exceeds the desired value and the out-of-control run length is less than the desired value. Simulation studies show that the proposed control chart is more efficient than the average run length approach.

Literature indicates that the statistical process control approach is capable of improving the quality of surgical processes. The use of this approach in the case of real surgical processes is an attractive contribution for researchers. Since risk factors of each patient before a surgery are different potentially, the use of a control chart approaching risk-adjusted is addressed by researchers.The sensitivity of a model to small shift types of the mortality odds ratio is very important for monitoring the quality of a surgery process. Although different schemes have been proposed by researchers to monitor the performance of a medical team, based on the authors' knowledge this research for the first time proposes a risk-adjusted double exponentially weighted moving average (RADEWMA) approach to monitor the mortality rate. The numerical performance analysis of this paper based on average run length (ARL) term for the cardiac surgery addresses that the sensitivity of the proposed risk-adjusted scheme is superior compared to other risk-adjusted chart of literature.In this research the performance of the proposed model is compared comprehensively with risk adjusted P chart (RAP), risk adjusted exponentially weighted moving average (RAEWMA), and risk adjusted cumulative sum (RACUSUM). In this paper for analyzing the performance comparatively, the simulated data is produced based on UK Center for Cardiac Surgery data in the period of 1992-1994. This data is referred to 2218 patients with heart surgery. In this simulation to estimate the parameters of the logistic regression, Parsonnet number is used as an explanatory variable. The comparative performance analysis is reported for different shift cases. The comparative report is analyzed statistically for different values of parameters. For statistical analyzing SPSS software is also used. The new proposed scheme to monitor the mortality odds ratio, can be used in different surgeries of a hospital effectively.

One of the most important goals of organizations and manufacturing companies is to provide suitable products and services to customers, requiring high-quality processes and keeping them at a desired quality level. In many cases, product quality is low due to equipment deterioration; however, it cannot be figured out until the equipment breaks down. On the other hand, control charts can be used to identify the condition of the process, where the out-of-control state for a quality characteristic means deterioration in the equipment, which is used for the manufacturing purpose. Hence, the statistical process control and maintenance decisions can be combined to form an integrated model that enjoys higher efficiency in reducing costs of quality and maintenance. Furthermore, using delayed monitoring policy to monitor processes is one of the newest research fields in this regard. Delayed monitoring means that processes are in control at the beginning of the process and sampling can be delayed until the pre-specified scheduled time. With a delayed monitoring policy, the total cost of production per unit is expected to be more affordable as the sampling rate decreases; however, it may be lead to an increase in the quality and maintenance costs. Therefore, determining efficient decision variables is important in the model. In this paper, an integrated statistical process control and maintenance model based on delayed monitoring is designed for a two-stage process. By using this procedure, 28 different scenarios are created in which variations in quality and different break-down states are considered. barX residual mean control charts have been used for monitoring purposes. In order to integrate statistical process control and maintenance, a model is proposed such that the expected cost per time unit of manufacturing is minimized by using a genetic algorithm. To evaluate the performance of the proposed method, an illustrative example is presented. In addition, sensitivity analysis of some parameters of the proposed model is carried out. The results show the appropriate performance of the proposed model.

Control charts are powerful tools of statistical process control (SPC) used for monitoring processes. Control charts are not only used to detect changes in process, but also enable the quality engineers to prepare the system for an appropriate response before production of the faulty products. The main objective of most researches on control charts is developing a favorable and cost-eective design as well as most appropriate sampling methods. In most of the researches that have been done in this eld, the control charts are designed based on simple random sampling (SRS). However, there are many charts in the literature of statistical process controlthatarenotbasedonSRSandusealternativemethods such as ranked set sampling (RSS) or generalized methods. The RSS method is an alternative to SRS when taking actual measurement of the quality characteristic is costly or production of the faulty products is expensive. In this paper, in order to improve the performance of mixed EWMA-CUSUM (MEC) and mixed CUSUM-EWMA(MCE)controlchartsinestimatingthe population mean, ranked set sampling method rather than common simple random sampling method is used to design these mixed control charts. The performances of the proposed MEC-RSS and MCE-RSS control charts are evaluated through simulation studies using the average run length (ARL) criterion. Moreover, the performances of the proposed control charts are compared withthoseofCUSUM-SRS,CUSUM-RSS,EWMA-SRS, EWMA-RSS, MEC-SRS, and MCE-SRS control charts. The results revealed that RSS method improves control charts performance compared to SRS method because it providesanecientestimationofthemeanandvariance ofthepopulation. Inaddition, usingtherankedsetsampling scheme makes the proposed mixed control charts moresensitivetodetectingthesmallandmoderateshifts in the process mean than the other corresponding classical control charts. Moreover, based on the same sampling methods, the performances of mixed control charts are better than those of the single CUSUM and EWMA control charts.

There is considerable interest in the use of Statistical Process Control (SPC) in healthcare in the recent years because SPC tools lead to continual improvement in healthcare process. Control chart is one of the main tools of the SPC, however, they usually signal the out-of-control status with delay respect to real time of the change known as change point. Hence, change point estimation is important in healthcare with considering relationship between quality engineering and hospital epidemiology. There are varieties of quality characteristics in healthcare which are should be monitored over time. Therefore, in this paper, first, g and h control charts are described since these control charts are famous tools for monitoring events in healthcare. Then, corresponding step change point estimators using Maximum Likelihood Estimation (MLE) are proposed. In this regard, Mont Carlo simulation is used to evaluate performances of the proposed estimators based on accuracy and precision measures under all kinds of shifts. In addition, cardinality and coverage probability of confidence set are presented for the proposed estimators based on the logarithm of the likelihood function.
The simulation studies are conducted under different magnitudes of the step shifts to evaluate performances of the proposed change point estimators in both control charts. The results show that as the magnitude of the step change in the parameter of the distributions increase, the performance of the proposed change point estimators improve significantly in terms of precision and accuracy measures. Also, cardinality and coverage percentage of confidence set estimators are calculated and plotted to show the relationship among these measures and increasing and decreasing shifts under the given reference values. In addition, the simulation studies demonstrate that as the magnitude of the step change in the parameters of the distributions enlarge, cardinality of set estimators reduces and coverage percentage of confidence set estimators increases under the given references values. In general, results show that the proposed change point estimators perform satisfactory under all types of shifts. Finally, the performance of one of the proposed change point estimators is illustrated through an applied example in healthcare.

In some quality control applications, quality of a product or process can be characterized by a relationship between two or more variables that is typically referred to as profile. Research on profile monitoring commenced with studying simple linear profiles nearly fifteen years ago and then was pursued by investigating other types of profiles including multiple linear profiles, polynomial profiles, generalized linear profiles, and recently multivariate linear profiles. This paper has focused on the last case where several correlated quality characteristics can be modeled as a set of linear functions of one or more explanatory variables. Since, for the sake of simplicity, the model structure of our study consists of only one predictor, this type of profile has been referred to as multivariate simple linear profile. This paper deals with statistical process control in phase one where parameters of the model are to be estimated in order to develop a control chart for process monitoring. While outliers may hamper the expected performance of the classical regression estimators leading to erroneous interpretation of the process, this study resorts to robust regression estimators as the remedy of the estimation problem in the presence of outliers. Two robust approaches are proposed in our study to estimate parameters of multivariate simple linear profiles on the basis of robust regression M-estimates with bi-square weight function. In the first approach, the aggregation of estimates in the sample level is done by averaging while the second approach employs a second M-estimate to aggregate parameter estimates. Extensive simulation studies are conducted to investigate and compare the performance of the proposed estimators in terms of robustness and efficiency. The results show that in the absence of outliers or for small amount of contamination, the robust methods perform as well as the classical method, while for medium and large amounts of contamination the proposed estimators perform considerably better than the classical ones.

Control charts are used to monitor the variation of productions and processes. They can detect an outof- control signal when an assignable cause is occurred. Recently, many researchers have investigated dierent types of proles. Simple linear prole is one of the most important types of proles, which has many applications in industry, especially in calibration. The simple linear prole is characterized by a relationship between a response variable and one explanatory variable. Monitoring simple linear proles in both phases I and II is well studied in the literature. In phase I, the parameters are unknown and are estimated by historical dataset while the process parameters are known in phase II and the main aim is detecting assignable causes as quickly as possible. One of the most popular methods in phase II monitoring of the simple linear prole is EWMA 3 scheme. In the EWMA 3 scheme, three EWMA control charts are used to monitor the regression parameters of the simple linear prole, including intercept, slope and standard deviation- separately. In this paper, we specically concentrate on phase II monitoring of the simple linear proles through EWMA 3 scheme. Since the third statistic in the EWMA 3 scheme does not follow any specic distribution, we rst propose an EWMA control chart for monitoring the standard deviation instead of the third control chart used in the EWMA 3 scheme. Then, a variable sampling interval (VSI) method is proposed to improve the performance of the modied EWMA 3 control chart. In the VSI procedure, the sampling interval for the next sample depends on the current sample situation on the control chart and it varies over time. The performance of the proposed VSI EWMA 3 control chart is evaluated in terms of the adjusted average time to signal (AATS) obtained by a Markov chain approach. A numerical example is provided to demonstrate the eectiveness of the proposed adaptive control chart. The results show that the VSI EWMA 3 control chart is more eective than the FSI EWMA 3 control chart.

Detection of change time of the process parameters is a crucial problem in statistical process control (SPC), because more detailed information on the time and the pattern of a change can provide process managers with more eective clues for root-cause analysis and corresponding corrective actions. Parameter changes may take dierent forms including monotonic, trend, step shift, and so on. The issue frequently considered in the relevant studies involves only a single shift, whereas an out-of-control condition may be caused by multiple changes occurring in dierent points. On the other hand, recently, the issue of prole monitoring in which the quality of a process or product is represented by a functional relationship between a dependent and a number of explanatory variables has attracted a great deal of attention as witnessed by the growing number of publications in this area. Our investigation showed that the studies dealing with change point estimation in prole monitoring had neglected the case of multiple change points. This gap is noticed as the primary subject of this research and a clustering-based algorithm is proposed for estimating the number, as well as the location of the change points, while monitoring a simple linear prole. This clustering-based method, which is implemented in an iterative manner, is an extension of a similar method in monitoring univariate individual quality measures using Shewhart control charts. A decision rule determined via simulation using a pre-specied signicance level enables the algorithm to detect multiple change points of the parameters in addition to identifying out-of-control conditions. The proposed method is applied in the phase I of process monitoring, where a historical dataset is available and the ultimate goal is to nd reliable estimates of the process parameters, including the intercept and the slope of a linear prole model. Extensive simulation scenarios were devised to declare the performance of the aforementioned method.

P‌r‌o‌f‌i‌l‌e m‌o‌n‌i‌t‌o‌r‌i‌n‌g i‌s o‌n‌e o‌f t‌h‌e n‌e‌w r‌e‌s‌e‌a‌r‌c‌h a‌r‌e‌a‌s i‌n s‌t‌a‌t‌i‌s‌t‌i‌c‌a‌l p‌r‌o‌c‌e‌s‌s c‌o‌n‌t‌r‌o‌l i‌n w‌h‌i‌c‌h t‌h‌e q‌u‌a‌l‌i‌t‌y o‌f a p‌r‌o‌c‌e‌s‌s o‌r p‌r‌o‌d‌u‌c‌t c‌a‌n b‌e c‌h‌a‌r‌a‌c‌t‌e‌r‌i‌z‌e‌d b‌y a r‌e‌l‌a‌t‌i‌o‌n‌s‌h‌i‌p b‌e‌t‌w‌e‌e‌n a r‌e‌s‌p‌o‌n‌s‌e v‌a‌r‌i‌a‌b‌l‌e a‌n‌d o‌n‌e o‌r m‌o‌r‌e e‌x‌p‌l‌a‌n‌a‌t‌o‌r‌y v‌a‌r‌i‌a‌b‌l‌e‌s. T‌h‌i‌s r‌e‌l‌a‌t‌i‌o‌n‌s‌h‌i‌p, w‌h‌i‌c‌h c‌a‌n b‌e l‌i‌n‌e‌a‌r, n‌o‌n‌l‌i‌n‌e‌a‌r, o‌r e‌v‌e‌n a c‌o‌m‌p‌l‌i‌c‌a‌t‌e‌d m‌o‌d‌e‌l, i‌s r‌e‌f‌e‌r‌r‌e‌d t‌o a‌s p‌r‌o‌f‌i‌l‌e. M‌o‌s‌t o‌f t‌h‌e c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t‌s i‌n p‌r‌o‌f‌i‌l‌e m‌o‌n‌i‌t‌o‌r‌i‌n‌g l‌i‌t‌e‌r‌a‌t‌u‌r‌e a‌r‌e d‌e‌s‌i‌g‌n‌e‌d o‌n t‌h‌e b‌a‌s‌i‌s o‌f f‌i‌x‌e‌d s‌a‌m‌p‌l‌i‌n‌g r‌a‌t‌e. I‌n o‌t‌h‌e‌r w‌o‌r‌d‌s i‌n t‌h‌e t‌r‌a‌d‌i‌t‌i‌o‌n‌a‌l c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t‌s, s‌a‌m‌p‌l‌e‌s w‌i‌t‌h f‌i‌x‌e‌d s‌i‌z‌e a‌r‌e t‌a‌k‌e‌n a‌t f‌i‌x‌e‌d i‌n‌t‌e‌r‌v‌a‌l‌s. D‌e‌s‌p‌i‌t‌e m‌a‌n‌y a‌d‌v‌a‌n‌t‌a‌g‌e‌s, t‌h‌e‌s‌e c‌h‌a‌r‌t‌s a‌r‌e n‌o‌t s‌e‌n‌s‌i‌t‌i‌v‌e e‌n‌o‌u‌g‌h t‌o d‌e‌t‌e‌c‌t s‌m‌a‌l‌l e‌v‌e‌n m‌o‌d‌e‌r‌a‌t‌e s‌h‌i‌f‌t‌s. C‌o‌n‌s‌e‌q‌u‌e‌n‌t‌l‌y, i‌n o‌r‌d‌e‌r t‌o i‌m‌p‌r‌o‌v‌e t‌h‌e p‌e‌r‌f‌o‌r‌m‌a‌n‌c‌e o‌f t‌h‌e c‌o‌n‌v‌e‌n‌t‌i‌o‌n‌a‌l f‌i‌x‌e‌d s‌a‌m‌p‌l‌i‌n‌g r‌a‌t‌e c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t‌s, a‌d‌a‌p‌t‌i‌v‌e c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t‌s h‌a‌v‌e b‌e‌e‌n p‌r‌o‌p‌o‌s‌e‌d. W‌h‌e‌n a‌d‌a‌p‌t‌i‌v‌e c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t‌s a‌r‌e a‌p‌p‌l‌i‌e‌d, o‌n‌e o‌r m‌o‌r‌e d‌e‌s‌i‌g‌n p‌a‌r‌a‌m‌e‌t‌e‌r‌s v‌a‌r‌y d‌u‌r‌i‌n‌g t‌h‌e p‌r‌o‌c‌e‌s‌s b‌a‌s‌e‌d o‌n r‌e‌c‌e‌n‌t d‌a‌t‌a o‌b‌t‌a‌i‌n‌e‌d f‌r‌o‌m t‌h‌e p‌r‌o‌c‌e‌s‌s. A‌s a r‌e‌s‌u‌l‌t t‌h‌e p‌r‌o‌c‌e‌s‌s c‌h‌a‌n‌g‌e‌s c‌o‌u‌l‌d b‌e d‌e‌t‌e‌c‌t‌e‌d m‌o‌r‌e q‌u‌i‌c‌k‌l‌y.l‌o‌o‌s‌e‌n‌e‌s‌s=1 I‌n t‌h‌i‌s p‌a‌p‌e‌r f‌o‌u‌r a‌d‌a‌p‌t‌i‌v‌e c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t‌s a‌r‌e c‌o‌n‌s‌i‌d‌e‌r‌e‌d f‌o‌r m‌o‌n‌i‌t‌o‌r‌i‌n‌g s‌i‌m‌p‌l‌e l‌i‌n‌e‌a‌r p‌r‌o‌f‌i‌l‌e‌s i‌n p‌h‌a‌s‌e I‌I b‌a‌s‌e‌d o‌n t‌h‌e m‌u‌l‌t‌i‌v‌a‌r‌i‌a‌t‌e T2 c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t. T‌h‌e‌s‌e c‌h‌a‌r‌t‌s i‌n‌c‌l‌u‌d‌e v‌a‌r‌i‌a‌b‌l‌e s‌a‌m‌p‌l‌e s‌i‌z‌e (V‌S‌S), v‌a‌r‌i‌a‌b‌l‌e s‌a‌m‌p‌l‌i‌n‌g i‌n‌t‌e‌r‌v‌a‌l (V‌S‌I), v‌a‌r‌i‌a‌b‌l‌e s‌a‌m‌p‌l‌e s‌i‌z‌e a‌n‌d s‌a‌m‌p‌l‌i‌n‌g i‌n‌t‌e‌r‌v‌a‌l (V‌S‌S‌I) a‌n‌d v‌a‌r‌i‌a‌b‌l‌e s‌a‌m‌p‌l‌i‌n‌g i‌n‌t‌e‌r‌v‌a‌l w‌i‌t‌h s‌a‌m‌p‌l‌i‌n‌g a‌t f‌i‌x‌e‌d t‌i‌m‌e‌s (V‌S‌I‌F‌T) T2 c‌h‌a‌r‌t‌s. T‌h‌e p‌e‌r‌f‌o‌r‌m‌a‌n‌c‌e o‌f t‌h‌e p‌r‌o‌p‌o‌s‌e‌d c‌h‌a‌r‌t‌s i‌s c‌o‌m‌p‌a‌r‌e‌d i‌n t‌e‌r‌m‌s o‌f t‌h‌e a‌v‌e‌r‌a‌g‌e t‌i‌m‌e t‌o s‌i‌g‌n‌a‌l (A‌T‌S). T‌o c‌o‌m‌p‌u‌t‌e t‌h‌e A‌T‌S, t‌h‌e M‌a‌r‌k‌o‌v c‌h‌a‌i‌n a‌p‌p‌r‌o‌a‌c‌h i‌s u‌s‌e‌d. N‌u‌m‌e‌r‌i‌c‌a‌l e‌x‌a‌m‌p‌l‌e‌s s‌h‌o‌w t‌h‌a‌t a‌d‌d‌i‌n‌g a‌d‌a‌p‌t‌i‌v‌e f‌e‌a‌t‌u‌r‌e‌s t‌o t‌r‌a‌d‌i‌t‌i‌o‌n‌a‌l c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t‌s i‌n‌c‌r‌e‌a‌s‌e t‌h‌e p‌o‌w‌e‌r o‌f T2 c‌o‌n‌t‌r‌o‌l c‌h‌a‌r‌t i‌n d‌e‌t‌e‌c‌t‌i‌n‌g t‌h‌e s‌h‌i‌f‌t‌s i‌n t‌h‌e p‌a‌r‌a‌m‌e‌t‌e‌r‌s o‌f t‌h‌e s‌i‌m‌p‌l‌e l‌i‌n‌e‌a‌r p‌r‌o‌f‌i‌l‌e‌s. A‌m‌o‌n‌g t‌h‌e m‌e‌n‌t‌i‌o‌n‌e‌d a‌d‌a‌p‌t‌i‌v‌e c‌h‌a‌r‌t‌s, V‌S‌S‌I T2 c‌h‌a‌r‌t i‌s t‌h‌e m‌o‌s‌t e‌f‌f‌i‌c‌i‌e‌n‌t o‌n‌e t‌o d‌e‌t‌e‌c‌t s‌h‌i‌f‌t‌s i‌n s‌l‌o‌p‌e a‌n‌d i‌n‌t‌e‌r‌c‌e‌p‌t w‌h‌i‌l‌e V‌S‌I a‌n‌d V‌S‌I‌F‌T T2 c‌h‌a‌r‌t‌s h‌a‌v‌e t‌h‌e b‌e‌s‌t a‌b‌i‌l‌i‌t‌y f‌o‌r d‌e‌t‌e‌c‌t‌i‌n‌g s‌h‌i‌f‌t‌s i‌n s‌t‌a‌n‌d‌a‌r‌d d‌e‌v‌i‌a‌t‌i‌o‌n.

One of the most important problems of the designs proposed by traditional economic-statistical approaches of control charts is inefficiency in the face of uncertainty. Uncertainty in the parameters of economic-statistical models may lead to failure in rapidly detecting changes in processes and impose greater costs to the organization. Monitoring the machining process in an automotive industry explains the necessity of considering the robust approach to the control charts. This research intends the control charts design for monitoring process quality characteristics in conditions of uncertainty in cost and process parameters. The robust design ensures that the proposed control chart alarms the process changes earlier than the time set by the user, despite the uncertainty in model parameters. The resulted robust optimal solutions not only ensure the efficiency of solutions in any realization of parameters, but also facilitate the practical implementation of control charts and reduce organization costs through improving the quality of process outgoings.

In this paper، drift change point estimation in the mean of polynomial profiles is considered. For this purpose، the proposed change point estimator is computed using maximum likelihood approach. Performance of the proposed estimator is evaluated using Monte Carlo simulations when T2 control chart issues an out-of-control signal. Simulation results show that the performance of the proposed estimator improves when the magnitude of shifts increases. Also، the desirable performance of the proposed estimator is clear in all range of shifts.

The main objective of performed researches in the field of multivariate statistical process control is to consider the correlation between multiple qualitative attributes for one step of process. In the second phase of the multivariate process control procedure, the rest of process is being studied whether it is under control, using the achieved control limits from the first phase and future observations. So, having found the outlier points of the first phase before the control limits to be computed, it is considered as an important issue. In order to detect these outlier points, Variety of techniques, that the majority of them rely on primary random samples, are proposed. These primary random points can effect on the precision of algorithms and final solution of the problem. In this paper, a robust estimator is issued applying hierarchical clustering technique that is not affected by outlier data in sample or unusual data, rather than the model assumptions and will detect the outlier points in multivariate control charts of the first phase in order to get them removed. Then, the proposed method is evaluated by creating the variety of scenarios from outlier points and the final outcome is compared with the Classical Hoteling and the least determinant covariance estimator. The evaluations represent that the proposed method detects more outlier points in less time rather than the former performed researches.