Charts In Terms Research Articles

Correlation of anthropometric measurements with pretransfusion haemoglobin in transfusion dependent thalassemia major children

Background: The etio-pathogenesis of growth failure in transfusion dependent thalassemia major (TDTM) children is mainly because of chronic anemia, iron overload and endocrine complications. Aim was to determine the association between anthropometric measurements with pre-transfusion haemoglobin in TDTM children. Methods: Present cross-sectional study included total 55 children between 5-18 years of age who were diagnosed as TDTM. Mean pre transfusion hemoglobin of last 1 year was obtained from previous medical records. Anthropometric measurements like weight in kg and height in cm measured while body mass index (BMI) was calculated using standard formula in each patient. Weight for age, height for age, and BMI were plotted on WHO 2006 and Indian academy of paediatrics 2015 combined growth charts in terms of percentile. Data was analysed statistically. Results: Total 55 children of TDTM between 5-18 years of age were studied. The mean age was 10.42(4.07) years. The mean weight (kg), height (cm) and BMI was 23.76 (7.5), 123.94 (18.13) and 15.24 (1.76) respectively. Fifty-one (92.73%) children were having BMI less than 3rd centile (Underweight). Forty (93.02%) and 11 (91.66%) children between age group of 5-10 years and more than 10 years were underweight and was statistically significant (p<0.05). Total 50 (94.33%) children were having mean pre transfusion hemoglobin below 9 gm/dl who were underweight which was statistically significant. Conclusions: Low pre-transfusion hemoglobin is one of the risk factors for growth failure in children with TDTM and it should be maintained above 9 gm/dl may for normal growth in children with TDTM.

Control charts using half-normal and half-exponential power distributions using repetitive sampling

This manuscript presents the development of an attribute control chart (ACC) designed to monitor the number of defective items in manufacturing processes. The charts are specifically tailored using time-truncated life test (TTLT) for two lifetime data distributions: the half-normal distribution (HND) and the half-exponential power distribution (HEPD) under a repetitive sampling scheme (RSS). To assess the effectiveness of the proposed control charts, both in-control (IC) and out-of-control (OOC) scenarios are considered by deriving the average run length (ARL). Various factors, including sample sizes, control coefficients, and truncated constants for shifted phases, are taken into account to evaluate the performance of the charts in terms of ARL. The behavior of ARLs is analyzed in the shifted process by introducing shifts in its parameters. The superiority of the HEPD-based chart is highlighted by comparing it with both the HND-based ACC and the ACC based on the Exponential distribution (ED) under TTLT using RSS. The results showcase the superior performance of the proposed HEPD-based chart, indicated by smaller ARL values. Additionally, the benefits of another proposed ACC using HND are compared with the ED-based ACC under RSS, further confirming the effectiveness of the HND-based approach through smaller ARLs Finally, the proposed control charts are evaluated through simulation testing and real-life implementation, emphasizing their practical applicability in real-world manufacturing settings.

Improving Sensitivity of the DEWMA Chart with Exact ARL Solution under the Trend AR(p) Model and Its Applications

The double exponentially weighted moving average (DEWMA) chart is a control chart that is a vital analytical tool for keeping track of the quality of a process, and the sensitivity of the control chart to the process is evaluated using the average run length (ARL). Herein, the aim of this study is to derive the explicit formula of the ARL on the DEWMA chart with the autoregressive with trend model and its residual, which is exponential white noise. This study shows that this proposed method was compared to the ARL derived using the numerical integral equation (NIE) approach, and the explicit ARL formula decreased the computing time. By changing exponential parameters that were relevant to evaluating in various circumstances, the sensitivity of AR(p) with the trend model with the DEWMA chart was investigated. These were compared with the EWMA and CUSUM charts in terms of the ARL, standard deviation run length (SDRL), and median run length (MRL). The results indicate that the DEWMA chart has the highest performance, and when it was small, the DEWMA chart had high sensitivity for detecting processes. Digital currencies are utilized to demonstrate the efficacy of the proposed method; the results are consistent with the simulated data. Doi: 10.28991/ESJ-2023-07-06-03 Full Text: PDF

THE PERFORMANCES OF MIXED EWMA-CUSUM CONTROL CHARTS BASED ON MEDIAN-BASED ESTIMATORS UNDER NON-NORMALITY

Exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts have been regularly used to monitor small process mean shifts. More recently, a mixture of EWMA and CUSUM charts known as mixed EWMA-CUSUM (MEC) control chart has been introduced for better small shift detection. However, like its predecessor, the MEC chart requires the normality assumption to ensure optimal performances. In the presence of outliers, which is the cause of non-normality, the parameters of the chart may be overestimated, leading to an unreliable monitoring process. To mitigate this problem, this paper employed median-based estimators namely, the median and modified one-step M-estimator (MOM), to control the location parameter via the MEC control chart. In this study, the performance of robust MEC charts for Phase II monitoring of location was compared with the standard MEC chart that is based on the sample mean. The performance of the robust MEC charts in terms of the average run length (ARL) on various g-and-h distributions clearly shows that a robust MEC chart based on the MOM estimator performs well regardless of the distributional shapes.

Adaptive multivariate dispersion control chart with application to bimetal thermostat data

Adaptive EWMA (AEWMA) control charts have gained remarkable recognition by monitoring productions over a wide range of shifts. The adaptation of computational statistic as per system shift is the main aspect behind the proficiency of these charts. In this paper, a function-based AEWMA multivariate control chart is suggested to monitor the stability of the variance–covariance matrix for normally distributed process control. Our approach involves utilizing an unbiased estimator applying the EWMA statistic to estimate the process shift in real-time and adapt the smoothing or weighting constant using a suggested continuous function. Preferably, the Monte Carlo simulation method is utilized to determine the characteristics of the suggested AEWMA chart in terms of proficient detection of process shifts. The underlying computed results are compared with existing EWMA and existing AEWMA charts and proved to outperform in providing quick detection for different sizes of shifts. To illustrate its real-life application, the authors employed the concept in the bimetal thermostat industry dataset. The proposed research contributes to statistical process control and provides a practical tool for the solution while monitoring covariance matrix changes.

A New Hybrid Exponentially Weighted Moving Average Control Chart with Repetitive Sampling for Monitoring the Coefficient of Variation

The implementation of Statistical Quality Control (SQC) has been tracked in various areas, such as agriculture, environment, industry, and health services. The employment of SQC methodologies is frequently employed for monitoring and identification of process irregularities across various fields. This research proposes and implements a novel SQC methodology in agricultural areas. A control chart is one of the SQC tools that facilitates real-time monitoring of multiple activities, including agricultural yield, industrial yield, and hospital outcomes. Advanced control charts with symmetrical data are being subjected to the new SQC method, which is suitable for this purpose. This research aims to develop a novel hybrid exponentially weighted moving average control chart for detecting the coefficient of variation (CV) using a repetitive sampling method called the HEWMARS-CV control chart. It is an effective tool for monitoring the mean and variance of a process simultaneously. The HEWMARS-CV control chart used the repetitive sampling scheme to generate two pairs of control limits to enhance the performance of the control chart. The proposed control chart is compared with the classical HEWMA and Shewhart control charts regarding the average run length (ARL) when the data has a normal distribution. The Monte Carlo simulation method is utilized to approximate the ARL values of the proposed control charts to determine their performance. The proposed control chart detects small shifts in CV values more effectively than the existing control chart. An illustrative application related to monitor the wheat yield at Rothamsted Experimental Station in Great Britain is also incorporated to demonstrate the efficiency of the proposed control chart. The efficiency of the proposed HEWMARS-CV control chart on the real data shows that the proposed control chart can detect a shift in the CV of the process, and it is superior to the existing control chart in terms of the average run length.

On efficient change point detection using a step cumulative sum control chart

Control charts are widely used to monitor the stability of processes and Shewhart is the most commonly used type of chart because of the simplicity of its structure. To improve the performance, there are several modifications on control chart suggested since its proposal like cumulative sum and exponentially weighted moving average control charts. The cumulative sum chart accumulates all the deviations from target mean higher than a specific design parameter which can be used to controls the sensitivity of this chart. The optimal choice of this design parameter is half of the target shift size. This results into an optimal performance in terms of quick detection of shift that is equal to the target shift size. For shift sizes other than target, the cumulative sum chart loses its performance and becomes inferior to other control charts in terms of average run length. Since we rarely know the process shift size that is going to occur in future, this study proposes a revision in the structure of cumulative sum chart. The adjusted cumulative sum chart is shown to be very flexible for a wide range of shifts, when designed against a target shift size. The proposed adjusted cumulative sum control chart is compared with cumulative sum, exponentially weighted, combined Shewhart-CUSUM, progressive mean, double progressive mean and mixed EWMA-CUSUM control charts in terms of zero- and steady-state average run lengths. The superiority zones are identified and the results are supported by a real-life implementation of the proposed scheme.

A joint monitoring of the process mean and variance with a generally weighted moving average maximum control chart

Single control charts are widely used to control assignable causes that shift the process due to variations in the mean and the dispersion. In the present article, the exponentially weighted moving average maximum (EWMA-Max) control chart is extended to the new single generally weighted moving average maximum (referred as GWMA-Max) control chart for joint monitoring of the process mean and variability. The proposed chart is compared with the EWMA-Max and DEWMA-Max charts in terms of the run-length performance measures. The results reveal that the GWMA-Max chart is very efficient in detecting small shifts in the process mean and variability concurrently. Finally, a practical implementation of the GWMA-Max chart is displayed using real and simulated datasets.

Improved Control Charts for Phase II Monitoring of Simple Linear Profiles Using Auxiliary Information

In some manufacturing or non-manufacturing systems, the process outcome is better characterized by a relationship between a main variable and some associated supporting variables. The monitoring of this functional relationship over time is termed as profile monitoring. This study aims to improve Phase II monitoring of simple linear profiles by using auxiliary information which is correlated with the main variable. To accomplish that, using a mean estimator, two auxiliary information-based (AIB) control charts namely AIB-MEWMA and AIB-DMEWMA charts are proposed for detecting different shifts in model parameters. Using two numerical examples based on simulation studies, the sensitivity of the proposed control charts is evaluated and compared with the existing MEWMA and DMEWMA charts in terms of the average run length (ARL) metric. The results of simulations reveal that the proposed charts perform better than the existing MEWMA and DMEWMA charts. The applicability of the proposed control charting methods is demonstrated using a real-life data example from the cylinder production process.

Efficient control chart-based monitoring of scale parameter for a process with heavy-tailed non-normal distribution

<abstract> <p>Statistical process control is a procedure of quality control that is widely used in industrial processes to enable monitoring by using statistical techniques. All production processes are faced with natural and unnatural variations. To maintain the stability of the production process and reduce variation, different tools are used. Control charts are significant tools to monitor a production process. In this article, we design an extended exponentially weighted moving average (EEWMA) chart under the assumption of inverse Maxwell (IM) distribution, an IM EEWMA (IMEEWMA) control chart. We have estimated the performance of the proposed chart in terms of various run-length (RL) properties, including the average RL, standard deviation of the RL and median RL. We have also carried out a comparative analysis of the proposed chart with the existing Shewhart-type chart for IM distribution (VIM chart) and IM exponential weighted moving average (IMEWMA) chart. We observed that the proposed IMEEWMA chart performed better than the VIM chart and IMEWMA chart in terms of the ability to detect small and moderate shifts. To demonstrate its practical application, we have applied the IMEEWMA chart, along with existing control charts, to monitor the lifetime of car brake pad data. This real-world example illustrates the superiority of the IMEEWMA chart over its counterparts in industrial scenarios.</p> </abstract>

A bivariate exponentially weighted moving average control chart based on exceedance statistics

Nonparametric control charts are more practical tools for statistical process control (SPC), as they are robust in situations in which the underlying distribution is unknown. Comprehensibility and simplicity of exceedance statistics provide great convenience to analysts in multivariate SPC applications. By using the exceedance statistics, analysts save time and avoid complex calculations. Therefore, in this study, a bivariate nonparametric exponentially weighted moving average (BEWMA-EX) control chart is proposed based on the exceedance statistics to detect the shifts in the location parameter. The performance of the proposed BEWMA-EX chart is compared with the multivariate sign EWMA (MSEWMA) control chart under some well-known bivariate distributions, such as bivariate normal, t, and gamma distributions. The BEWMA-EX chart outperforms the MSEWMA control chart in terms of run-length properties. To highlight the importance of the stated study, the BEWMA-EX chart is implemented on industrial engineering datasets related to coal power plant and aluminum electrolytic capacitor manufacturing processes. The findings are promising and support the simulated results.

The RNVP-based process monitoring with transforming non-normal data to multivariate normal data

In the modern industry, process monitoring with control charts is essential to improve processes by decreasing defects. Control charts, which are based on grounded statistical theory, have been used as an online process monitoring method to detect out-of-control. However, because control charts assume that the process data are normally distributed (a.k.a. the assumption of normality), they do not perform as well as failing to detect out-of-control when assumptions are not held. In practice, most process data violate the assumption of normality, the application of control charts in real manufacturing sites is limited. Therefore, to address this limitation, this study proposes a real-valued non-volume preserving (RNVP)-based control chart. The proposed method first transforms the process data to follow a multivariate normal distribution, and then monitors the transformed data using a control chart. As a result of conducting numerical experiments to evaluate the effectiveness of the proposed method, it was found that the performance of the proposed method was superior to that of the existing control charts in terms of Type II error rate and average run length.

A generalized multiple dependent state sampling chart based on ridge penalized likelihood ratio for high-dimensional covariance matrix monitoring

Online monitoring of high-dimensional processes variability in which the number of variables is larger than the sample size is a challenging issue for quality practitioners because the sample covariance matrix is not invariable. To deal with this challenge, a generalized multiple dependent state sampling (GMDS) chart based on ridge penalized likelihood ratio (RPLR) statistic is developed for Phase II monitoring of multivariate process variability under high-dimensional setting. The developed control chart benefits from three advantages: (1) departing from the conventional covariance matrix charts, it can be efficiently employed for both spars and non-spars covariance matrices; (2) it is able to detect spars shift patterns in which only a few covariance matrix elements are deviated from their nominal values; and (3) it outperforms the detectability of the RPLR chart in terms of average run length (ARL) and standard deviation of run length (SDRL). The performance of RPLR, MDS-RPLR, and GMDS-RPLR charts are compared using extensive simulation studies by considering different diagonal and/or off-diagonal covariance matrix disturbance. Moreover, sensitivity analysis are provided to analyze how the number of process variables and GMDS parameter affect the run length properties of the developed chart.

Enhanced CUSUM and dual CUSUM mean charts

The CUSUM (C) and dual C (DC) charts are well-known because of their sensitive nature against small-to-moderate shifts that occur in the process parameter(s). In this study, we propose new auxiliary information based (AIB) control charts for monitoring the process mean, which include Brownian motion-based C (BC), new BC (NBC), NBC with Crosier C (NBCC), dual NBC (DNBC) and dual NBCC (DNBCC) charts. In addition, fast initial response feature is also added to the proposed charts. We use Monte Carlo simulations to compute the run length properties of the control charts. Based on detailed run length comparisons, it is found that the proposed AIB charts are uniformly better than the existing AIB charts in terms of expected weighted and relative average run length benchmarks. An important concern all around the world is the quality and safety of drinking water. Several water quality tests are available that provide required information about the health of the waterway. Here, a water quality test dataset is taken on two variables, namely pH and total solids, and the proposed AIB charts are used to monitor the mean level of pH in the water.

Attribute Control Chart for Rayleigh Distribution Using Repetitive Sampling under Truncated Life Test

A control chart is an important tool in statistical process monitoring that is useful to monitor and improve production process quality. In this article, an attribute control chart using repetitive sampling under a truncated life test is proposed for monitoring the mean life of the product where the lifetime follows the Rayleigh distribution. The repetitive sampling parameters and the control limit coefficients of the chart are determined so that the in-control average run length (ARL) is very close to the target ARL. Tables of ARL values for various shift sizes in the scale parameter were presented, and the performance of the proposed chart is compared with the existing attribute control charts using the out-of-control ARL. The proposed control chart is shown to outperform the existing control charts in terms of ARL. An illustrative example is given to demonstrate the application of the proposed chart.

Deep learning-based residual control chart for count data

Statistical process control for count data has difficulty overcoming multicollinearity. In this paper, we propose a new deep learning residual control chart based on the asymmetrical count response variable when there are highly correlated explanatory variables. We implement and compare different methods such as neural network, deep learning, principal component analysis based Poisson regression, principal component analysis based negative binomial regression, nonlinear principal component analysis based Poisson regression, and nonlinear principal component analysis based negative binomial regression in terms of the root mean squared error. Using two asymmetrical simulated datasets generated by the combined multivariate normal, binary and copula functions, the neural network and deep learning have a smaller mean, median, and interquartile range when compared to the principal component analysis based Poisson regression, principal component analysis based negative binomial regression, nonlinear principal component analysis based Poisson regression, and nonlinear principal component analysis based negative binomial regression. We also compare the deep learning and neural network based residual control charts in terms of the average run length with the copula based asymmetrical simulated data and real takeover bids data.

Optimal design of an improved X¯ and R control chart for joint monitoring of process location and dispersion

Enhancing the detection power of control charts for detecting small to moderate process changes is always the focus of attention in academia. To improve the detection ability of conventional [Formula: see text] and R control charts, an improved joint [Formula: see text] and R chart, which combines the ordinary [Formula: see text] and R charts with runs rules of the type ‘ r out of m’, is proposed to monitor the process location and dispersion simultaneously. A finite Markov chain imbedding approach is employed to develop the resulting control scheme. A comparative study is conducted to investigate the performance of the proposed chart in terms of the out-of-control average run length. The statistical performance of the suggested chart when the process parameters are estimated is also evaluated. The numerical results indicate that (1) the proposed chart improves the detection ability of traditional [Formula: see text] and R charts in detecting small to moderate process shifts; (2) the suggested scheme performs better than the EWMA and CUSUM schemes in detecting large process fluctuations. Furthermore, when specific values of r and m are selected, the statistical performance of the proposed chart for detecting small shifts is close to or even better than that of its competitors; (3) the run length performance of the proposed chart is greatly affected by parameter estimation, especially for small process shifts.

Multivariate Hotelling T2 Control Chart for Monitoring Some Quality Characteristics in Medium Density Fiberboard Manufacturing Process

Statistical process control tools are of great importance in terms of controlling manufacturing processes and improving product quality. In this study, the manufacturing process of medium density fiberboards manufactured in a company operating in the forest products industry was monitored by using multivariate Hotelling T2 statistical process control chart in terms of some quality characteristics. The T2 values of the signals detected by the Hotelling T2 control chart were also decomposed. By the decomposition of T2 values, it was determined which quality characteristics contributed more to each signal. It was seen that the process was not in control for Hotelling T2 control chart, which reveals the shift level in the mean of quality characteristics. As a result, the application of Hotelling T2 allowed fast detection of possible abnormalities in the process. The decomposition of T2 values successfully revealed which quality characteristics contributed significantly to the signals. Besides, it was concluded that, for monitoring, the Hotelling T2 chart was able to employ simultaneously different quality characteristics of medium density fiberboard. The current application study also contributed to the emergence of the root causes of the large shifts in the process. In conclusion, the findings of the study enabled the company to ensure the process stability and to facilitate decision-making on actions to be taken for quality improvement.

Anchoring the Image of the Sea: Copying Coastlines on Manuscript Nautical Charts from the Late Middle Ages and the Early Modern Period

ABSTRACT The birth of the nautical chart in the late medieval period is seen as a watershed moment in the history of cartography. So far, however, the artisanal practices that permitted the proliferation of sea charts have remained poorly understood and little evidence has been recovered from extant charts on which to base the production history of the surviving charts. This article describes a systematic exploration of the techniques employed in the copying of coastlines on manuscript charts between the fourteenth and early seventeenth centuries. Attention is drawn to the ways different processes shaped contemporary late-medieval and early-modern understanding of the Mediterranean and what the techniques may reveal of that thinking. By reframing the charts in terms of their characteristics as drawings and placing map making in the broader context of two-dimensional graphic art, and by making use of the ever-growing corpus of high-resolution digital reproductions, we gain new insights into the chartmakers’ changing approaches to the transmission of geographical information. At the same time, a number of directions for further research are opened up.

Polish growth charts for preterm infants - comparison with reference Fenton charts.

Proper infant classification, particularly a preterm infant, as small or large for gestational age, is crucial to undertake activities to improve postnatal outcomes. This study aimed to assess the usability of the Fenton preterm growth charts to evaluate the anthropometric parameters of Polish preterm neonates. In this single-center, retrospective study data extracted from the medical documentation of preterm neonates born 2002-2013 were analyzed. Body weight, body length, and head circumference were evaluated and used to develop growth charts, which were compared with the reference Fenton growth charts. This study included 3,205 preterm neonates, of whom 937 were born before 30 weeks of pregnancy. Overall, 11.04%, 3.3%, and 5.2% of neonates were below the 10th percentile on the Fenton charts for birth weight, body length, and head circumference, respectively. Only 26 (6.67%) of 390 analyzed anthropological parameters differed significantly between the study and the Fenton groups. Statistically significant differences between the study and the Fenton populations were found only in body length for both sexes, and in head circumference for female neonates. The growth charts developed in this study for a population of Polish preterm neonates corresponded to the Fenton charts in terms of birth weight but differed in terms of body length and head circumference. Our findings suggest the need to evaluate growth charts for Polish preterm newborns.