Assessment on the mechanical,structural, and thermal attributes of green graphene-based water soluble polymer electrolyte composites

In the current study, graphene oxide (GO) was prepared using green chemistry with modified Hummer's method without incorporating sodium nitrate (NaNO₃). Solvent casting was employed to fabricate GO-doped poly(ethylene oxide) (PEO), that is, PEO/GO composites with various proportion of Na₂SO₄ and were then subjected to characterization via advanced spectroscopic techniques for different physicochemical aspects to estimate their potential applications as marketable products. XRD analysis explored that fabricated composites are more crystalline than neat PEO. PEO/GO/Na₂SO₄ composite films offered maximum crystallinity. SEM displayed the same trend. TG/DTA thermogram exposed better thermal stability than pristine polymer. FTIR studies confirmed complexation among hybrid's components. Elongation-at-break and Young's modulus displayed an enhancing behavior with an incremental loading of salt and filler. In terms of mechanical performance, composite of PEO with 0.37 wt % GO and 0.08 g salt was found to be an ideal composition during the course of study. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48376.     

Bayesian EWMA control chart with measurement error under different loss functions

This paper presents the Bayesian EWMA control chart under two different loss functions (i) squared error loss function (SELF) and (ii) linex loss function (LLF) in the presence of measurement error (ME). We take posterior and posterior predictive distribution under the conjugate prior. We used a linear covariate model in the existence of ME to evaluate the control chart. We also studied the effects of multiple measurements and linear increasing variance methods in the existence of the ME. The average run length and standard deviation of run length are used to measure the performance of the Bayesian EWMA control chart with ME. We conducted the Monte Carlo simulation study to evaluate the performance of Bayesian EWMA control chart with ME. A real-life data example is also presented to demonstrate the application of the control chart.     

Function-based adaptive exponentially weighted moving average dispersion control chart

In this study, a new process dispersion monitoring control chart is proposed based on a function-based adaptation to select the smoothing constant value, named as a function-based adaptive exponentially weighted moving average (EWMA) dispersion control chart. It is suggested to track shift ranges first expected in process dispersion by opting for smoothing constant computation with the help of a function. The shift magnitude assessment is made by an unbiased estimator that determines the smoothing constant value through the proposed function. The enhanced efficiency of the proposed chart can be assessed in terms of smaller run-length profiles, which are determined through Monte Carlo simulations. The proposed chart is compared with the existing adaptive EWMA dispersion chart, and it turned out to perform substantially efficiently in detecting all kinds of decreasing and increasing process dispersion shift magnitudes. Moreover, a real-life dataset application is explained in the example section to elaborate on the ease of implementation in the real-life scenario.     

Maximum hybrid exponentially weighted moving average control chart in the presence of measurement error by using auxiliary information

Hybrid control charts have become part of statistical process control (SPC) but still, need more emphasis. Researchers are developing charts for joint monitoring of process mean and variance shifts just like Max-EWMA and their hybrid version using auxiliary information but are ignoring the effect of measurement error on the efficiency of charts. We propose maximum hybrid exponentially weighted moving average with measurement error using auxiliary information and name it Max-HEWMAMEAI control chart. The efficiency of this chart is proved through calculations of average run lengths ($ARLs$) and standard deviations of run lengths (SDRLs) using the Monte Carlo simulations method whereas, $ARLs$ and $\text{◂⋅▸}SDRLs$ are shown in tabular form. The effect of measurement error on the efficiency of the chart has been analyzed and the impact of multiple measurements to reduce the error effect has been studied using the covariate model. Real-life application is also part of this article to support the simulation results.     

An adaptive EWMA control chart for monitoring the process mean in Bayesian theory under different loss functions

The Shewhart control chart is used for detecting the large shift and an exponentially weighted moving average (EWMA) control chart is used for detecting the small/moderate shift in the process mean. A scheme that combines both the Shewhart control chart and the EWMA control chart in a smooth way is called the adaptive EWMA (AEWMA) control chart. In this paper, we proposed a new AEWMA control chart for monitoring the process mean in Bayesian theory under different loss functions (LFs). We used informative (conjugate prior) under two different LFs: (1) squared error loss function and (2) linex loss function for posterior and posterior predictive distributions. We used the average run length and standard deviation of run length to measure the performance of the AEWMA control chart in the Bayesian theory. A comparative study is conducted for comparing the proposed AEWMA control chart in Bayesian theory with the existing Bayesian EWMA control chart. We conducted a Monte Carlo simulation study to evaluate the proposed AEWMA control chart. For the implementation purposes, we presented a real-data example.     

Bayesian EWMA control charts based on Exponential and transformed Exponential distributions

In this paper, we proposed the Bayesian exponentially weighted moving average (EWMA) control charts for mean under the nonnormal life time distributions. We used the time between events data which follow the Exponential distribution and proposed the Bayesian EWMA control charts for Exponential distribution and transformed Exponential distributions into Inverse Rayleigh and Weibull distributions. In order to develop the control charts, we used a uniform prior under five different symmetric and asymmetric loss functions (LFs), namely, squared error loss function (SELF), precautionary loss function (PLF), general entropy loss function (GELF), entropy loss function (ELF), and weighted balance loss function (WBLF). The average run length (ARL) and the standard deviation of run length (SDRL) are used to check the performance of the proposed Bayesian EWMA control charts for Exponential and transformed Exponential distributions. An extensive simulation study is conducted to evaluate the proposed Bayesian EWMA control chart for nonnormal distributions. It is observed from the results that the proposed control chart with the Weibull distribution produces the best results among the considered distributions under different LFs. A real data example is presented for implementation purposes.