Parameter free AEWMA control chart for dispersion in semiconductor manufacturing.

The study presents a new parameter free adaptive exponentially weighted moving average (AEWMA) control chart tailored for monitoring process dispersion, utilizing an adaptive approach for determining the smoothing constant. This chart is crafted to adeptly detect shifts within anticipated ranges in process dispersion by dynamically computing the smoothing constant. To assess its effectiveness, the chart's performance is measured through concise run-length profiles generated from Monte Carlo simulations. A notable aspect is the incorporation of an unbiased estimator in computing the smoothing constant through the suggested function, thereby improving the chart's capability to identify different levels of increasing and decreasing shifts in process dispersion. The comparison with an established adaptive EWMA-S2 dispersion chart highlights the considerable efficiency of the proposed chart in addressing diverse magnitudes of process dispersion shifts. Additionally, the study includes an application to a real-life dataset, showcasing the practicality and user-friendly nature of the proposed chart in real-world situations.

Max-mixed EWMA control chart for joint monitoring of mean and variance: an application to yogurt packing process.

The Max-Mixed EWMA Exponentially Weighted Moving Average (MM EWMA) control chart is a statistical process control technique used for joint monitoring of the mean and variance of a process. This control chart is designed to detect small and moderate shifts in the mean and variance of a process by comparing the maximum of two statistics, one based on the mean and the other on the variance. In this paper, we propose a new MM EWMA control chart. The proposed chart is compared with existing control charts using simulation studies, and the results show that the chart performs better in detecting small and moderate shifts in both the mean and variance. The proposed chart can be helpful in quality control applications, where joint monitoring of mean and variance is necessary to ensure a product's or process's quality. The real-life application of the proposed control chart on yogurt packing in a cup data set shows the outperformance of the MM EWMA control chart. Both simulations & real-life application results demonstrate the better performance of the proposed chart in detecting smaller shifts during the production process.

An adaptive Bayesian approach for improved sensitivity in joint monitoring of mean and variance using Max-EWMA control chart.

This article introduces an adaptive approach within the Bayesian Max-EWMA control chart framework. Various Bayesian loss functions were used to jointly monitor process deviations from the mean and variance of normally distributed processes. Our study proposes the mechanism of using a function-based adaptive method that picks self-adjusting weights incorporated in Bayesian Max-EWMA for the estimation of mean and variance. This adaptive mechanism significantly enhances the effectiveness and sensitivity of the Max-EWMA chart in detecting process shifts in both the mean and dispersion. The Monte Carlo simulation technique was used to calculate the run-length profiles of different combinations. A comparative performance analysis with an existing chart demonstrates its effectiveness. A practical example from the hard-bake process in semiconductor manufacturing is presented for practical context and illustration of the chart settings and performance. The empirical results showcase the superior performance of the Adaptive Bayesian Max-EWMA control chart in identifying out-of-control signals. The chart's ability to jointly monitor the mean and variance of a process, its adaptive nature, and its Bayesian framework make it a useful and effective control chart.

Risk adjusted EWMA control chart based on support vector machine with application to cardiac surgery data.

In the current study, we demonstrate the use of a quality framework to review the process for improving the quality and safety of the patient in the health care department. The researchers paid attention to assessing the performance of the health care service, where the data is usually heterogeneous to patient's health conditions. In our study, the support vector machine (SVM) regression model is used to handle the challenge of adjusting the risk factors attached to the patients. Further, the design of exponentially weighted moving average (EWMA) control charts is proposed based on the residuals obtained through SVM regression model. Analyzing real cardiac surgery patient data, we employed the SVM method to gauge patient condition. The resulting SVM-EWMA chart, fashioned via SVM modeling, revealed superior shift detection capabilities and demonstrated enhanced efficacy compared to the risk-adjusted EWMA control chart.

A novel flexible exponent power-X family of distributions with applications to COVID-19 mortality rate in Mexico and Canada.

This paper aims to introduce a novel family of probability distributions by the well-known method of the T-X family of distributions. The proposed family is called a "Novel Generalized Exponent Power X Family" of distributions. A three-parameters special sub-model of the proposed method is derived and named a "Novel Generalized Exponent Power Weibull" distribution (NGEP-Wei for short). For the proposed family, some statistical properties are derived including the hazard rate function, moments, moment generating function, order statistics, residual life, and reverse residual life. The well-known method of estimation, the maximum likelihood estimation method is used for estimating the model parameters. Besides, a comprehensive Monte Carlo simulation study is conducted to assess the efficacy of this estimation method. Finally, the model selection criterion such as Akaike information criterion (AINC), the correct information criterion (CINC), the Bayesian information criterion (BINC), the Hannan-Quinn information criterion (HQINC), the Cramer-von-Misses (CRMI), and the ANDA (Anderson-Darling) are used for comparison purpose. The comparison of the NGEP-Wei with other rival distributions is made by Two COVID-19 data sets. In terms of performance, we show that the proposed method outperforms the other competing methods included in this study.

Memory type Bayesian adaptive max-EWMA control chart for weibull processes.

The simultaneous monitoring of both the process mean and dispersion has gained considerable attention in statistical process control, especially when the process follows the normal distribution. This paper introduces a novel Bayesian adaptive maximum exponentially weighted moving average (Max-EWMA) control chart, designed to jointly monitor the mean and dispersion of a non-normal process. This is achieved through the utilization of the inverse response function, particularly suitable for processes conforming to a Weibull distribution. To assess the effectiveness of the proposed control chart, we employed the average run length (ARL) and the standard deviation of run length (SDRL). Subsequently, we compared the performance of our proposed control chart with that of an existing Max-EWMA control chart. Our findings suggest that the proposed control chart demonstrates a higher level of sensitivity in detecting out-of-control signals. Finally, to illustrate the effectiveness of our Bayesian Max-EWMA control chart under various Loss Functions (LFs) for a Weibull process, we present a practical case study focusing on the hard-bake process in the semiconductor manufacturing industry. This case study highlights the adaptability of the chart to different scenarios. Our results provide compelling evidence of the exceptional performance of the suggested control chart in rapidly detecting out-of-control signals during the hard-bake process, thereby significantly contributing to the improvement of process monitoring and quality control.

Memory type Max-EWMA control chart for the Weibull process under the Bayesian theory.

The simultaneous monitoring of both process mean and dispersion, particularly in normal processes, has garnered significant attention within the field. In this article, we present a new Bayesian Max-EWMA control chart that is intended to track a non-normal process mean and dispersion simultaneously. This is accomplished through the utilization of the inverse response function, especially in cases where the procedure follows a Weibull distribution. We used the average run length (ARL) and the standard deviation of run length (SDRL) to assess the efficacy of our suggested control chart. Next, we contrast our suggested control chart's performance with an already-existing Max-EWMA control chart. Our results show that compared to the control chart under consideration, the proposed control chart exhibits a higher degree of sensitivity. Finally, we present a useful case study centered around the hard-bake process in the semiconductor manufacturing sector to demonstrate the performance of our Bayesian Max-EWMA control chart under different Loss Functions (LFs) for a Weibull process. The case study highlights how flexible the chart is to various situations. Our results offer strong proof of the outstanding ability of the Bayesian Max-EWMA control chart to quickly identify out-of-control signals during the hard-bake procedure. This in turn significantly contributes to the enhancement of process monitoring and quality control.

After ECMO Decannulation, Are Patients Thriving or Just Surviving?

The use of extracorporeal membrane oxygenation (ECMO) to support patients with cardiac arrest, cardiogenic shock, and acute respiratory distress syndrome is rising worldwide.1 While ECMO may save the lives of some of our sickest patients, the outlook of ECMO survivorship remains uncertain. Defining longer-term functional and neuropsychiatric outcomes in ECMO survivors is important for 3 reasons. First, critically ill patients are at high risk of experiencing postintensive care syndrome (PICS), defined as new physical, cognitive, or psychological impairments that present in survivors of critical illness after hospital discharge.2 PICS is associated with more severe illness and longer intensive care unit length of stay.3 Because ECMO is reserved for patients with refractory shock or hypoxia, patients treated with ECMO represent a severely ill patient population with prolonged length of stay, putting them at particularly high risk of developing PICS. Second, ECMO is associated with direct neurologic injury, including both macrohemorrhages and microhemorrhages, infarcts, and diffuse hypoxic-ischemic brain injury that likely contribute to long-term outcomes.4 Finally, ECMO is very expensive. A recent study determined that the average cost per admission for patients with COVID-19 placed on ECMO was nearly $850,000 more than those who received only mechanical ventilation.5 Understanding patient-centered outcomes will be an integral part of future cost-effectiveness analyses.

Correction: Nutritional analysis and characterization of carbapenemase producing-Klebsiella pneumoniae resistant genes associated with bovine mastitis infected cow's milk.

[This corrects the article DOI: 10.1371/journal.pone.0293477.].

Neuromonitoring of Pediatric and Adult Extracorporeal Membrane Oxygenation Patients: The Importance of Continuous Bedside Tools in Driving Neuroprotective Clinical Care.

Extracorporeal membrane oxygenation (ECMO) is a form of temporary cardiopulmonary bypass for patients with acute respiratory or cardiac failure refractory to conventional therapy. Its usage has become increasingly widespread and while reported survival after ECMO has increased in the past 25 years, the incidence of neurological injury has not declined, leading to the pressing question of how to improve time-to-detection and diagnosis of neurological injury. The neurological status of patients on ECMO is clinically difficult to evaluate due to multiple factors including illness, sedation, and pharmacological paralysis. Thus, increasing attention has been focused on developing tools and techniques to measure and monitor the brain of ECMO patients to identify dynamic risk factors and monitor patients' neurophysiological state as a function in time. Such tools may guide neuroprotective interventions and thus prevent or mitigate brain injury. Current means to continuously monitor and prevent neurological injury in ECMO patients are rather limited; most techniques provide indirect or postinsult recognition of irreversible brain injury. This review will explore the indications, advantages, and disadvantages of standard-of-care, emerging, and investigational technologies for neurological monitoring on ECMO, focusing on bedside techniques that provide continuous assessment of neurological health.

A novel Bayesian Max-EWMA control chart for jointly monitoring the process mean and variance: an application to hard bake process.

In this article, we introduce a novel Bayesian Max-EWMA control chart under various loss functions to concurrently monitor the mean and variance of a normally distributed process. The Bayesian Max-EWMA control chart exhibit strong overall performance in detecting shifts in both mean and dispersion across various magnitudes. To evaluate the performance of the proposed control chart, we employ Monte Carlo simulation methods to compute their run length characteristics. We conduct an extensive comparative analysis, contrasting the run length performance of our proposed charts with that of existing ones. Our findings highlight the heightened sensitivity of Bayesian Max-EWMA control chart to shifts of diverse magnitudes. Finally, to illustrate the efficacy of our Bayesian Max-EWMA control chart using various loss functions, we present a practical case study involving the hard-bake process in semiconductor manufacturing. Our results underscore the superior performance of the Bayesian Max-EWMA control chart in detecting out-of-control signals.

Monitoring of semiconductor manufacturing process on Bayesian AEWMA control chart under paired ranked set sampling schemes.

Quality control often employs memory-type control charts, including the exponentially weighted moving average (EWMA) and Shewhart control charts, to identify shifts in the location parameter of a process. This article pioneers a new Bayesian Adaptive EWMA (AEWMA) control chart, built on diverse loss functions (LFs) such as the square error loss function (SELF) and the Linex loss function (LLF). The proposed chart aims to enhance the process of identifying small to moderate as well as significant shifts in the mean, signifying a notable advancement in the field of quality control. These are implemented utilizing an informative prior for both posterior and posterior predictive distributions, employing various paired ranked set sampling (PRSS) schemes. The effectiveness of the suggested chart is appraised using average run length (ARL) and the standard deviation of run length (SDRL). Monte Carlo simulations are employed to contrast the recommended approach against other control charts. The outcomes demonstrate the dignitary performance of the recommended chart in identifying out-of-control signals, especially applying PRSS designs, in comparison to simple random sampling (SRS). Finally, a practical application was conducted in the semiconductor manufacturing context to appraise the efficacy of the offered chart using various paired ranked set sampling strategies. The results reveal that the suggested control chart performed well in capturing the out-of-control signals far better than the already in use control charts. Overall, this study interposes a new technique with diverse LFs and PRSS designs, improving the precision and effectiveness in detecting process mean shifts, thereby contributing to advancements in quality control and process monitoring.

Monitoring the process mean under the Bayesian approach with application to hard bake process.

This study introduces the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart within the framework of measurement error, examining two separate loss functions: the squared error loss function and the linex loss function. We conduct an analysis of the posterior and posterior predictive distributions utilizing a conjugate prior. In the presence of measurement error (ME), we employ a linear covariate model to assess the control chart's effectiveness. Additionally, we explore the impacts of measurement error by investigating multiple measurements and a method involving linearly increasing variance. We conduct a Monte Carlo simulation study to assess the control chart's performance under ME, examining its run length profile. Subsequently, we offer a specific numerical instance related to the hard-bake process in semiconductor manufacturing, serving to verify the functionality and practical application of the suggested Bayesian AEWMA control chart when confronted with ME.

Correlations Between Quantitative EEG Parameters and Cortical Blood Flow in Patients Undergoing Extracorporeal Membrane Oxygenation With and Without Encephalopathy.

PURPOSE: The neurologic examination of patients undergoing extracorporeal membrane oxygenation (ECMO) is crucial for evaluating irreversible encephalopathy but is often obscured by sedation or neuromuscular blockade. Noninvasive neuromonitoring modalities including diffuse correlation spectroscopy and EEG measure cerebral perfusion and neuronal function, respectively. We hypothesized that encephalopathic ECMO patients with greater degree of irreversible cerebral injury demonstrate less correlation between electrographic activity and cerebral perfusion than those whose encephalopathy is attributable to medications. METHODS: We performed a prospective observational study of adults undergoing ECMO who underwent simultaneous continuous EEG and diffuse correlation spectroscopy monitoring. (Alpha + beta)/delta ratio and alpha/delta Rartio derived from quantitative EEG analysis were correlated with frontal cortical blood flow index. Patients who awakened and followed commands during sedation pauses were included in group 1, whereas patients who could not follow commands for most neuromonitoring were placed in group 2. (Alpha + beta)/delta ratio-blood flow index and ADR-BFI correlations were compared between the groups. RESULTS: Ten patients (five in each group) underwent 39 concomitant continuous EEG and diffuse correlation spectroscopy monitoring sessions. Four patients (80%) in each group received some form of analgosedation during neuromonitoring. (Alpha + beta)/delta ratio-blood flow index correlation was significantly lower in group 2 than group 1 (left: 0.05 vs. 0.52, P = 0.03; right: -0.12 vs. 0.39, P = 0.04). Group 2 ADR-BFI correlation was lower only over the right hemisphere (-0.06 vs. 0.47, P = 0.04). CONCLUSIONS: Correlation between (alpha + beta)/delta ratio and blood flow index were decreased in encephalopathic ECMO patients compared with awake ones, regardless of the analgosedation use. The combined use of EEG and diffuse correlation spectroscopy may have utility in monitoring cerebral function in ECMO patients.

A Weibull process monitoring with AEWMA control chart: an application to breaking strength of the fibrous composite.

In recent times, there has been a growing focus among researchers on memory-based control charts. The Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts and the adaptive control charting approaches got the attention. Control charts are commonly employed to oversee processes, assuming the monitored variable follows a normal distribution. However, it's worth noting that this assumption does not hold true in many real-world situations. The use of the algebraic expression for normalization, which can be used for all kinds of skewed distributions with a closed-form distribution function, using the proposed continuous function to adapt a smoothing constant, motivates this study. In the present manuscript, we design an EWMA statistic-based adaptive control chart to monitor the irregular variations in the mean of two parametric Weibull distribution and use Hasting approximation for normalization. The adaptive control charts are used to update the smoothing constant according to the estimated shift. Here we use the proposed continuous function to adapt the smoothing constant. The average run length and standard deviation of run length are calculated under different parameter settings. The effectiveness of the proposed chart is argued in terms of ARLs over the considered EWMA chart through Monte-Carlo (MC) simulation method. The proposed chart is examined, followed by a real data set to demonstrate the design and application procedures.

Bayesian AEWMA control chart under ranked set sampling with application to reliability engineering.

The article introduces a novel Bayesian AEWMA Control Chart that integrates different loss functions (LFs) like the square error loss function and Linex loss function under an informative prior for posterior and posterior predictive distributions, implemented across diverse ranked set sampling (RSS) designs. The main objective is to detect small to moderate shifts in the process mean, with the average run length and standard deviation of run length serving as performance measures. The study employs a hard bake process in semiconductor production to demonstrate the effectiveness of the proposed chart, comparing it with existing control charts through Monte Carlo simulations. The results underscore the superiority of the proposed approach, particularly under RSS designs compared to simple random sampling (SRS), in identifying out-of-control signals. Overall, this study contributes a comprehensive method integrating various LFs and RSS schemes, offering a more precise and efficient approach for detecting shifts in the process mean. Real-world applications highlight the heightened sensitivity of the suggested chart in identifying out-of-control signals compared to existing Bayesian charts using SRS.

Omega-3 long-chain polyunsaturated fatty acids: Metabolism and health implications.

Recently, omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFAs) have gained substantial interest due to their specific structure and biological functions. Humans cannot naturally produce these fatty acids (FAs), making it crucial to obtain them from our diet. This comprehensive review details n-3 LC-PUFAs and their role in promoting and maintaining optimal health. The article thoroughly analyses several sources of n-3 LC-PUFAs and their respective bioavailability, covering marine, microbial and plant-based sources. Furthermore, we provide an in-depth analysis of the biological impacts of n-3 LC-PUFAs on health conditions, with particular emphasis on cardiovascular disease (CVD), gastrointestinal (GI) cancer, diabetes, depression, arthritis, and cognition. In addition, we highlight the significance of fortification and supplementation of n-3 LC-PUFAs in both functional foods and dietary supplements. Additionally, we conducted a detailed analysis of the several kinds of n-3 LC-PUFAs supplements currently available in the market, including an assessment of their recommended intake, safety, and effectiveness. The dietary guidelines associated with n-3 LC-PUFAs are also highlighted, focusing on the significance of maintaining a well-balanced intake of n-3 PUFAs to enhance health benefits. Lastly, we highlight future directions for further research in this area and their potential implications for public health.

Nutritional analysis and characterization of carbapenemase producing-Klebsiella pneumoniae resistant genes associated with bovine mastitis infected cow's milk.

The current study was designed to analyze nutritional parameters and to characterize carbapenemase producing-Klebsiella pneumoniae isolates from bovine mastitic cow's milk. Out of 700 milk samples K. pneumoniae was identified by phenotypic and molecular techniques along with their antibiogram analysis and nutritional analysis was performed using the procedure of Association of Official Analytical Chemists. Carbapenemase-producing K. pneumoniae was detected by phenotypic CarbaNP test followed by molecular characterization of their associated resistant genes blaVIM, blaKPC, blaOXA-48, blaNDM, and blaIMP along with insertion sequence common region 1 (ISCR1) and integrons (Int1, Int2, and Int3) genes. Among nutritional parameters, fat content was observed (2.99%) followed by protein (2.78%), lactose (4.32%), and total solid (11.34%), respectively. The prevalence of K. pneumoniae among bovine mastitis was found 25.71%. Antibiogram analysis revealed that more effective antibiotics was ceftazidime (80%) followed by amikacin (72%), while highly resistant antibiotics was Fusidic acid (100%). Distribution of carbapenemase producer K. pneumoniae was found 44.4%. Among carbapenem resistant genes blaKPC was found 11.25%, blaVIM 2.75%, blaNDM 17.5%, and blaOXA-48 7.5%, while blaIMP gene was not detected. Furthermore, distribution of ISCR1 was found 40%, while integron 1 was found 61.2% followed by integron 2 (20%), and integron 3 (5%). In conclusion, the recent scenario of carbapenemase resistant K. pneumoniae isolates responsible for mastitis may affect not only the current treatment regime but also possess a serious threat to public health due to its food borne transmission and zoonotic potential.

Monitoring of manufacturing process using bayesian EWMA control chart under ranked based sampling designs.

Control charts, including exponentially moving average (EWMA) , are valuable for efficiently detecting small to moderate shifts. This study introduces a Bayesian EWMA control chart that employs ranked set sampling (RSS) with known prior information and two distinct loss functions (LFs), the Square Error Loss function (SELF) and the Linex Loss function (LLF), for posterior and posterior predictive distributions. The chart's performance is assessed using average run length (ARL) and standard deviation of run length (SDRL) profiles, and it is compared to the Bayesian EWMA control chart based on simple random sampling (SRS). The results indicate that the proposed control chart detects small to moderate shifts more effectively. The application in semiconductor manufacturing provides concrete evidence that the Bayesian EWMA control chart, when implemented with RSS schemes, demonstrates a higher degree of sensitivity in detecting deviations from normal process behavior. Comparison to the Bayesian EWMA control chart using SRS, it exhibits a superior ability to identify and flag instances where the manufacturing process is going out of control. This heightened sensitivity is critical for promptly addressing and rectifying issues, which ultimately contributes to improved quality control in semiconductor production.

Regulate structure and properties of κ-carrageenan/konjac glucomannan composite hydrogel by filling effects of Quillaja saponin-stabilized solid lipid nanostructure.

κ-Carrageenan/konjac glucomannan (κ-CA/KGM) composite hydrogels often fail to meet industrial requirements due to their low gel strength and poor mechanical properties, while solid lipid nanoparticles are potential materials to address this challenge due to their good biocompatibility. In the study, we propose using Quillaja saponin-stabilized solid lipid nanoparticle (QSLN) as nanofillers to enhance properties of κ-carrageenan/konjac glucan (κ-CA/KGM) composite hydrogels, and with emphasis on the effect of QSLN filling concentration on the structure and properties of composite hydrogels and the possible mechanisms were investigated. The best performance of QSLN-filled composite hydrogels was achieved at the QSLN concentration of 2.4 %. QSLN was uniformly distributed in the hydrogel matrix and formed electrostatic interactions and hydrogen bonding interactions with the matrix at an appropriate filling level, which enhanced the textural and rheological properties of the hydrogel greatly. In addition, the results of low-field NMR experiments showed that the filling of QSLN reduced the water mobility by enhancing the entanglement of polymer chains in the hydrogel matrix, which improved the freeze-thaw stability and regulated the swelling and deswelling behavior of the composite hydrogel. However, with the increasing of QSLN filling concentration, the above improvements were weakened by the depletion of van der Waals interactions due to the large amount of QSLN aggregation and the weakening of electrostatic interaction. In turn, the hydrogel was found to modulate the crystalline behavior of QSLN by X-ray diffraction and differential scanning calorimeter monitoring. Overall, the optimal synergistic effect between structure and properties could be achieved when the QSLN filling concentration was 2.4 %. These results provide a basis for the development of products that require excellent gel properties and structure.