New Control Chart Based On Neutrosophic Maxwell Distribution with Decision Making Applications

The neutrosophic approach is a potential area to provide a novel framework for dealing with uncertain data. This study aims to introduce the neutrosophic Maxwell distribution (M̃D) for dealing with imprecise data. The proposed notions are presented in such a manner that the proposed model may be used in a variety of circumstances involving indeterminate, ambiguous, and fuzzy data. The suggested distribution is particularly useful in statistical process control (SPC) for processing uncertain values in data collection. The existing formation of VSQ-chart is incapable of addressing uncertainty on the quality variables being investigated. The notion of neutrosophic VSQchart (Ṽ SQ) is developed based on suggested neutrosophic distribution. The parameters of the suggested Ṽ SQ-chart and other performance indicators, such as neutrosophic power curve (P̃C), neutrosophic characteristic curve (C̃C) and neutrosophic run length (R̃L) are established. The performance of the Ṽ SQ-chart under uncertain environment is also compared to the performance of the conventional model. The comparative findings depict that the proposed Ṽ SQ-chart outperforms in consideration of neutrosophic indicators. Finally, the implementation procedure for real data on the COVID-19 incubation period is explored to support the theoretical part of the proposed model © 2023,Neutrosophic Sets and Systems. All Rights Reserved.

An IoT-based and cloud-assisted AI-driven monitoring platform for smart manufacturing: design architecture and experimental validation: IMS

PurposeThis work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.Design/methodology/approachThe proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.FindingsThe proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.Practical implicationsThanks to the abnormal risk panel;human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.Originality/valueThe monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products' waste avoidance.

Statistical process monitoring techniques for covid-19

The global negative impact of Coronavirus 2019 (Covid-19) disease in vital sustainability points of social functionality, such as health and economics, is indisputable. State authorities and societies in general have found themselves confronted with new regularities while trying to understand and measure the behaviour of this new disease. Statistical process monitoring (SPM) constitutes a method of quality control which employs various powerful tools to monitor and control a process. Such tools should be able to illustrate the evolution over time of the behavior of quality characteristics, either measurable or countable, and at the same time detect anomalies. The use of SPM is very common among various organizations which make use of control charts for capturing the behavior of processes or systems that evolve over time. This chapter presents a real case study on Covid-19 cases regarding 10 countries of the Mediterranean basin based on the four-epoch/phase framework. © ISTE Ltd 2022.

Adaptive COVID-19 Screening of a Subpopulation

Methods are sought to test adaptively whether a subpopulation proportion follows the same time evolution as the population proportion. The motivating case study is the COVID-19 screening in a university community, taking into account the time evolution of the pandemic in the whole country. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Adaptive COVID-19 Screening of a Subpopulation

Methods are sought to test adaptively whether a subpopulation proportion follows the same time evolution as the population proportion. The motivating case study is the COVID-19 screening in a university community, taking into account the time evolution of the pandemic in the whole country. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Deviance and Pearson Residuals-Based Control Charts with Different Link Functions for Monitoring Logistic Regression Profiles: An Application to COVID-19 Data

In statistical process control, the control charts are an effective tool to monitor the process. When the process is examined based on an exponential family distributed response variable along with a single explanatory variable, the generalized linear model (GLM) provides better estimates and GLM-based charts are preferred. This study is designed to propose GLM-based control charts using different link functions (i.e., logit, probit, c-log-log, and cauchit) with the binary response variable. The Pearson residuals (PR)- and deviance residuals (DR)-based control charts for logistic regression are proposed under different link functions. For evaluation purposes, a simulation study is designed to evaluate the performance of the proposed control charts. The results are compared based on the average run length (ARL). Moreover, the proposed charts are implemented on a real application for COVID-19 death monitoring. The Monte Carlo simulation study and real applications show that the performance of the model-based control charts with the c-log-log link function gives a better performance as compared to model-based control charts with other link functions. © 2023 by the authors.

Tolerance limits for mixture-of-normal distributions with application to COVID-19 data

Tolerance intervals (TIs) are widely used in various applications including manufacturing engineers, clinical research, and pharmaceutical industries. TIs can be used to construct limits of control charts for monitoring quality characteristics. For manufacturing processes where multiple factors may contribute to defects or multiple-stream processes, a mixture distribution of several suitable probabilistic models may be a better choice than a simple distribution for modeling the data. TIs for the normal mixture distribution have been studied in the literature. This article reviews the TIs of the normal mixture distribution, the applications of the mixture distribution, and the control charts of the mixture distribution. A rule for constructing modified two-sided TIs of the normal mixture distribution is summarized, and this rule may be extended to construct modified two-sided TIs for general mixture distributions. The feasibility of using TIs to build control charts for mixture distributions is also discussed. A real data example of coronavirus disease 2019 is used to illustrate the method by linking the TI to control charts. This article is categorized under: Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification. © 2023 Wiley Periodicals LLC.

Insights into Variations in COVID-19 Infections in Malaysia

The present study aims to visualize the variations in the number of confirmed daily COVID-19 infections during the third wave in Malaysia through the application of control charts. This study also attempts to propose the target number of daily new cases that would bring the pandemic situation in Malaysia under control by utilizing the confirmed daily cases in Malaysia starting from 8(th) September 2020 until 30(th) June 2021. A modified Shewhart control chart was adopted to monitor the variations before and after the commencement of National Immunisation Programme (NIP). The chart shows a declining trend in the number of cases after the rollout of NIP whereby several days were brought to a state of statistical-in-control. But in less than three months after NIP commencement, there were huge variations in COVID-19 cases leading to drastic increase in the mean number of cases. These signal the presence of unnatural or assignable causes of variations which could be attributed to failure of curbing the risks of transmission, existence of various variants in the community, easing of containment measures and less adherence to the COVID-19 Standard Operating Procedures (SOPs). Significant shifts in the mean values prompt the development of a 3-phase modified Shewhart control chart. From the 3-phase chart, a series of daily number new cases that can be set as the target value to bring the pandemic situation in Malaysia under control, while flattening the epidemiological curve in the very near term.

Hybrid LSTM-ARMA Demand-Forecasting Model Based on Error Compensation for Integrated Circuit Tray Manufacturing

Demand forecasting plays a crucial role in a company’s operating costs. Excessive inventory can increase costs and unnecessary waste can be reduced if managers plan for uncertain future demand and determine the most favorable decisions. Managers are demanding increasing accuracy in forecasting as technology advances. Most of the literature discusses forecasting results’ inaccuracy by suspending the model and reloading the data for model retraining and correction, which is extensively employed but causes a bottleneck in practice since users do not have the sufficient ability to correct the model. This study proposes an error compensation mechanism and uses the individuals and moving-range (I-MR) control chart to evaluate the requirement for compensation to solve the current bottleneck using forecasting models. The approach is validated using the case companies’ historical data, and the model is developed using a rolling long short-term memory (LSTM) to output the predicted values;then, five indicators are proposed for screening to determine the prediction statistics to be subsequently employed. Root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) compare the LSTM, rolling LSTM combined index, and LSTM-autoregressive moving average (ARMA) models. The results demonstrate that the RMSE, MAPE, and MAE of LSTM-ARMA are smaller than those of the other two models, indicating that the error compensation mechanism that is proposed in this study can enhance the prediction’s accuracy.

Surveilling public health through statistical process monitoring: A literature review and a unified framework

A challenge, in the era of economic crisis and uncertainty, is to provide health care services in an efficient and effective manner. The protection of public health, the provision of quality healthcare services to patients, the location of health centers, the geographical distribution of patients, and the provision of specialist services are some of the topics that the government and/or a health organization responsible for health care services provision has to arrange. Other topics are the assessment of quality, safety, and effectiveness of healthcare services provided by healthcare providers. Moreover, a central pylon in designing healthcare policy is expenditure monitoring and control. However, among all these topics the most significant is the protection of public health;especially now that viruses such as Coronavirus are spreading rapidly worldwide. This paper aims to review the use of Statistical Process Monitoring techniques in the public health domain in order to improve health care decision-making under uncertainty and further on to provide an innovative three-layer framework for the collection, processing, and real-time analysis of related data like Coronavirus or any other infectious disease that will emerge in the future for both proper and effective case management and effective health policy planning. © 2022 Taylor & Francis Group, LLC.

Teledermatology to reduce face-to-face appointments in general practice during the COVID-19 pandemic: a quality improvement project.

Teledermatology is an important subspecialty of telemedicine that continues to evolve with advances in telecommunication and mobile phone technology. A 19-week primary care quality improvement project collected baseline data and tested three change ideas, using the Model for Improvement method, with primary and secondary aims: to increase the weekly percentage of remote dermatological consultations with supporting images that were successfully concluded remotely to greater than 80% and to reduce the weekly percentage of dermatological face-to-face consultations to less than 50%. We hypothesised that by improving the quality of patient images and the confidence of reception staff in triaging skin complaints, there would be a decrease in the weekly number of face-to-face dermatological appointments, thereby decreasing the risk of COVID-19 transmission within the practice and community. Two change ideas focused on supporting patients to improve image quality by introducing '4 Key Instructions' and a patient information leaflet (PIL). The third focused on increasing reception staff confidence in triaging skin complaints by introducing a triage pathway guidance tool. A total of 253 dermatological consultations were analysed: 170 of these were telephone consultations with 308 supporting images. Process measures showed clear improvements in the quality of images provided by patients which likely contributed to an increase in completed remote consultation. Our primary outcome measure was achieved. Our secondary outcome measure suggested that in the absence of high-quality images, it might not be possible to reduce dermatological face-to-face consultations much below 50% in primary care. Process measures showed clear improvements in the quality of images provided by patients which likely contributed to the increase in remote consultation. The implications of these findings for the theory of change are discussed.

Predicting of infected People with Corona virus (covid-19) by using Non-Parametric Quality Control Charts

Quality control Charts were used to monitor the number of infections with the emerging corona virus (Covid-19) for the purpose of predicting the extent of the disease's control, knowing the extent of its spread, and determining the injuries if they were within or outside the limits of the control charts. The research aims to use each of the control chart of the (Kernel Principal Component Analysis Control Chart) and (K- Nearest Neighbor Control Chart). As (18) variables representing the governorates of Iraq were used, depending on the daily epidemiological position of the Public Health Department of the Iraqi Ministry of Health. To compare the performance of the charts, a measure of average length of run was adopted, as the results showed that the number of infection with the new Corona virus is out of control, and that the (KNN) chart had better performance in the short term with a relative equality in the performance of the two charts in the medium and long rang

Applying Lean principles to create a high throughput mass COVID-19 vaccination site.

A high throughput COVID-19 vaccination site was created using Lean principles and tools. Mass-vaccination sites can achieve high output by creating a standard physical design for workspaces and standardised work protocols, and by timing each step in the vaccination process to create a value stream map that can identify and remove all wasteful steps. Reliability of the vaccination process can be assured by creating a visual checklist that monitors the individual steps as well as by building in second checks by downstream personnel. Finally, productivity can be closely monitored by recording the start and completion time for each vaccination and plotting run charts. With 78 personnel working efficiently and effectively together, a maximum throughput of 5024 injections over 10 hours was achieved. As compared with other published COVID-19 mass-vaccination sites, our site attained threefold-fourfold higher productivity. We share our approach to encourage others to reproduce our vaccination system.

Nutritional status of children living within institution-based care: a retrospective analysis with funnel plots and control charts for programme monitoring

ObjectivesThe aim of this study is to fill a key information gap on the nutrition-related epidemiology of orphaned and vulnerable children living within institution-based care (IBC) across six countries.DesignA retrospective analysis with Shewhart control charts and funnel plots to explore intersite and over time variations in nutritional status.SettingWe conducted a retrospective analysis of records from Holt International’s Child Nutrition Programme from 35 sites in six countries;Mongolia, India, Ethiopia, Vietnam, China and the Philippines.ParticipantsDeidentified health records from Holt International’s online nutrition screening database included records from 2926 children, 0–18 years old. Data were collected from 2013 to 2020 and included demographic and health information.ResultsAt initial screening, 717 (28.7%) children were anaemic, 788 (34.1%) underweight, 1048 (37.3%) stunted, 212 (12.6%) wasted, 135 (12%) overweight or obese and 339 (31%) had small head circumference. Many had underlying conditions: low birth weight, 514 (57.5%);prematurity, 294 (42.2%) and disabilities, 739 (25.3%). Children with disabilities had higher prevalence of malnutrition compared with counterparts without disabilities at baseline and 1-year screenings. There was marked intersite variation. Funnel plots highlight sites with malnutrition prevalence outside expected limits for this specific population taking into consideration natural variation at baseline and at 1 year. Control charts show changes in site mean z-scores over time in relation to site control limits.ConclusionsMalnutrition is prevalent among children living within IBC, notably different forms of undernutrition (stunting, underweight, wasting). Underlying risk factors are also common: prematurity, low birth weight and disability. Nutrition interventions should take into account the needs of this vulnerable population, especially for infants and those with disabilities. Using control charts to present data could be especially useful to programme managers as sites outside control limits could represent: problems to be investigated;good practices to be shared.

Six Sigma to distinguish patterns in COVID-19 approaches

PurposeCOVID-19 has changed life as we know. Data are scarce and necessary for making decisions on fighting COVID-19. The purpose of this paper is to apply Six Sigma techniques on the current COVID-19 pandemic to distinguish between special cause and common cause variation. In the DMAIC structure, different approaches applied in three countries are compared.Design/methodology/approachFor three countries the mortality is compared to the population to distinguish between special cause variation and common cause variation. This variation and the patterns in it are assessed to the countries' different approaches to COVID-19.FindingsIn the DMAIC problem-solving approach, patterns in the data are distinguished. The special cause variation is assessed to the special causes and approaches. The moment on which measures were taken has been essential, as well as policies on testing and distancing.Research limitations/implicationsCross-national data comparisons are a challenge as countries have different moments on which they register data on their population. Furthermore, different intervals are taken, varying from registering weekly to registering yearly. For the research, three countries with similar data registration and different approaches in fighting COVID-19 were taken.Originality/valueThis is the first study with Master Black Belts from different countries on the application of Six Sigma techniques and the DMAIC from the viewpoint of special cause variation on COVID-19.

Improving turnaround times for HLA-B*27 and HLA-B*57:01 gene testing: a Barts Health NHS Trust quality improvement project.

Among other tests, Barts Health NHS Trust clinical transplantation laboratory conducts two important gene-detection tests: human leucocyte antigen (HLA)-B*27 ('B27', associated with the diagnosis of ankylosing spondylitis) and HLA-B*57:01 ('B57', associated with prediction of abacavir hypersensitivity disorder). The turnaround time (TaT) from sample receipt to return of results is important to clinicians and their patients but was not monitored. Furthermore, we anticipated an imminent increase in demand from a forthcoming pathology service merger, together with long-term increases with the rise of personalised genetic medicine.In this quality improvement project, we identified current TaT performance and sources of delay. Over three plan-do-study-act (PDSA) cycles, we tested three change ideas, two involving using IT to remove manual administrative steps and alert us to samples needing progressing; both were retained. The other change involved separating out the targeted tests; we judged this not worthwhile with current demand levels, although something to be re-examined when volumes increase. During the project, we reduced mean TaT from 3.8 to 3.3 days and increased the proportion within our 5-day target from 78% to 100%. These have been sustained (at 3.4 days and 97%) for the 3 months following our PDSA cycles and illustrate that reducing variation can be as impactful as reducing the mean.We conducted this project during the COVID-19 disruption, which reduced demand substantially. We took advantage of this to allow staff to spend time on these improvement activities. Another interesting feature of the work is that during the project, we compared changes in performance on our targeted B27/B57 tests with that on another comparable test as a control, to consider the impact of the general increased attention (the Hawthorne effect). We found that performance on this control also increased comparably, but then fell away after our project finished, while it did not for B27/B57.

Strengthening sepsis care at a tertiary care teaching hospital in New Delhi, India.

INTRODUCTION: Failure of early identification of sepsis in the emergency department (ED) leads to significant delays in antibiotic administration which adversely affects patient outcomes. AIM: The primary objective of our Quality Improvement (QI) project was to reduce the door-to-antibiotic time (DTAT) by 30% from the preintervention in patients with suspected sepsis. Secondary objectives were to increase the blood culture collection rate by 30% from preintervention, investigate the predictors of improving DTAT and study the effect of these interventions on 24-hour in-hospital mortality. METHODS: This QI project was conducted in the ED of a tertiary care teaching hospital of North India; the ED receives approximately 400 patients per day. Adult patients with suspected sepsis presenting to our ED were included in the study, between January 2019 and December 2020. The study was divided into three phases; preintervention phase (100 patients), intervention phase (100 patients) and postintervention phase (93 patients). DTAT and blood cultures prior to antibiotic administration was recorded for all patients. Blood culture yield and 24-hour in-hospital mortality were also recorded using standard data templates. Change ideas planned by the Sepsis QI Team were implemented after conducting plan-do-study-act cycles. RESULTS: The median DTAT reduced from 155 min in preintervention phase to 78 min in postintervention phase. Drawing of blood cultures prior to antibiotic administration improved by 67%. Application of novel screening tool at triage was found to be an independent predictor of reduced DTAT. CONCLUSION: Our QI project identified the existing lacunae in implementation of the sepsis bundle which were dealt with in a stepwise manner. The sepsis screening tool and on-site training improved care of patients with sepsis. A similar approach can be used to deal with complex quality issues in other high-volume low-resource settings.

Operationalising outpatient antimicrobial stewardship to reduce system-wide antibiotics for acute bronchitis.

BACKGROUND: Antibiotics are not recommended for treatment of acute uncomplicated bronchitis (AUB), but are often prescribed (85% of AUB visits within the Veterans Affairs nationally). This quality improvement project aimed to decrease antibiotic prescribing for AUB in community-based outpatient centres from 65% to <32% by April 2020. METHODS: From January to December 2018, community-based outpatient clinics' 6 months' average of prescribed antibiotics for AUB and upper respiratory infections was 63% (667 of 1054) and 64.6% (314 of 486) when reviewing the last 6 months. Seven plan-do-study-act (PDSA) cycles were implemented by an interprofessional antimicrobial stewardship team between January 2019 and March 2020. Balancing measures were a return patient phone call or visit within 4 weeks for the same complaint. Χ2 tests and statistical process control charts using Western Electric rules were used to analyse intervention data. RESULTS: The AUB antibiotic prescribing rate decreased from 64.6% (314 of 486) in the 6 months prior to the intervention to 36.8% (154 of 418) in the final 6 months of the intervention. No change was seen in balancing measures. The largest reduction in antibiotic prescribing was seen after implementation of PDSA 6 in which 14 high prescribers were identified and targeted for individualised reviews of encounters of patients with AUB with an antimicrobial steward. CONCLUSIONS: Operational implementation of successful stewardship interventions is challenging and differs from the traditional implementation study environment. As a nascent outpatient stewardship programme with limited resources and no additional intervention funding, we successfully reduced antibiotic prescribing from 64.6% to 36.8%, a reduction of 43% from baseline. The most success was seen with targeted education of high prescribers.