Monitoring non-parametric profiles using adaptive EWMA control chart

To monitor the quality of a process in statistical process control (SPC), considering a functional relationship between a dependent variable and one or more independent variables (which is denoted as profile monitoring) is becoming an increasingly common approach. Most of the studies in the SPC literature considered parametric approaches in which the functional relationship has the same form in the in-control (IC) and out-of-control (OC) situations. Non-parametric profiles, which have a different functional relationship in the OC conditions are very common. This paper designs a novel control chart to monitor not only the regression parameters but also the variation of the profiles in Phase II applications using an adaptive approach. Adaptive control charts adjust the final statistic with regard to information of the previous samples. The proposed method considers the relative distance of the chart statistic to the control limits as a tendency index and provides some outcomes about the process condition. The results of Monte Carlo simulations show the superiority of the proposed monitoring scheme in comparison with the common non-parametric control charts. 2022, The Author(s).The publication of this article was funded by Qatar National Library.Scopu

The pathogenicity of genetic variants previously associated with left ventricular non-compaction

BACKGROUND: Left ventricular non‐compaction (LVNC) is a rare cardiomyopathy. Many genetic variants have been associated with LVNC. However, the number of the previous LVNC‐associated variants that are common in the background population remains unknown. The aim of this study was to provide an updated list of previously reported LVNC‐associated variants with biologic description and investigate the prevalence of LVNC variants in healthy general population to find false‐positive LVNC‐associated variants. METHODS AND RESULTS: The Human Gene Mutation Database and PubMed were systematically searched to identify all previously reported LVNC‐associated variants. Thereafter, the Exome Sequencing Project (ESP) and the Exome Aggregation Consortium (ExAC), that both represent the background population, was searched for all variants. Four in silico prediction tools were assessed to determine the functional effects of these variants. The prediction results of those identified in the ESP and ExAC and those not identified in the ESP and ExAC were compared. In 12 genes, 60 LVNC‐associated missense/nonsense variants were identified. MYH7 was the predominant gene, encompassing 24 of the 60 LVNC‐associated variants. The ESP only harbored nine and ExAC harbored 18 of the 60 LVNC‐associated variants. In total, eight out of nine ESP‐positive variants overlapped with the 18 variants identified in ExAC database. CONCLUSIONS: In this article, we identified 9 ESP‐positive and 18 ExAC‐positive variants of 60 previously reported LVNC‐associated variants, suggesting that these variants are not necessarily the monogenic cause of LVNC

Run Rules-Based EWMA Charts for Efficient Monitoring of Profile Parameters

In usual quality control methods, the quality of a process or product is evaluated by monitoring one or more quality characteristics using their corresponding distributions. However, when the quality characteristic is defined through the relationship between one or more response and independent variables, the regime is referred to as profiles monitoring. In this article, we improve the performance of the Exponentially Weighted Moving Average Range (EWMAR) control charts, which are implemented for monitoring linear profiles (i.e., intercept, slope and average residual between sample and reference lines) by integrating them with run rules in order to quickly detect various magnitudes of shifts in profile parameters. The validation of the proposed control chart is accomplished by examining its performance using the average run length (ARL) criteria. The proposed EWMAR chart with run rules exhibits a much better performance in detecting small and decreasing shifts than the other competing charts. Finally, an example from multivariate manufacturing industry is employed to illustrate the superiority of the EWMAR chart with run rules. 2013 IEEE.Scopu

Evolutionary support vector regression for monitoring Poisson profiles

Many researchers have shown interest in profile monitoring; however, most of the applications in this field of research are developed under the assumption of normal response variable. Little attention has been given to profile monitoring with non-normal response variables, known as general linear models which consists of two main categories (i.e., logistic and Poisson profiles). This paper aims to monitor Poisson profile monitoring problem in Phase II and develops a new robust control chart using support vector regression by incorporating some novel input features and evolutionary training algorithm. The new method is quicker in detecting out-of-control signals as compared to conventional statistical methods. Moreover, the performance of the proposed scheme is further investigated for Poisson profiles with both fixed and random explanatory variables as well as non-parametric profiles. The proposed monitoring scheme is revealed to be superior to its counterparts, including the likelihood ratio test (LRT), multivariate exponentially weighted moving average (MEWMA), LRT-EWMA and other machine learning-based schemes. The simulation results show superiority of the proposed method in profiles with fixed explanatory variables and non-parametric models in nearly all situations while it is not able to be the best in all the simulations when there are with random explanatory variables. A diagnostic method with machine learning approach is also used to identify the parameters of change in the profile. It is shown that the proposed profile diagnosis approach is able to reach acceptable results in comparison with other competitors. A real-life example in monitoring Poisson profiles is also provided to illustrate the implementation of the proposed charting scheme.Open Access funding provided by the Qatar National Library.http://link.springer.com/journal/500am2024StatisticsNon

Prevalence of Gastrointestinal Symptoms among Individuals with and without Diabetes: A Cross-Sectional Study from the PERSIAN Guilan Cohort Study

Objective: Gastrointestinal (GI) symptoms are the most common complaint among individuals with diabetes. This study investigated the prevalence of upper, lower, and general GI symptoms in individuals with and without diabetes among the Prospective Epidemiological Research Studies in Iran (PERSIAN) Guilan Cohort study (PGCS) population. Materials and methods: This cross-sectional study of PGCS was conducted on 2669 participants, 1364 with diabetes and 1305 without diabetes. The first part of the questionnaire collected demographical and clinical data, and the second part collected GI symptoms. A 4-point Likert Scale was used for each question. Data were analyzed using SPSS software version 16, and the significance level was considered < 0.05. Results: The mean age of the participants was 52.24 ± 8.75 years, and 55.5% were female. Patients with diabetes have an increased incidence of upper GI symptoms (adjusted odds ratio [aOR] = 1.19, 95% confidence interval [CI]: 1.00–1.42, p = 0.045) compared to individuals without diabetes. The most common upper GI symptom in patients with diabetes compared to those without diabetes was eructation (18.6% vs. 14.9%, p = 0.009). Conclusions: The prevalence of GI symptoms was high in patients both with and without diabetes, and the chance of developing GI upper symptoms was higher in patients with diabetes

The Role of Genetic Identification in the Scientific Discovery of Crime through the Review of DNA in the Crime Scene: Genetic Identification in the Scientific Discovery of Crime

Background and Aim: DNA fingerprinting, one of the great discoveries of the late 20th century, has revolutionized forensic investigations. DNA analysis is frequently used to acquire information from biological material to aid inquiries associated with criminal offenses, disaster victim identification, and missing person investigations. Methods: This analytical-descriptive research gathered relevant data in a literature search. After a description of the fundamentals and definitions, ethical texts were subsequently analyzed. Ethical Considerations: Ethical principles were considered in searching and citing the literature. Results: In our country, since the year 1388, the need for having a genetic database in Iran was felt and the head of the judiciary ordered the creation of a genealogy database in light of the sensitivity of the matter and helped to resolve the cases quickly. Legal genetic laboratories in Iran began their work in the 1970s. In Iran, firstly, the forensic laboratories in Tehran, Mashhad, and Isfahan began their activities. Conclusion: The actions of governments in the field of criminal law are not always conducive to accountability through the use of punishment. Today, the Special Criminal Tribunal is focused on taking preventive measures. Prevention of the commission of crimes is carried out in a variety of ways, in which we focus on preventive methods with an emphasis on genetic science in this paper. If the genetic information of the criminals present in the genealogy bank exists and is also an example of a crime scene, it would be possible to identify the offender before using other methods of identifying criminals and arresting individuals. It helps to quickly retrieve the accused and to succeed. Experiences in many countries have shown that by doing this, you can significantly reduce costs and achieve less favorable results with less testing. Corresponding Author: Behnaz Vahid Yeganeh; Email: [email protected];  ORCID: https://orcid.org/0000-0003-3638-001X Please cite this article as: Abbasi M, Vahid Yeganeh B. The Role of Genetic Identification in the Scientific Discovery of Crime through the Review of DNA in the Crime Scene. Bioeth Health Law J. 2022; 2(1): 1-8 (e3). http://doi.org/10.22037/bhl.v2i1.2453

Enhancing the detection ability of control charts in profile monitoring by adding RBF ensemble model

While numerous contributions and applications have been extended in profile monitoring, little attention has been paid to employing machine learning techniques in development of control charts. In this paper, a novel control chart based on artificial neural network is proposed to improve the performance of monitoring general linear profiles in Phase II. Specifically, an ensemble of radial basis functions (RBF) is added to the predefined base control chart to enhance the detection ability of the control chart for monitoring linear profile parameters based on the average run length (ARL) criterion. The performance of the proposed method is evaluated by adjusting the multivariate exponentially weighted moving average (MEWMA) control chart as a base control chart under simple and multiple linear profiles. The simulation results demonstrate that the proposed approach is very efficient than competing existing methods for monitoring linear profile parameters. Moreover, profile diagnosis actions, referring to the identification of shifted parameters, are provided based on the RBF networks. Finally, we provide an example from thermal management to illustrate the implementation of the proposed monitoring scheme and diagnostic method.This research is supported by Ferdowsi University of Mashhad, under Grant No. 51697

A Novel Simulation-Based Adaptive MEWMA Approach for Monitoring Linear and Logistic Profiles

As a common approach in the development of control charts in Statistical Process Control (SPC), an industrial process is monitored with one or more quality characteristics using their corresponding distributions. Note though, modelling the quality characteristics through a relation between some independent and dependent variables is an alternative approach which is designated as profiles monitoring. This study proposes the integration of the adaptive approach to the conventional Multivariate Exponentially Weighted Moving Average (MEWMA) control chart to improve its detection ability in phase II application. The run length characteristics of the adaptive MEWMA chart are measured with the use of Monte Carlo simulations by which better performance of the proposed method than numerous existing competitors including the conventional MEWMA chart is indicated in monitoring linear and logistic profiles. Finally, a real-life example from semiconductor manufacturing is provided to demonstrate the implementation and superiority of the proposed adaptive MEWMA chart over the conventional MEWMA chart

Employing evolutionary artificial neural network in risk-adjusted monitoring of surgical performance

Various applications of control charts in the field of health-care monitoring and surveillance can be found in the literature. As one of the major categories, monitoring binary outcomes of cardiac surgeries with the aim of logistic regression model for the patients' death probability has been extended by different researchers. For this aim, statistical control charts, such as cumulative sum (CUSUM) chart, are applied as a risk-adjusted method to monitoring patients' mortality rate. However, employing machine learning techniques such as artificial neural network (ANN) has not been paid attention. So, this paper proposes a novel ANN-based control chart with a heuristic training approach to monitor binary surgical outcomes by control charts. Performance of the proposed approach is investigated and compared with existing studies, based on the average run lengths (ARL) criterion and the results demonstrated a superior performance of the proposed approach. Nevertheless, to demonstrate the application of the proposed approach, some real-life applications are also provided in this paper. Furthermore, robustness of the proposed method is investigated by considering Beta distribution for the death rate in addition to the logistic model. 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.Scopu