Statistical Process Research Articles

Deep Learning and Statistical Process Control Approach to Improve Quality in Immigration Services

Technological advancements have profoundly influenced various sectors, including tourism, by simplifying international travel and increasing the demand for passports. In this context, public feedback on the Surabaya Immigration Office's services, gathered from Google Maps reviews, presents an invaluable dataset for analysis. This study introduces a novel approach to quality management systems in immigration services by applying the Convolutional Long Short-Term Memory (Co-LSTM) for sentiment analysis of public reviews and p-attribute control charts for statistical process control. Focusing on the Surabaya Immigration Office, we analyze public feedback from Google Maps to assess service quality. Our methodology preprocesses and classifies opinion data into sentiment classes, employing Co-LSTM for its superior accuracy over traditional models. The sentiment analysis reveals a predominance of positive over negative reviews, with classification accuracy demonstrating Area Under Curve (AUC) values of 98.61% for training and 85.66% for testing data. Furthermore, the p-attribute control charts are utilized to monitor service defects, identifying areas of uncontrolled variability and suggesting the necessity for service improvement interventions. The study uncovers that the primary public grievance relates to the perceived lack of friendliness and politeness from office staff. By integrating sentiment analysis with statistical process control, this research offers a comprehensive approach for immigration service providers to enhance service quality, respond proactively to public sentiment, and ensure customer satisfaction.

Coffee and Cases (C&C) - Enhancing Knowledge Creation and Sharing for Organizational Learning From Clinical Debriefs in a Helicopter Emergency Medical Service: A Quality Improvement Study

Objectives The objective of this quality improvement (QI) study was to improve organizational learning from clinical debriefs known as “Coffee and Cases” (C&C) in a helicopter emergency medical service (HEMS) by increasing weekly learning summaries (LS) and documented learning points (DLP) as well as the dissemination thereof by at least 50% from baseline. Methods The problem analysis for sub-optimal organizational learning from C&C identified several factors, including lack of responsibility, poor documentation quality, and limited sharing of learning points. Using the Model for Improvement (MFI), interventions enhanced the learning environment, and improved documentation and dissemination. Changes included dedicated computers, introducing standardized processes, a newsletter, and a searchable DLP database. Statistical process control (SPC) charts were used to assess the effectiveness of interventions. Results Prior to interventions (August 2022 to January 2023), baseline mean counts of weekly DLPs, LSs, DLPs internally disseminated, and DLPs openly disseminated were 3.18, 2.67, 3.18, and 2.96, respectively. Plan-Do-Study-Act (PDSA) cycles included declaring C&C as a non-clinical portfolio, redesigning documentation processes, initiating monthly newsletters, developing a DLP repository, and designing a C&C visual brand. Signals of special cause variation showed improvements, with mean counts increasing. Weekly DLPs increased to 14.92, LSs to 5.2, internally disseminated C&C Snippets to 11.88, and openly disseminated C&C Snippets to 8.64. Conclusions Recognizing barriers to effective knowledge creation and sharing, our QI study aimed to increase weekly DLPs and LSs by 50% from baseline. It aligned with the relationship between knowledge management and organizational learning, emphasizing the importance of utilizing knowledge for improved performance. Our interventions enhanced the learning environment, ensured robust capturing of learning points and effective communication thereof, ultimately contributing toward improving organizational dissemination of learning from clinical debriefs. Our QI study demonstrates how enhanced knowledge creation and sharing can widen the benefits of learning from clinical team debriefs.

Dimensions of Posterior Implant Restorations Related to Intra-Coronal Cantilever and Occlusal Forces: An In Vitro Study.

The aim of the present study was to investigate the relation of the implant position to the implant crown and analyze the location and magnitude of bite force. The dimensions of 49 posterior implant-supported restorations were recorded in terms of: 1. implant diameter, 2. crown length, 3. crown width, 4. crown height, 5. occlusal surface of the restoration. The crown/implant height was measured and the distance between the implant center and the restoration center revealed the size of intra-coronal cantilevers. The occlusal force analysis determined the point and magnitude of loading. Correlation coefficient r was used for the statistical process and statistical control was carried out with the aid of t-test with minimum level of significance p < 0.05. - The mean crown-to-implant ratio was 0.9 and in short implants (< 10mm) the ratio was 1.28. - Intra-coronal cantilever extensions ranged from 2.1 mm to 6.35 mm depending on crown size. - Occlusal loading was identified in cantilever areas of the crown. - Implant center deviates from crown center in 71%-82% of the cases depending on site location. - Implants with non-ideal position may support a functionally occluding crown. Intra-coronal cantilevers are a critical factor to avoid clinical complications and should be taken under consideration both in the planning and fabrication phase of implant restorations.

MARKETING PRIORITIES OF COMPANIES IN THE INTERNATIONAL PHARMACEUTICAL MARKET

The article presents the current trends in the development of the international pharmaceutical market and systematizes the priority areas for implementing marketing approaches of the leading companies. The purpose of this article is to reveal the marketing priorities of international pharmaceutical companies in the context of global challenges. To study this issue, a set of methods was used, namely: analysis and synthesis, statistical method, process and institutionalist approaches, case study.The results of the study show that the international pharmaceutical market is characterized by a high level of dynamism, preservation of the priority share of economically developed countries in revenue generation, significant influence of regulatory mechanisms at the national and international levels, and active processes of economic competition through mergers and acquisitions. The analysis has shown that the modern concept of pharmaceutical marketing goes far beyond the established narrow interpretation as a set of tools for the production, sale and promotion of a pharmaceutical product and is characterized by a high level of adaptability to the challenges of the global marketing environment of the twenty-first century. The article reveals the focus of marketing of leading companies on such priorities as: scaling up marketing budgets, ensuring synergy between marketing and financial indicators of pharmaceutical companies, modernizing communication strategies by optimizing interaction with key opinion leaders, intensifying multichannel marketing, etc.The theoretical significance of the study is to deepen the concept of international marketing in the context of its individual sectoral manifestations, taking into account the influence of global competition, institutional regulations, and technological progress. The practical value is due to the potential of using local or regional benchmarking for domestic companies participating in the international pharmaceutical market. Prospects for further research are considered in the direction of assessing the mechanisms and effects of the introduction of marketing innovations in the field of international pharmaceuticals.

Gaussian Denoising for the First Image from The James Webb Space Telescope “Carina Nebula” using Non-Linear Filters

Noise, including Gaussian noise, distorts images during transition or acquisition process, reducing required information. Removing or reducing this noise is crucial in image processing. The James Webb Space Telescope (JWST) is a vital tool for enhancing our understanding of the universe, providing valuable scientific data and inspiring global interest. In this paper we introduce several nonlinear (Non-Local Mean, Bilateral, and classical) filters to remove the Gaussian noise from the Carina Nebula Image, the first image taken by (JWST) on 12 July 2022. These nonlinear filters were therefore selected to highlight the significance of selecting the right technique that can handle, process, and preserve as many details as possible. They also serve to elucidate the degree of advancement achieved in the field of denoising and the distinction between the classical filters and the more sophisticated filters that have evolved to handle finer details. Classic filters deal with the pixels themselves and their neighbors and then perform the desired statistical process. While advanced filters consider the similarities and distances between the central pixel and its neighbors, they preserve the edges of the image as advanced features. Based on quality measurements (PSNR) and (SSIM), the filter results were compared. The results show that the Bilateral filter gives high performance in restoring images under different Gaussian noise densities compared with the other denoising filters where it gives values of (30.65) and (0.93) for (PSNR) and (SSIM) respectively Which is higher than the results of the filters. Paper type :Research paper.

Quality management of head and neck patient treatments using statistical process control techniques.

The treatment, planning, simulation, and setup of radiotherapy patients contain many processes subject to errors involving both staff and equipment. Cone-beam-CT (CBCT) provides a final check of patient positioning and corrections based on this can be made prior to treatment delivery. Statistical Process Control (SPC) techniques are used in various industries for quality management and error mitigation. The utility of SPC techniques to monitor process and equipment changes in our Head and Neck patient treatments was assessed by application to CBCT results from a quality-focused longitudinal study. Individuals and moving range (XmR) as well as exponentially-weighted moving average (EWMA) techniques were explored. The SPC techniques were sensitive to process changes and trends over the 12 years of data collected. A reduction in the random component of patient setup errors needing correction was observed. Systematic components of error remained more stable. An uptick in both datasets was observed correlating with the COVID-19 pandemic. Process control limits for use in prospective process monitoring were established. Challenges that arose from using SPC techniques in a retrospective study are outlined.

Acquiring better load estimates by combining anomaly and change point detection in power grid time-series measurements

In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems. By leveraging unsupervised methods with supervised optimization, our approach prioritizes interpretability while ensuring robust and generalizable performance on unseen data. Through experimentation, a combination of binary segmentation for change point detection and statistical process control for anomaly detection emerges as the most effective strategy, specifically when ensembled in a novel sequential manner. Results indicate the clear wasted potential when filtering is not applied. The automatic load estimation is also fairly accurate, with approximately 90% of estimates falling within a 10% error margin, with only a single significant failure in both the minimum and maximum load estimates across 60 measurements in the test set. Our methodology’s interpretability makes it particularly suitable for critical infrastructure planning, thereby enhancing decision-making processes.

Using structural equation modelling to investigate the mediating effects of TPACK on intention to use technology

This article aims to understand, through structural equation modelling (SEM), how the knowledge of technological pedagogical and content knowledge (TPACK) mediates the intention to accept and use technology by teachers’ college lecturers. The ultimate aim is to come up with a sem-generated model which teacher training colleges can use to train lecturers in technology use. Structural equation modeling is a sophisticated multivariate statistical process that enables researchers to analyze the direct and indirect effects of variables on one another, construct theoretical concepts, test the reliability of their measurements, hypothesize and test a theory about their relationships, and account for measurement errors. A questionnaire founded on the unified theory of acceptance and use of technology (UTAUT) was employed to gather data from instructors at ten teacher-training colleges. Simple random sampling was used to select 300 valid questionnaires used in the analysis. Statistic package R was used to run SEM and the results confirmed the mediating role of TPACK in the behavior intention to accept and use technology by lecturers.

Navigating the path to equitable rheumatologic care for underserved children with quality improvement.

The aim of this quality improvement project is to identify children with rheumatologic conditions to prevent delayed or missed diagnosis in underserved pediatric populations. Our focus is on prompt and accurate identification and subsequent treatment of rheumatologic symptoms in pediatric patients referred from Atrium Health safety-net primary care clinics that deliver care to families without private insurance, including those lacking insurance entirely. We collaborated with providers at one safety-net clinic to improve the processes of identification and subspecialty referral, resulting in an increase in the number of identified pediatric patients and referrals for these patients with potential rheumatologic disease. We used the Model for Improvement framework with rapid Plan-Do-Study-Act cycles and evaluated improvement with run and statistical process control charts. We achieved improvement, with zero referrals in the previous 5 years for the targeted population increasing to 15 patient referrals within 1 year of project initiation. Despite this increase in referrals, the rheumatology clinic was able to see all priority patients within 20 business days from referral. An awareness of concerning rheumatologic symptoms in safety-net primary care clinics, combined with the use of both visual and decision aids, allows care teams to efficiently recognize and accurately refer patients needing specialty care.

Adaptive sample size strategies for process dispersion using EWMA control chart

AbstractMonitoring the stability of measured dispersion in a process quality parameter is one of the critical steps in statistical process control (SPC) and is a paramount activity carried out by specialists. For this purpose, the current research presents a promising and computationally effective tool where the exponentially weighted moving average (EWMA) statistic utilizes the quadratic sigmoid function by incorporating sample size leading to the Quadratic Sigmoid Exponential weighted Moving Average (QSEWMA) control chart. The adaptive sample size is a strategy that increases or decreases the sample size according to the conditions of the process and accordingly, increases the sensitivity and response of the control chart. The proposed strategy improves the detection of process dispersion changes, making it a more efficient and effective way of quality assurance. Our proposed control chart is superior to the EWMA‐S2 chart, HHW‐EWMA chart CUSUM‐S2chart, and parametric free adaptive EWMA (AEWMA) charts for dispersion through extensive Monte Carlo simulations. The proposed QSEWMA chart has better detection sensitivity and fewer false alarms, and is, in general, more effective comparatively. At last, a real data application test is done to measure the practicability and highest accuracy of the above chart.

MONITORING THE SAUSAGE PRODUCT USING LANEY DEMERIT CHART BASED ANALYTICAL HIERARCHY PROCESS

Ready-to-eat sausage is a food product that has a limited shelf life. Therefore, regularly monitoring the quality of packaged ready-to-eat sausage products is important to ensure that the products meet the established quality standards. Twelve types of product defects need to be observed in the final checking process to meet the quality standards, namely Wrinkle, Dots, Leaking, Product Stain, Non-standard Form, Poor Print Quality, Vacuum Leaks, Weak Ties, Body Defects, Uneven Length, Broken Node, and Small Stain. This study aims to apply the Laney Demerit Control Chart (LDCC) and Analytical Hierarchy Process-Integrated Statistical Process Control (AHP-ISPC) methods to monitor the quality of packaged ready-to-eat sausage production at XYZ Inc. The data is from the quality testing of ready-to-eat sausage products taken from XYZ Inc. for six months from April 1, 2023, until September 30, 2023. The findings reveal that conventional control charts (u control chart, demerit control chart, and AHP-based demerit control chart) exhibited oversensitivity because it is attributed to the large number of samples produced by the company, prompting the need for a more balanced approach. Implementing the Laney u control chart, Laney demerit control chart, and the AHP-based Laney demerit control charts successfully achieved statistical control in phase I. In contrast, phase II still demonstrated challenges, particularly with the AHP-based Laney Demerit Control Chart detecting the highest number of out-of-control points. This suggests that phase II remains statistically out of control, necessitating further analysis or corrective measures to enhance process stability. Additionally, the process capability analysis indicated that the production process during the specified period lacked capability, as evidenced by a capability index value below one (0.883).

New approach to multivariate statistical process control and its application to wastewater treatment process monitoring

This paper presents a new process monitoring and fault diagnosis approach based on a modified Multivariate StatisticalProcess Control (MSPC) and evaluates its applicability to municipal wastewater treatment process monitoring. Firstly,a conventional MSPC, based on Principal Component Analysis (PCA), is adjusted to provide an easy-to-understand userinterface and then a new yet simplified reconfigurable diagnostic model is introduced. The user interface that has beendeveloped is designed to integrate MSPC seamlessly with existing process monitoring systems that use the so-called trendgraphs. The proposed diagnostic model is constructed by aggregating small models with either one or two inputs, whichenhances the tractability of the diagnostic model. The effectiveness of the modified MSPC is demonstrated through a series of offline and online experiments, using a set of real multivariate process data from a municipal wastewater treatment.plant.

Evaluation of statistical process control charts for infant mortality monitoring in Brazilian cities with different population sizes.

The control chart is a classic statistical technique in epidemiology for identifying trends, patterns, or alerts. One meaningful use is monitoring and tracking Infant Mortality Rates, which is a priority both domestically and for the World Health Organization, as it reflects the effectiveness of public policies and the progress of nations. This study aims to evaluate the applicability and performance of this technique in Brazilian cities with different population sizes using infant mortality data. In this article, we evaluate the effectiveness of the statistical process control chart in the context of Brazilian cities. We present three categories of city groups, divided based on population size and classified according to the quality of the analyses when subjected to the control method: consistent, interpretable, and inconsistent. In cities with a large population, the data in these contexts show a lower noise level and reliable results. However, in intermediate and small-sized cities, the technique becomes limited in detecting deviations from expected behaviors, resulting in reduced reliability of the generated patterns and alerts.

An artificial neural network approach for out‐of‐control stream identification in multiple stream processes

AbstractA multiple stream process (MSP) is a process at a point in time that generates several streams of output with quality variables and specifications that are identical in all streams. Statistical process control (SPC) schemes for MSPs are designed to issue an out‐of‐control (OC) signal when a possible change occurs from the in‐control (IC) state of the process. However, the SPM literature for MSPs lacks contributions regarding post‐hoc identification of the stream or group of streams responsible for an OC signal. In this article, we propose an artificial neural network (NN) specifically trained for this purpose and, through a Monte Carlo simulation, show its superiority in correctly identifying OC streams, since an OC signal has been correctly issued. The research is motivated by the need for a post hoc diagnosis of the heating, ventilation, and air conditioning (HVAC) systems installed on modern train coaches, generating multiple streams of quality variables of interest. A case study in this area based on the HVAC data (openly available online at https://github.com/unina‐sfere/NN4OCMSP) illustrates the practical applicability of the proposed approach, which is implemented in the Python package NN4OCMSP and published on the PyPI software repository.

0.28-nW level-shifter folded flipped source follower fourth-order low-pass-filter IC design

ABSTRACT Low-pass-filter (LPF) is strictly required to process signal in battery-operated wearable healthcare systems. However, in these battery-operated systems improving signal quality and power efficiency of LPF is a challenging task. This manuscript presents a 4th-order LPF architecture based on level-shifter and folded flipped source follower (LSFFSF) techniques which consumes power of 0.28 nW. The LSFFSF LPF is simulated in Cadence Virtuoso 180 nm technology using BSIM3V3 MOS model and is capable of delivering gain of −0.27 dB, cut-off frequency of 99.91 Hz, input referred noise of 79.9 μVrms, dynamic range (DR) of 50.47 dB, total harmonic distortion (THD) of −50.06 dB at 80.31 Hz. The figure of merit (FOM) of LSFFSF LPF is 2.11 × 10−15 J with area consumption of 0.021 mm2. Additionally, the statistical process and mismatch analysis values for gain, THD and DR with 400 iterations show coefficient of variation of 1.92, 0.21 and 0.013, respectively. Comparison of the proposed LSFFSF LPF with other LPF architectures establishes its potential advantage in terms of low-power consumption, enhanced THD, DR and least FOM. The LPF architecture presented in this work is suitable candidate for wearable EEG healthcare applications.

Taking an argumentation approach to statistical investigations: developing student data-ing practices

AbstractResearch literature has provided considerable insight as to how young students might come to deeper understandings of statistical concepts, processes and representations through investigations. However, ‘data-ing’—the critical thinking aspects of decision-making while engaging in statistical investigations—has shown to be more difficult to grasp. Data-ing aspects of statistical investigations identified in literature as particularly challenging include: generating researchable questions, knowing when or how to apply statistical knowledge, seeing a need for evidence to support conclusions, and knowing how to use data as evidence. Argumentation practices necessitate a focus on the use of evidence to logically support a claim, thereby offering potential to focus students on the sufficiency and quality of evidence and the conclusions that can be drawn. As such, argumentation offers potential to support students to engage in data decision-making practices. In the research described here, an argumentation focus was used to guide a statistical investigation with a class of 9–10-year-old students in a suburban Australian school. Analysis of classroom interactions and children's work samples suggested that taking an argument-based focus supported students’ developing capacity to overcome the aforementioned challenges and to engage in critical, decision-making practices with authentic, messy data within the context of the investigation.

Improving Time to Goals of Care Discussions in Invasively Ventilated Preterm Infants.

The challenge of identifying preterm infants with bronchopulmonary dysplasia (BPD) that need tracheostomy placement may delay goals of care (GOC) discussions. By identifying infants with a low probability of ventilation liberation, timely GOC discussions may reduce the time to tracheostomy. Our SMART aim was to reduce the postmenstrual age (PMA) of GOC discussions by 20% in infants with BPD and prolonged invasive ventilatory requirement by October 2020. Our group conducted a quality improvement initiative at the University of Alabama at Birmingham. Infants were included if born at <32 weeks' gestation and exposed to invasive ventilation for ≥2 weeks beyond 36 weeks' PMA. Interventions included (1) consensus of BPD infants at risk for tracheostomy dependence, (2) monthly multidisciplinary tracheostomy meetings, and (3) development and utilization of tracheostomy educational content for families. Statistical process control charts were used for all analyses. A total of 79 infants were included in analyses, of which 44 infants either received a tracheostomy or died. From X-mR control chart analysis, there was special cause variation in the time to GOC discussions, which decreased from 62 to 51 weeks' PMA related to monthly multidisciplinary conferences. The average PMA at tracheostomy decreased from 80 weeks to 63 weeks with no change in the frequency of tracheostomy placement or discordant GOC discussions in which infants survived to hospital discharge without a tracheostomy. In infants with ventilator-dependent BPD, standardization of GOC discussions reduced the PMA of GOC discussions and tracheostomy.

A-202 Quality control design survey reveals 399 ways to do it wrong

Abstract Background The effectiveness of statistical quality control processes to detect unacceptable patient risk depends on the choices that laboratory professionals make for the design of quality control charts and acceptable patient risk. Recommended best practice consists of putting a meaningful recent mean and SD on the QC chart, selecting rules based on the method’s margin for error to unacceptable risk or Sigma value, and basing acceptable risk on clinical need. Methods A survey was circulated through LinkedIn and Labvine. Forty respondents who included their credentials were included in the final analysis. Respondents were asked to select the one most correct answer four questions regarding the parameters that govern the effectiveness of quality control processes. 1. The best source of the mean on the QC chart? (Four choices) 2. Best source of the SD on the QC chart? (Five choices) 3. Best way to select QC rules for each QC sample? (Six choices) 4. If an analytical process “fails,” we should stop reporting before [errors are reported.] (Five choices) A total of 400 potential patterns of QC selections were chosen. (4 * 5 * 5 * 4). Results When asked acceptable error rate if an analytical process “fails,” responses were More than 5% errors are reported - 11%. Any errors are reported - 66% It varies with clinical use of the test - 16% It varies with capabilities of our method - 8% While most respondents chose at least one of the recommended answers for QC chart design and rules, only three chose the best choice for all parameters. Conclusions Despite decades of recommendations of best practice for quality control, typical practice throughout the laboratory community in general does not reflect this. It is time to consider a new approach to teaching, validating and certifying competency in medical laboratory quality control.

An image-based nonparametric multivariate EWMA control chart for metal additive manufacturing process monitoring

Additive manufacturing offers considerable promise in terms of manufacturing flexibility, but print quality remains a major obstacle to its widespread adoption. Consequently, timely and accurate detection of process variations or anomalies is critical for implementing corrective actions. Melt pool characteristics are crucial to final product quality, and current state-of-the-art studies on melt pool image flow have primarily focused on in-situ sensing, feature extraction, and their relationship with process settings and material properties. This study aims to develop a statistical process control approach to detect process variations immediately using a predefined distribution of monitoring statistics. Two main challenges are addressed: 1) the unknown distribution of melt pool image features, rendering traditional parametric control charts unreliable and 2) the autocorrelation of melt pool image flow features, violating the traditional control chart assumption of independent and identically distributed monitoring data. To overcome these challenges, we propose a multivariate nonparametric Exponential Weighted Moving Average (EWMA) control chart that can appropriately accommodate stationary serial data correlation to monitor process stability by tracking physical features extracted from the original images. Key features of the proposed control chart include its ability to eliminate serial correlation effects and its distribution-free nature, requiring no prior information about the data distribution. Comparative analysis of simulated and real data demonstrates that the proposed approach outperforms baseline methods in anomaly detection accuracy. An actual case study in laser metal deposition (LMD) further validates the effectiveness of the proposed method.

Assessment, analysis and optimization for high temperature sliding wear process parameter with additive built nickel base superalloy

Abstract Superalloys are highly demanding alloy for high temperature application. The conventional production process and metallurgical sustainability of the superalloy against the applications is the preamble of the existing research. Based on the review it is clear to say that the development of nickel base superalloy with advanced technique without compensating the quality is open for research. The nickel base superalloy is developed through additive manufacturing process following a metal laser sintering technique. The developed alloy is used to perform high temperature sliding wear analysis with the different input process conditions. Applied load, sliding duration and the working temperature are the defined process environment for the investigation. The process conditions are designed with twenty-seven set of experimental trials for statistical analysis and process assessment. The responses on surface roughness and the material loss with respect to the input process parameters are technically assessed and justifications made with the electron microscopic images. The surface topography has influenced due to applied load and the sliding duration. Applied load has influenced the contact area prone with severe wear and the ridges are notice from the microscopic analysis. The statistical analysis has proved that the influence of temperature is less and negotiable compared to the load and time factor. From the optimization process, the optimal parameter for the experimental design is 10 N, 100 °C and 30 min is the ultimate condition to produce best results from the high temperature sliding wear analysis.