Statistical Control Research Articles

Incorporating costs and benefits to the evaluation of uncertain research results: Applications to cancer research funding

Abstract Correctness is a key aspiration of the scientific process, yet recent studies suggest that many high-profile findings may be difficult to replicate or require considerable evidence for verification. Proposals to fix these issues typically ask for tighter statistical controls (e.g., stricter p-value thresholds or higher statistical power). However, these approaches often overlook the importance of contemplating research outcomes’ potential costs and benefits. Here, we develop a framework grounded in Bayesian decision theory that seamlessly integrates cost-benefit analysis into evaluating research programs with potentially uncertain results. We derive minimally acceptable pre-study odds and positive predictive values for cost and benefit levels. We show that tolerance to inaccurate results changes dramatically due to uncertainties posed by research. We also show that reducing uncertainties (e.g., by recruiting more subjects) may have limited effects on the expected benefit of continuing specific research programs. We apply our framework to several types of cancer research and their funding. Our analysis shows that highly exploratory research designs are easily justifiable due to their potential benefits, even when probabilistic models suggest otherwise. We discuss how the cost and benefit of research could and should always be part of the toolkit used by scientists, institutions, or funding agencies.

USING RETROSPECTIVE DATA TO ASSESS THE UNCERTAINTY OF INDICATORS OF BIOLOGICAL SAFETY OF FOOD AND FEED RAW MATERIALS

The results of the analysis of the advantages and disadvantages of the use of retrospective data for the implementation of the evaluation of uncertainty in the measurement of indicators of biological safety of food and feed raw materials are given. The model measurement procedure used quantitative determination of genetically modified organisms by means of polymerase chain reaction in real time 250 measurement results of certified reference material with a content of 0.1% genetically modified soybean line obtained under conditions of intra-laboratory reproducibility and 10 results of relevant inter-laboratory comparisons with the participation accredited provider. The suitability of retrospective data was assessed by the Shapiro-Wilk criterion and statistical controllability analysis using standardized score charts and modified Shewhart charts, uncertainty by 4 methods described in general and branch international guidelines and regulations. The results of using a combination of modified Shewhart maps demonstrate the acceptability of this approach for establishing the suitability of retrospective measurement results as input data for uncertainty assessment. By integrating additional "fitness limits" to a set of standardized control limits, this analysis acquires the ability to simultaneously establish not only stability, through the interpretation of geometric patterns, but also compliance with specific criteria. Using this approach, objective evidence of the suitability of retrospective data for estimating measurement uncertainty was obtained. The distribution of retrospective data obtained under conditions of intralaboratory reproducibility is assumed to be normal. As a result of the assessment of statistical controllability and compliance with the established criteria, objective evidence of the suitability of the data for uncertainty assessment was obtained. Analysis of uncertainty intervals of subgroups with the volume of 25 measurements, using 4 assessment methods, demonstrated significant variability in the results of measuring biological safety indicators. The uncertainty interval could narrow or widen by almost 100% of the value within fifty measurements. Median values obtained using different uncertainty estimation methods were 2.69%, 22.69%, 39.23, and 43.08%. This variability is due to the limited coverage of sources of uncertainty, which is inherent in each of the analyzed methods. The most optimal method is the evaluation based on the routine measurement of the certified reference material. Retrospective results of routine measurement of certified reference material and interlaboratory comparisons are an effective source of input data for assessing the uncertainty of the results of measuring biological safety indicators of agricultural raw materials. There is a need for further research to establish an assessment model that will provide an opportunity to take into account the optimal number of influential sources of uncertainty that are inherent in biological systems and cannot be simultaneously taken into account using existing approaches offered by standard assessment methods.

Home-Based Connected Devices Combined With Statistical Process Control for the Early Detection of Respiratory Exacerbations by Patients With Cystic Fibrosis: Pilot Interventional Study With a Pre-Post Design.

Currently, patients with cystic fibrosis do not routinely monitor their respiratory function at home. This study aims to assess the clinical validity of using different connected health devices at home to measure 5 physiological parameters to help prevent exacerbations on a personalized basis from the perspective of patient empowerment. A multicenter interventional pilot study including 36 patients was conducted. Statistical process control-the cumulative sum control chart (CUSUM)-was used with connected health device measures with the objective of sending patients alerts at a relevant time in order to identify their individual risk of exacerbations. Associated patient education was delivered. Quantitative and qualitative data were collected. One-half (18/36) of the patients completed the protocol through the end of the study. During the 12-month intervention, 6162 measures were collected with connected health devices, 387 alerts were sent, and 33 exacerbations were reported. The precision of alerts to detect exacerbations was weak for all parameters, which may be partly related to the low compliance of patients with the measurements. However, a decrease in the median number of exacerbations from 12 months before the study to after the 12-month intervention was observed for patients. The use of connected health devices associated with statistical process control showed that it was not acceptable for all patients, especially because of the burden related to measurements. However, the results suggest that it may be promising, after adaptations, for early identification and better management of exacerbations. ClinicalTrials.gov NCT03304028; https://clinicaltrials.gov/study/NCT03304028.

QC Constellation: a cutting-edge solution for risk and patient-based quality control in clinical laboratories.

Clinical laboratories face limitations in implementing advanced quality control (QC) methods with existing systems. This study aimed to develop a web-based application to addresses this gap, and improve QC practices. QC Constellation, a web application built using Python 3.11, integrates various statistical QC modules. These include Levey-Jennings charts with Westgard rules, sigma-metric calculations, exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) charts, and method decision charts. Additionally, it offers a risk-based QC section and a patient-based QC module aligning with modern QC practices. The codes and the web application links for QC Constellation were shared at https://github.com/hikmetc/QC_Constellation, and http://qcconstellation.com, respectively. Using synthetic data, QC Constellation demonstrated effective implementation of Levey-Jennings charts with user-friendly features like checkboxes for Westgard rules and customizable moving averages graphs. Sigma-metric calculations for hypothetical performance values of serum total cholesterol were successfully performed using allowable total error and maximum allowable measurement uncertainty goals, and displayed on method decision charts. The utility of the risk-based QC module was exemplified by assessing QC plans for serum total cholesterol, showcasing the application's capability in calculating risk-based QC parameters including maximum unreliable final patient results, risk management index, and maximum run size andoffering risk-based QC recommendations. Similarly, the patient-based QC and optimization modules were demonstrated using simulated sodium results. In conclusion, QC Constellation emerges as a pivotal tool for laboratory professionals, streamlining the management of quality control and analytical performance monitoring, while enhancing patient safety through optimized QC processes.

Analysis of quality control in clothing production using SQC (Statistical Quality Control) and 5W+1H methods

CV Tujuh Pusaka Tala is a start-up company engaged in the clothing and sports jersey convection industry, as well as marching band musical instruments known by the brands 7MJR Store for clothing and SevenMajor for marching band musical instruments. This study was conducted to analyze the quality control of clothing production at CV. Tujuh Pusaka Tala using the Statistical Quality Control (SQC) method and the 5W and 1H approach to minimize defective products. The SQC methods used include Checksheet, Control Map, Histogram, Pareto Diagram and Cause and Effect Diagram to identify and control variability in the production process. While the 5W and 1H approach is applied to understand the main causes of product defects and find the right solution. The results of the study showed that there were 4 types of defects in the production process at CV. Tujuh Pusaka Tala, namely printing defects, screen printing defects, stitching, and material defects. The main factors causing production defects include humans, materials, machines, methods, and the environment. It can be concluded that the quality control of clothing production at CV. Tujuh Pusaka Tala using the Statistical Quality Control (SQC) method and the 5W and 1H approach to minimize defective products has been implemented well so that it can minimize the existence of defective products.

Iterative Development of a Clinical Decision Support Tool to Enhance Naloxone Co-Prescribing.

Background Opioid overdoses have contributed significantly to mortality in the United States. Despite long-standing recommendations from the Centers for Disease Control and Prevention to co-prescribe naloxone for patients receiving opioids who are at high risk of overdose, compliance with these guidelines has remained low. Objectives The objective of this study was to develop and evaluate a hospital-wide electronic health record (EHR)-based clinical decision support (CDS) tool designed to promote naloxone co-prescription for high-risk opioids. Methods We employed an iterative approach to develop a point-of-order, interruptive EHR alert as the primary intervention and assessed naloxone prescription rates, EHR efficiency metrics, and barriers to adoption. Data was obtained from our EHR's clinical data warehouse and analyzed using statistical process control and Chi-square analyses to assess statistically significant differences in prescribing rates during the intervention periods. Results The initial implementation phase of the intervention, spanning from April 2019 to May 2022, yielded a nearly 3-fold increase in the proportion of high-risk patients receiving naloxone, rising from 13.4% [95% CI, 12.9% - 13.8%] to 36.4% [95% CI, 35.2% - 37.5%; p = 1 x 10-38]. Enhancements to the CDS design and logic during the subsequent iteration's study period, June 2022 and December 2023, reduced the number of CDS triggers by more than 30-fold while simultaneously driving an additional increase in naloxone receipt to 42.7% [95% CI, 40.6% - 44.8%; p = 2 x 10-5]. The efficiency of the CDS demonstrated marked improvement, with prescribers accepting the naloxone co-prescription recommendation provided by the CDS in 41.1% of the encounters in version two, compared to 6.2% in version one (p = 6 x 10-9). Conclusion This study offers a sustainable and scalable model to address low rates of naloxone co-prescription and may also be used to target other opportunities for improving guideline-concordant prescribing practices.

Modelling and monitoring multi-relational networks with ordinal information

Network relationships can be widely seen among entities in various fields such as social networks, supply networks and Internet of Things (IoT). Sometimes abnormal events such as cyber-attacks occur to cause an abrupt increase or decrease in the traffic of networks. Many anomaly detection methods have been developed to identify such abnormal events in networks. In recent years, statistical process control (SPC) has attracted more and more attention in network anomaly detection. However, many of the existing statistical models regard the interaction between two nodes in unweighted directed networks as a binary variable, i.e. presence and absence of contacts, which fails to reflect the intensity level of interactions. This article proposes a new model to describe the dyadic interactions with several ordinal levels and introduces special quantities to incorporate the ordinal information into the model. The model can be expressed in a matrix form to enable easy parameter estimation and derivation of a quadratic monitoring statistic. Numerous simulation studies show that the proposed methods detect anomalies in multi-relational networks more quickly than existing monitoring methods. A case study exhibits the implementation and superiority of the proposed method.

Robustness of Moving Average-Exponential Weighted Moving Average Control Chart with the Light-Tailed Distribution

Control charts are the most significant statistical process control tools for ensuring industrial process reliability and efficiency. This research uses moving average – exponentially weighted moving average (MA-EWMA) control charts to investigate the dispersion characteristics. The control chart performance compares the arithmetic mean of run length (AMRL) and standard deviation of run length (SDRL) profiles of MA-EWMA and synthetic charts. Student's t-distributions are compared for dispersion processes. Finally, we present a case to show how essential control charts are in practice.

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.

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.

Evaluation of the Hematology Analyzer Test Performance Using Six Sigma Metrics: A Cross Sectional Study

Abstract Introduction/Objective The six sigma metric is used to assess the performance of a measurement procedure in comparison to the medical requirement. In clinical laboratories, it can be used to assess the quality of instruments or processes. This study aims to evaluate the performance of hematology analyzers using the six sigma scale. Methods/Case Report This is a descriptive cross-sectional study wherein quality control data from hematology analyzers were obtained. The mean, standard deviation, coefficient of variation, bias and sigma value of the RBC, WBC, hemoglobin, hematocrit, and platelet parameters were determined and the performance of Beckman Coulter DxH 900 was assessed by plotting a normalized OPSpecs chart. Results (if a Case Study enter NA) The WBC and platelet parameters had sigma values of more than 6. Less stringent quality control measures could be applied these parameters which include fewer Westgard rules and fewer control runs. The RBC and hemoglobin parameters had sigma values of at least 3. The hematocrit parameter had a sigma value of less than 3. Improving this parameter could be done by performing optical channel evaluation, using multiple Westgard control rules, increasing the number of control materials and the number of runs. Conclusion This study has demonstrated the analytical performance of hematology analyzers on the six sigma scale, which in turn allows for the selection of the appropriate statistical quality control rule. Ultimately, statistical quality control is only one part of a laboratory’s quality management system. The sigma quality of the testing process, as well as choosing the appropriate control rules and number of controls, may provide the basis for the total quality control strategy of the laboratory. Other pre-analytic and post-analytic factors must also be considered in formulating a laboratory total quality control plan. This would allow the laboratory to provide the best possible quality of services to its clients by increasing efficiency, reducing wastage, and incorporating continuous changes to improve the system.

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.

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.

Proposal and appraisal of novel indicators and performance metrics for project monitoring

Project monitoring is a critical process for guaranteeing its excellence, ensuring the delivery of activities both on planned time and budget. Despite its importance, numerous discussions have arisen regarding which indicator is the most appropriate, and how and when to measure it. The primary objective of this article is to evaluate novel duration indicators based on the Earned Value Management (EVM), Earned Schedule Management (ESM) and Earned Duration Management (EDM) methodologies, and to assess new performance metrics. This involves the determination of the limits of the control charts through extensive Monte Carlo Simulations and their performance under several out-of-control scenarios. The robustness of the control charts is tested using performance metrics within a statistical control approach. The secondary objective consists of evaluating the impact of new probability distributions that describe the project activities, such as the Gamma and Conway–Maxwell–Poisson (CMP) distributions. The contributions of this study are threefold: firstly, the Duration Performance Index (DPI) from the EDM is a reliable indicator for monitoring the overall project duration. Secondly, the probability of overreaction (PO) and the accuracy (Ac) are the performance metrics that most produced robust indicators. Lastly, it is observed that different probability distributions, specifically Gamma and Conway–Maxwell–Poison (CMP) distributions, seem to interfere with the correct generation of control charts signals, although they do not significantly affect the overall duration of the entire project.

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.

ECOBRICK SOFA DESIGN IS ENVIRONMENTALLY FRIENDLY USING THE HOUSE OF QUALITY METHOD AND THE BOSTON CONSULTING GROUP (BCG) METHOD

Plastic is one of the longest decomposing types of waste with a decomposition time of up to 10 centuries. Plastic waste that is difficult to decompose can be minimized by using ecobrick techniques. This ecobrick technique aims to turn plastic waste that was initially worthless (zero value) into a product that has added value. This study uses four methods, namely the House of Quality (HOQ), Quality Function Deployment (QFD), Slovin, and Boston Consulting Group (BCG) methods. These methods are used to determine product planning and development, consumer desires, product position in market share, and systematic evaluation of its ability to produce products to satisfy consumers. This study involved 100 respondents obtained from the slovin method. Where the respondents are positioned as research subjects. The respondent data was obtained from distributing questionnaires through google forms. The data collected is calculated and analyzed for unique and minimalist sofa House of Quality (HOQ). The results of this study inform that customer feedback on the product or Voice of Customer can affect quality control and improvement by using the statistical method or Statistical Quality Control. Furthermore, unique and minimalist sofa companies each have different product development focuses. Following up on the findings in this study, the company can adjust the needs and desires of these consumers to the design and service standards as well as the obstacles that may occur.

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).

Observation of Three-Photon Cascaded Emission from Triexcitons in Giant CsPbBr3 Quantum Dots at Room Temperature.

Colloidal semiconductor nanocrystals have long been considered a promising source of time-correlated and entangled photons via the cascaded emission of multiexcitonic states. The spectroscopy of such cascaded emission, however, is hindered by efficient nonradiative Auger-Meitner decay, rendering multiexcitonic states nonemissive. Here we present room-temperature heralded spectroscopy of three-photon cascades from triexcitons in giant CsPbBr3 nanocrystals. We show that this system exhibits second- and third-order correlation function values, g(2)(0) and g(3)(0,0), close to unity, identifying very weak binding of both biexcitons and triexcitons. Combining fluorescence lifetime analysis, photon statistics, and spectroscopy, we can readily identify emission from higher multiexcitonic states. We use this to verify emission from a single emitter despite high emission quantum yields of multiply excited states and comparable emission lifetimes of singly and multiply excited states. Finally, we present potential pathways toward control of the photon number statistics of multiexcitonic emission cascades.