Repetitive Sampling Research Articles

Economic Design of Generalized Multiple Dependent State Sampling Plan Under Exponentiated Half Logistic Distribution

The main objective of this study is to propose an economic design of a generalized multiple dependent state sampling plan based on time truncated life test under exponentiated half logistic distribution for assuring median life. In this study, the optimal plan parameters are determined for various median ratios using the approach of two points on the operating characteristic curve. The performance of the proposed plan is compared with other sampling plans designed under exponentiated half logistic distribution. An investigation on the effect of misspecification of shape parameters on probabilities of lot acceptance is done and the industrial application of the proposed plan is explained with real time data. The results obtained from both conventional as well as economic designing show that the proposed plan outperforms the other existing sampling plans, such as multiple dependent state sampling plan, multiple dependent state repetitive group sampling plan, and single sampling plan.

Establishment of a Raman nanosphere based immunochromatographic system for the combined detection of influenza A and B viruses’ antigens on a single T-line

A simple and rapid system based on Raman nanosphere (R-Sphere) immunochromatography was developed in this study for the simultaneous detection of Influenza A, B virus antigens on a single test line (T-line). Two types of R-Sphere with different characteristic Raman spectrum were used as the signal source, which were labeled with monoclonal antibodies against FluA, FluB (tracer antibodies), respectively. A mixture of antibodies containing anti-FluA monoclonal antibody and anti-FluB monoclonal antibody (capture antibody) was sprayed on a single T-line and goat anti-chicken IgY antibody was coated as a C-line, and the antigen solution with known concentration was detected by the strip of lateral flow immunochromatography based on surface-enhanced Raman spectroscopy (SERS). The T-line was scanned with a Raman spectrometer and SERS signals were collected. Simultaneous specific recognition and detection of FluA and FluB were achieved on a single T-line by analyzing the SERS signals. The findings indicated that the test system could identify FluA and FluB in a qualitative manner in just 15 minutes, with a minimum detection threshold of 0.25 ng ml-1, excellent consistency, and specificity. There was no interference with the other four respiratory pathogens, and it exhibited 8 times greater sensitivity compared to the colloidal gold test strip method. The assay system is rapid, sensitive, and does not require repetitive sample pretreatment steps and two viruses can be detected simultaneously on a single T-line by titrating one sample, which improves detection efficiency, and provide a reference for developing multiplexed detection techniques for other respiratory viruses.

Utilization of the dialysate to characterize dialysis adequacy by using microwave urea biosensor based on complementary split-ring resonator (CSRR)

The aim of this study is going to develop a monitoring approach for measuring blood urea concentration. This method uses microwave technology and complementary split-ring resonator (CSRR) to assess dialysis adequacy. The robust electric field near CSRR sides produces a high-sensitivity surface to a change in the nearby urea level. The proposed biosensor detection range spans from 1 to 100 mg/dL by modifying resonance frequency and peak attenuation. The biosensor was developed to operate at the main resonance frequency of 2.4 GHz, but all harmonics within the frequency range of 1–10 GHz were analyzed. A container incorporates the dialysate as the material being tested on the top of the sensor. Accurate measurement of blood urea variations using spent dialysate compared to the blood-based method has benefits due primarily to the lack of repetitive blood sampling. The innovative biosensor offers a non-invasive and convenient method to accurately evaluate the quantitative urea concentration in spent dialysate without the necessity of disposable chemicals. The biosensor has been designed to possess noteworthy attributes such as detecting minuscule concentration variations, low-cost fabrication, compact size, high sensitivity, and its ability to provide real-time results. By merging these features, the sensor demonstrates remarkable potential for non-invasively monitoring blood urea levels while seamlessly integrating with other dialysis devices. The initiative purpose of this study is going to offer renal patients methods for assessing dialysis adequacy with minimal impact on human health.

LAMAIS: A library-aided approach for efficient 1D 1H NMR qualitative analysis in plant metabolomics

BackgroundOne-dimensional proton nuclear magnetic resonance (1D 1H NMR) spectroscopy is a non-destructive, non-targeted analytical technique providing both qualitative and quantitative insights, particularly beneficial for mixture analysis. However, the qualitative analysis of 1D 1H NMR spectra for mixture samples is laborious and time-consuming, involving extensive database searches and verification experiments like spiking. This process heavily relies on the analyst's expertise, leading to efficiency discrepancies. There is a pressing need for a reliable method to streamline operations and enhance the efficiency of qualitative analysis in complex mixtures. ResultsWe introduce a library-aided method for spectral profiling, named LAMAIS. This method achieves compound identification through similarity assessment between samples and template data, allowing rapid, automatic compound identification and full-spectrum peak assignment without the need for fitting. LAMAIS correctly identifies over 90 % of components in synthetic mixtures and more than 75 % in experimental mixtures, surpassing other representative methods with a higher F2 score. Our reference library, which currently includes 71 compounds, is tailored to capture the commonality of primary metabolites across diverse plant species. The analysis of real-world samples yielded encouraging results, underscoring LAMAIS's versatility as an auxiliary tool suitable for a variety of botanical sources. For analyst convenience, interactive graphics are utilized as the output format. SignificanceLAMAIS excels, demonstrating competitiveness and reliability. The approach minimizes repetitive tasks and sample wastage, improving the efficiency of 1D 1H NMR qualitative analysis. Constructing a reference library effectively preserves knowledge, mitigates reliance on human experience, and addresses gaps in the analysis of plant source samples.

Design of Control Charts Using Repetitive Sampling: A Comparative Study of Conditional Expected Delay

Design of Control Charts Using Repetitive Sampling: A Comparative Study of Conditional Expected Delay

Real-world experience of angiotensin receptor-neprilysin inhibitors in patients with heart failure and dialysis.

Although angiotensin receptor-neprilysin inhibitor (ARNI) has shown promise in patients with heart failure and reduced ejection fraction (HFrEF), the treatment effect in HFrEF patients with end-stage renal disease (ESRD) undergoing dialysis is uncertain. This study aimed to examine the real-world effects of ARNI vs. angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEI/ARB) in this subpopulation. This multi-institutional, retrospective study identified 349 HFrEF patients with ESRD on dialysis, who initiated either ARNI or ACEI/ARB therapy. Efficacy outcomes included rates of hospitalization for heart failure (HHF) and mortality, as well as changes in echocardiographic parameters. Safety outcomes encompassed hypotension and hyperkalemia. Treatment effects were assessed using Cox proportional hazards models, with additional sensitivity analyses for robustness. Out of 349 patients screened, 89 were included in the final analysis (42 in the ARNI group and 47 in the ACEI/ARB group). After 1 year of treatment, echocardiographic measures between the two groups were comparable. The primary composite rate of HHF or mortality was 20.6 events per 100 patient-years in the ARNI group and 26.1 in the ACEI/ARB group; the adjusted hazard ratio was 0.98 (95% CI: 0.28-3.43, P = 0.97). Their safety outcomes did not differ significantly. Sensitivity analyses, including repetitive sampling, propensity score matching, and extended follow-up, corroborated these findings. ARNI has proven effective in treating HFrEF patients; however, significant benefits were not observed in these patients with ESRD undergoing dialysis compared with ACEI/ARB in this real-world cohort. Future research employing a more extended follow-up period, larger sample size, or randomized design is warranted to investigate the treatment effects in this subpopulation.

Optimal and economic design of multiple deferred state repetitive group sampling plan for assuring mean life under a family of lifetime distributions

In this paper, a designing methodology of the multiple deferred (dependent) state repetitive group sampling plan is developed to ensure the mean lifetime of the products under the truncated life test, which follows a family of different lifetime distributions. An approach of two points on the operating characteristic curve is followed to obtain the optimal plan parameters with the known shape parameter values for various mean life ratios under different lifetime models. The proposed sampling plan is illustrated with a numerical example. The application of the proposed sampling plan is explained with a real-life example based on Burr XII distribution. Comparison is made among the performances of the proposed sampling plan using the different lifetime distributions; these performances are also compared with a single sampling plan using Burr XII distribution based on mean life assurance. Finally, an economic design of the proposed sampling plan is discussed, along with some cost models to minimize the total cost.

Prediction of postoperative dysphagia in patients with oral cancer: A prospective cohort study

ObjectivesThis study aims to identify autonomous risk factors for postoperative dysphagia in oral cancer patients and construct a nomogram prediction model to improve risk assessment accuracy and feasibility in clinical settings. MethodsA prospective cohort study was conducted from March to July 2022 among oral cancer patients undergoing surgical interventions at the Department of Head and Neck Surgery. Clinical data were collected using the Postoperative Dysphagia Risk Factor Questionnaire. Swallowing function was assessed with the Mann Assessment of Swallowing Ability-Oral Cancer (MASA-OC). Lasso regression identified potential predictor variables, followed by univariate and multivariate logistic regression analyses. A predictive model was developed using R Studio 4.1.2 and rigorously evaluated with ROC curves, Hosmer-Lemeshow tests, and calibration curves. Internal validation utilized Bootstrap methodology with 1000 repetitive samples. ResultsThe cohort included 257 oral cancer patients, with 73.9 % experiencing postoperative dysphagia. Independent predictors included functional status, depressive symptoms, pT stage, surgical techniques, glossoplasty, maxillectomy, and post-surgery nasopharyngeal tube retention. The predictive model achieved an AUC of 0.933, sensitivity of 90.9 %, and specificity of 81.7 %. Hosmer-Lemeshow test (P = 0.715) and C-index (0.934) indicated satisfactory model fit. Internal validation yielded an AUC of 0.912, sensitivity of 93.3 %, and specificity of 63.8 %. Calibration curves demonstrated alignment between predicted and observed outcomes. ConclusionA nomogram integrating recognized risk factors shows promise in predicting postoperative dysphagia in oral cancer patients, enhancing precision and aiding healthcare professionals in risk evaluation and patient care strategies.

Time Truncated Attribute Median Control Charts for Logistic-Exponential Process Distribution

Abstract In this article, time truncated attribute control chart is proposed for logistic-exponential distribution based on four acceptance sampling plans. The performance of the proposed control chart is evaluated in terms of average run length. Furthermore, the efficiency of the proposed attribute control chart is compared for all given acceptance sampling plans, viz., single sampling plan, repetitive sampling plan, multiple dependent state sampling plan and multiple dependent state repetitive sampling plan. Also, the application of the proposed control chart is illustrated by using real life example.

Designing acceptance sampling plans for polynomial profiles using EWMA statistics based on process yield index

Inspection of raw materials, semi-finished products or final products is an important part of a quality assurance system. Acceptance sampling is a procedure used to decide either to accept or reject suppliers’ submitted lots in accordance with a specific standard. In this manuscript, designing acceptance sampling plans for lots with polynomial profile as quality characteristic is considered. To use previous lot information on deciding for accept or reject the available lot, exponentially weighted moving average (EWMA) statistic based on process yield index (Spk ) of polynomial profiles with two-sided specification limits is applied. For this purpose, three acceptance sampling plans of repetitive group sampling (RGS), resubmitted and also combined RGS-resubmitted plans are developed in this paper. The Parameters of the proposed plans are determined using non-linear optimization model. Performance of the proposed acceptance sampling plans are compared using numerical methods and Monte-Carlo simulations. Simulation results emphasize that the proposed combined sampling plan mostly requires less average sampling number (ASN) and consequently leads to decreasing inspection time and costs. The application of the proposed combined plan is also demonstrated using a real case.

Utilizing Repetitive Sampling in the Construction of a Control Chart for Lindley Distribution with Time Truncation

The monitoring of the process relies on the average product lifetime, which adheres to the Lindley distribution and employs a truncated life test-based attribute control chart. Control limits for these charts are determined through a repetitive sampling procedure. Two inner and outer control limits are established based on the desired average run length (ARL). Tables are generated for varying ARL values by adjusting shift values while keeping the shape parameter of the Lindley distribution constant. The effectiveness of the control charts is assessed through application to real-time data, and the performance of the proposed chart is evaluated using simulation data. A comparative analysis is conducted with single sampling scheme based on ARL values, and the results are presented and discussed.

Statistical design of ARL-unbiased exponential chart with runs and scans rules using repetitive sampling

Statistical design of ARL-unbiased exponential chart with runs and scans rules using repetitive sampling

Multiple dependent state repetitive sampling plan odd log-logistic generalized exponential distribution with application to beverage product’s carbon dioxide pressure and concentration

Multiple dependent state repetitive sampling plan odd log-logistic generalized exponential distribution with application to beverage product’s carbon dioxide pressure and concentration

An improved skip-lot sampling scheme with resampling mechanism using an advanced capability index

In today’s competitive market, product quality holds significant importance for consumers, motivating companies to prioritise high-quality product development and enhance their competitiveness. Acceptance sampling plans have proven to be indispensable tools in quality control practices, enabling producers and consumers to protect their interests by establishing evaluation rules for product batches. As defective rates decrease in modern manufacturing, skip-lot sampling plans (SkSP) have emerged as a suitable way for lots exhibiting stable and excellent product quality. Additionally, repetitive group sampling plan (RGSP) offers a cost-effective way to maintain desired protection for both parties, especially in costly or destructive inspection scenarios. Therefore, this study integrates the benefits of process capability indices, SkSP, and RGSP to develop a new variables SkSP with RGSP as its reference plan based on an advanced capability index Cpmk (called Cpmk -based SkSP-RGSP). The proposed plan achieves comparable discriminatory power to traditional sampling plans while significantly reducing the required sample size. Detailed tables of plan parameters are given to facilitate effective implementation for practitioners.

Determination and economic design of repetitive group sampling plan under two parameter Lindley distribution

ABSTRACT A designing procedure of repetitive group sampling plan for assuring mean life under a two-parameter Lindley distribution is proposed, and the optimization model is developed for finding the optimal plan parameters such that the demands of both supplier and buyer are satisfied simultaneously. A real-time data set of ball bearings is used to explain the application of the proposed plan. A comparison between the proposed plan and the single sampling plan is done to reveal the better performance of the proposed plan based on operating characteristic function and average sample number. Applying the proposed plan to provide lifetime assurance reduces the inspection effort and related costs. We discuss the design of the proposed plan with an economic aspect. In addition, the sensitivity of the optimal sampling plans concerning the failure probability and cost parameters is analyzed. Finally, it is evident from the study that the proposed plan will be preferable to a single sampling plan for providing quality assurance with a minimum cost of the inspection.

Formation of Repetitive Group Sampling Plan for Life Tests Based on Percentiles using Odd Frechet Inverse Rayleigh Distribution

Objectives: This paper presents the idea of Repetitive Group Sampling Plans using Odd Frechet Inverse Rayleigh Distribution, when the life test is truncated at a pre-specified time. The main objective of the proposed sampling plan is to minimize the sample size because the analogous inspection time and inspection cost will be reduced. Methods: A truncated life test may be conducted to evaluate the smallest sample size to insure certain percentile lifetime of products. The minimum sample size and its ratio for the specified lifetime percentile were derived for the producer’s risk. The operating characteristic values are also obtained for the proposed sampling plan. The corresponding table values are obtained through Monto Carlo Simulation technique. Findings: The results from the sampling plan developed in this research can easily be adaptable in practical situation with small sample sizes and fewer experimental times and hence the plan yields a better result for reliability sampling plan. Novelty: The work designed and developed with the aim that sampling plan in this paper may be helpful for the engineers and statistical plan developers in the field of statistical quality control especially in attribute reliability sampling plan. The tables are generated which is useful for both producer and consumer. This plan can highly recommendable since the products are randomly sampled and also the procedure for sampling involves less experiment time and with minimum sample size. Keywords: Odd Frechet Inverse Rayleigh Distribution, Percentiles, Repetitive Group Sampling Plan (RGS), Producer’s risk

A novel generative adversarial network for improving crash severity modeling with imbalanced data

Traffic crash data is often greatly imbalanced with the majority of non-fatal crashes and only a small number of fatal crashes. Such data imbalance issue poses a challenge for crash severity modelling, especially for classifying and interpreting fatal crashes with very limited samples. To address the data imbalance issues, the data resampling techniques are commonly used methods to rebalance the number of samples among all categories of the dataset, such as under-sampling and over-sampling techniques. However, it is challenging for most traditional and existing deep learning-based resampling methods, e.g., synthetic minority oversampling technique (SMOTE) and Generative Adversarial Networks (GAN), to handle both continuous and discrete risk factors in traffic crash datasets, since they are built upon by smooth and continuous functions which are not applicable for processing discrete variables. Though some resampling methods are capable of handling both continuous and discrete variables, they may struggle with mode collapse issues associated with sparse discrete risk factors so that the diversity of the underlying data distribution can not be captured due to oversampling repetitive and similar samples. To address the aforementioned issues, the current study proposes a traffic crash data generation method based on the Conditional Tabular GAN (CTGAN) to rebalance crash datasets for improving performance of crash severity classification and interpretation. The designed experiments are conducted to evaluate contributions of the synthetic data for improving crash severity classification, the distribution consistency between synthetic and benchmark datasets, and the parameter recovery (i.e., the accuracy of parameter estimation and probability prediction) for various resampling strategies. A 4-year real-world dataset collected in Washington State, U.S., and Monte Carlo simulations are utilized for demonstrating the designed experiments. The results indicate that crash severity modeling using synthetic data generated by the mix-resampling of CTGAN and random under-sampling (CTGAN-RU) outperforms all baseline methods. In addition, the proposed deep generative method demonstrates the capability in maintaining distribution consistency and achieving accurate parameter recovery. This study can provide valuable insights for traffic safety researchers and engineers into crash severity modeling, especially when handling imbalanced crash data of various types.

A cost-effective skip-lot sampling scheme using loss-based capability index for product acceptance determination

Acceptance sampling plans are essential for quality control and assurance, empowering businesses to make well-informed decisions regarding lot disposition. Among these plans, the skip-lot sampling plan (SkSP) stands out as a cost-effective strategy for suppliers with consistently excellent product quality. In this article, we present an enhanced version of the SkSP, termed Cpm-based SkSP-RGSP, which incorporates a repetitive group sampling plan (RGSP) using the Taguchi capability index Cpm as its reference plan. We develop an optimization model to ensure that the proposed plan meets a two-point condition in its operating characteristic function and effectively minimizes the average sample number (ASN). Through investigations of two critical performance measures, ASN and discriminatory power, we demonstrate the superiority of our plan over conventional sampling approaches. Detailed plan parameters, including required sample size and lot sentencing criteria, are provided to assist examiners in practical implementation. Additionally, we showcase the plan's applicability and practicality through a case study. This research contributes to the field of quality control by offering an efficient and cost-effective sampling scheme for lot disposition.

Ertapenem's therapeutic potential for Mycobacterium avium lung disease in the hollow fibre model

IntroductionGuideline-based therapy for Mycobacterium avium complex (MAC) pulmonary disease achieves sustained sputum conversion rates in only 43–53% of patients. Repurposing of β-lactam antibiotics such as ertapenem could expedite design of more efficacious regimens, compared to developing new drugs. MethodsWe performed an ertapenem exposure-response study in the hollow fibre system model of intracellular MAC (HFS-MAC). We recapitulated human-like intrapulmonary concentration-time profiles of eight once-daily intravenous doses of ertapenem over 28 days and performed repetitive sampling for drug concentration-time profiles and MAC burden. The % of time concentration persisted above MIC (%TMIC) mediating either 50% or 80% of maximal effect (E50, EC80) were identified. The EC80 was used as target exposure in a 10 000 subject Monte Carlo experiments for ertapenem doses of 1G, 2G, or 4G administered once versus twice daily. ResultsThe ertapenem MIC ranged from 0.5 to 2 mg/L on three occasions. Ertapenem achieved a half-life of 4.04 ± 0.80 h in the HFS-MAC and killed a maximum of 2.17 log10 CFU/mL below day 0. The EC50 was %TMIC of 75.9% (95% confidence interval: 68.43%–86.54%) and the EC80 was %TMIC of 100%. Target attainment probability was >90% for 1G twice daily up to an MIC of 2 mg/L, while for 2G twice daily the susceptibility MIC breakpoint was 4–8 mg/L. ConclusionsErtapenem microbial kill below day 0 burden was better than guideline-based therapy drugs in the HFS-MAC in the past. Ertapenem is a promising drug for novel combination therapies for MAC lung disease.

Influence Assessment of Distance to the Source of Pulse Signals With Harmonic Components on the Temporal Distortion of Their Forms

Within the scope of this article, periodic pulse signal typical samples with harmonic components have been analyzed, including their spectral fluctuations, changes in their frequency range, and the form of signal typical samples depending on the distance. Collected statistical information regarding changes in the duration of typical samples affected by distance change from the signal source to the sensor based on data collected during field experiments. Signal features by which typical samples can be recognized have been outlined and their duration effectively measured. The dynamics of frequency spectrum change and duration of repetitive typical samples have been presented depending on the distance to the signal source. Additionally, the frequency range and average duration range of the researched typical samples, and their variations have been provided based on the gathered statistics data.