Average Sample Number Research Articles

Multiple dependent state repetitive sampling plans for exponentiated half logistic distribution

ABSTRACT In the textile and packing industries, the lifetime of the items is exhibited by some statistical distribution. This paper aims to develop a multiple dependent state repetitive (MDSR) sampling plan by assuming that the lifetime of an item comes from an exponentiated half logistic distribution (EHLD). The MDSR sampling plan will be more economical than the other sampling plans on hand such as the multiple dependent state sampling schemes, the repetitive sampling plan, the single sampling plan, and the resubmitted sampling plan concerning the size of the sample required to test the lot. The design parameters for the developed sampling plans are obtained by minimizing the average sample number when both the consumer’s and producer’s risks are satisfied at corresponding quality levels. Tables are given for practical use. The proposed MDSR sampling plan is compared with the single sampling plan and some examples are also provided.

Designing of variables repetitive group sampling plan for consumer protection

Due to the development of modern technology, it is possible to reduce the variation of products quality during the production. However, it is necessary to propose a designing methodology of sampling plans to guarantee the consumers protection in terms of maximum limit of outgoing non-conforming items while protecting the producers a good quality level. Hence, in this paper, we propose the designing methodology of variables repetitive group sampling plan indexed by acceptable quality level and average outgoing quality limit. We develop an optimization model for determining the optimal parameters with minimum average sample number at the acceptable quality level by simultaneously considering two inequalities. The optimal plan parameters are determined for both cases of known and unknown standard deviation. Results show that the proposed plan reduces the required sample size than the existing single sampling plan to provide protection to the consumers in terms of average outgoing quality.

Design of SkSP-R Plan for Popular Statistical Distributions

The design of a Skip-lot sampling plan of type SkSP-R is presented for time truncated life test for the Weibull, Exponentiated Weibull, and Birnbaum-Saunders lifetime distributions. The plan parameters of the SkSP-R plan under these three distributions are determined through a nonlinear optimization problem. Tables are also constructed for each distribution. The advantages of the proposed plan over the existing sampling schemes are discussed. Application of the proposed plan is explained with the help of an example. The Birnbaum-Saunders distribution is economically superior to other two distributions in terms of minimum average sample number.

TIME TRUNCATED ACCEPTANCE SAMPLING PLAN FOR ODDS EXPONENTIAL LOG-LOGISTIC DISTRIBUTION

In this paper, the multiple deferred state repetitive acceptance sampling plan (MDSRASP) is proposed and designed for assuring a 50th percentile lifetime of the products under odds exponential log-logistic (OELL) distribution. The optimum parameters of the proposed plan are determined based on satisfying various blends of producer’s risk and consumer’s risk for definite quality levels in light of 50th percentile. The aim is to minimize the average sample number (ASN), though the constraints are connected with the lot acceptance probability at the acceptable and limiting quality levels. The efficiency of the proposed plan is compared with single sampling plan using ASN. Tables are formed to present the outcomes and comparison of the proposed plan with existing sampling plan is done in terms of ASN.

A Novel Multiple Dependent State Sampling Plan Based on Time Truncated Life Tests Using Mean Lifetime

The design of a new adaptive version of the multiple dependent state (AMDS) sampling plan is presented based on the time truncated life test under the Weibull distribution. We achieved the proposed sampling plan by applying the concept of the double sampling plan and existing multiple dependent state sampling plans. A warning sign for acceptance number was proposed to increase the probability of current lot acceptance. The optimal plan parameters were determined simultaneously with nonlinear optimization problems under the producer’s risk and consumer’s risk. A simulation study was presented to support the proposed sampling plan. A comparison between the proposed and existing sampling plans, namely multiple dependent state (MDS) sampling plans and a modified multiple dependent state (MMDS) sampling plan, was considered under the average sampling number and operating characteristic curve values. In addition, the use of two real datasets demonstrated the practicality and usefulness of the proposed sampling plan. The results indicated that the proposed plan is more flexible and efficient in terms of the average sample number compared to the existing MDS and MMDS sampling plans.

Optimization of Bayesian repetitive group sampling plan for quality determination in Pharmaceutical products and related materials

Sampling plans are extensively used in pharmaceutical industries to test drugs or other related materials to ensure that they are safe and consistent. A sampling plan can help to determine the quality of products, to monitor the goodness of materials and to validate the yields whether it is free from defects or not. If the manufacturing process is precisely aligned, the occurrence of defects will be an unusual occasion and will result in an excess number of zeros (no defects) during the sampling inspection. The Zero Inflated Poisson (ZIP) distribution is studied for the given scenario, which helps the management to take a precise decision about the lot and it can certainly reduce the error rate than the regular Poisson model. The Bayesian methodology is a more appropriate statistical procedure for reaching a good decision if the previous knowledge is available concerning the production process. This article proposed a new design of the Bayesian Repetitive Group Sampling plan based on Zero Inflated Poisson distribution for the quality assurance in pharmaceutical products and related materials. This plan is studied through the Gamma-Zero Inflated Poisson (G-ZIP) model to safeguard both the producer and consumer by minimizing the Average Sample Number. Necessary tables and figures are constructed for the selection of optimal plan parameters and suitable illustrations are provided that are applicable for pharmaceutical industries.

THE RELATIONSHIP BETWEEN POSITIVE THINKING AND SELF ACCEPTANCE ON ELDERLY IN THE NURSING HOMES: A LITERATURE REVIEW

Background: Low elderly self-acceptance in nursing homes affects their quality of life. Elderly difficult to adapting so they feel stressed, lose of control of lives, lose identity. Positive thinking is a form of adaption that can be done by the elderly to have good self acceptance to live in nursong homes. Objective: To know the relationship betwen positive thinking and self-acceptance elderly in nursing homes. Design: A Whittemore and Khanfl’s integrative review was used in this review. Data Sources: This systematic review searched 4 electronic database Scopus, Proquest, Pubmed, Science Direct that published in 2015-2020. Review Methods: Key terms and phrases that utilized were positive thinking OR optimism, self acceptance, elderly OR aged, nursing homes OR orphanage. The JBI Guideline used to asses the quality of each study. Data tabulation and narrative analysis were performed. Results: A total of 11 papers were included in this review. The included studies were divided into three-part, positive thinking in the elderly (n=4), self-acceptance (n=3) and both (n=4). The average number of samples used in each study was 35 people. Conclusion: Positive thinking related to self-acceptance of the elderly in nursing homes. Elders who think positively have a positive mind about themselves, acknowledge and accept various aspects including the good and bad and look at life positively, so they get life satisfaction. The elderly more often feels happiness, enthusiasm to support life, actively appreciate the environment. Good quality of life will increase welfare.

An integrated operating mechanism for lot sentencing based on process yield

ABSTRACT A flexible sampling policy integrated with the consideration of past records is discussed in this paper for the quality evaluation of submission based on process yield. Lot sentencing plays the main role in buyer-seller business contracts for deliveries. A continuous partnership between the vendor and buyer can help in maintaining historically traceable results whereby the recording information is considered valuable. Hence, we propose a new sampling strategy with an integrated operating mechanism that considers the preceding inspection results to lessen the required average sample number based on process yield. Compared with the conventional methods, the proposed modified sampling strategy has the advantage of having a small number of samples for inspection while providing desirable protection under the same quality requirements and tolerable sampling risks. An application taken from the contact lens industry is presented to demonstrate the practicability of this research.

MI-Immune/1801: Lessons from an Ongoing, Multi-Center Trial Involving Biospecimen Collection for Prospective Microbiome and Immune Profiling in Patients Undergoing Reduced Intensity Conditioning Allogeneic HCT

MI-Immune/1801: Lessons from an Ongoing, Multi-Center Trial Involving Biospecimen Collection for Prospective Microbiome and Immune Profiling in Patients Undergoing Reduced Intensity Conditioning Allogeneic HCT

Multiple Deferred State Sampling Plan for Exponentiated New Weibull Pareto Distributed Mean Life Assurance

Abstract This article proposes a methodology of designing the multiple deferred state acceptance sampling plan for assuring lifetime of the products when the underlying distribution of lifetime is exponentiated new Weibull-Pareto. Inspection of the sample items is done based on attribute quality characteristics under a time truncated life test. A number of tables are constructed using attribute multiple deferred state acceptance sampling plans of some specified value for mean lifetime ratio that may be obtained under the exponentiated new Weibull-Pareto model. The consequence of misspecification of the shape parameter and the role of the number of successive lots needed for sentencing current lot are investigated. A procedure for implementing the proposed plan for life-testing applications is included. The performance of the proposed plan is compared in terms of the average sample number required as well as discriminating power.

Yield‐based variables repetitive group plan with a critical‐value‐adjusted mechanism

AbstractAcceptance sampling plans are preferably used when sample testing becomes destructive or costly to make reliable lot determination from a reasonable size of samples. Previous decades have seen various sampling strategies developed for specific purposes and the evolution of sampling methods continue to ensure that lot sentencing becomes more efficient and economic. In this study, we proposed a modified variables repetitive group sampling (VRGS) plan integrated with a critical‐value‐adjusted switching mechanism. Such a design provides an adaptive assessment of the varied submitted quality level. The operating characteristic function is derived from the sampling distribution of process yield index Spk, which possesses a one‐to‐one transformation to process yield. By solving the developed nonlinear minimization model, the plan parameters of the proposed method are satisfied with tolerable sampling risks and desirable quality levels simultaneously. Furthermore, by comparing the average sample number (ASN) reduction and discriminatory power with other approaches, the proposed method outperforms conventional methods. Numerical illustrations are provided to show its practicability in real applications.

Inspection plan for COVID-19 patients for Weibull distribution using repetitive sampling under indeterminacy

BackgroundThis research work is elaborated investigation of COVID-19 data for Weibull distribution under indeterminacy using time truncated repetitive sampling plan. The proposed design parameters like sample size, acceptance sample number and rejection sample number are obtained for known indeterminacy parameter.MethodsThe plan parameters and corresponding tables are developed for specified indeterminacy parametric values. The conclusion from the outcome of the proposed design is that when indeterminacy values increase the average sample number (ASN) reduces.ResultsThe proposed repetitive sampling plan methodology application is given using COVID-19 data belong to Italy. The efficiency of the proposed sampling plan is compared with the existing sampling plans.ConclusionsUsing the tables and COVID-19 data illustration, it is concluded that the proposed plan required a smaller sample size as examined with the available sampling plans in the literature.

On Sequential Probability Sampling Plan for a Truncated Life Tests Using Rayleigh Distribution

This paper proposed a sequential probability sampling plan for a truncated life test using a Rayleigh distribution from a designed double sampling plans where the interest was to obtain the minimum sample size necessary to assure that the average life time of a product is longer than the default life time at the specified consumer’s and producer’s confidence level. Estimations of minimum sample, acceptance and rejection numbers obtained are analyzed and presented to explain the usefulness of sequential plans in relation to single and double sampling plan. Probability of acceptance (Pa), Average sample number (ASN) and Average outgoing quality (AOQ) for the plans are computed. The three regions; acceptance, continue sampling and rejection were determined. The five points necessary to plot ASN curve were also computed and presented.

Design and construction of a quick-switching sampling system with a third-generation capability index

Acceptance sampling, the most widely used technique in practice, provides rules for making decisions on product acceptance with specified quality standards. A quick-switching sampling (QSS) system, which involves a Normal sampling plan and a Tightened sampling plan with switching rules between them, has recently been discussed. This paper proposes a variables QSS system with parameters based on a third-generation capability index, which takes process yield and quality loss into consideration. A nonlinear optimization model is formulated to determine the designed plan parameters by minimizing average sample number and subject to the quality and risk levels specified by both the producer and the consumer. The comparison study between the designed sampling system and conventional sampling plan is also conducted in terms of common metrics including operating characteristic curve, average sample number, and average run length. Finally, a practical example is presented along with a step-by-step description of the procedure.

Optimal parameters of EWMA Control Chart for Seasonal and Non-Seasonal Moving Average Processes

The main goal of this paper is to study optimal parameters of an Exponentially Weighted Moving Average (EWMA) control chart for seasonal and non-seasonal Moving Average (SMA and MA) processes. The characteristic of control chart is Average Run Length (ARL) which is the average number of samples taken before an action signal is given. Ideally, an acceptable ARL of in-control process should be large enough, so-called Average Run Length for in-control process (ARL 0). Otherwise, it should be small when the process is out-of-control, so-called Average Run Length for out-of-control process (ARL 1). We obtain explicit formulas of ARL for EWMA chart for Moving Average (MA) process with exponential white noise. In particular, the explicit analytical formulas for evaluating ARL 0 and ARL 1 are able to obtain a set of optimal parameters which depend on a smoothing parameter (λ) and a width of control limit (b) for designing EWMA chart with a minimum ARL 1 value. In addition, the explicit formulas for the EWMA control chart was applied with the practical data of the unemployment rate of Thailand.

Development of a Sequential Sampling Plan using Spatial Attributes of Cercospora Leaf Spot Epidemics of Table Beet in New York

Sampling strategies that effectively assess disease intensity in the field are important to underpin management decisions. To develop a sequential sampling plan for the incidence of Cercospora leaf spot (CLS), caused by Cercospora beticola, 31 table beet fields were assessed in the state of New York. Assessments of CLS incidence were performed in six leaves arbitrarily selected in 51 sampling locations along each of three to six linear transects per field. Spatial pattern analyses were performed, and results were used to develop sequential sampling estimation and classification models. CLS incidence (p) ranged from 0.13 to 0.92 with a median of 0.31, and beta-binomial distribution, which is reflective of aggregation, best described the spatial patterns observed. Aggregation was commonly detected (>95%) by methods using the point-process approach, runs analyses, and autocorrelation up to the fourth spatial lag. For Spatial Analysis by Distance Indices, or SADIE, 45% of the datasets were classified as a random pattern. In the sequential sampling estimation and classification models, disease units are sampled until a prespecified target is achieved. For estimation, the goal was sampling CLS incidence with a preselected coefficient of variation (C). Achieving the C = 0.1 was challenging with <51 sampling units, and only observed on datasets with incidence >0.3. Reducing the level of precision, i.e., increasing C to 0.2, allowed the preselected C to be achieved with a lower number of sampling units and with an estimated incidence ([Formula: see text]) close to the true value of p. For classification, the goal was to classify the datasets above or below prespecified thresholds (pt) used for CLS management. The average sample number, or ASN, was determined by Monte Carlo simulations, and was between 20 and 45 at disease incidence values close to pt, and approximately 11 when far from pt. Correct decisions occurred in >76% of the validation datasets. Results indicated these sequential sampling plans can be used to effectively assess CLS incidence in table beet fields.

Design and construction of a variables quick switching sampling system based on Taguchi capability index

Quick switching sampling (QSS) system comprises a pair of single sampling plans with switching rules and has been developed for lot disposition. The traditional QSS system for variables data only focused on products’ yield evaluation but ignored the additional loss caused when the quality deviated from the target value. Therefore, the purpose of this research is to develop a variables QSS system based on the loss-based capability index. To determine the designed plan parameters, a nonlinear optimization problem is constructed. The objective function is minimizing the required average sample number, and the constraints are set to satisfy the quality requests and allowable risks demanded by the producers and consumers, simultaneously. The performance of the proposed system is investigated and compared with other existing methods in terms of the operating characteristic, average sample number, and average run length. Finally, a practical case is presented for illustrating the applicability of the proposed plan.

Developing process-yield-based acceptance sampling plans for AR(1) auto-correlated process

Acceptance sampling plans include a sampling scheme and a set of rules to determine whether an inspection lot from a supplier should be accepted or rejected. This paper aims at developing two acceptance sampling plans by variables inspection, namely a quick switching sampling (QSS) system and the modified repetitive group sampling (Modified-RGS) plan based on the process yield index for the first-order auto-regressive (AR(1)) auto-correlated process. A minimization model is employed to solve the plan parameters. The performance of the proposed sampling plans is investigated and compared with the existing sampling plans through the operating characteristic (OC) curve and the average sample number (ASN) required for inspection. The simulation results confirm the advantages of the proposed plans over existing sampling plans. An example is presented to illustrate the applicability of the proposed plans.

Glass Transition in Rice Pasta as Observed by Combined Neutron Scattering and Time-Domain NMR.

Experimental protocols aiming at the characterisation of glass transition often suffer from ambiguity. The ambition of the present study is to describe the glass transition in a complex, micro heterogeneous system, the dry rice pasta, in a most unambiguous manner, minimising the influence of technique-specific bias. To this end, we apply an unprecedented combination of experimental techniques. Apart from the usually used NMR and DSC, we employ, in a concurrent manner, neutron transmission, diffraction, and Compton scattering. This enables us to investigate the glass transition over a range of spatio-temporal scales that stretches over seven orders of magnitude. The results obtained by neutron diffraction and DSC reveal that dry rice pasta is almost entirely amorphous. Moreover, the glass transition is evidenced by neutron transmission and diffraction data and manifested as a significant decrease of the average sample number density in the temperature range between 40 and 60 °C. At the microscopic level, our NMR, neutron transmission and Compton scattering results provide evidence of changes in the secondary structure of the starch within the dry rice pasta accompanying the glass transition, whereby the long-range order provided by the polymer structure within the starch present in the dry rice pasta is partially lost.

Spatial Distribution and Development of Sequential Sampling Plans for Diaphorina citri Kuwayama (Hemiptera: Liviidae)

Vector control in huanglongbing management has been conducted on a calendar basis resulting in high production costs. We addressed this issue and proposed a sequential sampling plan to support decision making for intervention against Diaphorina citri Kuwayama, which is involved in the transmission of the bacteria Candidatus Liberibacter asiaticus, associated with such lethal disease. We analyzed 3,264,660 records from samples gathered from the Mexican trapping program for the monitoring of D. citri; it included weekly inspection of 86,004 yellow sticky traps distributed in the country. Spatial distribution of the insect, estimation of a common k (kc), and sequential sampling plans based on Sequential Probability Ratio Test (SPRT) were determined. Taylor’s power law coefficients were ≥1 indicating aggregation in the spatial distribution of the insect. Common k ranged from 0.0183 to 0.2253 and varied independently of geographic zone or citrus species. We obtained 18 sequential sampling plans, one for each state. In the Average Sample Number (ASN) function, the minimal number of samples to make a decision ranged from 17 to 65. In the Operational Characteristic (OC) function, probabilities for a correct intervention at the threshold of 0.2 D. citri adults/trap in most cases were above 80%. In a field evaluation, the application of sampling plans yielded savings obtained by reduction in the number of interventions for insect control.