Average Sample Number Research Articles

Development of a "Geo-Tagged" tumor sample registry: intra-operative linkage of sample location to imaging.

There is a growing body of literature documenting glioma heterogeneity in terms of radiographic, histologic, molecular, and genetic characteristics. Incomplete spatial specification of intraoperative tumor samples may contribute to variability in the results of pathological and biological investigations. We have developed a system, termed geo-tagging, for routine intraoperative linkage of each tumor sample to its location via neuronavigation. This is a single-institution, IRB approved, prospective database of undergoing clinically indicated surgery. We evaluated relevant factors affecting data collection by this registry, including tumor and surgical factors (e.g. tumor volume, location, grade and surgeon). Over a 2-year period, 487 patients underwent craniotomy for an intra-axial tumor. Of those, 214 underwent surgery for a newly diagnosed or recurrent glioma. There was significant variation in the average number of samples collected per registered case, with a range of samples from 2.53 to 4.75 per tumor type. Histology and grade impacted on sampling with a range of 2.0 samples per tumor in Grade four, IDH-WT gliomas to 4.5 samples in grade four, IDH-mutant gliomas. The range of cases with sampling per surgeon was 6 to 99 with a mean of 47.6 cases and there was a statistically significant differences between surgeons. Tumor grade did not have a statistically significant impact on number of samples per case. No significant correlation was found between the number of samples collected and enhancing tumor volume, EOR, or volume of tumor resected. We are using the results of this analysis to develop a prospective sample collection protocol.

Optimized repetitive sampling X-bar control chart: performance evaluation and comparison with Shewhart control chart

When initial sample information falls short of enabling industrial engineers to confidently make decisions about lot quality assessment, repetitive sampling emerges as a solution. In this study, we present an optimized repetitive sampling control chart for X-bar values. Through meticulous analysis, we determined the optimal control chart coefficients. Additionally, we established the control chart parameters for scenarios where the sample size equals the average sample number, encompassing both in-control and out-of-control processes. To underscore the effectiveness of our proposed chart compared to the traditional Shewhart control chart, we provide comprehensive tables across various sample sizes. By meticulously examining these tables alongside the corresponding control charts, the chart's efficacy in relation to the Shewhart alternative becomes evident.

Impact of the total measurement error on sampling plan for bulk materials – An optimal sampling plan under ToS framework

Acceptance sampling plans are used in quality control between a producer and a buyer. However, in the literature of acceptance sampling for bulk materials, the term “sampling” refers to obtaining simple random samples in the traditional statistical sense, which may lead to heavy financial losses. In contrast, the Theory of Sampling (ToS) defines sampling as the collection of physical samples based on a thorough understanding of the material concerned. The goal of this research is to investigate the influence of incorrect sampling procedures on acceptance sampling in a mining value chain. To achieve this, the representative sampling approach of ToS was utilized, total measurement error was measured, and possibilities for improving sampling procedures were identified. A variable Quick Switch Sampling plan (QSS) based on the Beta distribution is applied. A nonlinear optimization model is used to estimate the plan parameters, which minimizes the necessary average sample number (ASN) while meeting the producer and consumer quality levels and maximum allowable risks using real-life data. Finally, A collaborative quality program is suggested for a solid producer-buyer relationship by using a correct sampling facility of the producer.

Economic design of multiple deferred state sampling plan for two parameter Lindley distributed mean life assurance

In this study, we propose a multiple deferred state sampling plan under two parameter Lindley distribution based on time truncated life test. One of the principal objectives of the proposed plan is to ensure that the products have higher mean life. The procedure for determining the optimal plan parameters of the proposed plan is described by two points on the operating characteristics curve approach. Tables are constructed to select the optimal plan parameters of the proposed plan for different values of shape parameters, consumer’s risk and producer’s risk. The implementation of the proposed plan is illustrated with a real life time data. In addition, the performance of the proposed plan is evaluated by comparing the average sample number and operating characteristic curve of the proposed plan to that of single sampling plan. Finally, a mathematical model for economic design of the proposed plan is developed by considering the waiting time to reduce the total inspection cost while satisfying both the producer and consumer requirements and a sensitivity analysis is also explored.

Control decision‐making systems for Anacampsis phytomiella in cashew orchards as a function of insecticide spray method and irrigation use

Abstract Cashew (Anacardium occidentale) is one of the main tropical fruit trees, and its chestnuts are the most consumed in the world. Cashew orchards are managed under irrigated and unirrigated conditions, and pesticides are applied manually (hand sprayer) or by a tractor‐powered sprayer. The chestnut moth Anacampsis phytomiella (Lepidoptera: Gelechiidae) is the primary pest that attacks cashew chestnuts, causing losses of up to 53% in yield in the orchards. Sampling plans and economic injury levels (EILs) are part of the decision‐making systems of integrated pest management (IPM) programmes. The EIL varies according to the method of insecticide spraying (hand sprayer or tractor) and in irrigated and unirrigated cashew orchards. This study aimed to determine the EIL and a sequential sampling plan for A. phytomiella in cashew orchards. Decision‐making systems were determined using data collected from 44 orchards over 3 years. EILs ranged from 0.53 to 1.53 for chestnuts attacked by the pest in bunches with 10 chestnuts, depending on irrigation and insecticide spray methods. The sequential sampling plan was validated using field‐collected data (providing correct decisions in 100% of the fields and an average time saving of 83.65%), operational characteristic (OC) and average sample number curves. In conclusion, determined decision‐making systems can be incorporated into IPM programmes for A. phytomiella in cashew orchards, as they enable correct, rapid and cost‐effective pest control decisions.

Effect of particle gradation on pore structure and seepage law of solution in weathered crust elution-deposited rare earth ores

Both CT and Avizo software were used to explore the effect of particle gradation on the evolution characteristics of pore structure and seepage paths in weathered crust elution-deposited rare earth ores during leaching. The results showed that the pore areas in four kinds of ore samples before leaching were mainly concentrated in 104–107 μm2, whose pore quantities accounted for 96.89%, 94.94%, 90.48%, and 89.45%, respectively, while the corresponding pore volume only accounted for 30.74%, 14.55%, 7.58%, and 2.84% of the total pore volume. With the decrease of fractal dimension, the average pore throat length increased, but pore throat quantities, the average pore throat radius and coordination number decreased. Compared with that before leaching, the change degree of pore structure during leaching increased with the fractal dimension decreasing. For example, the reduction rate of the average coordination number of ore samples was 14.36%, 21.30%, 28.00%, and 32.90%, respectively. Seepage simulation results indicated that seepage paths were uniformly distributed before leaching while the streamline density and seepage velocity increased with the fractal dimension decreasing. Besides, the phenomenon of the streamline interruption gradually reduced during leaching while preferential seepage got more obvious with the decrease of the fractal dimension.

Patient-based pre-classified real-time quality control (PCRTQC)

BackgroundPatient-based real-time quality control (PBRTQC) has gained increasing attention in clinical laboratory management. Although its valuable characteristics complement traditional quality control measures, its performance and practical application have faced scrutiny. In this study, patient-based pre-classified real-time quality control (PCRTQC), an extended approach was devised to enhance real-time quality control protocols. MethodsPCRTQC distinguishes itself by incorporating an additional patient pre-classification step utilising the OPTICS algorithm, thus addressing interference from diverse patient types. The complete set of patient test results obtained from a clinical chemistry analyser at The First Hospital of China Medical University in 2021 was utilised. Constant error (CE) and proportional error (PE) were introduced as analytical errors. Four analytes were selected to evaluate the PCRTQC, measuring probability for false rejection (Pfr) and the average number of patient samples until error detection (ANPed). Relevant error detection charts were generated. ResultsThe PCRTQC outperformed regression-adjusted real-time quality control (RARTQC) based on the ANPed by approximately 50% for both the CE and PE, compared to the RARTQC, particularly for the total allowable error threshold. ConclusionThe pre-classification step effectively reduced inter-individual variation and improved data preprocessing, filtering, and modelling. The PCRTQC is a robust framework for real-time quality control research.

Repetitive sampling inspection plan for cancer patients using exponentiated half-logistic distribution under indeterminacy

This piece of work deals with a time truncated sampling scheme for cancer patients using exponentiated half-logistic distribution (EHLD) based on indeterminacy. We have studied time truncated schemes like repetitive acceptance sampling plan (RASP) under indeterminacy. We have estimated the projected scheme parameters such as sample size and acceptance and rejection sample numbers for known indeterminacy parameters. In addition to the projected sampling scheme quantities, the corresponding tables are generated for various values of indeterminacy parameters. The results of a sampling scheme show that the average sample number (ASN) decreases as indeterminacy values increase. It leads that the indeterminacy parameter is played a crucial portrayal in ASN. A comparative study is carried out with existing sampling schemes based on indeterminacy and classical sampling schemes. The evaluated sampling schemes are exemplified with the help of cancer data. From tables and exemplification, we wind up that the projected RSP scheme under indeterminacy desired a smaller sample size than the existing schemes.

Application of the multiple dependent state sampling strategy to late adolescent suicide rates

A multiple dependent state sampling plan (MDSSP) is designed when the lifetimes of the variables follow New Lomax Rayleigh Distribution (NLRD). The decision to accept or reject a lot in the proposed methodology is based on the quality of the given present or previous lots. A binomial model-based operating characteristic curve (OC curve) for continuous lots of variables under similar settings in healthcare is used in finding the probability of acceptance, acceptance number, rejection number, and the number of preceding (succeeding) lots to consider. Time truncated life test based on the specified median of the NLRD is used in designing the current acceptance sampling plan. For specified values of the parameters of NLRD, quantile ratios, consumer’s risk and producer’s risk, average sampling number (ASN) and probability of acceptance of a lot are reported in tables. Real data on worldwide suicide rates of 15–19 years in the year 2019 from the World Health Organization (WHO) website is considered to illustrate this methodology. The minimum sample size required from the selected data to comment on worldwide suicide rates in late adolescents is explained with MDSSP. The results of the proposed acceptance sampling method are compared with the single-stage sampling plan.

Distribution and fixed-precision sampling plans for diamondback moth (Lepidoptera: Plutellidae), on winter-spring cabbage.

The diamondback moth (Plutella xylostella L.) is the most destructive insect pest on cabbage (Brassica oleracea var. capitata L.). Infestation by this pest usually results in the indiscriminate use of insecticides by farmers due to a lack of sampling plans for this pest. Sampling plans for P. xylostella management decisions on winter-spring cabbage in the Eastern Cape Province of South Africa were developed, through population monitoring that comprised weekly counts of immature stages of P. xylostella on 60 plants for 11 wk each during the winter and spring seasons. The mean density-variance relationship was used to describe the distribution of the pest, and number of infested plants was used to develop a fixed-precision sampling plan. All plant growth stages preceding maturation were vulnerable to P. xylostella damage resulting in yield losses. A high aggregation of P. xylostella on cabbage was observed in spring than in winter. The average sample number to estimate P. xylostella density within a 15% standard error of the mean was 35 plants. Furthermore, the estimated plant proportion action threshold (AT) was 51% with density action thresholds of 0.50 and 0.80 for spring and winter, respectively. Fitting P. xylostella cumulative counts in the winter and spring sampling plans resulted in 100% and 45% reduction in insecticide treatments. The similarity of sample size and ATs between both seasons provides evidence that a single sampling plan is practical for all cabbage growing seasons. The similarity of the estimated ATs to those acceptable in established integrated pest management programs indicates reliability.

Design and Optimization of c-Control Chart Using a Triple Sampling Scheme

Abstract In this paper, a c-control chart using a triple sampling scheme (TS-c) is studied. The chart design, procedure, and a bi-objective optimization model are given to optimize the TS-c-chart. The Average Run Length for in-control and out-of-control ( ARL 1 \mathrm{ARL}_{1} ), and Average Sample Number metrics are calculated. A Comparison among TS-c, Fixed parameters c (FP-c), VSS-c, EWMA-c, and Double Sampling c (DS-c) control charts are carried out in terms of ARL 1 \mathrm{ARL}_{1} . The proposed TS-c-chart has lower ARL 1 \mathrm{ARL}_{1} values for detecting small and moderate shifts in the mean number of non-conformities in control compared with FP-c, VSS-c, EWMA-c, and DS-c.

A Sequential Test of Traffic Intensity for the M/M/1 Queueing System

This paper deals with testing traffic intensity, which is the most important parameter of the queue system. The Wald-type sequential probability ratio test (SPRT) is performed to determine traffic intensity by taking advantage of the fact that the total number of customers arriving up to the kth service period, including the kth service period, has a negative binomial distribution. Acceptance and rejection limits obtained from the SPRT are arranged as a multiple sampling plan. This plan is applied with a finite Markov chain. Then, the Operating Characteristic (OC) function and the Average Sample Number (ASN) are obtained precisely. Obtaining the standard errors of the sample number compared to the studies on SPRT applications is the novelty brought by this paper. The sequential analysis method based on the finite Markov chain is also applied to a numerical sample.

Numerical solution of Kiefer-Weiss problems when sampling from continuous exponential families

In this article, we deal with problems of testing hypotheses in the framework of sequential statistical analysis. The main concern is the optimal design and performance evaluation of sampling plans in Kiefer-Weiss problems. The main goal of the Kiefer-Weiss problem is designing hypothesis tests that minimize the maximum average sample number, over all parameter values, as opposed to both the sequential probability tests (SPRTs) minimizing the average sample number only at two hypothesis points and the classical fixed-sample-size test. For observations that follow a distribution from an exponential family of the continuous type, we provide algorithms for optimal design in the modified Kiefer-Weiss problem and obtain formulas for evaluating the performance of sequential tests by calculating the operating characteristic function, the average sample number, and some related characteristics. These formulas cover, as a particular case, the SPRTs and their truncated versions, as well as optimal finite-horizon sequential tests. In the setting of the original Kiefer-Weiss problem we apply the method of our recent work (Sequential Analysis 2022, 41(2), 198–219) for numerical construction of the optimal tests. For the particular case of sampling from a normal distribution with a known variance, we make numerical comparisons of the Kiefer-Weiss solution with the SPRT and the fixed-sample-size test provided that the three tests have the same levels of the error probabilities. All of the algorithms are implemented in the form of computer code written in the R programming language and are available at the GitHub public repository (https://github.com/tosinabase/Kiefer-Weiss). Guidelines on the adaptation of the program code to other exponential family distributions are provided.

A new Spk -based sample-size tightening sampling system for lot determination

ABSTRACT This article presents a new Spk-based sample-size tightening sampling system by variables inspection for lot determination when the quality characteristic has bilateral specification limits. The proposed system has a flexible switching mechanism between normal and tightened inspection, which is adaptively employed for the occurrence of quality deterioration in submitted lots. The performances of discriminatory power, economy and sensitivity to quality shift of the proposed method are demonstrated by comparing the operating characteristic (OC) curve, average sample number (ASN) and average run length (ARL), respectively, with the existing sampling plans. Furthermore, for practical application, the solved plan parameters under certain quality conditions are tabulated for quick reference, and an example taken from the Multi-layer Ceramic Capacitor (MLCC) industry is studied for illustration.

Gearbox fault diagnosis based on frequency-domain Gramian angular difference field and deep convolutional neural network

Gearbox fault diagnosis is of great significance to avoid accidents and reduce operation and maintenance costs for machinery such as the quayside container crane. In recent years, mechanical fault diagnosis methods based on deep learning have developed rapidly. How to perform diagnosis simply based on deep learning in the case of limited labeled samples is still a challenging problem. To address this issue, this paper proposes a new fault diagnosis method combining the frequency-domain Gramian angular difference field (FDGADF) and deep convolutional neural network (DCNN), namely, FDGADF-DCNN. To explore the availability of the FDGADF-DCNN method, two experiment datasets are studied. When the proportion of labeled samples is 70%, the recognition accuracy of both datasets exceeds 98%. When only 20% of the labeled samples are available (the average number of training samples in each category is only dozens), FDGADF-DCNN can achieve a recognition accuracy of no less than 95% in two datasets, in which a benchmark dataset is 100% and the dataset collected from a scaled-down test bench is 95%. Compared with other 11 methods, the FDGADF-DCNN method achieves the highest recognition accuracy. These results indicate that the FDGADF-DCNN method is expected to be used for gearbox fault diagnosis under limited labeled samples.

Hip-hop sampling aesthetics and the legacy ofGrand Upright v. Warner

AbstractIn 1991, Gilbert O'Sullivan sued Biz Markie for sampling without permission: this lawsuit,Grand Upright v. Warner, became a landmark case for music copyright, and for some scholars, represented a symbolic end to hip hop's golden age. This paper uses the lawsuit as a point of entry into debates about hip hop during a time of aesthetic transformation. Specifically, I present a corpus study spanning 1988–1993, consisting of hip hop songs of various subgenres drawn from Billboard charts. Unlike previous studies on this period, I consider both canonical artists, whose mastery of sampling is widely admired (such as Public Enemy), and more commercially successful artists (like the Fresh Prince), who used fewer samples. My study reveals a decrease in the average number of samples per song, and a radical shift in how these remaining samples are used. I situate Grand Upright at the intersection of legal institutions and musical aesthetics

Characterization of the number of spot samples required for quantification of gas fluxes and metabolic heat production from grazing beef cows using a GreenFeed.

Enteric fermentation from cattle results in greenhouse gas production that is an environmental concern and also an energetic loss. Several methods exist to quantify gas fluxes; however, an open circuit gas quantification system (OCGQS) allows for unencumbered quantification of methane (CH4), carbon dioxide (CO2), and oxygen (O2) from grazing cattle. While previous literature has proven the accuracy of an OCGQS, little work has been done to establish the minimum number of spot samples required to best evaluate an individual grazing animal's gas fluxes and metabolic heat production. A GreenFeed system (C-Lock Inc.) was used to collect at least 100 spot samples each from 17 grazing cows. The mean gas fluxes and metabolic heat production were computed starting from the first 10 visits (forward) and increasing by increments of 10 until an animal had 100 visits. Mean gas fluxes and metabolic heat production were also computed starting from visit 100 (reverse) in increments of 10 using the same approach. Pearson and Spearman correlations were computed between the full 100 visits and each shortened visit interval. A large increase in correlations were seen between 30 and 40 visits. Thus, mean forward and reverse gas fluxes and metabolic heat production were also computed starting at 30 visits and increasing by 2 until 40 visits. The minimum number of spot samples was determined when correlations with the full 100 visits were greater than 0.95. The results indicated that the minimum numbers of spot samples needed for accurate quantification of CH4, CO2, and O2 gas fluxes are 38, 40, and 40, respectively. Metabolic heat production can be calculated using gas fluxes collected by the OCGQS with 36 spot samples. Practically, calculation of metabolic heat production will require 40 spot samples because the component gases for metabolic heat calculation require up to 40 spot samples. Published literature from nongrazing (confined) environments recommended a similar number of total spot samples. Large variation existed around the average number of spot samples for an animal per day, therefore a wide range of test durations may be needed to meet the same number of spot samples in different populations. For this reason, protocols for the OCGQS should be based on the total number of spot samples, rather than a test duration.

Designing Adaptive Multiple Dependent State Sampling Plan for Accelerated Life Tests

A novel adaptive multiple dependent state sampling plan (AMDSSP) was designed to inspect products from a continuous manufacturing process under the accelerated life test (ALT) using both double sampling plan (DSP) and multiple dependent state sampling plan (MDSSP) concepts. Under accelerated conditions, the lifetime of a product follows the Weibull distribution with a known shape parameter, while the scale parameter can be determined using the acceleration factor (AF). The Arrhenius model is used to estimate AF when the damaging process is temperature-sensitive. An economic design of the proposed sampling plan was also considered for the ALT. A genetic algorithm with nonlinear optimization was used to estimate optimal plan parameters to minimize the average sample number (ASN) and total cost of inspection (TC) under both producer’s and consumer’s risks. Numerical results are presented to support the AMDSSP for the ALT, while performance comparisons between the AMDSSP, the MDSSP and a single sampling plan (SSP) for the ALT are discussed. Results indicated that the AMDSSP was more flexible and efficient for ASN and TC than the MDSSP and SSP plans under accelerated conditions. The AMDSSP also had a higher operating characteristic (OC) curve than both the existing sampling plans. Two real datasets of electronic devices for the ALT at high temperatures demonstrated the practicality and usefulness of the proposed sampling plan.

Development of an adaptive sampling system based on a process capability index with flexible switching mechanism

The quick-switch sampling system (QSS) and tightened-normal-tightened sampling system (TSS) are efficient schemes for dispositioning a series of lots. However, the QSS mechanism for switching decision rules is too simple to satisfy the requirements of suppliers and buyers. Conversely, the TSS is more flexible due to its adaptable switching mechanism. The TSS was recently developed based on process capability indices (PCIs) to help practitioners make more reliable and accurate decisions in practice. The existing PCI-based TSSs are the required sample-size type (TSS-n). However, the TSS-n requires a large sample size for the tightened inspection, which is costly and time-consuming. We propose the acceptance-benchmark type TSS (TSS-k) based on the most commonly used PCI, to improve the lot-disposition sampling efficiency. The TSS-k adjusts the acceptance benchmark instead of the sample size to constitute tightened and normal inspections. We investigated combinations of TSS-k switching mechanism parameters and provided managerial suggestions for practitioners. Compared with the existing TSS-n, the proposed TSS-k can reduce the average sample number by more than 60% and has superior discrimination power. Moreover, we developed a cloud-computing programme to calculate the optimal system design online. Finally, we illustrate an industrial case to demonstrate the applicability of the proposed TSS-k.

Designing of Variables Double Sampling Plan Under Rectifying Inspection for Consumer Protection

Designing of acceptance sampling plans based on operating characteristics fail to prescribe the corrective action for rejected lots although they guarantee the producer’s and the consumer’s protection at their corresponding quality levels. But, rectification sampling inspection plan suggests 100% inspection for rejected lots and hence, the producer’s risk is reduced and also there is a guarantee to the consumer by restricting upper limit of the maximum expected proportion of non-conforming of the shipping lot. Although the variables single sampling plan for rectifying inspection is available, variables double sampling inspection plan under rectifying inspection has not yet been designed. Hence, in this paper, we design a variables double sampling plan for rectifying inspection indexed by acceptable quality level and average outgoing quality limit to meet the needs of the producer as well as the consumer. A non-linear optimization problem is developed to determine the optimal parameters with minimum average sample number at acceptable quality level for both cases of standard deviation are known and unknown. We investigate the validation and limitation of the proposed plan through the simulation study and industrial application of the proposed plan is also given. It can be confirmed from the investigations on the results that in many situations, variables double sampling plan designed under rectifying inspection is more economical than variables single sampling plan designed with the same conditions in providing the same protection to consumer.