The paper presents an economical model for DOUBLE variable acceptance SAMPLING with inspection errors. Taguchi cost function is used as acceptance cost while quality specification functions are normal with known variance. An optimization model is developed for DOUBLE variables acceptance SAMPLING scheme at the presence of inspection errors with either constant or monotone value functions. The monotone value functions could be descending or ascending exponentially. In the case that inspection errors have exponentially functions, we can find the best value for inspection errors regarding to the sample number and other economical parameters. Finally sensitivity analysis has done on model parameters and some numerical examples are given to demonstrate how the developed model is applied.

In zero acceptance number SAMPLING plans, the sample items of an incoming lot are inspected one by one. The proposed method in this research follows these rules: if the number of nonconforming items in the first sample is equal to zero, the lot is accepted but if the number of nonconforming items is equal to one, then second sample is taken and the policy of zero acceptance number would be applied for the second sample. In this paper, a mathematical model is developed to design single stage and DOUBLE stage SAMPLING plans. Proposed model can be used to determine the optimal tolerance limits and sample size. In addition, a sensitivity analysis is done to illustrate the effect of some important parameters on the objective function. The results show that the proposed two stage SAMPLING plan has better performance than single stage SAMPLING plan in terms of total loss function, sample size and robustness.

The nature of input materials is changed as long as the product reaches the consumer in many types of manufacturing processes. In designing and improving multi-stage systems, the study of the steps separately may not lead to the greatest possible improvement in the whole system, therefore the study of inputs and outputs of each stage can be effective in improving the output quality characteristics. In this study, the DOUBLE SAMPLING method is applied for inspection where decision variables are the sample size per SAMPLING time and the maximum amount of defective items in the first and second samples in each stage. Furthermore, uncertainty in parameters such as production, inspection, and replacement costs are included in the objective function and handled by a Monte-Carlo based optimization method. In order to show the efficacies of the proposed method, a numerical example has been designed, and further analyses on solutions have been conducted.

One of the main aspects of the production industry is the optimization of acceptance SAMPLING plans. SAMPLING plan performance depends on many uncertain factors that are difficult to model, especially in a multi-stage process. Therefore, it requires an innovative procedure to optimize it. This study presents an innovative DOUBLE SAMPLING plan for a multi-stage process based on discrete event simulation (DES) toward proposing an applicable plan to the inspection of the product whose accepting probability follows the hypergeometric distribution for a finite lot without replacement. This paper focuses on the five economic parameters of a DOUBLE SAMPLING plan that are determined by minimizing the average sample number (ASN) with the help of DES results and optimization methods. Several experiments based on DES were tested to determine the regression function of the ASN simulation study was carried out using Enterprise Dynamic software (ED). Our economic statistical lot acceptance SAMPLING plan based on minimizing the average sample number has been developed to determine the acceptance parameters including the first acceptance number, first rejection, number, first sample size, second sample size, and second acceptance number in a multi-stage process. According to all runs of the simulation model, we concluded at a 95% level of confidence that ASN ranges from 530.16 to 554.93, which is given in detail in the paper.

Optimum sample size and the method of SAMPLING for annual yield measurement are very important in measuring the standing crop in rangelands. For this purpose, a key area was selected in the Nir range research station as an indicator of steppic rangelands of Yazd province. SAMPLING was done using 150 plots (2 m2) on 5 transects where sample points had 25 m intervals. Forage yield were measured by clipping and then weighting in mid-May 2004. Optimum sample size was determined by graphic method based on the biomass data on 10 to 150 plots. Results showed that estimation of total forage yield is possible with clipping and weighting method on 30 quadrates of 2 m2. More accurate estimation for individual species biomass was found with 100 quadrates of 2 m2 and considering 20% CV. This shows that even with 20% CV estimating the annual biomass of individual plants in steppic rangeland of Yazd using clipping method is expensive and time-consuming. To overcome this problem DOUBLE SAMPLING is recommended with clipping 25 plots out of 1000 plots.

Given the manufacturer's obligation to meet expectations and financial constraints, monitoring a large number of quality characteristics with a limited sample size poses a significant challenge for researchers in statistical quality control. According to recent research, multivariate charts based on DOUBLE SAMPLING are developed under the assumption that the number of quality characteristics is less than the sample size. The objective of this paper is to monitor the dispersion matrix of high-dimensional processes using a combination of the ridge penalized likelihood ratio statistic and DOUBLE SAMPLING method. To evaluate the performance of the proposed chart, we first introduce seven out-of-control scenarios, which include three combined diagonal/non-diagonal patterns, two diagonal patterns, and two non-diagonal patterns. We then extract the run length properties and the expected value of the sample size associated with the proposed chart through simulation experiments. The simulation results indicate that, across all three pattern categories mentioned, the proposed chart's sensitivity in detecting disturbances in the covariance matrix improves as the magnitude of shifts in its components increases. The simulation results further indicate that as the number of components influenced by the assignable cause in the covariance matrix decreases, the proposed control chart can detect process disturbances over a longer period.

Importance of production information in range management has caused researchers be seeking to find suitable methods for yield estimation. As clipping and weighing method has been recognized as a time consuming, high cost and destructive method, researches have been conducted on indirect methods. Estimating yield from cover data can be considered as an important method. In the present study, relationship between cover and yield was determined at first. Then investigation was done to find how cover data can be used to estimate production using a DOUBLE SAMPLING procedure or pooled equation. Research was carried out in two rangelands of Manuka (a semi-arid rangeland) and Conservation (as an arid area). DOUBLE SAMPLING was formed by clipping and weighing method as direct SAMPLING and foliage or canopy cover measurement as indirect SAMPLING for each group of species. The number of paired of direct and indirect SAMPLING based on time consumption and accuracy were 4, 6, 8, 12, 16, 20, 24, 28, 32, 36 and 40. SPSS was used for data analysis. Data were collected during 2 or 3 years in different seasonal conditions of fair, poor and good. The results showed the possibility of estimating range production from cover measurement using equation based 8 to 12 paired direct and indirect SAMPLING. Correlation between cover and yield was higher for all collected data in both rangelands of Manuka and Conservation. According to the results, cover data can be used in both models of DOUBLE SAMPLING or pooled equation for accurate yield estimation.

Background and Objectives: Brucellae are gram-negative intracellular pathogens which cause zoonotic disease in humans. Clinical manifestations of brucellosis in human are variable and often are non-specific, and the diagnosis requires fast and accurate confirmation. Since the use of serum instead of whole-blood samples offers several advantages for nucleic acid amplification methods, in this study we developed an improved PCR assay for the rapid and specific laboratory diagnosis of human brucellosis directly from serum specimens.Material and Methods: DNA was extracted from 100 µL of serum from 30 patients with acute serologic brucellosis. The PCR reaction was carried out with Specific primers. Second PCR reaction for reamplification of the first reaction products was designed.Results: a 223 bp conserved region on the sequence encoding the 31-KDa immunogenic outer membrane protein which is specific to the genus Brucella (BCSP31) and present in all its biovars was amplified in all serum samples.Conclusion: For confirmation and efficient amplification of the specific target, reamplification of the first PCR products had a sharper banding patterns with high sensitivity and specificity that might be considered as a new useful method for diagnosis of human brucellosis in serum specimens.

BACKGROUND AND OBJECTIVE: In infertility without suffering tubal factor, IUI is the choice therapeutic method. Some studies recommended DOUBLE IUI for every cycle. Since DOUBLE IUI needs more laboratory materials and more expenses, so in this study it was tried to evaluate pregnancy rate in infertile couples after single and DOUBLE IUI.METHODS: This clinical trial study was performed on infertile couples referred to Fatemeh Alzahra or Babol Clinic hospital for IUI cycles. Patients divided into two groups. In the first group, 36 hours after HCG injection, IUI was done as a single protocol and in the other group; IUI was done 24 & 72 hours after HCG injection as DOUBLE protocol. Then data were analyzed by fishers exact test.FINDINGS: Two hundred and fifteen women participated in our study and their mean age was 26.02±5.5 and the mean (±SD) duration of infertility was 3.73 (±2.75) years. In the first group, pregnancy rate was 16.9% and in second group it was 19.6% without any significant difference between two groups. Also, there was no significant difference between groups in pregnancy rate with different etiology of infertility.CONCLUSION: Some studies recommended that DOUBLE IUI is more successful than single IUI But present findings suggest that there is no difference in pregnancy rate between single IUI and DOUBLE IUI.