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.

Moving target indication (MTI) filters are generally considered as the primary approach to remove echo caused by fixed unwanted targets as well as clutter, in real-world radar systems. The MTI filter is set up as a linear combination of the echoes received from the range cell under test, when the inter-pulse periods (IPPs) are staggered. Hence, the MTI filter is mainly characterized with the coefficients and the IPPs. The MTI filter DESIGN problem is to optimize both these parameters to maximize both the blind speed and the clutter rejection, while providing the required signal gain. In this paper, to calculate the coefficients, the deviation of the DESIGNed filter from the ideal one in pass-band is optimized using the least squares criterion. The coefficient optimization is done under the constraint to induce nulls at limited number of frequency spots in the clutter rejection band to increase clutter rejection. As to the IPPs, a random IPP search is done to maximize the minimum SCR in the pass-band. At the analysis stage, the performance of the proposed method is compared with the other existing methods, based on two criteria: Improvement Factor (IF) and the minimum SCR gain in the pass-band. Eventually, it is shown that the proposed method has better results.

A novel approach to determine optimal SAMPLING locations under parameter uncertainty in a water distribution system (WDS) for the purpose of its hydraulic model calibration is presented. The problem is formulated as a multi-objective optimization problem under calibration parameter uncertainty. The objectives are to maximise the calibrated model accuracy and to minimise the number of SAMPLING devices as a surrogate of SAMPLING DESIGN cost. Model accuracy is defined as the average of normalised traces of model prediction covariance matrices, each of which is constructed from a randomly generated sample of calibration parameter values. To resolve the computational time issue, the optimisation problem is solved using a multi-objective genetic algorithm and adaptive neural networks (MOGA-ANN). The verification of results is done by comparison of the optimal SAMPLING locations obtained using the MOGA-ANN model to the ones obtained using the Monte Carlo Simulation (MCS) method. In the MCS method, an equivalent deterministic SAMPLING DESIGN optimisation problem is solved for a number of randomly generated calibration model parameter samples. The results show that significant computational savings can be achieved by using MOGA-ANN compared to the MCS model or the GA model based on all full fitness evaluations without significant decrease in the final solution accuracy.

Sometimes in order to estimate population parameters such as mean and total values, we extract a random sample by cluster SAMPLING method, and after completing SAMPLING, we are interested in using the same sample to estimate the desired parameters in a subset of the population, which is said subpopulation. In this paper, we try to estimate subpopulation parameters in different cases when one-stage cluster SAMPLING DESIGN is used.

We introduce a modified Poisson SAMPLING, with a fixed lower bound of sample size. The DESIGN is a combination of simple random SAMPLING and Poisson SAMPLING. Simple random SAMPLING is used to compensate for the lack of sample size from remaining elements in the finite population, after execution of a Poisson SAMPLING. At the first stage, the units are sampled independently with given inclusion probabilities. But in the second stage, inclusion probabilities are dependent to each other. Because it is important to know, which of the elements are selected in the first stage and which of them are remained. Some advantages of our DESIGN are: simple performance, controlling sample size, ability to perform the method of probability proportional to size. The simulations show that the DESIGN can dominate its rival DESIGN in probability proportional to size SAMPLING.

Dear Editor, We read the valuable manuscript with the title: Association of modified Nordic diet with cardiovascular risk factors among type 2 diabetes patients: a cross-sectional study that published in J Cardiovasc Thorac Res. 1 In this manuscript authors have said “ cluster random SAMPLING methods were used to select participants. Throughout the five sectors of Isfahan, we randomly chose two centers (clinics) from each area. Based on previously calculated mean and standard deviations for BMI in this population, our target number of participants was 143”

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.

SAMPLING is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen. To draw a sample from the underlying population, a variety of SAMPLING methods can be employed, individually or in combination. Cut-off SAMPLING is a procedure commonly used by national statistical institutes to select samples. There are different types of cut-off SAMPLING methods employed in practice. In its simplest case, part of the target population is deliberately excluded from selection. For example, in business statistics it is not unusual to cut off (very) small enterprises from the SAMPLING frame. Indeed, it may be tempting not to use resources on enterprises that contribute little to the overall results of the survey. So, in this case, the frame and the sample are typically restricted to enterprises of at least a given size, e.g. a certain number of employees. It is assumed that the contribution of this part of the population is, if not negligible, at least small in comparison with the remaining population. In particular, cut-off SAMPLING is used when the distribution of the values Y1, . . . ,YN is highly skewed, and no reliable frame exists for the small elements. As explained above, such populations are often found in business surveys. A considerable portion of the population may consist of small business enterprises whose contribution to the total of a variable of interest (for example, sales) is modest or negligible. At the other extreme, such a population often contains some giant enterprises whose inclusion in the sample is virtually mandatory in order not to risk large error in an estimated total. One may decide in such a case to cut off (exclude from the frame, thus from sample selection) the enterprises with few employees, say five or less. The procedure is not recommended if a good frame for the whole population can be constructed without excessive cost. This method may reduce the response burden for these small enterprises. On the other hand, this elementary form of cut-off SAMPLING, which we refer to as type I cut-off SAMPLING, may be considered a dirty method, simply because (i) the SAMPLING probability is set equal to zero for some SAMPLING units and so it can be considered as a type of non-probability SAMPLING DESIGN, and (ii)it leads to biased estimates.However, the use of cut-off SAMPLING and its modified versions can be justified by many arguments. Among other one can argue, and justify the use of cut-off SAMPLING, that . It would cost too much, in relation to a small gain in accuracy, to construct and maintain a reliableframe for the entire population; . Excluding the units of population that give little contribution to the aggregates to be estimated usually implies a large decrease of the number of units which have to be surveyed in order to get a predefined accuracy level of the estimates;. Putting a constraint to the frame population and, as a consequence, to the sample allows to reduce the problem of empty strata; . The bias caused by the cut-off is deemed negligible. In this paper we discuss different types of cut-off SAMPLING methods with more emphasize on analyzing type III cut-off SAMPLING which consists of take all, take some, and take none criteria: Roughly speaking, in our discussed methods, the population is partitioned in two or three strata such that the units in each stratum are treated differently; in particular, a part of the target population is usually excluded a priori from sample selection. We discuss where we should consider cut-off SAMPLING as a permitted method and how to deal with it concerning estimation of the population mean or total using modelbased, model-assisted, and DESIGN-based strategies. Theoretical results will be given to show how the cut-off thresholds and the sample size should be chosen.Different error sources and their effects on the overall accuracy of our presented estimates are also addressed. The outline of the paper is as follows. In section 2, we briefly discuss different types of cut-off SAMPLING DESIGN and some of their properties. In section 3, we first introduce our notations and motivate the use of type III cut-off SAMPLING. We further discuss estimation of the population mean (or total) based on ignoring the population units in "take none" strata or by modeling them using auxiliary information. We study the problem of ratio estimation of the population mean and type III sample size determination (for given precision of estimation) using DESIGN-based, model-based, and model-assisted strategies. In this section, we also study the problem of threshold calculation and its approximation using different methods and under different conditions. Finally, in section 4, we present a simulation study and compare our obtained results with the ones under commonly used cut-off SAMPLING of type I and its modification.

In acceptance SAMPLING plans, the decisions on either accepting or rejecting a specific batch is still a challenging problem. In order to provide a desired level of protection for customers as well as manufacturers, in this paper, a new acceptance SAMPLING DESIGN is proposed to accept or reject a batch based on Bayesian modeling to update the distribution function of the percentage of nonconforming items. Moreover, to determine the required sample size the backwards induction methodology of the decision tree approach is utilized. A sensitivity analysis that is carried out on the parameters of the proposed methodology shows the optimal solution is affected by initial values of the parameters. Furthermore, an optimal (n, c) DESIGN is determined when there is a limited time and budget available and hence the maximum sample size is specified in advance.