Background: Birth weight and gestational age are two important variables in obstetric research. The primary measure of gestational age is based on a mother’ s recall of her last menstrual period. This recall may cause random or systematic errors. Therefore, the objective of this study is to utilize Bayesian mixture MODEL in order to identify implausible gestational age. Methods: In this cross-sectional study, medical documents of 502 preterm infants born and hospitalized in Hamadan Fatemieh Hospital from 2009 to 2013 were gathered. Preterm infants were classified to less than 28 weeks and 28 to 31 weeks. A TWO-COMPONENT Bayesian mixture MODEL was utilized to identify implausible gestational age; the first COMPONENT shows the probability of correct and the second one shows the probability of incorrect classification of gestational ages. The data were analyzed through OpenBUGS 3. 2. 2 and 'coda' package of R 3. 1. 1. Results: The mean (SD) of the second COMPONENT of less than 28 weeks and 28 to 31 weeks were 1179 (0. 0123) and 1620 (0. 0074), respectively. These values were larger than the mean of the first COMPONENT for both groups which were 815. 9 (0. 0123) and 1061 (0. 0074), respectively. Conclusion: Errors occurred in recording the gestational ages of these two groups of preterm infants included recording the gestational age less than the actual value at birth. Therefore, developing scientific methods to correct these errors is essential to providing desirable health services and adjusting accurate health indicators.

Nanofluids, in which nano-sized particles (typically less than 100 nm) are suspended in liquids, have emerged as a possible effective way of improving the heat transfer performance of common fluids. In this paper a numerical study is performed to analyze the wall shear stress and heat transfer coefficient of gAl2O3-water nanofluids under laminar forced convection through a circular pipe. It is assumed that the distribution of nanoparticles in the flow field is nonhomogeneous. The results obtained show that addition of gAl2O3 nanoparticles to pure water effectively enhances the convective heat transfer. Moreover, the wall shear stresses are increased. The increasing rate of heat transfer depends on the volume concentration such that for the lowest and highest values of particle volume concentration 0.03 and 0.05, considered in this study, the heat transfer enhancement is approximately 23% and 40%, respectively. Also, compared with the available experimental data, the MODEL used in this work is capable to predict the increasing rate of heat transfer of nanofluids properly.

The principal COMPONENT analysis (PCA) is one of the procedures that have been a successful performance in signal processing and dimension reduction of the signals. However, a requirement in applying PCA to the images is converting images into a vector.This process leads to loss spatial locality information. To solve this problem, the TWO-dimensional PCA was proposed. Also, most recently the sparse principal COMPONENT was introduced that not only keep the properties of standard PCA but also try to make a lot of elements of the basis vectors to zero. In this paper, inspired by the above two ideas, the TWO-dimensional sparse principal COMPONENT analysis (2-D. SPCA) is proposed.In this paper, the Least Angle Regression- Elastic Net formula, in addition, using l1 and l2 constraints is extended to TWO-dimensional MODEL with a few minor changes in its input to approach 2-D. SPCA.The TWO-dimensional sparse principal COMPONENT analysis is evaluated in image compression. Before applying the algorithm, the image is divided into several blocks with resolution 8×8 and a database of these blocks is formed. Comparison the performance of 2-D. SPCA and Discrete Cosine transform, for the same number of elements that are necessary to save the image after the conversion shows the good performance of the proposed algorithm. In addition, the proposed algorithm is applied to 8×8 blocks of 60 images with different textures, and the resulted TWO-dimensional sparse principal COMPONENTs could be used for other test images with a suitable performance.

Due to the high value of sea foods and importance usage of them in individual and social health, the present study was designed to investigate on the effective factors in aquatic consumption in Mashhad. The consumption information of 150 Mashhadian households that collected by random sampling has been used. The results have analyzed using Heckman’s TWO- stage MODEL and Double- Hurdle MODEL. The results showed that, household population, education level of households’ administrator, number of persons under 10 years old, income, factors related to taste, access to fish, knowledge of methods to preparing and cooking aquatic and health- related factors, are important for household to consumption of aquatic. Due to the results of Double- Hurdle MODEL, Education, income, occupation of households’ administrator, residential area, Factors associated with taste, knowledge of methods to preparing and cooking aquatic and aquatic health, have the significant effects on Action with households to consume aquatic. According to the results and Double- Hurdle MODEL excellence in this study, this method is recommended as an alternative method for use in studies.

The Present work addresses the relation between addiction and emotional entelligence COMPONENTs. For this purpose 276 addicts including males and females who using depressant (opium, heroin and opium tincture) and nondepressant (crystal, cocaine and cannabis) drugs refered to private and public institutes for with drawal in isfahan. They were selected through multi- stage cluster sampling. The subjects completed Bar-On's emotion intelligence test, demographic and addiction type questionnaires. To analyze the data, logistic regression was applied using statistical package for social science (15th ed). The results indicated that only problem –solving COMPONENT is a predictor of addiction to nondepressant drugs.

A gas permeation MODEL (Coupling MODEL) has been developed which has the flexibility to be used for different membrane module configurations. The aim of this work is to predict the performance of a single stage gas separation process using membranes and provide a comprehensive description of process parameters like flow rates, composition, stage cut and stream pressure. The significant feature of this work is the development of computational technique which combines the counter-current flow mode with co-current flow mode. In contrary to other counter-current MODELs (reported in literature), the MODEL reported in this work (Coupling MODEL) does not require initial conditions to start and also it is independent of any adjustment technique like shooting method. This MODEL is based on real membrane operation and works by the coupling of co-current and counter-current methods. After each iteration, output values of co-current mode become the input values for counter-current mode. This MODEL has the capability to take upto nine COMPONENTs, whereas MODEL reported in literature can handle 4-5 COMPONENTs in a gas mixture to be separated. The results obtained are validated with the published data and will be discussed to elaborate the operation of a gas separation membrane.

In this paper, optimization of periodic inspection interval for a TWO-COMPONENT system with failure dependency is presented. Failure of the first COMPONENT is soft, namely, it does not cause the system stop, but it increases the system operating costs. The second COMPONENT’s failure is hard, i.e. as soon as it occurs, the system stops operating. Any failure of the second COMPONENT increases the first COMPONENT’s failure rate. Failure of the first COMPONENT is only detected if inspection is performed. Thus, the first COMPONENT is periodically inspected and if found failed, it is perfectly repaired and it is restored to as good as new. Failure of the second COMPONENT is detected as soon as it occurs. Since this failure causes the system stop, it is immediately replaced. It is assumed that the time for replacement or repaired is negligible. We MODEL the first COMPONENT’s failure as a non-homogeneous Poisson process (NHPP) with increasing failure rate and the second COMPONENT’s failure as a homogeneous Poisson process (HPP) with constant failure rate. The objective is to find the optimal inspection interval for the first COMPONENT such that the expected total cost per unit time is minimized. A simplified numerical example along with sensitivity analysis on cost parameters is given.

In many real application of DEA, the MODEL presented, are designed to obtain an aggregatete efficiency score. However there are situations that the decision making units can be separated into different COMPONENTs in which each COMPONENT consumes inputs to produce outputs. In this case some of inputs are shared among COMPONENTs. In this paper, we present a DEA-like to determine the aggregate efficiency of decision making units a companying with COMPONENT measure.