Shahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80883119700101Simulation of coated paper drying and presentation of its experimental modelSimulation of coated paper drying and presentation of its experimental model12510437FAHosseinShahbeigSchool of Chemical Engineering, College of Engineering, University of Tehran, Tehran, IranAhmadHallajiSaniSchool of Chemical Engineering, College of Engineering, University of Tehran, Tehran, IranJournal Article19700101In this study, a simulation model which integrates the mechanism of heat and mass transfer in drying the coated paper is presented. In the simulation model, the paper divided to three layers, coated paper, wet base paper and dry base paper, where each thickness of layer is a function of drying conditions and time. The effects of fluid temperature, fluid type, time delay and the suspension, are studied. Two type of fluid (air and steam) in 200 ° C, 160° C, and 120 ° C were used. The delay time of 0.2 and 0.4 seconds were considered in accordance with industry requirements. Also, two type of suspension with different viscosity were used. Coated paper, divided to eight parts, and write the equation of mass and energy transfer are solved for each layer, the temperature and humidity of each layer were calculated.In this study, a simulation model which integrates the mechanism of heat and mass transfer in drying the coated paper is presented. In the simulation model, the paper divided to three layers, coated paper, wet base paper and dry base paper, where each thickness of layer is a function of drying conditions and time. The effects of fluid temperature, fluid type, time delay and the suspension, are studied. Two type of fluid (air and steam) in 200 ° C, 160° C, and 120 ° C were used. The delay time of 0.2 and 0.4 seconds were considered in accordance with industry requirements. Also, two type of suspension with different viscosity were used. Coated paper, divided to eight parts, and write the equation of mass and energy transfer are solved for each layer, the temperature and humidity of each layer were calculated.https://jamm.scu.ac.ir/article_10437_a68bed83e8deb268cc64099b6490cda9.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80883120130823Analysis of Mixed Discrete and Continuous Classified ResponsesAnalysis of Mixed Discrete and Continuous Classified Responses274410559FAEhsanBahramiDepartment of Statistics, Shahid Beheshti University, Tehran, IranHamid RezaAalipourDepartment of Statistics, Shahid Beheshti University, Tehran, IranJournal Article20130722In this paper, Our goal is classifying the responses which are mixed discrete and continuous<br />responses. So, first we are modeling them then get their classification rules using the Welch’s<br />classification rules (1939) along with their misclassification probabilities.. Also, sample classification<br />rules are reached using the MLE. Finally, simulation study and some real data are analyzed have<br />performed to show the performance of the rules.In this paper, Our goal is classifying the responses which are mixed discrete and continuous<br />responses. So, first we are modeling them then get their classification rules using the Welch’s<br />classification rules (1939) along with their misclassification probabilities.. Also, sample classification<br />rules are reached using the MLE. Finally, simulation study and some real data are analyzed have<br />performed to show the performance of the rules.https://jamm.scu.ac.ir/article_10559_c6dbbad787b6225d4ac864137e6c8455.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80883120130823Time series discrimination using likelihood function of discrete wavelet coefficientsTime series discrimination using likelihood function of discrete wavelet coefficients455910523FABehzadMansouriDepartment of Statistics, Shahid Chamran University, Ahvaz, IranRahimChinipardazDepartment of Statistics, Shahid Chamran University, Ahvaz, IranJournal Article20130703In this paper, the likelihood ratio of two normal density functions are approximated using discrete wavelet transform and a nonparametric criteria for discrimination of a stationary time series models in the wavelet domain are obtained. The performance of discriminant rule is shown in ARMA models using simulation techniques. In addition to do not requiring to parametric model, speed calculations for large time series and very low error rate are two characteristics of wavelet discriminate criteria.<br /><br />In this paper, the likelihood ratio of two normal density functions are approximated using discrete wavelet transform and a nonparametric criteria for discrimination of a stationary time series models in the wavelet domain are obtained. The performance of discriminant rule is shown in ARMA models using simulation techniques. In addition to do not requiring to parametric model, speed calculations for large time series and very low error rate are two characteristics of wavelet discriminate criteria.In this paper, the likelihood ratio of two normal density functions are approximated using discrete wavelet transform and a nonparametric criteria for discrimination of a stationary time series models in the wavelet domain are obtained. The performance of discriminant rule is shown in ARMA models using simulation techniques. In addition to do not requiring to parametric model, speed calculations for large time series and very low error rate are two characteristics of wavelet discriminate criteria.<br /><br />In this paper, the likelihood ratio of two normal density functions are approximated using discrete wavelet transform and a nonparametric criteria for discrimination of a stationary time series models in the wavelet domain are obtained. The performance of discriminant rule is shown in ARMA models using simulation techniques. In addition to do not requiring to parametric model, speed calculations for large time series and very low error rate are two characteristics of wavelet discriminate criteria.https://jamm.scu.ac.ir/article_10523_abfa7b4925c0c7e1594124c2f0454c5a.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80883120130823The Effects of Sample Size in Multilevel Models via Sub-sampling ApproachThe Effects of Sample Size in Multilevel Models via Sub-sampling Approach617710380FAMousaGolalizadehUniversity Lecturer / Tarbiat Modares UniversityOmidAkhgariTarbiat Modares UniversityJournal Article20130622There are numerous research topics in different fields of study including social, medical, and agricultural sciences which contain data having intra-class correlation structure. For such data, simple linear regression models do not have acceptable applicability since they do not consider the correlation involved. Models which are suitable for data of this kind are called 'multilevel' models. Determining appropriate sample size at different levels in multilevel models is among the issues which has attracted the interest of researchers in applied sciences. In the present study, taking a sub-sampling method approach, the influence of different sizes of the sample size at the first and second levels on the estimation of fixed and random effects was studied. Furthermore, due to the close relation between determining sample size and the power of the statistical test related to the parameters under study, and also due to other factors such as design effect and significance level, different design sizes were evaluated using simulation. Results of the study indicate that increasing sample size at the second level of the two-level model increases the power of the statistical test related to the calculation of the fixed and random effects.There are numerous research topics in different fields of study including social, medical, and agricultural sciences which contain data having intra-class correlation structure. For such data, simple linear regression models do not have acceptable applicability since they do not consider the correlation involved. Models which are suitable for data of this kind are called 'multilevel' models. Determining appropriate sample size at different levels in multilevel models is among the issues which has attracted the interest of researchers in applied sciences. In the present study, taking a sub-sampling method approach, the influence of different sizes of the sample size at the first and second levels on the estimation of fixed and random effects was studied. Furthermore, due to the close relation between determining sample size and the power of the statistical test related to the parameters under study, and also due to other factors such as design effect and significance level, different design sizes were evaluated using simulation. Results of the study indicate that increasing sample size at the second level of the two-level model increases the power of the statistical test related to the calculation of the fixed and random effects.https://jamm.scu.ac.ir/article_10380_b0dc5ed6e1f1fe7d33e0aa5d702e96ce.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80883120130823A Mathematical modeling of a two layered blood flow through constricted vesselsA Mathematical modeling of a two layered blood flow through constricted vessels799910562FAAhmadHaghighiDepartment of Mathematics, Urmia University of technology, Urmia, IranMohammadShahbazi AslDepartment of Mathematics, Urmia University of technology, Urmia, IranJournal Article20130917A mathematical model of pulsatile and two-layered blood flow through constriction vessels is simulated in this paper. The blood vessel has been assumed to be elastic and the blood flow is treated as a two-layered fluid, such that the core region is a micropolar fluid and the peripheral layer is a Newtonian plasma fluid. By applying suitable coordinate transformation, the governing equations have been solved numerically using the finite difference method, and the velocity profile of the two-layered blood flow has been achieved. The flow characteristics including the volumetric flow rate and the resistive impedance were obtained, and the effects of the stenosis size on these characteristic have been discussed.A mathematical model of pulsatile and two-layered blood flow through constriction vessels is simulated in this paper. The blood vessel has been assumed to be elastic and the blood flow is treated as a two-layered fluid, such that the core region is a micropolar fluid and the peripheral layer is a Newtonian plasma fluid. By applying suitable coordinate transformation, the governing equations have been solved numerically using the finite difference method, and the velocity profile of the two-layered blood flow has been achieved. The flow characteristics including the volumetric flow rate and the resistive impedance were obtained, and the effects of the stenosis size on these characteristic have been discussed.https://jamm.scu.ac.ir/article_10562_eec0daf7e6ab3df1e11a8bd8842fa51c.pdfShahid Chamran University of AhvazJournal of Advanced Mathematical Modeling2251-80883120130823Small area estimation and prediction of unemployment duration mean in Iran and its
effect on the provinces using multilevel modelsSmall area estimation and prediction of unemployment duration mean in Iran and its
effect on the provinces using multilevel models10112110560FASeyed Mohammad EbrahimHosseininasabDepartment of Statistics, Shahid Beheshti University, Tehran, IranRaziehAhmadlooDepartment of Statistics, Shahid Beheshti University, Tehran, IranJournal Article20130803Abstract<br />The need for small area estimates via survey data has long been recognized. The importance of using such estimates is that the sample size in some of small areas may be small or even zero so that direct estimates cannot be made with acceptable precision in each small area.<br />If data within small areas have hierarchical structure, multilevel models should be used to make estimates with acceptable precision. In this paper, the labor force survey data of 8 different Iranian provinces, collected by statistical center of Iran in 1388 (Iranian solar year), have been analyzed. In this study, response variable that is unemployment duration of the persons in the survey, is modeled according to auxiliary variables such as age, sex, connection with householder, education situation, marital status, educational level and previous labor experience. Since data structure is hierarchical, we used three-level models for modeling the response variable. In this modeling, the education situation and marital status variables are not significant. The fixed effects and variance components are also estimated by restricted maximum likelihood method and the random effects are predicted by the BLUP method and then the main results are reported and interpreted. Finally using this model, the small area estimations of unemployment duration for 8 different provinces are predicted.Abstract<br />The need for small area estimates via survey data has long been recognized. The importance of using such estimates is that the sample size in some of small areas may be small or even zero so that direct estimates cannot be made with acceptable precision in each small area.<br />If data within small areas have hierarchical structure, multilevel models should be used to make estimates with acceptable precision. In this paper, the labor force survey data of 8 different Iranian provinces, collected by statistical center of Iran in 1388 (Iranian solar year), have been analyzed. In this study, response variable that is unemployment duration of the persons in the survey, is modeled according to auxiliary variables such as age, sex, connection with householder, education situation, marital status, educational level and previous labor experience. Since data structure is hierarchical, we used three-level models for modeling the response variable. In this modeling, the education situation and marital status variables are not significant. The fixed effects and variance components are also estimated by restricted maximum likelihood method and the random effects are predicted by the BLUP method and then the main results are reported and interpreted. Finally using this model, the small area estimations of unemployment duration for 8 different provinces are predicted.https://jamm.scu.ac.ir/article_10560_3f7fd8ce6e866baf96e4ddfa122ed77e.pdf