Journal of Science,University of Tehran(not publish)3020040722--16794FAJournal Article19700101The compound lead carnonate were prepared in this lab and its characterization was made by X-Ray. The thermal properties were investigated by the thermogravimetry method (TGA).
It was shown that this compound decomposed at 600 in lead oxide. The same experiments on alkaline lead carbonate have shown that this compound decomposed in lead oxide at 330 . Investigation of the morphology of the compound, was made by electron microscopy blow up to 2500.The compound lead carnonate were prepared in this lab and its characterization was made by X-Ray. The thermal properties were investigated by the thermogravimetry method (TGA).
It was shown that this compound decomposed at 600 in lead oxide. The same experiments on alkaline lead carbonate have shown that this compound decomposed in lead oxide at 330 . Investigation of the morphology of the compound, was made by electron microscopy blow up to 2500.https://jos.ut.ac.ir/article_16794_fc895d3dbda09c84b0a62f43dd4ba898.pdfJournal of Science,University of Tehran(not publish)3020040722--16795FAJournal Article19700101The most important problem in the industry is corrosion and scale formation in cooling systems, the most effective solution to overcome the problem is the use of inhibitors. One of the most important and potent inhibitors for this purpose is HEDP. This important organo phosphorous compound is used in various fields such as:
Scale control in aqueous systems, corrosion and scale inhibitor in cooling waters, efficient preconcentration and separation of actinide elements from large soil and sediment samples, prescribed medication for osteoporosis, an inhibiting compound against the formation of crystalline mineral deposits on the heat exchanger tubes.
Regarding to extensive industrial application of HEDP, have it is attempted to production of Lab-scale HEDP by using the required chemically which are may manufactured in the country in a large scale. The conditions of the synthesis procedure is optimized and the purified product way characterized by means of 1HNMR, IR and elemental analysis. Optimization is preformed considering the kinetic aspects of the reaction as well as the significant variables such as stoichiometric ratio of CH3COOH/H2O, temperature, time and the rate of addition of PCl3 and an efficiency of about %98 is achieved at the optimum conditions.The most important problem in the industry is corrosion and scale formation in cooling systems, the most effective solution to overcome the problem is the use of inhibitors. One of the most important and potent inhibitors for this purpose is HEDP. This important organo phosphorous compound is used in various fields such as:
Scale control in aqueous systems, corrosion and scale inhibitor in cooling waters, efficient preconcentration and separation of actinide elements from large soil and sediment samples, prescribed medication for osteoporosis, an inhibiting compound against the formation of crystalline mineral deposits on the heat exchanger tubes.
Regarding to extensive industrial application of HEDP, have it is attempted to production of Lab-scale HEDP by using the required chemically which are may manufactured in the country in a large scale. The conditions of the synthesis procedure is optimized and the purified product way characterized by means of 1HNMR, IR and elemental analysis. Optimization is preformed considering the kinetic aspects of the reaction as well as the significant variables such as stoichiometric ratio of CH3COOH/H2O, temperature, time and the rate of addition of PCl3 and an efficiency of about %98 is achieved at the optimum conditions.https://jos.ut.ac.ir/article_16795_fb7004bf6a4346e7379de6887db9a9c8.pdfJournal of Science,University of Tehran(not publish)3020040722--16796FAJournal Article19700101We will introduce the Euler-Maruyama and Milstein Methods. By using pseudo-random number and implementing the Ito integral we will present an efficient algorithm for the pathwise numerical approximation of solutions to stochastic differential equations.
In the last section we will show the efficiceny of the algorithm by presenting the numerical results for some test problems.We will introduce the Euler-Maruyama and Milstein Methods. By using pseudo-random number and implementing the Ito integral we will present an efficient algorithm for the pathwise numerical approximation of solutions to stochastic differential equations.
In the last section we will show the efficiceny of the algorithm by presenting the numerical results for some test problems.https://jos.ut.ac.ir/article_16796_362bb07b6d78f0198dfb0fe7e5465987.pdfJournal of Science,University of Tehran(not publish)3020040722--16797FAJournal Article19700101Marginal model is one of the approaches that can be used in analyzing longitudinal data. In this model, to obtain valid inference, correlations between responses of the same individuals are considered as parameters to be estimated. The various marginal models for analyzing longitudinal data with binary responses such as marginal models with marginal odds ratio, conditional odds ratio, dependence ratio, multivariate probit and the method of generalized estimating equations (GEE) are reviewed and compared. Some residuals for examining the goodness of fit of these models are presented. In an empirical example, these models are fitted to some data.Marginal model is one of the approaches that can be used in analyzing longitudinal data. In this model, to obtain valid inference, correlations between responses of the same individuals are considered as parameters to be estimated. The various marginal models for analyzing longitudinal data with binary responses such as marginal models with marginal odds ratio, conditional odds ratio, dependence ratio, multivariate probit and the method of generalized estimating equations (GEE) are reviewed and compared. Some residuals for examining the goodness of fit of these models are presented. In an empirical example, these models are fitted to some data.https://jos.ut.ac.ir/article_16797_fa8994e71be726cdb064c133752179be.pdfJournal of Science,University of Tehran(not publish)3020040722--16798FAJournal Article19700101In this paper we briefly review some of the Bayesian techniques for sample size determination in different trials. The two main areas are inferential and decision theoretic frameworks. In the inferential approach we are usually concerned with inference about unknown parameter(s) of interest and sample sizes are determined by taking the parameters of posterior distribution into account. In the decision theoretic approach the problem is treated as a decision problem and using a proper utility function the optimal sample size is determined by optimizing an objective function.In this paper we briefly review some of the Bayesian techniques for sample size determination in different trials. The two main areas are inferential and decision theoretic frameworks. In the inferential approach we are usually concerned with inference about unknown parameter(s) of interest and sample sizes are determined by taking the parameters of posterior distribution into account. In the decision theoretic approach the problem is treated as a decision problem and using a proper utility function the optimal sample size is determined by optimizing an objective function.https://jos.ut.ac.ir/article_16798_cfc100e3a572c6f3b122af94214f5aaf.pdfJournal of Science,University of Tehran(not publish)3020040722--16799FAJournal Article19700101Kernel method is one of the most common nonparametric density estimation and recently B-spline is used for estimation of a probability density function. These two methods in some how depend on selecting a smoothing parameter that has an important effect on precision of the estimators. In this paper, we consider kernel and B-spline methods of density estimation and smoothing parameter selection for these two methods. Then, the accuracy of the obtained estimators is compared by their mean square errors. Also, the effect of the number and dispersion of data on precision of estimators are studied. The results show that for a symmetric probability density, if the dispersion of data increases, the precision of both estimators decreases. While, for an asymmetric probability density function, the precision of the estimators increases for dispersion data.Kernel method is one of the most common nonparametric density estimation and recently B-spline is used for estimation of a probability density function. These two methods in some how depend on selecting a smoothing parameter that has an important effect on precision of the estimators. In this paper, we consider kernel and B-spline methods of density estimation and smoothing parameter selection for these two methods. Then, the accuracy of the obtained estimators is compared by their mean square errors. Also, the effect of the number and dispersion of data on precision of estimators are studied. The results show that for a symmetric probability density, if the dispersion of data increases, the precision of both estimators decreases. While, for an asymmetric probability density function, the precision of the estimators increases for dispersion data.https://jos.ut.ac.ir/article_16799_5485d105bb2689b988752657173adc77.pdfJournal of Science,University of Tehran(not publish)3020040722--16800FAJournal Article19700101So far in solving the equations of gas flow within centrifuge rotor different modelings and analysis have been obtained. There are some errors and approximations in each model. In this article, it has been tried to be analysed the onsager’s analytical model (Gunzburger, et al., 1989) from eigen value procedure and for its real values with the help of package mathematicaSo far in solving the equations of gas flow within centrifuge rotor different modelings and analysis have been obtained. There are some errors and approximations in each model. In this article, it has been tried to be analysed the onsager’s analytical model (Gunzburger, et al., 1989) from eigen value procedure and for its real values with the help of package mathematicahttps://jos.ut.ac.ir/article_16800_70827b3d6b140a1ccae49bcf945e0345.pdfJournal of Science,University of Tehran(not publish)3020040722--16801FAJournal Article19700101Let G be a finite group and let NG denote the set of all non- trivial proper subgroups of G. A member K of NG is called n-decomposable if K is the union of n distinct conjugacy classes of G. The group G is called n-decomposable if and every member of NG is n-decomposable. In this paper we investigate the structure of n-decomposable finite groups for and classify them in the class of finite non-perfect groups.Let G be a finite group and let NG denote the set of all non- trivial proper subgroups of G. A member K of NG is called n-decomposable if K is the union of n distinct conjugacy classes of G. The group G is called n-decomposable if and every member of NG is n-decomposable. In this paper we investigate the structure of n-decomposable finite groups for and classify them in the class of finite non-perfect groups.https://jos.ut.ac.ir/article_16801_2ddcfd565a795f56d42f426285748788.pdfJournal of Science,University of Tehran(not publish)3020040722--16802FAJournal Article19700101Let be the product of two groups and . In this paper we show that if the groups and are almost central, then is almost soluble.
We remind that N.S. Cernikov in 1981 gave a very condensed proof in a short note, but here we present a completely different approach, which will be understandable for the interested readers.Let be the product of two groups and . In this paper we show that if the groups and are almost central, then is almost soluble.
We remind that N.S. Cernikov in 1981 gave a very condensed proof in a short note, but here we present a completely different approach, which will be understandable for the interested readers.https://jos.ut.ac.ir/article_16802_e3a7f657d9350e6b8a8007bc1e106c25.pdfJournal of Science,University of Tehran(not publish)3020040722--16803FAJournal Article19700101A common scientific purpose in spatial data analysis is prediction of a random field in unmeasured sites based on measured data in some sample sites. If the random field is Gaussian with parametric mean and covariance functions, optimal predictor and its mean square error can be determined. But in some applications, the data give evidence of non-Gausian features. In this case, if a nonlinear transformation of the random field is Gaussian, the spatial prediction is carried out. When the transformation is unknown, we assumed that it is belong to a certain parametric family of transformations. If the maximum likelihood estimators of the model parameters is determined and plugged in optimal predictor, optimality of the obtained predictor is doubt and, often, we can`t determine its MSE. Instead, in this paper, using the Bayesian approach, we determine the optimal predictor and its MSE. In a numerical example our method is used to deriving the Bayesian spatial prediction of rainfall at a given site.A common scientific purpose in spatial data analysis is prediction of a random field in unmeasured sites based on measured data in some sample sites. If the random field is Gaussian with parametric mean and covariance functions, optimal predictor and its mean square error can be determined. But in some applications, the data give evidence of non-Gausian features. In this case, if a nonlinear transformation of the random field is Gaussian, the spatial prediction is carried out. When the transformation is unknown, we assumed that it is belong to a certain parametric family of transformations. If the maximum likelihood estimators of the model parameters is determined and plugged in optimal predictor, optimality of the obtained predictor is doubt and, often, we can`t determine its MSE. Instead, in this paper, using the Bayesian approach, we determine the optimal predictor and its MSE. In a numerical example our method is used to deriving the Bayesian spatial prediction of rainfall at a given site.https://jos.ut.ac.ir/article_16803_57a8ce491b6a5a91d9f4e012f0158a6c.pdfJournal of Science,University of Tehran(not publish)3020040722--16804FAJournal Article19700101The anomalous behavior of angular distribution of fission fragments reported by different authors during last years. We can explain some part of these anomalous behavior by TSM, also associated it on large value spins of targets or projectiles or the fissionability parameter ( ) of compound nucleus. For large value of anisotropy, the SSM is more useful and the parameter , is obtained by best fitting the analytical relation to experimental data. In this research work the SSM is employed for explaining the anisotropies of these reactions ، ، ، ، . And we have show that we can explain the angular distribution of fission fragments by SSM when at least the spins of projectile or target is nonzero at moderate energies, Also the ratio of spherical moment of inertia to effective moment of transition nucleus is calculated by this model.The anomalous behavior of angular distribution of fission fragments reported by different authors during last years. We can explain some part of these anomalous behavior by TSM, also associated it on large value spins of targets or projectiles or the fissionability parameter ( ) of compound nucleus. For large value of anisotropy, the SSM is more useful and the parameter , is obtained by best fitting the analytical relation to experimental data. In this research work the SSM is employed for explaining the anisotropies of these reactions ، ، ، ، . And we have show that we can explain the angular distribution of fission fragments by SSM when at least the spins of projectile or target is nonzero at moderate energies, Also the ratio of spherical moment of inertia to effective moment of transition nucleus is calculated by this model.https://jos.ut.ac.ir/article_16804_f962a5e3c0f7886a1848cc6df34a02fe.pdf