JOURNAL OF SCIENCE (UNIVERSITY OF TEHRAN) (JSUT)
Year:2004 | Volume:30 | Issue:1|Papers Count:13

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 6000 C in lead oxide. The same experiments on alkaline lead carbonate (3PbCO3 .2Pb( OH)2) have shown that this compound decomposed in lead oxide at 3300 C. Investigation of the morphology of the compound, PbCO3 was made by electron microscopy blow up to 2500.

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 dements 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 or addition of PCl3 and an efficiency of about %98 is achieved at the optimum conditions.

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 efficiency of the algorithm by presenting the numerical results for some test problems.

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.

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

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.

So far in solving the equations of gas flow within centrifuge rotor different modeling 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 mathematica.

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 NG≠ǿ and every member of NG is n decomposable. In this paper we investigate the structure of n-decomposable finite groups for n ≤ 6 and classify them in the class of finite non-perfect groups.

Let G = A B be the product of two groups A and B. In this paper we show that if the groups A and B are almost central, then G is almost soluble.We remind that N.S. Cernikov in 1981 gave a very condensed proof in short note, but here we present a completely different approach, which will be understandable for the interested readers.

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.

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 (Z2/ A) of compound nucleus. For large value of anisotropy, the SSM is more useful and the parameter S02, 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 10B(64Mev)+237Np , 10B(60Mev)+232Th" 12C(72Mev)+237Np , 12 C(79Mev)+232Th " 160(88Mev)+232Th 16 O(94Mev)+237Np. 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.