Introduction: Many methods like Dose Point Kernels and MIRD calculations, have been evolved for improving the accuracy of dosimetry in the context of 131I radionuclide therapy. However studies have not shown a strong relationship between tumor absorbed dose and response, due to inaccuracies in activity and dose estimation.Methods: GATE Monte Carlo code was used in this study, because it simply takes into account the accurate inhomogeneities in organs and activity DISTRIBUTION and distant energy deposition in a voxel based dosimetry approach. The activity of all organs and tumors were derived, using data from thyroid cancer patients treated with radioiodine. The realistic human phantom of Zubal with 56 segmented tissues was used.Results: The tumor activity was considered constant but with uniform and nonuniform DISTRIBUTIONs in different simulation setups and its calculated mean absorbed dose was respectively 0.65 E-5 and 0.61 E-5 (mGy/Mq.s) which does not vary considerably. However the Dose volume histograms shown that the tumor nonuniform activity DISTRIBUTION decreases the effective dose to most parts of the tumor volume by a factor of 3.Conclusion: In this case it can be misleading to quote the mean or maximum absorbed dose, because overall response is likely to be dependent on the extent of the volume that receives a low absorbed dose. So the more comprehensive approach is to consider the activity and dose DISTRIBUTION throughout the tumor to improve the response assessment and treatment planning for radionuclide therapy.

Investigation on physical characteristics of suspended sediment is an important subject in river studies. The PARTICLE size DISTRIBUTION of the suspended sediment is one of these physical properties represents important links between sources and fluvial mechanisms in the watersheds. However, limited studies have been conducted in field of suspended sediment PARTICLE size DISTRIBUTION. The present study therefore aimed to investigate the PARTICLE size DISTRIBUTION of suspended sediments in Kojour River within the period of one year. Toward this attempt, 24 suspended sediment samples were collected at intervals of some 15 days. The PARTICLE size DISTRIBUTION was determined after necessary preparations by laser scattering PARTICLE size of HORIBA LA-950. The results showed that the suspended sediment diameter were in the range of 0.82 to 353.55 microns during time of sampling and in different conditions. Also, the silt PARTICLEs with partial contribution of 97.68 % had the largest contribution in the suspended sediment load. In addition, the results indicated that the precipitation and sand harvesting plays an important role in increasing the coarse PARTICLEs of suspended sediment load.

PARTICLE size DISTRIBUTION is an important characteristic in suspension polymerization. Special condition must satisfy for achievement of an appropriate PARTICLE size DISTRIBUTION. This condition is studied for some systems. In this work the effect of parameters affecting the suspension polymerization system of styrene was studied and then the approximate optimum range was determined to obtain narrow PARTICLE size DISTRIBUTION mainly in the range of 200 to 400 microns. The studied parameters are: position of impeller, impeller type, impeller speed, baffles, stabilizer concentration, initiator concentration and divinylbenzene concentration as cross-linking agent.

The objective of exploration seismology is to measure accurately reflected wavefields from subsurface interfaces in order to generate an image of the geological formations. More often the seismic data is NON-UNIFORMly sampled, i.e. the data is not acquired on an equidistantly spaced grid. This may result in artifacts during data processing that complicate interpretation. Sampling may be nonuniform because of human-related reasons such as: 1. Faulty equipment and positioning errors, 2. It may be due to environmental circumstances, such as cable feathering in marine acquisition induced by ocean currents or 3. Due to inaccessible areas in land acquisition (cities, rivers, and canyons).The generation of uniformly sampled data from nonuniformly sampled data is called reconstruction and many different methods to reconstruct seismic data have been published over the years. The method used in this paper is called reconstruction of nonuniformly sampled data with least squares Fourier transform. It is based on estimating the Fourier coefficients that describe the NON-UNIFORMly sampled data, and once these coefficients have been found the signal can be reconstructed on any suitable grid via an inverse Fourier transformation. The efficiency of the method is evaluated on both real and synthetic seismic. All necessary codes were written in MATLAB environment.

Soil water characteristics curve (SWC), is one of the most important traits of every soil and is employed in many aspects of soil management such as irrigation, drainage, land reclamation, infiltration and erosion. Laboratory method to determine this curve is time consuming, costly, difficult and involves some errors. For this reason researchers have tried to estimate it with the help of mathematical and statistical models. One of these models has been suggested by Arya and Paris. The primary concept of this model has been developed on the basis of shape similarity between SWC and cumulative soil PARTICLE size DISTRIBUTION (PSD) curves. An experimental coefficient (a) have been employed in this model. Arya and Paris obtained the mean of 1.38 for a. Evaluating the generality of this value for soils of various physical properties, particulary texture, was the main objective of this study. For this purpose, five soil series (Balestan, Arzanagh, Sharabian, Sarab and Gonbadan) were selected from the Sarab region in northwest of Iran. Within each series seven sites were specified and core (undisturbed) samples were taken from each site in triplicate. Composite disturbed samples also were taken. Percentage of soil PARTICLEs, saturated water contents, soil bulk and densities and PSD curves were determined for all samples, and finally SWC curves were simulated by employing Arya and Paris model. The experimental SWC were also prepared using suction and pressure plate apparatus. Comparison of experimental soil water content at specified soil water potential with those predicted from the model indicated that a values ranged from 1.3 to 1.43. Within this range 1.3 seems justifiable for the most series and implies that the model with a=1.3 simulates SWC curves accurately enough. The generalization, however, may need more investigation.

In recent years, many researchers have attempted to estimate the soil hydraulic functions (e.g. soil moisture characteristics curve, and hydraulic conductivity function) using PARTICLE-size DISTRIBUTION (PSD) curve. In these studies, an accurate mathematical representation of PSD is required for fitting the observed data. So far, some mathematical models were developed with different limitations. The goodness of fit is directly related to the number of the model parameters. However, estimating the parameters for higher-parameter models which have no mathematical or physical significance is a problem. Among the current models, 2-parameter Log-normal DISTRIBUTION model with mathematical significant parameters has been considered as a basis for many studies. In this study, it is indicated that the 2-parameter Log-normal DISTRIBUTION model can not be very accurate for representation of the PSD for all of soil textural classes. As an alternative, 2-parameter Gamma DISTRIBUTION model is proposed for more accurate representation of the PSD that its two parameters also are mathematical significant and readily computable. These two models have been compared in fitting the observed PSD data of 461 soil samples from UNSODA soil database. Gamma DISTRIBUTION model indicated a pronounced improvement in representation of the PSD. Based on Coefficient of determination (R2), in 362 samples and based on RMSE, in 323 samples, Gamma DISTRIBUTION model showed a better representation of the PSD than Log-normal. To evaluate the significance of the difference between two models, a t-test was performed. The results showed that, at confidence level of 1%, the R2-values of the Gamma model are significantly greater than those of Log-normal model. Also, at confidence level of 5%, a significant difference between the RMSE-values of two models was shown. Therefore, 2-parameter Gamma DISTRIBUTION model is judged to be better than 2-parameter Log-normal model for representation of PSD.