OjatAbad iron ore located in north east of Semnan city. Old indications of mining are evident in the area. Belich and Bragin (1993) introduced Semnan iron ores as hydrothermal deposits. Recent studies show that the iron ores are related to Oligo-Miocene magmatism. Most iron ores have magnetite which has high MAGNETIC susceptibility; therefore, MAGNETIC method is a conventional method for geophysical exploration of iron. In order to identify and detect this deposit, a MAGNETIC survey was carried out in OjatAbad area. The MAGNETIC DATA corrected for diurnal change of MAGNETIC field and then total MAGNETIC field of the Earth has been reduced. To do this, a reduce to pole (RTP) filter was implemented on the grid for locating the anomalies and their sources. This method entails removing the dependence of MAGNETIC DATA to the MAGNETIC inclination, i.e., converting the DATA which were recorded in the inclined Earth’s MAGNETIC field to what they would have been if the MAGNETIC field had been vertical. This method simplified the interpretation because for sub-vertical prisms or sub-vertical contacts (including faults), it transforms their asymmetric responses to simpler symmetric and anti-symmetric forms. The symmetric “highs” are directly centered on the body, while the maximum gradient of the anti-symmetric dipolar anomalies coincides exactly with the body edges. For depth estimation of anomalies, the upward continuation filter was implemented. This is a mathematical technique that projects the DATA taken at an elevation to a higher elevation. The effect is that the short-wavelength features are smoothed out because one is moving away from the anomaly. The upward continuation is a way of enhancing large scale (usually deep) features in the survey area. It attenuates the anomalies depending on their wavelengths; the shorter the wavelength, the greater the attenuation. Also upward continuation tends to accentuate the anomalies caused by deep sources at the expense of the anomalies caused by shallow sources. For 3D imaging of MAGNETIC DATA, we chose the inversion method of Li and Oldenburg (1998) that minimizes a function composed by (1) the DATA-misfit function defined in the DATA space as the L2 norm of the difference between the observed and predicted DATA, and (2) the stabilizing function defined in the parameter model space as the L2 norm of the first-order derivative of the weighted density distribution in both vertical and horizontal directions. They introduced a depth-weighting function to counteract the spatial decay of the kernel function with depth by giving more weight to rectangular prisms as depth increases. On the RTP MAGNETIC map of study area we can see 8 MAGNETIC anomalies (A, B, C, D, E, F, G and H) which are located at the northeast to the southwest trend. The H one has the lowest amplitude. The results of upward continuation filter show that the anomalies of F and G are shallower than the other anomalies. Also, H and B are only deeper than 100m. The inversion results recovered all of the 8 anomalous bodies and confirm the above results. They showed that the anomalous body H has lower MAGNETIC susceptibility and is deeper than the others and it seems likely that it is an intrusive body. So, iron mineralization is probably happened in the other bodies. The anomalous body B is the deepest mineralized body which elongates to 100 meter.

Background and Aims: Disc displacement is the most common temporomandibular joint disorder and MAGNETIC resonance imaging (MRI) is the gold standard in its diagnosis. This disorder can lead to changes in signal intensity of MAGNETIC resonance (MR). The purpose of this study was evaluation of correlation between relative signal intensity of MR images of retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle with type of anterior disk displacement and condylar head flattening in patients with temporomandibular disorder (TMD).Materials and Methods: In this retrospective study, 31 MR images of patients who had anterior disc displacement were evaluated. After relative signal intensity measurement for retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle, the correlation between relative signal intensity and type of anterior disc displacement was evaluated with repeated measure ANOVA test. In each of these 3 areas, t-test was used to compare the groups with and without condylar head flattening.Results: The correlation between relative signal intensity of MR images and type of anterior disc displacement in retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle was not significant. There was also no statistically significant correlation between relative signal intensity of MR images and flattening of condylar head in retrodiscal tissue, superior and inferior head of lateral ptrygoid muscle (P>0.05).Conclusion: According to findings of this study, relative signal intensity of MR images in retrodiscal tissue, superior and inferior head of ptrygoid muscle is not a good predictor for type of anterior disc displacement and flattening of condylar head. It seems that this cannot be used as a diagnostic marker for TMD progression.

The neoclassical growth model is tested by use of panel DATA procedure in this research. In the econometric test, simoultanously time series and cross detection will be compared on the basis of panel DATA method through which their observed points increase and consequently the estimation efficiency will be increased. The examination of neoclassical growth theory has been done with reference to external & internal factors of 52 selected countries from 1960 to 2000. The independent variable of model has been selected on the basis of the result of previous research which explains the result in three separate models: developed countries, developing countries, and whole countries. These factors are such as: Gross National Products with lag of period, work force age, growth rate, education level, the change of capital accumulation and economic trade volum. The consequences of this research is that: neoclassical growth model can explain the major part of economic growth of the countries with use of internal variables. Also with the use of panel procedure of fixed effect, we can see the fundamental differences and structure of the growth process for different countries; and show how the economic, and social conditions affect on the growth.

In this paper we used orthogonal basis functions and expansion coefficients for inverse modeling of MAGNETIC DATA. The basis functions chosen are normalized eigenvectors of second derivation of the objective function (Hessian matrix) calculate for an initial model. Limited number of basis vectors obtained in this way defines a new subspace in model parameters space. Anew objective function is defined in term of these new parameters and minimized in subspace of original space. As in geophysical inverse problems we need to inverse matrixes that are functions DATA and geometry of DATA and model parameters. The matrix inversion in new subspace of the original space will be better conditions due to less dimensionality in the inversion. Since the most significant eigenvectors corresponding the largest Eigen values in Singular Value Decomposition (SVD) of matrixes. Others eigenvectors have less influence in fitting DATA or lead inversion procedures to local minima. With apply subspace method inversion will be fast and stable against the noise. The efficiency of the method is tested with synthetic and real MAGNETIC DATA (acquired from Moghan area, north-west of Iran). The results proved fast convergence and stability of inversion against the noise.

Contrast-enhanced breast MAGNETIC resonance imaging (MRI) has been shown to have very high sensitivity in the detection of breast cancer, with a significant increase in the number of breast MR examination. In this study, the numerous morphologic and kinetic features have been described and reported for characterizing benign and malignant lesion. These features result in lack of appropriate descrip-tion of the lesions, frequent problems of communication with the referring physicians, or difficulties for comparing different MR studies in some patients. As with mammography and ultrasound, a breast MRI reporting system is used for describing abnormal MR findings, resulting in an standardized description, an organized report with a final assessment of the lesion.This article reviews the interpretation criteria in breast MR imaging based on breast imaging and reporting DATA system (BIRADS).Interesting and practical cases will also be present in this article in order to become familiar with BIRADS MRI.

Inverse theory was developed by scientists and has been used in different scientific applications, such as geophysical tomography, image enhancement, curve fitting and determination of earth structure from geophysical DATA. Inverse theory provides mathematical techniques to obtain useful information about measurements (DATA). The information resulting from inversion usually reveals some specific properties of the geological structures, called model parameters. Inverse theory, in contrast to forward theory, which predicts results of measurements on the basis of a suggested model relevant to the problem, uses models that are adjusted and estimates the model parameters by using the DATA and some general principles. It should be noted that inverse theory provides information about unknown model parameters directly using measured DATA. In contrast to forward theory, inverse theory doesn’t provide a basis for the model itself. Recently, considerable effort has been devoted to the explanation of gravity and MAGNETIC anomalies by employing DATA inversion in the spatial domain.Three major types of gravity and MAGNETIC DATA inversion are discussed in geophysical literature. The first is "Inverting DATA for solving both physical and shape parameters". In this case, the inverse problem is completely non-unique. The non-uniqueness of the problem is visible in the two-dimensional section as a large number of well-defined local minima, some of which are distinguished as unfeasible. In this class, unacceptable solutions can be confined by specifying some of the model parameters. The second type of gravity and MAGNETIC DATA inversion is “Inverting DATA for solving physical parameters”.In this approach, the earth is divided into a limited number of cells of fixed size with unknown physical parameters, such as density and magnetization. The non-uniqueness of the solution is evident and algorithms have been developed to produce a single model by minimizing an objective function. The third type of gravity and MAGNETIC DATA inversion is “Inverting DATA for solving shape parameters”. In this class, physical parameters are assumed to be known and nonlinear operators must be design to determine geometry of the geophysical sources. However, geophysical inversion methods are most effective when a linear operator is applied; thus, the problem is usually linearized about some initial model and the inverse problem is solved iteratively.This paper presents a robust, flexible and efficient algorithm to solve large scale nonlinear inverse problems in geoMAGNETIC surveys (the third type of gravity and MAGNETIC DATA inversion). Considering the sensitivity of inverting MAGNETIC DATA and the high level of noise in DATA acquisition, the inversion of MAGNETIC DATA should be performed using advanced methods. These methods have high performance to handle noise DATA. The method is iterative, and at each iteration a perturbation of model parameters in a Pdimensional subspace of an M-dimensional model space are sought (the primary model is updated using perturbation values of the model parameters at each iteration).This style of iterative subspace procedure is well adapted to non-linear inverse problems with many parameters and can be successfully applied to a variety of geopotential problems. The gradient subspace algorithm utilizes a model of parameterization in which the depth of each block is described as an unknown parameter.Model parameters are allocated to separate subspaces on the basis of different physical dimensionality (in this case, model parameters have the same physical dimension). Basis vectors of P-dimensional subspace are extracted by the SVD of a Hessian matrix (the second derivation of model parameters). M-dimensional model space is projected onto Pdimensional subspace using basis vectors.If effective basis vectors are chosen for inversion procedures, the projected matrix is accurate with respect to original one. In new and small dimensions, inversion can be performed with great speed and is stable against noise. This procedure is very effective in accelerating convergence and obtaining a more accurate solution. Also, the inversion is robust with respect to DATA errors and poor initial estimations. The efficiency of the method is compared with one of the conventional methods of inversion of non-linear problems (Marquardt-Levenberg); the results show that the gradient subspace has fast and stable convergence in comparison to its performance in the conventional method. The practical effectiveness of this method is demonstrated by inversion of synthetic and real examples. The real MAGNETIC DATA is acquired over the MOGHAN area, in the northwest of Iran. The results compared with those of seismic interpretation at the study area.