The main goal of this present study is to apply an improved zonality index to epithermal gold ores based on staged factor and number-size (N-S) fractal analysis. This technique was utilized in Bardaskan district, NE Iran, which is epithermal gold mineralization. An improved zonality index is a proportion of factors including ore and pathfinder elements based on rock samples. Consequently, two factors were selected after four stages of the staged factor analysis which consist on F-4 (As-Fe-Mo-S) and F 3-4 (Au-Ag). Based on these factors, (F1-4)/(F3-4) is determined as an improved zonality index. In addition, the improved zonality index was categorized by fractal modeling. The N-S model demonstrates that the major anomalies of this improved zonality index are associated with silicification as main alteration zone and the intersections of faults, particularly in the NE and northern parts of the Bardaskan region. Additionally, additional rock samples with Au higher than 100 ppb are located in anomalous parts of the improved zonality index and marginal parts of this area with high values of this index contain pathfinders of gold mineralization, especially As based on this methodology. This methodology could be strongly considered in the exploration of different types of mineral deposits and the classification of the target anomalies.

Anomaly separation based on stream sediment data is an important step for mineral exploration. In this article, three methods of cluster analysis, factor analysis and fractal geometry have been used to separate the anomalous and suspected mineralization areas from the background areas. By combining these three methods, a possible mineralization was found in the Maleksiahkuh area. In addition, the relationship between the anomalies and the anomaly's host rocks was discussed. Maleksiahkuh is located 35 kilometers north of Zahedan and in the eastern part of the Flysch zone, Iran. Multivariate statistical analysis was performed. The results show a positive correlation between copper and molybdenum. The amount of chromium from the field is relatively high. Chromium is rich in the host mafic rocks. The presence of large concentrations of chromium in the region can be attributed to the presence of mafic rocks. The highest positive correlation was observed between manganese and cobalt, which is about 0.997. In addition, iron with titanium has a correlation of 0.984. Cobalt with iron has a correlation of 0.975. The cluster analysis for the region confirmed the existence of three clusters. The third cluster containing elements As, Sr, Sn, Sb, Pb, Cu, and Ag is probably related to the base-metal mineralization. Factor analysis was performed on the elemental concentrations. The sixth factor, which Cu and Ag elements have the highest weightage, was considered as another mineralization factor. The location of the most concentrated copper in the map derived from the Number-size (N-S) fractal method corresponds to the highest score in the factor rating map. There is a good match between copper anomalies and mafic rocks. Green crests have always been associated with mineralization, and studies show that there is a good relationship between mineralization and these rocks.

The rheological property plays an important role in a free-form extrusion 3D printing process, no rheological model was available in open literature that could effectively take into account effects of both the non-Newtonian viscosity and the concentration of nano/micro particles in a paste. Here a fractal law for non-Newtonian fluids is suggested using a fractal derivative, the law can predict correctly the boundary effect of a viscous flow, and can model effectively the nonlinear velocity distribution across the section. A systematic derivation of a fractal rheological model is suggested using the basic laws in the fluid mechanics, which can provide a deep insight into the two-scale fractal interpretation of non-Newtonian fluids. An experiment was carefully designed to verify the model and to elucidate the relationship between the shear rate and viscosity of the SiC paste. 15wt. %, 25wt. %, 35wt. % and 45wt. % SiC pastes were prepared by using mixing, stirring and ball milling processes. The rheology of the paste can be controlled primarily through the SiC concentration, which affects the fractal order. The fractal model sheds a bright light on a simple but accurate approach to non-Newtonian fluids.

The primary purpose of this investigation is contamination mapping in surrounding areas of Irankuh Pb–Zn mine, located in central Iran, using an integrated approach of principal component analysis (PCA) with the Concentration-Area (C-A) and Power Spectrum-Area (S-A) fractal models. PCA categorized the 45 elements into eight principal components. Component 2, containing the toxic elements of Pb, Zn, As, Mn, Cd, and Ba, was identified as the contamination factor. This multivariate contamination factor was modeled using the C-A and S-A fractal methods (in spatial and frequency domains) to delineate pollution areas. Modeling of PCA data using the C-A fractal method showed four main populations for the contamination factors. Two populations with higher fractal dimensions are associated with contamination from mining activities or anthropogenic effects. Low fractal dimensions are considered the background population, which has not been affected or is less affected by these activities. Five geo-chemical populations were obtained for contamination factors using the S-A fractal modeling of PCA in the frequency domain. Therefore, various geo-chemical populations were achieved using geo-chemical filtering and two-dimensional inverse Fourier transformation. The geo-chemical populations related to classes 2, 3, and 4 containing intermediate frequency signals showed the pollution anomaly. The spatial distribution of pollutant geo-chemical signals exhibits excellent conformity with the mining operation limit and tailing dam location as pollutant sources. The results indicate that the elements Pb, Zn, Cd, and As have significant values in the surrounding soils rather than their concentrations in the earth’s crust. The results demonstrate that the S-A fractal models can more precisely delineate the environmental anomaly than the C-A fractal model, especially in intermediate frequency populations.

Multivariate normal-Poisson model has been recently introduced as a special case of normal stable Tweedie models. The model is composed of a univariate Poisson variable, and the remaining variables given the Poisson one are independent Gaussian variables with variance the value of the Poisson component. Two characterizations of this model are shown, first by variance function and then by generalized variance function which is the determinant of the variance function. The latter provides an explicit solution of a particular Monge-Ampère equation.

Background & Aim: One of the basic assumptions in simple linear regression models is the statistical independence of observations. Sometimes this assumption is not true for study subject and consequently the use of general regression models may not be appropriate. In this case, one of the leading methods is the use of multilevel models. The present study utilizes multivariate logistic regression model using a multilevel model to exhibit the chance of having elbow, wrist and knee disorders over the past year based on elbow, wrist and disorders during the past week. Methods & Materials: This study is a cross-sectional study that was carried out from April 2015 to May 2016 in Mobarakeh Steel Company, Isfahan. The study population includes 300 male employees of Mobarakeh Steel Company, with a mean age of 41. 40± 8. 17 years and an average working experience of 16. 0± 7. 66 years. Data were analyzed using SPSS (version 24) and MLwiN software. Results: Based on this study, results obtained from single variable and multivariable regression were different. Conclusion: Based on this study, it can be suggested that multivariable regression cause a better and more accurate deduction compared to single variable method.