Pedotransfer functions are the predictive models of a certain soil property from other easily, routinely, or cheaply measured properties. The common approach for fitting the pedotransfer functions is the use of the conventional statistical Regression method. Such an approach is heavily based on the crisp obervations and also the crisp relations among variables. In the modeling natural systems, like soil, we are dealing with imprecise observations and the vague relations among the variables. Therefore, we need an appropriate algorithm for modeling such a fuzzy structures. In the present study, the fuzzy Regression approach was used in order to fit some chemical and physical pedotransfer functions. The optimum Regression models with the fuzzy coefficients were obtained for modeling pedotransfer functions. Sensivity analysis was carried out by using the credibility level. The results indicated that the fuzzy Regression might be cOflsidered,as a suitable alternative or a complement to the statistical Regression, whenever a relationship between variables is imprecise and generally when dealing with the errors due to a vaguness in Regression models.

Growth analysis is a valuable method in the quantitative analysis of crop growth, development and crop production. In order to evaluate effects of nitrogen rates and plant density on physiological growth indices of safflower, an experiment was conducted at Khorramabad, Lorestan province in 2008. The experiment was carried out as split plot in basis of randomized complete block design with four replications. Three nitrogen application rates were as main plots (N1=control, N2=75 and N3=150 kg/ha net nitrogen) and plant density was as sub plots in 3 levels (D1=40, D2=50 and D3=60 plant/m2). The growth degree day index was used to examine more closely fitting growth curves using non-linear Regression models. Appropriate model was selected for each growth index. The results showed that application of nitrogen led to increasing of growth indexes including leaf area, total dry matter and crop growth rate. But, net assimilation rate was reduced. Growth indexes such as, leaf area, total dry matter, did not change with increasing plant density, due to branching of safflower. However, crop growth and net assimilation rate were reduced in plants. Overall, the results show that applied nitrogen has a more positive effect on safflower growth index compared to density changes.

Today, due to the limited natural resources of land, rapid population growth, rapid expansion of cities, future land use prediction is very important for land managers, planners and environmental specialists because land use change effect on ecosystem and also threaten vital resources . modeling and analysis of the phenomenon of urban development provide comprehensive information to urban planners and managers to have better and more scientific planning. The main objective of this research is modeling urban growth for the city of Sanandaj, in the west of Iran using satellite imagery, Geographic Information Systems and logistic Regression. The parameters are used in this study, including distance to developed area, distance to main roads, distance to green spaces, elevation, slope, distance to fault, distance to district centers and number of urban cell in a 3 by 3 neighborhood. Figure of Merit, Kappa coefficient and Percent Correct Match (PCM) have been used to evaluate goodness of fit of proposed model.

Background: A fundamental part of prevention is prediction. Potential predictors are the sine qua non of prediction models. However, whether incorporating novel predictors to prediction models could be directly translated to added predictive value remains an area of dispute. The difference between the predictive power of a predictive model with (enhanced model) and without (baseline model) a certain predictor is generally regarded as an indicator of the predictive value added by that predictor. Indices such as discrimination and calibration have long been used in this regard. Recently, the use of added predictive value has been suggested while comparing the predictive performances of the predictive models with and without novel biomarkers.Objectives: User-friendly statistical software capable of implementing novel statistical procedures is conspicuously lacking. This shortcoming has restricted implementation of such novel model assessment methods. We aimed to construct Stata commands to help researchers obtain the aforementioned statistical indices.Materials and Methods: We have written Stata commands that are intended to help researchers obtain the following.1, Nam-D’Agostino X2 goodness of fit test; 2, Cut point-free and cut point-based net reclassification improvement index (NRI), relative absolute integrated discriminatory improvement index (IDI), and survival-based Regression analyses. We applied the commands to real data on women participating in the Tehran lipid and glucose study (TLGS) to examine if information relating to a family history of premature cardiovascular disease (CVD), waist circumference, and fasting plasma glucose can improve predictive performance of Framingham’s general CVD risk algorithm.Results: The command is adpredsurv for survival models.Conclusions: Herein we have described the Stata package “adpredsurv” for calculation of the Nam-D’Agostino X2 goodness of fit test as well as cut point-free and cut point-based NRI, relative and absolute IDI, and survival-based Regression analyses. We hope this work encourages the use of novel methods in examining predictive capacity of the emerging plethora of novel biomarkers.

Background: modeling of wastewater treatment plants is necessary to predict their later works. In this research, three methods were compared to predict some parameters at the outlet of wastewater treatment plant in Hama city in Syria. Methods: In this paper, three methods (linear Regression, power Regression, and Regression trees) to model wastewater treatment plant in Hama city were compared to predict the parameters at the outlet of the plant (cBOD5out, CODout, TSSout) in terms of the parameters at the inlet of the plant (Qin, cBOD5in, CODin, TSSin). Results: When predicting cBOD5out, the values of RMSE of the test data set were 4.4105, 4.3875, and 3.8418; when predicting CODout, the values of RMSE of the test data set were 6.9325, 6.8003, and 5.3232; and when predicting TSSout, the values of root mean squared error (RMSE) of the test data set were 3.7781, 3.6936, and 3.2391 using linear Regression, power Regression, and Regression trees (RTs), respectively. Conclusion: According to the results, the RTs outperforms in predicting cBOD5out, CODout, and TSSout because this method achieved the least RMSE of the test data set.

Topological indices are the real number of a molecular structure obtained using molecular graph G. Topological indices are used for QSPR, QSAR and structural design in chemistry, nanotechnology, and pharmacology. Moreover, physicochemical properties such as the boiling point, the enthalpy of vaporization, and stability can be estimated by QSAR/QSPR models. In this study, the QSPR (Quantitative Structure-Property Relationship) models were designed using the Gutman index, the product connectivity Banhatti index, the Variance of degree index, and the Sigma index to predict the thermodynamic properties of monocarboxylic acids. The relationship analyses between the thermodynamic properties and the topological indices were done by using the curvilinear Regression method. It was used the linear, quadratic and cubic equations of the curvilinear Regression model. These Regression models were then compared.

Describing and quantifying the spatiotemporal dynamics of urban expansion in developing countries play a crucial role in determining the mechanisms of urban growth and decision making processes. Urban dissonant development is one of the most important issues in land using, and predicting urban development is not simply possible, but it needs a model to consider the complex nature of urban processes. Urban land uses constitute a complex system which is controlled both by human activities and spatial-temporal dynamics. The rapid population growth and urbanization in developing countries provoke uncontrolled urban sprawl and correspondingly pose a threat to the environment. This urbanization process in territorial structures led to a higher complexity and pressure on the natural environment Logistic Regression is one of the experimental predicting model. Determination of weight of mobile factors based on empirical rather than expert knowledge is one of the benefits of this model. Another superiority of this model is the ability to import more variables. Considering the importance of this issue, this study is working around the modeling the development of Bojnourd, using logistic Regression in 28 years. This method is a descriptive and analytic model that uses 9 independent variables for modelling. The Pseudo-R2 and ROC, respectively were 0. 2835 and 0. 9335 that are in an acceptable range. Therefore, this modeling is confirmed. Evaluating the sensitivity of the model, using the method of eliminating independent variables, represents high impacts of tilt and its distance of water surfaces in modelling. Southern urban areas and rural areas are the most suitable areas for the development in the future.