Streptococcusis is the one of the most important bacterial fish diseases with outbreak in rainbow trout farms in Iran. The fish farmers have been largely suffered from huge economic losses due to the Streptococcusis outbreaks in different rainbow trout farms in Iran. The present study assessed the effects of some environmental risk factors on incidence of streptococcusis in rainbow trout farms in Haraz River in Mazandaran Province, Iran. A suit of environmental factors including water temperature, nitrite, nitrate, ammonium, water turbidity, DO, water Debi and total count of bacteria were explored as influential factors. Fish and water samples were randomly collected from 10 farms on a monthly basis throughout a year. Isolation and recognition of strep strains were made using biochemical and PCR tests and the data were analyzed by logistic regression method. According to the results, 20% of the differences were explained by the logistic model. Management of these factors might decline the rate of disease outbreak.

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: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors. Methods: This is a cross-sectional study and conducted on 6460 people, over 30 years old, who have participated in the screening of diabetes plan in Mashhad city that it was done by Mashhad University of Medical Sciences from October to December 2010. According to the fasting blood sugar criteria, 5414 individuals were identified as healthy and 1046 individuals were identified as pre-diabetic. Age, gender, body mass index, systolic blood pressure, diastolic blood pressure and waist-to-hip ratio were measured for every participant. The data was entered into the Microsoft Excel 2013 (Microsoft Corp., Redmond, WA, USA) and then analysis of the data was done in R Project for Statistical Computing, Version R 3. 1. 2 (www. r-project. org). Ordinary logistic regression model was fitted on the data. The outliers were identified. Then Mallow, WBY and BY robust logistic regression models were fitted on the data. And then, the robust models were compared with each other and with ordinary logistic regression model according to goodness of fit and prediction ability using Pearson's chi-square and area under the receiver operating characteristic (ROC) curve respectively. Results: Among the variables that were included in the ordinary logistic regression model and three robust logistic models, age, body mass index and systolic blood pressure were statistically significant (P< 0. 01) but waist-to-hip ratio was not statistically significant (P> 0. 1). There were 552 outliers with misclassification error in the ordinary logistic regression model. Pearson's chi-square value and area under the ROC curve value in the Mallow model were almost the same as for ordinary logistic regression model. But it was relatively higher in BY and WBY models. Conclusion: Based on results of this study age, overweight and hypertension are risk factors of prediabetes. Also, WBY and BY models were better than ordinary logistic regression model, according to goodness of fit criteria and prediction ability.

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.

In this paper, we consider the issue of data classification in which the response (dependent) variable is two (or multi) valued and the predictor (independent) variables are ordinary variables. The errors could be nonprecise and random. In this case, the response variable is also a fuzzy random variable. Based on this and logistic regression, we formulate a model and find the estimation of the coefficients using the least squares method. We will describe the results with an example of one independent random variable. Finally, we provide recurrence relations for the estimation of parameters. This relation can be used in machine learning and big data classification.

Subsidence is an environmental phenomenon caused by the gradual subsidence or sudden subsidence of the earthchr('39')s surface. The phenomenon of subsidence in residential, industrial and agricultural areas can cause catastrophic damage. In most parts of Iran, there is a high correlation between land subsidence and the decrease of groundwater level and consequently the density of soil layers. In this study, using two time series of radar images with artificial apertures from Sentinel sensors belonging to 2014 and 2019, the amount of subsidence in Damaneh plain (Frieden city) was calculated. Wells were studied in the period 2014 to 2019, the results of the study of the correlation between land subsidence with changes in groundwater level at the level of 95% was significant. In the continuation of the research, using the logistic regression model, the subsidence trend in the study area was predicted and a subsidence probability map was prepared and created as a dependent variable for the logistic regression model. The independent variables used included altitude, slope, slope direction, geology, distance from the road, distance from the river, land use, distance from the village, groundwater level, piezometric wells. The output of the model is subsidence risk zoning map which was created in five classes. The accuracy and validation of the logistic regression model was evaluated using the system performance characteristic curve and the accuracy (0.89) was obtained. The good accuracy of the logistic regression model in producing the probability map Subsidence is in the study area. In the output of the model, it was found that the area of ​​1980 hectares, equivalent to 7.9%, has a very severe subsidence that has put the situation in a dangerous situation and the need for control and management to reduce this destructive effect.