Background and Objective: Phenol presence and its derivatives in water and waste water on human health and the environment is one the major concerns. Because of the toxicity of phenol and also because of the presence of even low concentrations in natural resources, water disinfection and oxidation processes can lead to the formation of additional components. This material is one of the most common organic pollutants in water. In this research, adsorption of phenol from wastewater by sawdust was simulated using intelligent techniques. Method: Intelligent techniques including multi-layer Perceptron, radial basis functions network and support vector regression were used. To design the network structure as well as the training and testing of 125 sets of experimental data is used. Performance evaluation criteria and stop network consists of % AARE and R2, which is used for all three models. Findings: All models compared results showed that the support vector regression with 0. 5132 and 0. 979, respectively, for %AARE and R2 is the best model. All models are better results than the quadratic polynomial model showed. Discussion and Conclusion: Models showed good agreement with experimental data. The optimum conditions for the removal of phenol were 127. 6 mg/l of initial phenol concentration, 0. 84 g/l of adsorbent dose, natural pH value of 3. 62 and 146. 9 min of contact time, under these conditions the maximum removal efficiency was 91. 23%.

Background and Objective: Having information about qualitative and quantitative parameters distribution of groundwater supplies is one of most important parameters in integrated groundwater management. Thus, in this study it has been attempted to determine a proper model and input combination for estimation of quality parameters including electrical conductivity (EC), calcium (Ca) and sodium (Na) ions in aquifers of Guilans plain. Method: In this study, the data from 132 observation wells during 2001 to 2013 were used and artificial neural network (ANN) and support vector model (SVM) were applied. In the first approach, estimations were conducted according to five different combinations, including water level, distance from see, total precipitation of six months and coordinates of observation wells. In the second approach, estimations were conducted based on combination of the selected qualitative parameters of gamma test with combinations of the best input in the first part. Findings: Comparison of the results from the first part indicated that SVM model outperformed the ANN mode in the estimation of Ca, Na and EC parameters. Support vector machine error values for estimating Ca, Na and EC variables at the test period were 1. 218 (meq/l), 0. 867(meq/l), and 175. 742 (μ mos/cm), while for artificial neural network these values were 1. 268 (meq/l), 0. 933 (meq/l), and 186/448 (μ mos/cm) respectively. The results from this part showed that adding the distance from see input improves the estimation of models in all cases. In the second part, using gamma test for measuring the nine quality parameters, the best combination of quality parameters was determined to estimate the three parameters: Ca, Na and EC. The results from the second part show that both ANN and SVM models have an excellent performance in the estimation of the three qualitative parameters. ANN model error values in estimating Ca, Na and EC variables in validation period were 0. 662 (meq/l), 0. 305(meq/l), and 47. 346 (μ mos/cm), while these values were 0. 671 (meq/l), 0. 356 (meq/l), and 55. 412 (μ mos/cm) for SVM model respectively. Obviously, the results from ANN model in this section were better than those from SVM model. Discussion and Conclusion: Results showed that both ANN and SVM models have a great ability in predicting qualitative parameters in the aquifers. Also, in less inputs, the results of SVM model are better than those of ANN model and in more inputs it is vice versa. Results of the second section showed that gamma test is fully practical and accurate in determining the effective input combinations.

The purpose of this research is Impact use of education model based on mobile learning environments on Social peresence and teaching peresence for Smart schools students in mathematical lesson. in this research goal, a combination research method was used. In this research we have been used some semi-experimental with pre-test and post-test design with control group. research statistical society are including all girl students are studying in Tehran Smart schools. For testing we have been used respected model volume with using of fast step cluster sampling way between Tehran Smart schools with suitable volume so that finally with pay attention to the resulted test decrease, 15 people have been settled in test group and 15 people in control group. Test group have been settled under provided education by researcher via mobile learning management system. That have been designed based on given model. For studying first and second we were used it by Multivaraite covariance analysis. Results was an indication of that using this sample at all dimensions of social and teaching presence in testing group had more increase than control group. Moreover, total score of social and teaching presence have been increased tangibly more than control group score on prosecution stage.