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%.

In the modern lifestyle, environmental stresses have substantially influenced human life. Evidence indicates that noise exposure can lead to anxiety-like behaviors. In recent years, advances in nanotechnology have favorably impacted industrial productions. In the present study, we aimed to evaluate the effect of Zinc/Iron oxide nanocomposites on anxiety-like behavior. Using gelatin, Zinc/Iron oxide nanocomposites were synthesized by the sol-gel modified method. Four groups of rats were exposed to sound stress for 12 days. Three groups received 1. 25, 2. 5, and 5 mg/kg of Zinc/Iron oxide nanocomposites before the noise stress. Also, three control animals received the same doses of the nanocomposites. One group of the stressed (ST) and control (CO) rats received saline. The animals were introduced to the elevated plus maze (EPM) for the assessment of anxiety. Entry to closed arms, duration of stay in open arms were considered for data analysis. We observed a significant difference between the behavior of the CO and ST animals so that the latter group showed more anxiety compared to the former one. Concerning the effect of Zinc/Iron oxide nanocomposites, the data analysis confirmed the positive effect of the dose of 2. 5 mg/kg where the stressed animals treated with this dose of the nanocomposites considerably improved their normal performance in the EPM.

The purpose of this descriptive-survey study is to design an optimal model for deploying interactive virtual interactive tutorials in Tehran University based on strategic analysis. To achieve this goal, a five-step research method after identifying beneficiary groups using the dimensions and components The virtual intelligent interactive training in Tehran University was identified from the viewpoint of the stakeholders, and after analyzing their views, a selection of experts from virtual education experts and policy makers of higher education was monitored and confirmed, according to which the optimal model was designed and group of experts was validated. The results of this research show that the possibility of deploying interactive virtual interactive tutorials in Tehran University in terms of high technology infrastructure in terms of human resources infrastructure is moderate and in terms of cultural and social infrastructure is lower than average is. Based on these results, optimal deployment model was designed based on five dimensions and 32 components.

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.