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%.

The purpose of this study in the first phase was to determine the relationships between the characteristics of the articles published by the Iranian top one percent researchers in 2020 (journal Impact Factor, and author impacts; paper, abstract, and title lengths; author affiliations, number of references, author-supplied keywords, and number of countries; access type, and funding) with the citation counts, readership counts, and altmetric attention score (AAS), which were respectively extracted from Web of Science (WoS), Mendeley and Altmetic.com. The research aim in the second phase was to demonstrate their presence in the international patents, Iranian research outputs indexed in WoS, the curriculum of their fields, profiling services, and their academic inbreeding status and collaborations patterns. The method of this applied study was descriptive in terms of purpose and scientometric in terms of approach, which was conducted based on citation analysis, altmetric and bibliometric techniques. The statistical population of the research was 12 Iranian top one percent researchers from the top one percent researchers listed on Clarivate Analytics in 2020. The findings demonstrated that the characteristics of the articles, including the number of affiliations, authors' h-index, journal impact factor, authors and references counts, and title and abstract length, have been correlated positively with their citations, AAS, and readership. In addition, articles published by authors with a higher h-index had a greater AAS and readership, and articles with longer titles had a lower AAS. However, no statistically significant correlation was found between the citation counts and the AAS with the author keywords counts, and paper lengths, also between the AAS with the countries count. The access type variable did not affect the number of citations, but non-open-access articles received higher AAS and readership than open-access articles. In addition, funded articles obtained less citations, AAS, and readership than non-funded articles. There was no relationship between academic inbreeding of Iranian top one percent researchers with their citations and AAS. However, there was a weak correlation between the average number of citations and AAS of the Iranian top one percent researchers. Eventually, these researchers did not have a high national impact based on the indicators of presence in the references of Iranian papers indexed in WoS and references/resources of the curriculum of their field. Just 1.5 percent of publications of these researchers' were cited in patents, and many of them were contributed by Omid Akhavan and Nosratullah Dabir Vaziri.

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.