University of Tabriz

About: University of Tabriz is a education organization based out in Tabriz, Iran. It is known for research contribution in the topics: Population & Nanocomposite. The organization has 12141 authors who have published 20976 publications receiving 313982 citations.

Application of Artificial Intelligence to Estimate Daily Pan Evaporation Using Available and Estimated Climatic Data in the Khozestan Province (South Western Iran)

Abstract: Estimation of evaporation, a major component of the hydrologic cycle, is required for a variety of purposes in water resources development and management. This paper investigates the abilities of genetic programming (GP) to improve the accuracy of daily evaporation estimation. In the first part of the study, different GP models, comprising various combinations of daily climatic variables, namely, air temperature, sunshine hours, wind speed, and relative humidity, were developed to evaluate the degree of the effect of each variable on daily pan evaporation. A dynamic modeling of evaporation was also performed, with the current climatic variables and one of the previous variables, to evaluate the effect of their time series on evaporation. In the second part of the study, the estimated solar radiation data were used as input vectors instead of recorded sunshine values. Statistics such as correlation coefficient (R), root mean square error (RMSE), coefficient of residual mass (CRM) and scatter index (SI) were used to measure the performance of models. Tthe dynamic model approach was shown to give the best results with relatively fewer errors and higher correlations. To assess the ability of GP relative to the neuro-fuzzy (NF) and artificial neural networks (ANN), several NF and ANN models were developed by using the same data set. The obtained results showed the superiority of GP to the NF and ANN approaches. The Stephen-Stewart and Christiansen methods were also considered for comparison. The results indicated that the proposed GP model performed quite well in modeling evaporation processes from the available climatic data. The results also showed that the estimated solar radiation data can be applied successfully instead of the recorded sunshine data.

Carboxymethylcellulose/MOF-5/Graphene oxide bio-nanocomposite as antibacterial drug nanocarrier agent

Abstract: Introduction: In recent years, more attention was dedicated to developing new methods for designing of drug delivery systems. The aim of present work is to improve the efficiency of the antibacterial drug delivery process, and to realize and to control accurately the release. Methods: First, graphene oxide (GO) was prepared according to the modified Hummers method then the GO was modified with carboxymethylcellulose (CMC) and Zn-based metal-organic framework (MOF-5) through the solvothermal technique. Results: Performing the various analysis methods including scanning electron microscope (SEM), X-ray diffraction (XRD), EDX, Fourier transform infrared (FTIR) spectroscopy and Zeta potentials on the obtained bio-nanocomposite showed that the new modified GO has been prepared. With using common analysis methods the structure of synthesized materials was determined and confirmed and finally, their antibacterial behavior was examined based on the broth microdilution methods. Conclusion: Carboxymethylcellulose/MOF-5/GO bio-nanocomposite (CMC/MOF-5/GO) was successfully synthesized through the solvothermal technique. Tetracycline (TC) was encapsulated in the GO and CMC/MOF-5/GO. The drug release tests showed that the TC-loaded CMC/MOF5/GO has an effective protection against stomach pH. With controlling the TC release in the gastrointestinal tract conditions, the long-time stability of drug dosing was enhanced. Furthermore, antibacterial activity tests showed that the TC-loaded CMC/MOF-5/GO has an antibacterial activity to negatively charge E. coli bacteria in contrast to TC-loaded GO.