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Background: Wastewater contaminated with dyes such as Reactive Blue 203 can produce a lot of health problems if it is released into the environment without a suitable treatment. Although there are several studies on dye removal from wastewater, removal of Reactive Blue 203 has not been investigated by hybrid methods. Therefore, the aim of this study was to investigate the removal of Reactive Blue 203 from aqueous solution, using combined processes of zinc oxide (ZnO) nanoparticles, Fe(VI) oxidation process, and UV radiation. Methods: The removal of dye from aqueous solution using ZnO nanoparticles, Fe(VI) oxidation process, and UV radiation was individually evaluated. Then, the results of combined methods were compared. Hydraulic retention time (HRT), pH, and temperature were the most important factors which were investigated in this study. Results: ZnO nanoparticles, Fe(VI) oxidation process, and UV radiation were able to remove 97%, 71%, and 47% of the dye in the optimal conditions, respectively. Also, the removal of dye using combination of Fe(VI) oxidation process/UV radiation, ZnO nanoparticles/Fe(VI) oxidation process, and ZnO nanoparticles/UV radiation under optimum conditions was 100%. It seems that the combined methods were significantly more effective than the methods alone for removal of dye from water. Conclusion: UV radiation alone is a simple and efficient method for removal of Reactive Blue 203 from water. Removal of Reactive Blue 203 using Fe(VI) oxidation process can be completed in a fraction of second, therefore, it can be categorized as a rapid reaction.

Background: Industrial dyes are toxic and carcinogenic, therefore, they should be removed from wastewater. The aim of this study was to investigate the removal of acid orange 7 Dye from wastewater using ultraviolet (UV) radiation, MgO nanoparticles, ultrasonic method alone and in combination with each other. Methods: The effects of some factors such as temperature, pH, hydraulic retention time (HRT), UV power, and concentration of MgO nanoparticles on the removal of Acid Orange 7 dye from synthetic wastewater using different methods were investigated. Also, adsorption isotherms for MgO nanoparticles and kinetics for UV radiation were investigated. Results: The optimum HRT was 55 minutes while the temperature was not effective in dye removal using the ultrasonic method. Under optimum conditions for UV irradiation method (HRT = 70 minutes, UV power = 170 mW/cm2, and temperature = 10˚C), 58% of the dye was removed. However, under optimum conditions for MgO nanoparticles method (HRT = 15 minutes, temperature = 20˚C, and ratio of MgO nanoparticles to the initial dye concentration = 67.2), 82% of the dye was removed. By combining these methods, the dye removal efficiency was significantly increased. The combination of ultrasonic method and MgO nanoparticles had no significant effect on increasing the dye removal efficiency from wastewater. It was revealed that dye removal using UV radiation can be described by the first-order kinetics. Conclusion: According to the results, UV radiation has a synergistic effect on the dye adsorption process by MgO nanoparticles. Therefore, the combination of these methods can be effective for the removal of dye from wastewater.

Whole genome evaluation of quantitative traits using suitable statistical methods enables researchers to predict genomic breeding values (GEBVs) more accurately. Recent studies suggested that the ability of methods in terms of predictive performance may depend on the genetic architecture of traits. Therefore, when choosing a statistical method, it is essential to consider the genetic architecture of the target traits. Herein, the performance of parametric methods i.e. GBLUP and BayesB and non-parametric methods i.e. Bagging GBLUP and Random Forest (RF) were compared for traits with different genetic architecture. Three scenarios of genetic architecture, including purely Additive (Add), purely Epistasis (Epis) and Additive-Dominance-Epistasis (ADE) were considered. To this end, an animal genome composed of five chromosomes, each chromosome harboring 1000 SNPs and four QTL was simulated. Predictive accuracies in the first generation of testing set under Additive genetic architectures for GBLUP, BayesB, Baging GBLUP and RF were 0.639, 0.731, 0.633 and 0.548, respectively, and were 0.278, 0.330, 0.275 and 0.444 under purely Epistatic genetic architectures. Corresponding values for the Additive-Dominance-Epistatic structure also were 0.375, 0.448, 0.369 and 0.458, respectively. The results showed that genetic architecture has a great impact on prediction accuracy of genomic evaluation methods. When genetic architecture was purely Additive, parametric methods and Bagging GBLUP were better than RF, whereas under Epistatic and Additive-Dominance-Epistatic genetic architectures, RF delivered better predictive performance than the other statistical methods.