This study depicts the removal of Manganese ions (Mn2+) from simulated wastewater by combined electrocoagulation/ electroflotation technologies. The effects of initial Mn concentration, current density (C.D.), electrolysis time, and different mesh numbers of stainless steel screen electrodes were investigated in a batch cell by adopting Taguchi experimental design to explore the optimum conditions for maximum removal efficiency of Mn. The results of multiple regression and signal to noise ratio (S/N) showed that the optimum conditions were Mn initial concentration of 100 ppm, C.D. of 4 mA/cm2, time of 120 min, and mesh no. of 30 (wire/inch). Also, the relative significance of each factor was attained by the analysis of variance (ANOVA) which indicates that the percentage of contribution followed the order: time (47.42%), C.D. (37.13%), Mesh number (5.73%), and Mn initial Conc. (0.05%). The electrolysis time and C.D. were the most effective operating parameters and mesh no. had a fair influence on Mn removal efficiency, while the initial conc. of Mn. had no significant effect in the studied ranges of control factors. Regression analysis (R2= 90.16%) showed an acceptable agreement between the experimental and the predicted values, and confirmation test results revealed that the removal efficiency of Mn at optimum conditions was higher than 99%.
Polycystic ovary syndrome (PCOS) is a prevalent condition in women of reproductive age. It is characterized by androgen excess and chronic anovulation. Some trace elements, macroelements, and heavy metals have been linked to pathophysiological mechanisms of PCOS .
To study the alterations in the serum levels of the trace element manganese (Mn), some macroelements, magnesium(Mg) and calcium (Ca), and the heavy metals cadmium (Cd) and lead (Pb), in obese and non-obese PCOS patients; and the association of these alterations with some of the hormonal changes occurring in PCOS.
The study was carried out at Kamal Al-Samarrai Hospital (Center for Infertility treatment and in vitro Fertilization "IVF") Baghdad- Iraq. Eig
... Show MoreSimulated annealing (SA) has been an effective means that can address difficulties related to optimization problems. is now a common discipline for research with several productive applications such as production planning. Due to the fact that aggregate production planning (APP) is one of the most considerable problems in production planning, in this paper, we present multi-objective linear programming model for APP and optimized by . During the course of optimizing for the APP problem, it uncovered that the capability of was inadequate and its performance was substandard, particularly for a sizable controlled problem with many decision variables and plenty of constraints. Since this algorithm works sequentially then the current state wi
... Show MoreNurse scheduling problem is one of combinatorial optimization problems and it is one of NP-Hard problems which is difficult to be solved as optimal solution. In this paper, we had created an proposed algorithm which it is hybrid simulated annealing algorithm to solve nurse scheduling problem, developed the simulated annealing algorithm and Genetic algorithm. We can note that the proposed algorithm (Hybrid simulated Annealing Algorithm(GS-h)) is the best method among other methods which it is used in this paper because it satisfied minimum average of the total cost and maximum number of Solved , Best and Optimal problems. So we can note that the ratios of the optimal solution are 77% for the proposed algorithm(GS-h), 28.75% for Si
... Show MoreIn this work, a novel design for the NiO/TiO2 heterojunction solar cells is presented. Highly-pure nanopowders prepared by dc reactive magnetron sputtering technique were used to form the heterojunctions. The electrical characteristics of the proposed design were compared to those of a conventional thin film heterojunction design prepared by the same technique. A higher efficiency of 300% was achieved by the proposed design. This attempt can be considered as the first to fabricate solar cells from highly-pure nanopowders of two different semiconductors.
Correct grading of apple slices can help ensure quality and improve the marketability of the final product, which can impact the overall development of the apple slice industry post-harvest. The study intends to employ the convolutional neural network (CNN) architectures of ResNet-18 and DenseNet-201 and classical machine learning (ML) classifiers such as Wide Neural Networks (WNN), Naïve Bayes (NB), and two kernels of support vector machines (SVM) to classify apple slices into different hardness classes based on their RGB values. Our research data showed that the DenseNet-201 features classified by the SVM-Cubic kernel had the highest accuracy and lowest standard deviation (SD) among all the methods we tested, at 89.51 % 1.66 %. This
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