Combining different treatment strategies successively or simultaneously has become recommended to achieve high purification standards for the treated discharged water. The current work focused on combining electrocoagulation, ion-exchange, and ultrasonication treatment approaches for the simultaneous removal of copper, nickel, and zinc ions from water. The removal of the three studied ions was significantly enhanced by increasing the power density (4–10 mA/cm2) and NaCl salt concentration (0.5–1.5 g/L) at a natural solution pH. The simultaneous removal of these metal ions at 4 mA/cm2 and 1 g NaCl/L was highly improved by introducing 1 g/L of mordenite zeolite as an ion-exchanger. A remarkable removal of heavy metals was reported, as the initial concentration of each metal decreased from approximately 50 ppm to 1.19 for nickel, 3.06 for zinc, and less than 1 ppm for copper. In contrast, ultrasonication did not show any improvement in the treatment process. The extended Langmuir isotherm model convincingly described the experimental data; the Temkin and Dubinin-Radushkevich isotherm models have proven that the removal processes were physical and exothermic. Finally, the pseudo-second-order kinetics model appropriately explained the kinetics of the process with correlation coefficients of 0.9337 and 0.9016, respectively.
Cleft / palate is one of the common congenital deformities in craniofacial region, associated with different types of dental anomalies like (Tooth agenesis, impaction, and supernumerary teeth) with marked changes in palatal dimensions. This study aimed to determine the prevalence of teeth agenesis and dental anomalies in cleft lip/palate patients using CBCT, and to compare the palatal dimension of cleft group with control subjects. Twenty-eight cleft cases collected during the period from 2015 to 2022, CBCT images evaluated, the study sample classified into two groups (14 bilateral and 14 unilateral cleft lip/palate) and the non-cleft control group (14 CBCT images). The presence of dental anomalies was assessed in relation to clef
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We have synthesized many metal (II) complexes using curcumin L1 as the major ligand and 2-(1H-Benzimidazol-2-yl) aniline L2 as a supporting ligand. The complexes were characterized by spectroscopy methods such as; molar conductivity, elements microanalysis, Fourier-transform spectroscopy (FT-IR), UV-vis, and mass spectroscopy. Both curcumin ligands and L2 were found to be capable of binding to M(II) and metal ions via their two N atoms, according to the data. The formula for the complexes is the same. [M (L1)(L2)H2OCl], where M is Ni(II), Co(II), Cu(II), Cd(II), and Hg(II) (II). Octahedral complexes are proposed for the prepared compounds. The bio-actives suggested that the complexes are effective against bacteria and fungus on a mi
... Show MoreThis study aimed at identifying the counseling needs for classroom teachers specialization in
Jerash university. The sample of the study consisted (112) students. To achieve this goal a
scale with (39) items was built by the researcher, this scale has four domains they are as
follows; family, social, psychological and academic domain. The results of this study showed
that counseling needs obtain the heighest average. As well as, the results of the study revealed
that there were statistical differences in favor of gender and teaching year variable. Finally the
study stated a list of recommendations.
Extracorporeal Shock Wave Lithotripsy (ESWL) is the most commonplace remedy for kidney stone. Shock waves from outside the body frame are centered at a kidney stone inflicting the stone to fragment. The success of the (ESWL) treatment is based on some variables such as age, sex, stone quantity stone period and so on. Thus, the prediction the success of remedy by this method is so important for professionals to make a decision to continue using (ESWL) or tousing another remedy technique. In this study, a prediction system for (ESWL) treatment by used three techniques of mixing classifiers, which is Product Rule (PR), Neural Network (NN) and the proposed classifier called Nested Combined Classi
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