A field-pilot scale slow sand filter (SSF) was constructed at Al-Rustamiya Sewage Treatment Plant (STP) in Baghdad city to investigate the removal efficiency in terms of Biochemical Oxygen Demand (BOD5), Chemical oxygen demand (COD), Total Suspended Solids (TSS) and Chloride concentrations for achieving better secondary effluent quality from this treatment plant. The SSF was designed at a 0.2 m/h filtration rate with filter area 1 m2 and total filter depth of 2.3 m. A filter sand media 0.35 mm in size and 1 m depth was supported by 0.2 m layer of gravel of size 5 mm. The secondary effluent from Al-Rustamiya STP was used as the influent to the slow sand filter. The results showed that the removal of BOD5, COD, TSS, and Chloride were

The removal of commercial orange G dye from its aqueous solution by adsorption on tobacco leaves (TL) was studied in respect to different factor that affected the adsorption process. These factors including the tobacco leaves does, period of orange G adsorption, pH, and initial orange G dye concentration .Different types of isotherm models were used to describe the orange G dye adsorption onto the tobacco leaves. The experimental results were compared using Langmuir, and frundlich adsorption isotherm, the constants for these two isotherm models was determined. The results fitted frundlich model with value of correlation coefficient equal to (0.981). The capacity of adsorption for the orange G dye was carried out using various kinetic models

   This study aims to prepare new compounds and investigate them spectroscopically and biologically against selected types of positive and negative bacteria and fungi to demonstrate their biological effectiveness. The prepared ligand combining formaldehyde, indole, sulfa benzamide, and 2-mercapto benzimidazole, a Mannich base ligand (L) was synthesized. The six metal ions including Cobalt (II), Nickel (II), Copper (II), Palladium (II), Platinum (IV), and gold (III) have interacted with the ligand and formed new complexes. Different spectroscopic methods, including C.H.N.S., FTIR, UV- Range visible, 1HNMR, 13CNMR, mass spectra, magnetic moment, and molar conductivity were used to suggest the new geometry of the complexes. The resul

Four metal complexes of Schi base ligand were prepared. To characterize both the ligand and the metal complexes, various techniques were employed, including elemental analysis, FT-IR spectroscopy, UV-Vis spectroscopy, molar conductivity measurements, magnetic moment determination, melting point, mass spectroscopy, 1H-NMR and 13C-NMR spectroscopy. The results showed that the metal complexes formed have the formula [M(L)2Cl2]Cl(n).H2O, where LD Schi base ligand and MDCr(III), Rh(III), Pt(IV) and Au(III), n D 1, 2. Based on spectroscopic analysis, coordination with metal ions involves the ’N’ donor atom of N atoms of the imine and the N-H amid group, and four complexes are suggested to have a six-coordinated octahedral structure. Molar c

Frictional resistance occurs whenever sliding happens, negatively impacting treatment outcomes and duration. It is a clinical challenge and must be dealt with efficiently to achieve the best orthodontic results. Aims of this study: compare and evaluate the static frictional forces under the wet condition to mimic the oral environment produced by using a polycrystalline ceramic bracket, monocrystalline ceramic bracket, 0.014 of an inch nickel-titanium (Rhodium coated archwires, and ilusio aesthetic archwires), and 0.019 x 0.025 of an inch stainless steel (Rhodium coated archwires, and ilusio aesthetic archwires). Ninety-six aesthetic brackets (48 monocrystalline and 48 polycrystalline brackets) were used and stored in different incub

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an ob

Codes of red, green, and blue data (RGB) extracted from a lab-fabricated colorimeter device were used to build a proposed classifier with the objective of classifying colors of objects based on defined categories of fundamental colors. Primary, secondary, and tertiary colors namely red, green, orange, yellow, pink, purple, blue, brown, grey, white, and black, were employed in machine learning (ML) by applying an artificial neural network (ANN) algorithm using Python. The classifier, which was based on the ANN algorithm, required a definition of the mentioned eleven colors in the form of RGB codes in order to acquire the capability of classification. The software's capacity to forecast the color of the code that belongs to an object under de