Abstract : A research was conducted to study the process parameters affecting hexavalent chromium Cr (VI) (carcinogenic compound) the removal percentage from the electrical industries company waste water that contain 88 mg/l of Cr (VI) concentration by adsorption onto tea wastes. Synthetic water with 88 mg/l Cr (VI) concentration was used. Several operation parameters affecting Cr (VI) removal efficiency were investigated, such as pH, initial Cr (VI) concentration, stirring time and tea wastes dose. The experimental results reveal that maximum Cr (VI) removal reached up to 94.26% at pH of 2, stirring time of 180 minute, tea wastes do

The present study is to investigate the possibility of using wastes in the form of scrap iron (ZVI) and/ or aluminum ZVAI for the detention and immobilization of the chromium ions in simulated wastewater. Different batch equilibrium parameters such as contact time (0-250) min, sorbent dose (2-8 g ZVI/100 mL and 0.2-1 g ZVAI/100 mL), initial pH (3-6), initial pollutant concentration of 50 mg/L, and speed of agitation (0-250) rpm were investigated. Maximum contaminant removal efficiency corresponding to (96 %) at 250 min contact time, 1g ZVAI/ 6g ZVI sorbent mass ratio, pH 5.5, pollutant concentration of 50 mg/L initially, and 250 rpm agitation speed were obtained.

The best isotherm model for the batch single Cr(III) uptake by ZVI

Background: Tooth extraction is one of the most commonly performed procedures in dentistry. It is usually a traumatic process often resulting in immediate destruction and loss of alveolar bone and surrounding soft tissues. Various instruments have been described to perform atraumatic extractions which can prevent damage to the paradental structures. The physics forceps is one of those innovations in dental extraction technologies that claim to provide an efficient means for atraumatic dental extractions. Materials and method: A randomized clinical trial was conducted to compare the physics forceps with the conventional forceps for the removal of 28 mandibular single rooted teeth under the following parameters: incidence of crown, root, b

Coronavirus disease (COVID-19) is an acute disease that affects the respiratory system which initially appeared in Wuhan, China. In Feb 2019 the sickness began to spread swiftly throughout the entire planet, causing significant health, social, and economic problems. Time series is an important statistical method used to study and analyze a particular phenomenon, identify its pattern and factors, and use it to predict future values. The main focus of the research is to shed light on the study of SARIMA, NARNN, and hybrid models, expecting that the series comprises both linear and non-linear compounds, and that the ARIMA model can deal with the linear component and the NARNN model can deal with the non-linear component. The models

The relationship between costs of environment and costs of product life – cycle.    Boubtlessly when the economical unit exercise their productive works, they lead to pollution in water, air and soil as well as all stages of product life- cycle from Rans Dstage, production stage, packaging stage and finally abandonment stage-             Pollution causes environmental costs. Lgnoring or hiding environmental costs and no taking them in consideration with product cost lead to a wrong account of preduot cost.

Therefore, environmental costs should be included and matched for all stages with in product costs to know which activities, processes

This research aims to study the methods of reduction of dimensions that overcome the problem curse of dimensionality when traditional methods fail to provide a good estimation of the parameters So this problem must be dealt with directly . Two methods were used to solve the problem of high dimensional data, The first method is the non-classical method Slice inverse regression ( SIR ) method and the proposed weight standard Sir (WSIR) method and principal components (PCA) which is the general method used in reducing dimensions,    (SIR ) and (PCA) is based on the work of linear combinations of a subset of the original explanatory variables, which may suffer from the problem of heterogeneity and the problem of linear

Diabetes mellitus type 2 [DMT2] is a disturbance of metabolism and complex diseases influenced by environmental, genetic agents, and linked with inflammation, happens when the pancreas either does not use the insulin as it should or the body does not make enough insulin, lead to insulin resistance [IR] alongside with gradual loss of ß-cell secretory ability. The aim of this study was to investigate the role of soluble L-selectin (sL-selectin) in diabetes mellitus type 2 patients in Iraqi Arabs patient. Study includes seventy six Iraqi Arabs patients (male and female) having newly diagnosed type 2 diabetes mellitus (T2DM), with Fifty three Iraqi Arabs healthy subjects matched in age, sex and ethnic group. Patients and healthy subjec

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

Gestational diabetes mellitus (GDM) is a growing health concern that usually appears during the second and third trimester stage of pregnancy and is characterized by carbohydrate intolerance of variable severity. The aim of the present study was to scrutinize the relationship between the G972R polymorphism of the insulin receptor substrate-1 (IRS-1) gene with GDM in the Iraqi female population. One hundred and twenty of blood samples taken from healthy women (control) and women with gestational diabetes mellitus in 3rd trimester stage of pregnancy, fasting blood glucose (FBG) and HbA1c% measured to diagnose GDM, lipid profile (cholesterol, triglyceride, HDL, LDL, and VLDL), insulin concentration, insulin resistance and beta cell function to