The herein research was carried out in order to identified the presence of bacteria in cervix and uterine lumen in Iraqi cattle during the different estrus phase with focusing on Protus and E coli. Estrus phases were determined by the structures which found on ovary (follicular growth for pro-estrus, mature growing follicle for estrus, hemorrhagic corpus luteam for meta-estrus and active corpus luteam for di-eatrus). Forty cervical swabs (ten for each estrus phase) and forty uterine swabs (ten for each estrus phase) were taken from macroscopically healthy reproductive animals after slaughtering and cultivated on nutrient agar and blood agar, the bacterial isolation were identified with biochemical teats. The present study found that

Glassy carbon electrode (GCE) was modified with carbon nanotubes CNT and C60 by attachment and solution evaporation techniques, respectively. CNT/Li+/GCE and C60/Li+/GCE were prepared by modifying CNT/GCE and C60/GCE in Li+ solution via cyclic voltammetry (CV) potential cycling. The sensing characteristics of the modified film electrodes, demonstrated in this study for interference of Mn2+ in different heavy metals ion esp. Hg2+, Cd2+ and Cu2+. The interfering effect was investigated that exert positive interference on the redox peaks of Mn2+. The modification of GCE with nano materials and Li+ act an enhancement for the redox current peaks to observe the effect of interference for Mn2+ in 1:1 ratio with different heavy metals ion.

The new organic reagent 2-[Benzo thiazolyl azo]-4,5-diphenyl imidazole was prepared and used as complexing agent for separation and spectrophotometric determination of Cu2+ ion in some samples include plants, soil, water and human blood serum. Initially determined all factors effect on extraction method and the results show optimum pH was (pHex=9), optimum concentration was 40?g/5mLCu2+ and optimum shaking time was (15min.), as well stoichiometry study appears the complex structure was 1:1 Cu2+: BTADPI. Interferences effect of cations were studied. Synergism effect shows MIBK gave increasing in distribution ratio (D). Organic solvent effect appears there is no any linear relation between dielectric constant for organic solvent used and dis

Background: The aim of this study was to evaluate the push-out bond strength of four different obturation materials to intraradicular dentin and to determine the failure mode. Materials and method: forty straight palatal roots of the maxillary first molars teeth were used in this study, the roots were instrumented using crown down technique and rotary EndoSequence system, the roots were randomly divided into four groups according to the materials used for obturation (n=10).Group (1): AH Plus sealer and gutta-percha. Group (2): Activ GP glass ionomer sealer and Activ GP gutta-percha (Activ GP system). Group (3): Bioceramic sealer and Bioceramic gutta-percha. Group (4): GuttaFlow2 sealer and gutta-percha. For all groups single cone obturatio

In this work, a novel single-slope solar distillator of floating perforated absorber inserted with wicks (cotton ribbons), and a stepped distillator are designed and manufactured with the aim of developing the conventional distillator. They are examined experimentally at Baghdad, Iraq (33.3°N Latitude, 44.4°E Longitude) in order to enhance the freshwater productivity and the efficiency of the conventional distillator. Results showed that the daily productivity and efficiency of the stepped distillator are higher than that for conventional solar distillator by 30% and 36.19% respectively. The daily productivity and thermal efficiency for the distillator with the floating absorber are higher than that for the conventional distillator by 16%

<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the c