The study in duded isolation and identification of microbial isolates from oral cavity to 10 volunteers, diagnosed within the three groups: Staphylococcus aureus, Staphylococcus epidermidis, Streptococcus spp. and Candida albicans . The sensitivity test of all isolates bacteria Streptococcus spp. , S. aureus and S. epidermidis showed high resistance to Ampicillin(100)%,followed Methicillin (88.88)% and Amoxicillin / clavulanic acid(77.77)%, while the resistance for each of Vancomycin and Amoxicillin were (66.66)%, and the resistance to Erythromycin and Pencillin (55.55)% to each of them. The results showed less resistance to Trimethoprim (22.22)% and Cefalotine (11.11)% of all bacteria isolate. Investigation of the pre

Density functional theory (DFT) with B3LYP level and 6-311G[Formula: see text] basis sets for light atoms like N and O and SDD basis sets for heavy atoms like Sn is used to examine the interaction of tin dioxide nanocrystals with nitrogen dioxide as a function of temperature from 273[Formula: see text]K to 373[Formula: see text]K through a Gaussian 09W software program. Gibbs free energy, enthalpy, and entropy of activation and reaction are calculated. The situation of transition of SnO₂ clusters toward nitrogen dioxide is investigated. According to the findings, the activation energy of SnO₂ clusters with nitrogen dioxide increases as the temperature rises (in negative value). Gauss view 0

Objective: To review and see the pattern of histopathological diagnoses of one year appendectomy specimens.
Methodology: This retrospective study was carried in Sulaimani Teaching Hospital over the period of one year (from 1st
of January to 31st of December 2009). All pathological reports were reviewed retrospectively for patient’s age, sex,
histopathological diagnosis and operative findings (if present). Histopathological diagnoses then were classified into
either positive or negative for acute inflammation. Any associated findings or any surgical specimen removed with the
appendix was recorded. The obtained data were analyzed by using the statistical package social sciences (SPSS) version
19; with Chi square to test

Abstract

This paper concerned with study the effect of a graphite micro powder mixed in the kerosene dielectric fluid during powder mixing electric discharge machining (PMEDM) of high carbon high chromium AISI D2 steel. The type of electrode (copper and graphite), the pulse current and the pulse-on time and mixing powder in kerosene dielectric fluid are taken as the process main input parameters. The material removal rate MRR, the tool wear ratio TWR and the work piece surface roughness (SR) are taken as output parameters to measure the process performance. The experiments are planned using response surface methodology (RSM) design procedure. Empirical models are developed for MRR, TWR and SR, using the analysis

      Faces of the individual in his life many stressful events, which includes expertise undesirable, and events may involve a lot of sources of tension and the risk factors and threats in all areas of life, and this would make the stressful events play a role in the genesis of many diseases physical.            

The high blood pressure is one of the most Actual manifestations of mental stress in the present scale physical disorders which may frequently in men relative to women, which may be caused by spasms in the blood vessels.      

Quantum dots of CdSe, CdS and ZnS QDs were prepared by chemical reaction and used to fabricate organic quantum dot hybrid junction device. QD-LEDs were fabricated using layers of ITO/TPD: PMMA/CdSe/Alq₃, ITO/TPD: PMMA/CdS/Alq₃ and ITO/TPD: PMMA/ZnS/Alq₃ devices which prepared by phase segregation method. The hybrid white light emitting devices consists, of three-layers deposited successively on the ITO glass substrate; the first layer was of N, N’-bis (3-methylphenyl)-N, N’-bis (phenyl) benzidine (TPD) polymer mixed with polymethyl methacrylate (PMMA) polymers. The second layer was QDs while the third layer was tris (8-hydroxyquinoline) aluminium (Alq₃

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca