Objective: The study aims to determine the effectiveness of the continuing nursing education
program on nursing staffs knowledge in kidney transplantation unit and to find out the relationship
between nursing staffs knowledge and demographic characteristics (age, gender, education level, and
years of experiences in kidney transplantation unit).
Methodology: A quasiexperemental design (One-group Pretest - Posttest design) was carried out in
kidney transplantation units at Baghdad Teaching Hospitals, from December 2011 to July 2012. A nonprobability
(purposive sample) of (16) nurses were selected from kidney transplant units at Baghdad
teaching hospitals, the choice was based on the study criteria. The data were collected through the
use of constructed questionnaire and consist from two major parts, part one consist of demographic
characteristics contain (9) and part two consist of (58) items of a multiple choice questions
distributed in (8) major sections. Validity of the instrument was determined through a panel of (8)
experts, and reliability through a pilot study. The data were analyzed through the application of
descriptive and inferential statistical analysis procedures.
Results: The findings of the present study indicate that the continuing nursing education program
was effective on knowledge improvement of the participant’s nurses. The total percent of the
improvements resulted by the effects of applying the continuing nursing education program was
(43.31%). And there was a non-significant relationship between nurse’s knowledge and demographic
characteristics (age, gender, education level, and years of experiences in kidney transplantation unit).
Recommendation: Based on the result of the present study the researcher recommends to carrying
out additional studies on application of nursing education programs about nurses practice on kidney
transplantation in kidney transplant units, and nurses should be encouraged to participate in
continuing education programs and training sessions about kidney transplantation.
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Three mesoporous silica with different functional group were prepared by one-step synthesis based on the simultaneous hydrolysis and condensation of sodium silicate with organo - silane in the presence of template surfactant polydimethylsiloxane - polyethyleneoxide (PDMS - PEO). The prepared materials were characterized by Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), atomic force microscopy (AFM) and nitrogen adsorption/desorption experiments. The results indicate that the preparation of methyl and phenyl functionalized silica were successful and the mass of methyl and phenyl groups bonded to the silica structure are 15, 38 mmol per gram silica. The average diameter of the silica particles are 103.51,
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The purpose of my thesis is to synthesis two new bidentate ligands which were used to prepare series of metal complexes by reacting the ligands with (M+2 = Mn, Co, Ni, Cu, Cd and Hg) Succinyl chloride was used as starting material to synthesis two bidentate ligands (L1) and (L2) by reaction it with 4-chloroaniline (L1) and (4-aminoacetophenone) (L2) in dichloromethane as a solvent, that are: (L1) = N1,N4-bis (4-chloro phenyl ) succinamide (L2) =N1,N4-bis(4-acetylphenyl)succinamide The new ligands were characterize by using spectroscopic study (Fourier-transform infrared spectroscopy (FT-IR), electronic spectra ( UV-Vis) ,nuclear magnetic resonance(1H,13C-NMR), Mass spectra
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne
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To assess the contribution of Doppler broadening and examine the
Compton profile, the Compton energy absorption cross sections are
measured and calculated using formulas based on a relativistic
impulse approximation. The Compton energy-absorption cross
sections are evaluated for different elements (Fe, Zn, Ag, Au and Hg)
and for a photon energy range (1 - 100 keV). With using these crosssections,
the Compton component of the mass–energy absorption
coefficient was derived, where the electron momentum prior to the
scattering event caused a Doppler broadening of the Compton line.
Also, the momentum resolution function was evaluated in terms of
incident and scattered photon energy and scattering angle. The res