Abstract:
Objective: To self-evaluate the effect of SBAR (Situation, Background, Assessment, and Recommendation) educational program on nurse and midwives practices in maternal health report documentation accuracy.
Methods: A quasi- experimental design was carried with the application of pre- post test for nurses and midwives’ knowledge and practices regarding SBAR communication tool. The study was held in Al-Elwia maternity teaching hospital, Al –Karckh maternity hospital and Al-Yarmouk teaching Hospital. purposive sample as it was convenient with inclusion criteria consisted of (84) nurse and midwives. The questionnaire comprised of demographic data, nurses- midwives practices of SBAR using (5) level Likert scale for assessme

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,

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 ‎

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