Objective: The aim of the present study is to assess the practices of nurses towards standard precautions at Azady
Teaching Hospital in the City of Kirkuk.
Methodology: A descriptive study, which uses the assessment approach and it was conducted on nurses from January
18th, 2009 to September 30th, 2009, using non-probability sampling a purposive sample of (37) subject (male and
female nurses) who worked at surgical wards in Azady Teaching Hospital in Kirkuk city was selected. Two study
instruments were utilized for proper data collection [questionnaire and observational checklist]; a questionnaire was
developed for the purpose of the study. It was comprised of two parts which included the nurses' demographic
characteristics and Information related to the setting of the sample (surgical wards). A pilot study was carried out for
the period of January 18th, 2009, to February 2nd, 2009 to determine the observaƟonal checklist reliability through
the use of (Inter–rater). A panel of (19) experts was involved in the determinaƟon of the observaƟonal checklist
content validity. Data were analyzed through the application of descriptive statistical data analysis approach
(frequency, percentage, mean of scores and standard deviation), and inferential data analysis approach (chi-square).
Results: The study findings revealed that the majority (91.9%) of the nurses did not get training sessions regarding
infecƟon control and (83.4%) of them had not parƟcipated conƟnuous learning about infecƟon control. According to
the level of practices towards standard precautions, it has revealed that poor practices of standard precautions by
surgical wards nurses had shown in surgical wards.
Recommendations: The study recommended that special education programs can be designed and presented to the
surgical ward nurses and to all the hospital staff in order to increase their awareness towards infection.
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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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Release of industrial effluents comprising dyes in water bodies is one of the foremost causes of water pollution. Therefore, the proper and proficient treatment of these dyes contaminated left-over material before their release is crucial. Herein, an eco-friendly biological macromolecule Gum-Acacia (GA) integrated Fe3O4 nanoparticles composite hydrogel was manufactured via co-precipitation technique for effective adsorption of Congo red (CR) dye existing in water bodies. The as-prepared magnetic GA/Fe3O4 composite hydrogel was characterized by FTIR, XRD, EDX, VSM, SEM, and BET techniques. These studies discovered the fruitful fabrication of biodegradable magnetic GA/Fe3O4 composite hydrogel possessing porous structure with large surface are
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Detecting the optimum layer for well placement, which requires a diverse assortment of tools and techniques, represents a significant challenge in petroleum studies due to its critical impact on minimizing drilling costs and time. This study aims to evaluate integrated geological, petrophysical, seismic, and geomechanical data to identify the optimum zones for well placement. Three different reservoirs were analyzed to account for lateral and vertical variations in reservoir properties. The integrated data from these reservoirs provides many tools for reservoir development, especially to detect appropriate well placement zones based on evaluations of reservoir and geomechanical quality. The Mechanical Earth Model (MEM) was construct
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New derivatives of Schiff bases were synthesized from the aldehyde derivative (Ma2) which was produced by reacting the mefenamic acid (Ma) with thionyl chloride to obtain the acid halide derivative (Ma1). Compound (Ma1) was dissolved in DMF and mixed with p-hydroxybenzaldehyde which was previously dissolved with pyridine to obtain the aldehyde derivative (Ma2). In the final step, derivatives of Schiff bases were synthesized by reacting the aldehyde (Ma2) with a number of different aromatic primary amines in the presence of glacial acetic acid to obtain the new derivatives Ma [3-10]. The new prepared compounds were characterized by melting points and with spectral data FT-IR, 13C-NMR and 1H-MNR (some of them). The vital effectiven
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