Objectives: The study aims at identifying the nurses’ knowledge about peritoneal dialysis complications, to
construct an education program for nurses in peritoneal dialysis units, to determine the effectiveness of the
education program upon the nurses' knowledge about complications of peritoneal dialysis, and to identify the
relationship between the nurses’ knowledge and their demographic characteristics of level of education and
years of experience.
Methodology: A quasi-experimentai study was carried out at the peritoneal dialysis units of Baghdad teaching
hospitals, from April 2004 to April 2006.
٨ purposive sample of (50) nurse was selected from Baghdad teaching hospitals. These nurses working at the
peritoneal dialysis units to provide nursing care for a patient who has undergone peritoneal dialysis.
The data were collected through the use of constructed questionnaire, which comprised of (56) item distributed
in (7)sections.
The reliability of the instrument was determined through test-retest and the validity through a panel study of
experts.
The data were analyzed through the application of descriptive statistical analysis that included (frequency,
mean, mean of scores, standard deviation, and percentage) and the application of inferential statistical analysis
that included (test for independent sample, test for dependent sample pre-test and post-test, pearson
correlation coefficient, Chi-square, and analysis of variance for the difference between pre-test, post-testi, and
post-test2 of the study group).
Results: The results of the study indicated that the education program had a positive impact on the knowledge
of nurses in peritoneal dialysis units regarding peritoneal dialysis complications and significant association was
identified between the nurses' knowledge relation to their education level at the study group (pre-test).
Recommendations: Based on the result of the study, the study recommended that Nurses who are working at
the peritoneal dialysis unit should be encouraged to participate in education program and training session for
their benefit of their practice in such units, in service continuing education program should be presented to
nurse at the peritoneal dialysis unit on a regular base to maintain their level of knowledge.
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A simple, environmental friendly and selective sample preparation technique employing porous membrane protected micro-solid phase extraction (μ-SPE) loaded with molecularly imprinted polymer (MIP) for the determination of ochratoxin A (OTA) is described. After the extraction, the analyte was desorbed using ultrasonication and was analyzed using high performance liquid chromatography. Under the optimized conditions, the detection limits of OTA for coffee, grape juice and urine were 0.06 ng g−1, 0.02 and 0.02 ng mL−1, respectively while the quantification limits were 0.19 ng g−1, 0.06 and 0.08 ng mL−1, respectively. The recoveries of OTA from coffee spiked at 1, 25 and 50 ng g−1, grape juice and urine samples at 1, 25 and 50 ng mL
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A chemometric method, partial least squares regression (PLS) was applied for the simultaneous determination of piroxicam (PIR), naproxen (NAP), diclofenac sodium (DIC), and mefenamic acid (MEF) in synthetic mixtures and commercial formulations. The proposed method is based on the use of spectrophotometric data coupled with PLS multivariate calibration. The Spectra of drugs were recorded at concentrations in the linear range of 1.0 - 10 μg mL-1 for NAP and from 1.0 - 20 μg mL-1 for PIR, DIC, and MEF. 34 sets of mixtures were used for calibration and 10 sets of mixtures were used for validation in the wavelength range of 200 to 400 nm with the wavelength interval λ = 1 nm in methanol. This method has been used successfully to quant
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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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