Objective(s): To evaluate nurses' practices who work in respiratory intensive care units to control the
complications of patients admitted at this unit and determine the relationship between nurses' sociodemographic
characteristics and their practices.
Methodology: A descriptive study was carried out at Respiratory Care Unit at Baghdad teaching hospitals that
started from February 22th, 2013 to August 30th, 2013. A purposive "non-probability" sample of (70) nurses who
work in Respiratory Care Unit was selected from Baghdad teaching hospitals. The data were collected through the
use of constructed questionnaire that consists of two parts; (l) Demographic data form that consists of 7items and
(2) nurses' practice form that consists of 4sections (112) items. Data were collected by means of direct observation
technique with the nurses. The reliability of the questionnaire was determined through a pilot study that was
carried out through the period from January 6th 2013 through February 10th 2013.Descriptive statistical measures
(frequency, percent, mean of score, Standard deviation and Weighted mean) and inferential statistical (Regression)
was used for the data analysis.
Result: The findings of the study indicated that there is a practice deficit of Respiratory Care Unit nurses in some
aspects relative to control of patient complication. significant relationship was found between nurses' practice and
their (age, gender, marital status, level of education, Years of working in nursing, Years of working in RCU,
Participation in training courses, Number of training courses related to RCU, Place of training courses in RCU,
Duration of training courses in RCU, and in Respiratory Care Unit Nurses' practice to control of patient
complication in Respiratory Care Unit.
Recommendations: The researchers recommend that special training session, concerning patient complication and
standard Respiratory Care Unit nurse practice toward patient complication that should be followed in Respiratory
Care Unit wards and booklets should be designated and presented to all Respiratory Care nurses.
Manganese sulfate and Punica granatum plant extract were used to create MnO2 nanoparticles, which were then characterized using techniques like Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, atomic force microscopy, X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The crystal's size was calculated to be 30.94nm by employing the Debye Scherrer equation in X-ray diffraction. MnO2 NPs were shown to be effective in adsorbing M(II) = Co, Ni, and Cu ions, proving that all three metal ions may be removed from water in one go. Ni(II) has a higher adsorption rate throughout the board. Co, Ni, and Cu ion removal efficiencies were 32.79%, 75
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... Show MoreThe Hubble telescope is characterized by the accuracy of the image formed in it, as a result of the fact that the surrounding environment is free of optical pollutants. Such as atmospheric gases and dust, in addition to light pollution emanating from industrial and natural light sources on the earth's surface. The Hubble telescope has a relatively large objective lens that provides appropriate light to enter the telescope to get a good image. Because of the nature of astronomical observation, which requires sufficient light intensity emanating from celestial objects (galaxies, stars, planets, etc.). The Hubble telescope is classified as type of the Cassegrain reflecting telescopes, which gives it the advantage of eliminating chromat
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... Show MoreMetasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat
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