Objective(s): To assess nurses' practices for neurological unconscious patients in intensive care units.
Methodology: A descriptive study was conducted that included (50) nurse who are working in intensive care
units in hospitals and departments of the nervous system in (4) hospitals (neuroscience hospital, teaching
neurosurgical hospital, surgical specialist hospital, and sheck zaied hospital) in Baghdad city from March, 30th
,
2009 to July, 30th 2009 for the purpose of assessing their skills towards unconscious patients. A purposive "nonprobability
sample" was selected that consisted of (50) nurse who are working in intensive care units. A
questionnaire format and observational checklist were used which consist of (2) parts, the first part includes
demographic information of the sample and the second part includes tools which consists of the observation
(7) parts distributed to (64) items. Reliability and validity of questionnaire and observational checklist was
estimated through a pilot study and a panel of expert. The data were analyzed by using descriptive statistical
measures which included frequencies, percentages, and standard deviation, as well as the use of inferential
statistical measures which include the correlation coefficient and coefficient of probability.
Results: The results revealed the inadequacy of skills of nurses towards unconscious patients' care in the field
of airway care, observation and record level of consciousness, hygiene, stimulate the senses and care of urinary
and digestive tract and the adequacy of skills of nurses in the field of measuring and recording vital signs,
nutrition, and the environment.
Recommendations: Based on the results of research, the study recommends initiating training courses in the
field of intensive care and care of for unconscious patients as well as to design a special mini-booklet for the
purpose of care for unconscious patients.
<p>The directing of a wheeled robot in an unknown moving environment with physical barriers is a difficult proposition. In particular, having an optimal or near-optimal path that avoids obstacles is a major challenge. In this paper, a modified neuro-controller mechanism is proposed for controlling the movement of an indoor mobile robot. The proposed mechanism is based on the design of a modified Elman neural network (MENN) with an effective element aware gate (MEEG) as the neuro-controller. This controller is updated to overcome the rigid and dynamic barriers in the indoor area. The proposed controller is implemented with a mobile robot known as Khepera IV in a practical manner. The practical results demonstrate that the propo
... Show MoreA few examinations have endeavored to assess a definitive shear quality of a fiber fortified polymer (FRP)- strengthened solid shallow shafts. Be that as it may, need data announced for examining the solid profound pillars strengthened with FRP bars. The majority of these investigations don't think about the blend of the rigidity of both FRP support and cement. This examination builds up a basic swagger adequacy factor model to evaluate the referenced issue. Two sorts of disappointment modes; concrete part and pulverizing disappointment modes were examined. Protection from corner to corner part is chiefly given by the longitudinal FRP support, steel shear fortification, and cement rigidity. The proposed model has been confirmed util
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... Show MoreHigh peak to average power ration (PAPR) in orthogonal frequency division multiplexing (OFDM) is an important problem, which increase the cost and complexity of high power amplifiers. One of the techniques used to reduce the PAPR in OFDM system is the tone reservation method (TR). In our work we propose a modified tone reservation method to decrease the PAPR with low complexity compared with the conventional TR method by process the high and low amplitudes at the same time. An image of size 128×128 is used as a source of data that transmitted using OFDM system. The proposed method decrease the PAPR by 2dB compared with conventional method with keeping the performance unchanged. The performance of the proposed method is tested with
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Explainable Artificial Intelligence (XAI) techniques enable transparency and trust in automated visual inspection systems by making black-box machine learning models understandable. While XAI has been widely applied, prior reviews have not addressed the specific demands of industrial and medical inspection tasks. This paper reviews studies applying XAI techniques to visual inspection across industrial and medical domains. A systematic search was conducted in IEEE Xplore, Scopus, PubMed, arXiv, and Web of Science for studies published between 2014 and 2025, with inclusion criteria requiring the application of XAI in inspection tasks using public or domain-specific datasets. From an initial pool of studies, 75 were included and categorized in
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