This research dealt with the process of reducing costs through some strategic methods of management accounting targeted cost analysis unassembled and Alkeisen, where he focused this research through his theory on a review of some administrative accounting strategic technologies, while the second practical side through the application of targeted cost analysis unassembled and Alkeisen, acquired Search importance of focusing on the decisions to cut costs, through the use of some administrative accounting strategic methods and this we can unassembled analysis, continuous improvement, and the cost of quality) when applied quality, "in light of this has been reached to a set of conclusions that the most important of the  company's relian

Background: Moral distress, as one of the most important issues in the nursing profession, can negatively affect the healthcare system Objectives: To examine the effect of moral distress on decision making. Methodology: A descriptive cross-sectional study a non-probability sampling method was used and a total sample collected was (126) ICU nurses Result: Moral distress among ICUs nurses were moderate and decision making was good Conclusion: Although nurses in ICUs perceive good decision making but suffered from moderate MD which affected their decision making. MD is still an important issue that nurses may face in ICUs Conclusion: Although nurses in ICUs perceive good decision making but suffered from moderate MD which affected the

RA is a complex chronic disease, primarily affects the lining of the synovial joints and can cause progressive disability, premature death, and socioeconomic burdens. The clinical manifestations of symmetrical joint involvement include arthralgia, swelling, redness, and even limiting the range of motion. The pathogenesis is not known therefore the study included Rheumatoid arthritis (RA) is one of systemic inflammatory diseases that characterized by a progressive disabling course. The study included patients suffering from RA and as healthy controls. immunological and genetic factor were evaluated in each subject by using serum level of cytokine and genetic factor. The etiopathology of the disease is not well known for this reason the study

Rapid and accurate identification of Methicillin Resistant Staphylococcus aureus is essential in limiting the spread of this bacterium. The aim of study is the detection of Methicillin Resistant Staphylococcus aureus (MRSA) and determining their susceptibility to some antimicrobial agent. A total of fifty clinical Staphylococcus aureus, isolated from the nose of health work staff in surgery unit of Kalar general hospital and from ear of patients attended to the same hospital. The susceptibilities of isolates were determined by the disc diffusion method with oxacillin (1 ?g) and cefoxitin (30 ?g), and by the mannitol salt agar supplemented with cefoxitin (MSA-CFOX), susceptibilities of isolates to other antimicrobial agent were determined b

Adverse drug reactions (ADR) are important information for verifying the view of the patient on a particular drug. Regular user comments and reviews have been considered during the data collection process to extract ADR mentions, when the user reported a side effect after taking a specific medication. In the literature, most researchers focused on machine learning techniques to detect ADR. These methods train the classification model using annotated medical review data. Yet, there are still many challenging issues that face ADR extraction, especially the accuracy of detection. The main aim of this study is to propose LSA with ANN classifiers for ADR detection. The findings show the effectiveness of utilizing LSA with ANN in extracting AD

Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob