This study aims to explore the potential mediation role of person-centeredness between the effects of the work environment and nurse reported quality and patient safety. A quantitative cross-sectional survey collected data from 1055 nurses, working in medical and surgical units, in twelve Malaysian private hospitals. The data collection used structured questionnaires. The Hayes macro explored the mediation effect of person-centeredness between the associations of work environment dimensions and care outcomes, controlling nurses’ demographics and practice characteristics. A total of 652 nurses responded completely to the survey (61.8% response rate). About 47.7% of nurses worked 7-h shifts, and 37.0% were assigned more than 15 pati

This study was conducted on the effect of the sedimentary source (the sediments coming from both the Iraqi-Iranian borderline and the Tigris river) on the optical and textural features, especially sphericity and roundness of feldspar minerals (potassium and plagioclase types) in soils of the southern part of the alluvial plain. Eight pedons were selected to represent the study area, five of them represented sediments coming from the borderline, which included pedons of (Badra, Taj Al-Din, Al-Shihabi, Jassan, and Galati), while two of them represent the sediments of the Tigris River (Essaouira, Al-Dabouni), the pedon of Ali Al-Gharbi was represented the mixing area of sediments of all the floods coming from the borderline and the sediments o

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

The growth of developments in machine learning, the image processing methods along with availability of the medical imaging data are taking a big increase in the utilization of machine learning strategies in the medical area. The utilization of neural networks, mainly, in recent days, the convolutional neural networks (CNN), have powerful descriptors for computer added diagnosis systems. Even so, there are several issues when work with medical images in which many of medical images possess a low-quality noise-to-signal (NSR) ratio compared to scenes obtained with a digital camera, that generally qualified a confusingly low spatial resolution and tends to make the contrast between different tissues of body are very low and it difficult to co

Abstract

 Metal cutting processes still represent the largest class of manufacturing operations. Turning is the most commonly employed material removal process. This research focuses on analysis of the thermal field of the oblique machining process. Finite element method (FEM) software DEFORM 3D V10.2 was used together with experimental work carried out using infrared image equipment, which include both hardware and software simulations. The thermal experiments are conducted with AA6063-T6, using different tool obliquity, cutting speeds and feed rates. The results show that the temperature relatively decreased when tool obliquity increases at different cutting speeds and feed rates, also it

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Administrative leaders conserned to understand the challenges which are faced their organizations and try to assimilate and adapt with the extent that achieves to it efficiency and effective- ess, and standing face to face to faceing any challenge.that threaten it’s existence thro- ugh using modern inputs reached to that level of these  challenges and applied the study on a sample deliberate random from teaching hospitals of the Directorate General for Health Baghdad Karkh, and the Directorate General for Health Baghdad Rusafa and the City of Medicine , The importance of t