This work reports the development of an analytical method for the simultaneous analysis of three fluoroquinolones; ciprofloxacin (CIP), norfloxacin (NOR) and ofloxacin (OFL) in soil matrix. The proposed method was performed by using microwave-assisted extraction (MAE), solid-phase extraction (SPE) for samples purification, and finally the pre-concentrated samples were analyzed by HPLC detector. In this study, various organic solvents were tested to extract the test compounds, and the extraction performance was evaluated by testing various parameters including extraction solvent, solvent volume, extraction time, temperature and number of the extraction cycles. The current method showed a good linearity over the concentration ranging from

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

This paper deals with, Bayesian estimation of the parameters of Gamma distribution under Generalized Weighted loss function, based on Gamma and Exponential priors for the shape and scale parameters, respectively. Moment, Maximum likelihood estimators and Lindley’s approximation have been used effectively in Bayesian estimation. Based on Monte Carlo simulation method, those estimators are compared in terms of the mean squared errors (MSE’s).

The main objective of this study is to characterize the main factors which may affect the behavior of segmental prestressed concrete beams comprised of multi segments. The 3-D finite element program ABAQUS was utilized. The experimental work was conducted on twelve simply supported segmental prestressed concrete beams divided into three groups depending on the precast segments number. They all had an identical total length of 3150mm, but each had different segment numbers (9, 7, and 5 segments), in other words, different segment lengths. To simulate the genuine fire disasters, nine beams were exposed to high-temperature flame for one hour, the selected temperatures were 300°C (572°F), 500°C (932°F) and 700°C (1292°F) as recomm

Objective Thalassemic patients present with multiple immune abnormalities that may predispose them to oral Candida, however this has not been investigated. The aim of this study was to assess oral candidal colonization in a group of patients with β-thalassemia major both qualitatively and quantitatively. Study design The oral mycologic flora of 50 β-thalassemia major patients and 50 age- and sex-matched control subjects was assessed using the concentrated oral rinse technique. Candida species were identified using the germ tube test and the Vitek yeast identification system. Results Oral Candida was isolated from 37 patients (74%) and 28 healthy subjects (56%; P = .04). The mean candidal count was significantly higher in thalassemic patie

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