One of the goals of adding adjuvants to agricultural spray solutions is to enhance the droplet size characteristics of this spray. Droplet size, in turn, has an influence in the deposited spray quality, in addition to the drift and losses of spray to off-target places. The aim of this research was to evaluate the effect of adding adjuvants to two types of water from different sources on the droplet size characteristics. Two types of adjuvants were employed in the tests: the active substance content of the first adjuvant was a 50% aqueous solution of sodium salt of alkylbenzenesulfonic acid—10% (HY), whereas the second was from rapeseed oil (natural origin)—85% (OL). Both adjuvants were tested in two concentrations: the first was

Background: Helicobacter pylori are important gastrointestinal pathogen associated with gastritis, peptic ulcers, and an increased risk of gastric carcinoma. There are several popular methods for detection of H. pylori (invasive and non-invasive methods) each having its own advantages, disadvantages, and limitations, and by using PCR technique the ability to detect H. pylori in saliva samples offers a potential for an alternative test for detection of this microorganism. Materials and methods: The study sample consists of fifty participants of both genders, who undergo Oesophageo-gastrodudenoscopy at the Gastroenterology Department of Al-Kindy Teaching Hospital Baghdad/ Iraq, during five months period from January 2014 to May 2014. They we

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

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

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

م. د. ولاء طارق حميد, Al. Qadisiya journal for the Sciences of Physical Education, 2017

Background: This study aimed to evaluate the effect addition of polyester fibers on the some mechanical properties of heat cured acrylic resin (implant strength, flexural strength and hardness) Materials and methods: Ninety specimens were used in the study. Thirty specimens were used for impact strength measurements (80mm X 10mm X 4mm) length, width and thickness respectively. The specimens divided into three test groups (n=10), first group formed from heat cure acrylic resin without fiber reinforcement. Second group was formed from heat cure acrylic resin was reinforced with 2 mm length polyester fiber and third group was formed from heat cure acrylic resin reinforced with 4mm length polyester fiber, impact strength measured by impact test

This study was conducted to determine the effect of vitamin A ( 10 mg/kg ) on avearage testis weight and sexual glands ( Prostate and Seminal Vesicle ) for albino male mice treated with Hexavalent chromium ( 1000 ppm ) .The current study 40 mice were divided into fife groups : 1st group treated with distilled water and considered an control group (C) / the 2nd group treated with sesame oil ( T1) / 3rd group was givin hexavalent chromium ( 1000 ppm ) (T2) / 4th group treated with vitamin A ( 10 mg / kg ) and exposed to hexavalent chromium ( 1000 ppm ) (T3) / 5th group treated with vitamin A ( 10 mg kg ) (T4) . The expermint lasted 35 day . the results showed a significant ( P ? 0.05 ) decrease in avearage testis weight and sexual glan