The current study was conducted on 504(Ros-308) broiler chicks reared in Animal farms belong to College of Agriculture, University of Baghdad during the period 28/9/2017- 9/11/2018 to determine the effect of ginseng additive on the performance of chicks. Results of study showed a significant effect (p≤0.05) of exposure period an Red blood cells, 3.56×106ml3 of blood was in bird, which exposure to 2hr at heat shock. In 42 day at age 106 ×38 ml3 of blood can noticed in the blood at birds, which exposure to 2hr in 21-42 days at 3 days of age. No significant effect at ginseng on blood cells. The results showed a significant effect (p≤0.05) of interaction on red blood cells at 21 and 42 days of age and the average cells between these ages

Energy use is second to staffing in building operating costs. Sustainable technology in the energy sector is based on utilizing renewable sources of energy such as solar, wind, glazing systems, insulation. Other areas of focus include heating, ventilation and air conditioning; novel materials and construction methods; improved sensors and monitoring systems; and advanced simulation tools that can help building designers make more energy efficient choices. The objective of this research is studying the effect of insulations on energy consumption of buildings in Iraq and identifying the amount of energy savings from application th

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

Gingival crevicular fluid (GCF) may reflect the events associated with orthodontic tooth movement. Attempts have been conducted to identify biomarkers reflecting optimum orthodontic force, unwanted sequallea (i.e. root resorption) and accelerated tooth movement. The aim of the present study is to find out a standardized GCF collection, storage and total protein extraction method from apparently healthy gingival sites with orthodontics that is compatible with further high-throughput proteomics. Eighteen patients who required extractions of both maxillary first premolars were recruited in this study. These teeth were randomly assigned to either heavy (225g) or light force (25g), and their site specific GCF was collected at baseline and aft

This work describes the development of new spectrophotometric techniques for 3-aminophenol assessment. The first technique involves using benzidine in an alkaline solution to convert 3-aminophenol into a colored complex. The produced complex has a red color with an absorbance of 462 nm. Between the concentration range 5–14 μg mL−1, Beer's law is obeyed with a correlation coefficient (R2) of 0.99781, a limit of detection (LOD) of 0.0423 μg mL−1, and a limit of quantification (LOQ) of 0.1411 μg mL−1. The recovery was between 87.2–95.43%, the relative standard deviation (%RSD) was 2.40–3.31% and the molar absorptivity was 3.545 × 103 L mol−1 cm−1. Secondly, cloud point extraction (CPE) was used to determ

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

Organic light emitting diodes (OLEDs) were fabricated containing host molecule of conjugated polymer MEH-PPV poly (2-methoxy-5-(2'-ethylhexyloxy)-1,4-phenlenevinylene) which was dissolved in chloroform solvent and doped with guest molecules MWCNTs that were dissolved in DMF solvent with different volume ratios 0.05 and 0.15, nanocomposite system which acts as active layer in OLEDs device. The Poly(3,4-ethylenedioxythiophene):poly-(styrenesulfonate) "PEDOT:PSS" and Tris98-hydroxyquinoline) aluminum "Alq3" as hole transport layer HTL and electron transport layer ETL were used respectively, to enhanced the injection charge from the electrodes to the active layer. Spin coating method used for achieving facile and low cost OLED. The absorption s

The goal of this study was to investigate the protein peroxidation role by measuring serum levels of advanced oxidation protein products (AOPP) in type 2 diabetic patients with or without retinopathy and comparing them to controls to see if circulating AOPP levels can be used as a detection biomarker for DR. And see which of the two widely used antidiabetic treatment groups had the most impact on this oxidative stress marker. The groups were divided into two subgroups: 1) 70 type 2 diabetic patients (36 male, 34 female), 35 with diabetic retinopathy (DR) and 35 with no evidence of DR, and 2) non-diabetic controls (11 male, 9 female) were chosen from Ibn AL-Haitham Hospital for Ophthalmology and a Specialized Center for Endocrinology and Dia