As the prices of the fuel and power had fluctuated many times in the last decade and new policies appeared and signed by most of the world countries to eliminate global warming and environmental impact on the earth surface and humanity exciting, an urgent need appeared to develop the renewable energy harnessing technologies on the short-term and long-term and one of these promising technologies are the vertical axis wind turbines, and mostly the combined types. The purpose of the present work is to combine a cavity type Savonius with straight bladed Darrieus to eliminate the poor self-starting ability for Darrieus type and low performance for Savonius type and for this purpose, a three-bladed Darrieus type with symmetrical S1046 airfoil was tested experimentally and numerically at different wind speeds (4.5 m/s, 8 m/s and 10 m/s) and it showed a poor self-starting ability at low wind speed although its higher performance at high wind speed. However when adding the cavities in two setup configuration and testing it at the same conditions, it was found that when adding the cavities as reversed cups in the core of the turbine, the performance increased and the power coefficient reached a maximum value at 10 m/s wind speed and it was observed to be 0.0914 , but when the solidity increased by adding three cavities, the performance was higher at low wind speed (4.5 m/s) but it tragically decreased at higher wind speed which indicates that the performance depends on the solidity and the turbine configuration. On the other hand, the numerical simulation showed a good match with the experimental results although it under-predicted the performance.
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
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Background: Essential oils extracted from plants have been widely used in antimicrobial activity, particularly the Callistemon viminalis, with a high number of essential oils extracted. Objectives: To identify the chemical composition of essential oil derived from Callistemon viminalis and evaluates its antimicrobial activity against selected bacterial and fungal strains. Subjects and methods: During the study, the antimicrobial activity of different selected essential oils on some bacteria (Escherichia coli, Pseudomonas aeruginosa, Salmonella enteritidis, Staphylococcus aureus, and Streptococcus pneumonia) and fungus (Candida albicans) was evalua |
Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification. Different
... Show MoreThe current study was conducted to demonstrate the effects of air pollution on different biochemical parameters inCitrus aurantium plant and calculation the Air Pollution Tolerance Index (APTI). Five sites were selected tocollect plant leaves, four of them within the city of Baghdad, namely Al-Jadriya, Al-Andlous, Al-Doura and closeto the private generators to represent the urban areas and Abu Ghraib site to represent the rural area. Seasonalsamples were collected for the period from October 2016 to June 2017. Eleven biochemical parameters totalchlorophyll, ascorbic acid, leaf extract pH, relative water content, total nitrogen, total protein content, total sugarcontent, proline, electrical conductivity, cadmium and lead. The results of Citr
... Show MoreRemoval of heavy metal ions such as, cadmium ion (Cd 2+) and lead ion (Pb 2+) from aqueous solution onto Eichhornia (water hyacinth) activated carbon (EAC) by physiochemical activation with potassium hydroxide (KOH) and carbon dioxide (CO2) as the activating agents were investigated. The Eichhornia activated carbon was characterized by Brunauer Emmett Teller (BET), Fourier Transform Infrared spectroscopy (FTIR), and Scanning Electron Microscopy (SEM) techniques. Whereas, the effect of adsorbent dosage, contact time of pH, and metal ion concentration on the adsorption process have been investigated using the batch process t
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
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