The aim of study to evaluated cinnamic acid and its activity on complete blood count(RBC,WBC,HG,HCV,MCH,MCHC and Plat.)and removed the cytoxan damage which caused bone marrow failure and leukemia and other that due to linked the cytoxan in 7- nitrogen of guanine based of DNA that lead to dead cells. Two concentration from pure cinnamic acid (5.6, 2.8 mg ? mice weight) in first step to choice the perfect concentration in comparison with each negative control ,positive control of cytoxan and the comparison group represent vitamin C. The second step to understand cinnamic acid mechanism activity towards cytoxan by used pre- cytoxan and post – cytoxan in interaction with perfect concentration of cinnamic acid dose (2.8 mg ? mice we

Cladophora and Spirulina algae biomass have been used for the removal of Tetracycline (TC) antibiotic from aqueous solution. Different operation conditions were varied in batch process, such as initial antibiotic concentration, different biomass dosage and type, contact time, agitation speed, and initial pH. The result showed that the maximum removal efficiencies by using 1.25 g/100 ml Cladophora and 0.5 g/100 ml Spirulina algae biomass were 95% and 94% respectively. At the optimum experimental condition of temperature 25°C, initial TC concentration 50 mg/l, contact time 2.5hr, agitation speed 200 rpm and pH 6.5. The characterization of Cladophora and Spirulina biomass by Fourier transform infrared (FTIR) indicates that the presenc

The increased use of hybrid PET /CT scanners combining detailed anatomical information along withfunctional data has benefits for both diagnostic and therapeutic purposes. This presented study is to makecomparison of cross sections to produce 18F , 82Sr and68Ge via different reactions with particle incident energy up to 60 MeV as a part of systematic studies on particle-induced activations on enriched natNe, natRb, natGa 18O,85Rb, and 69Ga targets, theoretical calculation of production yield, calculation of requiredtarget and suggestion of optimum reaction to produce: Fluorine-18 , Strontium-82 andGermanium-68 touse in Hybrid Machines PET/CT Scanners.

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.