The relationship between F2- layer critical frequencies (foF2), total sunspot number (Ri), northern hemisphere sunspot number (Rn) and southern hemisphere sunspot number (Rs) at station located in mid- latitudes on latitude near to latitude of Iraq (Rome station, lat.: 42o N and lon.: 13o E) and for 2003(the descending phase of solar cycle 23) were studied.
This research work aims to know the correlation range between them, through correlation coefficients which correlate between them, and hence, the dependence on that index for predicting F2- layer critical frequencies. When the correlation coefficients between foF2, Ri, Rn and Rs were compared for different seasons of 2003, It is found that, correlation coefficient between foF2 and Ri is higher in Winter and Summer than it between foF2, Rn and foF2, Rs in Winter and Summer, too. While, correlation coefficient between foF2 and Rn is higher in Spring and Autumn than it between foF2, Ri and foF2, Rs in Spring and Autumn, too. Also, it is found that, the correlation coefficients between foF2, Ri and foF2, Rn and foF2, Rs were so small that foF2 values are approximately constant as Ri, Rn and Rs values increased, in Autumn.
A Genetic Algorithm optimization model is used in this study to find the optimum flow values of the Tigris river branches near Ammara city, which their water is to be used for central marshes restoration after mixing in Maissan River. These tributaries are Al-Areed, AlBittera and Al-Majar Al-Kabeer Rivers. The aim of this model is to enhance the water quality in Maissan River, hence provide acceptable water quality for marsh restoration. The model is applied for different water quality change scenarios ,i.e. , 10%,20% increase in EC,TDS and BOD. The model output are the optimum flow values for the three rivers while, the input data are monthly flows(1994-2011),monthly water requirements and water quality parameters (EC, TDS, BOD, DO and
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In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho
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The researcher studied transportation problem because it's great importance in the country's economy. This paper which ware studied several ways to find a solution closely to the optimization, has applied these methods to the practical reality by taking one oil derivatives which is benzene product, where the first purpose of this study is, how we can reduce the total costs of transportation for product of petrol from warehouses in the province of Baghdad, to some stations in the Karsh district and Rusafa in the same province. Secondly, how can we address the Domandes of each station by required quantity which is depending on absorptive capacity of the warehouses (quantities supply), And through r
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