Diyala river is the most important tributaries in Iraq, this river suffering from pollution, therefore, this research aimed to predict organic pollutants that represented by biological oxygen demand BOD, and inorganic pollutants that represented by total dissolved solids TDS for Diyala river in Iraq, the data used in this research were collected for the period from 2011-2016 for the last station in the river known as D17, before the river meeting Tigris river in Baghdad city. Analysis Neural Network ANN was used in order to find the mathematical models, the parameters used to predict BOD were seven parameters EC, Alk, Cl, K, TH, NO3, DO, after removing the less importance parameters. While the parameters that used to predict TDS were fourteen parameters pH, DO, BOD, PO4, NO3,Ca, Mg, TH, K, Na, SO4,Cl, EC, Alk. The results indicated that the best correlation coefficient is 86.5% for BOD, and the most important parameter is Chloride Cl, and the best correlation coefficient is 95.4% for TDS and the most important parameters are total hardness TH and electrical conductivity EC, according to direct relation between these parameters and TDS.
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In this paper, the construction of Hermite wavelets functions and their operational matrix of integration is presented. The Hermite wavelets method is applied to solve nth order Volterra integro diferential equations (VIDE) by expanding the unknown functions, as series in terms of Hermite wavelets with unknown coefficients. Finally, two examples are given
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Longitudinal data is becoming increasingly common, especially in the medical and economic fields, and various methods have been analyzed and developed to analyze this type of data.
In this research, the focus was on compiling and analyzing this data, as cluster analysis plays an important role in identifying and grouping co-expressed subfiles over time and employing them on the nonparametric smoothing cubic B-spline model, which is characterized by providing continuous first and second derivatives, resulting in a smoother curve with fewer abrupt changes in slope. It is also more flexible and can pick up on more complex patterns and fluctuations in the data.
The longitudinal balanced data profile was compiled into subgroup
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In this study a concentration of uranium was measured for twenty two samples of soil distributed in many regions (algolan, almoalmeen, alaskary and nasal streets) from Falluja Cityin AL-Anbar Governorate in addition to other region (alandlos street) as a back ground on the Falluja City that there is no military operations happened on it. The uranium concentrations in soil samples measured by using fission tracks registration in (PM-355) track detector that caused by the bombardment of (U) with thermal neutrons from (241Am-Be) neutron source that has flux of (5×103n cm-2 s-1). The concentrations values were calculated by a comparison with standard samples. The results shows that the uranium concentrations algolan street varies from(1.
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Portland Cement is manufactured by adding 3% gypsum to clinker which is produced by grinding, pulverizing, mixing, and then burning a raw mix of silica, and calcium carbonate. Limestone is the main source of carbonates, while clay collected from arable land is the main source of silica. The marl in the Euphrates Formation was studied as an alternative to arable lands. Nine boreholes drilled and penetrated the marl layer in selected locations at the Kufa cement quarry. Forty-one samples of marl from boreholes and four samples of limestone from the closed area were collected. The chemical content of the major oxides and the hardness of the marl layer was very encouraging as a raw material for Portland Cement as they are SiO2 (17.60),
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Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat
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