The aim of this paper is to design artificial neural network as an alternative accurate tool to estimate concentration of Cadmium in contaminated soils for any depth and time. First, fifty soil samples were harvested from a phytoremediated contaminated site located in Qanat Aljaeesh in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. The inputs are the soil depth, the time, and the soil parameters but the output is the concentration of Cu in the soil for depth x and time t. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Cadmium. The performance of the ANN technique was compared with the traditional laboratory inspecting using the training and test data sets. The results of this work show that the ANN technique trained on experimental measurements can be successfully applied to the rapid estimation of Cadmium concentration
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Earth dams are constructed mainly from soil. A homogenous earth dam is composed of only one material. The seepage through such dams is quite high. Upstream impervious blanket is one of the methods used to control seepage through the dam foundations. Bennet's method is one of the commonly used methods to design an impervious upstream blanket. Design charts are developed relating the length of blanket, total reservoir head, total base width of the dam (excluding downstream drainage), the coefficient of permeability of the blanket material, blanket thickness, foundation thickness, and coefficient of permeability of the foundation soil, based on the equations governing the Bennet's method for a homogenous earth dam with a blanket of uniform
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In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.
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In this paper, we propose a method using continuous wavelets to study the multivariate fractional Brownian motion through the deviations of the transformed random process to find an efficient estimate of Hurst exponent using eigenvalue regression of the covariance matrix. The results of simulations experiments shown that the performance of the proposed estimator was efficient in bias but the variance get increase as signal change from short to long memory the MASE increase relatively. The estimation process was made by calculating the eigenvalues for the variance-covariance matrix of Meyer’s continuous wavelet details coefficients.
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Spatial Autoregressive Model (SAR) is one of the modeling frameworks that indicates a spatial dependence in the response variable. SAR model has a weakness, which is represented by the unknown variance of the residuals. Therefore, an alternative model has used titled Spatial Autoregressive Quantile Regression (SARQR) model That which is obtained by combining SAR and Quantile Regression (QR) models, is a regression method with the approach of dividing the data into particular quantiles that are likely to have different estimate values. This alternative model addresses the variance issues in SAR models. Additionally, the SARQR model not only resolves the issue of spatial variance but also serves as a solution for dealing with non-normal data
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This paper presents a comparison between the electroencephalogram (EEG) channels during scoliosis correction surgeries. Surgeons use many hand tools and electronic devices that directly affect the EEG channels. These noises do not affect the EEG channels uniformly. This research provides a complete system to find the least affected channel by the noise. The presented system consists of five stages: filtering, wavelet decomposing (Level 4), processing the signal bands using four different criteria (mean, energy, entropy and standard deviation), finding the useful channel according to the criteria’s value and, finally, generating a combinational signal from Channels 1 and 2. Experimentally, two channels of EEG data were recorded fro
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ABSTRACT: Pathogenic bacteria responsible for the causation of many common diseases have been identified on currency notes. The present investigation was carried out on one hundred currency notes of all the denominations (50, 100, 250, 500 and 1000RY), obtained from different occupational mainly bus drivers, hawker street, vegetable vendor, restaurants and butchers and fish seller groups in Taiz city,Yemen. Identification and characterization revealed active participation of the following species of organisms in the ascending order of percentage as E. coli(50.28 %),Staphylococci aureus(14.04 %), Klebsiellaspp(4.39 %),proteus(4.39 %), salmonella(1.25 %), shigella(0.72 %), Coagulase negative staphylococcus(0.60 %), pseudomonas(0.50 %),
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