The Quantitative high-resolution planktonic foraminiferal analysis of the subsurface section in three selected wells in the Ajeel Oil Field (Aj-8, Aj-12, and Aj-15) in Tikrit Governorate, Central Iraq has revealed that Shiranish Formation deposited in Late Campanian- Latest Maastrichtian age. This formation consists mainly of marly and marly limestone yielding diverse planktonic foraminiferal assemblages and calcareous benthic foraminifera, with a total of 46 species that belong to 23 genera, Three zones and four subzones, which cover the Late Campanian to the Latest Maastrichtian, were identified based on the recorded planktonic foraminifera and their ranges. They are as follows:1. Globotruncana aegyptiaca Zone that dated to be Lat

Abstract

      In this study, we compare between the autoregressive approximations (Yule-Walker equations, Least Squares , Least Squares ( forward- backword ) and Burg’s (Geometric and Harmonic ) methods, to determine the optimal approximation to the time series generated from the first - order moving Average non-invertible process, and fractionally - integrated noise process, with several values for d (d=0.15,0.25,0.35,0.45) for different sample sizes (small,median,large)for two processes . We depend on figure of merit function which proposed by author Shibata in 1980, to determine the theoretical optimal order according to min

Abstract:

Robust statistics Known as, resistance to errors caused by deviation from the stability hypotheses of the statistical operations (Reasonable, Approximately Met, Asymptotically Unbiased, Reasonably Small Bias, Efficient ) in the data selected in a wide range of probability distributions whether they follow a normal distribution or a mixture of other distributions deviations different standard .

power spectrum function lead to, President role in the analysis of Stationary random processes, form stable random variables organized according to time, may be discrete random variables or continuous. It can be described by measuring its total capacity as function in frequency.

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  This study is concerned with the estimation of constant  and time-varying parameters in non-linear ordinary differential equations, which do not have analytical solutions. The estimation is done in a multi-stage method where constant and time-varying parameters are estimated in a straight sequential way from several stages. In the first stage, the model of the differential equations is converted to a regression model that includes the state variables with their derivatives and then the estimation of the state variables and their derivatives in a penalized splines method and compensating the estimations in the regression model. In the second stage, the pseudo- least squares method was used to es

Objecte The study aims to test the effect of using the appropriate quantitative method of demand forecasting in improving the performance of supply chain of the aviation fuel product ( The study sample), One of the products of the Doura refinery (The study site), By testing a set of quantitative methods of demand forecasting using forecasting error measurements, and choosing the least faulty, most accurate and reliable method and adept it in the building  chain.

Is the study of problem through a starting with the fol

In this work, the effect of anodizing duration on the morphology and photoelectrochemical properties of TiO2 nanotubes arrays (NTAs) has been investigated The samples were characterized by X-ray diffraction (XRD) and energy dispersive X-ray (EDX) to characterize their crystalline structure and compositional. Surface morphological and their dimensional variation was examined by field emission scanning electron microscopy (FESEM). The anodizing duration played a significant role in the formation of TiO2 nanotubes arrays. Moreover, the photoelectrochemical properties (PEC) were studied through photocurrent measurements. Optimum anodizing duration of 60 min at 40 V exhibited maximum photocurrent of 0.03 mA cm-2 under illumination of hal

The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm