The purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals

Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

Objective: This study aims to assess the efficacy of CT-guided true-cut biopsy as a less invasive and cost-effective diagnostic technique for peripherally placed lung lesions.   Methods: fourty patients with solitary lung nodule were involved in this study, true cut biopsies under Ct guide was taken then processed for routine H&E staining.   Results: different pathological features can be identified with different pathological features giving primary diagnostic screening for lung cancer   Conclusion: CT guided thoracic lesion biopsy is very efficient, cost-effective and less invasive technique when compared with the thoracic surgery

The air flow pattern in a co-current pilot plant spray dryer fitted with a rotary disk atomizer was determined experimentally and modelled numerically using Computational Fluid Dynamics (CFD) (ANSYS Fluent ) software. The CFD simulation used a three dimensions system, Reynolds-Average Navier-Stokes equations (RANS), closed via the RNG k −ε turbulence model. Measurements were carried out at a rotation of the atomizer (3000 rpm) and when there is no rotation using a drying air at 25 ^oC and  air velocity at the inlet of  5 m/s without swirl. The air flow pattern was predicted experimentally using cotton tufts and digital anemometer. The CFD simulation predicted a downward central flowing air core surrounded by a slow

Background: The development of orthodontic biomaterials that attract less biofilm has been a goal for decades. Adhesion and colonization of cariogenic streptococci are considered to play key roles in the development of enamel demineralization related to orthodontic materials. The aim of this study was to quantitatively evaluate the Mutans streptococci adhesion to coated orthodontic archwires (Epoxy and Teflon) and uncoated archwires (stainless steel and nickel-titanium) with respect to incubation time in the presence and absence of saliva. Material and Method: Six types of archwires stainless steel and nickel titanium with two type of coating (Epoxy, Teflon) were used in this study. Twelve specimens of each archwire were incubated in steri

The research discusses the most important goals and means of IGAD in making peace in Sudan and then regional peace.