The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as the hybrid model consists of integrating the linear component, which represents Box Jenkins models and the non-linear component, which represents one of the methods of artificial intelligence, which is long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed h

Forecasting is one of the important topics in the analysis of time series, as the importance of forecasting in the economic field has emerged in order to achieve economic growth. Therefore, accurate forecasting of time series is one of the most important challenges that we seek to make the best decision, the aim of the research is to suggest employing hybrid models to predict daily crude oil prices. The hybrid model consists of integrating the linear component, which represents Box Jenkins models, and the non-linear component, which represents one of the methods of artificial intelligence, which is the artificial neural network (ANN), support vector regression (SVR) algorithm and it was shown that the proposed hybrid models in the predicti

Background: evaluate the effects of three different intracoronal bleaching agents on the shear bond strengths (SBS) and failure site of stainless steel and monocrystalline (sapphire) orthodontic brackets bonded to endodontically treated teeth using light cured orthodontic adhesive in vitro. Materials and methods: Eighty extracted sound human upper first premolars were selected, endondontically treated and randomly divided equally (according to the type of the brackets used) into two main groups (n = 40 per group). Each main group were subdivided (according to the bleaching agent used) into four subgroups 10 teeth each; as following : control (un bleached) group, hydrogen peroxide group (Hp) 35%, carbamide peroxide group (CP) 37% group and s

Gestational diabetes mellitus is glucose intolerance of varying degree with onset or first detection duringpregnancy,it can causelong and short term morbidities in both the mother and the child, such as shoulder dystocia,preeclampsia, and high blood pressure. The most powerful endogenous vasoconstrictor peptide, urotensin II, andits receptor are involved in the etiology of gestational diabetes mellitus.Aim of the study: The study’s goal was to see if there is a link between Urotensin II levels and insulin resistancein pregnant women with gestational diabetes.Patients and method: A case-control study that was conducted in obstetrics and gynecology department atBaghdad Teaching hospital from the first of January 2019 to the end of D

Retainers have the potential to detrimentally impact periodontal health and contribute to tooth decay.

This study aims to evaluate and compare the cytotoxicity and biocompatibility of a modified heat-cured acrylic denture base material containing 15% phosphoric acid 2-hydroxyethyl methacrylate ester (PA2HEME) with those of nonmodified PMMA. Discs with a diameter of 12 mm and a thickness of 2 mm were prepared using a heat-cured PMMA denture base material and divided into control and experimental groups. The experimental group was modified with 15% phosphoric acid 2-hydroxyethyl methacrylate ester (PA2HEME). The modified and nonmodified materials were tested via FTIR, and the effect of modification on surface roughness was evaluated with AFM. An in vitro test was conducted to examine the cytotoxicity and biocompatibility of heat-cured acry

Prediction of penetration rate (ROP) is important process in optimization of drilling due to its crucial role in lowering drilling operation costs. This process has complex nature due to too many interrelated factors that affected the rate of penetration, which make difficult predicting process. This paper shows a new technique of rate of penetration prediction by using artificial neural network technique. A three layers model composed of two hidden layers and output layer has built by using drilling parameters data extracted from mud logging and wire line log for Alhalfaya oil field. These drilling parameters includes mechanical (WOB, RPM), hydraulic (HIS), and travel transit time (DT). Five data set represented five formations gathered