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

The data presented in this paper are related to the research article entitled “Novel dichloro(bis{2-[1-(4-methylphenyl)-1H-1,2,3-triazol-4-yl-κN3 ]pyridine-κN})metal(II) coordination compounds of seven transition metals (Mn, Fe, Co, Ni, Cu, Zn and Cd)” (Conradie et al., 2018) [1]. This paper presents characterization and structural data of the 2-(1-(4-methyl-phenyl)-1H-1,2,3-triazol-1-yl)pyridine ligand (L2 ) (Tawfiq et al., 2014) [2] as well as seven dichloro(bis{2- [1-(4-methylphenyl)-1H-1,2,3-triazol-4-yl-κN3 ]pyridine-κN})metal (II) coordination compounds, [M(L2 )2Cl2], all containing the same ligand but coordinated to different metal ions. The data illustrate the shift in IR, UV/VIS, and NMR (for diamagnetic complexes) peaks wh

The behavior and shear strength of full-scale (T-section) reinforced concrete deep beams, designed according to the strut-and-tie approach of ACI Code-19 specifications, with various large web openings were investigated in this paper. A total of 7 deep beam specimens with identical shear span-to-depth ratios have been tested under mid-span concentrated load applied monotonically until beam failure. The main variables studied were the effects of width and depth of the web openings on deep beam performance. Experimental data results were calibrated with the strut-and-tie approach, adopted by ACI 318-19 code for the design of deep beams. The provided strut-and-tie design model in ACI 318-19 code provision was assessed and found to be u

 Since there is no market for bond issuance by companies in the Iraqi market and the difficulty of borrowing, companies must resort to proprietary financing to finance their investments. However, in the framework of the literature of financial management, the type of financing used by the company sends signals to investors and therefore reflected on the market value. Therefore, the problem of the study revolves around the variables of the study (Equity financing within the framework the signal theory, price of common stock in the Iraqi market).     

The study aims to verify the impact of the capital increase through the issuance of new stock on the price of

In the present work, experimental tests was done to explain the effect of insulation and water level on the yield output. Linear basin, single slope solar still used to do this purpose. The test was done from May to August 2017 in Mosul City-Iraq (Latitude: Longitude: Elevation: 200 m, and  South-East face). Experimental results showed that the yield output of the still increased by 20.785% and 19.864% in case of using thermal insulation at 4cm and 5cm respectively, also the yield output decrease by 15.134% as the water level increase from 4 to 5cm, with the presence of insulation and 14.147% without it. It has been conclude that the insulation and water level play important role in the process of passive

Porous silicon (P-Si) has been produced in this work by photoelectrochemical (PEC) etching process. The irradiation has been achieved using diode laser of (2 W) power and 810 nm wavelength. The influence of various irradiation times on the properties of P-Si material such as P-Si layer thickness, surface aspect, pore diameter and the thickness of walls between pores as well as porosity and etching rate was investigated by depending on the scanning electron micrograph (SEM) technique and gravimetric measurements.

Asphalt binder is a thermoplastic material that conducts as an elastic solid at lower service temperatures or throughout fast loading rate. At a high temperature or slow rate of loading, asphalt binder conducts as a different liquid. The classical duplication generates a required to assess the mechanical properties of asphalt concrete at the anticipated service temperature to reduce the stress cracking, which happens at lower temperatures, fatigue, and the plastic deformation at higher temperatures (rutting). In this study, an achievement was made to assess the effect of temperature on the mechanical characteristics of asphalt concrete mixes. A total of 132 asphalt concrete samples were attended utilizing two asphalt cement grades (40-50) a

Asphalt pavement properties in Iraq are highly affected by elevated summer air temperatures. One of these properties is stiffness (resilient modulus). To explain the effect of air temperatures on stiffness of asphalt concrete, it is necessary to determine the distribution of temperatures through the pavement asphalt concrete layers. In this study, the distribution of pavement temperatures at three depths (2cm,7cm, 10cm) below the pavement surface is determined by using the temperature data logger instrument. A relationship for determining pavement temperature as related to depth and air temperature has been suggested. To achieve the objective of this thesis, the prepared specimens have been tested for indirect tension in accordance with

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.