The study investigates the relationship between the volatility of the Iraqi Stock Exchange Index (ISX), and the volatility of global oil prices benchmarks, Brent and West Intermediate Texas (WTI), in additional to the Iraqi Oil, Basra Crude Light (BSL) which represents the most exported Iraqi oil and the major influential factor on the Iraqi governmental revenues. Using monthly data covering the period: 1/2005-12/1205, econometrical and technical tools represented by Co-incretion, Vector Error Correction Model – VECM, Granger Causality, and Bollinger band were employed in order to explore the relationship between the variables.

The econometric analysis revealed the impact of the oil prices volatility on

Interval methods for verified integration of initial value problems (IVPs) for ODEs have been used for more than 40 years. For many classes of IVPs, these methods have the ability to compute guaranteed error bounds for the flow of an ODE, where traditional methods provide only approximations to a solution. Overestimation, however, is a potential drawback of verified methods. For some problems, the computed error bounds become overly pessimistic, or integration even breaks down. The dependency problem and the wrapping effect are particular sources of overestimations in interval computations. Berz (see [1]) and his co-workers have developed Taylor model methods, which extend interval arithmetic with symbolic computations. The latter is an ef

     In the present investigation two different types of fiber reinforced polymer composites were prepared by hand lay-up method using three different parameters (curing temperature, pressing load and fiber volume fraction). These composites were prepared from the polyester resin as the matrix material reinforced with glass fibers as first group of samples and mat Kevlar fibers as the second group, both with different volume fractions (4%, 8%, and 12%) of fibers. They were then tested by tensile strength and impact strength. The main objective in this study is to use Taguchi method for predicting the better parameters that give the better tensile and impact strength to the composites, and then preparing composites at

Abstract: Colloidal gold nanoparticles (ringworm Palm or in the form of paper willow) have been prepared from HAuCl4 containing aqueous solution by hot chemical reduction method. The colloidal gold nanoparticles were characterized by SEM, EDX, and UV-VIS absorption spectroscopy. It was found that the variation of reduction time from boiling point affects the size of the nanoparticles and also in chemical reduction approach the size of nanoparticles can be controlled by varying the amount of variation the volume of reductant material with respect to the volume of HAuCL4.

Medicinal plant life have performed an integral role in the development of human lifestyle being used as resources for the manufacturing of contemporary medicines, antibiotics. Microorganisms are responsible for many issues in industry and remedy because of biofilm formation. This study aimed to take a look at the results of Thymus vulgaris alcoholic extract on Staphylococcus aureus the causative marketers of acne in human particularly the women. The extract was acquired mixing 25 g of Thyme powder with 350 ml of 80% ethanol for 6 hours at 40°. Antimicrobial activities of the plant extract and integral oil towards the S. aureus were decided using the agar diffusion technique. The consequences proven that special concentrations of alcoholic

The research deals with the important and modern two subjects, strategic leadership which have six demotions and knowledge management
(four demotions') . the gools & the research is to know the relation & the effect them in the oil ministry (project department) , the sample was (50) persons who works in the department the questionnaire was the tool of data gathering .

The research divided to four parties, the first  to the theotical review of the research variables, the second to the research methrology, the third to analysis and discoed the empirical results the last to the conclusions and recommendations .

Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F