Unconfined compressive strength (UCS) of rock is the most critical geomechanical property widely used as input parameters for designing fractures, analyzing wellbore stability, drilling programming and carrying out various petroleum engineering projects. The USC regulates rock deformation by measuring its strength and load-bearing capacity. The determination of UCS in the laboratory is a time-consuming and costly process. The current study aims to develop empirical equations to predict UCS using regression analysis by JMP software for the Khasib Formation in the Buzurgan oil fields, in southeastern Iraq using well-log data. The proposed equation accuracy was tested using the coefficient of determination (R²), the average absolute

Purepolyaniline and doped with hydrochloric acid was prepared in different molarities at room temperature. The a.c electrical properties were stadied.AC conductivityσac (ω), is found to vary as ωS in the frequency range (100Hz-10MH), S< 1and decreases indicating a dominate hopping process. Thedielectric constant ε1and dielectric loss ε2 have been determined for bulk polyaniline. ε1 decrease with the increase frequency. Electrical conductivity measurements increase with the increases both of the amount of HCl and the dose of radiation. The dielectric investigations show decrease with dose radiation.

Background: Hashimoto's thyroiditis has been found to coexist with differentiated thyroid cancer in surgical specimens, but an association between the two conditions has been discounted by the medical literature. So, we performed this research to determine any potential relationship between Hashimoto's thyroiditis and the risk of developing differentiated thyroid cancer in clinical status. we assessed the related clinical factors linking these conditions, especially serum thyroid-stimulating hormone concentration, family history of thyroid disease, gender&amp; young age group. Aim of study: to determine that hashimoto’s thyroiditis increases risk for differentiated thyroid carcinoma. Patients and method: This study is a Cross-sectiona

The subject of this research involves studying adsorption to remove hexavalent chromium Cr(VI) from aqueous solutions. Adsorption process on bentonite clay as adsorbent was used in the Cr(VI) concentration range (10-100) ppm at different temperatures (298, 303, 308 and 313)K, for different periods of time. The adsorption isotherms were obtained by obeying Langmuir and Freundlich adsorption isotherm with R2 (0.9921-0.9060) and (0.994-0.9998), respectively. The thermodynamic parameters were calculated by using the adsorption process at four different temperatures the values of ?H, ?G and ?S was [(+6.582 ? +6.547) kJ.mol-1, (-284.560 ? -343.070) kJ.mol-1 and (+0.977 ? +1.117) kJ.K-1.mol-1] respectively. This data indicates the spontaneous sorp

The problem of the research lies in choosing agility tests suitable to the test taker to observe the relative changes in some players. In addition to that, there are a lot of agility tests that lack special test models that coordinate gender and age. This means the youth basketball player on one hand and time and distance in applying the tests on the other. The importance of the research lies in designing agility tests for youth basketball players to achieve variations in tests a matter that will benefit coaches in their training. The subjects of the research were (30) youth basketball players from the specialized school of the National Center that sponsor gifted basketball players in Baghdad for the season 2014 – 2015. The data was colle

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi

Visual analytics becomes an important approach for discovering patterns in big data. As visualization struggles from high dimensionality of data, issues like concept hierarchy on each dimension add more difficulty and make visualization a prohibitive task. Data cube offers multi-perspective aggregated views of large data sets and has important applications in business and many other areas. It has high dimensionality, concept hierarchy, vast number of cells, and comes with special exploration operations such as roll-up, drill-down, slicing and dicing. All these issues make data cubes very difficult to visually explore. Most existing approaches visualize a data cube in 2D space and require preprocessing steps. In this paper, we propose a visu