The aim of the present research is to illustrate γ-ray levels emitting from soil and bitumen producing from 238U, 232Th, and 40K along Abu-Jir Fault Zone. in the area extended from Al-Marj valley to Abu-Jir village using scintillometer device. Such study is important in environmental assessment to buildup data base about radioactivity. The concentration of natural radionuclides in the study area was determined to be occurring mostly in the clay minerals and organic matter. A high purity germanium spectrometer was used to detect the activity of these elements which ranged between 00.6±18.1- 1526±102, 0- 8.4±1.4, and 70.1±10.9- 328.2±73 in soil, and 28.2±5.6- 94±22.1, 0- 2.2±0.5, and 38.4±7.9- 70.1±10.9 in bitumen for 226Ra

In the present study, radon gas concentration in the shallow groundwater samples of the Abu-Jir region in Anbar governorate was measured by using Rad-7 detector. The highest radon gas level in the samples is up to 9.3 Bq/L, while the lowest level is 2.1 Bq/L, with an average of 6.44±1.8 Bq/L. The annual effective dose is varied from 33.945 μSv/y to 7.66 μSv/y, with an average of 0.145±0.06 μSv/y. Consequently, the radon level in the groundwater studied is lower than the standard recommended value (11 Bq/L) reported by the United States Environmental Protection Agency (USEPA). The potential source of radon is uranium-rich hydrocarbons that are leakage to the surface along the Abu-Jir Fault. This research did not indicate any ris

 Researchers have increased interest in recent years in determining the optimum sample size to obtain sufficient accuracy and estimation and to obtain high-precision parameters in order to evaluate a large number of tests in the field of diagnosis at the same time. In this research, two methods were used to determine the optimum sample size to estimate the parameters of high-dimensional data. These methods are the Bennett inequality method and the regression method. The nonlinear logistic regression model is estimated by the size of each sampling method in high-dimensional data using artificial intelligence, which is the method of artificial neural network (ANN) as it gives a high-precision estimate commensurate with the dat

A new two-way nesting technique is presented for a multiple nested-grid ocean modelling system. The new technique uses explicit center finite difference and leapfrog schemes to exchange information between the different subcomponents of the nested-grid system. The performance of the different nesting techniques is compared, using two independent nested-grid modelling systems. In this paper, a new nesting algorithm is described and some preliminary results are demonstrated. The validity of the nesting method is shown in some problems for the depth averaged of 2D linear shallow water equation.

A system was used to detect injuries in plant leaves by combining machine learning and the principles of image processing. A small agricultural robot was implemented for fine spraying by identifying infected leaves using image processing technology with four different forward speeds (35, 46, 63 and 80 cm/s). The results revealed that increasing the speed of the agricultural robot led to a decrease in the mount of supplements spraying and a detection percentage of infected plants. They also revealed a decrease in the percentage of supplements spraying by 46.89, 52.94, 63.07 and 76% with different forward speeds compared to the traditional method.