The Gas Assisted Gravity Drainage (GAGD) process has become one of the most important processes to enhance oil recovery in both secondary and tertiary recovery stages and through immiscible and miscible modes.  Its advantages came from the ability to provide gravity-stable oil displacement for improving oil recovery, when compared with conventional gas injection methods such as Continuous Gas Injection (CGI) and Water – Alternative Gas (WAG). Vertical injectors for CO2   gas were placed at the top of the reservoir to form a gas cap which drives the oil towards the horizontal oil producing wells which are located above the oil-water-contact. The GAGD process was developed and tested in vertical wells to increase oil r

The fetal heart rate (FHR) signal processing based on Artificial Neural Networks (ANN),Fuzzy Logic (FL) and frequency domain Discrete Wavelet Transform(DWT) were analysis in order to perform automatic analysis using personal computers. Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces was based on the visual analysis of patterns that describing the variability of fetal heart rate signal. Fetal heart rate data of pregnant women with pregnancy between 38 and 40 weeks of gestation were studied. The first stage in the system was to convert the cardiotocograghy (CTG) tracing in to digital series so that the system can be analyzed ,while the second stage ,the FHR time series was t

In this study two types of extraction solvents were used to extract the undesirable polyaromatics, the first solvent was furfural which was used today in the Iraqi refineries and the second was NMP (N-methyl-2-pyrrolidone).
The studied effecting variables of extraction are extraction temperature ranged from 70 to 110°C and solvent to oil ratio in the range from 1:1 to 4:1.
The results of this investigation show that the viscosity index of mixed-medium lubricating oil fraction increases with increasing extraction temperature and reaches 107.82 for NMP extraction at extraction temperature 110°C and solvent to oil ratio 4:1, while the viscosity index reaches to 101 for furfural extraction at the same extraction temperature and same

Autism is a lifelong developmental deficit that affects how people perceive the world and interact with each others. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. The commonly used tools for analyzing the dataset of autism are FMRI, EEG, and more recently "eye tracking". A preliminary study on eye tracking trajectories of patients studied, showed a rudimentary statistical analysis (principal component analysis) provides interesting results on the statistical parameters that are studied such as the time spent in a region of interest. Another study, involving tools from Euclidean geometry and non-Euclidean, the trajectory of eye patients also showed interesting results. In this

In this work, satellite images for Razaza Lake and the surrounding area
district in Karbala province are classified for years 1990,1999 and
2014 using two software programming (MATLAB 7.12 and ERDAS
imagine 2014). Proposed unsupervised and supervised method of
classification using MATLAB software have been used; these are
mean value and Singular Value Decomposition respectively. While
unsupervised (K-Means) and supervised (Maximum likelihood
Classifier) method are utilized using ERDAS imagine, in order to get
most accurate results and then compare these results of each method
and calculate the changes that taken place in years 1999 and 2014;
comparing with 1990. The results from classification indicated that

In this study, stabilization of expansive soils using waste materials namely; Cement Kiln Dust (CKD), and waste plastic bottles (WPB) was experimentally investigated. Using CKD and WPB are exponentially increasing day by day, due to their capability to solve both environmental and geotechnical problems successfully. Expansive soils were collected from locations with a wide range of plasticity index (PI) (15 - 27) and liquid limit (LL) (35% - 64%). Stabilizer percentages were varied from 0% to 20%, and curing durations for CKD cases were 7 and 28 days. Results showed the best percentages of CKD and WPB are 12% of each one respectively. LL, plastic limit (PL), and swelling percent (SP) loss were observed, which are 46%, 55%, and 96% respec

The availability of different processing levels for satellite images makes it important to measure their suitability for classification tasks. This study investigates the impact of the Landsat data processing level on the accuracy of land cover classification using a support vector machine (SVM) classifier. The classification accuracy values of Landsat 8 (LS8) and Landsat 9 (LS9) data at different processing levels vary notably. For LS9, Collection 2 Level 2 (C2L2) achieved the highest accuracy of (86.55%) with the polynomial kernel of the SVM classifier, surpassing the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) at (85.31%) and Collection 2 Level 1 (C2L1) at (84.93%). The LS8 data exhibits similar behavior. Conv

Ration power plants, to generate power, have become common worldwide. One such one is the steam power plant. In such plants, various moving parts of heavy machines generate a lot of noise. Operators are subjected to high levels of noise. High noise level exposure leads to psychological as well physiological problems; different kinds of ill effects. It results in deteriorated work efficiency, although the exact nature of work performance is still unknown. To predict work efficiency deterioration, neuro-fuzzy tools are being used in research. It has been established that a neuro-fuzzy computing system helps in identification and analysis of fuzzy models. The last decade has seen substantial growth in development of various neuro-fuzzy systems

The proposal of nonlinear models is one of the most important methods in time series analysis, which has a wide potential for predicting various phenomena, including physical, engineering and economic, by studying the characteristics of random disturbances in order to arrive at accurate predictions.

In this, the autoregressive model with exogenous variable was built using a threshold as the first method, using two proposed approaches that were used to determine the best cutting point of [the predictability forward (forecasting) and the predictability in the time series (prediction), through the threshold point indicator]. B-J seasonal models are used as a second method based on the principle of the two proposed approaches in dete