Many carbonate reservoirs in the world show a tilted in originally oil-water contact (OOWC) which requires a special consideration in the selection of the capillary pressure curves and an understanding of reservoir fluids distribution while initializing the reservoir simulation models.
An analytical model for predicting the capillary pressure across the interface that separates two immiscible fluids was derived from reservoir pressure transient analysis. The model reflected the entire interaction between the reservoir-aquifer fluids and rock properties measured under downhole reservoir conditions.
This model retained the natural coupling of oil reservoirs with the aquifer zone and treated them as an explicit-region composite system

In recent years, the world witnessed a rapid growth in attacks on the internet which resulted in deficiencies in networks performances. The growth was in both quantity and versatility of the attacks. To cope with this, new detection techniques are required especially the ones that use Artificial Intelligence techniques such as machine learning based intrusion detection and prevention systems. Many machine learning models are used to deal with intrusion detection and each has its own pros and cons and this is where this paper falls in, performance analysis of different Machine Learning Models for Intrusion Detection Systems based on supervised machine learning algorithms. Using Python Scikit-Learn library KNN, Support Ve

In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.

Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

Illegal web platforms known as phishing websites adopted high-risk threats to mimic legitimate online platforms in order to steal important data of users, like login credentials and financial information. These forged platforms often involve internet tactics to attract unsuspecting victims through URL manipulation. Many recent machine learning techniques are proposed for detecting web attackers. However, under an increasing number of internet users, they struggle to reveal recent phishing strategies for stealing sensitive information covered by fake websites. This study presents a comprehensive methodology for detecting phishing websites through a proposed hybrid technique (RF_PSO) by involving the Random Forest (RF) classification algorith

The effect of short range correlations on the inelastic longitudinal
Coulomb form factors for different states of J   4 , T  1with
excitation energies 3.553,7.114, 8.960 and 10.310 MeV in 18O is
analyzed. This effect (which depends on the correlation parameter )
is inserted into the ground state charge density distribution through
the Jastrow type correlation function. The single particle harmonic
oscillator wave function is used with an oscillator size parameter b.
The parameters  and b are considered as free parameters, adjusted
for each excited state separately so as to reproduce the experimental
root mean square charge radius of 18O. The model space of 18O does
not contribute to the tra

In this paper, the time-history responses of a square plan two-story reinforced concrete prototype building, considering the elastic and inelastic behavior of the materials, were studied numerically. ABAQUS software was used in three-dimensional (3D) nonlinear dynamic analysis to predict the inelastic response of the buildings. Concrete Damage Plasticity Model (CDPM) has been used to model the inelastic behavior of the reinforced concrete building under seismic excitation. The input data included geometric information, material properties, and the ground motion. The building structure was designed only for gravity load according to ACI 318 with

to evaluate the effect of various Nd:YAG frequency doubled laser parameters on the acid
dissolution and the progression of in vitro caries like lesions in human enamel.
Materials and Methods: Human extracted caries free upper first premolar teeth were collected for
this study. The irradiated teeth were divided into two groups. The first group was irradiated with
continuous Nd:YAG laser radiation, and the second group was irradiated with chopped Nd:YAG laser
radiation. For the first group, power and exposure time were changed while for the second group
power and number of pulses were changed. The spot diameter was kept constant for all the samples.
Results: using 1.5 W and 3 seconds (exposure time), best results among

In this work, results of a mathematical analysis of the role of workpiece preheating in laser keyhole welding were presented. This analysis considered the steady-state welding as well as certain range of boundary conditions over which preheating effect would be indicated. This work is an attempt to interpret the role of preheating to increase welding depth and perform keyhole welding with high quality using physical and thermal properties of steel alloys.

The electrical properties of the AlNiCo thin films with thickness (1000oA) deposited on glass substrates using Ion – Beam sputtering (IBS) technique under vacuum <10-6 torr have been studied . Also it studied the effect of annealing temperature from this films , It is found that the effective energy decrease with increase of temperature and the conductivity decrease with increase temperature 323oK but after this degree the conductivity increasing .