The Dirichlet process is an important fundamental object in nonparametric Bayesian modelling, applied to a wide range of problems in machine learning, statistics, and bioinformatics, among other fields. This flexible stochastic process models rich data structures with unknown or evolving number of clusters. It is a valuable tool for encoding the true complexity of real-world data in computer models. Our results show that the Dirichlet process improves, both in distribution density and in signal-to-noise ratio, with larger sample size; achieves slow decay rate to its base distribution; has improved convergence and stability; and thrives with a Gaussian base distribution, which is much better than the Gamma distribution. The performance depen

A 3D geological model is an essential step to reveal reservoir heterogeneity and reservoir properties distribution. In the present study, a three-dimensional geological model for the Mishrif reservoir was built based on data obtained from seven wells and core data. The methodology includes building a 3D grid and populating it with petrophysical properties such as (facies, porosity, water saturation, and net to gross ratio). The structural model was built based on a base contour map obtained from 2D seismic interpretation along with well tops from seven wells. A simple grid method was used to build the structural framework with 234x278x91 grid cells in the X, Y, and Z directions, respectively, with lengths equal to 150 meters. The to

order to increase the level of security, as this system encrypts the secret image before sending it through the internet to the recipient (by the Blowfish method). As The Blowfish method is known for its efficient security; nevertheless, the encrypting time is long. In this research we try to apply the smoothing filter on the secret image which decreases its size and consequently the encrypting and decrypting time are decreased. The secret image is hidden after encrypting it into another image called the cover image, by the use of one of these two methods" Two-LSB" or" Hiding most bits in blue pixels". Eventually we compare the results of the two methods to determine which one is better to be used according to the PSNR measurs

Nuclear structure of 20,22Ne isotopes has been studied via the shell model with Skyrme-Hartree-Fock calculations. In particular, the transitions to the low-lying positive and negative parity excited states have been investigated within three shell model spaces; sd for positive parity states, spsdpf large-basis (no-core), and zbme model spaces for negative parity states. Excitation energies, reduced transition probabilities, and elastic and inelastic form factors were estimated and compared to the available experimental data. Skyrme interaction was used to generate a one-body potential in the Hartree-Fock calculations for each selected excited states, which is then used to calculate the single-particle matrix elements. Skyrme interac

A single-crystalline semi-polar gallium nitride (11-22) was grown on m-plane (10-10) sapphire substrate by metal organic chemical vapor deposition. Three-step approach was introduced to investigate the grain size evolution for semi-polar (11-22) GaN. Such approach was achieved due to the optimized gallium to ammonia ratio and temperature variations, which led to high quality (11-22) oriented gallium nitride epilayers. The full width at half maximum values along (-1-123) and (1-100) planes for the overgrowth temperature of 1080°C were found to be as low as 0.37° and 0.49°, respectively. This was an indication of the enhanced coalescence and reduction in root mean square roughness as seen by atomic force microscopy. Surface analysi

In this paper, third order non-polynomial spline function is used to solve 2nd kind Volterra integral equations. Numerical examples are presented to illustrate the applications of this method, and to compare the computed results with other known methods.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes