In the field of data security, the critical challenge of preserving sensitive information during its transmission through public channels takes centre stage. Steganography, a method employed to conceal data within various carrier objects such as text, can be proposed to address these security challenges. Text, owing to its extensive usage and constrained bandwidth, stands out as an optimal medium for this purpose. Despite the richness of the Arabic language in its linguistic features, only a small number of studies have explored Arabic text steganography. Arabic text, characterized by its distinctive script and linguistic features, has gained notable attention as a promising domain for steganographic ventures. Arabic text steganography harn

This paper proposes a completion that can allow fracturing four zones in a single trip in the well called “Y” (for confidential reasons) of the field named “X” (for confidential reasons). The steps to design a well completion for multiple fracturing are first to select the best completion method then the required equipment and the materials that it is made of. After that, the completion schematic must be drawn by using Power Draw in this case, and the summary installation procedures explained. The data used to design the completion are the well trajectory, the reservoir data (including temperature, pressure and fluid properties), the production and injection strategy. The results suggest that multi-stage hydraulic fracturing can

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

This paper focuses on developing a self-starting numerical approach that can be used for direct integration of higher-order initial value problems of Ordinary Differential Equations. The method is derived from power series approximation with the resulting equations discretized at the selected grid and off-grid points. The method is applied in a block-by-block approach as a numerical integrator of higher-order initial value problems. The basic properties of the block method are investigated to authenticate its performance and then implemented with some tested experiments to validate the accuracy and convergence of the method.

Ebastine (EBS) is a poorly water-soluble antihistaminic drug; it belongs to the class II group according to the biopharmaceutical classification system (BCS). The aim of the present work was to enhance the solubility, dissolution rate and micromeritic properties of the drug, by formulating it as spherical crystal agglomerates by Quasi Emulsion Solvent Diffusion (QESD) method.

Spherical crystal agglomerates (SCAs) were prepared in presence of three solvents dichloromethane (DCM), water and chloroform as a good solvent, poor solvent and bridging solvent respectively.  Agglomeration of EBS involved the use of some hydrophilic polymers like polyethylene glycol 4000 (PEG 4000), polyvinyl pyrrolidine K30 (PVP K30), D-?-tocopheryl

The study showed significant differences between the average weight lens and the average amount protein in the lens between that Kestrel Falco tinnunculus L. and the Collared Dove Streptopelia decaocto F. , also the study electrical migration of lens proteins having one bundle of crystalline –? in Kestrel compared with three bundles in Collared Dove, two bundles of crystalline – ? in both , and crystalline – ? appeared as one bundle in both birds.

In the current work various types of epoxy composites were added to concrete to enhance its effectiveness as a gamma- ray shield. Four epoxy samples of (E/clay/B4C) S1, (E/Mag/B4C) S2, (EPIL) S3 and (Ep) S4 were used in a comparative study of gamma radiation attenuation properties of these shields that calculating using Mont Carlo code (MCNP-5). Adopting Win X-com software and Artificial Neural Network (ANN), µ/ρ revealed great compliance with MCNP-5. By applying (µ/ρ) output for gamma at different energies, HVL, TVL and MFP have been also estimated. ANN technique was simulated to estimate (µ/ρ) and dose rates. According to the results, µ/ρ of all epoxy samples scored higher than standard concrete. Both S2 and S3 samples having h