In this work, polyvinylpyrrolidone (PVP)/ Multi-walled carbon nanotubes (MWCNTs) nanocomposites were prepared with two concentrations of MWCNTs by casting method. Morphological, structural characteristics and electrical properties were investigated. The state of MWCNTs dispersion in a PVP matrix was indicated by Field Effect-Scanning Electron Microscopy (FESEM) which showed a uniform dispersion of MWCNTs within the PVP matrix. X-ray Diffraction (XRD) indicate strong bonding of carbonyl groups of PVP composite chains with MWCNTs. Fourier transfer infrared (FTIR) studies shows characteristics of various stretching and bending vibration bands, as well as shifts in some band locations and intensity changes in others. Hall effect was stu

Data steganography is a technique used to hide data, secret message, within another data, cover carrier. It is considered as a part of information security. Audio steganography is a type of data steganography, where the secret message is hidden in audio carrier. This paper proposes an efficient audio steganography method that uses LSB technique. The proposed method enhances steganography performance by exploiting all carrier samples and balancing between hiding capacity and distortion ratio. It suggests an adaptive number of hiding bits for each audio sample depending on the secret message size, the cover carrier size, and the signal to noise ratio (SNR). Comparison results show that the proposed method outperforms state of the art methods

Three new hydrazone derivatives of Etodolac were synthesized and evaluated for their anti-inflammatory activity by using egg white induced paw edema method. All the synthesized target compounds were characterized by CHN- microanalysis, FT-IR spectroscopy, and ¹HNMR analysis. The synthesis of the target (P1-P3) compounds was accomplished following multistep reaction procedures. The synthesized target compounds were found to be active in reducing paw edema thickness and their anti-inflammatory effect was comparable to that of the standard (Etodolac).

A pioneering idea for increasing the thermal performance of heat transfer fluids was to use ultrafine solid particles suspended in the base fluid. Nanofluids, synthesized by mixing solid nanometer sized particles at low concentrations with the base fluid, were used as a new heat transfer fluid and developed a remarkable effect on the thermophysical properties and heat transfer coefficient. For any nanofluid to be usable in heat transfer applications, the main concern is its long-term stability. The aim of this research is to investigate the effect of using four different surfactants (sodium dodecyl benzene sulfonate (SDBS), sodium dodecyl sulfate (SDS), cetyl trimethylammonium bromide (CTAB), and gum Arabic (GA)), each with three different

Four new binuclear Schiff base metal complexes [(MCl₂)₂L] {M = Fe 1, Co 2, Cu 3, Sn 4, L = N,N’-1,4-Phenylenebis (methanylylidene) bis (ethane-1,2-diamine)} have been synthesized using direct reaction between proligand (L) and the corresponding metal chloride (FeCl₂, CoCl₂, CuCl₂ and SnCl₂). The structures of the complexes have been conclusively determined by a set of spectroscopic techniques (FT-IR, ¹H-NMR, and mass spectra). Finally, the biological properties of the complexes have been investigated with a comparative approach against different species of bacteria (E. coli G-, Pseudomonas G-, Bacillus G+,

Classifying an overlapping object is one of the main challenges faced by researchers who work in object detection and recognition. Most of the available algorithms that have been developed are only able to classify or recognize objects which are either individually separated from each other or a single object in a scene(s), but not overlapping kitchen utensil objects. In this project, Faster R-CNN and YOLOv5 algorithms were proposed to detect and classify an overlapping object in a kitchen area.  The YOLOv5 and Faster R-CNN were applied to overlapping objects where the filter or kernel that are expected to be able to separate the overlapping object in the dedicated layer of applying models. A kitchen utensil benchmark image database and

Drilling deviated wells is a frequently used approach in the oil and gas industry to increase the productivity of wells in reservoirs with a small thickness. Drilling these wells has been a challenge due to the low rate of penetration (ROP) and severe wellbore instability issues. The objective of this research is to reach a better drilling performance by reducing drilling time and increasing wellbore stability.

In this work, the first step was to develop a model that predicts the ROP for deviated wells by applying Artificial Neural Networks (ANNs). In the modeling, azimuth (AZI) and inclination (INC) of the wellbore trajectory, controllable drilling parameters, unconfined compressive strength (UCS), formation