Steganography is a mean of hiding information within a more obvious form of
communication. It exploits the use of host data to hide a piece of information in such a way
that it is imperceptible to human observer. The major goals of effective Steganography are
High Embedding Capacity, Imperceptibility and Robustness. This paper introduces a scheme
for hiding secret images that could be as much as 25% of the host image data. The proposed
algorithm uses orthogonal discrete cosine transform for host image. A scaling factor (a) in
frequency domain controls the quality of the stego images. Experimented results of secret
image recovery after applying JPEG coding to the stego-images are included.

The effect of internal acoustic excitation on the leading-edge, separated boundary layers and the aerodynamic performance of NACA23015 cross section airfoil are examined as a function of excitation location with ranging frequency range (50-400) Hz of the introduced acoustic. Tests are separately conducted in two sections, open type wind tunnels at the Reynolds number of 3.3x105 for measurement at angle of attack (0, 3, 6, 9 &12) deg. and 3x104 for the visualization at angle of attack (12) deg. based on the airfoil chord. Results indicated that the excitation frequency and the excitation location are the key parameters to alter the flow properties and thus to improve the aerodynamic performance. The most effective excitation frequency

The study aimed to explore the effectiveness of using rational judgment strategy in teaching science to develop scientific thinking for second-grade students. The researcher utilized the quasi-experimental approach based on (the pre/post designing) of two groups: experimental and control. As for tools: a test of scientific thinking prepared by the researcher that proved its verification of their validity and reliability. The test applied on a random sample of (66) students, divided into two groups: (34) experimental, and (32) control. The results showed that the experimental group outperformed the control group in the post-application of the scientific thinking test, In each skill separately, and in the total skills. The study recommende

Introduction and Aim: Pseudomonas aeruginosa is a nosocomial infection with an ability to develop high levels of antibiotic resistance. The efflux pump system is one of the mechanisms that is linked to multidrug resistance in P. aeruginosa. In this study, we employed siRNA loaded on gold nanoparticles against the MexA efflux pump gene to decrease the MexA gene expression in P. aeruginosa and estimated antibiotic resistance after gene silencing.   Materials and Methods: This study examined four strains of P. aeruginosa isolated from patients in various hospitals in Baghdad. Bacteria isolated were identified by biochemical tests and Vitek compact 2 system.  Single-stranded siRNA (33bp) designed in this study was loaded onto gold

DBN Rashid, IMPAT: International Journal of Research in Humanities, Arts, and Literature, 2016 - Cited by 5

Background: The efficacy of educational strategies is crucial for nursing students to competently perform pediatric procedures like nasogastric tube insertion. Specific Background: This study evaluates the effectiveness of simulation, blended, and self-directed learning strategies in enhancing these skills among nursing students. Knowledge Gap: Previous research lacks a comprehensive comparison of these strategies' impacts on skill development in pediatric nursing contexts. Aims: The study aims to assess the effectiveness of different educational strategies on nursing students' ability to perform pediatric nasogastric tube insertions. Methods: A pre-experimental design was employed at the College of Nursing, University of Baghdad, i

QJ Rashid, IH Abdul-Abbas, MR Younus, PalArch's Journal of Archaeology of Egypt/Egyptology, 2021 - Cited by 4

Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achi