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.

Hemorrhoids are one of the most common surgical conditions. The hemorrhoid may cause symptoms that are: bleeding, pain, prolapse, itching, spoilage of feces, and psychologic discomfort. There are many methods for treatment of hemorrhoid like, medical therapy, rubber band ligation, electerocoagulation, stapled hemorrhoidpexy, photocoagulation, sclerothereapy, doppler guided artery ligation, Cryosurgery, and surgery. All methods for treatment of hemorrhoids have advantages, disadvantages, and limitations. Conventional haemorrhoidectomy was the traditional operation for the treatment of hemorrhoids. But recently other modalities of treatment had been used as an alternative operations including CO2 laser haemorrhoidectomy. This work aims to

In this paper, an algorithm for reconstruction of a completely lost blocks using Modified
Hybrid Transform. The algorithms examined in this paper do not require a DC estimation
method or interpolation. The reconstruction achieved using matrix manipulation based on
Modified Hybrid transform. Also adopted in this paper smart matrix (Detection Matrix) to detect
the missing blocks for the purpose of rebuilding it. We further asses the performance of the
Modified Hybrid Transform in lost block reconstruction application. Also this paper discusses
the effect of using multiwavelet and 3D Radon in lost block reconstruction.

Expansive soils are recognized by their swelling potential upon wetting due to the existence of some clay minerals such as  montmorillonite. An effective solution was found to avoid the danger of such soils by using piles. A single pile embedded in an elasto-plastic expansive soil has been analyzed by using one of the available software which is ABAQUS to investigate the effect of applied loads on pile’s top and investigate the effect of swelling soils on load carrying capacity of the pile. The result shows that as the pile is axially loaded at its top, the axial force along the pile gradually changes from (tension) to (compression) and the pile tends to move downward. The applied load needed to initiate pile’s settlement depend

Afamin, which is a human plasma glycoprotein, a putative multifunctional transporter of hydrophobic molecules and a marker for metabolic syndrome. Afamin concentration have been proposed to have a significant role as a predictor of metabolic disorders. Since NAFLD is associated with metabolic risk factors, e.g., dyslipidemia, insulin resistance and visceral obesity, it is considered as the hepatic manifestation of the metabolic syndrome. The objective of this study is to determine Afamin levels in hypothyroid patients with and without fatty liver disease and compare the results with controls. Also to study the relationship of Afamin level with the Anthropometric and Clinical Features (Age, Gender, BMI and Duration of Hypothyroidism) , Serum

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor