Objective: The purpose of this study was to assess the effectiveness of Vibriophage Universiti Sains Malaysia 8 (VPUSM 8), a bacteriophage that destroys bacteria, in managing the proliferation of Vibrio cholerae, specifically the El Tor serotype, as an alternate therapeutic strategy. Methods: The study entailed subjecting water samples from Kelantan, Malaysia, to reproduce the natural circumstances that promote the growth of V. cholerae. Subsequently, the samples were contaminated with the V. cholerae O1 El Tor Inaba strain and treated using VPUSM 8. The study employed a controlled experimental design, wherein the samples were divided into three groups, each experiencing different treatment methods. Quantifying the number of colony-

Background: It is well-known that silicon oil (SO) injection into the vitreous cavity after pars plana vitrectomy is usually associated with high intraocular pressure.

Objectives: To determine the influence of silicon oil (SO) removal on IOP level after pars plana vitrectomy for spontaneous rhegmatogenous retinal detachment (RRD)

Subjects and Methods: A prospective study was conducted at Ibn Al-Haitham eye teaching hospital, Baghdad- Iraq. Intraocular pressure (IOP) was measured pre and post SO removal in patients who have underwent retinal detachment surgery with SO injection of 1000 centistokes (cSt) viscosity. Baseline IOP was measured for all the patient before the SO

Novel bidentate Schiff bases having nitrogen-sulphur donor sequence was synthesized from condensation of racemate camphor, (R)-camphor and (S)-camphor with Methyl hydrazinecarbodithioate (SMDTC). Its metal complexes were also prepared through the reaction of these ligands with silver and bismuth salts. All complexes were characterized by elemental analyses and various physico-chemical techniques. These Schiff bases behaved as uninegatively charged bidentate ligands and coordinated to the metal ions via ?-nitrogen and thiolate sulphur atoms. The NS Schiff bases formed complexes of general formula, [M(NS)2] or [M(NS)2.H2O] where M is BiIII or AgI, the expected geometry is octahedral for Bi(III) complexes while Ag(I) is expected to oxidized t

The aim of this research is to assess the validity of Detailed Micro-Modeling (DMM) as a numerical model for masonry analysis. To achieve this aim, a set of load-displacement curves obtained based on both numerical simulation and experimental results of clay masonry prisms loaded by a vertical load. The finite element method was implemented in DMM for analysis of the experimental clay masonry prism. The finite element software ABAQUS with implicit solver was used to model and analyze the clay masonry prism subjected to a vertical load. The load-displacement relationship of numerical model was found in good agreement with those drawn from experimental results. Evidence shows that load-displacement curvefound from the finite element m

An analytical approach based on field data was used to determine the strength capacity of large diameter bored type piles. Also the deformations and settlements were evaluated for both vertical and lateral loadings. The analytical predictions are compared to field data obtained from a proto-type test pile used at Tharthar –Tigris canal Bridge. They were found to be with acceptable agreement of 12% deviation.

               Following ASTM standards D1143M-07e1,2010, a test schedule of five loading cycles were proposed for vertical loads and series of cyclic loads to simulate horizontal loading .The load test results and analytical data of 1.95

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Recently, the internet has made the users able to transmit the digital media in the easiest manner. In spite of this facility of the internet, this may lead to several threats that are concerned with confidentiality of transferred media contents such as media authentication and integrity verification. For these reasons, data hiding methods and cryptography are used to protect the contents of digital media. In this paper, an enhanced method of image steganography combined with visual cryptography has been proposed. A secret logo (binary image) of size (128x128) is encrypted by applying (2 out 2 share) visual cryptography on it to generate two secret share. During the embedding process, a cover red, green, and blue (RGB) image of size (512