In this paper a mathematical model that describes the flow of infectious disease in a population is proposed and studied. It is assumed that the disease divided the population into four classes: susceptible individuals (S), vaccinated individuals (V), infected individuals (I) and recover individuals (R). The impact of immigrants, vaccine and external sources of disease, on the dynamics of SVIRS epidemic model is studied. The existence, uniqueness and boundedness of the solution of the model are discussed. The local and global stability of the model is studied. The occurrence of local bifurcation as well as Hopf bifurcation in the model is investigated. Finally the global dynamics of the proposed model is studied numerically.

Non tuberculous mycobacteria (NTM) present in the environment, mainly in water and not transmitted from human to human. The lung is the most common target of NTM infections in human. The symptoms of NTM infection are: fever, weight loss and abdominal pain.
114 sputum samples were examined microscopically using Ziehl-Nelsen stain, all were positive. They cultured on Lowenstein-Jensen media.
The results of this study indicate presence of 3 (2.6%) of rapidly growing mycobacteria, phenotypically they resemble T.B. bacilli. They were diagnosed by biochemical tests, they were positive for catalase and pyruvate, negative for niacin, nitrate reduction test, sensitive for Thiophene-2-carboxyl acid hydrozide test& resistant for Para nitr

Plant tissue culture considers a benefit biotechnological technique for scientific research especially the production of undifferentiation callus cells and regeneration through suspension or static media. The seedlings of Peganum harmala was used as a source to produce callus mass in vitro in static media through different tissue culture media supplemented by varying combinations of plant growth regulators (PGR). The result illustrates that 2 mg/l of Kinitine with 0.5 mg/l of 2, 4-D was efficient to stimulate callus induction with percent 100% in stem and root of P. harmala and this combination gave a high fresh weight, 1954 mg in root and 1170
mg in stem and high dry weight in root and stem was 74.6

Abstract                                                         

The issue of the protection of the environment is a shared responsibility between several destinations and sectors, and constitutes a main subject in which they can achieve sustainable development. In the sectors of government programs can be set up towards the establishment of the government sector to the green environment, so to be the implementati

A simple, rapid and sensitive method for the analysis of Atenolol in pure and pharmaceutical preparation as an alternative analytical procedure were developed by continuous flow injection analysis via turbidimetric (T180o) and scattered light effect at two opposite position (2N90o). The method is based upon the formation of white precipitate for the ion pair compound by phosphomolybidic acid with Atenolol in aqueous medium. The precipitate is measured via the attenuation of incident light and scattering of the incident light in two opposite direction namely +90o and -90o angle were measured. Chemical and physical parameters were investigated. The linearity of Atenolol is ranged from (0.1-11) mmol.L-1, with correlation coefficient r=0.993

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

     Sentiment Analysis is a research field that studies human opinion, sentiment, evaluation, and emotions towards entities such as products, services, organizations, events, topics, and their attributes. It is also a task of natural language processing. However, sentiment analysis research has mainly been carried out for the English language. Although the Arabic language is one of the most used languages on the Internet, only a few studies have focused on Arabic language sentiment analysis.

     In this paper, a review of the most important research works in the field of Arabic text sentiment analysis using deep learning algorithms is presented. This review illustrates the main steps used in these studies, which include

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show