Deflection angle of photon from magnetized black hole and effect of nonlinear electrodynamics

In this paper, we analyze deflection angle of photon from magnetized black hole within non-linear electrodynamics with parameter $\beta$. In doing so, we find the corresponding optical spacetime metric and then we calculate the Gaussian optical curvature. Using the Gauss-Bonnet theorem, we obtain the deflection angle of photon from magnetized black hole in weak field limits and show the effect of non-linear electrodynamics on weak gravitational lensing. We also analyzed that our results reduces into Maxwell's electrodynamics and Reissner-Nordstr\"{o}m (RN) solution with the reduction of parameters. Moreover, we also investigate the graphical behavior of deflection angle w.r.t correction parameter, black hole charge and impact parameter.Comment: 9 page

Assessment of the Health Status and Needs of Bahraini Women.

Women’s health problems in Bahrain are varied and create a major challenge for the health system, increasing demand on health centers and requiring provision of comprehensive health services for women throughout their life span. Lack of women’s involvement in planning health care services and health policies, along with limited research and literature regarding women’s health in Bahrain, has resulted in health disparities such as increased chronic diseases.This was a cross-sectional study designed to examine the perceived and actual health status, health practices and needs for Bahraini women aged 18 to 64 years, while examining the reliability and validity of the SF-36 scale with Bahraini population, during the period August to October 2009. The SF-36 Health Status Survey (Arabic Version), a structured questionnaire, and medical chart reviews for chronic conditions were used. In addition, women’s blood pressure, weight and height were measured. A systematic random sample of 258 women was selected from local health centers. The SF-36 perceived health status scale was found to be reliable and valid when used with this population, however the reliability of the scale could be improved if the scale were modified to the local Bahraini dialect. The perceived health status scores for Bahraini women were similar to the Arabic populations and different from US populations. Obesity, anemia, hyperlipidemia, diabetes and hypertension were identified as the most common women’s health problems in Bahrain. Results showed that 70% of the women were overweight or obese and the number of chronic conditions they had was a significant determinant of their health status. Bahraini women identified access to women’s health specialists, annual screening for chronic conditions, nutritional, health education, and exercise programs as their highest priority health care needs. Recommendations to re-examine the SF-36 scale when modified to the local language, re-evaluation of women’s health services, and planning future services according to their health needs is fundamental to improve health of Bahraini women’s.Ph.D.NursingUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77851/1/mukhaim_1.pd

Impact of Fungiform Papillae Count on Taste Perception and Different Methods of Taste Assessment and their Clinical Applications: A comprehensive review

Fungiform papillae are raised lingual structures which contain taste buds and thus play an important role in taste perception. These structures vary in number due to their relative sensitivity to a range of systemic and local factors which affect the dorsum of the tongue. Taste sensation can be measured using both chemical and electrical methods; however, the number of fungiform papillae has a direct effect on chemogustometric and electrogustometric values during evaluation. This review provides a general overview of fungiform papillae, their quantification methods and the various factors which may affect these structures. In addition, numerous methods of recording taste sensation and their clinical applications are highlighted.Keywords: Sensation; Taste; Taste Perception; Tongue; Taste Buds; Investigative Techniques

Named Entity Disambiguation using Hierarchical Text Categorization

Named entity extraction is an important step in natural language processing. It aims at finding the entities which are present in text such as organizations, places or persons. Named entities extraction is of a paramount importance when it comes to automatic translation as different named entities are translated differently. Named entities are also very useful for advanced search engines which aim at searching for a detailed information regarding a specific entity. Named entity extraction is a difficult problem as it usually requires a disambiguation step as the same word might belong to different named entities depending on the context. This work has been conducted on the ANERCorp named entities database. This Arabic database contains four different named entities: person, organization, location and miscellaneous. The database contains 6099 sentences, out of which 60% are used for training 20% for validation and 20% for testing. Our method for named entity extraction contains two main steps: the first step predicts the list of named entities which are present at the sentence level. The second step predicts the named entity of each word of the sentence. The prediction of the list of named entities at the sentence level is done through separating the document into sentences using punctuation marks. Subsequently, a binary relation between the set of sentences (x) and the set of words (y) is created from the obtained list of sentences. A relation exists between the sentence (x) and the word (y) if, and only if, (x) contains (y). A binary relation is created for each category of named entities (person, organization, location and miscellaneous). If a sentence contains several named entities, it is duplicated in the relation corresponding to each one of them. Our method then extracts keywords from the obtained binary relations using the hyper concept method [1]. This method decomposes the original relation into non-overlapping rectangles and highlights for each rectangle the most representative keyword. The output is a list of keywords sorted in a hierarchical ordering of importance. The obtained keyword list associated with each category of named entities are fed into a random forest classifier of 10000 random trees in order to predict the list of named entities associated with each sentence. The random forest classifier produces for each sentence the list of probabilities corresponding to the existence of each category of named entities within the sentence. Random Forest [sentence(i)] = (P(Person),P(Organization),P(Location),P(miscellaneous)). Subsequently, the sentence is associated with the named entities for which the corresponding probability is larger than a threshold set empirically on the validation set. In the second step, we create a lookup table associating to each word in the database, the list of named entities to which it corresponds in the training set. For unseen sentences of the test set, the list of named entities predicted at the sentence level is produced, and for each word, the list of predicted named entities is also produced using the lookup table previously built. Ultimately, for each word, the intersection between the two predicted lists of named entities (at the sentence and the word level) will give the final predicted named entity. In the case where more than one named entity is produced at this stage, the one with the maximum probability is kept. We obtained an accuracy of 76.58% when only considering lookup tables of named entities produced at the word level. When performing the intersection with the list produced at the sentence level the accuracy reaches 77.96%. In conclusion, the hierarchical named entity extraction leads to improved results over direct extraction. Future work includes the use of other linguist features and larger lookup table in order to improve the results. Validation on other state of the art databases is also considered. Acknowledgements This contribution was made possible by NPRP grant #06-1220-1-233 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.qscienc

New Normal and Abnormal Red Blood Cells Features for Improved Classification

This paper focused obtaining new features for improved classification of red blood cells (RBCs). RBCs varies according to shapes, colors and sizes. Abnormal RBCs may be caused by anemia. Abnormal RBCs has great similarities among each other causing difficulties in medical diagnosis. In this work, spatial, spectral statistical features and geometrical features of RBCs are extracted from 1000 normal and abnormal RBCs. The extracted features are reduced using Principal Component Analysis (PCA) and tested with different types of machine learning algorithms for classification. Classifications were evaluated for high sensitivity, specificity, and kappa statistical parameters. The classifications yielded accuracy rates of 97.9%, 98% and 98% for discriminative (SVM), generative (RBFNN) and clustering (K-NN) algorithm respectively, which is an improvement over previous works

Human immunodeficiency virus infection and chemotherapy treatment in the Kingdom of Bahrain

Human immunodeficiency virus (HIV) is a lentivirus which that may progress to immunodeficiency syndrome (AIDS) and predispose for opportunistic infections and malignancies. According to recent reports,  1.5 million people died of AIDS in 2013 worldwide which is a 35% decrease since 2005. The number of deaths has decreased in part due to antiretroviral treatment (ART) wide spread use. In Bahrain, in 2011, a multidisciplinary team was established for HIV management involving major stakeholders: public health, infectious diseases, pharmacists, nursing and virologists. A retrospective descriptive study is done about HIV positive patient in Bahrain, their current treatment regimens and other blood parameters were collected, aiming to have a general idea about their health status in a way to help in their medical care. The data was collected retrospectively from the 2014 registry about all patients who are diagnosed to have HIV. Their ART regimen, CD 4 count and viral load were gathered and entered in Excel sheet.. A total of 208 patients were diagnosed to have HIV up to 2014. However, only 108 of them have their full data. In addition, a review of the frequency of admission of these patients over a 10 year period was done too. On reviewing the medical records of the patients admitted over the last ten years from 2004 till 2014, it was found that the total admission of HIV positive patients were 107. Hepatitis C was the most common co infection among those patients with a percentage of 24%. It was found that with better ART treatment and better structure of HIV team and program, we are getting more patients to be controlled. There is a clear increase in the number of patients with improving CD4 count. Based on the results of our study, the HIV multi-disciplinary management team is an essential part for the best management of these patients

Multistage optimal homotopy asymptotic method for solving initial-value problems

In this paper, a new approximate analytical algorithm namely multistage optimal homotopy asymptotic method (MOHAM) is presented for the first time to obtain approximate analytical solutions for linear, nonlinear and system of initial value problems (IVPs).This algorithm depends on the standard optimal homotopy asymptotic method (OHAM), in which it is treated as an algorithm in a sequence of subinterval. The main advantage of this study is to obtain continuous approximate analytical solutions for a long time span.Numerical examples are tested to highlight the important features of the new algorithm.Comparison of the MOHAM results, standard OHAM, available exact solution and the fourth-order Runge Kutta (RK4) reveale that this algorithm is effective, simple and more impressive than the standard OHAM for solving IVPs

Comparative Study Using WEKA for Red Blood Cells Classification

Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as "anemia". Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively

Numerical solution of n’th order fuzzy initial value problems by six stages

The purpose of this paper is to present a numerical approach to solve fuzzy initial value problems (FIVPs) involving n-th order ordinary differential equations.The idea is based on the formulation of the six stages Runge-Kutta method of order five (RKM56) from crisp environment to fuzzy environment followed by the stability definitions and the convergence proof.It is shown that the n-th order FIVP can be solved by RKM56 by transforming the original problem into a system of first-order FIVPs. The results indicate that the method is very effective and simple to apply.An efficient procedure is proposed of RKM56 on the basis of the principles and definitions of fuzzy sets theory and the capability of the method is illustrated by solving second-order linear FIVP involving a circuit model problem