In this paper, several combination algorithms between Partial Update LMS (PU LMS) methods and previously proposed algorithm (New Variable Length LMS (NVLLMS)) have been developed. Then, the new sets of proposed algorithms were applied to an Acoustic Echo Cancellation system (AEC) in order to decrease the filter coefficients, decrease the convergence time, and enhance its performance in terms of Mean Square Error (MSE) and Echo Return Loss Enhancement (ERLE). These proposed algorithms will use the Echo Return Loss Enhancement (ERLE) to control the operation of filter's coefficient length variation. In addition, the time-varying step size is used.The total number of coefficients required was reduced by about 18% , 10% , 6%

The research aims to find out the relationship between the medical and nursing staffing and its impact on the quality of provided health service in the Yarmouk Teaching Hospital, and to understand the nature of the relationship between doctors and nurses, it has been the adoption of cooperation and conflict Kmngaran independent knowledge of the relationship between doctors and nurses and the quality of health service as a variable certified by eating five dimensions (tangibility, reliability, safety, responsiveness, empathy). The research was adopted to collect data on the questionnaire prepared medical and nursing to the angel and adopted included Likert Quintet her, and distributed to doctors working in the Yarmouk hospital in

   This research includes the application of non-parametric methods in estimating the conditional survival function represented in a method (Turnbull) and (Generalization Turnbull's) using data for Interval censored of breast cancer and two types of treatment, Chemotherapy and radiation therapy and age is continuous variable, The algorithm of estimators was applied through using (MATLAB) and then the use average Mean Square Error (MSE) as amusement  to the estimates and the results showed (generalization of Turnbull's) In estimating the conditional survival function and for both treatments ,The estimated survival of the patients does not show very large differences

Human cytomegalovirus (CMV) is the globally highly prevalent herpesvirus worldwide. CMV infects populations of all ages according to the Center for Disease Control and Prevention (CDC) and World Health Organization (WHO). CMV infections remain the most common viral complication potentially multiple in humans and are a major cause of congenital normality in women, which is why they are critical for diagnosis in several times when it happens during pregnancy. Pregnant women with CMV infection can be in charge of abortion or congenital expandaedby. This study involves the collection a total of (90) samples taken from each aborted and pregnant woman (70 with abortion cases and 20 of pregnant without history of abortion as control subjects) r

A new series polymers was synthesized from reaction starting material Bisacodyl A or [(2-Pyridinylmethylene) di-4, 1-phenylene di acetate] with hydrogen bromide, then the products were polymerized by addition polymerization from used adipoyl and glutaroyl chloride. The structure of these compounds was characterized by FT-IR, melting points, TLC, X-Ray, DSC and 1H-NMR for starting material. These compounds were also screened for their antibacterial activists?

<p><strong>Objective: </strong>The aim of our study was to compare between flavonoids and phenolic acids contents of leaves and fruits of <em>Melia azedarach</em> since no phytochemical investigation had done previously in Iraq.</p><p><strong>Methods: </strong>The leaves and fruits of <em>Melia azedarach </em>were extracted by soxhlet using 80% ethanol then the dried extract was suspended in water and fractionated using petroleum ether, chloroform, ethyl acetate, and n-butanol. The n-butanol fraction was hydrolyzed by acid and partitioned with ethyl acetate. The different fractions containing flavonoids and phenolic acids were analyzed by HPLC and HPTLC.</p><

Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an