To study the effect of iron overload due to continuous blood transfusions on peroxidation products, such as malondialdehyde (MDA) and peroxynitrite, with evaluation of some antioxidants like, glutathione (GSH), superoxide dismutase (SOD), vitamin A, vitamin C, vitamine E, Ceruloplasmin, uric acid and albumin in thalassemia patients. Forty patients with thalassemia major, aged 5 to 15 years, were carried out in Abn-Alatheer Teaching Hospital in Mosul city, during the period from October 2007 to April 2008. They were on Chelation therapy with desfer­rioxamine. They were divided into two groups, the first one without iron overload (90,97±12.92), and the second one with iron overload (157.75±7.57). All the patien

This research focuses on the synthesis of carbon nanotube (CNT) and Poly(3-hexylthiophene) (P3HT) (pristine polymer) with Ag doped (CNT/ P3HT@Ag) nanocomposite thin films to be utilised in various practical applications. First, four samples of CNT solution and different ratios of the polymer (P3HT) [0.1, 0.3, 0.5, and 0.7 wt.%] are prepared to form thin layer of P3HT@CNT nanocomposites by dip-coating method of Ag. To investigate the absorption and conductivity properties for use in various practical applications, structure, morphology, optical, and photoluminescence properties of CNT/P3HT @Ag nanocomposite are systematically evaluated in this study. In this regard, the UV/Vis/NIR spectrophotometer in the wavelength range of 350 to 7

Circular data (circular sightings) are periodic data and are measured on the unit's circle by radian or grades. They are fundamentally different from those linear data compatible with the mathematical representation of the usual linear regression model due to their cyclical nature. Circular data originate in a wide variety of fields of scientific, medical, economic and social life. One of the most important statistical methods that represents this data, and there are several methods of estimating angular regression, including teachers and non-educationalists, so the letter included the use of three models of angular regression, two of which are teaching models and one of which is a model of educators. ) (DM) (MLE) and circular shrinkage mod

Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such a

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