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

         Serum adenosine deaminase (ADA) activity was determined in 30 blood sample of type 1 diabetic individuals 30 blood sample for the type 2 and 15 normal children as a control for type 1 15 normal adults as control for type 2. The mean ADA activity and specific activity in type 1 was (8.85± 5.55 U/mg of protein) which is compared with control (32.11± 1.54 U/mg of protein) while in type 2 was (48.46±11.91 U/mg of protein) is compared with control (5.18± 2.27 U/mg of protein ). We conclude that the altered blood level of ADA activity may help in predicting immunological dysfunction in diabetic individuals and also has a prognostic value.

Arum maculatum is traditionally used for the control of many diseases and illnesses such as kidney pain, liver injury, hemorrhoids. However, the detailed biomedical knowledge about this species is still lacking. This study reports on the bioactive components and the possible mechanisms underlying the antioxidant, anti-inflammatory and cytotoxic activity of A. maculatum leaf extract. Gas chromatography-mass spectrometry (GC-MS) was used for phytochemical analysis. Assay of 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide ) (MTT) was used to determine the cytotoxicity in the murine cell line L20B upon exposure to different extract concentrations for 24 h. Enzyme-linked immunosorbent assay (ELISA) was used to detect pro-in

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