A histological study showed the wall of the stomach in Pica pica and Herpestes javanicus consists of four layers: mucosa, submucosa, muscularis externa and serosa. Also, the present study showed many  differences in the histological structures of the stomach for each in both types. The stomach of P. pica consists of two portions: the proventiculus and gizzard, while the stomach of H. javanicus consists of three portions: cardiac, fundic and pyloric regions. The mucosa layer formed short gastric folds, named plicae. In the proventiculus of P. pica, sulcus is found between each two plicae, but the folds called gastric p

          A histological study showed the wall of the stomach in Pica pica and Herpestes javanicus consists of four layers: mucosa, submucosa, muscularis externa and serosa. Also, the present study showed many  differences in the histological structures of the stomach for each in both types. The stomach of P. pica consists of two portions: the proventiculus and gizzard, while the stomach of H. javanicus consists of three portions: cardiac, fundic and pyloric regions. The mucosa layer formed short gastric folds, named plicae. In the proventiculus of P. pica, sulcus is found between each two plicae, but the folds called gastric pits in the gizzard, which are full with koilin. Lamina properia in both types contained gastric g

HR Al-Hamamy, AA Noaimi, HA Salman, NAA Jabbar, American Journal of Dermatology and Venereology, 2013

      Long memory analysis is one of the most active areas in econometrics and time series where various methods have been introduced to identify and estimate the long memory parameter in partially integrated time series. One of the most common models used to represent time series that have a long memory is the ARFIMA (Auto Regressive Fractional Integration Moving Average Model) which diffs are a fractional number called the fractional parameter. To analyze and determine the ARFIMA model, the fractal parameter must be estimated. There are many methods for fractional parameter estimation. In this research, the estimation methods were divided into indirect methods, where the Hurst parameter is estimated fir

Background: The Titanium and its alloys are suitable for dental implant and medical applications. Biocompatibility of the materials is a major factor in determining the success of the implant and has a great impact on their rate of osseointegration. The aim of this study was to evaluate the biocompatibility and cytotoxicity of Ti2AlC in comparison to CPTi & Ti6Al7Nb in rabbits. Materials and Methods: 10 male New Zealand White rabbits, weighing (2-2.5 kg), aged (10-12 months) were used in this study. Cylindrical implants were prepared from the study materials (CPTi, Ti6Al7Nb and Ti2AlC) with (8mm) height and (3mm) diameter for the evaluation of tissue response and disc specimens were prepared with (6 mm) diameter and (2 mm) thickness for ev

Background: Thermocycling simulates the temperature dynamics in the oral environment. This in vitro study done to measure and compare the effect of thermocycling on the shear bond strength of stainless steel and sapphire brackets bonded to human enamel teeth using light cured orthodontic adhesive and debonded at various time, and to measure adhesive remnant index after debonding. Materials and Methods: one-hundred-twenty extracted upper first premolars for orthodontic reason were used in this study; depending on weather thermocycled or not, the sample was divided into two main groups, then within each group 30 teeth were used for stainless-steel brackets (Bionic®) and for sapphire brackets (Pure®). Both groups were subdivided into three

Time series is an important statistical method adopted in the analysis of phenomena, practices, and events in all areas during specific time periods and predict future values ​​contribute to give a rough estimate of the status of the study, so the study aimed to adopt the ARIMA models to forecast the volume of cargo handled and achieved in four ports (Umm Qasr Port, Khor Al Zubair Port, Abu Flus Port, and Maqal Port(, Monthly data on the volume of cargo handled for the years (2006-2018) were collected (156) observations. The study found that the most efficient model is ARIMA (1,1,1).

The volume of go