A new approach for baud time (or baud rate) estimation of a random binary signal is presented. This approach utilizes the spectrum of the signal after nonlinear processing in a way that the estimation error can be reduced by simply increasing the number of the processed samples instead of increasing the sampling rate. The spectrum of the new signal is shown to give an accurate estimate about the baud time when there is no apriory information or any restricting preassumptions. The performance of the estimator for random binary square waves perturbed by white Gaussian noise and ISI is evaluated and compared with that of the conventional estimator of the zero crossing detector.

The goal beyond this Research is to review methods that used to estimate Logistic distribution parameters. An exact estimators method which is the Moment method, compared with other approximate estimators obtained essentially from White approach such as: OLS, Ridge, and Adjusted Ridge as a suggested one to be applied with this distribution. The Results of all those methods are based on Simulation experiment, with different models and variety of  sample sizes. The comparison had been made with respect to two criteria: Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE).  

Alpha shape theory for 3D visualization and volumetric measurement of brain tumor progression using magnetic resonance images

This study aimed to determine the optimal conditions for extracting basil seed gum in addition to determine the chemical components of basil seeds. Additionally, the study aimed to investigate the effect of the mixing ratio of gum to ethanol when deposited on the basis of the gum yield which was1:1, 1:2, 1:3 (v/v) respectively. The best mixing ratio was one size of gum to two sizes of ethanol, which recorded the highest yield. Based on the earlier, the optimal conditions for extracting basil seed gum in different levels which included pH, temperature, mixing ratio seeds: water and the soaking duration were studied. The optimal conditions were: pH 8, temperature of 60°C, mixing ratio seeds: water 1:65 (w/v) and soaking duration of 30 min

 Alternative  distribution  to estimate the Dose – Response  model in bioassay  excrement

This research   concern to study five different distribution (Probit , Logistic, Arc sine , extreme value , One hit  ), to estimate  dose –response model by using m.l.e  and probit method This is done by determining different  weights in each  distribution in addition find all particular statistics for vital model . 

Olanzapine (OLZ) is classified as a typical antipsychotic drug utilized for the treatment of schizophrenia. Its oral bioavailability is 60% due to its low solubility and pre-systemic metabolism. Hence, the present work aims to formulate and evaluate OLZ nanoparticles dissolving microneedles (MNs) for transdermal delivery to overcome the problems associated with drug administration orally. OLZ nanoparticles were prepared by the nanoprecipitation method. The optimized OLZ nanoparticle formula was utilized for the fabrication of dissolving MNs by loading OLZ nanodispersion into polydimethylsiloxane (PDMS) micromould cavities, followed by casting the polymeric solution of polyvinylpyrrolidone(PVP-K30) and polyvinyl alcohol (PVA) to form

The aim of the present study is to investigate whether or not xanthine oxidase (XO)–derived reactive oxygen species (ROS) may play a role in the pathogenesis of alloxan (ALX)–induced diabetes in rats using the specific XO inhibitor and hydroxyl radical scavenger, allopurinol

The involvement of oxidative stress in ALX – diabetes was assessed by the measurement of plasma and various tissues lipid peroxides levels ( using thiobarbituric acid ( TBA ) reactive substances ). Furthermore, the ability of allopurinol to influence these and other biochemical parameters, including plasma and urine ketones levels were also investigated in diabetic rats.

Rats were divided into four groups: control, untreated diabe

The purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals