Abstract

The Classical Normal Linear Regression Model Based on Several hypotheses, one of them is Heteroscedasticity as it is known that the wing of least squares method (OLS), under the existence of these two problems make the estimators, lose their desirable properties, in addition the statistical inference becomes unaccepted table. According that we put tow alternative,  the first one is  (Generalized Least Square) Which is denoted by (GLS), and the second alternative is to (Robust covariance matrix estimation) the estimated parameters method(OLS), and that the way (GLS) method neat and certified, if the capabilities (Efficient) and the statistical inference Thread on the basis of an acceptable

Isolation of fungi was performed from February to July, 2019. One hundred clinical specimens were collected from King Abdullah Hospital (KAH) Bisha, Saudi Arabia. Samples were collected from twenty patients of different ages (30 - 70 years old) ten males and ten females. The samples were collected from patients with the two types of diabetics. Specimens included blood, hair, nail, oral swabs and skin.  Specimens were inoculated on Sabourauds Dextrose agar containing chloramphenicol. Thirteen fungal species were isolated and identified. The isolated species were: Aspergillus flavus, A. niger, A. terrus, A. nidulans, A. fumigatus, Candida albicans, C. krusei, C. parapsilosis, C. Tropicalis, Curvularia lunata, Fusarium solani, Penicill

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.

A new technique for embedding image data into another BMP image data is presented. The image data to be embedded is referred to as signature image, while the image into which the signature image is embedded is referred as host image. The host and the signature images are first partitioned into 8x8 blocks, discrete cosine transformed “DCT”, only significant coefficients are retained, the retained coefficients then inserted in the transformed block in a forward and backward zigzag scan direction. The result then inversely transformed and presented as a BMP image file. The peak signal-to-noise ratio (PSNR) is exploited to evaluate the objective visual quality of the host image compared with the original image.

Background: Poly (methyl methacrylate) has several disadvantages (poor mechanical properties) like impact and transverse strength. In order to overcome these disadvantages, several methods were used to strengthen the acrylic resin by using different fibers or fillers. This study was conducted to evaluate the effect of Plasma treatment of the fiber on mechanical properties Poly (methyl methacrylate) denture base material. Materials and methods: Specimens were prepared from poly methyl metha acrylic (PMMA) divided according to present of fiber into 4 groups (first group without fiber as control group, second group with Plasma treated polyester fibers, third group with Plasma treated polyamide fibers and fourth group Plasma treated combination

ABSTRACT Porous silicon has been produced in this work by photochemical etching process (PC). The irradiation has been achieved using ordinary light source (150250 W) power and (875 nm) wavelength. The influence of various irradiation times and HF concentration on porosity of PSi material was investigated by depending on gravimetric measurements. The I-V and C-V characteristics for CdS/PSi structure have been investigated in this work too.

            This study tests the effect of a large number of independent variables that control the growth of the total productivity, which amounted to 112 variables, gathered from what is mentioned in the specialized theoretical and applied literature. The data for these variables were taken from global reports of sound international organizations and reliable databases covering the period 1991-2016. The data of the dependent variable, the growth of the total factor productivity, were taken from the database of the world development indicators. The study covered 61 countries for which data were available. The study included three regression models to explain