The research dealt with a comparative study between some semi-parametric estimation methods to the Partial linear Single Index Model using simulation. There are two approaches to model estimation two-stage procedure and MADE to estimate this model. Simulations were used to study the finite sample performance of estimating methods based on different Single Index models, error variances, and different sample sizes , and the mean average squared errors were used as a comparison criterion between the methods were used. The results showed a preference for the two-stage procedure depending on all the cases that were used

      Some cases of common fixed point theory for classes of generalized nonexpansive maps are studied. Also, we show that the Picard-Mann scheme can be employed to approximate the unique solution of a mixed-type Volterra-Fredholm functional nonlinear integral equation.

Because of the experience of the mixture problem of high correlation and the existence of linear MultiCollinearity between the explanatory variables, because of the constraint of the unit and the interactions between them in the model, which increases the existence of links between the explanatory variables and this is illustrated by the variance inflation vector (VIF), L-Pseudo component to reduce the bond between the components of the mixture.

    To estimate the parameters of the mixture model, we used in our research the use of methods that increase bias and reduce variance, such as the Ridge Regression Method and the Least Absolute Shrinkage and Selection Operator (LASSO) method a

The aim of our study is to solve a nonlinear epidemic model, which is the COVID-19 epidemic model in Iraq, through the application of initial value problems in the current study. The model has been presented as a system of ordinary differential equations that has parameters that change with time. Two numerical simulation methods are proposed to solve this model as suitable methods for solving systems whose coefficients change over time. These methods are the Mean Monte Carlo Runge-Kutta method (MMC_RK) and the Mean Latin Hypercube Runge-Kutta method (MLH_RK). The results of numerical simulation methods are compared with the results of the numerical Runge-Kutta 4th order method (RK4) from 2021 to 2025 using the absolute error, which prove

1267 Objectives Aim to evaluate 198Au nanoparticles (AuNP) biodistribution and uptake in a human prostate model for treatment. Many phytochemicals are known to have anti-tumor properties but have short half-lives in vivo. We hypothesized that using these phytochemicals to formulate and coat AuNP would inhibit enzyme cleavage and enhance their anti-tumor properties. Initial evaluations were performed in SCID mice bearing PC3 tumors. Methods : 198AuNP were formulated with the following gum Arabic, epigalocatechin gallate (EGCg) pomegranate extract and mangiferin extract. The resultant nanoparticles were evaluated in normal mice and in human prostate bearing SCID mice. The tumor bearing mice were injected intratumorally with 3-5 uCi of 198A

Recently, wireless communication environments with high speeds and low complexity have become increasingly essential. Free-space optics (FSO) has emerged as a promising solution for providing direct connections between devices in such high-spectrum wireless setups. However, FSO communications are susceptible to weather-induced signal fluctuations, leading to fading and signal weakness at the receiver. To mitigate the effects of these challenges, several mathematical models have been proposed to describe the transition from weak to strong atmospheric turbulence, including Rayleigh, lognormal, Málaga, Nakagami-m, K-distribution, Weibull, Negative-Exponential, Inverse-Gaussian, G-G, and Fisher-Snedecor F distributions. This paper extensive

بهذا البحث نقارن معاييرالمعلومات التقليدية (AIC , SIC, HQ , FPE ) مع معيارمعلومات الانحراف المحور (MDIC) المستعملة لتحديد رتبة انموذج الانحدارالذاتي (AR) للعملية التي تولد البيانات,باستعمال المحاكاة وذلك بتوليد بيانات من عدة نماذج للأنحدارالذاتي,عندما خضوع حد الخطأ للتوزيع الطبيعي بقيم مختلفة لمعلماته

      The detection of diseases affecting plant is very important as it relates to the issue of food security, which is a very serious threat to human life. The system of diagnosis of diseases involves a series of steps starting with the acquisition of images through the pre-processing, segmentation and then features extraction that is our subject finally the process of classification. Features extraction is a very important process in any diagnostic system where we can compare this stage to the spine in this type of system. It is known that the reason behind this great importance of this stage is that the process of extracting features greatly affects the work and accuracy of classification. Proper selection of

Background: The rhizome of ginger is used in cooking and for medicinal purposes such as anti-bacterial, anti-fungal, anti-inflammatory and antioxidant. The aims of the study were to test the effect of ethanolic extract of ginger on growth, adherence and acidogenicity of mutans streptococci in comparison to chlorhexidine gluconate 0.2% and de-ionized water. Materials and methods: From saliva often volunteers (dental students 20-22 years); mutans streptococci was isolated, purified and diagnosed according to morphological characteristic and biochemical tests. Ginger was powdered and extracted, different concentrations of ginger extract were prepared. Chlorhexidine gluconate 0.2% used as a control positive; while de-ionized water was used as a