Many of the dynamic processes in different sciences are described by models of differential equations. These models explain the change in the behavior of the studied process over time by linking the behavior of the process under study with its derivatives. These models often contain constant and time-varying parameters that vary according to the nature of the process under study in this We will estimate the constant and time-varying parameters in a sequential method in several stages. In the first stage, the state variables and their derivatives are estimated in the method of penalized splines(p- splines) . In the second stage we use pseudo lest square to estimate constant parameters, For the third stage, the rem

Background: The ultimate purpose of this prospective study is to estimate and measure swelling associated with surgical extrac¬tion of impacted mandibular third molars in different four post-operative times and to identify the risk factors associated with determination of their risk degree. Material and Methods: In this prospective cohort study 159 consecutive cases in which removal of impacted lower third molars in 107outpatients were evaluated. Five groups of variables have been studied which are regarded as a potential factor for swelling after mandibular third removal which will enable the surgeon to predict and counsel high risk patients in order to offer a preventive strategy. Results: Facial measurements were carried out on 1st, 2

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the

 A mixture model is used to model data that come from more than one component. In recent years, it became an effective tool in drawing inferences about the complex data that we might come across in real life. Moreover, it can represent a tremendous confirmatory tool in classification observations based on similarities amongst them. In this paper, several mixture regression-based methods were conducted under the assumption that the data come from a finite number of components. A comparison of these methods has been made according to their results in estimating component parameters. Also, observation membership has been inferred and assessed for these methods. The results showed that the flexible mixture model outperformed the others

The analysis of survival and reliability considered of topics and methods of vital statistics at the present time because of their importance in the various demographical, medical, industrial and engineering fields. This research focused generate random data for samples from the probability distribution Generalized Gamma: GG, known as: "Inverse Transformation" Method: ITM, which includes the distribution cycle integration function incomplete Gamma integration making it more difficult classical estimation so will be the need to illustration to the method of numerical approximation and then appreciation of the function of survival function. It was estimated survival function by simulation the way "Monte Carlo". The Entropy method used for the

Exponential distribution is one of most common distributions in studies and scientific researches with wide application in the fields of reliability, engineering and in analyzing survival function therefore the researcher has carried on extended studies in the characteristics of this distribution.

In this research, estimation of survival function for truncated exponential distribution in the maximum likelihood  methods and Bayes first and second method, least square method and Jackknife dependent in the first place on the maximum likelihood method, then on Bayes first method then comparing then using simulation, thus to accomplish this task, different size samples have been adopted by the searcher us

Developing routes to produce cellulose nanocrystals (CNCs) from high-lignin wood residues is essential for expanding sustainable nanocellulose feedstocks. In this work, Meranti (Shorea sp.) sawdust was valorized into CNCs by integrating ammonium persulfate (APS) pretreatment with subsequent sulfuric acid hydrolysis. To establish a severity map and define an operating window, APS pretreatment severity was systematically varied by adjusting APS concentration and the sawdust-to-APS (solid-to-liquid) ratio, and its influence on CNC structural properties was evaluated. APS pretreatment partially disrupted and oxidized the lignocellulosic matrix, enabling acid hydrolysis to liberate crystalline cellulose domains. XRD analysis confirmed th

Chronic periodontitis (CP) is an inflammatory disease affecting tooth supporting structures in response to bacterial dental plaque causing irreversible tissue destruction. Cyclooxygenase-2 (COX-2) is an effective mediator in the pathogenesis of periodontitis. Polymorphisms in the COX-2 gene may contribute to its overexpression and increased disease susceptibility. To evaluate the association between -1195 A/G single nucleotide polymorphism (SNP) in the promotor area of the cyclooxygenase-2(COX-2) gene and severity of chronic periodontitis in a sample of Iraqi population. -1195A/ G COX-2 SNP was investigated in 70 chronic periodontitis (CP) cases and 30 healthy controls. CP cases composed of 2 subgroups (35 moderate CP cases and 35 severe CP