In this paper, we derived an estimator of reliability function for Laplace distribution with two parameters using Bayes method with square error loss function, Jeffery’s formula and conditional probability random variable of observation. The main objective of this study is to find the efficiency of the derived Bayesian estimator compared to the maximum likelihood of this function and moment method using simulation technique by Monte Carlo method under different Laplace distribution parameters and sample sizes. The consequences have shown that Bayes estimator has been more efficient than the maximum likelihood estimator and moment estimator in all samples sizes

Background: Chronic otitis media (COM) of mucosal or squamous type is a common problem in otolaryngology practice, the active form of COM is characterized by discharge of pus and is treated by antibiotics to start with, the appropriate antibiotic should be prescribed to avoid antibiotic abuse and guarantee good outcome. Objectives:The objective of this study is to identify the causative organisms of active chronic active otitis media both (mucosal, squamous) type and test their sensitivity to various anti- microbial agents &compare with abroad studies.Methods:A prospective study was done on eighty patients, different ages and sexes were taken and carful history and examination was done, examination under microscope was done with carf

The use of non-parametric models and subsequent estimation methods requires that many of the initial conditions that must be met to represent those models of society under study are appropriate, prompting researchers to look for more flexible models, which are represented by non-parametric models                  

          In this study, the most important and most widespread estimations of the estimation of the nonlinear regression function were investigated using Nadaraya-Watson and Regression Local Ploynomial, which are one of the types of non-linear

Software-defined networking (SDN) presents novel security and privacy risks, including distributed denial-of-service (DDoS) attacks. In response to these threats, machine learning (ML) and deep learning (DL) have emerged as effective approaches for quickly identifying and mitigating anomalies. To this end, this research employs various classification methods, including support vector machines (SVMs), K-nearest neighbors (KNNs), decision trees (DTs), multiple layer perceptron (MLP), and convolutional neural networks (CNNs), and compares their performance. CNN exhibits the highest train accuracy at 97.808%, yet the lowest prediction accuracy at 90.08%. In contrast, SVM demonstrates the highest prediction accuracy of 95.5%. As such, an

Stuck pipe is a prevalent and costly issue in drilling operations, with the potential to cost the petroleum industry billions of dollars annually. To reduce the likelihood of this issue, efforts have been made to identify the causes of stuck pipes. The main mechanisms that cause stuck pipes include drill cutting of the formation, inappropriate hole-cleaning, wellbore instability, and differential sticking forces, particularly in highly deviated wellbores. The significant consequences of a stuck pipe include an increase in well costs and Non-Productive Time (NPT), and in the worst-case scenario, the loss of a wellbore section and down-hole equipment, or the need to sidetrack, plug, or abandon the well. This paper provides a comprehensive

Colorectal cancer (CRC) remains a leading cause of cancer-related deaths worldwide, with tumor angiogenesis playing a pivotal role in its progression and metastasis. CD144 (VE-cadherin), a calcium-dependent adhesion molecule, is critical for endothelial cell integrity and has been linked to tumor angiogenesis and cancer stem cell phenotypes. This study aimed to evaluate the immunohistochemical expression of CD144 in benign colorectal lesions, normal adjacent tumor tissue (NRAT), and tumor tissues to elucidate its role in colorectal cancer progression. Multiple techniques, including immunohistochemistry, flow cytometry, Western blot, and qPCR, were used to assess CD144 expression and its association with the VEGF/VEGFR2 signaling pat

Historically, medicinal herbs have been utilized as an important origin of chemicals with particular therapeutic potentials, and they continue to be a great place to find new medication candidates. Parthenocissus quinquefolia L. is a member of the grape-growing family Vitaceae. It is indigenous to Central and North America. It is widely dispersed in Iraqi gardens and plant houses from north to south. Traditionally, it has many uses, like relieving constipation, treating jaundice, expectorant, emetic, and others. At the same time, its proven activities include antioxidant activity, antimicrobial, anti-diabetic, thrombin inhibitor effect, and medicine for treating eyelid eczema. Parthenocissus quinquefolia contains valuable phytochemicals lik

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

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