Background: The aim of this study was to evaluate the push-out bond strength of four different obturation materials to intraradicular dentin and to determine the failure mode. Materials and method: forty straight palatal roots of the maxillary first molars teeth were used in this study, the roots were instrumented using crown down technique and rotary EndoSequence system, the roots were randomly divided into four groups according to the materials used for obturation (n=10).Group (1): AH Plus sealer and gutta-percha. Group (2): Activ GP glass ionomer sealer and Activ GP gutta-percha (Activ GP system). Group (3): Bioceramic sealer and Bioceramic gutta-percha. Group (4): GuttaFlow2 sealer and gutta-percha. For all groups single cone obturatio

Titanium alloy surface properties have an essential role in the interaction of dental implants with bone, and alteration of the surface of the implant could improve osseointegration. This study was designed to investigate the effect of different heat treatment temperatures on titanium alloy surface properties for dental implants. The effect of different temperatures of heat treatment (750°C, 850°C, 950°C and 1050°C) were investigated on the surface topography, surface chemistry, titanium oxide layer thickness, blood contact angle, & blood drop diameter of titanium alloy. The disks were prepared from titanium alloy (Ti-6Al-4V) and the samples were divided into five groups depending on the different temperatures of heat treatment. The hea

<span lang="EN-US">Diabetes is one of the deadliest diseases in the world that can lead to stroke, blindness, organ failure, and amputation of lower limbs. Researches state that diabetes can be controlled if it is detected at an early stage. Scientists are becoming more interested in classification algorithms in diagnosing diseases. In this study, we have analyzed the performance of five classification algorithms namely naïve Bayes, support vector machine, multi layer perceptron artificial neural network, decision tree, and random forest using diabetes dataset that contains the information of 2000 female patients. Various metrics were applied in evaluating the performance of the classifiers such as precision, area under the c

Polycystic ovary syndrome(PCOS) is a heterogeneous disorder of uncertain etiology , it is the most common endocrinopathy in women and most common cause of anovulatery infertility ,characterized by chronic anovulation and hyperandrogenemia .The present study was designed to investigate the effect of silymarin which is known to have antioxidant and insulin sensitivity effects on the  levels of glucose, insulin ,testosterone ,leutinizing hormone(LH) and progesterone .Ovulation rate and Homeostasis Model Assessment of insulin Resistance (HOMA) ratio were determined .A 3-months of treatment were conducted in 60 PCOS patients in three well-matched groups .The first one (n=20),received silymarin(750mg/day)  .The second group received

A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m

Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage