Rock mechanical properties are critical parameters for many development techniques related to tight reservoirs, such as hydraulic fracturing design and detecting failure criteria in wellbore instability assessment. When direct measurements of mechanical properties are not available, it is helpful to find sufficient correlations to estimate these parameters. This study summarized experimentally derived correlations for estimating the shear velocity, Young's modulus, Poisson's ratio, and compressive strength. Also, a useful correlation is introduced to convert dynamic elastic properties from log data to static elastic properties. Most of the derived equations in this paper show good fitting to measured data, while some equations show scatters

Virtual reality, VR, offers many benefits to technical education, including the delivery of information through multiple active channels, the addressing of different learning styles, and experiential-based learning. This paper presents work performed by the authors to apply VR to engineering education, in three broad project areas: virtual robotic learning, virtual mechatronics laboratory, and a virtual manufacturing platform. The first area provides guided exploration of domains otherwise inaccessible, such as the robotic cell components, robotic kinematics and work envelope.  The second promotes mechatronics learning and guidance for new mechatronics engineers when dealing with robots in a safe and interactive manner. And the thir

Excessive skewness which occurs sometimes in the data is represented as an obstacle against normal distribution. So, recent studies have witnessed activity in studying the skew-normal distribution (SND) that matches the skewness data which is regarded as a special case of the normal distribution with additional skewness parameter (α), which gives more flexibility to the normal distribution. When estimating the parameters of (SND), we face the problem of the non-linear equation and by using the method of Maximum Likelihood estimation (ML) their solutions will be inaccurate and unreliable. To solve this problem, two methods can be used that are: the genetic algorithm (GA) and the iterative reweighting algorithm (IR) based on the M

Background: Determination of local bone mineral density (BMD) with cortical thickness and bone height may offer a comprehensive description of the bone the surgeon will encounter when he or she actually sets the implant. Quantitative computed tomography (CT) (i.e., quantitative interpretation of values derived from Hounsfield units with a suitable calibration procedure) is the modality of choice to determine BMD. The aim of the present clinical study is to determine the local bone density in dental implant recipient sites using computerized tomography. Material and method: The sample consisted of (72) Iraqi patients whom referred to Al-Kharkh General hospital, Spiral CT scan Department for bone quality and quantity assessment after one wee

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

Abstract

In the field of construction materials the glass reinforced mortar and Styrene Butadiene mortar are modern composite materials. This study experimentally investigated the effect of addition of randomly dispersed glass fibers and layered glass fibers on density and compressive strength of mortar with and without the presence of Styrene Butadiene Rubber (SBR). Mixtures of 1:2 cement/sand ratio and 0.5 water/cement ratio were prepared for making mortar. The glass fibers were added by two manners, layers and random with weight percentages of (0.54, 0.76, 1.1 and 1.42). The specimens were divided into two series: glass-fiber reinforced mortar without SBR and glass-fiber reinforced mortar with 7% SBR of mixture water. All s

This paper presents the theoretical and experimental results of drilling high density
polyethylene sheet with thickness of 1 mm using millisecond Nd:YAG pulsed laser. Effects of laser
parameters including laser energy, pulse duration and peak power were investigated. To describe and
understand the mechanism of the drilling process Comsol multiphysics package version 4.3b was used to
simulate the process. Both of the computational and experimental results indicated that the drilling
process has been carried out successfully and there are two phases introduced in the drilling process,
vaporization and melting. Each portion of these phases depend on the laser parameters used in the
drilling process

Abstract

  Bivariate time series modeling and forecasting have become a promising field of applied studies in recent times. For this purpose, the Linear Autoregressive Moving Average with exogenous variable ARMAX model is the most widely used technique over the past few years in modeling and forecasting this type of data. The most important assumptions of this model are linearity and homogenous for random error variance of the appropriate model. In practice, these two assumptions are often violated, so the Generalized Autoregressive Conditional Heteroscedasticity (ARCH) and (GARCH) with exogenous varia

The multiple linear regression model is an important regression model that has attracted many researchers in different fields including applied mathematics, business, medicine, and social sciences , Linear regression models involving a large number of independent variables are poorly performing due to large variation and lead to inaccurate conclusions , One of the most important problems in the regression analysis is the multicollinearity Problem, which is considered one of the most important problems that has become known to many researchers  , As well as their effects on the multiple linear regression model, In addition to multicollinearity, the problem of outliers in data is one of the difficulties in constructing the reg