Background: The PMMA polymer denture base materials are low mechanical properties, adaptation of the denture base to underlying tissue is important for retention and stability of denture. The aim of the study was toevaluate the effect of mixtureZrO2-Al2O3 nanoparticles on impact strength, transverse strength, hardness, roughness, denture base adaptation of heat cured acrylic resin denture base material. Materials and methods: One hundred (100) specimens were prepared, the specimens were divided into five groups (20 specimens to each) according to the test type, each group was subdivided in to two subgroups (control and experimental) each subgroup consist of 10 specimens, the experimental group included mixture of 2% (ZrO2-Al2O3ratio2:1) b

The continuous increase in population has led to the development of underground structures like tunnels to be of great importance due to several reasons. One of these reasons is that tunnels do not affect the living activities on the surface, nor they interfere with the existing traffic network. More importantly, they have a less environmental impact than conventional highways and railways. This paper focuses on using numerical analysis of circular tunnels in terms of their behavior during construction and the deformations that may occur due to overburden and seismic loads imposed on them. In this study, the input data are taken from an existing Cairo metro case study; results were found for the lateral and vertical displacements, the Peak

In this research, the effect of adding two different types of reinforcing particles was investigated, which included: nano-zirconia (nano-ZrO₂) particles and micro-lignin particles that were added with different volume fractions of 0.5%, 1%, 1.5% and 2% on the mechanical properties of polymer composite materials. They were prepared in this research, as a complete prosthesis and partial denture base materials was prepared, by using cold cure poly methyl methacrylate (PMMA) resin matrix. The composite specimens in this research consist of two groups according to the types of reinforced particles, were prepared by using casting methods, type (Hand Lay-Up) method. The first group consists of PMMA resin reinforced by (nano-ZrO

Precise forecasting of pore pressures is crucial for efficiently planning and drilling oil and gas wells. It reduces expenses and saves time while preventing drilling complications. Since direct measurement of pore pressure in wellbores is costly and time-intensive, the ability to estimate it using empirical or machine learning models is beneficial. The present study aims to predict pore pressure using artificial neural network. The building and testing of artificial neural network are based on the data from five oil fields and several formations. The artificial neural network model is built using a measured dataset consisting of 77 data points of Pore pressure obtained from the modular formation dynamics tester. The input variables

In this study, gamma-ray spectrometry with an HPGe detector was used to measure the specific activity concentrations of ²²⁶Ra, ²³²Th, and ⁴⁰K in soil samples collected from IT1 oil reservoirs in Kirkuk city, northeast Iraq. The “spectral line Gp” gamma analysis software package was used to analyze the spectral data. ²²⁶Ra specific activity varies from 9  0.34 Bq.kg^-1 to 17  0.47 Bq.kg^-1. ²³²Th specific activity varies from 6.2  0.08 Bq.kg^-1 to 18  0.2 Bq.kg^-1. ⁴⁰K specific activity varies from 25  0.19 Bq.kg^-1 to 118  0.41 Bq.kg^-1. The radiological hazard due to the radiation emitted from natural r