Background: The finite element method (FEM) is expected to be one of the most effective computational tools for measuring the stress on implant-supported restorations. This study was designed using the 3D-FEM to evaluate the effect of two adhesive luting types of cement on the occlusal stress and deformation of a hybrid crown cemented to a mono-implant. Materials and Method: The mono-screw STL file was imported into the CAD/CAM system library from a database supported by De-Tech Implant Technology. This was to assist in the accurate reproduction of details and design of a simulated implant abutment. Virtually, a digital crown was designed to be cemented on an abutment screw. A minimum occlusal thickness of 1mm and marginal fitting of 1.2

    Identification of complex communities in biological networks is a critical and ongoing challenge since lots of network-related problems correspond to the subgraph isomorphism problem known in the literature as NP-hard. Several optimization algorithms have been dedicated and applied to solve this problem. The main challenge regarding the application of optimization algorithms, specifically to handle large-scale complex networks, is their relatively long execution time. Thus, this paper proposes a parallel extension of the PSO algorithm to detect communities in complex biological networks. The main contribution of this study is summarized in three- fold; Firstly, a modified PSO algorithm with a local search operator is proposed

Chemical pollution is a very important issue that people suffer from and it often affects the nature of health of society and the future of the health of future generations. Consequently, it must be considered in order to discover suitable models and find descriptions to predict the performance of it in the forthcoming years. Chemical pollution data in Iraq take a great scope and manifold sources and kinds, which brands it as Big Data that need to be studied using novel statistical methods. The research object on using Proposed Nonparametric Procedure NP Method to develop an (OCMT) test procedure to estimate parameters of linear regression model with large size of data (Big Data) which comprises many indicators associated with chemi

A new simultaneous spectrophotometric-kinetic method was developed to determine phenylephrine (PHEN) and tetracycline (TETR) via H-point standard addition method (HPSAM). The proposed procedures rely on the measurements of the difference in the rate of charge-transfer (CT) reaction between each of PHEN and TETR as electron donors with p-Bromanil (p-Br) as an electron acceptor. Different experimental factors which affect the extent of the complex formation were investigated by monitoring the value of absorbance at 446 nm. Time pair of 50 -100 sec was selected and employed, among different examined pairs since it results in the highest accuracy for HPSAM-plot. Linear calibration graphs in the concentration ranges of 10.0-40.0 and 10.0–50.0

Impact strength of self-compacted concrete is a field of interest, mostly when the concrete is produced from sustainable materials. This research's main objective is to clarify the ability to use two types of Portland limestone cement (Karasta and Tasluja) in self compacted concrete under impact loading, further to the economic and environmental benefits of the limestone cement. The impact loading was applied by a low-speed test, using the drop ball on concrete. Moreover, the study reveals the resistance of the grids reinforced concrete to impact loading by using polymer grid, and steel grid reinforced concrete slabs. Mixes reinforced by steel mesh had the highest results, indicating that the steel mesh was more robust because it had

Support vector machines (SVMs) are supervised learning models that analyze data for classification or regression. For classification, SVM is widely used by selecting an optimal hyperplane that separates two classes. SVM has very good accuracy and extremally robust comparing with some other classification methods such as logistics linear regression, random forest, k-nearest neighbor and naïve model. However, working with large datasets can cause many problems such as time-consuming and inefficient results. In this paper, the SVM has been modified by using a stochastic Gradient descent process. The modified method, stochastic gradient descent SVM (SGD-SVM), checked by using two simulation datasets. Since the classification of different ca

This research includes the study of dual data models with mixed random parameters, which contain two types of parameters, the first is random and the other is fixed. For the random parameter, it is obtained as a result of differences in the marginal tendencies of the cross sections, and for the fixed parameter, it is obtained as a result of differences in fixed limits, and random errors for each section. Accidental bearing the characteristic of heterogeneity of variance in addition to the presence of serial correlation of the first degree, and the main objective in this research is the use of efficient methods commensurate with the paired data in the case of small samples, and to achieve this goal, the feasible general least squa