Purpose: Despite the high clinical accuracy of dynamic navigation, inherent sources of error exist. The purpose of this study was to improve the accuracy of dynamic navigated surgical procedures in the edentulous maxilla by identifying the optimal configuration of intra-oral points that results in the lowest possible registration error for direct clinical implementation. Materials and methods: Six different 4-area configurations were tested by 3 operators against positive and negative controls (8-areas and 3-areas, respectively) using a skull model. The two dynamic navigation systems (X-Guide® and NaviDent®) and the two registration methods (bone surface tracing and fiducial markers) produced four registration groups. The accuracy of the

The peculiarity of worship spaces in the Islamic architecture is evident by its symbolic connotations with doctrinal connections, thus the niche has a major status in that symbolic connotation, which transformed due to the cultural interaction from a rock on the wall directed towards Mecca into an element of integrated structural entity with performative and aesthetic characteristics. The spread of the Islamic religion contributed to subjecting it to a design acculturation process, thus the problem of the research was raised by the following question: has the evolutionary tendency of acculturation been able to effect a major transformation in the niche design? The research aims at identifying the design acculturation and its translation

In this work, a new development of predictive voltage-tracking control algorithm for Proton Exchange Membrane Fuel Cell (PEMFCs) model, using a neural network technique based on-line auto-tuning intelligent algorithm was proposed. The aim of proposed robust feedback nonlinear neural predictive voltage controller is to find precisely and quickly the optimal hydrogen partial pressure action to control the stack terminal voltage of the (PEMFC) model for N-step ahead prediction. The Chaotic Particle Swarm Optimization (CPSO) implemented as a stable and robust on-line auto-tune algorithm to find the optimal weights for the proposed predictive neural network controller to improve system performance in terms of fast-tracking de

Survival analysis is widely applied to data that described by the length of time until the occurrence of an event under interest such as death or other important events. The purpose of this paper is to use the dynamic methodology which provides a flexible method, especially in the analysis of discrete survival time, to estimate the effect of covariate variables through time in the survival analysis on dialysis patients with kidney failure until death occurs. Where the estimations process is completely based on the Bayes approach by using two estimation methods: the maximum A Posterior (MAP) involved with Iteratively Weighted Kalman Filter Smoothing (IWKFS) and in combination with the Expectation Maximization (EM) algorithm. While the other

Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

    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

Objective: To assess the impact of a social support for pregnant women upon their pregnancy outcome Methodology: A descriptive purposive study was used to assess the impact of a social support on their pregnancy outcomes. The study was conducted from (22 \ September \ 2020 to 15 \ February \ 2021). A non-probability sample (purposive sample) was selected from 100 women. Data were collected through an interview with the mother in the counseling clinic, during the third trimester of pregnancy, as well as after childbirth in the labor wards to assess the outcome of pregnancy. Data were analyzed through descriptive statistics (frequency and percentages). Results: The most important thing observed in this study was the positive pregnancy outcome

Of non-Muslim minorities In the Muslim community