Background: It has been well known that the success of mandibular implant- retained overdenture heavily depends on initial stability, retention and long term osseointegration this is might be due to optimal stresses distribution in surrounding bones. Types of mandibular implant- retained overdenture anchorage system and number of dental implants play an important role in stresses distribution at the implant-bone interface. It is necessary to keep the stresses below the physiologic tolerance level of the bone .since. And it is difficult to measure these stresses around bone in vivo. In the present study, finite element analysis used to study the stresses distribution around dental implant supporting Mandible implant retained overdenture Materials and methods: Eight models were constructed including four designs of anchorage system (ball-cup, ball-O Ring, bar without distal extension and bar with distal extension).The first group of models were supported by four dental implant and second group of models were supported by two dental implant only. Models constructed from the data obtained directly from patient The contour of bone was obtained from C.T scan image of patient, then data transferred to ANSYS program for modeling then load applied and solve the equation by the program, Specified nodes were selected at the rings of crestal bone (cortical bone) and cortical cancellous interface around each dental implant and fixed for all models to monitor the stress change in that regions of different design of MIR-OD.. After load application, Specified nodes were selected at the rings of crestal bone (cortical bone) and cortical cancellous interface around each dental implant and fixed for all models to monitor the stress change in that regions of different design of MIR-OD . Results: In the present study the stress distribution and maximum stresses value around dental implant had a relationship to the number of dental implant. , The result appeared that the maximum stresses and means of stresses value was lower in the first group of models (which was supported through the use four dental implant) than the second group of models (which was supported through the use of two dental implant only). For the first group of models the maximum stresses value around mesial implant was11.67, 10.51, 10.98 and 10.72 Mpa, while the maximum stresses around distal implant was 21.33, 18.51, 18.86, and17.56 Mpa for models 1,2,3 and 4respectively ,and the stresses around implant supporting second group of models was 22.52, 22.16, 20.51 and 19.60 Mpa for models 5,6,7and8 respectively .Statistical analyses of means value appeared that there was statistically significant difference in stresses means value around implant of the second group with that’s values around mesial and distal implant supporting first group of model . Regarding the result of both ball and bar system, it has been demonstrated that stress was greater with ball attachment and MIR-OD supported by the use of four dental implants and anchored by bar attachments with distal extension gives the minimum values of stresses than the rest models. Also the results show that higher stresses value was appeared at the cortical bone ring surrounding dental implant especially the distal implant nearest to the free end extension area. Also it was appeared that the best model was Mandible implant- retained overdenture that’s anchored by bar with distal extension and support by four dental implant. Conclusions: Bar-clips with distal extension mode of attachment considered the best type in producing the least stresses around dental implant regardless number of dental implant used.
(3)
(3)
In this paper, we investigate the behavior of the bayes estimators, for the scale parameter of the Gompertz distribution under two different loss functions such as, the squared error loss function, the exponential loss function (proposed), based different double prior distributions represented as erlang with inverse levy prior, erlang with non-informative prior, inverse levy with non-informative prior and erlang with chi-square prior.
The simulation method was fulfilled to obtain the results, including the estimated values and the mean square error (MSE) for the scale parameter of the Gompertz distribution, for different cases for the scale parameter of the Gompertz distr
... Show More
Passive optical network (PON) is a point to multipoint, bidirectional, high rate optical network for data communication. Different standards of PONs are being implemented, first of all PON was ATM PON (APON) which evolved in Broadband PON (BPON). The two major types are Ethernet PON (EPON) and Gigabit passive optical network (GPON). PON with these different standards is called xPON. To have an efficient performance for the last two standards of PON, some important issues will considered. In our work we will integrate a network with different queuing models such M/M/1 and M/M/m model. After analyzing IPACT as a DBA scheme for this integrated network, we modulate cycle time, traffic load, throughput, utilization and overall delay
... Show MoreExtracting, studying and interpreting the morphological database of a basin is a basic building block for building a correct geomorphological understanding of this basin. In this work, Arc GIS 10.8 software and SRTM DEM satellite images were used. The principle of data integration was adopted by extracting the quantitative values of the morphometric characteristics that are affected by the geomorphological condition of the studied basin, then eliciting an optimal conception of the geomorphological condition of the basin from the meanings and connotations of these combined transactions. Hypsometric integration was extracted for each region in the basin separately with the value of integration of the plot curve for the relative heights of
... Show More
(1)
In general, the importance of cluster analysis is that one can evaluate elements by clustering multiple homogeneous data; the main objective of this analysis is to collect the elements of a single, homogeneous group into different divisions, depending on many variables. This method of analysis is used to reduce data, generate hypotheses and test them, as well as predict and match models. The research aims to evaluate the fuzzy cluster analysis, which is a special case of cluster analysis, as well as to compare the two methods—classical and fuzzy cluster analysis. The research topic has been allocated to the government and private hospitals. The sampling for this research was comprised of 288 patients being treated in 10 hospitals. As t
... Show More
(6)
(3)
The fractional order partial differential equations (FPDEs) are generalizations of classical partial differential equations (PDEs). In this paper we examine the stability of the explicit and implicit finite difference methods to solve the initial-boundary value problem of the hyperbolic for one-sided and two sided fractional order partial differential equations (FPDEs). The stability (and convergence) result of this problem is discussed by using the Fourier series method (Von Neumanns Method).
The estimation of the parameters of linear regression is based on the usual Least Square method, as this method is based on the estimation of several basic assumptions. Therefore, the accuracy of estimating the parameters of the model depends on the validity of these hypotheses. The most successful technique was the robust estimation method which is minimizing maximum likelihood estimator (MM-estimator) that proved its efficiency in this purpose. However, the use of the model becomes unrealistic and one of these assumptions is the uniformity of the variance and the normal distribution of the error. These assumptions are not achievable in the case of studying a specific problem that may include complex data of more than one model. To
... Show More
Throughout this paper, a generic iteration algorithm for a finite family of total asymptotically quasi-nonexpansive maps in uniformly convex Banach space is suggested. As well as weak / strong convergence theorems of this algorithm to a common fixed point are established. Finally, illustrative numerical example by using Matlab is presented.
(15)
(4)