The best proximity point is a generalization of a fixed point that is beneficial when the contraction map is not a self-map. On other hand, best approximation theorems offer an approximate solution to the fixed point equation . It is used to solve the problem in order to come up with a good approximation. This paper's main purpose is to introduce new types of proximal contraction for nonself mappings in fuzzy normed space and then proved the best proximity point theorem for these mappings. At first, the definition of fuzzy normed space is given. Then the notions of the best proximity point and - proximal admissible in the context of fuzzy normed space are presented. The notion of α ̃–ψ ̃- proximal contractive mapping is introduced.

      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.

Background: Maxillary canines are important aesthetically and functionally, but impacted canines are more difficult and time consuming to treat, the aim of this study is to investigate with multi-detector computed tomography the correlation between the bone density and the upper canine impaction. Material and method: A sample of Unilaterally impacted maxillary canines from 24 patients (19 female, 5 male) who were referred to accurately localize the impacted canines at al- Karkh general hospital were evaluated by a volumetric 3-d images by the multi-detector computed tomography to accurately measure the bone density of the maxillary cortical palate of the maxillary impacted canine side and compare it with the other side of the normally erupt

The very fast developments of web and data collection technologies have enabled non-experts to collect and disseminate geospatial datasets through web applications. This new type of spatial data is usually known as collaborative mapping or volunteered geographic information VGI. There are various countries around the world could benefit from collaborative mapping data because it is cost free data, easy to access and it provides more customised data. However, there is a concern about its quality because the data collectors may lack the sufficient experience and training about geospatial data production. Most previous studies which have outlined and analysed VGI quality focused on positional and linear features. The current research has been