Comportamento mecânico e biológico de implante dentário com interface cônica interna

The general aim of this study was to evaluate the conical interface of pilar/implant. The specific aims were to evaluate the influence of hexagonal internal index in the microleakage and mechanical strength of Morse taper implants; the effect of axial loading on the deformation in cervical region of Morse taper implants of different diameters through strain gauge; the effect of axial loading in cervical deformation and sliding of abutment into the implant by tridimensional measurements; the integrity of conical interface before and after dynamic loading by microscopy and microleakage; and the stress distribution in tridimensional finite element models of Morse taper implants assembled with 2 pieces abutment. According to the obtained results, could be concluded that the diameter had influence in the cervical deformation of Morse taper implants; the presence of internal hexagonal index in the end of internal cone of implant didn´t influenced the bacterial microleakage under static loading neither reduced the mechanical strength of implants; one million cycles of vertical and off-center load had no negative influence in Morse taper implant integrity.

Uma abordagem baseada em filtragem colaborativa integrada a mapas de saliência para a recomendação de imagens

Nowadays, the amount of customers using sites for shopping is greatly increasing, mainly due to the easiness and rapidity of this way of consumption. The sites, differently from physical stores, can make anything available to customers. In this context, Recommender Systems (RS) have become indispensable to help consumers to find products that may possibly pleasant or be useful to them. These systems often use techniques of Collaborating Filtering (CF), whose main underlying idea is that products are recommended to a given user based on purchase information and evaluations of past, by a group of users similar to the user who is requesting recommendation. One of the main challenges faced by such a technique is the need of the user to provide some information about her preferences on products in order to get further recommendations from the system. When there are items that do not have ratings or that possess quite few ratings available, the recommender system performs poorly. This problem is known as new item cold-start. In this paper, we propose to investigate in what extent information on visual attention can help to produce more accurate recommendation models. We present a new CF strategy, called IKB-MS, that uses visual attention to characterize images and alleviate the new item cold-start problem. In order to validate this strategy, we created a clothing image database and we use three algorithms well known for the extraction of visual attention these images. An extensive set of experiments shows that our approach is efficient and outperforms state-of-the-art CF RS.