Abstract

Abstract
Dental implant recognition in patients without available records is a time-consuming and not straightforward task. The traditional method is a complete user-dependent process, where the expert compares a 2D X-ray image of the dental implant with a generic database. Due to the high number of implants available and the similarity between them, automatic/semi-automatic frameworks to aide implant model detection are essential. In this study, a novel computer-aided framework for dental implant recognition is suggested. The proposed method relies on image processing concepts, namely: (i) a segmentation strategy for semi-automatic implant delineation; and (ii) a machine learning approach for implant model recognition. Although the segmentation technique is the main focus of the current study, preliminary details of the machine learning approach are also reported. Two different scenarios are used to validate the framework: (1) comparison of the semi-automatic contours against implant's manual contours of 125 X-ray images; and (2) classification of 11 known implants using a large reference database of 601 implants. Regarding experiment 1, 0.97+/-0.01, 2.24+/-0.85 pixels and 11.12+/-6 pixels of dice metric, mean absolute distance and Hausdorff distance were obtained, respectively. In experiment 2, 91% of the implants were successfully recognized while reducing the reference database to 5% of its original size. Overall, the segmentation technique achieved accurate implant contours. Although the preliminary classification results prove the concept of the current work, more features and an extended database should be used in a future work.

Abstract
The Web is rapidly becoming the prime medium for human socialization. The resources that enable that process (social web sites, blogs, media objects, etc.) present growing complexity and, collectively, weave an ever more intricate web of relationships. Current technology for declaring those relationships is predominantly implicit, ambiguous and semantically poor. As a consequence, their automatic assessment is complex and error prone, preventing the satisfaction of users' needs such as effective semantic searches. To address these limitations, whilst enabling the explicit declaration of semantically unambiguous relationships between digital resources, a solution employing structured semantic descriptors and ontologies was conceived, based on MPEG-21. This paper explains the functioning of the devised mechanism, and goes beyond that, into the definition of two novel employment venues for it, at the service of two real-world usage scenarios. These demonstrate the mechanism's added value as a powerful alternative for the semantically aware interconnection of web resources, and highlight the increased QoE that said mechanism enables.

Abstract
Over the last years, the numerical simulation of integrated processes has become the major challenge in virtual try-out of sheet metal components, including trimming operations that may occur between forming steps. Detailed simulation of trimming processes is a challenging task, particularly when integrated with other forming operations such as deep drawing or hemming. A simplified approach can be adopted in which elements outside the trim surface are deleted from the finite element (FE) model adjusting the remaining to the surface. Following this approach, the state variables are mapped from the old FE mesh to the new trimmed mesh to continue the simulation. This paper addresses this simplified approach to the trimming process exploring a previously presented algorithm (Finite Elem Anal Des 42: 1053-1060, Baptista et al. 2006), which allows the treatment of hexahedral finite element meshes. Particularly, it focuses on the performance evaluation of the implemented strategies for correcting the FE mesh to the trimming surface, including the treatment of pentahedral-shaped elements. Different correction and treatment strategies are evaluated on different types of meshes, based on numerical simulation results of simple mechanical tests: uniaxial tensile test and simple bending test. Finally, two practical applications are given where the local effect of the trimming algorithm is highlighted.

Abstract
The objective of the retailer in medium-term planning is managing the portfolio of contracts from different sources as well as determining the optimal selling price offered to its customers. When supplying the electricity sold to the costumers, two main challenges are faced by retailers. The first problem occurs during the electricity procurement procedure. In this stage, the retailer must deal with the uncertainty due to the pool price that propels the retailers to move towards agreeing to forward contracts signed at higher average prices. Besides, when the retailer decides on selling the electricity, another problem is to face the uncertainty caused by the demand while taking into consideration the possibility of reducing its clients in the case of high selling price. In this regard, this paper proposes a stochastic multi-objective framework for the retailer with profit maximization and risk minimization as two objective functions. The risk, due to the market price uncertainty, is modeled, employing the expected downside risk. The problem is formulated as mixed-integer programming while the stochastic optimization problem is characterized using the roulette wheel mechanism and lattice Monte Carlo simulation. Furthermore, lexicographic optimization and augmented epsilon-constraint method are used to solve the proposed multi-objective problem, and the best compromise solution is determined employing a fuzzy satisfying method. The presented model has been implemented using a realistic case study to verify the effectiveness of the method used in this paper. © 2015 John Wiley & Sons, Ltd.

Abstract