Abstract

Abstract
Renewable based power generation has significantly increased over the last years. However, this process has evolved separately from electricity markets, leading to an inadequacy of the present market models to cope with huge quantities of renewable energy resources, and to take full advantage of the presently existing and the increasing envisaged renewable based and distributed energy resources. This paper proposes the modelling of electricity markets at several levels (continental, regional and micro), taking into account the specific characteristics of the players and resources involved in each level and ensuring that the proposed models accommodate adequate business models able to support the contribution of all the resources in the system, from the largest to the smaller ones. The proposed market models are integrated in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets), using the multi agent approach advantages for overcoming the current inadequacy and significant limitations of the presently existing electricity market simulators to deal with the complex electricity market models that must be adopted.

Abstract
System resilience is a key challenge to building extreme scale systems. A large number of HPC applications are inherently resilient, but application programmers lack mechanisms to convey their fault tolerance knowledge to the system. We present a cross-layer approach to resilience in which we propose a set of programming model extensions and develop a runtime inference framework that can reason about the context and significance of faults, as they occur, to the application programmer's fault tolerance expectations. We demonstrate using a set accelerated fault injection experiments the validity of our approach with a set of real scientific and engineering codes. Our experiments show that a cross-layer approach that explicitly engages the programmer in expressing fault tolerance knowledge which is then leveraged across the layers of system abstraction can significantly improve the dependability of long running HPC applications. © 2012 IEEE.

Abstract
For manufacturers of physical goods, conducting market tests of product prototypes to assess consumers' preferences can be costly and complex. To some extent, production and logistic processes need to be in place, not just the marketing rationale. We put forth the hypothesis that virtual worlds may be a feasible environment to conduct early market tests of product prototypes for some physical products. The rationale for this hypothesis is that such tests with virtual versions of product prototypes may be conducted with minimal overhead, based on resources from marketing and design departments, without resources from production or logistics. They could be a first filter or selection process to determine which product prototypes demonstrate better acceptance by the public, with reduced costs and complexity. An expectation is that by having simpler and less costly tests, a wider variety of product prototypes can be considered, and test number and frequency increased, supporting better information gathering. A central question in this regard is the level of similarity between the preferences of the public when presented with virtual items, in comparison with the actual physical items. To attain data about this, we carried out an exploratory study, creating a set of both virtual and physical prototype versions of a physical product: t-shirts. We then invited virtual-world users to experience the virtual t-shirts on their avatars and express their preferences. Finally, we presented users with the option to buy the physical t-shirts with their own money, at promotional cost (as a reward for participating in the virtual trial), but explicitly told them, as they held the various physical versions in their hands, that they could change their preference at no extra cost. The results identified the level of similarity and differences between buyers' preferences in these two situations, pointing to the significant potential of using a virtual world to conduct market tests to assess consumers' preferences on prototypes of physical t-shirts.

Abstract

Abstract
Pectus excavatum is the most common congenital deformity of the anterior chest wall, in which an abnormal formation of the rib cage gives the chest a caved-in or sunken appearance. Today, the surgical correction of this deformity is carried out in children and adults through Nuss technic, which consists in the placement of a prosthetic bar under the sternum and over the ribs. Although this technique has been shown to be safe and reliable, not all patients have achieved adequate cosmetic outcome. This often leads to psychological problems and social stress, before and after the surgical correction. This paper targets this particular problem by presenting a method to predict the patient surgical outcome based on pre-surgical imagiologic information and chest skin dynamic modulation. The proposed approach uses the patient pre-surgical thoracic CT scan and anatomical-surgical references to perform a 3D segmentation of the left ribs, right ribs, sternum and skin. The technique encompasses three steps: a) approximation of the cartilages, between the ribs and the sternum, trough b-spline interpolation; b) a volumetric mass spring model that connects two layers - inner skin layer based on the outer pleura contour and the outer surface skin; and c) displacement of the sternum according to the prosthetic bar position. A dynamic model of the skin around the chest wall region was generated, capable of simulating the effect of the movement of the prosthetic bar along the sternum. The results were compared and validated with patient postsurgical skin surface acquired with Polhemus FastSCAN system.