Abstract
The scope of this work is the segmentation of the orders of Bivolino, a Belgian company that sells custom tailored shirts. The segmentation is done based on clustering, following a Data Mining approach. We use the K-Medoids clustering method because it is less sensitive to outliers than other methods and it can handle nominal variables, which are the most common in the data used in this work. We interpret the results from both the design and marketing perspectives. The results of this analysis contain useful knowledge for the company regarding its business. This knowledge, as well as the continued usage of clustering to support both the design and marketing processes, is expected to allow Bivolino to make important business decisions and, thus, obtain competitive advantage over its competitors. © Springer International Publishing Switzerland 2013.

Abstract
There is good evidence that naltrexone, an opioid antagonist, has a strong neuroprotective role and may be a potential drug for the treatment of fibromyalgia. In previous work, some of the authors used experimental clinical data to identify input-output linear time invariant models that were used to extract useful information about the effect of this drug on fibromyalgia symptoms. Additional factors such as anxiety, stress, mood, and headache, were considered as additive disturbances. However, it seems reasonable to think that these factors do not affect the drug actuation, but only the way in which a participant perceives how the drug actuates on herself. Under this hypothesis the linear time invariant models can be replaced by State-Space Affine Linear Parameter Varying models where the disturbances are seen as a scheduling signal signal only acting at the parameters of the output equation. In this paper a new algorithm for identifying such a model is proposed. This algorithm minimizes a quadratic criterion of the output error. Since the output error is a linear function of some parameters, the Affine Linear Parameter Varying system identification is formulated as a separable nonlinear least squares problem. Likewise other identification algorithms using gradient optimization methods several parameter derivatives are dynamical systems that must be simulated. In order to increase time efficiency a canonical parametrization that minimizes the number of systems to be simulated is chosen. The effectiveness of the algorithm is assessed in a case study where an Affine Parameter Varying Model is identified from the experimental data used in the previous study and compared with the time-invariant model.

Abstract
In order to further improve the performance of current genetic algorithms aiming at discovering communities, a local search based genetic algorithm (GALS) is here proposed. The core of GALS is a local search based mutation technique. In order to overcome the drawbacks of traditional mutation methods, the paper develops the concept of marginal gene and then the local monotonicity of modularity function Q is deduced from each node's local view. Based on these two elements, a new mutation method combined with a local search strategy is presented. GALS has been evaluated on both synthetic benchmarks and several real networks, and compared with some presently competing algorithms. Experimental results show that GALS is highly effective and efficient for discovering community structure.

Abstract
A Fabry-Perot microcavity tip sensor fabricated by post-processing of a special design double-cladding optical fiber is proposed. The produced fiber has a pure silica core, an outer cladding, and an inner silica cladding surrounding the core doped with phosphorous. When subjected to chemical etching post-processing, the whole ring region is removed and light is guided in the core region. The sensing head is created by splicing this fiber to single mode fiber and applying chemical etching to the fiber end. The core is forming a tip and it is thus surrounded by air. The Fabry-Perot microcavity tip sensor is subjected to temperature, and a sensitivity of 15.5 pm/degrees C is obtained.

Abstract

Abstract
Video Games may be used with different applications in mind, for entertainment, training and simulation or even as teaching tools. However, the production of contents for such games, namely game scenarios with virtual worlds composed by cities and related elements (streets, buildings, furniture and so on) may require a laborious effort by multidisciplinary teams which lead to an expensive and time consuming solution. In order to avoid the development burden, procedural modeling techniques have been created and applied to generate virtual models of buildings and cities expeditiously with high degree of detail and realism. Nevertheless, most of the existing techniques use complex sets of rules to produce models without being aware of the architectonic structures and its relationships. Thereby, in order to simplify the generation process and rule dealing we propose, in this paper, an ontology-based procedural modeling methodology which integrates an ontological structure, describing city elements and the relationships between each other, and a combined set of procedural techniques to generate roman houses. The preliminary results point to a method capable of producing roman houses expeditiously, including the interiors and the exteriors.