Abstract
Manufacturing environments require a real-time adaptation and optimization method to dynamically and intelligently maintain the current scheduling plan feasible. This way, the organization keeps clients satisfied and achieves its objectives (costs are minimized and profits maximized). This paper proposes an optimization approach - Selection Constructive based Hyper-heuristic for Dynamic Scheduling - to deal with these dynamic events, with the main goal of maintaining the current scheduling plan feasible and robust as possible. The development of this dynamic adaptation approach is inspired on evolutionary computation and hyper-heuristics. Our empirical results show that a selection constructive hyper-heuristic could be advantageous on solving dynamic adaptation optimization problems.

Abstract
In this paper we introduce a recursive subspace system identification algorithm for MIMO linear parameter varying systems driven by general inputs and a white noise time varying parameter vector. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state-space approximations, thus considered a Picard based method. Such methods have proven to be convergent for the bilinear state-space system identification problem. The key to the proposed algorithm is the fact that the bilinear term between the time varying parameter vector and the state vector behaves like a white noise process. Using a linear Kalman filter model, the bilinear term can be efficiently estimated and then used to construct an augmented input vector at each iteration. Since the previous state is known at each iteration, the system becomes linear, which can be identified with a linear-deterministic subspace algorithm such as MOESP, N4SID, or CVA. Furthermore, the model parameters obtained with the new algorithm converge to those of a linear parameter varying model. Finally, the dimensions of the data matrices are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality. © 2007 EUCA.

Abstract
Incentivized Sustainable Mobility is a conceptual business model which involves four stakeholders: citizens, municipalities, commerce and mobility services. A platform named ISUMO (Incentivized Sustainable Mobility) provides technological support to this business model, integrating a set of metaservices that unifies the existing ICTs of transportation plus a unique patented QR-based (Quick Response) low-cost charging device for electric vehicles. Essentially, the system tracks and registers citizens' transportation activities (anonymously and voluntarily) and evaluates each through a scoring system while their ecological footprint is calculated. Afterwards, citizens are able to exchange their accumulated points for discount QR coupons, to be redeemed in the associated commerce in order to purchase their products or services. The breakthrough of this business model is that it enhances awareness of sustainable mobility practices, increasing their attractiveness as perceived by the stakeholders with diverse benefits; citizens (and indirectly, the municipalities) initiate a new consumption pattern of coupons culture linked to sustainable mobility, the urban economy is stimulated, and the use of mobility services grows, providing a new business opportunity regarding electric vehicles. It is expected that continuous exploration of the model and implementation will contribute to sustainable social and economic development aiming at CO2 emissions reduction, headline targets of the Europe 2020 strategy.

Abstract

Abstract
The availability of cheap wireless sensors boosted the emergence of unstructured networks using wireless technologies with decentralised administration. However, a simple task such as learning the temperature needs a discovery service to find a thermometer among all the sensors. In general, resource discovery relies on flooding mechanisms that waste energy and compromises system availability. Energy efficient strategies limit the exploration area, but with a significant impact on latency. The paper proposes ABC (Adaptive Broadcast Cancellation), a new algorithm that uses the knowledge acquired in previous discoveries to accelerate queries towards the resource. Knowledge is stored in a variation of Bloom filters, thus contributing for an efficient utilization of the sensors limited memory.

Abstract
This work presents a fiber-optic Cavity Ring-Down (CRD) configuration using an added-signal for curvature sensing. An Optical Time-Domain Reflectometer (OTDR) was used to send impulses down into the fiber loop cavity, inside of which a long period grating (LPG) was placed to act as sensing device. The added-signal was obtained by the sum of several conventional CRD impulses, thus providing an improvement on the curvature sensitivity when compared to the conventional CRD signal processing. Sensitivity to applied curvature of 15.3 mu s/m(-1) was obtained. This result was found to be 20-fold the one obtained for the conventional CRD signal processing.