Abstract
This paper describes a novel Electric Vehicle (EV) charging management system which was designed to control the EV load considering simultaneously the EV owners requirements and the electrical network technical limitations. The system was developed to be integrated with existing commercial equipment for smart grids, such as distribution transformer controllers, SCADA systems and Electrical Vehicles Charging Stations. The performance of the smart charging system was evaluated using a typical Portuguese low voltage network as test case, where several EV were assumed to exist. The results obtained prove the effectiveness of the system, as it allowed charging all the EV according to their owners' preferences without increasing the network peak load or creating voltages or overload problems.

Abstract
The two-dimensional cutting stock problem (2DCSP) consists in the minimization of the number of plates used to cut a set of items. In industry, typically, an instance of this problem is considered at the beginning of each planning time period, what may result in solutions of poor quality, that is, excessive waste, when a set of planning periods is considered. To deal with this issue, we consider an integrated problem, in which the 2DCSP is extended from the solution in only a single production planning period to a solution in a set of production planning periods. The main difference of the approach in this work and the ones in the literature is to allow sufficiently large residual plates (leftovers) to be stored and cut in a subsequent period of the planning horizon, which may further help in the minimization of the waste. We propose two integrated integer programming models to optimize the combined two-dimensional cutting stock and lot-sizing problems, minimizing the total cost, which includes material, waste and storage costs. Two heuristics based on the industrial practice to solve the problem were also presented. Computational results for the proposed models and for the heuristics are presented and discussed.

Abstract
This paper determines to within a single measurement the minimum number of measurements required to successfully reconstruct a signal drawn from a Gaussian mixture model in the low-noise regime. The method is to develop upper and lower bounds that are a function of the maximum dimension of the linear subspaces spanned by the Gaussian mixture components. The method not only reveals the existence or absence of a minimum mean-squared error (MMSE) error floor (phase transition) but also provides insight into the MMSE decay via multivariate generalizations of the MMSE dimension and the MMSE power offset, which are a function of the interaction between the geometrical properties of the kernel and the Gaussian mixture. These results apply not only to standard linear random Gaussian measurements but also to linear kernels that minimize the MMSE. It is shown that optimal kernels do not change the number of measurements associated with theMMSE phase transition, rather they affect the sensed power required to achieve a target MMSE in the low-noise regime. Overall, our bounds are tighter and sharper than standard bounds on the minimum number of measurements needed to recover sparse signals associated with a union of subspaces model, as they are not asymptotic in the signal dimension or signal sparsity.

Abstract
Wireless visual sensor networks can provide significant information for a large set of monitoring and surveillance applications. In these networks, mobile sinks are often used to reduce energy consumption over the network, where many algorithms have been proposed for higher energy efficiency. Frequently, visual sensors may have different relevancies for the monitoring functions of the applications, according to their potential to provide significant data. Additionally, the relevancies of visual sensors may be quickly adjusted according to the occurrence of some critical event. In such cases, higher relevant source nodes may be concurrently transmitting visual information with higher quality or frequency, potentially increasing energy consumption in intermediate nodes from those sources toward the sink. We propose an autonomous balanced positioning algorithm for mobile sinks in order to shorten the transmission paths from higher relevant sources, directly benefiting multi-hop sensor networks with multiple active visual source nodes.

Abstract
This paper presents an overview of the different methodologies and mathematical optimization models developed in the framework of the EU-funded project SiNGULAR towards the optimal exploitation and efficient short-term operation of RES production in insular electricity networks. Specifically, the algorithms employed for the creation of system load and RES production scenarios that capture the spatial and temporal correlations of the corresponding variables as well as the procedure followed for the creation of units' availability scenarios using Monte Carlo simulation are discussed. In addition, the advanced unit commitment and economic dispatch models, that have been developed for the short-term scheduling of the conventional and RES generating units in different short-term time-scales (day-ahead, intra-day, and real-time) are presented. Indicative test results from the implementation of all models in the pilot system of the island of Crete, Greece, are illustrated and valuable conclusions are drawn.

Abstract
In traffic & transportation system analysis the way individuals make choices plays a paramount role as these will affect the general efficiency with which people can travel. Modifications on the system by means of policy intervention affect commuters' perspective impacting on the performance of the network and eventually on the society's welfare. The emergence of system's behaviour, as a result of decisions at individual level, provides the traffic manager with the opportunity to evaluate modifications that have been implemented on the system. However, there has been a slow advance in appropriately representing users and their behaviour in all social dimensions of intelligent transportation systems. This paper discusses on a social-oriented modelling & simulation framework for Artificial Transportation Systems, which accounts for different social dimensions of the system in the assessment and application of policy procedures. We illustrate how a social agent-based model can be a useful tool to test the appropriateness and efficiency of transportation policies.