Abstract
Microgrids are assumed to be established at the low voltage distribution level, where distributed energy sources, storage devices, controllable loads and electric vehicles are integrated in the system and need to be properly managed. The microgrid system is a flexible cell that can be operated connected to the main power network or autonomously, in a controlled and coordinated way. The use of storage devices in microgrids is related to the provision of some form of energy buffering during autonomous operating conditions, in order to balance load and generation. However, frequency variations and limited storage capacity might compromise microgrid autonomous operation. In order to improve microgrid resilience in the moments subsequent to islanding, this paper presents innovative functionalities to run online, which are able to manage microgrid storage considering the integration of electric vehicles and load responsiveness. The effectiveness of the proposed algorithms is validated through extensive numerical simulations.

Abstract
New governmental legislation introduced e-prescription as mandatory in the Portuguese health system. This changes consequences were not properly considered, which caused security problems related to patient and prescriber's data, such as digital identity fraud or access to prescriptions history to build clinical profiles. In order to evaluate the e-prescribing software users awareness to those risks, a survey took place, and the results revealed ignorance of certain obligations and procedures of the e-prescribing process. A significant part of doctors are not conscious about where the patient's data is stored neither about the risks related with prescription's information. © 2013 IEEE.

Abstract
We introduce Score As You Lift (SAYL), a novel Statistical Relational Learning (SRL) algorithm, and apply it to an important task in the diagnosis of breast cancer. SAYL combines SRL with the marketing concept of uplift modeling, uses the area under the uplift curve to direct clause construction and final theory evaluation, integrates rule learning and probability assignment, and conditions the addition of each new theory rule to existing ones. Breast cancer, the most common type of cancer among women, is categorized into two subtypes: an earlier in situ stage where cancer cells are still confined, and a subsequent invasive stage. Currently older women with in situ cancer are treated to prevent cancer progression, regardless of the fact that treatment may generate undesirable side-effects, and the woman may die of other causes. Younger women tend to have more aggressive cancers, while older women tend to have more indolent tumors. Therefore older women whose in situ tumors show significant dissimilarity with in situ cancer in younger women are less likely to progress, and can thus be considered for watchful waiting. Motivated by this important problem, this work makes two main contributions. First, we present the first multi-relational uplift modeling system, and introduce, implement and evaluate a novel method to guide search in an SRL framework. Second, we compare our algorithm to previous approaches, and demonstrate that the system can indeed obtain differential rules of interest to an expert on real data, while significantly improving the data uplift. © 2013 Springer-Verlag.

Abstract
Software connectors encapsulate interaction patterns between services in complex, distributed service-oriented applications. Such patterns evolve over time, in response to faults, changes in the expected QoS levels, emergent requirements or the reassessment of contextual conditions. This paper builds up on a model for connector reconfiguration to introduce notions of reconfiguration equivalence and refinement allowing for reasoning about them. This paves the way towards a (still missing) calculus of connector reconfigurations. Copyright 2013 ACM.

Abstract
Over the last years there has been some interest in models that combine first-order logic and probabilistic graphical models to describe large scale domains, and in efficient ways to perform inference on these domains. Prolog Factor Language (PFL) is a extension of the Prolog language that allows a natural representation of these first-order probabilistic models (either directed or undirected). PFL is also capable of solving probabilistic queries on these models through the implementation of four inference algorithms: variable elimination, belief propagation, lifted variable elimination and lifted belief propagation. We show how these models can be easily represented using PFL and then we perform a comparative study between the different inference algorithms in four artificial problems. © 2013 Springer-Verlag.

Abstract
Mathematical formulations for production planning are increasing complexity, in order to improve their realism. In short-term planning, the desirable level of detail is particularly high. Exact solvers fail to generate good quality solutions for those complex models on medium- and large-sized instances within feasible time. Motivated by a real-world case study in the pulp and paper industry, this paper provides an efficient solution method to tackle the short-term production planning and scheduling in an integrated mill. Decisions on the paper machine setup pattern and on the production rate of the pulp digester (which is constrained to a maximum variation) complicate the problem. The approach is built on top of a mixed integer programming (MIP) formulation derived from the multi-stage general lotsizing and scheduling problem. It combines a Variable Neighbourhood Search procedure which manages the setup-related variables, a specific heuristic to determine the digester's production speeds and an exact method to optimize the production and flow movement decisions. Different strategies are explored to speed-up the solution procedure and alternative variants of the algorithm are tested on instances based on real data from the case study. The algorithm is benchmarked against exact procedures.