Abstract
Clinical decisions are sometimes based on a variety of patient's information such as: age, weight or information extracted from image exams, among others. Depending on the nature of the disease or anatomy, clinicians can base their decisions on different image exams like mammographies, positron emission tomography scans or magnetic resonance images. However, the analysis of those exams is far from a trivial task. Over the years, the use of image descriptors-computational algorithms that present a summarized description of image regions-became an important tool to assist the clinician in such tasks. This paper presents an overview of the use of image descriptors in healthcare contexts, attending to different image exams. In the making of this review, we analyzed over 70 studies related to the application of image descriptors of different natures-e.g., intensity, texture, shape-in medical image analysis. Four imaging modalities are featured: mammography, PET, CT and MRI. Pathologies typically covered by these modalities are addressed: breast masses and microcalcifications in mammograms, head and neck cancer and Alzheimer's disease in the case of PET images, lung nodules regarding CTs and multiple sclerosis and brain tumors in the MRI section.

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The advancement of constraint solvers and model checkers has enabled the effective analysis of high-level formal specification languages. However, these typically handle a specification in an opaque manner, amalgamating all its constraints in a single monolithic verification task, which often proves to be a performance bottleneck. This paper addresses this issue by proposing a solving strategy that exploits user-provided partial knowledge, namely by assigning symbolic bounds to the problem’s variables, to automatically decompose a verification task into smaller ones, which are prone to being independently analyzed in parallel and with tighter search spaces. An effective implementation of the technique is provided as an extension to the Kodkod relational constraint solver. Evaluation shows that, in average, the proposed technique outperforms the regular amalgamated verification procedure. © Springer International Publishing AG 2017.

Abstract
Due to the uncertainty and stochastic behavior of wind and photovoltaic production introduced in conventional power systems, the correct overall management considering all the technical and economic constraints is faced with more challenges. To address also the specificities of insular power systems, several strategies have been proposed in last years, including energy storage systems with the aim of increasing system flexibility. Accurate forecasting tools may also help to reduce overall uncertainty. Other scheduling tools based on probabilistic, heuristic and stochastic programming have also been considered. In this work, a new scheduling strategy is proposed considering the integration of wind production in an insular power system. To this end, some arbitrarily chosen scenarios from wind production are introduced in the scheduling process, and a comparative study is carried out, with and without renewable production, providing an acceptable computational time.

Abstract
Although a well-organized power system is less subject to blackouts, the existence of a proper restoration plan is nevertheless still essential. The goal of a restoration plan is to bring the power system back to its normal operating conditions in the shortest time after a blackout occurs and to minimize the impact of the blackout on society. This paper presents a decentralized multi-agent system (MAS)-based restoration method for a low voltage (LV) microgrid (MG). In the proposed method, the MG local controllers are assigned to the specific agents who interact with each other to achieve a common decision in the restoration procedure. The evaluation of the proposed decentralized technique using a benchmark low-voltage MG network demonstrates the effectiveness of the proposed restoration plan.