Abstract
This paper presents a new and efficient methodology for voltage control and network reconfiguration of distribution networks using fuzzy rules, EPSO and graph theory. A simpler representation of the network is built through a graph, were all the network loops are identified, both closed and open loops. This information is incorporated into the EPSO operators to create feasible solutions for the topological problem, avoiding convergence problems and reducing the computational burden. The initial EPSO population is created from the initial feasible solution, using appropriated heuristics to create feasible and possibly better initial solutions. At the same time a heuristic based strategy is used to perform local voltage control actions. Finally a fuzzy inference based algorithm is employed to achieve the optimal transformer and capacitor bank tap position. The proposed methodology was tested in a 13-bus test system, and in a real distribution system with 3200 buses.

Abstract
Background: The optic disc (OD) centre and boundary are important landmarks in retinal images and are essential for automating the calculation of health biomarkers related with some prevalent systemic disorders, such as diabetes, hypertension, cerebrovascular and cardiovascular diseases. Methods: This paper presents an automatic approach for OD segmentation using a multiresolution sliding band filter (SBF). After the preprocessing phase, a low-resolution SBF is applied on a down-sampled retinal image and the locations of maximal filter response are used for focusing the analysis on a reduced region of interest (ROI). A high-resolution SBF is applied to obtain a set of pixels associated with the maximum response of the SBF, giving a coarse estimation of the OD boundary, which is regularized using a smoothing algorithm. Results: Our results are compared with manually extracted boundaries from public databases (ONHSD, MESSIDOR and INSPIRE-AVR datasets) outperforming recent approaches for OD segmentation. For the ONHSD, 44% of the results are classified as Excellent, while the remaining images are distributed between the Good (47%) and Fair (9%) categories. An average overlapping area of 83%, 89% and 85% is achieved for the images in ONHSD, MESSIDOR and INSPIR-AVR datasets, respectively, when comparing with the manually delineated OD regions. Discussion: The evaluation results on the images of three datasets demonstrate the better performance of the proposed method compared to recently published OD segmentation approaches and prove the independence of this method when from changes in image characteristics such as size, quality and camera field of view.

Abstract

Abstract
Clinical record integration and visualisation is one of the most important abilities of modern health information systems (HIS). Its use on clinical encounters plays a relevant role in the efficacy and efficiency of health care. One solution is to consider a virtual patient record (VPR), created by integrating all clinical records, which must collect documents from distributed departmental HIS. However, the amount of data currently being produced, stored and used in these settings is stressing information technology infrastructure: integrated VPR of central hospitals may gather millions of clinical documents, so accessing data becomes an issue. Our vision is that, making clinical reports to be stored either in primary (fast) or secondary (slower) storage devices according to their likelihood of visualisation can help manage the workload of these systems. The aim of this work was to develop a model that predicts the probability of visualisation, within 24h after production, of each clinical report in the VPR, so that reports less likely to be visualised in the following 24 hours can be stored in secondary devices. We studied log data from an existing virtual patient record (n=4975 reports) with information on report creation and report first-time visualisation dates, along with contextual information. Bayesian network classifiers were built and compared with logistic regression, revealing high discriminating power (AUC around 90%) and accuracy in predicting whether a report is going to be accessed in the 24 hours after creation.

Abstract
Computer networking is a central topic in computer science courses curricula offered by higher education institutions. Network virtualization and simulation tools, like GNS3, allows students and practitioners to test real world networking configuration scenarios and to configure complex network scenarios by configuring virtualized equipments, such as routers and switches, through each one's virtual console. The configuration of advanced network topics in GNS3 requires that students have to apply basic and very repetitive IP configuration tasks in all network equipments. As the network topology grows, so does the amount of network equipments to be configured, which may lead to logical configuration errors. In this paper we propose an extension for GNS3 network virtualizer, to automatically generate a valid configuration of all the network equipments in a GNS3 scenario. Our implementation is able to automatically produce an initial IP and routing configuration of all the Cisco virtual equipments by using the GNS3 specification files. We tested this extension against a set of networked scenarios which proved the robustness, readiness and speedup of the overall configuration tasks. In a learning environment, this feature may save time for all networking practitioners, both beginners or advanced, who aim to configure and test network topologies, since it automatically produces a valid and operational configuration for all the equipments designed in a GNS3 environment.

Abstract
Many state-machine Byzantine Fault Tolerant (BFT) protocols have been introduced so far. Each protocol addressed a different subset of conditions and use-cases. However, if the underlying conditions of a service span different subsets, choosing a single protocol will likely not be a best fit. This yields robustness and performance issues which may be even worse in services that exhibit fluctuating conditions and workloads. In this paper, we reconcile existing state-machine BFT protocols in a single adaptive BFT system, called ADAPT, aiming at covering a larger set of conditions and use-cases, probably the union of individual subsets of these protocols. At anytime, a launched protocol in ADAPT can be aborted and replaced by another protocol according to a potential change (an event) in the underlying system conditions. The launched protocol is chosen according to an 'evaluation process' that takes into consideration both: protocol characteristics and its performance. This is achieved by applying some mathematical formulas that match the profiles of protocols to given user (e.g., service owner) preferences. ADAPT can assess the profiles of protocols (e.g., throughput) at run-time using Machine Learning prediction mechanisms to get accurate evaluations. We compare ADAPT with well known BFT protocols showing that it outperforms others as system conditions change and under dynamic workloads. © 2015 IEEE.