Abstract
Worldwide microgrid capacity is expected to reach 7 GW and a market value of $35 billion dollars in the next few years. The decentralization of the generation dispatch role and different ownership models concerning microgrids, will contribute to increase the complexity of the future power systems. Analyzing new policies and strategies as well as evaluating those impacts is only possible with the use of sophisticated simulation tools. This paper presents a scalable computational simulation to address microgrid dispatch and the impact in the electricity market. Computational intelligence techniques are integrated to improve the effectiveness of the simulation tool. These techniques include CPLEX; differential search algorithm and quantum particle swaiin optimization. Each microgrid player is able to solve a day-ahead scheduling problem and submit bids to the electricity market agent (spot market), which calculates the market clearing price. The developed case study with a large number of players totaling about 150,000 consumers suggest the relevance of the developed computational framework.

Abstract
Application frameworks are a powerful technique for large-scale reuse, but require a considerable effort to understand them. Good documentation is costly, as it needs to address different audiences with disparate learning needs. When code and documentation prove insuficient, developers turn to their network of experts. Nevertheless, this proves difficult, mainly due to the lack of expertise awareness (who to ask), wasteful interruptions of the wrong people and unavailability ( either due to intrusion or time constraints). The DRIVER platform is a collaborative learning environment where framework users can, in a non-intrusive way, store and share their learning knowledge while following the best practices of framework understanding (patterns). Developed by the authors, it provides a framework documentation repository, mounted on a wiki, where the learning paths of the community of learners can be captured, shared, rated, and recommended. Combining these social activities, the DRIVER platform promotes collaborative learning, mitigating intrusiveness, unavailability of experts and loss of tacit knowledge. This paper presents the assessment of DRIVER using a controlled academic experiment that measured the performance, effectiveness and framework knowledge intake of MSc students. The study concluded that, especially for novice learners, the platform allows for a faster and more effective learning process.

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Abstract

Abstract
Setting: Confirmation of tuberculosis (TB) in children is difficult, so clinicians use different procedures when deciding to treat. Objective: Identify criteria to initiate and maintain TB treatment in children younger than 5 years-old, without diagnosis confirmation. Design: A web-based survey was distributed by email to the corresponding authors of journal articles on childhood TB. The observations were clustered into disjoint groups, and analyzed by Ward's method. Results: We sent out 260 questionnaires and received 64 (24.6%) responses. Forty-six respondents (71.9%) said that microbiological confirmation was not important for initiation of anti-TB treatment, and that the epidemiological context and signs/symptoms suggestive of disease were most important. Sixty-one respondents (95.3%) said that the decision to continue therapy was mainly dependent on clinical improvement. A cluster of older respondents (median age: 52 years-old) who were active at a hospital or primary health care centre placed the most value on immunological test results and chest X-rays. A cluster of younger respondents (median age: 38 years-old) who were less experienced in management of TB placed more value on Interferon Gamma Release Assay (IGRA) results and chest computed tomography (CT) scans. A cluster of respondents with more experience in treating TB and working at specialized TB centres placed greater value on the clinical results and specific radiological alterations ("tree-in-bud" pattern and pleural effusion). Conclusion: TB management varied according to the age, work location and experience of the clinicians. It is necessary to establish standardized guidelines used for the diagnosis and decision to treat TB in children. (C) 2017 Sociedade Portuguesa de Pneumologia. Published by Elsevier Espana, S.L.U.

Abstract
This two-part work presents a new multistage and stochastic mathematical model, developed to support the decision-making process of planning distribution network systems (DNS) for integrating large-scale "clean" energy sources. Part I is devoted to the theoretical aspects and mathematical formulations in a comprehensive manner. The proposed model, formulated from the system operator's viewpoint, determines the optimal sizing, timing, and placement of distributed energy technologies (particularly, renewables) in coordination with energy storage systems and reactive power sources. The ultimate goal of this optimization work is to maximize the size of renewable power absorbed by the system, while maintaining the required/standard levels of power quality and system stability at a minimum possible cost. From the methodological perspective, the entire problem is formulated as a mixed integer linear programming optimization, allowing one to obtain an exact solution within a finite simulation time. Moreover, it employs a linearized ac network model which captures the inherent characteristics of electric networks and balances well accuracy with computational burden. The IEEE 41-bus radial DNS is used to test validity and efficiency of the proposed model, and carry out the required analysis from the standpoint of the objectives set. Numerical results are presented and discussed in Part II of this paper to unequivocally demonstrate the merits of the model.