The Brazilian government is maintaining several digital inclusion projects, providing computers and Internet connection to developing regions around the country. However, these projects can only succeed if they are constantly assessed; namely, the projects infrastructure deployment must be closely monitored and evaluated. In this paper, we introduce a system called SIMMC, which is currently monitoring and evaluating more than 4,500 computing devices from Brazilian digital inclusion projects. This system is innovative because, in addition to being used by the government for managing and expanding its projects, the collected data is also publicly available on a web page, allowing the citizens to follow the projects' deployment. We describe the SIMMC architecture, reporting some techniques used to optimize its data analysis processes, and describe how the information acquired and presented by the system has been used to enable public administration overhaul and improve efficiency on the project management, as well as its strategic use for security, theft, and defrauding. 1 Gesac project (Portuguese only): http://www.mc.gov.br/gesac. 2 Quilombos are communities founded in colonial Brazil, organized by fugitive slaves and located in inaccessible areas. 3 Digital Cities project (Portuguese only): http://www.mc.gov.br/ cidades-digitais. 4 Telecentres project (Portuguese only): http://www.mc.gov.br/telecentros.
BackgroundStochastic mapping is frequently used in comparative biology to simulate character evolution, enabling the probabilistic computation of statistics such as number of state transitions along a tree and distribution of states in its internal nodes. Common implementations rely on Continuous-time Markov Chain simulations whose parameters are difficult to adjust and subjected to inherent inaccuracy. Thus, researchers must run a large number of simulations in order to obtain adequate estimates. Although execution time tends to be relatively small when simulations are performed on a single tree assumed to be the “true” topology, it may become an issue if analyses are conducted on several trees, such as the ones that make up posterior distributions obtained via Bayesian phylogenetic inference. Working with such distributions is preferable to working with a single tree, for they allow the integration of phylogenetic uncertainty into parameter estimation. In such cases, detailed character mapping becomes less important than parameter integration across topologies. Here, we present an R-based implementation (SFREEMAP) of an analytical approach to obtain accurate, per-branch expectations of numbers of state transitions and dwelling times. We also introduce an intuitive way of visualizing the results by integrating over the posterior distribution and summarizing the parameters onto a target reference topology (such as a consensus or MAP tree) provided by the user.ResultsWe benchmarked SFREEMAP’s performance against make.simmap, a popular R-based implementation of stochastic mapping. SFREEMAP confirmed theoretical expectations outperforming make.simmap in every experiment and reducing computation time of relatively modest datasets from hours to minutes. We have also demonstrated that SFREEMAP returns estimates which were not only similar to the ones obtained by averaging across make.simmap mappings, but also more accurate, according to simulated data. We illustrate our visualization strategy using previously published data on the evolution of coloniality in scleractinian corals.ConclusionSFREEMAP is an accurate and fast alternative to ancestral state reconstruction via simulation-based stochastic mapping.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1554-7) contains supplementary material, which is available to authorized users.
Part 8: Case Studies and Demonstrations of Open Source ProjectsInternational audienceThis paper briefly presents a model for monitoring a large, heterogeneous and geographically scattered computer park. The data collection is performed by a software agent. The collected data are sent to the central server over the Internet, and stored by the storage system. An on-line portal makes up the visualization system, featuring charts, reports, and other tools for assessing the state of the park. This system is currently monitoring circa 150,000 machines
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