Comando e proteção de IGBTs em paralelo

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Eletrotécnica Ramo de Automação e Eletrónica Industrial

Data analytics in the cloud with flexible mapreduced workflows

Data analytic applications are characterized by large data sets that are subject to a series of processing phases. Some of these phases are executed sequentially but others can be executed concurrently or in parallel on clusters, grids or clouds. The MapReduce programming model has been applied to process large data sets in cluster and cloud environments. For developing an application using MapReduce there is a need to install/configure/access specific frameworks such as Apache Hadoop or Elastic MapReduce in Amazon Cloud. It would be desirable to provide more flexibility in adjusting such configurations according to the application characteristics. Furthermore the composition of the multiple phases of a data analytic application requires the specification of all the phases and their orchestration. The original MapReduce model and environment lacks flexible support for such configuration and composition. Recognizing that scientific workflows have been successfully applied to modeling complex applications, this paper describes our experiments on implementing MapReduce as subworkflows in the AWARD framework (Autonomic Workflow Activities Reconfigurable and Dynamic). A text mining data analytic application is modeled as a complex workflow with multiple phases, where individual workflow nodes support MapReduce computations. As in typical MapReduce environments, the end user only needs to define the application algorithms for input data processing and for the map and reduce functions. In the paper we present experimental results when using the AWARD framework to execute MapReduce workflows deployed over multiple Amazon EC2 (Elastic Compute Cloud) instances.

Autonomic Activities in the Execution of Scientific Workflows: Evaluation of the AWARD Framework

Workflows have been successfully applied to express the decomposition of complex scientific applications. However the existing tools still lack adequate support to important aspects namely, decoupling the enactment engine from tasks specification, decentralizing the control of workflow activities allowing their tasks to run in distributed infrastructures, and supporting dynamic workflow reconfigurations. We present the AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic) model of computation, based on Process Networks, where the workflow activities (AWA) are autonomic processes with independent control that can run in parallel on distributed infrastructures. Each AWA executes a task developed as a Java class with a generic interface allowing end-users to code their applications without low-level details. The data-driven coordination of AWA interactions is based on a shared tuple space that also enables dynamic workflow reconfiguration. For evaluation we describe experimental results of AWARD workflow executions in several application scenarios, mapped to the Amazon (Elastic Computing EC2) Cloud.

Fungi spores dimension matters in health effects: a methodology for more detail fungi exposure assessment

Health effects resulting from dust inhalation in occupational environments may be more strongly associated with specific microbial components, such as fungi, than to the particles. The aim of the present study is to characterize the occupational exposure to the fungal burden in four different occupational settings (two feed industries, one poultry and one waste sorting industry), presenting results from two air sampling methods – the impinger collector and the use of filters. In addition, the equipment used for the filter sampling method allowed a more accurate characterization regarding the dimension of the collected fungal particles (less than 2.5 μm size). Air samples of 300L were collected using the impinger Coriolis μ air sampler. Simultaneously, the aerosol monitor (DustTrak II model 8532, TSI®) allowed assessing viable microbiological material below the 2.5 μm size. After sampling, filters were immersed in 300 mL of sterilized distilled water and agitated for 30 min at 100 rpm. 150 μl from the sterilized distilled water were subsequently spread onto malt extract agar (2%) with chloramphenicol (0.05 g/L). All plates were incubated at 27.5 ºC during 5–7 days. With the impinger method, the fungal load ranged from 0 to 413 CFU.m-3 and with the filter method, ranged from 0 to 64 CFU.m-3. In one feed industry, Penicillium genus was the most frequently found genus (66.7%) using the impinger method and three more fungi species/genera/complex were found. The filter assay allowed the detection of only two species/genera/complex in the same industry. In the other feed industry, Cladosporium sp. was the most found (33.3%) with impinger method and three more species/genera/complex were also found. Through the filter assay four fungi species/genera/complex were found. In the assessed poultry, Rhyzopus sp. was the most frequently detected (61.2%) and more three species/genera/complex were isolated. Through the filter assay, only two fungal species/genera/complex were found. In the waste sorting industry Penicillium sp. was the most prevalent (73.6%) with the impinger method, being isolated two more different fungi species/genera/complex. Through the filter assay only Penicillium sp. was found. A more precise determination of occupational fungal exposure was ensured, since it was possible to obtain information regarding not only the characterization of fungal contamination (impinger method), but also the size of dust particles, and viable fungal particles, that can reach the worker ́s respiratory tract (filters method). Both methods should be used in parallel to enrich discussion regarding potential health effects of occupational exposure to fungi.

Synthesis of optimization of [11C] Pittsburgh compound B by the captive solvent method

Mestrado em Medicina Nuclear.

Avaliação de qualidade de energia de um sistema fotovoltaico numa rede elétrica

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo de Energia

Utilização de materiais anti-vibráteis e de isolamento acústico na via férrea

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Vias de Comunicação e Transportes

As parcerias público-privadas e o seu impacto na sustentabilidade da dívida pública

Mestrado em Auditoria

A gestão pública e a auditoria: perspectiva dos gestores sobre a importância, as funções e o papel da auditoria

Mestrado em Auditoria

Robótica móvel: sistema de condução autónoma

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo Automação e Electrónica Industrial

Vou-te dar um brinquedo de menino: um carro … ou o desmistificar as questões de género no Jardim de Infância

Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

Diz-me as letras e eu escrevo: a descoberta da escrita no Jardim de Infância

Relatório da Prática Profissional Supervisionada Mestrado em Educação Pré-Escolar

Implementação em hardware reconfigurável de método de separação de dados hiperespetrais

Relatório do Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

Dimensionamento de um edifício misto de receção de areias e pirites

Trabalho de Projeto para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização em Estruturas

Autonomic workflow activities: the award framework

Workflows have been successfully applied to express the decomposition of complex scientific applications. This has motivated many initiatives that have been developing scientific workflow tools. However the existing tools still lack adequate support to important aspects namely, decoupling the enactment engine from workflow tasks specification, decentralizing the control of workflow activities, and allowing their tasks to run autonomous in distributed infrastructures, for instance on Clouds. Furthermore many workflow tools only support the execution of Direct Acyclic Graphs (DAG) without the concept of iterations, where activities are executed millions of iterations during long periods of time and supporting dynamic workflow reconfigurations after certain iteration. We present the AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic) model of computation, based on the Process Networks model, where the workflow activities (AWA) are autonomic processes with independent control that can run in parallel on distributed infrastructures, e. g. on Clouds. Each AWA executes a Task developed as a Java class that implements a generic interface allowing end-users to code their applications without concerns for low-level details. The data-driven coordination of AWA interactions is based on a shared tuple space that also enables support to dynamic workflow reconfiguration and monitoring of the execution of workflows. We describe how AWARD supports dynamic reconfiguration and discuss typical workflow reconfiguration scenarios. For evaluation we describe experimental results of AWARD workflow executions in several application scenarios, mapped to a small dedicated cluster and the Amazon (Elastic Computing EC2) Cloud.