Material compósito - propriedades do aglomerado de gesso e partículas fragmentadas de bambu (phyllostachys edulis)

Neste trabalho estudou-se um compósito de gesso FGD reforçado com fibras vegetais. As fibras utilizadas neste estudo são provenientes de bambu da espécie Phyllostachys edulis e foram trituradas até se obter uma granulometria apropriada à composição de uma pasta de gesso que permitisse a execução de placas de gesso laminado. As placas produzidas foram ensaiadas à flexão e posteriormente submetidas à análise de humidade para aferir a percentagem de água de cristalização nas amostras. Foram ainda produzidos provetes cúbicos com 7cm de aresta para permitira a execução de ensaio à compressão. Foram produzidos dois tipos de pastas, uma sem a adição de partículas de bambu (controlo) e outra com adição de 15% de partículas de bambu.

Nano-CF: a Coordination Framework for macro-programming in wireless sensor networks

Wireless Sensor Networks (WSN) are being used for a number of applications involving infrastructure monitoring, building energy monitoring and industrial sensing. The difficulty of programming individual sensor nodes and the associated overhead have encouraged researchers to design macro-programming systems which can help program the network as a whole or as a combination of subnets. Most of the current macro-programming schemes do not support multiple users seamlessly deploying diverse applications on the same shared sensor network. As WSNs are becoming more common, it is important to provide such support, since it enables higher-level optimizations such as code reuse, energy savings, and traffic reduction. In this paper, we propose a macro-programming framework called Nano-CF, which, in addition to supporting in-network programming, allows multiple applications written by different programmers to be executed simultaneously on a sensor networking infrastructure. This framework enables the use of a common sensing infrastructure for a number of applications without the users having to worrying about the applications already deployed on the network. The framework also supports timing constraints and resource reservations using the Nano-RK operating system. Nano- CF is efficient at improving WSN performance by (a) combining multiple user programs, (b) aggregating packets for data delivery, and (c) satisfying timing and energy specifications using Rate- Harmonized Scheduling. Using representative applications, we demonstrate that Nano-CF achieves 90% reduction in Source Lines-of-Code (SLoC) and 50% energy savings from aggregated data delivery.