978 resultados para Micro territorial experience


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Computer aided technologies, medical imaging, and rapid prototyping has created new possibilities in biomedical engineering. The systematic variation of scaffold architecture as well as the mineralization inside a scaffold/bone construct can be studied using computer imaging technology and CAD/CAM and micro computed tomography (CT). In this paper, the potential of combining these technologies has been exploited in the study of scaffolds and osteochondral repair. Porosity, surface area per unit volume and the degree of interconnectivity were evaluated through imaging and computer aided manipulation of the scaffold scan data. For the osteochondral model, the spatial distribution and the degree of bone regeneration were evaluated. In this study the versatility of two softwares Mimics (Materialize), CTan and 3D realistic visualization (Skyscan) were assessed, too.

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Structural health monitoring has been accepted as a justified effort for long-span bridges, which are critical to a region's economic vitality. As the most heavily instrumented bridge project in the world, WASHMS - Wind And Structural Health Monitoring System has been developed and installed on the cable-supported bridges in Hong Kong (Wong and Ni 2009a). This chapter aims to share some of the experience gained through the operations and studies on the application of WASHMS. It is concluded that Structural Health Monitoring should be composed of two main components: Structural Performance Monitoring (SPM) and Structural Safety Evaluation (SSE). As an example to illustrate how the WASHMS could be used for structural performance monitoring, the layout of the sensory system installed on the Tsing Ma Bridge is briefly described. To demonstrate the two broad approaches of structural safety evaluation - Structural Health Assessment and Damage Detection, three examples in the application of SHM information are presented. These three examples can be considered as pioneer works for the research and development of the structural diagnosis and prognosis tools required by the structural health monitoring for monitoring and evaluation applications.

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The aim of this study was to explore the experience of mental health nurses undertaking doctoral studies. The study was conducted in Victoria, Australia. A descriptive exploratory approach to inquiry was used for this study. Participants were mental health nurses who had successfully completed a doctoral qualification. Eligibility for inclusion required participants to be residing in Victoria (irrespective of where their doctoral studies were undertaken) and to have conducted their research within the domain of mental health and/or currently employed in the field of mental health nursing. Of the 20 potential participants invited, 16 accepted the invitation. Five emergent themes were explicated from narrative analyses. These themes were being a trail blazer, positioning for professional advancement, achieving a balance between competing priorities, maintaining a commitment to the development of the profession, and a point of affirmation. An understanding of the experience of undertaking doctoral studies can be used to influence the development of strategies to encourage more mental health nurses to consider undertaking a doctoral degree.

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This work is focussed on developing a commissioning procedure so that a Monte Carlo model, which uses BEAMnrc’s standard VARMLC component module, can be adapted to match a specific BrainLAB m3 micro-multileaf collimator (μMLC). A set of measurements are recommended, for use as a reference against which the model can be tested and optimised. These include radiochromic film measurements of dose from small and offset fields, as well as measurements of μMLC transmission and interleaf leakage. Simulations and measurements to obtain μMLC scatter factors are shown to be insensitive to relevant model parameters and are therefore not recommended, unless the output of the linear accelerator model is in doubt. Ultimately, this note provides detailed instructions for those intending to optimise a VARMLC model to match the dose delivered by their local BrainLAB m3 μMLC device.