291 resultados para runoff processes


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This paper examines Initial Teacher Education students’ experiences of participation in health and physical education (HPE) subject department offices and the impact on their understandings and identity formation. Pierre Bourdieu’s concepts of habitus, field, and practice along with Wenger’s communities of practice form the theoretical frame used in the paper. Data were collected using surveys and interviews with student‐teachers following their teaching practicum and analysed using coding and constant comparison. Emergent themes revealed students’ participation in masculine‐dominated sports, gendered body constructions, and repertoires of masculine domination. Findings are discussed in relation to their impact on student‐teachers’ learning, identity formation, and marginalizing practices in the department offices. Implications for teacher education and HPE are explored.

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Impervious surfaces in an urban catchment are the primary stormwater pollutant contributing areas. Appropriate treatment of stormwater runoff from these impervious surfaces is essential to safeguard the urban water environment. While urban roads have received significant research attention in this regard, roofs have not been well investigated. Key pollutant processes such as build-up on roads and roofs can be different due to the different surface characteristics. This entails different treatment strategies being needed for road and roofs. The research study characterized roof pollutants build-up by differentiating with road surfaces. It was noted that pollutants are more highly concentrated on particles and particularly finer particles in the case of roof surfaces, compared to road surfaces. Additionally, pollutants built-up on roof surfaces tend to be relatively more variable from one day to another in terms of pollutant loads. These results highlight the significance of roofs as a stormwater pollutant source and the important need for a specific stormwater treatment strategy rather than the application of a combined approach for treating stormwater runoff from both, roads and roofs.

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Metal nanoparticle photocatalysts have attracted recent interest due to their strong absorption of visible and ultraviolet light. The energy absorbed by the metal conduction electrons and the intense electric fields in close proximity, created by the localized surface plasmon resonance effect, makes the crucial contribution of activating the molecules on the metal nanoparticles which facilitates chemical transformation. There are now many examples of successful reactions catalyzed by supported nanoparticles of pure metals and of metal alloys driven by light at ambient or moderate temperatures. These examples demonstrate these materials are a novel group of efficient photocatalysts for converting solar energy to chemical energy and that the mechanisms are distinct from those of semiconductor photocatalysts. We present here an overview of recent research on direct photocatalysis of supported metal nanoparticles for organic synthesis under light irradiation and discuss the significant reaction mechanisms that occur through light irradiation.

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Stationary processes are random variables whose value is a signal and whose distribution is invariant to translation in the domain of the signal. They are intimately connected to convolution, and therefore to the Fourier transform, since the covariance matrix of a stationary process is a Toeplitz matrix, and Toeplitz matrices are the expression of convolution as a linear operator. This thesis utilises this connection in the study of i) efficient training algorithms for object detection and ii) trajectory-based non-rigid structure-from-motion.

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The current approach for protecting the receiving water environment from urban stormwater pollution is the adoption of structural measures commonly referred to as Water Sensitive Urban Design (WSUD). The treatment efficiency of WSUD measures closely depends on the design of the specific treatment units. As stormwater quality is influenced by rainfall characteristics, the selection of appropriate rainfall events for treatment design is essential to ensure the effectiveness of WSUD systems. Based on extensive field investigations in four urban residential catchments based at Gold Coast, Australia, and computer modelling, this paper details a technically robust approach for the selection of rainfall events for stormwater treatment design using a three-component model. The modelling results confirmed that high intensity-short duration events produce 58.0% of TS load while they only generated 29.1% of total runoff volume. Additionally, rainfall events smaller than 6-month average recurrence interval (ARI) generates a greater cumulative runoff volume (68.4% of the total annual runoff volume) and TS load (68.6% of the TS load exported) than the rainfall events larger than 6-month ARI. The results suggest that for the study catchments, stormwater treatment design could be based on the rainfall which had a mean value of 31 mm/h average intensity and 0.4 h duration. These outcomes also confirmed that selecting smaller ARI rainfall events with high intensity-short duration as the threshold for treatment system design is the most feasible approach since these events cumulatively generate a major portion of the annual pollutant load compared to the other types of events, despite producing a relatively smaller runoff volume. This implies that designs based on small and more frequent rainfall events rather than larger rainfall events would be appropriate in the context of efficiency in treatment performance, cost-effectiveness and possible savings in land area needed.

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Existing business process drift detection methods do not work with event streams. As such, they are designed to detect inter-trace drifts only, i.e. drifts that occur between complete process executions (traces), as recorded in event logs. However, process drift may also occur during the execution of a process, and may impact ongoing executions. Existing methods either do not detect such intra-trace drifts, or detect them with a long delay. Moreover, they do not perform well with unpredictable processes, i.e. processes whose logs exhibit a high number of distinct executions to the total number of executions. We address these two issues by proposing a fully automated and scalable method for online detection of process drift from event streams. We perform statistical tests over distributions of behavioral relations between events, as observed in two adjacent windows of adaptive size, sliding along with the stream. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in the detection of typical change patterns, and performs significantly better than the state of the art.