Hiu Fung Chan

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[1] Daily water demand forecasts are an important component of cost-effective and sustainable management and optimization of urban water supply systems. In this study, a method based on coupling discrete wavelet transforms (WA) and artificial neural networks (ANNs) for urban water demand forecasting applications is proposed and tested. Multiple linear(More)
Himalayan watersheds are characterized by mountainous topography and a lack of available data. Due to the complexity of rainfall–runoff relationships in mountainous watersheds and the lack of hydrological data in many of these watersheds, process-based models have limited applicability for runoff forecasting in these areas. In light of this, accurate(More)
The rapid advances in synthetic biology and biotechnology are increasingly demanding high-throughput screening technology, such as screening of the functionalities of synthetic genes for optimization of protein expression. Compartmentalization of single cells in water-in-oil (W/O) emulsion droplets allows screening of a vast number of individualized assays,(More)
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