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Shanghai has experienced rapid urbanization over the past 30 years, with a large number of rural areas being transformed into semi-urbanized areas with mixed functions. The main drivers were top-down state-led development, market forces, and bottom-up rural industrialization. The scale and speed of this process have far exceeded expectations, leading to a series of socio-ecological problems such as population loss, industrial decline, and environmental pollution. Based on the analysis, this dissertation identifies two development directions: industrial restructuring and ecological protection, to cope with the uncertainty of population growth, a scenario-building approach is used to explore the optimal strategies under different circumstances. Design strategies are extracted through scenario evaluation and applied to strategic planning., Architecture, Urbanism and Building Sciences | Urbanism

AR2U086 R&D Studio – Spatial Strategies for the Global Metropolis, Architecture, Urbanism and Building Sciences | Urbanism

AR2U086 R&D Studio – Spatial Strategies for the Global Metropolis, Architecture, Urbanism and Building Sciences | Urbanism

In this study, we investigate a strategy to accelerate the data assimilation (DA) algorithm. Based on evaluations of the computational time, the analysis step of the assimilation turns out to be the most expensive part. After a study of the characteristics of the ensemble ash state, we propose a mask-state algorithm which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. This will reduce the computational cost in the analysis step. Experimental results show the mask-state algorithm significantly speeds up the analysis step. Subsequently, the total amount of computing time for volcanic ash DA is reduced to an acceptable level. The mask-state algorithm is generic and thus can be embedded in any ensemble-based DA framework. Moreover, ensemble-based DA with the mask-state algorithm is promising and flexible, because it implements exactly the standard DA without any approximation and it realizes the satisfying performance without any change in the full model., Mathematical Physics

In this study, we investigate a strategy to accelerate the data assimilation (DA) algorithm. Based on evaluations of the computational time, the analysis step of the assimilation turns out to be the most expensive part. After a study of the characteristics of the ensemble ash state, we propose a mask-state algorithm which records the sparsity information of the full ensemble state matrix and transforms the full matrix into a relatively small one. This will reduce the computational cost in the analysis step. Experimental results show the mask-state algorithm significantly speeds up the analysis step. Subsequently, the total amount of computing time for volcanic ash DA is reduced to an acceptable level. The mask-state algorithm is generic and thus can be embedded in any ensemble-based DA framework. Moreover, ensemble-based DA with the mask-state algorithm is promising and flexible, because it implements exactly the standard DA without any approximation and it realizes the satisfying performance without any change in the full model., Mathematical Physics