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Significant computation challenges are emerging as agent-based modeling becomes more complicated and dynamically data-driven. In this context, parallel simulation is an attractive solution when dealing with massive data and computation requirements. Nearly all the available distributed simulation systems, however, do not support geospatial phenomena(More)
We report on the fabrication of Cu2+-sensing thermoresponsive poly(N-isopropylacrylamide) (PNIPAM) microgels labeled with metal-chelating acceptor and fluorescent reporter moieties. Cu2+ detection sensitivity can be considerably enhanced via thermo-induced collapse of the sensing matrix, which can easily optimize the relative spatial distribution of(More)
More and more vector-based cellular automata (VCA) models have been built to leverage parallel computing to model rapidly changing cities and urban regions. During parallel simulation, common task decomposition methods based on space partitioning, e.g., grid partitioning (GRID) and recursive binary space partitioning (BSP), do not work well given the(More)
Spatial data processing often requires massive datasets, and the task/data scheduling efficiency of these applications has an impact on the overall processing performance. Among the existing scheduling strategies, hypergraph-based algorithms capture the data sharing pattern in a global way and significantly reduce total communication volume. Due to(More)
Massive spatial data requires considerable computing power for real-time processing. With the help of the development of multicore technology and computer component cost reduction in recent years, high performance clusters become the only economically viable solution for this requirement. Massive spatial data processing demands heavy I/O operations however,(More)
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