Unauthorized Use Change and Control System of China’s Industrial Buildings: Taking S District of Chongqing as an Example

This paper uses both theoretical research and empirical research in analyzing the changing scale and characteristics of spatial-temporal variations of the unauthorized change of the use of the industrial buildings in Chongqing’s S District. Through the in-depth exploration of the driving factors and mechanism of China’s unauthorized change of the use of the industrial buildings, this paper finally builds scientific and reasonable use change control mechanism for industrial buildings. It has been found through the empirical study that unauthorized use change causes great loss of state-owned land resources and serious impact on commercial real estate and leads to very baneful social consequences. Use change of industrial buildings includes five driving factors: economy, structure of land supply, laws, industry development and system. To avoid such use changes, we must improve the existing laws and regulations and vitalize the industrial building resources; optimize both land supply structure and the spatial arrangement of industrial buildings; explore to develop supervisory control system based on the building certification process and principle of rent-to-grant; construct multi-sector linked supervision system for the use change of industrial buildings; effectively use economic levers to squeeze the profit brought about by the use change of industrial buildings; and know clearly about the industry direction and settled businesses

Modelling arterial pressure waveforms using Gaussian functions and two-stage particle swarm optimizer

Changes of arterial pressure waveform characteristics have been accepted as risk indicators of cardiovascular diseases. Waveform modelling using Gaussian functions has been used to decompose arterial pressure pulses into different numbers of subwaves and hence quantify waveform characteristics. However, the fitting accuracy and computation efficiency of current modelling approaches need to be improved. This study aimed to develop a novel two-stage particle swarm optimizer (TSPSO) to determine optimal parameters of Gaussian functions. The evaluation was performed on carotid and radial artery pressure waveforms (CAPW and RAPW) which were simultaneously recorded from twenty normal volunteers. The fitting accuracy and calculation efficiency of our TSPSO were compared with three published optimization methods: the Nelder-Mead, the modified PSO (MPSO), and the dynamic multiswarm particle swarm optimizer (DMS-PSO). The results showed that TSPSO achieved the best fitting accuracy with a mean absolute error (MAE) of 1.1% for CAPW and 1.0% for RAPW, in comparison with 4.2% and 4.1% for Nelder-Mead, 2.0% and 1.9% for MPSO, and 1.2% and 1.1% for DMS-PSO. In addition, to achieve target MAE of 2.0%, the computation time of TSPSO was only 1.5 s, which was only 20% and 30% of that for MPSO and DMS-PSO, respectively