Industrial Agglomeration, Production Networks and FDI Promotion The Case Study of China

Chinas Industrial clustering is a distinguished economic phenomenon over the last 20 years. It began to enter into its fast track in the mid-1990s and developed rapidly in recent years. Both market-driven force and government-driven force contribute to Chinese industrial clusters. The opening and stable macroeconomic policies create a favorable climate for the industrial clustering. Local government has made its contribution to construction on both hardware and software environments for industrial clusters. The major contribution of FDI to the local industrial clustering lies in helping integrating Chinese domestic industries into international division of labor and at the same time forging a relatively integrated production chain for Chinese domestic industries. At present, China has stepped into the new phase of industrial clusters upgrading. Chinese government is gradually improving the local software infrastructure for industry clustering.Industrial Agglomeration, China, Production Networks, FDI, foreign direct investment

Quantum Transport in Magnetic Topological Insulator Thin Films

The experimental observation of the long-sought quantum anomalous Hall effect was recently reported in magnetically doped topological insulator thin films [Chang et al., Science 340, 167 (2013)]. An intriguing observation is a rapid decrease from the quantized plateau in the Hall conductance, accompanied by a peak in the longitudinal conductance as a function of the gate voltage. Here, we present a quantum transport theory with an effective model for magnetic topological insulator thin films. The good agreement between theory and experiment reveals that the measured transport originates from a topologically nontrivial conduction band which, near its band edge, has concentrated Berry curvature and a local maximum in group velocity. The indispensable roles of the broken structure inversion and particle-hole symmetries are also revealed. The results are instructive for future experiments and transport studies based on first-principles calculations.Comment: 5 pages, 4 figure

Tunnel Settlement Prediction by Transfer Learning

Tunnel settlement has a significant impact on property security and personal safety. Accurate tunnel-settlement predictions can quickly reveal problems that may be addressed to prevent accidents. However, each acquisition point in the tunnel is only monitored once daily for around two months. This paper presents a new method for predicting tunnel settlement via transfer learning. First, a source model is constructed and trained by deep learning, then parameter transfer is used to transfer the knowledge gained from the source model to the target model, which has a small dataset. Based on this, the training complexity and training time of the target model can be reduced. The proposed method was tested to predict tunnel settlement in the tunnel of Shanghai metro line 13 at Jinshajiang Road and proven to be effective. Artificial neural network and support vector machines were also tested for comparison. The results showed that the transfer-learning method provided the most accurate tunnel-settlement prediction