Bulk effects on topological conduction on the surface of 3-D topological insulators

The surface states of a topological insulator in a fine-tuned magnetic field are ideal candidates for realizing a topological metal which is protected against disorder. Its signatures are (1) a conductance plateau in long wires in a finely tuned longitudinal magnetic field and (2) a conductivity which always increases with sample size, and both are independent of disorder strength. We numerically study how these experimental transport signatures are affected by bulk physics in the interior of the topological insulator sample. We show that both signatures of the topological metal are robust against bulk effects. However the bulk does substantially accelerate the metal's decay in a magnetic field and alter its response to surface disorder. When the disorder strength is tuned to resonance with the bulk band the conductivity follows the predictions of scaling theory, indicating that conduction is diffusive. At other disorder strengths the bulk reduces the effects of surface disorder and scaling theory is systematically violated, signaling that conduction is not fully diffusive. These effects will change the magnitude of the surface conductivity and the magnetoconductivity

Research on the Prediction of Rigid Frame-Continuous Girder Bridge Deflection Using BP and RBF Neural Networks

To solve the problem of excessive deflection in the post-operation process of a rigid frame-continuous girder bridge and provide a basis for the setting of its initial camber, this paper, based on the results of finite element analysis, uses three methods to predict and verify the deflection of a rigid frame-continuous girder bridge. The results show that the average deflection method can be used to fit the average deflection value for a relatively long period of time and predict the average deflection value for the next longer period of time. Both the back-propagation (BP) neural network model and the radial basis function (RBF) neural network model can predict deflection well, but the RBF neural network model has higher prediction accuracy, with a mean absolute error (MAE) of 2.55 cmm and a relative error not exceeding 1%. The prediction model established by the RBF neural network has higher stability, better generalization ability, and better overall prediction performance. The established model has some reference significance for similar engineering projects and can achieve the optimization of structural parameters