We determined the complete genome sequence of a Vietnamese isolate of Southern rice black-streaked dwarf virus (SRBSDV). Whole genome comparisons and phylogenetic analysis showed that the genome of the Vietnamese isolate shared high nucleotide sequence identities of over 97.5% with those of the reported Chinese isolates, confirming a common origin of them. Moreover, the greatest divergence between different SRBSDV isolates was found in the segments S1, S3, S4 and S6, which differs from the sequence alignment results between SRBSDV and Rice black streaked dwarf virus (RBSDV), implying that SRBSDV evolved in a unique way independent of RBSDV. This is the first report of a complete nucleotide sequence of SRBSDV from Vietnam and our data provides new clues for further understanding of molecular variation and epidemiology of SRBSDV in Southeast Asia.

Non-linear analysis of autoclaved aerated concrete (AAC) block masonry composite walls with a door opening was conducted with finite element software ABAQUS in order to study its seismic performance. The finite element results were firstly verified with laboratory results and then the effects of vertical stress on seismic performance of the wall was investigated using finite element analysis. This study indicates that seismic performance can be improved with the increase of vertical stress; however, the effect of vertical stress on ultimate load is less than that on the displacement under ultimate load.

Finite element analysis has been performed with software ABAQUS in order to study the effect of door opening’s size and position on seismic performance of autoclaved aerated concrete block composite walls. On the basis of good agreement between finite element results and experimental results, the effect of door opening’s size and position on seismic performance of autoclaved aerated concrete block composite walls has been studied using finite element analysis. This study indicates that with the increase in the door width, the decrease in the wall’s bearing capacity is proportional to the decrease in the wall’s net area; and the change of door’s position has less effect on seismic performance of the composite wall.

Based on mode shape, a new parameter was put forward—mode shape curvature ratio, for detecting structure damages. And it was also the input vector of the RBF neural network. Then through finite element analysis and calculating, the training and forecasting samples were got for the network. The trained neural network can identify the damage location and degree of the frame structure. It proved that this method is simple and valid.