Acoustic emission characteristics of rock under impact loading

Acoustic emission tests were performed using a split Hopkinson pressure bar system(SHPB) on 50-mm-diameter bars of granite, limestone, sandstone and skarn. The results show that the amplitude distribution of hits is not well centralized around 50 d B, and that some hits with large amplitudes, usually larger than 70 d B, occur in the early stages of each test, which is different from the findings from static and low-loading-rate tests. Furthermore, the dominant frequency range of the recorded acoustic emission waveforms is between 300 k Hz and 500 k Hz, and frequency components higher than 500 k Hz are not significant. The hit with the largest values of amplitude, counts, signal strength, and absolute energy in each test, displays a waveform with similar frequency characteristics and greater correlation with the waveform obtained from the elastic input bar of the split Hopkinson pressure bar system compared with the waveforms of the other hits. This indicates that the hit with the largest values of amplitude, counts, signal strength, and absolute energy is generated by elastic wave propagation instead of fracture within the rock specimen.

Bagging with CTD–A Novel Signature for the Hierarchical Prediction of Secreted Protein Trafcking in Eukaryotes

Protein trafficking or protein sorting in eukaryotes is a complicated process and is carried out based on the information contaified in the protein. Many methods reported prediction of the subcellular location of proteins from sequence information. However, most of these prediction methods use a flat structure or parallel architecture to perform prediction. In this work, we introduce ensemble classifiers with features that are extracted directly from full length protein sequences to predict locations in the protein-sorting pathway hierarchically. Sequence driven features, sequence mapped features and sequence autocorrelation features were tested with ensemble learners and their performances were compared. When evaluated by independent data testing, ensemble based-bagging algorithms with sequence feature composition, transition and distribution （CTD） successfully classified two datasets with accuracies greater than 90%. We compared our results with similar published methods, and our method equally performed with the others at two levels in the secreted pathway. This study shows that the feature CTD extracted from protein sequences is effective in capturing biological features among compartments in secreted pathways.