Acoustic emission and yield in sand

An experimental investigation into the Acoustic Emission (AE) response of sand has been undertaken, and the use of AE as a method of yield point identification has been assessed. Dense, saturated samples of sand were tested in conventional triaxial apparatus. The measurements of stresses and strains were carried out according to current research practice. The AE monitoring system was integrated with the soil mechanics equipment in such a way that sample disturbance was minimised. During monotonically loaded, constant cell pressure tests the total number of events recorded was found to increase at an increasing rate in a manner which may be approximated by a power law. The AE response of the sand was found to be both stress level and stress path dependent. Undrained constant cell pressure tests showed that, unlike drained tests, the AE event rate increased at an increasing rate; this was shown to correlate with the mean effective stress variation. The stress path dependence was most noticeable in extension tests, where the number of events recorded was an order of magnitude less than that recorded in comparable compression tests. This stress path dependence was shown to be due to the differences in the work done by the external stresses. In constant cell pressure tests containing unload/reload cycles it was found that yield could be identified from a discontinuity in the event rate/time curve which occurred during reloading. Further tests involving complex stress paths showed that AE was a useful method of yield point identification. Some tests involving large stress reversals were carried out, and AE identified the inverse yield points more distinctly than conventional methods of yield point identification.

Event trigger identification for biomedical events extraction using domain knowledge

Motivation: In molecular biology, molecular events describe observable alterations of biomolecules, such as binding of proteins or RNA production. These events might be responsible for drug reactions or development of certain diseases. As such, biomedical event extraction, the process of automatically detecting description of molecular interactions in research articles, attracted substantial research interest recently. Event trigger identification, detecting the words describing the event types, is a crucial and prerequisite step in the pipeline process of biomedical event extraction. Taking the event types as classes, event trigger identification can be viewed as a classification task. For each word in a sentence, a trained classifier predicts whether the word corresponds to an event type and which event type based on the context features. Therefore, a well-designed feature set with a good level of discrimination and generalization is crucial for the performance of event trigger identification. Results: In this article, we propose a novel framework for event trigger identification. In particular, we learn biomedical domain knowledge from a large text corpus built from Medline and embed it into word features using neural language modeling. The embedded features are then combined with the syntactic and semantic context features using the multiple kernel learning method. The combined feature set is used for training the event trigger classifier. Experimental results on the golden standard corpus show that >2.5% improvement on F-score is achieved by the proposed framework when compared with the state-of-the-art approach, demonstrating the effectiveness of the proposed framework. © 2014 The Author 2014. The source code for the proposed framework is freely available and can be downloaded at http://cse.seu.edu.cn/people/zhoudeyu/ETI_Sourcecode.zip.