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.

LP → LQ - Estimates for the Fractional Acoustic Potentials and some Related Operators

Mathematics Subject Classification: 47B38, 31B10, 42B20, 42B15.

Multiple Dipole Source Models for Scalp-Recorded Event-Related Potentials: Example from Complex Visual Processing in the Human Brain

2000 Mathematics Subject Classification: 62P10, 92C20

Effects of an extreme temperature event on the behavior and age structure of an estuarine top predator, Carcharhinus leucas

The frequency of extreme environmental events is predicted to increase in the future. Understanding the short- and long-term impacts of these extreme events on large-bodied predators will provide insight into the spatial and temporal scales at which acute environmental disturbances in top-down processes may persist within and across ecosystems. Here, we use long-term studies of movements and age structure of an estuarine top predator—juvenile bull sharks Carcharhinus leucas—to identify the effects of an extreme ‘cold snap’ from 2 to 13 January 2010 over short (weeks) to intermediate (months) time scales. Juvenile bull sharks are typically year-round residents of the Shark River Estuary until they reach 3 to 5 yr of age. However, acoustic telemetry revealed that almost all sharks either permanently left the system or died during the cold snap. For 116 d after the cold snap, no sharks were detected in the system with telemetry or captured during longline sampling. Once sharks returned, both the size structure and abundance of the individuals present in the nursery had changed considerably. During 2010, individual longlines were 70% less likely to capture any sharks, and catch rates on successful longlines were 40% lower than during 2006−2009. Also, all sharks caught after the cold snap were young-of-the-year or neonates, suggesting that the majority of sharks in the estuary were new recruits and several cohorts had been largely lost from the nursery. The longer-term impacts of this change in bull shark abundance to the trophic dynamics of the estuary and the importance of episodic disturbances to bull shark population dynamics will require continued monitoring, but are of considerable interest because of the ecological roles of bull sharks within coastal estuaries and oceans.

Event mining for system and service management

Modern IT infrastructures are constructed by large scale computing systems and administered by IT service providers. Manually maintaining such large computing systems is costly and inefficient. Service providers often seek automatic or semi-automatic methodologies of detecting and resolving system issues to improve their service quality and efficiency. This dissertation investigates several data-driven approaches for assisting service providers in achieving this goal. The detailed problems studied by these approaches can be categorized into the three aspects in the service workflow: 1) preprocessing raw textual system logs to structural events; 2) refining monitoring configurations for eliminating false positives and false negatives; 3) improving the efficiency of system diagnosis on detected alerts. Solving these problems usually requires a huge amount of domain knowledge about the particular computing systems. The approaches investigated by this dissertation are developed based on event mining algorithms, which are able to automatically derive part of that knowledge from the historical system logs, events and tickets. ^ In particular, two textual clustering algorithms are developed for converting raw textual logs into system events. For refining the monitoring configuration, a rule based alert prediction algorithm is proposed for eliminating false alerts (false positives) without losing any real alert and a textual classification method is applied to identify the missing alerts (false negatives) from manual incident tickets. For system diagnosis, this dissertation presents an efficient algorithm for discovering the temporal dependencies between system events with corresponding time lags, which can help the administrators to determine the redundancies of deployed monitoring situations and dependencies of system components. To improve the efficiency of incident ticket resolving, several KNN-based algorithms that recommend relevant historical tickets with resolutions for incoming tickets are investigated. Finally, this dissertation offers a novel algorithm for searching similar textual event segments over large system logs that assists administrators to locate similar system behaviors in the logs. Extensive empirical evaluation on system logs, events and tickets from real IT infrastructures demonstrates the effectiveness and efficiency of the proposed approaches.^

(Table 1) Mean statistics for acoustic parameters measured for both brown skua (Catharacta antarctica lonnbergi) sexes

Bird vocalisations are often essential for sex recognition, especially in species that show little morphological sex dimorphism. Brown skuas (Catharacta antarctica lonnbergi), which exhibit uniform plumage across both sexes, emit three main calls: the long call, the alarm call and the contact call. We tested the potential for sex recognition in brown skua calls of 42 genetically sexed individuals by analysing 8-12 acoustic parameters in the temporal and frequency domains of each call type. For every call type, we failed to find sex differences in any of the acoustic parameters measured. Stepwise discriminant function analysis (DFA) revealed that sexes cannot be unambiguously classified, with increasing uncertainty of correct classification from contact calls to long calls to alarm calls. Consequently, acoustic signalling is probably not the key mechanism for sex recognition in brown skuas.

(Table XIV-1, Page 137-139) Physical characteristics of manganese nodules collected in the Pacific Ocean during cruise GH76-1 of R/V Hakurei Maru

Nodule samples obtained were described and studied on board for 1) observation of occurrence and morphology in and outside samplers, size classification, measurement of weight and calculation of population density (kg/m2); 2) photographing whole nodules on the plate marked with the frames of unit areas of both 0cean-70 (0.50 m2) and freefall grab (0.13 m2), and that of typical samples on the plate with a 5 cm grid scale: 3) observation of internal structures of the nodules on cut section; and 4) determination of mineral composition by X-ray diffractometer. The relation between nodule types and geological environment or chemical composition was examined by referring to other data of related studies, such as sedimentology. acoustic survey, and chemical analysis.