161 research outputs found

    Patterns of Vertical Habitat Use by Atlantic Blue Marlin (Makaira nigricans) in the Gulf of Mexico

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    We examined data from pop-up archival transmitting (PAT) tags (n = 18) to characterize aspects of vertical habitat use by blue marlin (Makaira nigricans) from the Gulf of Mexico (GOM). Two of these tags were recovered and provided fine-scale information about diving patterns and the relationship between time at depth and temperature. Similar to previous studies, blue marlin in the GOM spent most of their time at the surface and at temperatures within 3° C of surface temperatures. Time at depth was multimodal and the magnitude of the smaller modes was dependent upon the strength and depth of the thermocline. Importantly, time at depth was a complex function of the temperature change relative to the surface, time of day, lunar phase, and water column structure. Temperature change with depth between the western and eastern GOM and the adjacent western Atlantic Ocean was also examined. The depth range (maximum depths varied between 68 and 388 m) varied widely between fish and did not appear to correspond with any particular magnitude of temperature change relative to the surface. Although these data may help to improve stock assessments that are based upon habitat standardizations of CPUE, progress will be limited until the distribution of feeding activity with depth and other aspects of blue marlin behavior in relation to capture probability are elucidated

    Managing China's energy sector: between the market and the state

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    As China has now become the largest energy consumer in the world, its energy sector has understandably huge domestic and global implications. In this Special Issue, which is an interdisciplinary one, comprising a set of eight in-depth empirical studies by leading international experts in the field, we set out to examine the management of the transformation of China's conventional and renewable energy sectors, with special attention to state–business relations and their link to the market

    Error motion trajectory-driven diagnostics of kinematic and non-kinematic machine tool faults

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    Error motion trajectory data are routinely collected on multi-axis machine tools to assess their operational state. There is a wealth of literature devoted to advances in modelling, identification and correction using such data, as well as the collection and processing of alternative data streams for the purpose of machine tool condition monitoring. Until recently, there has been minimal focus on combining these two related fields. This paper presents a general approach to identifying both kinematic and non-kinematic faults in error motion trajectory data, by framing the issue as a generic pattern recognition problem. Because of the typically-sparse nature of datasets in this domain – due to their infrequent, offline collection procedures – the foundation of the approach involves training on a purely simulated dataset, which defines the theoretical fault-states observable in the trajectories. Ensemble methods are investigated and shown to improve the generalisation ability when predicting on experimental data. Machine tools often have unique ‘signatures’ which can significantly-affect their error motion trajectories, which are largely repeatable, but specific to the individual machine. As such, experimentally-obtained data will not necessarily be easily defined in a theoretical simulation. A transfer learning approach is introduced to incorporate experimentally-obtained error motion trajectories into classifiers which were trained primarily on a simulation domain. The approach was shown to significantly improve experimental test set performance, whilst also maintaining all theoretical information learned in the initial, simulation-only training phase. The ultimate approach represents a viable and powerful automated classifier for error motion trajectory data, which can encode theoretical fault-states with efficacy whilst also remain adaptable to machine-specific signatures
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