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Noah Robinson
Noah Robinson

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Carbon Nanotubes (CNT) embedded in composite materials like CFRP, polymers or ceramics, can improve specific performance characteristics such as e.g. electrical conductivity, mechanical fatigue and crack propagation, mechanical properties, alpha/epsilon values, PIM-reduction, EMC shielding, etc.CNT skeletons, also called Bucky papers and Bucky discs, are macroscopic aggregates of Carbon Nanotubes. These skeletons are used in composites with different matrices, namely metal, ceramic or polymer or directly used in CFRP composites.The aim is to increase the performance of composite space structures by increasing the material characteristics or provide composites with additional sensing abilities like structural health monitoring.

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The sharp proliferation of high power electronics and electrical vehicles has promoted growing demands for power sources with both high energy and power densities. Under these circumstances, battery-supercapacitor hybrid devices are attracting considerable attention as they combine the advantages of both batteries and supercapacitors. Here, a novel type of hybrid device based on a carbon skeleton/Mg 2 Ni free-standing electrode without the traditional nickel foam current collector is reported, which has been designed and fabricated through a dispersing-freeze-drying method by employing reduced graphene oxide (rGO) and multiwalled carbon nanotubes (MWCNTs) as a hybrid skeleton. As a result, the Mg 2 Ni alloy is able to deliver a high discharge capacity of 644 mAh g -1 and, more importantly, a high cycling stability with a retention of over 78% after 50 charge/discharge cycles have been achieved, which exceeds almost all the results ever reported on the Mg 2 Ni alloy. Simultaneously, the electrode could also exhibit excellent supercapacitor performances including high specific capacities (296 F g -1 ) and outstanding cycling stability (100% retention after 100 cycles). Moreover, the hybrid device can switch between battery and supercapacitor modes immediately as needed during application. These features make the C skeleton/alloy electrode a highly promising candidate for battery-supercapacitor hybrid devices with high power/energy density and favorable cycling stability.

Object skeletons are useful for object representation and object detection. They are complementary to the object contour, and provide extra information, such as how object scale (thickness) varies among object parts. But object skeleton extraction from natural images is very challenging, because it requires the extractor to be able to capture both local and non-local image context in order to determine the scale of each skeleton pixel. In this paper, we present a novel fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network. The network is trained by multi-task learning, where one task is skeleton localization to classify whether a pixel is a skeleton pixel or not, and the other is skeleton scale prediction to regress the scale of each skeleton pixel. Supervision is imposed at different stages by guiding the scale-associated side outputs toward the groundtruth skeletons at the appropriate scales. The responses of the multiple scale-associated side outputs are then fused in a scale-specific way to detect skeleton pixels using multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors. Additionally, the usefulness of the obtained skeletons and scales (thickness) are verified on two object detection applications: Foreground object segmentation and object proposal detection.

Living vertebrates are divided into those that possess a fully formed and fully mineralised skeleton (gnathostomes) versus those that possess only unmineralised cartilaginous rudiments (cyclostomes). As such, extinct phylogenetic intermediates of these living lineages afford unique insights into the evolutionary assembly of the vertebrate mineralised skeleton and its canonical tissue types. Extinct jawless and jawed fishes assigned to the gnathostome stem evidence the piecemeal assembly of skeletal systems, revealing that the dermal skeleton is the earliest manifestation of a homologous mineralised skeleton. Yet the nature of the primitive dermal skeleton, itself, is poorly understood. This is principally because previous histological studies of early vertebrates lacked a phylogenetic framework required to derive evolutionary hypotheses. Nowhere is this more apparent than within Heterostraci, a diverse clade of primitive jawless vertebrates. To this end, we surveyed the dermal skeletal histology of heterostracans, inferred the plesiomorphic heterostracan skeleton and, through histological comparison to other skeletonising vertebrate clades, deduced the ancestral nature of the vertebrate dermal skeleton. Heterostracans primitively possess a four-layered skeleton, comprising a superficial layer of odontodes composed of dentine and enameloid; a compact layer of acellular parallel-fibred bone containing a network of vascular canals that supply the pulp canals (L1); a trabecular layer consisting of intersecting radial walls composed of acellular parallel-fibred bone, showing osteon-like development (L2); and a basal layer of isopedin (L3). A three layered skeleton, equivalent to the superficial layer L2 and L3 and composed of enameloid, dentine and acellular bone, is possessed by the ancestor of heterostracans + jawed vertebrates. We conclude that an osteogenic component is plesiomorphic with respect to the vertebrate dermal skeleton. Consequently, we interpret the

Carbonate minerals have precipitated from seawater for the last 3.8 billion years, but where and how they precipitate has changed through geologic time. The earliest carbonates precipitated on the seafloor as crystal fans until ocean oxygenation, coupled with aerobic microbial respiration, made the sediment-water interface caustic for carbonate sedimentation (Bergmann et al., 2013; Higgins et al., 2009). The locus of carbonate precipitation and the dominant carbonate sediments can be used as a high-resolution proxy, in both space and time, for oxygenation and seawater chemistry. Geobiologists have successfully used large datasets to track fluctuations in Earth's chemical and biological cycles. Few geobiologists, however, have studied Earth history by compiling a high-resolution database of global carbonate sedimentation. We have built such an archive: a dataset of Earth's 3.8 billion to 500 million years old carbonate rocks, which are our best proxy for carbonate sedimentation in deep time. The Catalogue of Carbonate Sedimentology and Stratigraphy (C2S2) currently contains 144 formations, digitized at the meter scale and classified by environment of deposition. Lithofacies details are recorded for each platform, including a range of microbial fabrics, mineralogy, depositional environmental, age and location. C2S2, a temporal-spatial compilation of trends in sediments and fossils, represents a research tool not previously available to geobiologists. With C2S2 we can, for example, track global trends in bioturbation depth, microbial morphotypes and habitat. We can also pinpoint the depth-dependent timing of oxygenation and the transition from anaerobic to aerobic respiration at the seafloor. C2S2 tracks dolomitization through time which can be correlated with other proxies for changing seawater chemistry. Applying C2S2 to ecologic questions will allow us to better understand the habitats critical to evolution.

The double cut and join (DCJ) model of genome rearrangement is well studied due to its mathematical simplicity and power to account for the many events that transform gene order. These studies have mostly been devoted to the understanding of minimum length scenarios transforming one genome into another. In this paper we search instead for rearrangement scenarios that minimize the number of rearrangements whose breakpoints are unlikely due to some biological criteria. One such criterion has recently become accessible due to the advent of the Hi-C experiment, facilitating the study of 3D spacial distance between breakpoint regions. We establish a link between the minimum number of unlikely rearrangements required by a scenario and the problem of finding a maximum edge-disjoint cycle packing on a certain transformed version of the adjacency graph. This link leads to a 3/2-approximation as well as an exact integer linear programming formulation for our problem, which we prove to be NP-complete. We also present experimental results on fruit flies, showing that Hi-C data is informative when used as a criterion for rearrangements. A new variant of the weighted DCJ distance problem is addressed that ignores scenario length in its objective function. A solution to this problem provides a lower bound on the number of unlikely moves necessary when transforming one gene order into another. This lower bound aids in the study of rearrangement scenarios with respect to chromatin structure, and could eventually be used in the design of a fixed parameter algorithm with a more general objective function.

A new approach to skeletonization was developed for gridded potential-field data. Generally, skeletonization is a pattern-recognition technique allowing automatic recognition of near-linear features in the images, measurement of their parameters, and analyzing them for similarities. Our approach decomposes the images into arbitrarily-oriented "wavelets" characterized by positive or negative amplitudes, orientation angles, spatial dimensions, polarities, and other attributes. Orientations of the wavelets are obtained by scanning the azimuths to detect the strike direction of each anomaly. The wavelets are connected according to the similarities of these attributes, which leads to a "skeleton" map of the potential-field data. In addition, 2-D filtering is conducted concurrently with the wavelet-identification process, which allows extracting parameters of background trends and reduces the adverse effects of low-frequency background (which is often strong in potential-field maps) on skeletonization.. By correlating the neighboring wavelets, linear anomalies are identified and characterized. The advantages of this algorithm are the generality and isotropy of feature detection, as well as being specifically designed for gridded data. With several options for background-trend extraction, the stability for identification of lineaments is improved and optimized. The algorithm is also integrated in a powerful processing system which allows combining it with numerous other tools, such as filtering, computation of analytical signal, empirical mode decomposition, and various types of plotting. The method is applied to potential-field data for the Western Canada Sedimentary Basin, in a study area which extends from southern Saskatchewan into southwestern Manitoba. The target is the structure of crystalline basement beneath Phanerozoic sediments. The examples illustrate that skeletonization aid in the interpretation of complex structures at different scale lengths. The results




  • Adhvika Gour
    Adhvika Gour
  • Dwayne Smith
    Dwayne Smith
  • Riva Motwani
    Riva Motwani
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