1,721,989 research outputs found

    Cao Guo Rong, 1992

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    Cao Guo Rong, Research Associate specialising in numerical simulation of metal flow, will take up a twelve-month fellowship at the University of Birmingham's Jaguar Research Centre. Photograph originally appeared in the 'Swinburne Staff News', 12th November 1992

    sj-docx-1-cll-10.1177_09636897231188300 – Supplemental material for METTL3 Promotes the Growth and Invasion of Melanoma Cells by Regulating the lncRNA SNHG3/miR-330-5p Axis

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    Supplemental material, sj-docx-1-cll-10.1177_09636897231188300 for METTL3 Promotes the Growth and Invasion of Melanoma Cells by Regulating the lncRNA SNHG3/miR-330-5p Axis by Shaojun Chu, Yulong Li, Baojin Wu, Guo Rong, Qiang Hou, Qin Zhou, Dexiang Du and Yufei Li in Cell Transplantation</p

    A framework to design interaction control of aerial slung load systems: transfer from existing flight control of under-actuated aerial vehicles

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    This paper establishes a framework within which interaction control is designed for the aerial slung load system composed of an underactuated aerial vehicle, a cable and a load. Instead of developing a new control law for the system, we propose the interaction control scheme by the controllers for under-actuated aerial systems. By selecting the deferentially flat output as the configuration, the equations of motion of the two systems are described in an identical form. The flight control task of the under-actuated aerial vehicle is thus converted into the control of the aerial slung load system. With the help of an admittance filter, the compliant trajectory is generated for the load subject to external interaction force. Moreover, the convergence of the whole system is proved by using the boundedness of the tracking error of vehicle attitude tracking as well as the estimation error of external force. Based on the developed theoretical results, an example is provided to illustrate the design algorithm of interaction controller for the aerial slung load via an existing flight controller directly. The correctness and applicability of the obtained results are demonstrated via the illustrative numerical example

    sj-tif-2-cll-10.1177_09636897231188300 – Supplemental material for METTL3 Promotes the Growth and Invasion of Melanoma Cells by Regulating the lncRNA SNHG3/miR-330-5p Axis

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    Supplemental material, sj-tif-2-cll-10.1177_09636897231188300 for METTL3 Promotes the Growth and Invasion of Melanoma Cells by Regulating the lncRNA SNHG3/miR-330-5p Axis by Shaojun Chu, Yulong Li, Baojin Wu, Guo Rong, Qiang Hou, Qin Zhou, Dexiang Du and Yufei Li in Cell Transplantation</p

    sj-tif-4-cll-10.1177_09636897231188300 – Supplemental material for METTL3 Promotes the Growth and Invasion of Melanoma Cells by Regulating the lncRNA SNHG3/miR-330-5p Axis

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    Supplemental material, sj-tif-4-cll-10.1177_09636897231188300 for METTL3 Promotes the Growth and Invasion of Melanoma Cells by Regulating the lncRNA SNHG3/miR-330-5p Axis by Shaojun Chu, Yulong Li, Baojin Wu, Guo Rong, Qiang Hou, Qin Zhou, Dexiang Du and Yufei Li in Cell Transplantation</p

    sj-tif-3-cll-10.1177_09636897231188300 – Supplemental material for METTL3 Promotes the Growth and Invasion of Melanoma Cells by Regulating the lncRNA SNHG3/miR-330-5p Axis

    No full text
    Supplemental material, sj-tif-3-cll-10.1177_09636897231188300 for METTL3 Promotes the Growth and Invasion of Melanoma Cells by Regulating the lncRNA SNHG3/miR-330-5p Axis by Shaojun Chu, Yulong Li, Baojin Wu, Guo Rong, Qiang Hou, Qin Zhou, Dexiang Du and Yufei Li in Cell Transplantation</p

    Expanding the transfer entropy to identify information circuits in complex systems

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    We propose a formal expansion of the transfer entropy to put in evidence irreducible sets of variables which provide information for the future state of each assigned target. Multiplets characterized by a large contribution to the expansion are associated to the informational circuits present in the system, with an informational character which can be associated to the sign of the contribution. For the sake of computational complexity, we adopt the assumption of Gaussianity and use the corresponding exact formula for the conditional mutual information. We report the application of the proposed methodology on two electroencephalography (EEG) data sets
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