2,334 research outputs found

    Solar Irradiance Variability is Caused by the Magnetic Activity on the Solar Surface

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    The variation in the radiative output of the Sun, described in terms of solar irradiance, is important to climatology. A common assumption is that solar irradiance variability is driven by its surface magnetism. Verifying this assumption has, however, been hampered by the fact that models of solar irradiance variability based on solar surface magnetism have to be calibrated to observed variability. Making use of realistic three-dimensional magnetohydrodynamic simulations of the solar atmosphere and state-of-the-art solar magnetograms from the Solar Dynamics Observatory, we present a model of total solar irradiance (TSI) that does not require any such calibration. In doing so, the modeled irradiance variability is entirely independent of the observational record. (The absolute level is calibrated to the TSI record from the Total Irradiance Monitor.) The model replicates 95% of the observed variability between April 2010 and July 2016, leaving little scope for alternative drivers of solar irradiance variability at least over the time scales examined (days to years).Comment: Supplementary Materials; https://journals.aps.org/prl/supplemental/10.1103/PhysRevLett.119.091102/supplementary_material_170801.pd

    One year survival with poorly differentiated metastatic pancreatic carcinoma following chemoembolization with gemcitabine and cisplatin.

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    While hepatic arterial chemoembolization is efficacious for a number of malignancies, there is scant data regarding treatment of pancreatic adenocarcinoma. We report a complete radiographic response at one year from diagnosis of metastatic pancreatic carcinoma. Gemcitabine/cisplatin based chemoembolization may be of potential benefit for patients with liver-dominant metastases from pancreatic carcinoma. Given the typical survival of 6 months or less in this patient group with standard therapies, further research is warranted

    Metastable Dynamics of the Hard-Sphere System

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    The reformulation of the mode-coupling theory (MCT) of the liquid-glass transition which incorporates the element of metastability is applied to the hard-sphere system. It is shown that the glass transition in this system is not a sharp one at the special value of the density or the packing fraction, which is in contrast to the prediction by the conventional MCT. Instead we find that the slowing down of the dynamics occurs over a range of values of the packing fraction. Consequently, the exponents governing the sequence of time relaxations in the intermediate time regime are given as functions of packing fraction with one additional parameter which describes the overall scale of the metastable potential energy for defects in the hard-sphere system. Implications of the present model on the recent experiments on colloidal systems are also discussed.Comment: 21 pages, 5 figures (available upon request), RevTEX3.0, JFI Preprint

    Nonparametric nonlinear model predictive control

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    Model Predictive Control (MPC) has recently found wide acceptance in industrial applications, but its potential has been much impeded by linear models due to the lack of a similarly accepted nonlinear modeling or databased technique. Aimed at solving this problem, the paper addresses three issues: (i) extending second-order Volterra nonlinear MPC (NMPC) to higher-order for improved prediction and control; (ii) formulating NMPC directly with plant data without needing for parametric modeling, which has hindered the progress of NMPC; and (iii) incorporating an error estimator directly in the formulation and hence eliminating the need for a nonlinear state observer. Following analysis of NMPC objectives and existing solutions, nonparametric NMPC is derived in discrete-time using multidimensional convolution between plant data and Volterra kernel measurements. This approach is validated against the benchmark van de Vusse nonlinear process control problem and is applied to an industrial polymerization process by using Volterra kernels of up to the third order. Results show that the nonparametric approach is very efficient and effective and considerably outperforms existing methods, while retaining the original data-based spirit and characteristics of linear MPC
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