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Relevant bibliographies by topics / Coarse-grid

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Academic literature on the topic 'Coarse-grid'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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Journal articles on the topic "Coarse-grid"

1

Alber, David M., and Luke N. Olson. "Parallel coarse-grid selection." Numerical Linear Algebra with Applications 14, no. 8 (2007): 611–43. http://dx.doi.org/10.1002/nla.541.

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Evazi, M., and H. Mahani. "Unstructured-Coarse-Grid Generation Using Background-Grid Approach." SPE Journal 15, no. 02 (March 3, 2010): 326–40. http://dx.doi.org/10.2118/120170-pa.

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Summary Reservoir flow simulation involves subdivision of the physical domain into a number of gridblocks. This is best accomplished with optimized gridpoint density and a minimized number of gridblocks, especially for coarse-grid generation from a fine-grid geological model. In any coarse-grid generation, proper distribution of gridpoints, which form the basis of numerical gridblocks, is a challenging task. We show that this can be achieved effectively by a novel grid-generation approach based on a background grid that stores gridpoint spacing parameters. Spacing parameter (L) can be described by Poisson's equation (∇2L = G), where the local density of gridpoints is controlled by a variable source term (G); see Eq. 1. This source term can be based on different gridpoint density indicators, such as permeability variations, fluid velocity, or their combination (e.g., vorticity) where they can be extracted from the reference fine grid. Once a background grid is generated, advancing-front triangulation (AFT) and then Delaunay tessellation are invoked to form the final (coarse) gridblocks. The algorithm produces grids varying smoothly from high- to low-density gridpoints, thus minimizing use of grid-smoothing and -optimization techniques. This algorithm is quite flexible, allowing choice of the gridding indicator, hence providing the possibility of comparing the grids generated with different indicators and selecting the best. In this paper, the capabilities of approach in generation of unstructured coarse grids from fine geological models are illustrated using 2D highly heterogeneous test cases. Flexibility of algorithm to gridding indicator is demonstrated using vorticity, permeability variation, and velocity. Quality of the coarse grids is evaluated by comparing their two-phase-flow simulation results to those of fine grid and uniform coarse grid. Results demonstrate the robustness and attractiveness of the approach, as well as relative quality/performance of grids generated by using different indicators.

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Kehl, René, and Reinhard Nabben. "Avoiding singular coarse grid systems." Linear Algebra and its Applications 507 (October 2016): 137–52. http://dx.doi.org/10.1016/j.laa.2016.05.025.

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Cho, Yongchae, and Richard L. Gibson, Jr. "Reverse time migration via frequency-adaptive multiscale spatial grids." GEOPHYSICS 84, no. 2 (March 1, 2019): S41—S55. http://dx.doi.org/10.1190/geo2018-0292.1.

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Reverse time migration (RTM) is widely used because of its ability to recover complex geologic structures. However, RTM also has a drawback in that it requires significant computational cost. In RTM, wave modeling accounts for the largest part of the computing cost for calculating forward- and backward-propagated wavefields before applying an imaging condition. For this reason, we have applied a frequency-adaptive multiscale spatial grid to enhance the efficiency of the wave simulations. To implement wave modeling for different values of the spatial grid interval, we apply a model reduction technique, the generalized multiscale finite-element method (GMsFEM), which solves local spectral problems on a fine grid to simulate wave propagation on a coarser grid. We can enhance the speed of computation without sacrificing accuracy by using coarser grids for lower frequency waves, while applying a finer grid for higher frequency waves. In the proposed method, we can control the size of the coarse grid and level of heterogeneity of the wave solutions to tune the trade-off between speedup and accuracy. As we increase the expected level of complexity of the wave solutions, the GMsFEM wave modeling can capture more detailed features of waves. After computing the forward and backward wavefield on the coarse grid, we reproject the coarse wave solutions to the fine grid to construct the RTM gradient image. Although wave solutions are computed on a coarse grid, we still obtain the RTM images without reducing the image resolution by projecting coarse wave solutions to the fine grid. We determine the efficiency of the proposed imaging method using the Marmousi-2 model. We compare the RTM images using GMsFEM with a fixed coarse mesh and a multiple frequency-adaptive coarse meshes to indicate the image quality and computational speed of the new approach.

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MacLachlan, S., and Yousef Saad. "A Greedy Strategy for Coarse-Grid Selection." SIAM Journal on Scientific Computing 29, no. 5 (January 2007): 1825–53. http://dx.doi.org/10.1137/060654062.

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Petrovskaya, Natalia B., and Sergei V. Petrovskii. "The coarse-grid problem in ecological monitoring." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 466, no. 2122 (April 29, 2010): 2933–53. http://dx.doi.org/10.1098/rspa.2010.0023.

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Obtaining information about pest-insect population size is an important problem of pest monitoring and control. Usually, this problem has to be solved based on scarce spatial data about the population density. The problem of monitoring can thus be linked to a more general mathematical problem of numerical integration on a coarse grid. Numerical integration on coarse grids has rarely been considered in literature as it is usually assumed that the grid can be refined. However, this is not the case in ecological monitoring where fine grids are not available. In this paper, we introduce a method of numerical integration that allows one to accurately evaluate an integral on a coarse grid. The method is tested on several functions with different properties to show its effectiveness. We then use the method to obtain an estimate of the population size for different population distributions and show that an ecologically reasonable accuracy can be achieved on a very coarse grid consisting of just a few points. Finally, we summarize our mathematical findings as a protocol of ecological monitoring, thus sending a clear and practically important message to ecologists and pest-control specialists.

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Bender, Iring, Roger Horsley, and Werner Wetzel. "Coarse grid Yukawa interaction for staggered fermions." Nuclear Physics B 349, no. 1 (January 1991): 277–304. http://dx.doi.org/10.1016/0550-3213(91)90198-7.

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Krogstad, S., V. L. L. Hauge, and A. F. F. Gulbransen. "Adjoint Multiscale Mixed Finite Elements." SPE Journal 16, no. 01 (August 23, 2010): 162–71. http://dx.doi.org/10.2118/119112-pa.

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Summary We develop an adjoint model for a simulator consisting of a multiscale pressure solver and a saturation solver that works on flow-adapted grids. The multiscale method solves the pressure on a coarse grid that is close to uniform in index space and incorporates fine-grid effects through numerically computed basis functions. The transport solver works on a coarse grid adapted by a fine-grid velocity field obtained by the multiscale solver. Both the multiscale solver for pressure and the flow-based coarsening approach for transport have shown earlier the ability to produce accurate results for a high degree of coarsening. We present results for a complex realistic model to demonstrate that control settings based on optimization of our multiscale flow-based model closely match or even outperform those found by using a fine-grid model. For additional speed-up, we develop mappings used for rapid system updates during the timestepping procedure. As a result, no fine-grid quantities are required during simulations and all fine-grid computations (multiscale basis functions, generation of coarse transport grid, and coarse mappings) become a preprocessing step. The combined methodology enables optimization of waterflooding on a complex model with 45,000 grid cells in a few minutes.

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Ziv, Alexander, and Elena Solov’eva. "Approximate noise maps as instrument for evaluation of the city environment quality." Noise Mapping 8, no. 1 (January 1, 2021): 260–67. http://dx.doi.org/10.1515/noise-2021-0021.

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Abstract The paper discusses noise mapping from the prospective of general evaluation of the state of the city environment. Suggested is a noise evaluation procedure based on a two-step spatial discretization - coarse and fine grids. The coarse grid is used for evaluation of average noise levels (background noise). For this, rather simple method is proposed, where average noise levels are estimated directly for the whole coarse grid cells instead of averaging the noise levels computed point-wise. The fine grid is used for finding the obstacle density to apply in calculations over the coarse grid. It may be used also for additional noise levels detailing in the close vicinity of noise sources where noise propagation is strongly affected by surrounding structures. The detailed results allow correction of the averages over the coarse grid. In comparison with other approaches, the suggested procedure takes little computing time to execute for the entire city. Test example shows reasonable agreement with results computed using the ‘Ecolog-Noise’ software package that has gained popularity in Russian Federation since its introduction in 2008. Another example describes the application of the proposed method for a moderate size densely built city.

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Gavrilieva, Uygulana, Maria Vasilyeva, and Eric T. Chung. "Generalized Multiscale Finite Element Method for Elastic Wave Propagation in the Frequency Domain." Computation 8, no. 3 (July 7, 2020): 63. http://dx.doi.org/10.3390/computation8030063.

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In this work, we consider elastic wave propagation in fractured media. The mathematical model is described by the Helmholtz problem related to wave propagation with specific interface conditions (Linear Slip Model, LSM) on the fracture in the frequency domain. For the numerical solution, we construct a fine grid that resolves all fracture interfaces on the grid level and construct approximation using a finite element method. We use a discontinuous Galerkin method for the approximation by space that helps to weakly impose interface conditions on fractures. Such approximation leads to a large system of equations and is computationally expensive. In this work, we construct a coarse grid approximation for an effective solution using the Generalized Multiscale Finite Element Method (GMsFEM). We construct and compare two types of the multiscale methods—Continuous Galerkin Generalized Multiscale Finite Element Method (CG-GMsFEM) and Discontinuous Galerkin Generalized Multiscale Finite Element Method (DG-GMsFEM). Multiscale basis functions are constructed by solving local spectral problems in each local domains to extract dominant modes of the local solution. In CG-GMsFEM, we construct continuous multiscale basis functions that are defined in the local domains associated with the coarse grid node and contain four coarse grid cells for the structured quadratic coarse grid. The multiscale basis functions in DG-GMsFEM are discontinuous and defined in each coarse grid cell. The results of the numerical solution for the two-dimensional Helmholtz equation are presented for CG-GMsFEM and DG-GMsFEM for different numbers of multiscale basis functions.

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Dissertations / Theses on the topic "Coarse-grid"

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Mahani, Hassan. "Upscaling and optimal coarse grid generation for the numerical simulation of two-phase flow in porous media." Thesis, Imperial College London, 2005. http://hdl.handle.net/10044/1/11814.

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Viellieber, Mathias [Verfasser]. "Coarse-Grid-CFD für industrielle Anwendungen: Integrale Analysen detaillierter generischer Simulationen zur Schließung von Feinstrukturtermen eines Multiskalenansatzes / Mathias Viellieber." Karlsruhe : KIT Scientific Publishing, 2017. http://www.ksp.kit.edu.

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Kashefi, Ali. "A Finite-Element Coarse-GridProjection Method for Incompressible Flows." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/79948.

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Coarse grid projection (CGP) methodology is a novel multigrid method for systems involving decoupled nonlinear evolution equations and linear elliptic Poisson equations. The nonlinear equations are solved on a fine grid and the linear equations are solved on a corresponding coarsened grid. Mapping operators execute data transfer between the grids. The CGP framework is constructed upon spatial and temporal discretization schemes. This framework has been established for finite volume/difference discretizations as well as explicit time integration methods. In this article we present for the first time a version of CGP for finite element discretizations, which uses a semi-implicit time integration scheme. The mapping functions correspond to the finite-element shape functions. With the novel data structure introduced, the mapping computational cost becomes insignificant. We apply CGP to pressure correction schemes used for the incompressible Navier Stokes flow computations. This version is validated on standard test cases with realistic boundary conditions using unstructured triangular meshes. We also pioneer investigations of the effects of CGP on the accuracy of the pressure field. It is found that although CGP reduces the pressure field accuracy, it preserves the accuracy of the pressure gradient and thus the velocity field, while achieving speedup factors ranging from approximately 2 to 30. Exploring the influence of boundary conditions on CGP, the minimum speedup occurs for velocity Dirichlet boundary conditions, while the maximum speedup occurs for open boundary conditions. We discuss the CGP method as a guide for partial mesh refinement of incompressible flow computations and show its application for simulations of flow over a backward facing step and flow past a cylinder.
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Özgen, Ilhan [Verfasser], Reinhard [Akademischer Betreuer] Hinkelmann, Dongfang [Akademischer Betreuer] Liang, Reinhard [Gutachter] Hinkelmann, Philippe [Gutachter] Gourbesville, Frank [Gutachter] Molkenthin, and Dongfang [Gutachter] Liang. "Coarse grid approaches for the shallow water model / Ilhan Özgen ; Gutachter: Reinhard Hinkelmann, Philippe Gourbesville, Frank Molkenthin, Dongfang Liang ; Reinhard Hinkelmann, Dongfang Liang." Berlin : Technische Universität Berlin, 2017. http://d-nb.info/1156275369/34.

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Hardy, Benjamin Arik. "A New Method for the Rapid Calculation of Finely-Gridded Reservoir Simulation Pressures." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1123.pdf.

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Books on the topic "Coarse-grid"

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Heidmann, James D. Coarse grid modeling of turbine film cooling flows using volumetric source terms. [Cleveland, Ohio]: National Aeronautics and Space Administration, Glenn Research Center, 2001.

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Book chapters on the topic "Coarse-grid"

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Cleary, Andrew J., Robert D. Falgout, Van Emden Henson, and Jim E. Jones. "Coarse-grid selection for parallel algebraic multigrid." In Solving Irregularly Structured Problems in Parallel, 104–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0018531.

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Alcin, H., O. Allain, and A. Dervieux. "Volume-Agglomeration Coarse Grid In Schwarz Algorithm." In Finite Volumes for Complex Applications VI Problems & Perspectives, 3–11. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20671-9_1.

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Wu, Yongwei, Qing Wang, Guangwen Yang, and Weiming Zheng. "Coarse-Grained Distributed Parallel Programming Interface for Grid Computing." In Grid and Cooperative Computing, 255–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24679-4_54.

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Zawidzki, Machi. "Architectural Functional Layout Optimization in a Coarse Grid." In Discrete Optimization in Architecture, 3–34. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1106-1_1.

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Gander, Martin J., Laurence Halpern, and Kévin Santugini Repiquet. "A New Coarse Grid Correction for RAS/AS." In Lecture Notes in Computational Science and Engineering, 275–83. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05789-7_24.

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Vuik, C., and J. Frank. "A Parallel Block Preconditioner Accelerated by Coarse Grid Correction." In High Performance Computing and Networking, 99–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45492-6_11.

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Emans, Maximilian. "Parallel Coarse-Grid Treatment in AMG for Coupled Systems." In Parallel Processing and Applied Mathematics, 361–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31500-8_37.

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Da, Mingjun, Jing-ying Zhao, Guojie Suo, and Hai Guo. "Online Handwritten Naxi Pictograph Digits Recognition System Using Coarse Grid." In Computer Science for Environmental Engineering and EcoInformatics, 390–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22694-6_55.

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Rafi, A., Saha Dauji, and Kapilesh Bhargava. "Estimation of SPT from Coarse Grid Data by Spatial Interpolation Technique." In Lecture Notes in Civil Engineering, 1079–91. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6086-6_87.

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Germano, Eduardo B. M. R., and Rodrigo Nicoletti. "Shape Optimization of Plates for Desired Natural Frequencies from Coarse Grid Results." In Topics in Model Validation and Uncertainty Quantification, Volume 4, 167–74. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-2431-4_17.

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Conference papers on the topic "Coarse-grid"

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A. Hewett, T., K. Suzuki, and M. A. Christie. "Discretization Effects in Coarse-Grid Pseudofunctions." In ECMOR VI - 6th European Conference on the Mathematics of Oil Recovery. European Association of Geoscientists & Engineers, 1998. http://dx.doi.org/10.3997/2214-4609.201406645.

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Mahani, Hassan, and Ann Helen Muggeridge. "Improved Coarse Grid Generation using Vorticity." In SPE Europec/EAGE Annual Conference. Society of Petroleum Engineers, 2005. http://dx.doi.org/10.2118/94319-ms.

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Mahani, H., and A. H. Muggeridge. "Improved Coarse Grid Generation Using Vorticity (SPE94319)." In 67th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 2005. http://dx.doi.org/10.3997/2214-4609-pdb.1.h021.

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Tessmer, E. "3D Coarse Grid GPR Modelling of Dipping Interfaces." In 59th EAGE Conference & Exhibition. European Association of Geoscientists & Engineers, 1997. http://dx.doi.org/10.3997/2214-4609-pdb.131.gen1997_p026.

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Evazi, Mohammad, and Hassan Mahani. "Unstructured Coarse Grid Generation for Reservoir Flow Simulation Using Background Grid Approach." In SPE Middle East Oil and Gas Show and Conference. Society of Petroleum Engineers, 2009. http://dx.doi.org/10.2118/120170-ms.

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Fujimoto, Noriyuki. "On Non-Approximability of Coarse-Grained Workflow Grid Scheduling." In 2008 International Symposium on parallel Architectures, Algorighms and Networks I-SPAN. IEEE, 2008. http://dx.doi.org/10.1109/i-span.2008.35.

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Viellieber, Mathias, and Andreas G. Class. "Anisotropic Porosity Formulation of the Coarse-Grid-CFD (CGCFD)." In 2012 20th International Conference on Nuclear Engineering and the ASME 2012 Power Conference. ASME, 2012. http://dx.doi.org/10.1115/icone20-power2012-54539.

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Leye, Stefen, Jan Himmelspach, Matthias Jeschke, Roland Ewald, and Adelinde M. Uhrmacher. "A Grid-Inspired Mechanism for Coarse-Grained Experiment Execution." In 2008 12th IEEE International Symposium on Distributed Simulation and Real-Time Applications (DS-RT). IEEE, 2008. http://dx.doi.org/10.1109/ds-rt.2008.33.

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Wischhusen, Stefan, Timo Tumforde, and Hans-Herrmann Wurr. "FluidDynamics Library for Coarse-Grid CFD-Simulation in Modelica." In The 2nd Japanese Modelica Conference Tokyo, Japan, May 17-18, 2018. Linköping University Electronic Press, 2019. http://dx.doi.org/10.3384/ecp1814871.

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Shen, Gangxiang (Steven), and Qi Yang. "From Coarse Grid to Mini-Grid to Gridless: How Much can Gridless Help Contentionless?" In Optical Fiber Communication Conference. Washington, D.C.: OSA, 2011. http://dx.doi.org/10.1364/ofc.2011.otui3.

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Reports on the topic "Coarse-grid"

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Sankaran Sundaresan. COARSE-GRID SIMULATION OF REACTING AND NON-REACTING GAS-PARTICLE FLOWS. Office of Scientific and Technical Information (OSTI), March 2004. http://dx.doi.org/10.2172/822872.

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Sankaran Sundaresan. COARSE-GRID SIMULATION OF REACTING AND NON-REACTING GAS-PARTICLE FLOWS. Office of Scientific and Technical Information (OSTI), October 2004. http://dx.doi.org/10.2172/836624.

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