Relevant bibliographies by topics / Methode gauss newton

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Academic literature on the topic 'Methode gauss newton'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Methode gauss newton"

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Kang, Jun Won, and Alireza Pakravan. "Performance Evaluation of a Time-domain Gauss-Newton Full-waveform Inversion Method." Journal of the Computational Structural Engineering Institute of Korea 26, no. 4 (August 30, 2013): 223–31. http://dx.doi.org/10.7734/coseik.2013.26.4.223.

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Wang, Yong. "Gauss-Newton method." Wiley Interdisciplinary Reviews: Computational Statistics 4, no. 4 (February 24, 2012): 415–20. http://dx.doi.org/10.1002/wics.1202.

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Kim, Yong-Hee, Dong-Gyu Kim, Jin-Woo Han, Kyu-Ha Song, and Hyoung-Nam Kim. "Gauss-Newton Based Emitter Location Method Using Successive TDOA and FDOA Measurements." Journal of the Institute of Electronics and Information Engineers 50, no. 7 (July 25, 2013): 76–84. http://dx.doi.org/10.5573/ieek.2013.50.7.076.

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Argyros, Ioannis K., and Saïd Hilout. "On the Gauss–Newton method." Journal of Applied Mathematics and Computing 35, no. 1-2 (January 16, 2010): 537–50. http://dx.doi.org/10.1007/s12190-010-0377-8.

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Shakhno, S. M., and H. P. Yarmola. "Local convergence of the Gauss-Newton-Kurchatov method under generalized Lipschitz conditions." Carpathian Mathematical Publications 13, no. 2 (July 28, 2021): 305–14. http://dx.doi.org/10.15330/cmp.13.2.305-314.

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We investigate the local convergence of the Gauss-Newton-Kurchatov method for solving nonlinear least squares problems. This method is a combination of Gauss-Newton and Kurchatov methods and it is used for problems with the decomposition of the operator. The convergence analysis of the method is performed under the generalized Lipshitz conditions. The conditions of convergence, radius and the convergence order of the considered method are established. Given numerical examples confirm the theoretical results.
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Fliege, Jörg, Andrey Tin, and Alain Zemkoho. "Gauss–Newton-type methods for bilevel optimization." Computational Optimization and Applications 78, no. 3 (January 10, 2021): 793–824. http://dx.doi.org/10.1007/s10589-020-00254-3.

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AbstractThis article studies Gauss–Newton-type methods for over-determined systems to find solutions to bilevel programming problems. To proceed, we use the lower-level value function reformulation of bilevel programs and consider necessary optimality conditions under appropriate assumptions. First, under strict complementarity for upper- and lower-level feasibility constraints, we prove the convergence of a Gauss–Newton-type method in computing points satisfying these optimality conditions under additional tractable qualification conditions. Potential approaches to address the shortcomings of the method are then proposed, leading to alternatives such as the pseudo or smoothing Gauss–Newton-type methods for bilevel optimization. Our numerical experiments conducted on 124 examples from the recently released Bilevel Optimization LIBrary (BOLIB) compare the performance of our method under different scenarios and show that it is a tractable approach to solve bilevel optimization problems with continuous variables.
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Fraley, C. "Computational Behavior of Gauss–Newton Methods." SIAM Journal on Scientific and Statistical Computing 10, no. 3 (May 1989): 515–32. http://dx.doi.org/10.1137/0910033.

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Cartis, Coralia, and Lindon Roberts. "A derivative-free Gauss–Newton method." Mathematical Programming Computation 11, no. 4 (May 20, 2019): 631–74. http://dx.doi.org/10.1007/s12532-019-00161-7.

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Rymarczyk, Tomasz, and Przemysław Adamkiewicz. "MONITORING DAMAGE AND DAMPNESS IN FLOOD EMBANKMENT BY ELECTRICAL IMPEDANCE TOMOGRAPHY." Informatics Control Measurement in Economy and Environment Protection 7, no. 1 (March 30, 2017): 59–62. http://dx.doi.org/10.5604/01.3001.0010.4584.

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The article presents a model of the measuring system for image reconstruction. Electrical impedance tomography was used to determine the moisture of the test flood blank on a specially built model. The Gauss-Newton methods have been applied very successfully in many areas of the scientific modelling. The basic information about the built model system is given. The finite element method was used to solve the forward problem. The level set method and the Gauss-Newton method were applied to solve the inverse problem.
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Chen, Ke, and Mauricio D. Sacchi. "Time-domain elastic Gauss–Newton full-waveform inversion: a matrix-free approach." Geophysical Journal International 223, no. 2 (July 21, 2020): 1007–39. http://dx.doi.org/10.1093/gji/ggaa330.

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SUMMARY We present a time-domain matrix-free elastic Gauss–Newton full-waveform inversion (FWI) algorithm. Our algorithm consists of a Gauss–Newton update with a search direction calculated via elastic least-squares reverse time migration (LSRTM). The conjugate gradient least-squares (CGLS) method solves the LSRTM problem with forward and adjoint operators derived via the elastic Born approximation. The Hessian of the Gauss–Newton method is never explicitly formed or saved in memory. In other words, the CGLS algorithm solves for the Gauss–Newton direction via the application of implicit-form forward and adjoint operators which are equivalent to elastic Born modelling and elastic reverse time migration, respectively. We provide numerical examples to test the proposed algorithm where we invert for P- and S-wave velocities simultaneously. The proposed algorithm performs positively on mid-size problems where we report solutions of slight improvement than those computed using the conventional non-linear conjugate gradient method. In spite of the aforementioned limited gain, the theory developed in this paper contributes to a better understanding of time-domain elastic Gauss–Newton FWI.
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Dissertations / Theses on the topic "Methode gauss newton"

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Chen, Min. "Excitation optimale d'un systeme parabolique en vue de son identification." Nantes, 1987. http://www.theses.fr/1987NANT2050.

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Le systeme considere est de type parabolique non lineaire. On montre l'existence et l'unicite de la solution du systeme et on le resoud numeriquement. On utilise les methodes d'optimisation du gradient conjugue et de gauss-newton pour l'identification des parametres avec l'excitation du systeme donnee puis on determine l'excitation optimale pour l'estimation des parametres dans le cas ou les parametres sont fonction de l'etat
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Simonis, Joseph P. "Newton-Picard Gauss-Seidel." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-051305-162036/unrestricted/simonis.pdf.

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Simonis, Joseph P. "Newton-Picard Gauss-Seidel." Digital WPI, 2005. https://digitalcommons.wpi.edu/etd-dissertations/285.

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Newton-Picard methods are iterative methods that work well for computing roots of nonlinear equations within a continuation framework. This project presents one of these methods and includes the results of a computation involving the Brusselator problem performed by an implementation of the method. This work was done in collaboration with Andrew Salinger at Sandia National Laboratories.
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Parkhurst, Steven Christopher. "Solution of equations arising in reservoir simulation by the truncated Gauss-Newton method." Thesis, University of Hertfordshire, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.283463.

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Meadows, Leslie J. "Iteratively Regularized Methods for Inverse Problems." Digital Archive @ GSU, 2013. http://digitalarchive.gsu.edu/math_diss/13.

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We are examining iteratively regularized methods for solving nonlinear inverse problems. Of particular interest for these types of methods are application problems which are unstable. For these application problems, special methods of numerical analysis are necessary, since classical algorithms tend to be divergent.
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Aguiar, Ademir Alves. "Análise semi-local do método de Gauss-Newton sob uma condição majorante." Universidade Federal de Goiás, 2014. http://repositorio.bc.ufg.br/tede/handle/tede/4251.

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Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-03-05T14:28:50Z No. of bitstreams: 2 Dissertação - Ademir Alves Aguiar - 2014.pdf: 1975016 bytes, checksum: 31320b5840b8b149afedc97d0e02b49b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)
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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
In this dissertation we present a semi-local convergence analysis for the Gauss-Newton method to solve a special class of systems of non-linear equations, under the hypothesis that the derivative of the non-linear operator satisfies a majorant condition. The proofs and conditions of convergence presented in this work are simplified by using a simple majorant condition. Another tool of demonstration that simplifies our study is to identify regions where the iteration of Gauss-Newton is “well-defined”. Moreover, special cases of the general theory are presented as applications.
Nesta dissertação apresentamos uma análise de convergência semi-local do método de Gauss-Newton para resolver uma classe especial de sistemas de equações não-lineares, sob a hipótese que a derivada do operador não-linear satisfaz uma condição majorante. As demonstrações e condições de convergência apresentadas neste trabalho são simplificadas pelo uso de uma simples condição majorante. Outra ferramenta de demonstração que simplifica o nosso estudo é a identificação de regiões onde a iteração de Gauss-Newton está “bem-definida”. Além disso, casos especiais da teoria geral são apresentados como aplicações.
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Dolák, Martin. "Nelineární regrese v programu R." Master's thesis, Vysoká škola ekonomická v Praze, 2015. http://www.nusl.cz/ntk/nusl-193088.

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This thesis deals with solutions of nonlinear regression problems using R programming language. The introductory theoretical part is devoted to familiarization with the principles of solving nonlinear regression models and of their applications in the program R. In both, theoretical and practical part, the most famous and used differentiator algorithms are presented, particularly the Gauss-Newton's and of the steepest descent method, for estimating the parameters of nonlinear regression. Further, in the practical part, there are some demo solutions of particular tasks using nonlinear regression methods. Overall, a large number of graphs processed by the author is used in this thesis for better comprehension.
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Bokka, Naveen. "Comparison of Power Flow Algorithms for inclusion in On-line Power Systems Operation Tools." ScholarWorks@UNO, 2010. http://scholarworks.uno.edu/td/1237.

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The goal of this thesis is to develop a new, fast, adaptive load flow algorithm that "automatically alternates" numerical methods including Newton-Raphson method, Gauss-Seidel method and Gauss method for a load flow run to achieve less run time. Unlike the proposed method, the traditional load flow analysis uses only one numerical method at a time. This adaptive algorithm performs all the computation for finding the bus voltage angles and magnitudes, real and reactive powers for the given generation and load values, while keeping track of the proximity to convergence of a solution. This work focuses on finding the algorithm that uses multiple numerical techniques, rather than investigating programming techniques and programming languages. The convergence time is compared with those from using each of the numerical techniques. The proposed method is implemented on the IEEE 39-bus system with different contingencies and the solutions obtained are verified with PowerWorld Simulator, a commercial software for load flow analysis.
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Gumpert, Ben Allen. "A recursive Gauss-Newton method for model independent eye-in-hand visual servoing / by Ben Allen Gumpert." Thesis, Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/17260.

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Mollevik, Iris. "Bundle adjustment for large problems - The effect of a truncated Gauss-Newton method on performance and precision." Thesis, Umeå universitet, Institutionen för datavetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-155346.

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We implement a truncated Gauss-Newton algorithm and apply it to the bundle adjustment problem in a photogrammetry application. The normal equations are solved approximately using the conjugate gradient method preconditioned with the incomplete Cholesky factor.  Our implementation is compared to an exact Gauss-Newton implementation.  Improvements in time performance are found in some cases. The observed relative errors in estimated parameters are of order 10^−10 or smaller.  The preconditioner proves to be very important, as does the permutation of the Jacobian. Excluding the time to re-permute the Jacobian, execution times are lowered by up to 24%. The truncated algorithm is observed to improve performance for larger datasets but not for smaller ones.
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Books on the topic "Methode gauss newton"

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Hohmann, Andreas. Inexact Gauss Newton methods for parameter dependent nonlinear problems. Aachen: Shaker, 1994.

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Book chapters on the topic "Methode gauss newton"

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Heinkenschloss, M., M. Laumen, and E. W. Sachs. "Gauss-Newton methods with grid refinement." In International Series of Numerical Mathematics / Internationale Schriftenreihe zur Numerischen Mathematik / Série Internationale d’Analyse Numérique, 161–74. Basel: Birkhäuser Basel, 1991. http://dx.doi.org/10.1007/978-3-0348-6418-3_11.

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Deuflhard, Peter. "Least Squares Problems: Gauss-Newton Methods." In Newton Methods for Nonlinear Problems, 173–231. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23899-4_4.

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Binder, Tanja, and Ekaterina Kostina. "Gauss–Newton Methods for Robust Parameter Estimation." In Contributions in Mathematical and Computational Sciences, 55–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30367-8_3.

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Anastassiou, George A., and Ioannis K. Argyros. "Inexact Gauss-Newton Method for Singular Equations." In Intelligent Numerical Methods: Applications to Fractional Calculus, 263–81. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26721-0_16.

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Kokurin, Mikhail Yu, and Anatoly B. Bakushinsky. "Iteratively Regularized Gauss-Newton Methods under Random Noise." In Inverse Problems and Applications, 1–11. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-12499-5_1.

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Argyros, Ioannis K., and Á. Alberto Magreñán. "Gauss-Newton method with applications to convex optimization." In Iterative Methods and Their Dynamics with Applications, 282–94. Boca Raton, FL : CRC Press, [2016] | “A science publishers book.”: CRC Press, 2017. http://dx.doi.org/10.1201/9781315153469-18.

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Argyros, Ioannis K., and Á. Alberto Magrenan. "Inexact Gauss-Newton method for least square problems." In Iterative Methods and Their Dynamics with Applications, 144–60. Boca Raton, FL : CRC Press, [2016] | “A science publishers book.”: CRC Press, 2017. http://dx.doi.org/10.1201/9781315153469-8.

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Gautschi, Walter, and Sotirios E. Notaris. "Newton’s Method and Gauss-Kronrod Quadrature." In International Series of Numerical Mathematics / Internationale Schriftenreihe zur Numerischen Mathematik / Série internationale d’Analyse numérique, 60–71. Basel: Birkhäuser Basel, 1988. http://dx.doi.org/10.1007/978-3-0348-6398-8_6.

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Rösch, Arnd. "A Gauss-Newton Method for the Identification of Nonlinear Heat Transfer Laws." In Optimal Control of Complex Structures, 217–30. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8148-7_18.

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Kaur, Amanpreet, Padam Kumar, and Govind P. Gupta. "Sensor Nodes Localization for 3D Wireless Sensor Networks Using Gauss–Newton Method." In Smart Innovations in Communication and Computational Sciences, 187–98. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8968-8_16.

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Conference papers on the topic "Methode gauss newton"

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Gao, G., H. Jiang Inc., J. C. Vink, P. H. van Hagen, and T. J. Wells. "Performance Enhancement Of Gauss-Newton Trust Region Solver For Distributed Gauss-Newton Optimization Method." In ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery. Netherlands: EAGE Publications BV, 2018. http://dx.doi.org/10.3997/2214-4609.201802228.

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Wang, Kunlun, and Tonggang Zhang. "Gauss-Newton method for DEM co-registration." In International Conference on Intelligent Earth Observing and Applications, edited by Guoqing Zhou and Chuanli Kang. SPIE, 2015. http://dx.doi.org/10.1117/12.2207244.

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Sheen, Dong‐Hoon, Chang‐Eob Baag, Kagan Tuncay, and Peter J. Ortoleva. "Elastic waveform inversion using Gauss‐Newton method." In SEG Technical Program Expanded Abstracts 2005. Society of Exploration Geophysicists, 2005. http://dx.doi.org/10.1190/1.2148036.

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Yan, Junlin, Christian Tiberius, Giovanni Bellusci, and Gerard Janssen. "Feasibility of Gauss-Newton method for indoor positioning." In 2008 IEEE/ION Position, Location and Navigation Symposium. IEEE, 2008. http://dx.doi.org/10.1109/plans.2008.4569986.

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Erlangga, Yogi A., and Felix J. Herrmann. "Seismic waveform inversion with Gauss‐Newton‐Krylov method." In SEG Technical Program Expanded Abstracts 2009. Society of Exploration Geophysicists, 2009. http://dx.doi.org/10.1190/1.3255332.

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Mouzer Figueiró, W., and S. Valilevich Goldin. "Gauss-Newton vs. Newton Method for the Estimation of Changing Reflector Slopes." In 4th International Congress of the Brazilian Geophysical Society. European Association of Geoscientists & Engineers, 1995. http://dx.doi.org/10.3997/2214-4609-pdb.313.326.

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Wang, Tengfei, Wencai Xu, Jiubing Cheng, and Jianhua Geng. "Practical reflection waveform inversion using truncated Gauss-Newton method." In SEG Technical Program Expanded Abstracts 2020. Society of Exploration Geophysicists, 2020. http://dx.doi.org/10.1190/segam2020-3427176.1.

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Chen, Bilei, and Satish Nagarajaiah. "Flexibility-based structural damage identification using Gauss-Newton method." In The 14th International Symposium on: Smart Structures and Materials & Nondestructive Evaluation and Health Monitoring, edited by Masayoshi Tomizuka, Chung-Bang Yun, and Victor Giurgiutiu. SPIE, 2007. http://dx.doi.org/10.1117/12.716215.

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Akcelik, V., G. Biros, and O. Ghattas. "Parallel Multiscale Gauss-Newton-Krylov Methods for Inverse Wave Propagation." In ACM/IEEE SC 2002 Conference. IEEE, 2002. http://dx.doi.org/10.1109/sc.2002.10002.

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Minot, Ariana, Yue Lu, and Na Li. "A distributed Gauss-Newton method for power system state estimation." In 2016 IEEE Power and Energy Society General Meeting (PESGM). IEEE, 2016. http://dx.doi.org/10.1109/pesgm.2016.7741288.

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Reports on the topic "Methode gauss newton"

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Dennis, Jr, Songbai J. E., Vu Sheng, and Phuong A. A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems. Fort Belvoir, VA: Defense Technical Information Center, February 1985. http://dx.doi.org/10.21236/ada454936.

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