Relevant bibliographies by topics / Feedforward

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Feedforward'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Feedforward"

1

Back, A. D., and A. C. Tsoi. "FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling." Neural Computation 3, no. 3 (1991): 375–85. http://dx.doi.org/10.1162/neco.1991.3.3.375.

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A new neural network architecture involving either local feedforward global feedforward, and/or local recurrent global feedforward structure is proposed. A learning rule minimizing a mean square error criterion is derived. The performance of this algorithm (local recurrent global feedforward architecture) is compared with a local-feedforward global-feedforward architecture. It is shown that the local-recurrent global-feedforward model performs better than the local-feedforward global-feedforward model.
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Xia, Lian, Jing Qiu, and Jiang Han. "Linear Motor Control Algorithm and Experimental Research Based on Feedforward Fuzzy PID." Key Engineering Materials 620 (August 2014): 363–68. http://dx.doi.org/10.4028/www.scientific.net/kem.620.363.

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In this paper, theory analysis, the MATLAB research and experimental verification about feedforward fuzzy PID control have been performed by combining the characteristics of the PID, feedforward control and fuzzy control. Simulation results show that the feedforward fuzzy PID control could improve the response speed of the system and reduce the tracking error of the system which shows the obvious superiority compared with the PID, feedforward PID, and fuzzy PID. Load experiment for such four kinds of control modes is done on the linear motor platform, and the experimental results show that the
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MANGAL, MANISH, and MANU PRATAP SINGH. "ANALYSIS OF MULTIDIMENSIONAL XOR CLASSIFICATION PROBLEM WITH EVOLUTIONARY FEEDFORWARD NEURAL NETWORKS." International Journal on Artificial Intelligence Tools 16, no. 01 (2007): 111–20. http://dx.doi.org/10.1142/s0218213007003229.

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This paper describes the application of two evolutionary algorithms to the feedforward neural networks used in classification problems. Besides of a simple backpropagation feedforward algorithm, the paper considers the genetic algorithm and random search algorithm. The objective is to analyze the performance of GAs over the simple backpropagation feedforward in terms of accuracy or speed in this problem. The experiments considered a feedforward neural network trained with genetic algorithm/random search algorithm and 39 types of network structures and artificial data sets. In most cases, the e
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Douglas, Michael R. "Holomorphic feedforward networks." Pure and Applied Mathematics Quarterly 18, no. 1 (2022): 251–68. http://dx.doi.org/10.4310/pamq.2022.v18.n1.a7.

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Hovd, Morten, and Robert R. Bitmead. "Feedforward for stabilization." IFAC Proceedings Volumes 42, no. 11 (2009): 602–6. http://dx.doi.org/10.3182/20090712-4-tr-2008.00097.

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Conaghan, P., and A. Lockey. "Feedback to feedforward." Notfall + Rettungsmedizin 12, S2 (2009): 45–48. http://dx.doi.org/10.1007/s10049-009-1222-1.

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Kluger, Avraham N., and Dina Nir. "The feedforward interview." Human Resource Management Review 20, no. 3 (2010): 235–46. http://dx.doi.org/10.1016/j.hrmr.2009.08.002.

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Brought to you by ACS Careers. "Feedforward, not feedback." C&EN Global Enterprise 101, no. 38 (2023): 37. http://dx.doi.org/10.1021/cen-10138-careertips.

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Hirose, Noriaki, and Ryosuke Tajima. "Deadbeat Feedforward Compensation with Frequency Shaping of Position Feedforward Controller." IEEJ Journal of Industry Applications 6, no. 2 (2017): 100–106. http://dx.doi.org/10.1541/ieejjia.6.100.

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Jensen, Konrad Johan, Morten Kjeld Ebbesen, and Michael Rygaard Hansen. "Adaptive Feedforward Control of a Pressure Compensated Differential Cylinder." Applied Sciences 10, no. 21 (2020): 7847. http://dx.doi.org/10.3390/app10217847.

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This paper presents the design, simulation and experimental verification of adaptive feedforward motion control for a hydraulic differential cylinder. The proposed solution is implemented on a hydraulic loader crane. Based on common adaptation methods, a typical electro-hydraulic motion control system has been extended with a novel adaptive feedforward controller that has two separate feedforward states, i.e, one for each direction of motion. Simulations show convergence of the feedforward states, as well as 23% reduction in root mean square (RMS) cylinder position error compared to a fixed ga
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Dissertations / Theses on the topic "Feedforward"

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Johansson, Björn. "Feedforward Control in Dynamic Situations." Licentiate thesis, Linköping University, Linköping University, CSE - Cognitive Systems Engineering Laboratory, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5690.

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<p>This thesis proposal discusses control of dynamic systems and its relation to time. Although much research has been done concerning control of dynamic systems and decision making, little research exists about the relationship between time and control. Control is defined as the ability to keep a target system/process in a desired state. In this study, properties of time such as fast, slow, overlapping etc, should be viewed as a relation between the variety of a controlling system and a target system. It is further concluded that humans have great difficulties controlling target systems that
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Johansson, Björn. "Feedforward control in dynamic situations /." Linköping : Univ, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5690.

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Smith, Alison M. "A wideband adaptive feedforward amplifier lineariser." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq24241.pdf.

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Khan, Altaf Hamid. "Feedforward neural networks with constrained weights." Thesis, University of Warwick, 1996. http://wrap.warwick.ac.uk/4332/.

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The conventional multilayer feedforward network having continuous-weights is expensive to implement in digital hardware. Two new types of networks are proposed which lend themselves to cost-effective implementations in hardware and have a fast forward-pass capability. These two differ from the conventional model in having extra constraints on their weights: the first allows its weights to take integer values in the range [-3,3] only, whereas the second restricts its synapses to the set {-1,0,1} while allowing unrestricted offsets. The benefits of the first configuration are in having weights w
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Shah, Jagesh V. (Jagesh Vijaykumar). "Learning dynamics in feedforward neural networks." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36541.

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Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1995.<br>Includes bibliographical references (leaves 108-115).<br>by Jagesh V. Shah.<br>M.S.
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Lotter, Paul. "Development of feedforward RF power amplifier." Thesis, Cape Peninsula University of Technology, 2006. http://hdl.handle.net/20.500.11838/2206.

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Thesis (MTech(Electrical Engineering))--Cape Peninsula University of Technology, 2006.<br>Electronic communication systems have become an integral part of our everyday lives. RF (Radio Frequency) power amplifiers form part of the fundamental building blocks of an electronic communication system. RF power amplifiers can also be one of the major causes of distortion in an electronic communication system. This thesis describes the linearity requirement for a RF power amplifier that is used in a transmitter section of an electronic communication system. Furthermore, five different linearisat
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Leonard, Julia Anne. "The feedforward control of posture and movement." Thesis, McGill University, 2013. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=114142.

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Goal-directed arm movements performed in the standing position potentially disturb the body's equilibrium as a result of the multi-linked structure of the musculoskeletal system. To compensate for these disturbances and ensure that stability is maintained, the central nervous system (CNS) organizes postural adjustments preceding and accompanying the voluntary movement in a feedforward manner (Massion 1992) using knowledge of the dynamics of the body (Bouisset and Zattara 1981). To date, most studies investigating the control of posture during voluntary movements in humans have focused on eithe
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Unar, Mukhtiar Ali. "Ship steering control using feedforward neural networks." Thesis, University of Glasgow, 1999. http://theses.gla.ac.uk/4493/.

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One significant problem in the design of ship steering control systems is that the dynamics of the vessel change with operating conditions such as the forward speed of the vessel, the depth of the water and loading conditions etc. Approaches considered in the past to overcome these difficulties include the use of self adaptive control systems which adjust the control characteristics on a continuous basis to suit the current operating conditions. Artificial neural networks have been receiving considerable attention in recent years and have been considered for a variety of applications where the
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Tebbs, Robert. "Functionality constraints in feedforward neuromorphic learning systems." Thesis, University of Surrey, 1995. http://epubs.surrey.ac.uk/804354/.

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Chen, Francis Xinghang. "Modeling human vision using feedforward neural networks." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/112824.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 81-86).<br>In this thesis, we discuss the implementation, characterization, and evaluation of a new computational model for human vision. Our goal is to understand the mechanisms enabling invariant perception under scaling,
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Books on the topic "Feedforward"

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Johansson, Björn. Feedforward control in dynamic situations. Department of Computer and Information Science, Linköpings universitet, 2003.

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Zevlaris, Charalambos. Feedforward squarewave FM data link. UMIST, 1994.

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Konstantinou, K. Feedforward linearization of microwave transmitter amplifiers. UMIST, 1995.

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Khan, Altaf Hamid. Feedforward neural networks with constrained weights. typescript, 1996.

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W, Sandberg I., ed. Nonlinear dynamical systems: Feedforward neural network perspectives. John Wiley, 2001.

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Duggan, D. M. Investigation of random feedforward Boolean neural networks. UMIST, 1993.

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Wally, Merrill, and United States. National Aeronautics and Space Administration., eds. A comparative robustness evaluation of feedforward neurofilters. National Aeronautics and Space Administration, 1993.

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Liu, Biao. Adaptive feedforward controllers for active noise control. Shaker, 2001.

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Mortimer, I. Bounding the cognitive domain in feedforward neural networks. UMIST, 1997.

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Kuan, Chung-Ming. Forecasting exchange rates using feedforward and recurrent neural networks. University of Illinois at Urbana-Champaign, 1993.

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Book chapters on the topic "Feedforward"

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Banegas, Darío Luis, and Yolanda Ruiz de Zarobe. "Feedforward." In Content and Language Integrated Learning in South America. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52986-3_13.

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Masters, Timothy. "Feedforward Networks." In Deep Belief Nets in C++ and CUDA C: Volume 3. Apress, 2018. http://dx.doi.org/10.1007/978-1-4842-3721-2_1.

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Isermann, Rolf. "Feedforward Control." In Digital Control Systems. Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-86420-9_6.

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Fleming, Andrew J., and Kam K. Leang. "Feedforward Control." In Design, Modeling and Control of Nanopositioning Systems. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06617-2_9.

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Visioli, Antonio, and Qing-Chang Zhong. "Feedforward Control." In Control of Integral Processes with Dead Time. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-070-0_6.

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Thathachar, M. A. L., and P. S. Sastry. "Feedforward Networks." In Networks of Learning Automata. Springer US, 2004. http://dx.doi.org/10.1007/978-1-4419-9052-5_3.

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Gooch, Jan W. "Feedforward Control." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_4818.

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Brause, Rüdiger W. "Einschichtige feedforward Netze." In Neuronale Netze. Vieweg+Teubner Verlag, 1995. http://dx.doi.org/10.1007/978-3-322-93994-4_3.

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Gegov, Alexander. "Feedforward Fuzzy Networks." In Fuzzy Networks for Complex Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15600-7_7.

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Zhang, Xiang-Sun. "Feedforward Neural Networks." In Nonconvex Optimization and Its Applications. Springer US, 2000. http://dx.doi.org/10.1007/978-1-4757-3167-5_6.

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Conference papers on the topic "Feedforward"

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Gambrell, Oliver, Zahra Vahdat, and Abhyudai Singh. "Feedforward Regulation of Interneuronal Communication." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886083.

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Li, Yunyan. "Asymptotic Observers for Nonlinear Feedforward Systems." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10864995.

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Xian Li, Qing-Guo Wang, and Wen-Jian Cai. "Approximate feedforward control." In 2015 10th Asian Control Conference (ASCC). IEEE, 2015. http://dx.doi.org/10.1109/ascc.2015.7244555.

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Li, Xian, Shuai Liu, Kok Kiong Tan, Qing-Guo Wang, and Wen-Jian Cai. "Predictive feedforward control." In 2016 12th IEEE International Conference on Control and Automation (ICCA). IEEE, 2016. http://dx.doi.org/10.1109/icca.2016.7505377.

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Pearson, Ronald K. "Analysis of feedforward networks." In San Diego '92, edited by Su-Shing Chen. SPIE, 1992. http://dx.doi.org/10.1117/12.130827.

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DeBruin, James C., James M. B. Royalty, Marty Wand, and Edwin Allen. "Feedforward stabilization test bed." In Aerospace/Defense Sensing and Controls, edited by Michael K. Masten and Larry A. Stockum. SPIE, 1996. http://dx.doi.org/10.1117/12.241916.

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Hickson, M. T., D. K. Paul, P. Gardner, and K. Konstantinou. "High Efficiency Feedforward Linearizers." In 24th European Microwave Conference, 1994. IEEE, 1994. http://dx.doi.org/10.1109/euma.1994.337313.

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Ishihara, Abraham, Yoo Yeh, Parth Kumar, Nick Alley, and Jim Neidhoeffer. "Adaptive Feedforward Aircraft Control." In AIAA Infotech@Aerospace 2010. American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-3418.

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Garcia, R. Ferreiro, F. J. Perez Castelo, and J. Vidal Paz. "Multivariable fuzzy feedforward compensation." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076449.

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Ying Luo, YangQuan Chen, and YouGuo Pi. "Fractional order adaptive feedforward cancellation." In 2011 American Control Conference. IEEE, 2011. http://dx.doi.org/10.1109/acc.2011.5991265.

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Reports on the topic "Feedforward"

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Noga, Andrew J. An Introduction to a Feedforward Demodulator. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada380206.

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Brabel, Michael J. Basin Sculpting a Hybrid Recurrent Feedforward Neural Network. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada336386.

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Schmidt, Vincent A., and Jane M. Binner. Analyzing Divisia Rules Extracted from a Feedforward Neural Network. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada457596.

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Rao, N. S. V., V. Protopopescu, R. C. Mann, E. M. Oblow, and S. S. Iyengar. Learning algorithms for feedforward networks based on finite samples. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10190716.

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Mu, Ruihui, and Xiaoqin Zeng. Improved Webpage Classification Technology Based on Feedforward Backpropagation Neural Network. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, 2018. http://dx.doi.org/10.7546/crabs.2018.09.11.

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Vassilev, Apostol. BowTie – A deep learning feedforward neural network for sentiment analysis. National Institute of Standards and Technology, 2019. http://dx.doi.org/10.6028/nist.cswp.04222019.

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Vassilev, Apostol. BowTie – A deep learning feedforward neural network for sentiment analysis. National Institute of Standards and Technology, 2019. http://dx.doi.org/10.6028/nist.cswp.8.

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Roberts, Matthew L. A Feedforward Compensation Technique for Use in Mitigating Platform Induced Jitter. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada548933.

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Pati, Y. C., and P. S. Krishnaprasad. Analysis and Synthesis of Feedforward Neural Networks Using Discrete Affine Wavelet Transformations. Defense Technical Information Center, 1990. http://dx.doi.org/10.21236/ada444558.

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Raka, E. Some Remarks on Feedback and Feedforward Employed to Reduce Beam Induced Voltages. Office of Scientific and Technical Information (OSTI), 1988. http://dx.doi.org/10.2172/1119133.

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