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Journal articles on the topic 'Fuzzy processes'

Author: Grafiati
Published: 4 June 2021
Last updated: 9 February 2022

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1

Matłoka, Marian. "Convex fuzzy processes." Fuzzy Sets and Systems 110, no. 1 (February 2000): 109–14. http://dx.doi.org/10.1016/s0165-0114(98)00053-0.

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2

Chalco-Cano, Y., M. A. Rojas-Medar, and R. Osuna-Gómez. "s-Convex fuzzy processes." Computers & Mathematics with Applications 47, no. 8-9 (April 2004): 1411–18. http://dx.doi.org/10.1016/s0898-1221(04)90133-2.

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3

Matłoka, Marian. "h-PREINVEX FUZZY PROCESSES." Śląski Przegląd Statystyczny, no. 14 (2016): 27–39. http://dx.doi.org/10.15611/sps.2016.14.02.

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4

Stojaković, Mila. "Fuzzy martingales - a simple form of fuzzy processes∗." Stochastic Analysis and Applications 14, no. 3 (January 1996): 355–67. http://dx.doi.org/10.1080/07362999608809443.

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5

Shen, Qiang, Ruiqing Zhao, and Wansheng Tang. "Random fuzzy alternating renewal processes." Soft Computing 13, no. 2 (April 22, 2008): 139–47. http://dx.doi.org/10.1007/s00500-008-0307-y.

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6

Li, Shunqin, Qiang Shen, Wansheng Tang, and Ruiqing Zhao. "Random fuzzy delayed renewal processes." Soft Computing 13, no. 7 (September 20, 2008): 681–90. http://dx.doi.org/10.1007/s00500-008-0372-2.

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7

Kim, Byoung Kyun, and Jai Heui Kim. "Stochastic Integrals of Set-Valued Processes and Fuzzy Processes." Journal of Mathematical Analysis and Applications 236, no. 2 (August 1999): 480–502. http://dx.doi.org/10.1006/jmaa.1999.6461.

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8

Yuji Yoshida. "A time-average fuzzy reward criterion in fuzzy decision processes." Information Sciences 110, no. 1-2 (September 1998): 103–12. http://dx.doi.org/10.1016/s0020-0255(97)10079-2.

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9

Kaminskas, Vytautas, and Raimundas Liutkevičius. "Learning Fuzzy Control of Nonlinear Processes." Informatica 16, no. 4 (January 1, 2005): 571–86. http://dx.doi.org/10.15388/informatica.2005.116.

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10

Fann, W. R., and P. L. Hsu. "Fuzzy Adaptive Control of Milling Processes." IFAC Proceedings Volumes 25, no. 28 (October 1992): 88–92. http://dx.doi.org/10.1016/s1474-6670(17)49470-5.

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11

Sarmasti Emami, M. R. "Fuzzy Logic Applications In Chemical Processes." Journal of Mathematics and Computer Science 01, no. 04 (December 25, 2010): 339–48. http://dx.doi.org/10.22436/jmcs.001.04.11.

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12

F. Hurdle, John. "Lightweight fuzzy processes in clinical computing." Artificial Intelligence in Medicine 11, no. 1 (September 1997): 55–73. http://dx.doi.org/10.1016/s0933-3657(97)00023-7.

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13

Gegov, Alexander, and Hans-Joachim Nern. "Distributed Fuzzy Control of Multivariable Processes." IFAC Proceedings Volumes 28, no. 5 (May 1995): 455–60. http://dx.doi.org/10.1016/s1474-6670(17)47266-1.

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14

Wang, Yin, Gang Rong, and Shuqing Wang. "Hybrid fuzzy modeling of chemical processes." Fuzzy Sets and Systems 130, no. 2 (September 2002): 265–75. http://dx.doi.org/10.1016/s0165-0114(01)00242-1.

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15

Zhang, H., and C. M. Tam. "Fuzzy simulation of flexible construction processes." International Journal of Computer Applications in Technology 20, no. 1/2/3 (2004): 15. http://dx.doi.org/10.1504/ijcat.2004.003832.

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16

Syau, Yu-Ru, Chin-Yao Low, and Tai-Hsi Wu. "A note on convex fuzzy processes." Applied Mathematics Letters 15, no. 2 (February 2002): 193–96. http://dx.doi.org/10.1016/s0893-9659(01)00117-3.

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17

Matłoka, Marian. "A Note on -Convex Fuzzy Processes." Advances in Fuzzy Systems 2012 (2012): 1–4. http://dx.doi.org/10.1155/2012/290845.

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18

Parchami, Abbas, and Mashaallah Mashinchi. "Testing the Capability of Fuzzy Processes." Quality Technology & Quantitative Management 6, no. 2 (January 2009): 125–36. http://dx.doi.org/10.1080/16843703.2009.11673189.

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19

Qiang Shen, Ruiqing Zhao, and Wansheng Tang. "Modeling Random Fuzzy Renewal Reward Processes." IEEE Transactions on Fuzzy Systems 16, no. 5 (October 2008): 1379–85. http://dx.doi.org/10.1109/tfuzz.2008.2005014.

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20

Wang, Guangyuan, and Yue Zhang. "The theory of fuzzy stochastic processes." Fuzzy Sets and Systems 51, no. 2 (October 1992): 161–78. http://dx.doi.org/10.1016/0165-0114(92)90189-b.

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21

Buckley, J. J. "Controllable processes and the fuzzy controller." Fuzzy Sets and Systems 53, no. 1 (January 1993): 27–31. http://dx.doi.org/10.1016/0165-0114(93)90520-r.

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22

Trauwaert, E., R. Reynders, and T. Van Roy. "Fuzzy optimization and nuclear production processes." Fuzzy Sets and Systems 74, no. 1 (August 1995): 93–102. http://dx.doi.org/10.1016/0165-0114(95)98165-q.

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23

Ramya, Thangavel, A. C. Kannan, R. S. Balasenthil, and B. Anusuya Bagirathi. "Fuzzy Logic Modeling for Decision Making Processes Using MATLAB." Advanced Materials Research 984-985 (July 2014): 425–30. http://dx.doi.org/10.4028/www.scientific.net/amr.984-985.425.

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— This paper demonstrates to build a Fuzzy Inference System (FIS) for any model utilizing the Fuzzy Logic Toolbox graphical user interface (GUI) tools. A different conception for decision making process, based on the fuzzy approach, is propounded by authors of the paper.The paper is worked out in two sections. Description about the Fuzzy Logic Tool box is done in the first section.Illustration with an introductory example concludes the second section. Based on various assumptions the authors construct the rule statements which are then converted into fuzzy rules and the GUI tools of the Fuzzy Logic Toolbox built using MATLAB numeric computing environment is used to construct a fuzzy inference system for this process.The output membership functions are expected to be fuzzy sets in Mamdani-type inference.Defuzzification of fuzzy set for each output variable generated after the aggregation process has to be carried out. Application of information technology for Decisions in today's environment which is highly competitive are undeniable principles of organizations and helps managers in making useful decisions meaningfully.
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24

Hwang, Chao-Ming, and Miin-Shen Yang. "On fuzzy renewal processes for fuzzy random variables and extended theorems." International Journal of Intelligent Systems 26, no. 2 (November 15, 2010): 115–28. http://dx.doi.org/10.1002/int.20457.

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25

Blahová, Lenka, Ján Dvoran, and Jana Kmeťová. "Neuro-fuzzy control design of processes in chemical technologies." Archives of Control Sciences 22, no. 2 (January 1, 2012): 233–50. http://dx.doi.org/10.2478/v10170-011-0022-2.

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Neuro-fuzzy control design of processes in chemical technologies The paper presents design of neuro-fuzzy control and its application in chemical technologies. Our approach to neuro-fuzzy control is a combination of the neural predictive controller and the neuro-fuzzy controller (Adaptive Network-based Fuzzy Inference System - ANFIS). These controllers work in parallel. The output of ANFIS adjusts the output of the neural predictive controller to enhance the control performance. Such design of an intelligent control system is applied to control of the continuous stirred tank reactor and laboratory mixing process.
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26

Liu, Chiun-Ming, Mei-Yu Ji, and Wen-Chieh Chuang. "Fuzzy TOPSIS for Multiresponse Quality Problems in Wafer Fabrication Processes." Advances in Fuzzy Systems 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/496158.

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The quality characteristics in the wafer fabrication process are diverse, variable, and fuzzy in nature. How to effectively deal with multiresponse quality problems in the wafer fabrication process is a challenging task. In this study, the fuzzy technique for order preference by similarity to an ideal solution (TOPSIS), one of the fuzzy multiattribute decision-analysis (MADA) methods, is proposed to investigate the fuzzy multiresponse quality problem in integrated-circuit (IC) wafer fabrication process. The fuzzy TOPSIS is one of the effective fuzzy MADA methods for dealing with decision-making problems under uncertain environments. First, a fuzzy TOPSIS methodology is developed by considering the ambiguity between quality characteristics. Then, a detailed procedure for the developed fuzzy TOPSIS approach is presented to show how the fuzzy wafer fabrication quality problems can be solved. Real-world data is collected from an IC semiconductor company and the developed fuzzy TOPSIS approach is applied to find an optimal combination of parameters. Results of this study show that the developed approach provides a satisfactory solution to the wafer fabrication multiresponse problem. This developed approach can be also applied to other industries for investigating multiple quality characteristics problems.
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27

Devi, R. Lakshmi, and Dr R. Amalraj Dr. R. Amalraj. "Performance analysis of Human Resource Processes using Fuzzy data mining approach." Indian Journal of Applied Research 1, no. 2 (October 1, 2011): 16–18. http://dx.doi.org/10.15373/2249555x/nov2011/5.

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28

Ohta, Robison, Valerio A. P. Salomon, and Messias B. Silva. "Classical, fuzzy, hesitant fuzzy and intuitionistic fuzzy analytic hierarchy processes applied to industrial maintenance management." Journal of Intelligent & Fuzzy Systems 38, no. 1 (January 9, 2020): 601–8. http://dx.doi.org/10.3233/jifs-179433.

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29

Li, Shunqin. "Some properties of fuzzy alternating renewal processes." Mathematical and Computer Modelling 54, no. 9-10 (November 2011): 1886–96. http://dx.doi.org/10.1016/j.mcm.2011.04.011.

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30

Haber, R. E., J. R. Alique, A. Alique, J. Hernández, and R. Uribe-Etxebarria. "Embedded fuzzy-control system for machining processes." Computers in Industry 50, no. 3 (April 2003): 353–66. http://dx.doi.org/10.1016/s0166-3615(03)00022-8.

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31

SHIN, Y. C., and P. VISHNUPAD. "Neuro-fuzzy control of complex manufacturing processes." International Journal of Production Research 34, no. 12 (December 1996): 3291–309. http://dx.doi.org/10.1080/00207549608905091.

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32

Gleason, J. M. "Fuzzy set computational processes in risk analysis." IEEE Transactions on Engineering Management 38, no. 2 (May 1991): 177–78. http://dx.doi.org/10.1109/17.78415.

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33

van Der Rhee, F., H. R. van Nauta Lemke, and J. G. Dijkman. "Applying Fuzzy Set Theory to Modeling Processes." IFAC Proceedings Volumes 20, no. 5 (July 1987): 343–48. http://dx.doi.org/10.1016/s1474-6670(17)55225-8.

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34

Puyin, Liu. "Fuzzy-valued Markov processes and their properties." Fuzzy Sets and Systems 91, no. 1 (October 1997): 45–52. http://dx.doi.org/10.1016/s0165-0114(96)00142-x.

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35

Horiuchi, Jun-Ichi. "Fuzzy modeling and control of biological processes." Journal of Bioscience and Bioengineering 94, no. 6 (December 2002): 574–78. http://dx.doi.org/10.1016/s1389-1723(02)80197-9.

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36

Berthold, M. R., and R. Silipo. "Input features' impact on fuzzy decision processes." IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) 30, no. 6 (2000): 821–34. http://dx.doi.org/10.1109/3477.891144.

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37

Ruiqing Zhao and Wansheng Tang. "Some properties of fuzzy random renewal processes." IEEE Transactions on Fuzzy Systems 14, no. 2 (April 2006): 173–79. http://dx.doi.org/10.1109/tfuzz.2005.864088.

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38

Cheng, Shuenn‐Ren, Bi‐Min Hsu, and Ming‐Hung Shu. "Fuzzy testing and selecting better processes performance." Industrial Management & Data Systems 107, no. 6 (July 3, 2007): 862–81. http://dx.doi.org/10.1108/02635570710758761.

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39

Skomorokhov, Alexander O., K. H. Reinhardt, G. Roche, and M. Tielemann. "Fuzzy control of technological processes in APL2." ACM SIGAPL APL Quote Quad 25, no. 4 (June 8, 1995): 179–84. http://dx.doi.org/10.1145/206944.207001.

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40

Semmouri, Abdellatif, Mostafa Jourhmane, and Zineb Belhallaj. "Discounted Markov decision processes with fuzzy costs." Annals of Operations Research 295, no. 2 (September 7, 2020): 769–86. http://dx.doi.org/10.1007/s10479-020-03783-6.

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41

Aliev, R. A., and G. A. Mamedova. "Analysis of fuzzy models of industrial processes." Fuzzy Sets and Systems 37, no. 1 (August 1990): 13–21. http://dx.doi.org/10.1016/0165-0114(90)90059-f.

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42

Xu, Chen-Wei. "Linguistic decoupling control of fuzzy multivariable processes." Fuzzy Sets and Systems 44, no. 2 (November 1991): 209–17. http://dx.doi.org/10.1016/0165-0114(91)90004-a.

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43

Wang, Shuming, and Yan-Kui Liu. "Modeling renewal processes in fuzzy decision system." Applied Mathematical Modelling 39, no. 5-6 (March 2015): 1536–53. http://dx.doi.org/10.1016/j.apm.2014.09.014.

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44

Fathi Vajargah, Behrouz, and Sara Ghasemalipour. "Some applications of random fuzzy alternating renewal processes based on fuzzy simulation." Journal of Fuzzy Set Valued Analysis 2013 (2013): 1–8. http://dx.doi.org/10.5899/2013/jfsva-00153.

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45

Skalozub, Vladislav, and Oleg Murashov. "Modeling of monitoring processes with uneven and fuzzy observation intervals." System technologies 4, no. 135 (April 5, 2021): 135–48. http://dx.doi.org/10.34185/1562-9945-4-135-2021-14.

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The paper presents the results of applying a separable mathematical model for analyzing fuzzy time series with uneven and fuzzy data sampling intervals. The study of the efficiency of an advanced quantile modeling algorithm is presented. The implementation of models of measurement sequences with fuzzy steps is conducting by applying the approach based on α-levels. The center of weight method was used for scalarization the fuzzy result. A separable model was used for modeling the processes of clinical monitoring of patients with diabetes.
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46

Reyna, Valerie F. "Data, development, and dual processes in rationality." Behavioral and Brain Sciences 23, no. 5 (October 2000): 694–95. http://dx.doi.org/10.1017/s0140525x0054343x.

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Although Stanovich & West (S&W) are likely to be criticized for not proposing a process model, results of such a model (fuzzy-trace theory) support many of their conclusions. However, arguments concerning evolution and Gricean intelligence are weak. Finally, developmental data are relevant to rationality, but contradictory results suggest a dual-processes approach that differs from S&W's based on fuzzy-trace theory.
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47

Shu, Ming Hung, Jui Chan Huang, Thanh Lam Nguyen, Bi Min Hsu, and Thanh Hien Lam. "Evaluating Manufacturing Processes with a Novel Approach of Making Fuzzy Judgement." Applied Mechanics and Materials 470 (December 2013): 416–19. http://dx.doi.org/10.4028/www.scientific.net/amm.470.416.

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To monitor manufacturing processes with the inevitable fuzzy (imprecise) information, several extensions of the traditional Shewhart-type control charts have been proposed over the past few years. Among them, fuzzy and s charts have been recently developed with certain categorization rules which can discriminate conditions of a manufacturing process into four consequences, including in-control, rather in-control, rather out-of-control and out-of-control. However, weakness of the fuzzy-number evaluation for the classification rules has been found. Thus, in this paper, an improved fuzzy-number ranking approach for the fuzzy charts is proposed to provide more sufficient and justified classification results for monitoring the online manufacturing processes.
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48

You, Cui Lian, and Wei Qing Wang. "Some Properties of Complex Fuzzy Process." Applied Mechanics and Materials 614 (September 2014): 425–27. http://dx.doi.org/10.4028/www.scientific.net/amm.614.425.

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A complex fuzzy process is a fundamental connection between two dimensional fuzzy processes. Some properties such as independence, calculation formulas of complex fuzzy processes are studied in this paper.
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49

Skalozub, V. V., V. M. Horiachkin, and O. V. Murachov. "Complex Models of Ordering Multi-Sequences with Fuzzy Parameters." Science and Transport Progress. Bulletin of Dnipropetrovsk National University of Railway Transport, no. 2(92) (April 15, 2021): 50–64. http://dx.doi.org/10.15802/stp2021/237291.

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Purpose. The aim of the article is to develop complex constructive mathematical models of ordering processes for multi-sequences of elements with fuzzy parameters. At the same time, the following requirements for fuzzy ordering of multi-sequences with complexity evaluation (FOMSCE) were established: accounting fuzzy estimates of the formation operations complexity, the need to define fuzzy classes for ordering the initial elements, as well as building individual fuzzy models for the processes of receiving orders from different sources. Methodology. To solve the problems of optimal planning of non-deterministic processes of clinical monitoring of the patients’ treatment, the formation of complex constructive mathematical models of the processes of ordering multi-sequences of elements with fuzzy FMLCPM parameters was applied. For forming models of FOMSCE tasks, a methodology is used to create models with multilayer structures. To implement fuzzy problems, methods and procedures for discretizing a system of fuzzy quantities using sets of α-levels are applied. Findings. The article proposes an approach to solving the problems of analysis and optimal planning of the processes of clinical monitoring of the patients’ treatment, represented as flow control in service systems under uncertainty. For its formalization and implementation, complex multilayer constructive-production models for ordering multi-sequences with fuzzy parameters have been developed. Originality. The work has developed constructive-production methods for modeling complex systems, presented in the form of a multilayer model FMLCPM, which are designed for the processes of ordering multi-sequences of elements with fuzzy parameters. In FMLCPM, layer models are proposed that provide accounting for fuzzy estimates of the complexity of ordering operations, classification of fuzzy parameters of output elements, the formation and analysis of individual fuzzy models of the processes of receipt of orders in service systems. Practical value. The practical value of the results obtained lies in the spectrum development of applications of the problems of optimal planning of the processes in the service systems, presented as an ordering of multi-sequences with fuzzy parameters. The complex models of FOMSCE processes developed in the article are suitable and effective for formalizing the tasks of analysis and optimal planning of clinical monitoring processes, as well as a wide range of other tasks for monitoring non-deterministic transport processes, logistics and service systems.
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50

Demenkov, N. P., E. A. Mirkin, and I. A. Mochalov. "Markov and Semi-Markov Processes with Fuzzy States. Part 1. Markov Processes." Informacionnye tehnologii 26, no. 6 (June 23, 2020): 323–34. http://dx.doi.org/10.17587/it.26.323-334.

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