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Volumn 1, Issue 4, 2016, Pages 247-257

A study of granular computing in the agenda of growth of artificial neural networks

Author keywords

Fuzzy logic; Granular computing; Granular neural networks; Information granules; Modular

Indexed keywords

FUZZY LOGIC; FUZZY NEURAL NETWORKS; GRANULATION; INFORMATION GRANULES; PATTERN RECOGNITION;

EID: 84975137706     PISSN: 23644966     EISSN: 23644974     Source Type: Journal    
DOI: 10.1007/s41066-016-0020-7     Document Type: Article
Times cited : (75)

References (60)
  • 1
    • 84892668535 scopus 로고    scopus 로고
    • A granular computing approach to the design of optimized graph classification systems
    • Bianchi FM et al (2014) A granular computing approach to the design of optimized graph classification systems. Soft Computing 18(2):393–412 DOI: 10.1007/s00500-013-1065-z
    • (2014) Soft Computing , vol.18 , Issue.2 , pp. 393-412
    • Bianchi, F.M.1
  • 2
    • 79953666016 scopus 로고    scopus 로고
    • ANCFIS: a neuro-fuzzy architecture employing complex fuzzy sets
    • Chen Z, Aghakhani S, Man J, Dick S (2011) ANCFIS: a neuro-fuzzy architecture employing complex fuzzy sets. IEEE Trans Fuzzy Syst 19:305–322 DOI: 10.1109/TFUZZ.2010.2096469
    • (2011) IEEE Trans Fuzzy Syst , vol.19 , pp. 305-322
    • Chen, Z.1    Aghakhani, S.2    Man, J.3    Dick, S.4
  • 3
    • 84888638820 scopus 로고    scopus 로고
    • Genetic interval neural networks for granular data regression
    • Cimino MGCA, Lazzerini B, Marcelloni F et al (2014) Genetic interval neural networks for granular data regression. Inf Sci 257(2):313–330 DOI: 10.1016/j.ins.2012.12.049
    • (2014) Inf Sci , vol.257 , Issue.2 , pp. 313-330
    • Cimino, M.G.C.A.1    Lazzerini, B.2    Marcelloni, F.3
  • 4
    • 84961050970 scopus 로고    scopus 로고
    • Surrogate modeling based on an adaptive network and granular computing
    • Cruz-Vega I, Escalante HJ, Reyes CA et al (2015) Surrogate modeling based on an adaptive network and granular computing. Soft Comput. doi:10.1007/s00500-015-1605-9
    • (2015) Soft Comput
    • Cruz-Vega, I.1    Escalante, H.J.2    Reyes, C.A.3
  • 5
    • 0010178372 scopus 로고    scopus 로고
    • Granular computing in neural networks
    • Pedrycz W, (ed), Physica Verlag, New York
    • Dick S, Kandel A (2001) Granular computing in neural networks. In: Pedrycz W (ed) Granular computing: an emerging paradigm. Physica Verlag, New York, pp 275–305 DOI: 10.1007/978-3-7908-1823-9_12
    • (2001) Granular computing: an emerging paradigm , pp. 275-305
    • Dick, S.1    Kandel, A.2
  • 6
    • 84879603249 scopus 로고    scopus 로고
    • A granular neural network: performance analysis and application to re-granulation
    • Dick S, Tappenden A, Badke C et al (2013) A granular neural network: performance analysis and application to re-granulation. Int J Approx Reason 54(8):1149–1167 DOI: 10.1016/j.ijar.2013.01.012
    • (2013) Int J Approx Reason , vol.54 , Issue.8 , pp. 1149-1167
    • Dick, S.1    Tappenden, A.2    Badke, C.3
  • 7
    • 84894505142 scopus 로고    scopus 로고
    • Granular neural networks
    • Ding S, Jia H, Chen J (2014) Granular neural networks. Springer Sci 41:373–384
    • (2014) Springer Sci , vol.41 , pp. 373-384
    • Ding, S.1    Jia, H.2    Chen, J.3
  • 8
    • 84958950191 scopus 로고    scopus 로고
    • Data mining using dynamically constructed recurrent fuzzy neural networks
    • Frayman Y, Wang L (1998) “Data mining using dynamically constructed recurrent fuzzy neural networks,” in Proc. PAKDD-98, pp 122–131
    • (1998) Proc. PAKDD-98 , pp. 122-131
    • Frayman, Y.1    Wang, L.2
  • 9
    • 80052819788 scopus 로고    scopus 로고
    • Fuzzy rough granular neural networks, fuzzy granules, and classification
    • Ganivada A, Dutta S, Pal SK (2011) Fuzzy rough granular neural networks, fuzzy granules, and classification. Theor Comput 412(42):5834–5853 DOI: 10.1016/j.tcs.2011.05.038
    • (2011) Theor Comput , vol.412 , Issue.42 , pp. 5834-5853
    • Ganivada, A.1    Dutta, S.2    Pal, S.K.3
  • 10
    • 84883207118 scopus 로고    scopus 로고
    • Fuzzy rough sets, and a granular neural network for unsupervised feature selection
    • Ganivada A, Ray SS, Pal SK (2013) Fuzzy rough sets, and a granular neural network for unsupervised feature selection. Neural Netw 48:91–108 DOI: 10.1016/j.neunet.2013.07.008
    • (2013) Neural Netw , vol.48 , pp. 91-108
    • Ganivada, A.1    Ray, S.S.2    Pal, S.K.3
  • 11
    • 37249082568 scopus 로고    scopus 로고
    • A neurofuzzy system to analyze liquefaction-induced lateral spread
    • García SR, Romo MP, Botero E (2008) A neurofuzzy system to analyze liquefaction-induced lateral spread. Soil Dyn Earthq Eng 28(3):169–180 DOI: 10.1016/j.soildyn.2007.06.014
    • (2008) Soil Dyn Earthq Eng , vol.28 , Issue.3 , pp. 169-180
    • García, S.R.1    Romo, M.P.2    Botero, E.3
  • 12
    • 84891745412 scopus 로고    scopus 로고
    • Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction
    • Gaxiola F, Melin P, Valdez F, Castillo O (2014) Interval type-2 fuzzy weight adjustment for backpropagation neural networks with application in time series prediction. Inf Sci 260:1–14 DOI: 10.1016/j.ins.2013.11.006
    • (2014) Inf Sci , vol.260 , pp. 1-14
    • Gaxiola, F.1    Melin, P.2    Valdez, F.3    Castillo, O.4
  • 13
    • 84919762230 scopus 로고    scopus 로고
    • Signal processing and time series description: a perspective of computational intelligence and granular computing
    • Grace A (2015) Signal processing and time series description: a perspective of computational intelligence and granular computing. Appl Soft Computing 27:590–601 DOI: 10.1016/j.asoc.2014.06.030
    • (2015) Appl Soft Computing , vol.27 , pp. 590-601
    • Grace, A.1
  • 14
    • 0027601884 scopus 로고
    • ANFIS: adaptive-network-based fuzzy inference system
    • Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685 DOI: 10.1109/21.256541
    • (1993) IEEE Trans Syst Man Cybern , vol.23 , Issue.3 , pp. 665-685
    • Jang, J.-S.R.1
  • 16
    • 58749086417 scopus 로고
    • Fuzzy sets and neural networks
    • Lee SC, Lee ET (1974) Fuzzy sets and neural networks. J Cybern 4:83–103 DOI: 10.1080/01969727408546068
    • (1974) J Cybern , vol.4 , pp. 83-103
    • Lee, S.C.1    Lee, E.T.2
  • 17
    • 84870233667 scopus 로고    scopus 로고
    • Evolving granular neural networks from fuzzy data streams
    • Leite D et al (2013) Evolving granular neural networks from fuzzy data streams. Neural Netw 38(2):1–16 DOI: 10.1016/j.neunet.2012.10.006
    • (2013) Neural Netw , vol.38 , Issue.2 , pp. 1-16
    • Leite, D.1
  • 20
    • 79952615753 scopus 로고    scopus 로고
    • Fusion of learning automata theory and granular inference systems: ANLAGIS. Applications to pattern recognition and machine learning
    • Maravall D, Lope JD (2011) Fusion of learning automata theory and granular inference systems: ANLAGIS. Applications to pattern recognition and machine learning. Neurocomputing 74(8):1237–1242 DOI: 10.1016/j.neucom.2010.07.024
    • (2011) Neurocomputing , vol.74 , Issue.8 , pp. 1237-1242
    • Maravall, D.1    Lope, J.D.2
  • 21
    • 44649143992 scopus 로고    scopus 로고
    • Approximation and prediction of wages based on granular neural network
    • Wang G, Li T, Grzymala-Busse JW, Miao D, Skowron A, Yao Y, (eds), Springer, Berlin, Heidelberg
    • Marček M, Marček D (2008) Approximation and prediction of wages based on granular neural network. In: Wang G, Li T, Grzymala-Busse JW, Miao D, Skowron A, Yao Y (eds) Rough sets and knowledge technology, vol 5009. Springer, Berlin, Heidelberg, pp 556–563 DOI: 10.1007/978-3-540-79721-0_75
    • (2008) Rough sets and knowledge technology , vol.5009 , pp. 556-563
    • Marček, M.1    Marček, D.2
  • 22
    • 51249194645 scopus 로고
    • A logical calculus of the ideas immanent in nervous activity
    • McCulloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133 DOI: 10.1007/BF02478259
    • (1943) Bull Math Biophys , vol.5 , pp. 115-133
    • McCulloch, W.1    Pitts, W.2
  • 23
    • 67949117116 scopus 로고    scopus 로고
    • A granular reflex fuzzy min-max neural network for classification
    • Nandedkar AV, Biswas PK (2009) A granular reflex fuzzy min-max neural network for classification. Neural Netw IEEE Trans 20(7):1117–1134 DOI: 10.1109/TNN.2009.2016419
    • (2009) Neural Netw IEEE Trans , vol.20 , Issue.7 , pp. 1117-1134
    • Nandedkar, A.V.1    Biswas, P.K.2
  • 24
    • 84881541728 scopus 로고    scopus 로고
    • A design of granular-oriented self-organizing hybrid fuzzy polynomial neural networks
    • Oh S, Kim W, Park B et al (2013) A design of granular-oriented self-organizing hybrid fuzzy polynomial neural networks. Neurocomputing 119(16):292–307 DOI: 10.1016/j.neucom.2013.03.029
    • (2013) Neurocomputing , vol.119 , Issue.16 , pp. 292-307
    • Oh, S.1    Kim, W.2    Park, B.3
  • 25
    • 84884381061 scopus 로고    scopus 로고
    • Title paper: natural computing: a problem solving paradigm with granular information processing
    • Pal SK, Meher SK (2013) Title paper: natural computing: a problem solving paradigm with granular information processing. Appl Soft Computing 13(9):3944–3955 DOI: 10.1016/j.asoc.2013.06.026
    • (2013) Appl Soft Computing , vol.13 , Issue.9 , pp. 3944-3955
    • Pal, S.K.1    Meher, S.K.2
  • 26
    • 0026927426 scopus 로고
    • Multilayer perceptron, fuzzy sets, and classification
    • Pal SK, Mitra S (1992) Multilayer perceptron, fuzzy sets, and classification. IEEE Trans Neural Netw 3(5):683–697 DOI: 10.1109/72.159058
    • (1992) IEEE Trans Neural Netw , vol.3 , Issue.5 , pp. 683-697
    • Pal, S.K.1    Mitra, S.2
  • 27
    • 70350272885 scopus 로고    scopus 로고
    • Granular neural networks and their development through context-based clustering and adjustable dimensionality of receptive fields
    • Park HS, Pedrycz W, Oh SK (2009) Granular neural networks and their development through context-based clustering and adjustable dimensionality of receptive fields. Neural Netw IEEE Trans 20(10):1604–1616 DOI: 10.1109/TNN.2009.2027319
    • (2009) Neural Netw IEEE Trans , vol.20 , Issue.10 , pp. 1604-1616
    • Park, H.S.1    Pedrycz, W.2    Oh, S.K.3
  • 28
    • 81855167014 scopus 로고    scopus 로고
    • Modeling of the charging characteristic of linear-type superconducting power supply using granular-based radial basis function neural networks
    • Park H, Pedrycz W et al (2012) Modeling of the charging characteristic of linear-type superconducting power supply using granular-based radial basis function neural networks. Expert Syst Appl 39(1):1021–1039 DOI: 10.1016/j.eswa.2011.07.103
    • (2012) Expert Syst Appl , vol.39 , Issue.1 , pp. 1021-1039
    • Park, H.1    Pedrycz, W.2
  • 29
    • 0030142628 scopus 로고    scopus 로고
    • Conditional fuzzy c-means
    • Pedrycz W (1996) Conditional fuzzy c-means. Pattern Recogn Lett 17:625–631 DOI: 10.1016/0167-8655(96)00027-X
    • (1996) Pattern Recogn Lett , vol.17 , pp. 625-631
    • Pedrycz, W.1
  • 30
    • 0032122726 scopus 로고    scopus 로고
    • Conditional fuzzy clustering in the design of radial basis function neural networks
    • Pedrycz W (1998) Conditional fuzzy clustering in the design of radial basis function neural networks. IEEE Trans Neural Netw 9(4):601–612 DOI: 10.1109/72.701174
    • (1998) IEEE Trans Neural Netw , vol.9 , Issue.4 , pp. 601-612
    • Pedrycz, W.1
  • 31
    • 35349023071 scopus 로고    scopus 로고
    • Computational intelligence and visual computing: an emerging technology for software engineering
    • Pedrycz W (2002) Computational intelligence and visual computing: an emerging technology for software engineering. Soft Comput 7(1):33–44 DOI: 10.1007/s00500-002-0170-1
    • (2002) Soft Comput , vol.7 , Issue.1 , pp. 33-44
    • Pedrycz, W.1
  • 32
    • 84879642774 scopus 로고    scopus 로고
    • Granular computing as a framework of system modeling
    • Pedrycz W (2013) Granular computing as a framework of system modeling. J Control Autom Electr Syst 24(1–2):81–86 DOI: 10.1007/s40313-013-0010-9
    • (2013) J Control Autom Electr Syst , vol.24 , Issue.1-2 , pp. 81-86
    • Pedrycz, W.1
  • 33
    • 70350714258 scopus 로고    scopus 로고
    • Logic-oriented neural networks for fuzzy neurocomputing
    • Pedrycz W, Aliev RA (2009) Logic-oriented neural networks for fuzzy neurocomputing. Neurocomputing 73:10–23 DOI: 10.1016/j.neucom.2008.10.027
    • (2009) Neurocomputing , vol.73 , pp. 10-23
    • Pedrycz, W.1    Aliev, R.A.2
  • 35
    • 79957974279 scopus 로고    scopus 로고
    • Analytic hierarchy process (AHP) in group decision-making and its optimization with an allocation of information granularity
    • Pedrycz W, Song M (2011) Analytic hierarchy process (AHP) in group decision-making and its optimization with an allocation of information granularity. IEEE Trans Fuzzy Syst 19(3):527–539 DOI: 10.1109/TFUZZ.2011.2116029
    • (2011) IEEE Trans Fuzzy Syst , vol.19 , Issue.3 , pp. 527-539
    • Pedrycz, W.1    Song, M.2
  • 36
    • 0034493984 scopus 로고    scopus 로고
    • Granular neural networks
    • Pedrycz W, Vukovich G (2001) Granular neural networks. Neurocomputing 36:205–224 DOI: 10.1016/S0925-2312(00)00342-8
    • (2001) Neurocomputing , vol.36 , pp. 205-224
    • Pedrycz, W.1    Vukovich, G.2
  • 37
    • 0442326749 scopus 로고    scopus 로고
    • Guest editorial special issue on computational intelligence in telecommunications networks and internet services-part III
    • Pedrycz W, Vasilakos A, Karnouskos S (2004) Guest editorial special issue on computational intelligence in telecommunications networks and internet services-part III. Syst Man Cybern Part C Appl Rev IEEE Trans 34(1):1–3 DOI: 10.1109/TSMCC.2003.820305
    • (2004) Syst Man Cybern Part C Appl Rev IEEE Trans , vol.34 , Issue.1 , pp. 1-3
    • Pedrycz, W.1    Vasilakos, A.2    Karnouskos, S.3
  • 38
    • 55949110248 scopus 로고    scopus 로고
    • A granular-oriented development of functional radial basis function neural networks
    • Pedrycz W, Park HS, Oh SK (2008) A granular-oriented development of functional radial basis function neural networks. Neurocomputing 72:420–435 DOI: 10.1016/j.neucom.2007.12.016
    • (2008) Neurocomputing , vol.72 , pp. 420-435
    • Pedrycz, W.1    Park, H.S.2    Oh, S.K.3
  • 39
    • 2642559663 scopus 로고    scopus 로고
    • Building a software experience factory using granular-based models
    • Reformat M, Pedrycz W, Pizzi N (2004) Building a software experience factory using granular-based models. Fuzzy Sets Syst 145(1):111–139 DOI: 10.1016/j.fss.2003.10.007
    • (2004) Fuzzy Sets Syst , vol.145 , Issue.1 , pp. 111-139
    • Reformat, M.1    Pedrycz, W.2    Pizzi, N.3
  • 40
    • 84884209840 scopus 로고    scopus 로고
    • A granular computing-based approach to credit scoring modeling
    • Saberi M, Mirtalaie MS, Hussain FK et al (2013) A granular computing-based approach to credit scoring modeling. Neurocomputing 122:100–115 DOI: 10.1016/j.neucom.2013.05.020
    • (2013) Neurocomputing , vol.122 , pp. 100-115
    • Saberi, M.1    Mirtalaie, M.S.2    Hussain, F.K.3
  • 42
    • 84870551481 scopus 로고    scopus 로고
    • Multi-objective hierarchical genetic algorithm for modular neural network optimization using a granular approach
    • Castillo O, Melin P, Kacprzyk J, (eds), Springer, Berlin
    • Sánchez D, Melin P (2013) Multi-objective hierarchical genetic algorithm for modular neural network optimization using a granular approach. In: Castillo O, Melin P, Kacprzyk J (eds) Recent advances on hybrid Intelligent systems, vol 451. Springer, Berlin, Heidelberg, pp 107–120 DOI: 10.1007/978-3-642-33021-6_9
    • (2013) Recent advances on hybrid Intelligent systems , vol.451 , pp. 107-120
    • Sánchez, D.1    Melin, P.2
  • 43
    • 84888228006 scopus 로고    scopus 로고
    • Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure
    • Sánchez D, Melin P (2014) Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure. Eng Appl AI 27:41–56 DOI: 10.1016/j.engappai.2013.09.014
    • (2014) Eng Appl AI , vol.27 , pp. 41-56
    • Sánchez, D.1    Melin, P.2
  • 44
    • 84881593878 scopus 로고    scopus 로고
    • Modular granular neural networks optimization with Multi-Objective Hierarchical Genetic Algorithm for human recognition based on iris biometric
    • Sánchez D, Melin P, Castillo O, Valdez F (2013) Modular granular neural networks optimization with Multi-Objective Hierarchical Genetic Algorithm for human recognition based on iris biometric. IEEE Congress on Evolutionary Computation 772–778
    • (2013) IEEE Congress on Evolutionary Computation , pp. 772-778
    • Sánchez, D.1    Melin, P.2    Castillo, O.3    Valdez, F.4
  • 45
    • 84927559754 scopus 로고    scopus 로고
    • Optimization of modular granular neural networks using a hierarchical genetic algorithm based on the database complexity applied to human recognition
    • Sánchez D, Melin P, Castillo O (2015) Optimization of modular granular neural networks using a hierarchical genetic algorithm based on the database complexity applied to human recognition. Inf Sci 309:73–101 DOI: 10.1016/j.ins.2015.02.020
    • (2015) Inf Sci , vol.309 , pp. 73-101
    • Sánchez, D.1    Melin, P.2    Castillo, O.3
  • 46
    • 80053314740 scopus 로고    scopus 로고
    • From local neural networks to granular neural networks: a study in information granulation
    • Song M, Pedrycz W (2011) From local neural networks to granular neural networks: a study in information granulation. Neurocomputing 74(18):3931–3940 DOI: 10.1016/j.neucom.2011.08.009
    • (2011) Neurocomputing , vol.74 , Issue.18 , pp. 3931-3940
    • Song, M.1    Pedrycz, W.2
  • 47
    • 84875896163 scopus 로고    scopus 로고
    • Granular neural networks: concepts and development schemes
    • Song M, Pedrycz W (2013) Granular neural networks: concepts and development schemes. Neural Net Learn Syst IEEE Trans 24(4):542–553 DOI: 10.1109/TNNLS.2013.2237787
    • (2013) Neural Net Learn Syst IEEE Trans , vol.24 , Issue.4 , pp. 542-553
    • Song, M.1    Pedrycz, W.2
  • 48
    • 17244366734 scopus 로고    scopus 로고
    • Granular neural networks for land use classification
    • Vasilakos A, Stathakis D (2005) Granular neural networks for land use classification. Soft Comput 9(5):332–340 DOI: 10.1007/s00500-004-0412-5
    • (2005) Soft Comput , vol.9 , Issue.5 , pp. 332-340
    • Vasilakos, A.1    Stathakis, D.2
  • 49
    • 84941253004 scopus 로고    scopus 로고
    • Granular computing with multiple granular layers for brain big data processing
    • Wang G, Xu J (2014) Granular computing with multiple granular layers for brain big data processing. Brain Inform 1:1–10 DOI: 10.1007/s40708-014-0001-z
    • (2014) Brain Inform , vol.1 , pp. 1-10
    • Wang, G.1    Xu, J.2
  • 51
    • 0003123776 scopus 로고    scopus 로고
    • Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems
    • Zadeh A (1998) Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft Comput 2(1):23–25 DOI: 10.1007/s005000050030
    • (1998) Soft Comput , vol.2 , Issue.1 , pp. 23-25
    • Zadeh, A.1
  • 53
    • 14644418483 scopus 로고    scopus 로고
    • Constructive granular systems with universal approximation and fast knowledge discovery
    • Zhang Y (2005) Constructive granular systems with universal approximation and fast knowledge discovery. Fuzzy Syst IEEE Trans 13(1):48–57 DOI: 10.1109/TFUZZ.2004.839657
    • (2005) Fuzzy Syst IEEE Trans , vol.13 , Issue.1 , pp. 48-57
    • Zhang, Y.1
  • 56
    • 34249306232 scopus 로고    scopus 로고
    • Statistical fuzzy interval neural networks for currency exchange rate time series prediction
    • Zhang YQ, Wan X (2007) Statistical fuzzy interval neural networks for currency exchange rate time series prediction. Appl Soft Computing 7(4):1149–1156 DOI: 10.1016/j.asoc.2006.01.002
    • (2007) Appl Soft Computing , vol.7 , Issue.4 , pp. 1149-1156
    • Zhang, Y.Q.1    Wan, X.2
  • 57
    • 0034186991 scopus 로고    scopus 로고
    • Granular neural networks for numerical-linguistic data fusion and knowledge discovery
    • Zhang Y, Gagliano RA et al (2000) Granular neural networks for numerical-linguistic data fusion and knowledge discovery. Neural Netw IEEE Trans 11(3):658–667 DOI: 10.1109/72.846737
    • (2000) Neural Netw IEEE Trans , vol.11 , Issue.3 , pp. 658-667
    • Zhang, Y.1    Gagliano, R.A.2
  • 58
    • 85107889291 scopus 로고    scopus 로고
    • Genetic granular neural networks
    • Zhang Y, Jin B, Tang Y (2007) Genetic granular neural networks. Adv Neural Netw, pp 1455–1463
    • (2007) Adv Neural Netw , pp. 1455-1463
    • Zhang, Y.1    Jin, B.2    Tang, Y.3
  • 59
    • 42549168022 scopus 로고    scopus 로고
    • Granular neural networks with evolutionary interval learning
    • Zhang Y et al (2008) Granular neural networks with evolutionary interval learning. Fuzzy Syst IEEE Trans 16(2):309–319 DOI: 10.1109/TFUZZ.2007.895975
    • (2008) Fuzzy Syst IEEE Trans , vol.16 , Issue.2 , pp. 309-319
    • Zhang, Y.1


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