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Controlled elitist multi-objective genetic algorithm joined with neural network to study the effects of nano-clay percentage on cell size and polymer foams density of PVC/clay nanocomposites

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Abstract

The parameters of foaming and nano-clay percentage on the density of polymer foam and cell size with the PVC field is studied. Cell size and density have a significant impact on the strength of foam and its insulation (including sounds and thermal insulation). By optimizing cell size and density, foam can be produced with the best mechanical properties. In foaming process of the nanocomposite samples by mass method, the design variables (input parameters) are foaming time and temperature and MMT content. The controlled elitist multi-objective GA is applied to minimize both the foam density and the cell size. To that end, the population size and the Pareto fraction are selected as 100 and 0.5, respectively. The noninferior solution obtained by the controlled elitist multi-objective GA is illustrated. When both the MMT and the temperature are high, the resulting foam does not have ideal characteristics.

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References

  1. Klempner D, Sendijarevic V. Handbook of polymeric foams and foam technology. Cincinnatti: Hanser Gardner Publications; 2004.

    Google Scholar 

  2. Kolich M, Hoke P, Dooge D, Doroudian M, Litovsky E, Kleiman J. Influence of temperature and mechanical compression on thermophysical properties of car interior foam plastics insulation. J Elast Plast. 2014;46(2):132–43.

    CAS  Google Scholar 

  3. Saraeian P, Tavakoli HR, Ghassemi A. Production of polystyrene-nanoclay nanocomposite foam and effect of nanoclay particles on foam cell size. J Comp Mater. 2012;47(18):2211–7.

    Google Scholar 

  4. Juntunen PR, Kumar V, Weller J. Impact strength of high density microcellular poly(vinyl chloride) foams. J Vinyl Addit Technol. 2000;6(2):93–9.

    CAS  Google Scholar 

  5. Liliana M, Ryszard P, Alan C, et al. Processing and characterization of polyurethane nanocomposite foam reinforced with montmorillonite–carbon nanotube hybrids. Compos A. 2013;44:1–7.

    Google Scholar 

  6. Zenkert D, Burman M. Tension, compression and shear fatigue of a closed cell polymer foam. J Comp Sci Technol. 2009;69:785–92.

    CAS  Google Scholar 

  7. Manninen A, Naguib H, Nawaby A, Liao X, Day M. The effect of clay content on PMMA-clay nanocomposite foams. J Cell Polym. 2005;24:49–70.

    CAS  Google Scholar 

  8. Reza J, Behrad K, Arash R. Effects of organically modified nanoclay on cellular morphology, tensile properties, and dimensional stability of flexible polyurethane foams. J Nanostruct Chem. 2013;3(1):82.

    Google Scholar 

  9. Yu E, Manabu I, Masami O. Foam processing and cellular structure of polylactide-based nanocomposites. Polymer. 2006;47(15):5350–9.

    Google Scholar 

  10. Changchun Z, Hossieny N, Zhang Ch, Wang B, Walsh ShM. Morphology and tensile properties of PMMA carbon nanotubes nanocomposites and nanocomposites foams. J Compos Sci Technol. 2013;82:29–37.

    Google Scholar 

  11. Wong A, Wijnands SFL, Kuboki T, Park ChB. Mechanisms of nanoclay-enhanced plastic foaming processes: effects of nanoclay intercalation and exfoliation. J Nanopart Res. 2013;15:1815.

    Google Scholar 

  12. Keramati M, Ghasemi I, Karrabi M, Azizi H. Microcellular foaming of PP/EPDM/organoclay nanocomposites: the effect of the distribution of nanoclay on foam morphology. Polym J. 2012;44:433–8.

    CAS  Google Scholar 

  13. Wang XCh, Jing X, Peng YY, Ma Zh, Liu ChT, Turng LSh, Shen ChY. The effect of nanoclay on the crystallization behavior, microcellular structure, and mechanical properties of thermoplastic polyurethane nanocomposite foams. Polym Eng Sci. 2016;65(3):319–27.

    Google Scholar 

  14. Jadidi H, Shahrajabian H, Moghri M. Using the mass method to produce PVC/clay nanocomposite foams: the effect of nano-clay and foaming conditions on density and cell size. J Inorg Organomet Polym. 2016;26:881–8.

    CAS  Google Scholar 

  15. Barmouz M, Behravesh AH. Statistical and experimental investigation on low density microcellular foaming of PLA-TPU/cellulose nano-fiber bio-nanocomposites. Polym Test. 2017;61:300–13.

    CAS  Google Scholar 

  16. Matuana M, Faruk O. Effect of gas saturation conditions on the expansion ratio of microcellular poly(lactic acid)/wood-flour composites. eXPRESS Polym Lett. 2010;4(10):621–31.

    CAS  Google Scholar 

  17. Barma P, Rhodes MB, Salovey RJ. Mechanical properties of CRETE, a polyurethane foam. Appl Phys. 1978;49:4985–91.

    CAS  Google Scholar 

  18. Bureau M, Gendron R. Mechanical–morphology relationship of PS foam. J Cellul Plast. 2003;39(5):353–67.

    Google Scholar 

  19. Zhao N, Qi C, Chen T, Tang J, Cui X. Experimental study on influences of cylindrical grooves on thermal efficiency, exergy efficiency and entropy generation of CPU cooled by nanofluids. Int J Heat Mass Transf. 2019;135:16–32.

    CAS  Google Scholar 

  20. Mei S, Qi C, Liu M, Fan F, Liang L. Effects of paralleled magnetic field on thermo-hydraulic performances of Fe3O4–water nanofluids in a circular tube. Int J Heat Mass Transf. 2019;134:707–21.

    CAS  Google Scholar 

  21. Wang G, Qi C, Liu M, Li C, Yan Y, Liang L. Effect of corrugation pitch on thermo-hydraulic performance of nanofluids in corrugated tubes of heat exchanger system based on exergy efficiency. Energy Convers Manag. 2019;186:51–65.

    CAS  Google Scholar 

  22. Safaei MR, Hajizadeh A, Afrand M, Qi C, Yarmand H, Zulkifli NWBM. Evaluating the effect of temperature and concentration on the thermal conductivity of ZnO–TiO2/EG hybrid nanofluid using artificial neural network and curve fitting on experimental data. Phys A. 2019;519:209–16.

    CAS  Google Scholar 

  23. Vo DD, Alsarraf J, Moradikazerouni A, Afrand M, Salehipour H, Qi C. Numerical investigation of ? –AlOOH nano-fluid convection performance in a wavy channel considering various shapes of nanoadditives. Powder Technol. 2019;345:649–57.

    CAS  Google Scholar 

  24. Zhai X, Qi C, Pan Y, Luo T, Liang L. Effects of screw pitches and rotation angles on flow and heat transfer characteristics of nanofluids in spiral tubes. Int J Heat Mass Transf. 2019;130:989–1003.

    CAS  Google Scholar 

  25. Zhao N, Guo L, Qi C, Chen T, Cui X. Experimental study on thermo-hydraulic performance of nanofluids in CPU heat sink with rectangular grooves and cylindrical bugles based on exergy efficiency. Energy Convers Manag. 2019;181:235–46.

    CAS  Google Scholar 

  26. Karimipour A, Esfe MH, Safaei MR, Semiromi DT, Jafari S, Kazi SN. Mixed convection of copper–water nanofluid in a shallow inclined lid driven cavity using the lattice Boltzmann method. Phys A. 2014;402:150–68.

    CAS  Google Scholar 

  27. Karimipour A, Nezhad AH, D’Orazio A, Esfe MH, Safaei MR, Shirani E. Simulation of copper–water nanofluid in a microchannel in slip flow regime using the lattice Boltzmann method. Eur J Mech B/Fluids. 2015;49:89–99.

    Google Scholar 

  28. Goodarzi Marjan, D’Orazio Annunziata, Keshavarzi Ahmad, Mousavi Sayedali, Karimipour Arash. Develop the nano scale method of lattice Boltzmann to predict the fluid flow and heat transfer of air in the inclined lid driven cavity with a large heat source inside, two case studies: pure natural convection & mixed convection. Phys A. 2018;509:210–33.

    CAS  Google Scholar 

  29. Ranjbarzadeh R, Isfahani AM, Afrand M, Karimipour A, Hojaji M. An experimental study on heat transfer and pressure drop of water/graphene oxide nanofluid in a copper tube under air cross-flow: applicable as a heat exchanger. Appl Therm Eng. 2017;125:69–79.

    CAS  Google Scholar 

  30. Ranjbarzadeh R, Karimipour A, Afrand M, Isfahani AHM, Shirneshan A. Empirical analysis of heat transfer and friction factor of water/graphene oxide nanofluid flow in turbulent regime through an isothermal pipe. Appl Therm Eng. 2017;126:538–47.

    CAS  Google Scholar 

  31. Karimipour A, D’Orazio A, Goodarzi M. Develop the lattice Boltzmann method to simulate the slip velocity and temperature domain of buoyancy forces of FMWCNT nanoparticles in water through a micro flow imposed to the specified heat flux. Phys A. 2018;509:729–45.

    CAS  Google Scholar 

  32. Afrand M, Karimipour A, Nadooshan AA, Akbari M. The variations of heat transfer and slip velocity of FMWNT–water nano-fluid along the micro-channel in the lack and presence of a magnetic field. Phys E. 2016;84:474–81.

    CAS  Google Scholar 

  33. Alrashed AA, Karimipour A, Bagherzadeh SA, Safaei MR, Afrand M. Electro-and thermophysical properties of water-based nanofluids containing copper ferrite nanoparticles coated with silica: experimental data, modeling through enhanced ANN and curve fitting. Int J Heat Mass Transf. 2018;127:925–35.

    CAS  Google Scholar 

  34. Esfe MH, Esforjani SSM, Akbari M, Karimipour A, Mixed-convection flow in a lid-driven square cavity filled with a nanofluid with variable properties: effect of the nanoparticle diameter and of the position of a hot obstacle. Heat Transf Res. 2014;45(6):563–78.

    Google Scholar 

  35. Karimipour A, Bagherzadeh SA, Goodarzi M, Alnaqi AA, Bahiraei M, Safaei MR, Shadloo MS. Synthesized CuFe2O4/SiO2 nanocomposites added to water/EG: Evaluation of the thermophysical properties beside sensitivity analysis & EANN. Int J Heat Mass Transf. 2018;127:1169–79.

    CAS  Google Scholar 

  36. Arabpour A, Karimipour A, Toghraie D, Akbari OA. Investigation into the effects of slip boundary condition on nanofluid flow in a double-layer microchannel. J Therm Anal Calorim. 2018;131(3):2975–91.

    CAS  Google Scholar 

  37. Safaei MR, Karimipour A, Abdollahi A, Nguyen TK. The investigation of thermal radiation and free convection heat transfer mechanisms of nanofluid inside a shallow cavity by lattice Boltzmann method. Phys A. 2018;509:515–35.

    CAS  Google Scholar 

  38. Karimipour A, Taghipour A, Malvandi A. Developing the laminar MHD forced convection flow of water/FMWNT carbon nanotubes in a microchannel imposed the uniform heat flux. J Magn Magn Mater. 2016;419:420–8.

    CAS  Google Scholar 

  39. Zadkhast M, Toghraie D, Karimipour A. Developing a new correlation to estimate the thermal conductivity of MWCNT–CuO/water hybrid nanofluid via an experimental investigation. J Therm Anal Calorim. 2017;129(2):859–67.

    CAS  Google Scholar 

  40. Goodarzi M, Javid S, Sajadifar A, Nojoomizadeh M, Motaharipour SH, Bach QV, Karimipour A. Slip velocity and temperature jump of a non-Newtonian nanofluid, aqueous solution of carboxy-methyl cellulose/aluminum oxide nanoparticles, through a microtube. Int J Numer Methods Heat Fluid Flow. 2019;29(5):1606–28.

    Google Scholar 

  41. Arabpour A, Karimipour A, Toghraie D. The study of heat transfer and laminar flow of kerosene/multi-walled carbon nanotubes (MWCNTs) nanofluid in the microchannel heat sink with slip boundary condition. J Therm Anal Calorim. 2018;131(2):1553–66.

    CAS  Google Scholar 

  42. Hassani M, Karimipour A. Discrete ordinates simulation of radiative participating nanofluid natural convection in an enclosure. J Therm Anal Calorim. 2018;134(3):2183–95.

    CAS  Google Scholar 

  43. Mozaffari M, Karimipour A, D’Orazio A. Increase lattice Boltzmann method ability to simulate slip flow regimes with dispersed CNTs nanoadditives inside. J Therm Anal Calorim. 2019;137(1):229–43.

    CAS  Google Scholar 

  44. Dehghani Y, Abdollahi A, Karimipour A. Experimental investigation toward obtaining a new correlation for viscosity of WO3 and Al2O3 nanoparticles-loaded nanofluid within aqueous and non-aqueous basefluids. J Therm Anal Calorim. 2019;135(1):713–28.

    CAS  Google Scholar 

  45. Arasteh H, Mashayekhi R, Toghraie D, Karimipour A, Bahiraei M, Rahbari A. Optimal arrangements of a heat sink partially filled with multilayered porous media employing hybrid nanofluid. J Therm Anal Calorim. 2019;137(3):1045–58.

    CAS  Google Scholar 

  46. Pordanjani AH, Aghakhani S, Karimipour A, Afrand M, Goodarzi M. Investigation of free convection heat transfer and entropy generation of nanofluid flow inside a cavity affected by magnetic field and thermal radiation. J Therm Anal Calorim. 2019;137(3):997–1019.

    Google Scholar 

  47. D’Orazio A, Karimipour A. A useful case study to develop lattice Boltzmann method performance: gravity effects on slip velocity and temperature profiles of an air flow inside a microchannel under a constant heat flux boundary condition. Int J Heat Mass Transf. 2019;136:1017–29.

    Google Scholar 

  48. Sulgani MT, Karimipour A. Improve the thermal conductivity of 10w40-engine oil at various temperature by addition of Al2O3/Fe2O3 nanoparticles. J Mol Liq. 2019;283:660–6.

    Google Scholar 

  49. Nafchi PM, Karimipour A, Afrand M. The evaluation on a new non-Newtonian hybrid mixture composed of TiO2/ZnO/EG to present a statistical approach of power law for its rheological and thermal properties. Phys A. 2019;516:1–18.

    CAS  Google Scholar 

  50. Ershadi H, Karimipour A. Present a multi-criteria modeling and optimization (energy, economic and environmental) approach of industrial combined cooling heating and power (CCHP) generation systems using the genetic algorithm, case study: a tile factory. Energy. 2018;149:286–95.

    Google Scholar 

  51. Nojoomizadeh M, Karimipour A, Firouzi M, Afrand M. Investigation of permeability and porosity effects on the slip velocity and convection heat transfer rate of Fe3O4/water nanofluid flow in a microchannel while its lower half filled by a porous medium. Int J Heat Mass Transf. 2018;119:891–906.

    CAS  Google Scholar 

  52. Nazari S, Ellahi R, Sarafraz MM, Safaei MR, Asgari A, Akbari OA, Numerical study on mixed convection of a non-Newtonian nanofluid with porous media in a two lid-driven square cavity. J Therm Anal Calorim. 2019. https://doi.org/10.1007/s10973-019-08841-1.

    Article  Google Scholar 

  53. Maithani R, Kumar A, Zadeh PG, Safaei MR, Gholamalizadeh E, Empirical correlations development for heat transfer and friction factor of a solar rectangular air passage with spherical-shaped turbulence promoters. J Therm Anal Calorim. 2019. https://doi.org/10.1007/s10973-019-08551-8

    Article  Google Scholar 

  54. Safaei MR, Rahmanian B, Goodarzi M. Numerical study of laminar mixed convection heat transfer of power-law non-Newtonian fluids in square enclosures by finite volume method. Int J Phys Sci. 2011;6(33):7456–70.

    CAS  Google Scholar 

  55. Goodarzi M, Safaei MR, Vafai K, Ahmadi G, Dahari M, Kazi SN, Jomhari N. Investigation of nanofluid mixed convection in a shallow cavity using a two-phase mixture model. Int J Therm Sci. 2014;75:204–20.

    CAS  Google Scholar 

  56. Sarafraz MM, Tlili I, Tian Z, Khan AR, Safaei MR, Thermal analysis and thermo-hydraulic characteristics of zirconia–water nanofluid under a convective boiling regime. J Therm Anal Calorim. 2019. https://doi.org/10.1007/s10973-019-08435-x.

    Article  Google Scholar 

  57. Hooman K, Tamayol A, Dahari M, Safaei MR, Togun H, Sadri R. A theoretical model to predict gas permeability for slip flow through a porous medium. Appl Therm Eng. 2014;70(1):71–6.

    Google Scholar 

  58. Rahmanian B, Safaei MR, Kazi SN, Ahmadi G, Oztop HF, Vafai K. Investigation of pollutant reduction by simulation of turbulent non-premixed pulverized coal combustion. Appl Therm Eng. 2014;73(1):1222–35.

    CAS  Google Scholar 

  59. Garoosi F, Safaei MR, Dahari M, Hooman K. Eulerian–Lagrangian analysis of solid particle distribution in an internally heated and cooled air-filled cavity. Appl Math Comput. 2015;250:28–46.

    Google Scholar 

  60. Aboulhasan Alavi SM, Safaei MR, Mahian O, Goodarzi M, Yarmand H, Dahari M, Wongwises S. A hybrid finite-element/finite-difference scheme for solving the 3-D energy equation in transient nonisothermal fluid flow over a staggered tube bank. Numer Heat Transf B Fundam. 2015;68(2):169–83.

    CAS  Google Scholar 

  61. Kherbeet AS, Safaei MR, Mohammed HA, Salman BH, Ahmed HE, Alawi OA, Al-Asadi MT. Heat transfer and fluid flow over microscale backward and forward facing step: a review. Int Commun Heat Mass Transf. 2016;76:237–44.

    CAS  Google Scholar 

  62. Safaei M, Goodarzi M, Mohammadi M. Numerical modeling of turbulence mixed convection heat transfer in air filled enclosures by finite volume method. Int J Multiphys. 2016. https://doi.org/10.1260/1750-9548.5.4.307.

    Article  Google Scholar 

  63. Kherbeet AS, Mohammed HA, Ahmed HE, Salman BH, Alawi OA, Safaei MR, Khazaal MT. Mixed convection nanofluid flow over microscale forward-facing step—effect of inclination and step heights. Int Commun Heat Mass Transf. 2016;78:145–54.

    CAS  Google Scholar 

  64. Jamalabadi MYA, Daqiqshirazi M, Nasiri H, Safaei MR, Nguyen TK. Modeling and analysis of biomagnetic blood Carreau fluid flow through a stenosis artery with magnetic heat transfer: a transient study. PLoS ONE. 2018;13(2):e0192138.

    Google Scholar 

  65. Safdari Shadloo M. Numerical simulation of compressible flows by lattice Boltzmann method. Numer Heat Transf A Appl. 2019;75(3):167–82.

    Google Scholar 

  66. Hopp-Hirschler M, Shadloo MS, Nieken U. Viscous fingering phenomena in the early stage of polymer membrane formation. J Fluid Mech. 2019;864:97–140.

    CAS  Google Scholar 

  67. Sadeghi R, Shadloo MS, Hopp-Hirschler M, Hadjadj A, Nieken U. Three-dimensional lattice Boltzmann simulations of high density ratio two-phase flows in porous media. Comput Math Appl. 2018;75(7):2445–65.

    Google Scholar 

  68. Hopp-Hirschler M, Shadloo MS, Nieken U. A smoothed particle hydrodynamics approach for thermo-capillary flows. Comput Fluids. 2018;176:1–19.

    Google Scholar 

  69. Shadloo MS, Hadjadj A. Laminar–turbulent transition in supersonic boundary layers with surface heat transfer: a numerical study. Numer Heat Transf A Appl. 2017;72(1):40–53.

    CAS  Google Scholar 

  70. Sharma S, Shadloo MS, Hadjadj A, Kloker MJ. Control of oblique-type breakdown in a supersonic boundary layer employing streaks. J Fluid Mech. 2019;873:1072–89.

    CAS  Google Scholar 

  71. Méndez M, Shadloo MS, Hadjadj A, Ducoin A. Boundary layer transition over a concave surface caused by centrifugal instabilities. Comput Fluids. 2018;171:135–53.

    Google Scholar 

  72. Piquet A, Zebiri B, Hadjadj A, Safdari Shadloo M, A parallel high-order compressible flows solver with domain decomposition method in the generalized curvilinear coordinates system. Int J Numer Methods Heat Fluid Flow. 2019. https://doi.org/10.1108/HFF-01-2019-0048.

    Article  Google Scholar 

  73. Nguyen MQ, Shadloo MS, Hadjadj A, Lebon B, Peixinho J. Perturbation threshold and hysteresis associated with the transition to turbulence in sudden expansion pipe flow. Int J Heat Fluid Flow. 2019;76:187–96.

    Google Scholar 

  74. Shenoy DV, Shadloo MS, Peixinho J, Hadjadj A, Direct numerical simulations of laminar and transitional flows in diverging pipes. Int J Numer Methods Heat Fluid Flow. 2019, in press.

  75. Shojaeizadeh A, Safaei MR, Alrashed AA, Ghodsian M, Geza M, Abbassi MA. Bed roughness effects on characteristics of turbulent confined wall jets. Measurement. 2018;122:325–38.

    Google Scholar 

  76. Haghighi SS, Goshayeshi HR, Safaei MR. Natural convection heat transfer enhancement in new designs of plate-fin based heat sinks. Int J Heat Mass Transf. 2018;125:640–7.

    Google Scholar 

  77. Mahdisoozani H, Mohsenizadeh M, Bahiraei M, Kasaeian A, Daneshvar A, Goodarzi M, Safaei MR. Performance enhancement of internal combustion engines through vibration control: state of the art and challenges. Appl Sci. 2019;9(3):406.

    CAS  Google Scholar 

  78. Li Z, Shahrajabian H, Bagherzadeh SA, Jadidi H, Karimipour A, Tlili I, Effects of nano-clay content, foaming temperature and foaming time on density and cell size of PVC matrix foam by presented least absolute shrinkage and selection operator statistical regression via suitable experiments as a function of MMT content. Phys A Stat Mech Its Appl. 2020;537:122637.

    CAS  Google Scholar 

  79. Shahrajabian H, Farahnakian M. Modeling and multi-constrained optimization in drilling process of carbon fiber reinforced epoxy composite. Int J Precis Eng Manuf. 2013;14(10):1829–37.

    Google Scholar 

  80. Moghri M, Shamaee H, Shahrajabian H, Ghannadzadeh A. The effect of different parameters on mechanical properties of PA-6/clay nanocomposite through genetic algorithm and response surface methods. Int Nano Lett. 2015;5(3):133–40.

    CAS  Google Scholar 

  81. Azad R, Shahrajabian H. Experimental study of warpage and shrinkage in injection molding of HDPE/rPET/wood composites with multi objective optimization. Mater Manuf Process. 2019;34(3):274–82.

    CAS  Google Scholar 

  82. Shahrajabian H, Farahnakian M. Multi-constrained optimization in ball-end machining of carbon fiber-reinforced epoxy composites by PSO. Cogent Eng. 2015;2(1):993157.

    Google Scholar 

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Acknowledgements

The first author acknowledges the support provided by the Fujian Province Natural Science Foundation (No: 2018J01506), and University-industry cooperation program of Department of Science and Technology of Fujian Province (No. 2019H6018), and Fuzhou Science and Technology Planning Project (Nos. 2018S113, 2018G92), and the Educational Research Projects of Young Teachers of Fujian Province (Nos. JK2017038, JAT170439), and the 2017 Outstanding Young Scientist Training Program of Colleges in Fujian Province.

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Corresponding author at Sustainable Management of Natural Resources and Environment Research Group, Faculty of Environment and Labour Safety, Ton Duc Thang University, 19 Nguyen Huu Tho, Tan Phong Ward, District 7, Ho Chi Minh City, Vietnam.

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He, W., Bagherzadeh, S.A., Shahrajabian, H. et al. Controlled elitist multi-objective genetic algorithm joined with neural network to study the effects of nano-clay percentage on cell size and polymer foams density of PVC/clay nanocomposites. J Therm Anal Calorim 139, 2801–2810 (2020). https://doi.org/10.1007/s10973-019-09059-x

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