REFERENCES

1. Mo, F.; Rehman, H. U.; Monetti, F. M.; et al. A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence. Robot. Comput. Integr. Manuf. 2023, 82, 102524.

2. De Winter, J.; Ei Makrini, I.; Van de Perre, G.; Nowé, A.; Verstraten, T.; Vanderborght, B. Autonomous assembly planning of demonstrated skills with reinforcement learning in simulation. Auton. Robot. 2021, 45, 1097-110.

3. Ibrahim, A.; Kumar, G. Selection of Industry 4.0 technologies for Lean Six Sigma integration using fuzzy DEMATEL approach. Int. J. Lean. Six. Sigma. 2024, 15, 1025-42.

4. Zafar, M. H.; Langås, E. F.; Sanfilippo, F. Exploring the synergies between collaborative robotics, digital twins, augmentation, and industry 5.0 for smart manufacturing: a state-of-the-art review. Robot. Comput. Integr. Manuf. 2024, 89, 102769.

5. Soori, M.; Arezoo, B.; Dastres, R. Artificial intelligence, machine learning and deep learning in advanced robotics, a review. Cogn. Robot. 2023, 3, 54-70.

6. Hao, M.; Li, H.; Luo, X.; Xu, G.; Yang, H.; Liu, S. Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE. Trans. Ind. Inf. 2020, 16, 6532-42.

7. Velazquez, L.; Palardy, G.; Barbalata, C. A robotic 3D printer for UV-curable thermosets: dimensionality prediction using a data-driven approach. Int. J. Comput. Integr. Manuf. 2024, 37, 772-89.

8. Canas-Moreno, S.; Piñero-Fuentes, E.; Rios-Navarro, A.; Cascado-Caballero, D.; Perez-Peña, F.; Linares-Barranco, A. Towards neuromorphic FPGA-based infrastructures for a robotic arm. Auton. Robot. 2023, 47, 947-61.

9. Intisar, M.; Monirujjaman Khan, M.; Rezaul Islam, M.; Masud, M. Computer vision based robotic arm controlled using interactive GUI. Intell. Autom. Soft. Comput. 2021, 27, 533-50.

10. Cazacu, C.; Iorga, I.; Parpală, R. C.; Popa, C. L.; Coteț, C. E. Optimizing assembly in wiring boxes using API technology for digital twin. Appl. Sci. 2024, 14, 9483.

11. Ardanza, A.; Moreno, A.; Segura, Á.; de la Cruz, M.; Aguinaga, D. Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm. Int. J. Prod. Res. 2019, 57, 4045-59.

12. Tsai, Y.; Lee, C.; Liu, T.; et al. Utilization of a reinforcement learning algorithm for the accurate alignment of a robotic arm in a complete soft fabric shoe tongues automation process. J. Manuf. Syst. 2020, 56, 501-13.

13. Jeong, J. H.; Shim, K. H.; Kim, D. J.; Lee, S. W. Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals. IEEE. Trans. Neural. Syst. Rehabil. Eng. 2020, 28, 1226-38.

14. Liu, J.; Chen, X.; Yu, S. From junk to genius: robotic arms and AI crafting creative designs from scraps. Buildings 2024, 14, 4076.

15. Liu, Q.; Ji, Z.; Xu, W.; Liu, Z.; Yao, B.; Zhou, Z. Knowledge-guided robot learning on compliance control for robotic assembly task with predictive model. Expert. Syst. Appl. 2023, 234, 121037.

16. Cao, G.; Bai, J. Multi-agent deep reinforcement learning-based robotic arm assembly research. PLoS. One. 2025, 20, e0311550.

17. Gao, T. Optimizing robotic arm control using deep Q-learning and artificial neural networks through demonstration-based methodologies: a case study of dynamic and static conditions. Robot. Auton. Syst. 2024, 181, 104771.

18. Li, T.; Zeng, Q.; Li, J.; et al. An adaptive control method and learning strategy for ultrasound-guided puncture robot. Electronics 2024, 13, 580.

19. Kumar, A.; Sharma, R. Linguistic Lyapunov reinforcement learning control for robotic manipulators. Neurocomputing 2018, 272, 84-95.

20. Juarez-Lora, A.; Ponce-Ponce, V. H.; Sossa, H.; Rubio-Espino, E. R-STDP spiking neural network architecture for motion control on a changing friction joint robotic arm. Front. Neurorobot. 2022, 16, 904017.

21. Guo, C.; Luk, W. FPGA-accelerated sim-to-real control policy learning for robotic arms. IEEE. Trans. Circuits. Syst. II. 2024, 71, 1690-4.

22. Katona, K.; Neamah, H. A.; Korondi, P. Obstacle avoidance and path planning methods for autonomous navigation of mobile robot. Sensors 2024, 24, 3573.

23. Ušinskis, V.; Nowicki, M.; Dzedzickis, A.; Bučinskas, V. Sensor-fusion based navigation for autonomous mobile robot. Sensors 2025, 25, 1248.

24. Eren, B.; Demir, M. H.; Mistikoglu, S. Recent developments in computer vision and artificial intelligence aided intelligent robotic welding applications. Int. J. Adv. Manuf. Technol. 2023, 126, 4763-809.

25. Fu, J.; Rota, A.; Li, S.; et al. Recent advancements in augmented reality for robotic applications: a survey. Actuators 2023, 12, 323.

26. Rho, E.; Kim, W.; Mun, J.; Yu, S. Y.; Cho, K.; Jo, S. Impact of physical parameters and vision data on deep learning-based grip force estimation for fluidic origami soft grippers. IEEE. Robot. Autom. Lett. 2024, 9, 2487-94.

27. Yang, C.; Kang, J.; Eom, D. Enhancing ToF sensor precision using 3D models and simulation for vision inspection in industrial mobile robots. Appl. Sci. 2024, 14, 4595.

28. Mena-Almonte, R. A.; Zulueta, E.; Etxeberria-Agiriano, I.; Fernandez-Gamiz, U. Efficient robot localization through deep learning-based natural fiduciary pattern recognition. Mathematics 2025, 13, 467.

29. Wu, H.; Zhang, W.; Lu, W.; Chen, J.; Bao, J.; Liu, Y. Automated part placement for precast concrete component manufacturing: an intelligent robotic system using target detection and path planning. J. Comput. Civ. Eng. 2025, 39, 04024044.

30. Nguyen, V.; Nguyen, P.; Su, S.; Tan, P. X.; Bui, T. Vision-based pick and place control system for industrial robots using an eye-in-hand camera. IEEE. Access. 2025, 13, 25127-40.

31. Wei, D.; Cao, J.; Gu, Y. Robot grasp in cluttered scene using a multi-stage deep learning model. IEEE. Robot. Autom. Lett. 2024, 9, 6512-9.

32. Huang, C.; Su, G.; Shao, Y.; Wang, Y.; Yang, S. Rapid-learning collaborative pushing and grasping via deep reinforcement learning and image masking. Appl. Sci. 2024, 14, 9018.

33. Wang, S.; Zhang, E.; Zhou, L.; Han, Y.; Liu, W.; Hong, J. 3DWDC-Net: an improved 3DCNN with separable structure and global attention for weld internal defect classification based on phased array ultrasonic tomography images. Mech. Syst. Signal. Process. 2025, 229, 112564.

34. Hassan, S. A.; Beliatis, M. J.; Radziwon, A.; Menciassi, A.; Oddo, C. M. Textile fabric defect detection using enhanced deep convolutional neural network with safe human–robot collaborative interaction. Electronics 2024, 13, 4314.

35. Zhou, S.; Le, D. V.; Jiang, L.; et al. RoboCam: model-based robotic visual sensing for precise inspection of mesh screens. ACM. Trans. Sens. Netw. 2025, 21, 1-23.

36. Rocha, F.; Garcia, G.; Pereira, R. F. S.; et al. ROSI: a robotic system for harsh outdoor industrial inspection - system design and applications. J. Intell. Robot. Syst. 2021, 103, 1459.

37. Surindra, M. D.; Alfarisy, G. A. F.; Caesarendra, W.; et al. Use of machine learning models in condition monitoring of abrasive belt in robotic arm grinding process. J. Intell. Manuf. 2025, 36, 3345-58.

38. Chew, S. Y.; Asadi, E.; Vargas-Uscategui, A.; et al. In-process 4D reconstruction in robotic additive manufacturing. Robot. Comput. Integr. Manuf. 2024, 89, 102784.

39. Ni, H.; Hu, T.; Deng, J.; Chen, B.; Luo, S.; Ji, S. Digital twin-driven virtual commissioning for robotic machining enhanced by machine learning. Robot. Comput. Integr. Manuf. 2025, 93, 102908.

40. Chen, Y.; Lai, C. An intuitive pre-processing method based on human–robot interactions: zero-shot learning semantic segmentation based on synthetic semantic template. J. Supercomput. 2023, 79, 11743-66.

41. Ghafarian Tamizi, M.; Honari, H.; Nozdryn-Plotnicki, A.; Najjaran, H. End-to-end deep learning-based framework for path planning and collision checking: bin-picking application. Robotica 2024, 42, 1094-112.

42. Arents, J.; Greitans, M. Smart industrial robot control trends, challenges and opportunities within manufacturing. Appl. Sci. 2022, 12, 937.

43. De Roovere, P.; Moonen, S.; Michiels, N.; wyffels, F. Sim-to-real dataset of industrial metal objects. Machines 2024, 12, 99.

44. Lee, J.; Chang, C.; Cheng, E.; Kuo, C.; Hsieh, C. Intelligent robotic palletizer system. Appl. Sci. 2021, 11, 12159.

45. Hou, R.; Yin, J.; Liu, Y.; Lu, H. Research on multi-hole localization tracking based on a combination of machine vision and deep learning. Sensors 2024, 24, 984.

46. Song, Y.; Wen, J.; Fei, Y.; Yu, C. Deep robotic prediction with hierarchical RGB-D fusion. arXiv 2019, arXiv:1909.06585. Available online: https://doi.org/10.48550/arXiv.1909.06585. (accessed 3 December 2025).

47. Lin, C.; Lin, P.; Shih, C. Vision-based robotic arm control for screwdriver bit placement tasks. Sens. Mater. 2024, 36, 1003.

48. Lee, S. K. H.; Simeth, A.; Hinchy, E. P.; Plapper, P.; O’dowd, N. P.; Mccarthy, C. T. A vision-based hole quality assessment technique for robotic drilling of composite materials using a hybrid classification model. Int. J. Adv. Manuf. Technol. 2023, 129, 1249-58.

49. Comari, S.; Carricato, M. Autonomous scanning and cleanliness classification of pharmaceutical bins through artificial intelligence and robotics. IEEE. Access. 2024, 12, 117256-70.

50. Kohut, P.; Skop, K. Vision systems for a UR5 cobot on a quality control robotic station. Appl. Sci. 2024, 14, 9469.

51. Cheng, A.; Lu, S.; Gao, F. Anomaly detection of tire tiny text: mechanism and method. IEEE. Trans. Autom. Sci. Eng. 2024, 21, 1911-28.

52. Lin, C.; Jhang, J.; Gao, Y.; Huang, H. Vision-based robotic arm in defect detection and object classification applications. Sens. Mater. 2024, 36, 655.

53. Pereira, F. R.; Rodrigues, C. D.; Souza, H. D. S. E.; et al. Force and vision-based system for robotic sealing monitoring. Int. J. Adv. Manuf. Technol. 2023, 126, 391-403.

54. Mukherjee, D.; Gupta, K.; Chang, L. H.; Najjaran, H. A survey of robot learning strategies for human-robot collaboration in industrial settings. Robot. Comput. Integr. Manuf. 2022, 73, 102231.

55. Mendez, E.; Ochoa, O.; Olivera-Guzman, D.; et al. Integration of deep learning and collaborative robot for assembly tasks. Appl. Sci. 2024, 14, 839.

56. Bartyzel, G. Multimodal variational DeepMDP: an efficient approach for industrial assembly in high-mix, low-volume production. IEEE. Robot. Autom. Lett. 2024, 9, 11297-304.

57. Li, T.; Polette, A.; Lou, R.; Jubert, M.; Nozais, D.; Pernot, J. Machine learning-based 3D scan coverage prediction for smart-control applications. Comput. Aided. Des. 2024, 176, 103775.

58. Roos-Hoefgeest, S.; Roos-Hoefgeest, M.; Alvarez, I.; González, R. C. Reinforcement learning approach to optimizing profilometric sensor trajectories for surface inspection. arXiv 2024, arXiv:2409.03429. Available online: https://doi.org/10.48550/arXiv.2409.03429. (accessed 3 December 2025).

59. Simoni, A.; Borghi, G.; Garattoni, L.; Francesca, G.; Vezzani, R. D-SPDH: improving 3D robot pose estimation in Sim2Real scenario via depth data. IEEE. Access. 2024, 12, 166660-73.

60. Mangat, A. S.; Mangler, J.; Rinderle-Ma, S. Interactive process automation based on lightweight object detection in manufacturing processes. Comput. Ind. 2021, 130, 103482.

61. Gao, Z.; Elibol, A.; Chong, N. Y. Zero moment two edge pushing of novel objects with center of mass estimation. IEEE. Trans. Autom. Sci. Eng. 2023, 20, 1487-99.

62. Celikel, R.; Aydogmus, O. NARMA-L2 controller for single link manipulator. In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), Malatya, Turkey, September 28-30, 2018. IEEE; 2018. p. 1-6.

63. Faroni, M.; Umbrico, A.; Beschi, M.; Orlandini, A.; Cesta, A.; Pedrocchi, N. Optimal task and motion planning and execution for multiagent systems in dynamic environments. IEEE. Trans. Cybern. 2024, 54, 3366-77.

64. Calderón-Cordova, C.; Sarango, R.; Castillo, D.; Lakshminarayanan, V. A deep reinforcement learning framework for control of robotic manipulators in simulated environments. IEEE. Access. 2024, 12, 103133-61.

65. Wu, P.; Su, H.; Dong, H.; Liu, T.; Li, M.; Chen, Z. An obstacle avoidance method for robotic arm based on reinforcement learning. Ind. Robot. 2025, 52, 9-17.

66. Lindner, T.; Milecki, A. Reinforcement learning-based algorithm to avoid obstacles by the anthropomorphic robotic arm. Appl. Sci. 2022, 12, 6629.

67. Liu, Q.; Liu, Z.; Xiong, B.; Xu, W.; Liu, Y. Deep reinforcement learning-based safe interaction for industrial human-robot collaboration using intrinsic reward function. Adv. Eng. Inform. 2021, 49, 101360.

68. Honelign, L.; Abebe, Y.; Tullu, A.; Jung, S. Deep reinforcement learning-based enhancement of robotic arm target-reaching performance. Actuators 2025, 14, 165.

69. Ji, Z.; Liu, G.; Xu, W.; Yao, B.; Liu, X.; Zhou, Z. Deep reinforcement learning on variable stiffness compliant control for programming-free robotic assembly in smart manufacturing. Int. J. Prod. Res. 2024, 62, 7073-95.

70. Men, Y.; Jin, L.; Cui, T.; Bai, Y.; Li, F.; Song, R. Policy fusion transfer: the knowledge transfer for different robot peg-in-hole insertion assemblies. IEEE. Trans. Instrum. Meas. 2023, 72, 1-10.

71. Zhou, H.; Lin, X. Intelligent redundant manipulation for long-horizon operations with multiple goal-conditioned hierarchical learning. Adv. Robot. 2025, 39, 291-304.

72. Koubaa, A.; Ammar, A.; Boulila, W. Next-generation human-robot interaction with ChatGPT and robot operating system. Softw. Pract. Exp. 2025, 55, 355-82.

73. Gupta, S.; Yao, K.; Niederhauser, L.; Billard, A. Action contextualization: adaptive task planning and action tuning using large language models. IEEE. Robot. Autom. Lett. 2024, 9, 9407-14.

74. Hou, W.; Xiong, Z.; Yue, M.; Chen, H. Human-robot collaborative assembly task planning for mobile cobots based on deep reinforcement learning. Proc. Inst. Mech. Eng. C. 2024, 238, 11097-114.

75. Angelidis, A.; Plevritakis, E.; Vosniakos, G.; Matsas, E. An open extended reality platform supporting dynamic robot paths for studying human–robot collaboration in manufacturing. Int. J. Adv. Manuf. Technol. 2025, 138, 3-15.

76. Zhao, D.; Ding, Z.; Li, W.; Zhao, S.; Du, Y. Cascaded fuzzy reward mechanisms in deep reinforcement learning for comprehensive path planning in textile robotic systems. Appl. Sci. 2024, 14, 851.

77. Lin, J.; Wei, X.; Xian, W.; et al. Continuous reinforcement learning via advantage value difference reward shaping: a proximal policy optimization perspective. Eng. Appl. Artif. Intell. 2025, 151, 110676.

78. Bing, Z.; Zhou, H.; Li, R.; et al. Solving robotic manipulation with sparse reward reinforcement learning via graph-based diversity and proximity. IEEE. Trans. Ind. Electron. 2023, 70, 2759-69.

79. Yu, C.; Yang, Y.; Cheng, Y.; Wang, Z.; Shi, M. Trajectory tracking control of an unmanned aerial vehicle with deep reinforcement learning for tasks inside the EAST. Fusion. Eng. Des. 2023, 194, 113894.

80. Bartyzel, G.; Półchłopek, W.; Rzepka, D. Reinforcement learning with stereo-view observation for robust electronic component robotic insertion. J. Intell. Robot. Syst. 2023, 109, 1970.

81. Yu, S.; Tan, G. Inverse kinematics of a 7-degree-of-freedom robotic arm based on deep reinforcement learning and damped least squares. IEEE. Access. 2025, 13, 4857-68.

82. Amirnia, A.; Keivanpour, S. Real-time sustainable cobotic disassembly planning using fuzzy reinforcement learning. Int. J. Prod. Res. 2025, 63, 3798-821.

83. Tian, B.; Kaul, H.; Janardhanan, M. Balancing heterogeneous assembly line with multi-skilled human-robot collaboration via adaptive cooperative co-evolutionary algorithm. Swarm. Evol. Comput. 2024, 91, 101762.

84. Wang, T.; Fan, J.; Zheng, P. An LLM-based vision and language cobot navigation approach for human-centric smart manufacturing. J. Manuf. Syst. 2024, 75, 299-305.

85. Li, C.; Chrysostomou, D.; Zhang, X.; Yang, H. IRWoZ: constructing an industrial robot Wizard-of-OZ dialoguing dataset. IEEE. Access. 2023, 11, 28236-51.

86. Li, C.; Zhang, X.; Chrysostomou, D.; Yang, H. ToD4IR: a humanised task-oriented dialogue system for industrial robots. IEEE. Access. 2022, 10, 91631-49.

87. Čakurda, T.; Trojanová, M.; Pomin, P.; Hošovský, A. Deep learning methods in soft robotics: architectures and applications. Adv. Intell. Syst. 2025, 7, 2400576.

88. Bilancia, P.; Locatelli, A.; Tutarini, A.; Mucciarini, M.; Iori, M.; Pellicciari, M. Online motion accuracy compensation of industrial servomechanisms using machine learning approaches. Robot. Comput. Integr. Manuf. 2025, 91, 102838.

89. Bi, Z.; Luo, C.; Miao, Z.; Zhang, B.; Zhang, W.; Wang, L. Safety assurance mechanisms of collaborative robotic systems in manufacturing. Robot. Comput. Integr. Manuf. 2021, 67, 102022.

90. Shan, S.; Pham, Q. Fine robotic manipulation without force/torque sensor. IEEE. Robot. Autom. Lett. 2024, 9, 1206-13.

91. Deng, W.; Ardiani, F.; Nguyen, K. T.; Benoussaad, M.; Medjaher, K. Physics informed machine learning model for inverse dynamics in robotic manipulators. Appl. Soft. Comput. 2024, 163, 111877.

92. Zhou, H.; Ma, S.; Wang, G.; Deng, Y.; Liu, Z. A hybrid control strategy for grinding and polishing robot based on adaptive impedance control. Adv. Mech. Eng. 2021, 13, 168781402110040.

93. Ma, H.; Zhang, Y.; Li, Z.; Zhang, J.; Wu, X.; Chen, W. Research on 3C compliant assembly strategy method of manipulator based on deep reinforcement learning. Comput. Electr. Eng. 2024, 119, 109605.

94. Zhang, S.; Wang, Y.; Liang, S.; Han, H.; Jiang, Z.; Zhang, M. Research on robotic peg-in-hole assembly method based on variable admittance. Appl. Sci. 2025, 15, 2143.

95. Hu, X.; Liu, G.; Ren, P.; et al. An admittance parameter optimization method based on reinforcement learning for robot force control. Actuators 2024, 13, 354.

96. Zheyuan, C.; Rahman, M. A.; Tao, H.; Liu, Y.; Pengxuan, D.; Yaseen, Z. M. Need for developing a security robot-based risk management for emerging practices in the workplace using the advanced human-robot collaboration model. Work 2021, 68, 825-34.

97. Xin, X.; Keoh, S. L.; Sevegnani, M.; Saerbeck, M.; Khoo, T. P. Adaptive model verification for modularized industry 4.0 applications. IEEE. Access. 2022, 10, 125353-64.

98. Hickman, X.; Lu, Y.; Prince, D. Hybrid safe reinforcement learning: tackling distribution shift and outliers with the Student-t’s process. Neurocomputing 2025, 634, 129912.

99. Kana, S.; Lakshminarayanan, S.; Mohan, D. M.; Campolo, D. Impedance controlled human–robot collaborative tooling for edge chamfering and polishing applications. Robot. Comput. Integr. Manuf. 2021, 72, 102199.

100. Amaya, C.; von Arnim, A. Neurorobotic reinforcement learning for domains with parametrical uncertainty. Front. Neurorobot. 2023, 17, 1239581.

101. Mahdi, M. M.; Bajestani, M. S.; Noh, S. D.; Kim, D. B. Digital twin-based architecture for wire arc additive manufacturing using OPC UA. Robot. Comput. Integr. Manuf. 2025, 94, 102944.

102. Li, C.; Zheng, P.; Li, S.; Pang, Y.; Lee, C. K. AR-assisted digital twin-enabled robot collaborative manufacturing system with human-in-the-loop. Robot. Comput. Integr. Manuf. 2022, 76, 102321.

103. Zhang, T.; Zhang, K.; Lin, J.; Louie, W. G.; Huang, H. Sim2real learning of obstacle avoidance for robotic manipulators in uncertain environments. IEEE. Robot. Autom. Lett. 2022, 7, 65-72.

104. Zhang, Z.; Zhang, Z.; Wang, L.; Zhu, X.; Huang, H.; Cao, Q. Digital twin-enabled grasp outcomes assessment for unknown objects using visual-tactile fusion perception. Robot. Comput. Integr. Manuf. 2023, 84, 102601.

105. Liu, Y.; Xu, H.; Liu, D.; Wang, L. A digital twin-based sim-to-real transfer for deep reinforcement learning-enabled industrial robot grasping. Robot. Comput. Integr. Manuf. 2022, 78, 102365.

106. Wang, R.; Tian, Y.; Kashima, K. Density estimation based soft actor-critic: deep reinforcement learning for static output feedback control with measurement noise. Adv. Robot. 2024, 38, 398-409.

107. Salehi, A.; Rühl, S.; Doncieux, S. Adaptive asynchronous control using meta-learned neural ordinary differential equations. IEEE. Trans. Robot. 2024, 40, 403-20.

108. Shin, G.; Yun, S.; Kim, W. A novel policy distillation with WPA-based knowledge filtering algorithm for efficient industrial robot control. IEEE. Access. 2024, 12, 154514-25.

109. Wang, S.; Tao, J.; Jiang, Q.; Chen, W.; Liu, C. Manipulator joint fault localization for intelligent flexible manufacturing based on reinforcement learning and robot dynamics. Robot. Comput. Integr. Manuf. 2024, 86, 102684.

110. Kim, H. Robot dynamics-based cable fault diagnosis using stacked transformer encoder layers. Electr. Eng. 2025, 107, 3697-708.

111. Lanese, I.; Schultz, U. P.; Ulidowski, I. Reversible execution for robustness in embodied AI and industrial robots. IT. Prof. 2021, 23, 12-7.

112. Hsieh, Y.; Xu, F.; Lin, S. Deep convolutional generative adversarial network for inverse kinematics of self-assembly robotic arm based on the depth sensor. IEEE. Sens. J. 2023, 23, 758-65.

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