Cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative artificial intelligence and cyber–physical manufacturing systems in the immersive industrial metaverse
DOI:
https://doi.org/10.24136/eq.3131Keywords:
cognitive digital twin, Internet of Robotic Things, sensor, extended reality, simulation modeling, generative artificial intelligence, cyber–physical manufacturing system, immersive industrial metaverseAbstract
Research background: Connected Internet of Robotic Things (IoRT) and cyber-physical process monitoring systems, industrial big data and real-time event analytics, and machine and deep learning algorithms articulate digital twin smart factories in relation to deep learning-assisted smart process planning, Internet of Things (IoT)-based real-time production logistics, and enterprise resource coordination. Robotic cooperative behaviors and 3D assembly operations in collaborative industrial environments require ambient environment monitoring and geospatial simulation tools, computer vision and spatial mapping algorithms, and generative artificial intelligence (AI) planning software. Flexible industrial and cloud computing environments necessitate sensing and actuation capabilities, cognitive data visualization and sensor fusion tools, and image recognition and computer vision technologies so as to lead to tangible business outcomes.
Purpose of the article: We show that generative AI and cyber–physical manufacturing systems, fog and edge computing tools, and task scheduling and computer vision algorithms are instrumental in the interactive economics of industrial metaverse. Generative AI-based digital twin industrial metaverse develops on IoRT and production management systems, multi-sensory extended reality and simulation modeling technologies, and machine and deep learning algorithms for big data-driven decision-making and image recognition processes. Virtual simulation modeling and deep reinforcement learning tools, autonomous manufacturing and virtual equipment systems, and deep learning-based object detection and spatial computing technologies can be leveraged in networked immersive environments for industrial big data processing.
Methods: Evidence appraisal checklists and citation management software deployed for justifying inclusion or exclusion reasons and data collection and analysis comprise: Abstrackr, Colandr, Covidence, EPPI Reviewer, JBI-SUMARI, Rayyan, RobotReviewer, SR Accelerator, and Systematic Review Toolbox.
Findings & value added: Modal actuators and sensors, robot trajectory planning and computational intelligence tools, and generative AI and cyber–physical manufacturing systems enable scalable data computation processes in smart virtual environments. Ambient intelligence and remote big data management tools, cloud-based robotic cooperation and industrial cyber-physical systems, and environment mapping and spatial computing algorithms improve IoT-based real-time production logistics and cooperative multi-agent controls in smart networked factories. Context recognition and data acquisition tools, generative AI and cyber–physical manufacturing systems, and deep and machine learning algorithms shape smart factories in relation to virtual path lines, collision-free motion planning, and coordinated and unpredictable smart manufacturing and robotic perception tasks, increasing economic performance. This collective writing cumulates and debates upon the most recent and relevant literature on cognitive digital twin-based Internet of Robotic Things, multi-sensory extended reality and simulation modeling technologies, and generative AI and cyber–physical manufacturing systems in the immersive industrial metaverse by use of evidence appraisal checklists and citation management software.
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Agarwal, A., & Alathur, S. (2023). Metaverse revolution and the digital transformation: Intersectional analysis of Industry 5.0. Transforming Government: People, Process and Policy, 17, 688‒707. DOI: https://doi.org/10.1108/TG-03-2023-0036
View in Google Scholar
Aggogeri, F., Pellegrini, N., & Taesi, C. (2024). Towards industrial robots’ maturity: An Italian case study. Robotics, 13(3), 42. DOI: https://doi.org/10.3390/robotics13030042
View in Google Scholar
Anwar, M. S., Choi, A., Ahmad, S., Aurangzeb, K., Laghari, A. A., Gadekallu, T. R., & Hines, A. (2024). A moving Metaverse: QoE challenges and standards requirements for immersive media consumption in autonomous vehicles. Applied Soft Computing, 159, 111577. DOI: https://doi.org/10.1016/j.asoc.2024.111577
View in Google Scholar
Aromaa, S., Heikkilä, P., Kaasinen, E., Lammi, H., Tammela, A., & Salminen, K. (2024). Human factors and ergonomics considerations in the industrial metaverse. International Journal of Human Factors and Ergonomics, 11(1), 4‒27. DOI: https://doi.org/10.1504/IJHFE.2024.137128
View in Google Scholar
Aung, N., Dhelim, S., Chen, L., Ning, H., Atzori, L., & Kechadi, T. (2024). Edge-enabled metaverse: The convergence of metaverse and mobile edge computing. Tsinghua Science and Technology, 29(3), 795‒805. DOI: https://doi.org/10.26599/TST.2023.9010052
View in Google Scholar
Bellalouna, F., & Puljiz, D. (2023). Use case for the application of the industrial metaverse approach for engineering design review. Procedia CIRP, 119, 638‒643. DOI: https://doi.org/10.1016/j.procir.2023.03.116
View in Google Scholar
Bhattacharya, P., Saraswat, D., Savaliya, D., Sanghavi, S., Verma, A., Sakariya, V., Tanwar, S., Sharma, R., Raboaca, M. S., & Manea, D. L. (2023). Towards future Internet: The metaverse perspective for diverse industrial applications. Mathematics, 11(4), 941. DOI: https://doi.org/10.3390/math11040941
View in Google Scholar
Cao, J., Zhu, X., Sun, S., Wei, Z., Jiang, Y., Wang, J., & Lau, V. K. N. (2023). Toward industrial metaverse: Age of information, latency and reliability of short-packet transmission in 6G. IEEE Wireless Communications, 30(2), 40‒47. DOI: https://doi.org/10.1109/MWC.2001.2200396
View in Google Scholar
Carrión, C. (2024). Research streams and open challenges in the metaverse. Journal of Supercomputing, 80, 1598–1639. DOI: https://doi.org/10.1007/s11227-023-05544-1
View in Google Scholar
Chang, L., Zhang, Z., Li, P., Xi, S., Guo, W., Shen, Y., Xiong, Z., Kang, J., Niyato, D., Qiao, X., & Wu, Y. (2022). 6G-enabled edge AI for metaverse: Challenges, methods, and future research directions. Journal of Communications and Information Networks, 7(2), 107‒121. DOI: https://doi.org/10.23919/JCIN.2022.9815195
View in Google Scholar
Chen, C., Fu, H., Zheng, Y., Tao, F., & Liu, Y. (2023a). The advance of digital twin for predictive maintenance: The role and function of machine learning. Journal of Manufacturing Systems, 71, 581‒594. DOI: https://doi.org/10.1016/j.jmsy.2023.10.010
View in Google Scholar
Chen, C., Zhang, H., Hou, J., Zhang, Y., Zhang, H., Dai, J., Pang, S., & Wang, C. (2023b). Deep learning in the ubiquitous human–computer interactive 6G era: Applications, principles and prospects. Biomimetics, 8(4), 343. DOI: https://doi.org/10.3390/biomimetics8040343
View in Google Scholar
Chen, Y., Huang, W., Jiang, X., Zhang, T., Wang, Y., Yan, B., Wang, Z., Chen, Q., Xing, Y., Li, D., & Long, G. (2023c). UbiMeta: A ubiquitous operating system model for metaverse. International Journal of Crowd Science, 7(4), 180‒189. DOI: https://doi.org/10.26599/IJCS.2023.9100028
View in Google Scholar
Chowdhury, M. (2023). Icon: An intelligent resource slicing and task coordination framework for Web 3.0 and metaverse-based service execution over 6G-based immersive edge computing network. International Journal of Ad Hoc and Ubiquitous Computing, 44(3), 167‒202. DOI: https://doi.org/10.1504/IJAHUC.2023.134763
View in Google Scholar
Chukwunonso Amaizu, G., Nkechinyere Njoku, J., Lee, J.-M., & Kim, D.-S. (2024).
View in Google Scholar
Metaverse in advanced manufacturing: Background, applications, limitations, open issues & future directions. ICT Express, 10(2), 233‒255. DOI: https://doi.org/10.1016/j.icte.2024.02.010
View in Google Scholar
Cui, Z., Yang, X., Yue, J., Liu, X., Tao, W., Xia, Q., & Wu, C. (2023). A review of digital twin technology for electromechanical products: Evolution focus throughout key lifecycle phases. Journal of Manufacturing Systems, 70, 264‒287. DOI: https://doi.org/10.1016/j.jmsy.2023.07.016
View in Google Scholar
Dzedzickis, A., Vaičiūnas, G., Lapkauskaitė, K., Viržonis, D., & Bučinskas, V. (2024). Recent advances in human–robot interaction: Robophobia or synergy. Journal of Intelligent Manufacturing. DOI: https://doi.org/10.1007/s10845-024-02362-x
View in Google Scholar
Endres, H., Indulska, M., & Ghosh, A. (2024). Unlocking the potential of Industrial Internet of Things (IIOT) in the age of the industrial metaverse: Business models and challenges. Industrial Marketing Management, 119, 90‒107. DOI: https://doi.org/10.1016/j.indmarman.2024.03.006
View in Google Scholar
Erman, B., & Martino, C. D. (2023). Generative network performance prediction with network digital twin. IEEE Network, 37(2), 286‒292. DOI: https://doi.org/10.1109/MNET.002.2200515
View in Google Scholar
Fabra, L., Solanes, J. E., Muñoz, A., Martí-Testón, A., Alabau, A., & Gracia, L. (2024). Application of Neural Radiance Fields (NeRFs) for 3D model representation in the industrial metaverse. Applied Sciences, 14(5), 1825. DOI: https://doi.org/10.3390/app14051825
View in Google Scholar
Ferrari, F., & McKelvey, F. (2023). Hyperproduction: A social theory of deep generative models. Distinktion: Journal of Social Theory, 24(2), 338‒360. DOI: https://doi.org/10.1080/1600910X.2022.2137546
View in Google Scholar
Gattullo, M., Laviola, E., Evangelista, A., Fiorentino, M., & Uva, A. E. (2022). Towards the evaluation of augmented reality in the metaverse: Information presentation modes. Applied Sciences, 12(24), 12600. DOI: https://doi.org/10.3390/app122412600
View in Google Scholar
Ghobakhloo, M., Iranmanesh, M., Fathi, M., Rejeb, A., Foroughi, B., & Nikbin, D. (2024). Beyond Industry 4.0: A systematic review of Industry 5.0 technologies and implications for social, environmental and economic sustainability. Asia-Pacific Journal of Business Administration. DOI: https://doi.org/10.1108/APJBA-08-2023-0384
View in Google Scholar
Grieves, M. (2023). Digital twin certified: Employing virtual testing of digital twins in manufacturing to ensure quality products. Machines, 11(8), 808. DOI: https://doi.org/10.3390/machines11080808
View in Google Scholar
Hajian, A., Daneshgar, S., Sadeghi R., K., Ojha, D., & Katiyar, G. (2024). From theory to practice: Empirical perspectives on the metaverse’s potential. Technological Forecasting and Social Change, 201, 123224. DOI: https://doi.org/10.1016/j.techfore.2024.123224
View in Google Scholar
Hong, Y., Guo, S., Zeng, X., & Zhang, J. (2024). Human cognition modeling for the metaverse-oriented design system. IEEE Network. DOI: https://doi.org/10.1109/MNET.2024.3377909
View in Google Scholar
Hou, X., Wang, J., Jiang, C., Meng, Z., Chen, J., & Ren, Y. (2024). Efficient federated learning for metaverse via dynamic user selection, gradient quantization and resource allocation. IEEE Journal on Selected Areas in Communications, 42(4), 850‒866. DOI: https://doi.org/10.1109/JSAC.2023.3345393
View in Google Scholar
Jagatheesaperumal, S. K., & Rahouti, M. (2022). Building digital twins of cyber physical systems with metaverse for Industry 5.0 and beyond. IT Professional, 24(6), 34‒40. DOI: https://doi.org/10.1109/MITP.2022.3225064
View in Google Scholar
Jagatheesaperumal, S. K., Yang, Z., Yang, Q., Huang, C., Xu, W., Shikh-Bahaei, M., & Zhang, Z. (2023). Semantic-aware digital twin for metaverse: A comprehensive review. IEEE Wireless Communications, 30(4), 38‒46. DOI: https://doi.org/10.1109/MWC.003.2200616
View in Google Scholar
Jaimini, U., Zhang, T., Brikis, G. O., & Sheth, A. (2022). iMetaverseKG: Industrial metaverse knowledge graph to promote interoperability in design and engineering applications. IEEE Internet Computing, 26(6), 59‒67. DOI: https://doi.org/10.1109/MIC.2022.3212085
View in Google Scholar
Jauhiainen, J. S. (2024). The Metaverse: Innovations and generative AI. International Journal of Innovation Studies, 8(3), 262–272. DOI: https://doi.org/10.1016/j.ijis.2024.04.004
View in Google Scholar
Kaarlela, T., Padrao, P., Pitkäaho, T., Pieskä, S., & Bobadilla, L. (2023a). Digital twins utilizing XR-technology as robotic training tools. Machines, 11(1), 13. DOI: https://doi.org/10.3390/machines11010013
View in Google Scholar
Kaarlela, T., Pitkäaho, T., Pieskä, S., Padrão, P., Bobadilla, L., Tikanmäki, M., Haavisto, T., Blanco Bataller, V., Laivuori, N., & Luimula, M. (2023b). Towards metaverse: Utilizing extended reality and digital twins to control robotic systems. Actuators, 12(6), 219. DOI: https://doi.org/10.3390/act12060219
View in Google Scholar
Kaigom, E. G. (2023). Metarobotics for industry and society: Vision, technologies, and opportunities. IEEE Transactions on Industrial Informatics. DOI: https://doi.org/10.36227/techrxiv.170862099.91234205/v1
View in Google Scholar
Keegan, B. J., McCarthy, I. P., Kietzmann, J., & Canhoto, A. I. (2024). On your marks, headset, go! Understanding the building blocks of metaverse realms. Business Horizons, 67(1), 107‒119. DOI: https://doi.org/10.1016/j.bushor.2023.09.002
View in Google Scholar
Kshetri, N. (2023a). The economics of the industrial metaverse. IT Professional, 25(1), 84‒88. DOI: https://doi.org/10.1109/MITP.2023.3236494
View in Google Scholar
Kshetri, N. (2023b). Metaverse technologies in product management, branding and communications: Virtual and augmented reality, artificial intelligence, non-fungible tokens and brain‒computer interface. Central European Management Journal, 31(4), 511‒521. DOI: https://doi.org/10.1108/CEMJ-08-2023-0336
View in Google Scholar
Kumar, A., Shankar, A., Agarwal, R., Agarwal, V., & Alzeiby, E. A. (2024). With enterprise metaverse comes great possibilities! Understanding metaverse usage intention from an employee perspective. Journal of Retailing and Consumer Services, 78, 103767. DOI: https://doi.org/10.1016/j.jretconser.2024.103767
View in Google Scholar
Kuo, H.-T., & Choi, T.-M. (2024). Metaverse in transportation and logistics operations: An AI-supported digital technological framework. Transportation Research Part E: Logistics and Transportation Review, 185, 103496. DOI: https://doi.org/10.1016/j.tre.2024.103496
View in Google Scholar
Laviola, E., Gattullo, M., Manghisi, V. M., Fiorentino, M., & Uva, A. E. (2022). Minimal AR: Visual asset optimization for the authoring of augmented reality work instructions in manufacturing. International Journal of Advanced Manufacturing Technology, 119, 1769–1784. DOI: https://doi.org/10.1007/s00170-021-08449-6
View in Google Scholar
Lee, J., & Kundu, P. (2022). Integrated cyber-physical systems and industrial metaverse for remote manufacturing. Manufacturing Letters, 34, 12‒15. DOI: https://doi.org/10.1016/j.mfglet.2022.08.012
View in Google Scholar
Li, X., Tian, Y., Ye, P., Duan, H., & Wang, F.-Y. (2023). A novel scenarios engineering methodology for foundation models in metaverse. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(4), 2148‒2159. DOI: https://doi.org/10.1109/TSMC.2022.3228594
View in Google Scholar
Liu, S., Xie, J., & Wang, X. (2023). QoE enhancement of the industrial metaverse based on mixed reality application optimization. Displays, 79, 102463. DOI: https://doi.org/10.1016/j.displa.2023.102463
View in Google Scholar
Lyu, Z., & Fridenfalk, M. (2023). Digital twins for building industrial metaverse. Journal of Advanced Research. DOI: https://doi.org/10.1016/j.jare.2023.11.019
View in Google Scholar
Magalhães, L. C., Magalhães, L. C., Ramos, J. B., Moura, L. R., de Moraes, R. E. N., Gonçalves, J. B., Hisatugu, W. H., Souza, M. T., de Lacalle, L. N. L., & Ferreira, J. C. E. (2022). Conceiving a digital twin for a flexible manufacturing system. Applied Sciences, 12(19), 9864. DOI: https://doi.org/10.3390/app12199864
View in Google Scholar
Mahmoud, K. H., Abdel-Jaber, G. T., & Sharkawy, A-N. (2024). Neural network-based classifier for collision classification and identification for a 3-DOF industrial robot. Automation, 5(1), 13‒34. DOI: https://doi.org/10.3390/automation5010002
View in Google Scholar
Mancuso, I., Messeni Petruzzelli, A., Urbinati, A., & Matzler, K. (2024). Leadership in the metaverse: Building and integrating digital capabilities. Business Horizons, 67(4), 331‒343. DOI: https://doi.org/10.1016/j.bushor.2024.04.005
View in Google Scholar
Martínez-Gutiérrez, A., Díez-González, J., Perez, H., & Araújo, M. (2024). Towards industry 5.0 through metaverse. Robotics and Computer-Integrated Manufacturing, 89, 102764. DOI: https://doi.org/10.1016/j.rcim.2024.102764
View in Google Scholar
Meng, Z., Chen, K., Diao, Y., She, C., Zhao, G., Imran, M. A., & Vucetic, B. (2024). Task-oriented cross-system design for timely and accurate modeling in the metaverse. IEEE Journal on Selected Areas in Communications, 42(3), 752‒766. DOI: https://doi.org/10.1109/JSAC.2023.3345398
View in Google Scholar
Mourad, N., Alsattar, H. A., Qahtan, S., Zaidan, A. A., Deveci, M., Sangaiah, A. K., & Pedrycz, W. (2024). Optimising control engineering tools using digital twin capabilities and other cyber-physical metaverse manufacturing system components. IEEE Transactions on Consumer Electronics, 70(1), 3212‒3221. DOI: https://doi.org/10.1109/TCE.2023.3326047
View in Google Scholar
Nagy, M., Lăzăroiu, G., & Valaskova, K. (2023). Machine intelligence and autonomous robotic technologies in the corporate context of SMEs: Deep learning and virtual simulation algorithms, cyber-physical production networks, and Industry 4.0-based manufacturing systems. Applied Sciences, 13(3), 1681. DOI: https://doi.org/10.3390/app13031681
View in Google Scholar
Negri, E., & Abdel-Aty, T. A. (2023). Clarifying concepts of metaverse, digital twin, digital thread and AAS for CPS-based production systems. IFAC-PapersOnLine, 56(2), 6351‒6357. DOI: https://doi.org/10.1016/j.ifacol.2023.10.818
View in Google Scholar
Ooi, K.-B., Wei-Han Tan, G., Al-Emran, M., Al-Sharafi, M. A., Arpaci, I., Zaidan, A. A., Lee, V.-H., Wong, L.-W., Deveci, M., & Iranmanesh, M. (2024). The metaverse in engineering management: Overview, opportunities, challenges, and future research agenda. IEEE Transactions on Engineering Management, 71, 13882‒13889. DOI: https://doi.org/10.1109/TEM.2023.3307562
View in Google Scholar
Patterson, E. A. (2024). Engineering design and the impact of digital technology from computer-aided engineering to industrial metaverses: A perspective. Journal of Strain Analysis for Engineering Design, 59(4), 303‒305. DOI: https://doi.org/10.1177/03093247241233325
View in Google Scholar
Qu, Q., Hatami, M., Xu, R., Nagothu, D., Chen, Y., Li, X., Blasch, E., Ardiles-Cruz, E., & Chen, G. (2024). The microverse: A task-oriented edge-scale metaverse. Future Internet, 16(2), 60. DOI: https://doi.org/10.3390/fi16020060
View in Google Scholar
Ren, L., Dong, J., Zhang, L., Laili, Y., Wang, X., Qi, Y., Li, B. H., Wang, L., Yang, L. T., & Deen, M. J. (2024). Industrial metaverse for smart manufacturing: Model, architecture, and applications. IEEE Transactions on Cybernetics, 54(5), 2683‒2695. DOI: https://doi.org/10.1109/TCYB.2024.3372591
View in Google Scholar
Sai, S., Prasad, M., Upadhyay, A., Chamola, V., & Herencsar, N. (2024). Confluence of digital twins and metaverse for consumer electronics: Real world case studies. IEEE Transactions on Consumer Electronics, 70(1), 3194‒3203. DOI: https://doi.org/10.1109/TCE.2024.3351441
View in Google Scholar
Sarwatt, D. S., Lin, Y., Ding, J., Sun, Y., & Ning, H. (2024). Metaverse for intelligent transportation systems (ITS): A comprehensive review of technologies, applications, implications, challenges and future directions. IEEE Transactions on Intelligent Transportation Systems. DOI: https://doi.org/10.1109/TITS.2023.3347280
View in Google Scholar
Starly, B., Koprov, P., Bharadwaj, A., Batchelder, T., & Breitenbach, B. (2023). ‘Unreal’ factories: Next generation of digital twins of machines and factories in the industrial metaverse. Manufacturing Letters, 37, 50‒52. DOI: https://doi.org/10.1016/j.mfglet.2023.07.021
View in Google Scholar
Stary, C. (2023). Digital process twins as intelligent design technology for engineering metaverse/XR applications. Sustainability, 15(22), 16062. DOI: https://doi.org/10.3390/su152216062
View in Google Scholar
Stavroulakis, G. E., Charalambidi, B. G., & Koutsianitis, P. (2022). Review of computational mechanics, optimization, and machine learning tools for digital twins applied to infrastructures. Applied Sciences, 12(23), 11997. DOI: https://doi.org/10.3390/app122311997
View in Google Scholar
Tantawi, K., Fidan, I., Huseynov, O., Musa, Y., & Tantawy, A. (2024). Advances in industry 4.0: From intelligentization to the industrial metaverse. International Journal on Interactive Design and Manufacturing. DOI: https://doi.org/10.1007/s12008-024-01750-0
View in Google Scholar
Tlili, A., Huang, R., & Kinshuk (2023). Metaverse for climbing the ladder toward ‘Industry 5.0’ and ‘Society 5.0’? Service Industries Journal, 43(3/4), 260‒287. DOI: https://doi.org/10.1080/02642069.2023.2178644
View in Google Scholar
Tuli, E. A., Lee; J.-M., & Kim, D.-S. (2024). Integration of quantum technologies into metaverse: Applications, potentials, and challenges. IEEE Access, 12, 29995–30019. DOI: https://doi.org/10.1109/ACCESS.2024.3366527
View in Google Scholar
Wang, X., Wang, Y., Yang, J., Jia, X., Li, L., Ding, W., & Wang, F. Y. (2024). The survey on multi-source data fusion in cyber-physical-social systems: Foundational infrastructure for industrial metaverses and industries 5.0. Information Fusion, 107, 102321. DOI: https://doi.org/10.1016/j.inffus.2024.102321
View in Google Scholar
Wang, Y., Tian, Y., Wang, J., Cao, Y., Li, S., & Tian, B. (2022). Integrated inspection of QoM, QoP, and QoS for AOI industries in metaverses. IEEE/CAA Journal of Automatica Sinica, 9(12), 2071‒2078. DOI: https://doi.org/10.1109/JAS.2022.106091
View in Google Scholar
Xinyi, T., Juuso, A., Riku, A.-L., Chao, Y., Pauli, S., & Kari, T. (2023). TwinXR: method for using digital twin descriptions in industrial eXtended reality applications. Frontiers in Virtual Reality, 4, 1019080. DOI: https://doi.org/10.3389/frvir.2023.1019080
View in Google Scholar
Yang, J., Wang, X., & Zhao, Y. (2022). Parallel manufacturing for industrial metaverses: A new paradigm in smart manufacturing. IEEE/CAA Journal of Automatica Sinica, 9(12), 2063‒2070. DOI: https://doi.org/10.1109/JAS.2022.106097
View in Google Scholar
Yao, X., Ma, N., Zhang, J., Wang, K., Yang, E., & Faccio, M. (2024). Enhancing wisdom manufacturing as industrial metaverse for industry and society 5.0. Journal of Intelligent Manufacturing, 35, 235–255. DOI: https://doi.org/10.1007/s10845-022-02027-7
View in Google Scholar
Zaidan, A. A., Alsattar, H. A., Qahtan, S., Deveci, M., Pamucar, D., & Hajiaghaei-Keshteli, M. (2023). Uncertainty decision modeling approach for control engineering tools to support industrial cyber-physical metaverse smart manufacturing systems. IEEE Systems Journal, 17(4), 5303‒5314. DOI: https://doi.org/10.1109/JSYST.2023.3266842
View in Google Scholar
Zhang, L., Du, Q., Lu, L., & Zhang, S. (2023). Overview of the integration of communications, sensing, computing, and storage as enabling technologies for the metaverse over 6G networks. Electronics, 12(17), 3651. DOI: https://doi.org/10.3390/electronics12173651
View in Google Scholar
Zheng, T., Grosse, E. H., Morana, S., & Glock, C. H. (2024). A review of digital assistants in production and logistics: applications, benefits, and challenges. International Journal of Production Research. DOI: https://doi.org/10.1080/00207543.2024.2330631
View in Google Scholar
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