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Emerging Enabling Technologies for Industry 4.0 and Beyond

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Abstract

Rapid advances in technology have spurred tremendous progress in developing the next generation of Industry 4.0 that was initially introduced in 2011 as a German strategic initiative for revolutionizing the manufacturing sector. Ten years have passed since 2011. In these ten years, numerous new and promising technologies and applications have been developed. The original concept of Industry 4.0, including the conceptual framework, technology framework, and enabling technologies, has experienced tremendous changes. As such, the new generation of Industry 4.0 emerges, which is also called Industry 5.0. Today, we are on the cusp of the Industry 4.0 evolution supported by a new set of enabling technologies. In such evolution of Industry 4.0, future Industry 4.0 requires a combination of recently emerging new technologies, which is giving rise to the emergence of the next generation of Industry 4.0 or Industry 5.0. Such technologies originate from different disciplines, including Artificial Intelligence (AI), 5G/6G, Quantum Computing, and others. The technologies in the original Industry 4.0 framework, such as Cyber-Physical Systems, IoT, etc., will be affected by Artificial Intelligence (AI), 5G/6G, and Quantum Computing. At this present moment, the emergence of a new era of Industry 4.0 can be seen. In this paper, we briefly survey the main emerging enabling technologies in Industry 4.0 as it relates to industries.

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References

  • Airbus (2021a). https://www.airbus.com/innovation/industry-4-0/artificial-intelligence.html. Accessed 7 Dec 2021

  • Airbus (2021b). https://www.airbus.com/innovation/industry-4-0/quantum-technologies.html. Accessed 7 Dec 2021

  • Amaba, B., Cohen, P., Kessentini, M., Testani, M., & Yilmaz, E. (2020). Industry 4.0. and Artificial Intelligence as Industrial Engineering Professionals. In IIE Annual Conference. Proceedings (pp. 943–948). Institute of Industrial and Systems Engineers (IISE).

  • Bécue, A., Praça, I., & Gama, J. (2021). Artificial intelligence, cyber-threats and Industry 4.0: Challenges and opportunities. Artificial Intelligence Review, 1–38.

  • Breque, M. (2021). From Industry 4.0 to 5.0 (online only) - Benelux Section Chapter, TEM14 on 06-April-2021. https://www.ieee.be/?q=node/211

  • Chaudhry, S. S., Varano, M. W., & Xu, L. (2000). Systems research, genetic algorithms and information systems. Systems Research and Behavioral Science, 17(2), 149–162.

    Google Scholar 

  • Chen, Z. B., & Xu, L. D. (2001). An object-oriented intelligent CAD system for ceramic kiln. Knowledge-Based Systems, 14(5–6), 263–270.

    Google Scholar 

  • Chen, H., Li, L., & Chen, Y. (2021). Explore success factors that impact artificial intelligence adoption on telecom industry in China. Journal of Management Analytics, 8(1), 36–68

  • Chi-Hsien, K., & Nagasawa, S. (2019). Applying machine learning to market analysis: Knowing your luxury consumer. Journal of Management Analytics, 6(4), 404–419.

    Google Scholar 

  • Chowdhury, M. Z., Shahjalal, M., Ahmed, S., & Jang, Y. M. (2020). 6G wireless communication systems: Applications, requirements, technologies, challenges, and research directions. IEEE Open Journal of the Communications Society, 1, 957–975.

    Google Scholar 

  • Chun, K. W., Kim, H., & Lee, K. (2018). A study on research trends of technologies for industry 4.0; 3D printing, artificial intelligence, big data, cloud computing, and internet of things. In Advanced Multimedia and Ubiquitous Engineering (pp. 397–403). Springer.

  • Duan, L., & Xu, L. (2012). Business intelligence for enterprise systems: A survey. IEEE Transactions on Industrial Informatics, 8(3), 679–687.

    Google Scholar 

  • Duan, N., Liu, L. Z., Yu, X. J., Li, Q., & Yeh, S. C. (2019). Classification of multichannel surface-electromyography signals based on convolutional neural networks. Journal of Industrial Information Integration, 15, 201–206.

    Google Scholar 

  • Dudukalov, E. V., Munister, V. D., Zolkin, A. L., Losev, A. N., & Knishov, A. V. (2021). The use of artificial intelligence and information technology for measurements in mechanical engineering and in process automation systems in Industry 4.0. In Journal of Physics: Conference Series (Vol. 1889, No. 5, p. 052011). IOP Publishing.

  • Ericsson (2021). https://www.ericsson.com/en/industries/manufacturing/five-use-cases. Accessed 7 Dec 2021

  • EU (2006). Commission staff working document on Quantum Technologies. https://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1497617477882&uri=CELEX:52016SC0107. Accessed 7 Dec 2021

  • Feng, S., & Xu, L. D. (1996a). A hybrid knowledge-based system for urban development. Expert Systems with Applications, 10(1), 157–163.

    Google Scholar 

  • Feng, S., & Xu, L. D. (1996b). Integrating knowledge-based simulation with aspiration-directed model-based decision support system. Journal of Systems Engineering and Electronics, 7(2), 25–33.

    Google Scholar 

  • Feng, S., & Xu, L. D. (1997). An integrated knowledge-based system for urban planning decision support. Knowledge-Based Systems, 10(2), 103–109.

    Google Scholar 

  • Feng, S., & Xu, L. (1999a). An intelligent decision support system for fuzzy comprehensive evaluation of urban development. Expert Systems with Applications, 16(1), 21–32.

    Google Scholar 

  • Feng, S., & Xu, L. (1999b). Hybrid artificial intelligence approach to urban planning. Expert Systems, 16(4), 248–261.

    Google Scholar 

  • Feng, S., Li, L. X., & Cen, L. (2001). An object-oriented intelligent design tool to aid the design of manufacturing systems. Knowledge-Based Systems, 14(5–6), 225–232.

    Google Scholar 

  • Feng, S., Xu, L., Tang, C., & Yang, S. (2003). An intelligent agent with layered architecture for operating systems resource management. Expert Systems, 20(4), 171–178.

    Google Scholar 

  • Finogeev, A., Finogeev, А., Fionova, L., Lyapin, A., & Lychagin, K. A. (2019). Intelligent monitoring system for smart road environment. Journal of Industrial Information Integration, 15, 15–20.

    Google Scholar 

  • Gao, Q., Da Xu, L., & Liang, N. (2001). Dynamic modelling with an integrated ecological knowledge-based system. Knowledge-Based Systems, 14(5–6), 281–287.

    Google Scholar 

  • Guo, F., Yu, F., Zhang, H., Li, X., Ji, H., & Leung, C. (2021). Enabling massive IoT toward 6G: A comprehensive survey. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2021.3063686

  • Gyongyosi, L., & Imre, S. (2019). A survey on quantum computing technology. Computer Science Review, 31, 51–71.

    Google Scholar 

  • Haenlein, M., Kaplan, A., Tan, C. W., & Zhang, P. (2019). Artificial intelligence (AI) and management analytics. Journal of Management Analytics, 6(4), 341–343.

    Google Scholar 

  • Huang, B., Huan, Y., Xu, L. D., Zheng, L., & Zou, Z. (2019a). Automated trading systems statistical and machine learning methods and hardware implementation: A survey. Enterprise Information Systems, 13(1), 132–144.

    Google Scholar 

  • Huang, C., Cai, H., Xu, L., Xu, B., Gu, Y., & Jiang, L. (2019b). Data-driven ontology generation and evolution towards intelligent service in manufacturing systems. Future Generation Computer Systems, 101, 197–207.

    Google Scholar 

  • Ilchenko, M., Uryvsky, L., & Osypchuk, S. (2019). World trends of modern information and telecommunication technologies development. In 2019 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo) (pp. 1–7). IEEE.

  • Intel. (2021). Intel debuts 2nd-Gen Horse Ridge cryogenic quantum control chip. https://www.intc.com/news-events/press-releases/detail/1429/intel-debuts-2nd-gen-horse-ridge-cryogenic-quantum-control. Accessed 7 Dec 2021

  • Jiang, Y., Xu, L., Wang, H., & Wang, H. (2009). Influencing factors for predicting financial performance based on genetic algorithms. Systems Research and Behavioral Science, 26(6), 661–673.

    Google Scholar 

  • Jiang, W., Han, B., Habibi, M. A., & Schotten, H. D. (2021). The road towards 6G: A comprehensive survey. IEEE Open Journal of the Communications Society, 2, 334–366.

    Google Scholar 

  • Jin, C., Li, F., Wilamowska-Korsak, M., Li, L., & Fu, L. (2014). BSP-GA: A new genetic algorithm for system optimization and excellent Schema selection. Systems Research and Behavioral Science, 31(3), 337–352.

    Google Scholar 

  • Kang, Y., Cai, Z., Tan, C. W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139–172.

    Google Scholar 

  • Kim, J. H. (2021). 6G and internet of things: A survey. Journal of Management Analytics, 1–17.

  • Kim, D. H., Kim, T. J., Wang, X., Kim, M., Quan, Y. J., Oh, J. W., ... & Ahn, S. H. (2018). Smart machining process using machine learning: A review and perspective on machining industry. International Journal of Precision Engineering and Manufacturing-Green Technology, 5(4), 555–568.

  • Kim, D., Kang, J., Kim, T. W., Pan, Y., & Park, J. H. (2021). The future of quantum information: Challenges and vision. Journal of Information Processing Systems, 17(1), 151–162.

    Google Scholar 

  • Knight, P., & Walmsley, I. (2019). UK National Quantum technology programme. Quantum Science and Technology, 4(4), 040502.

    Google Scholar 

  • Kullaya Swamy, A., & Sarojamma, B. (2020). Bank transaction data modeling by optimized hybrid machine learning merged with ARIMA. Journal of Management Analytics, 7(4), 624–648

  • Kumar, S. S., Bale, A. S., Matapati, P. M., & Vinay, N. (2021). Conceptual Study of Artificial Intelligence in Smart Cities with Industry 4.0. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 575–577). IEEE.

  • Kurade, S. S., & Latpate, R. (2020). Demand and deterioration of items per unit time inventory models with shortages using genetic algorithm. Journal of Management Analytics, 8(3), 502–529

  • Law, K. S., & Chung, F. L. (2020). Knowledge-driven decision analytics for commercial banking. Journal of Management Analytics, 7(2), 209–230.

    Google Scholar 

  • Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018). Industrial artificial intelligence for industry 4.0-based manufacturing systems. Manufacturing letters, 18, 20–23.

    Google Scholar 

  • Li, L. (1999a). Proposing an architectural framework of a hybrid knowledge-based system for production rescheduling. Expert Systems, 16(4), 273–279.

    Google Scholar 

  • Li, L. (1999b). Knowledge-based problem solving: An approach to health assessment. Expert Systems with Applications, 16(1), 33–42.

    Google Scholar 

  • Li, L. (2018). China's manufacturing locus in 2025: With a comparison of “made-in-China 2025” and “industry 4.0”. Technological Forecasting and Social Change, 135, 66–74.

    Google Scholar 

  • Li, L. (2020). Education supply chain in the era of industry 4.0. Systems Research and Behavioral Science, 37(4), 579–592.

    Google Scholar 

  • Li, H., & Li, L. (1999). Representing diverse mathematical problems using neural networks in hybrid intelligent systems. Expert Systems, 16(4), 262–272.

    Google Scholar 

  • Li, H., & Xu, L. (2000). A neural network representation of linear programming. European Journal of Operational Research, 124(2), 224–234.

    Google Scholar 

  • Li, T., Feng, S., & Li, L. X. (2001). Information visualization for intelligent decision support systems. Knowledge-Based Systems, 14(5–6), 259–262.

    Google Scholar 

  • Li, J., Li, L., Tang, L., & Wu, H. (2006). A case of rule-based heuristics for scheduling hot rolling seamless steel tube production. Expert Systems, 23(3), 145–158.

    Google Scholar 

  • Li, F., Xu, L., Jin, C., & Wang, H. (2011a). Intelligent bionic genetic algorithm (IB-GA) and its convergence. Expert Systems with Applications, 38(7), 8804–8811.

    Google Scholar 

  • Li, F., Xu, L., Jin, C., & Wang, H. (2011b). Structure of multi-stage composite genetic algorithm (MSC-GA) and its performance. Expert Systems with Applications, 38(7), 8929–8937.

    Google Scholar 

  • Li, F., Xu, L., Jin, C., & Wang, H. (2012). Random assignment method based on genetic algorithms and its application in resource allocation. Expert Systems with Applications, 39(15), 12213–12219.

    Google Scholar 

  • Lu, Y. (2019). Artificial intelligence: A survey on evolution, models, applications and future trends. Journal of Management Analytics, 6(1), 1–29.

    Google Scholar 

  • Lu, Y., & Ning, X. (2020). A vision of 6G–5G's successor. Journal of Management Analytics, 7(3), 301–320.

    Google Scholar 

  • Lu, L., Xu, L., Xu, B., Li, G., & Cai, H. (2018). Fog computing approach for music cognition system based on machine learning algorithm. IEEE Transactions on Computational Social Systems, 5(4), 1142–1151.

    Google Scholar 

  • Malhotra, D., & Rishi, O. P. (2019). A comprehensive review from hyperlink to intelligent technologies based personalized search systems. Journal of Management Analytics, 6(4), 365–389.

    Google Scholar 

  • Masahiro, T. (2021). Future of quantum ICT and its impact on our social life. NICT News, 486(2), 1–3.

    Google Scholar 

  • Mazurek, G., & Małagocka, K. (2019). Perception of privacy and data protection in the context of the development of artificial intelligence. Journal of Management Analytics, 6(4), 344–364.

    Google Scholar 

  • Mehrabi, A., Siekkinen, M., & Ylä-Jääski, A. (2019). Energy-aware QoE and backhaul traffic optimization in green edge adaptive mobile video streaming. IEEE Transactions on Green Communications and Networking, 3(3), 828–839.

    Google Scholar 

  • Merayo, D., Rodriguez-Prieto, A., & Camacho, A. M. (2019). Comparative analysis of artificial intelligence techniques for material selection applied to manufacturing in industry 4.0. Procedia Manufacturing, 41, 42–49.

    Google Scholar 

  • Panigrahi, B. K., Nath, T. K., & Senapati, M. R. (2019). An application of local linear radial basis function neural network for flood prediction. Journal of Management Analytics, 6(1), 67–87.

    Google Scholar 

  • Peres, R. S., Jia, X., Lee, J., Sun, K., Colombo, A. W., & Barata, J. (2020). Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE Access, 8, 220121–220139.

    Google Scholar 

  • Pradhan, K., & Chawla, P. (2020). Medical internet of things using machine learning algorithms for lung cancer detection. Journal of Management Analytics, 7(4), 591–623.

    Google Scholar 

  • Qiu, G. F., Li, H. Z., Xu, L. D., & Zhang, W. X. (2003). A knowledge processing method for intelligent systems based on inclusion degree. Expert Systems, 20(4), 187–195.

    Google Scholar 

  • Quantum Matter Institute (2021). National Quantum strategy funded in Government of Canada Budget. https://qmi.ubc.ca/news/apr-21-2021/national-quantum-strategy-funded-government-canada-budget. Accessed 7 Dec 2021

  • Ray, P. P., Kumar, N., & Guizani, M. (2021). A vision on 6G-enabled NIB: Requirements, technologies, deployments and prospects. IEEE Wireless Communications. https://doi.org/10.1109/MWC.001.2000384

  • Rosatom. (2021). Russia sets up National Quantum Lab. https://rosatom.ru/en/press-centre/news/russia-sets-up-national-quantum-lab/. Accessed 7 Dec 2021

  • Saad, W., Bennis, M., & Chen, M. (2020). A vision of 6G wireless systems: Applications, trends, technologies, and open research problems. IEEE Network, 34(3), 134–142.

    Google Scholar 

  • Senekane, M., Maseli, M., & Taele, M. B. (2020). Noisy, intermediate-scale quantum computing and industrial revolution 4.0. Lecture Notes in Electrical Engineering, 674, 205–225.

    Google Scholar 

  • Shi, S., Xu, L. D., & Liu, B. (1996). Applications of artificial neural networks to the nonlinear combination of forecasts. Expert Systems, 13(3), 195–201.

    Google Scholar 

  • Shi, S. M., Xu, L., & Liu, B. (1999). Improving the accuracy of nonlinear combined forecasting using neural networks. Expert Systems with Applications, 16(1), 49–54.

    Google Scholar 

  • Slalmi, A., Chaibi, H., Chehri, A., Saadane, R., & Jeon, G. (2021). Toward 6G: Understanding network requirements and key performance indicators. Transactions on Emerging Telecommunications Technologies, 32(3), e4201.

    Google Scholar 

  • Spiller, T. P. (2003). Quantum information technology. Materials Today, 6(1), 30–36.

    Google Scholar 

  • Stanford Institute for Human-Centered Artificial Intelligence (2020). The AI Index Report. Available: https://hai.stanford.edu/sites/default/files/ai_index_2019_report.pdf Accessed 7 April 2020.

  • Sun, B., Da Xu, L., Pei, X., & Li, H. (2003). Scenario-based knowledge representation in case-based reasoning systems. Expert Systems, 20(2), 92–99.

    Google Scholar 

  • Tan, W., Shen, W., Xu, L., Zhou, B., & Li, L. (2008). A business process intelligence system for enterprise process performance management. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(6), 745–756.

    Google Scholar 

  • Tung, K. (2019). AI, the internet of legal things, and lawyers. Journal of Management Analytics, 6(4), 390–403.

    Google Scholar 

  • Turing, A. M. (1950). I.—Computing machinery and intelligence. Mind, LIX(236), 433–460. https://doi.org/10.1093/mind/LIX.236.433

    Article  Google Scholar 

  • US Congress (2018) H.R.6227 - National Quantum Initiative Act. https://www.congress.gov/bill/115th-congress/house-bill/6227. Accessed 7 Dec 2021

  • Villalba-Diez, J., & Zheng, X. (2020). Quantum strategic organizational design: Alignment in industry 4.0 complex-networked cyber-physical lean management systems. Sensors, 20(20), 5856.

    Google Scholar 

  • Wang, Y., Li, H., Warfield, J., & Xu, L. (2006). Knowledge management in the ERP era. Systems Research and Behavioral Science, 23(2), 125–129.

    Google Scholar 

  • Wang, L., Xu, L., Wang, X., You, W. J., & Tan, W. (2009). Knowledge portal construction and resources integration for a large scale hydropower dam. Systems Research and Behavioral Science, 26(3), 357–366.

    Google Scholar 

  • Wang, P., Xu, L., Zhou, S. M., Fan, Z., Li, Y., & Feng, S. (2010). A novel Bayesian learning method for information aggregation in modular neural networks. Expert Systems with Applications, 37(2), 1071–1074.

    Google Scholar 

  • Wang, P., Zhang, J., Xu, L., Wang, H., Feng, S., & Zhu, H. (2011). How to measure adaptation complexity in evolvable systems–a new synthetic approach of constructing fitness functions. Expert Systems with Applications, 38(8), 10414–10419.

    Google Scholar 

  • Wanigasekara, C., Oromiehie, E., Swain, A., Prusty, B. G., & Nguang, S. K. (2021). Machine learning-based inverse predictive model for AFP based thermoplastic composites. Journal of Industrial Information Integration, 22, 100197100197.

    Google Scholar 

  • Xu, L. (1995). Case-based reasoning. IEEE Potentials, 13(5), 10–13.

    Google Scholar 

  • Xu, L. (1996). An integrated rule-and case-based approach to AIDS initial assessment. International Journal of Bio-Medical Computing, 40(3), 197–207.

    Google Scholar 

  • Xu, L. (2016). Inaugural Issue Editorial. Journal of Industrial Information Integration, 1, 1–2. https://doi.org/10.1016/j.jii.2016.04.001

  • Xu, L. (2020). Industry 4.0-Frontiers of the fourth industrial revolution. Systems Research and Behavioral Science, 37(4), 531–534.

    Google Scholar 

  • Xu, L. (2021). Special issue on system research on artificial intelligence. Systems Research and Behavioral Science, 2021. https://doi-org.proxy.lib.odu.edu/10.1002/sres.2776. Accessed 7 Dec 2021

  • Xu, L., Liang, N., & Gao, Q. (2001). An integrated knowledge-based system for grasslands ecosystems. Knowledge-Based Systems, 14(5–6), 271–280.

    Google Scholar 

  • Xu, L., Wang, C., Luo, X., & Shi, Z. (2006). Integrating knowledge management and ERP in enterprise information systems. Systems Research and Behavioral Science, 23(2), 147–156.

    Google Scholar 

  • Xu, L., Liang, N., & Gao, Q. (2008). An integrated approach for agricultural ecosystem management. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 38(4), 590–599.

    Google Scholar 

  • Xu, L., Cai, L., Zhao, S., & Ge, B. (2016). Editorial: Inaugural Issue. Journal of Industrial Integration and Management, 1(1).

  • Xu, L., Xu, E., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962.

    Google Scholar 

  • Yang, B., Li, L. X., Xie, Q., & Xu, J. (2001). Development of a KBS for managing bank loan risk. Knowledge-Based Systems, 14(5–6), 299–302.

    Google Scholar 

  • Yuan, R., Li, Z., Guan, X., & Xu, L. (2010). An SVM-based machine learning method for accurate internet traffic classification. Information Systems Frontiers, 12(2), 149–156.

    Google Scholar 

  • Zhang, C., & Lu, Y. (2021). Study on Artificial Intelligence: The State of the Art and Future Prospects. Journal of Industrial Information Integration, 100224

  • Zhang, L., Liang, Y. C., & Niyato, D. (2019a). 6G visions: Mobile ultra-broadband, super internet-of-things, and artificial intelligence. China Communications, 16(8), 1–14.

    Google Scholar 

  • Zhang, Q., Xu, F., Li, L., Liu, N. L., & Pan, J. W. (2019b). Quantum information research in China. Quantum Science and Technology, 4(4), 040503.

    Google Scholar 

  • Zhang, W., Xiang, Y., Liu, X., & Zhang, P. (2019c). Domain ontology development of knowledge base in cardiovascular personalized health management. Journal of Management Analytics, 6(4), 420–455.

    Google Scholar 

  • Zhang, Z., Xiao, Y., Ma, Z., Xiao, M., Ding, Z., Lei, X., Karagiannidis, G. K., & Fan, P. (2019d). 6G wireless networks: Vision, requirements, architecture, and key technologies. IEEE Vehicular Technology Magazine, 14(3), 28–41.

    Google Scholar 

  • Zhou, S., & Xu, L. D. (1999). Dynamic recurrent neural networks for a hybrid intelligent decision support system for the metallurgical industry. Expert Systems, 16(4), 240–247.

    Google Scholar 

  • Zhou, S. M., & Xu, L. (2001). A new type of recurrent fuzzy neural network for modeling dynamic systems. Knowledge-Based Systems, 14(5–6), 243–251.

    Google Scholar 

  • Zhou, S. M., Li, H. X., & Xu, L. D. (2003). A variational approach to intensity approximation for remote sensing images using dynamic neural networks. Expert Systems, 20(4), 163–170.

    Google Scholar 

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Authors and Affiliations

  1. Russian Technological University, Moscow, Russian Federation

    Leonid Ratkin

  2. Russian Academy of Sciences, Moscow, Russian Federation

    Leonid A. Ivanov

  3. Russian Academy of Engineering, Moscow, Russian Federation

    Li Da Xu

  4. Old Dominion University, Norfolk, VA, USA

    Alexander Sigov

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  1. Alexander Sigov
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Sigov, A., Ratkin, L., Ivanov, L.A. et al. Emerging Enabling Technologies for Industry 4.0 and Beyond. Inf Syst Front (2022). https://doi.org/10.1007/s10796-021-10213-w

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