Business and Social Sciences

Alabadi, M., Habbal, A., & Wei, X. (2022). Industrial Internet of Things: Requirements, architecture, challenges, and future research directions. IEEE Access, 10, 66374–66400. https://doi.org/10.1109/ACCESS.2022.3185049

Alexopoulos, K., Nikolakis, N., & Chryssolouris, G. (2020). Digital twin-driven supervised machine learning for the development of artificial intelligence applications in manufacturing. International Journal of Computer Integrated Manufacturing, 33(5), 429–439. https://doi.org/10.1080/0951192X.2020.1747642

Baker, L., & Sovacool, B. K. (2017). The political economy of technological capabilities and global production networks in South Africa’s wind and solar photovoltaic (PV) industries. Political Geography, 60, 1–12. https://doi.org/10.1016/j.polgeo.2017.03.003

Chen, Y. (2017). Integrated and intelligent manufacturing: Perspectives and enablers. Engineering, 3(5), 588–595. https://doi.org/10.1016/j.eng.2017.04.009

Çinar, Z. M., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in Industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211

Firouzi, F., Farahani, B., Daneshmand, M., Grise, K., Song, J., Saracco, R., Wang, L. L., Lo, K., Angelov, P., Soares, E., Loh, P., Talebpour, Z., Moradi, R., Goodarzi, M., Ashraf, H., Talebpour, M., Talebpour, A., Romeo, L., Das, R., … Luo, A. (2021). Harnessing the power of smart and connected health to tackle COVID-19: IoT, AI, robotics, and blockchain for a better world. IEEE Internet of Things Journal, 8(16), 12826–12846. https://doi.org/10.1109/JIOT.2021.3073904

Friesen, M., Wisniewski, L., & Jasperneite, J. (2022). Machine learning for zero-touch management in heterogeneous industrial networks: A review. In Proceedings of the IEEE International Workshop on Factory Communication Systems (WFCS) (pp. 1–8). https://doi.org/10.1109/WFCS53837.2022.9779183

Gupta, P., Krishna, C., Rajesh, R., Ananthakrishnan, A., Vishnuvardhan, A., Patel, S. S., Kapruan, C., Brahmbhatt, S., Kataray, T., Narayanan, D., Chadha, U., Alam, A., Selvaraj, S. K., Karthikeyan, B., Nagalakshmi, R., & Chandramohan, V. (2022). Industrial internet of things in intelligent manufacturing: A review, approaches, opportunities, open challenges, and future directions. International Journal on Interactive Design and Manufacturing (IJIDeM). https://doi.org/10.1007/s12008-022-01075-w

Haleem, A., Javaid, M., Singh, R. P., Rab, S., & Suman, R. (2021). Hyperautomation for the enhancement of automation in industries. Sensors International, 2, 100124. https://doi.org/10.1016/j.sintl.2021.100124

Hasidi, O., Abdelwahed, E. H., Qazdar, A., Boulaamail, A., Krafi, M., Benzakour, I., Bourzeix, F., Baïna, S., Baïna, K., Cherkaoui, M., & Bendaouia, A. (2022). Digital twins-based smart monitoring and optimisation of mineral processing industry. In Communications in Computer and Information Science (pp. 411–424). https://doi.org/10.1007/978-3-031-20490-6_33

Hu, W., Lim, K. Y. H., & Cai, Y. (2022). Digital twin and Industry 4.0 enablers in building and construction: A survey. Buildings, 12(11), 2004. https://doi.org/10.3390/buildings12112004

Jwo, J., Lee, C., & Lin, C. (2022). Data twin-driven cyber-physical factory for smart manufacturing. Sensors, 22(8), 2821. https://doi.org/10.3390/s22082821

Khalil, R. A., Saeed, N., Masood, M., Fard, Y. M., Alouini, M., & Al-Naffouri, T. Y. (2021). Deep learning in the industrial Internet of Things: Potentials, challenges, and emerging applications. IEEE Internet of Things Journal, 8(14), 11016–11040. https://doi.org/10.1109/JIOT.2021.3051414

Khan, S., Arslan, T., & Ratnarajah, T. (2022). Digital twin perspective of fourth industrial and healthcare revolution. IEEE Access, 10, 25732–25754. https://doi.org/10.1109/ACCESS.2022.3156062

Kor, M., Yitmen, I., & Alizadehsalehi, S. (2022). An investigation for integration of deep learning and digital twins towards Construction 4.0. Smart and Sustainable Built Environment, 12(3), 461–487. https://doi.org/10.1108/SASBE-08-2021-0148

Kumar, S. A. P., Madhumathi, R., Chelliah, P. R., Tao, L., & Wang, S. (2018). A novel digital twin-centric approach for driver intention prediction and traffic congestion avoidance. Journal of Reliable Intelligent Environments, 4(4), 199–209. https://doi.org/10.1007/s40860-018-0069-y

Lee, J., Ni, J., Singh, J., Jiang, B., Azamfar, M., & Feng, J. (2020). Intelligent maintenance systems and predictive manufacturing. Journal of Manufacturing Science and Engineering, 142(11), Article 110804. https://doi.org/10.1115/1.4047856

Li, Q., Yang, Y., & Jiang, P. (2022). Remote monitoring and maintenance for equipment and production lines on industrial Internet: A literature review. Machines, 11(1), 12. https://doi.org/10.3390/machines11010012

Mousavi, A., & Al-Raweshidy, H. (2021). Advanced digital twins for conditions monitoring, examinations, diagnosis and predictive remaining lifecycles based artificial intelligence [Technical report/Repository manuscript]. Brunel University Research Archive. http://bura.brunel.ac.uk/handle/2438/28074

Puranik, A., Dandekar, P., & Jain, R. (2022). Exploring the potential of machine learning for more efficient development and production of biopharmaceuticals. Biotechnology Progress, 38(6), e3291. https://doi.org/10.1002/btpr.3291

Sahal, R., Alsamhi, S. H., Brown, K. N., O’Shea, D., McCarthy, C., & Guizani, M. (2021). Blockchain-empowered digital twins collaboration: Smart transportation use case. Machines, 9(9), 193. https://doi.org/10.3390/machines9090193

Sampaio, R. P., Costa, A. A., & Flores-Colen, I. (2022). A systematic review of artificial intelligence applied to facility management in the building information modeling context and future research directions. Buildings, 12(11), 1939. https://doi.org/10.3390/buildings12111939

Santhi, A. R., & Muthuswamy, P. (2022). Pandemic, war, natural calamities, and sustainability: Industry 4.0 technologies to overcome traditional and contemporary supply chain challenges. Logistics, 6(4), 81. https://doi.org/10.3390/logistics6040081

Shahat, E., Hyun, C. T., & Yeom, C. (2021). City digital twin potentials: A review and research agenda. Sustainability, 13(6), 3386. https://doi.org/10.3390/su13063386

Suhail, S., Hussain, R., Jurdak, R., Oracevic, A., Salah, K., Hong, C. S., & Matulevicius, R. (2022). Blockchain-based digital twins: Research trends, issues, and future challenges. ACM Computing Surveys, 54(11s), 1–34. https://doi.org/10.1145/3517189

Tabrizi, M. K., Famiglietti, J., Bonalumi, D., & Campanari, S. (2023). The carbon footprint of hydrogen produced with state-of-the-art photovoltaic electricity using life-cycle assessment methodology. Energies, 16(13), 5190. https://doi.org/10.3390/en16135190

Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., Todd, M. D., Mahadevan, S., Hu, C., & Hu, Z. (2022). A comprehensive review of digital twin—Part 1: Modeling and twinning enabling technologies. Structural and Multidisciplinary Optimization, 65(12), Article 361. https://doi.org/10.1007/s00158-022-03425-4

Tuli, T. B., Kohl, L., Chala, S. A., Manns, M., & Ansari, F. (2021). Knowledge-based digital twin for predicting interactions in human–robot collaboration. In Proceedings of the IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). https://doi.org/10.1109/ETFA45728.2021.9613342

Tyagi, A. K., Fernandez, T. F., Mishra, S., & Kumari, S. (2021). Intelligent automation systems at the core of Industry 4.0. In Advances in Intelligent Systems and Computing (pp. 1–18). https://doi.org/10.1007/978-3-030-71187-0_1

Vermesan, O., Bahr, R., Ottella, M., Serrano, M., Karlsen, T., Wahlstrøm, T., Sand, H. E., Ashwathnarayan, M., & Gamba, M. T. (2020). Internet of robotic things intelligent connectivity and platforms. Frontiers in Robotics and AI, 7, 104. https://doi.org/10.3389/frobt.2020.00104

Woschank, M., Rauch, E., & Zsifkovits, H. (2020). A review of further directions for artificial intelligence, machine learning, and deep learning in smart logistics. Sustainability, 12(9), 3760. https://doi.org/10.3390/su12093760

Zhang, F., & Gallagher, K. S. (2016). Innovation and technology transfer through global value chains: Evidence from China’s PV industry. Energy Policy, 94, 191–203. https://doi.org/10.1016/j.enpol.2016.04.014

Zhang, X., Dumbrell, R., Li, W., Xu, M., Yan, D., Jin, J., Wang, Z., Zheng, P., Liu, C., & Yang, J. (2022). Mass production of crystalline silicon solar cells with polysilicon-based passivating contacts: An industrial perspective. Progress in Photovoltaics: Research and Applications, 31(4), 369–379. https://doi.org/10.1002/pip.3618