2511002373
  • Open Access
  • Review

Part I: Industrial Information Integration Review 2020–2025

  • Jinzhi Li

Received: 14 Jul 2025 | Revised: 13 Oct 2025 | Accepted: 24 Nov 2025 | Published: 06 Jan 2026

Abstract

Industrial Information Integration Engineering (IIIE) has become increasingly essential for improving operational efficiency and harmonizing heterogeneous industrial systems through advanced digital integration approaches. Fueled by rapid advancements in Industry 4.0 technologies—including digital twins, artificial intelligence, immersive interfaces, and IoT infrastructures—IIIE is substantially transforming traditional enterprise architecture and integration frameworks. This systematic review synthesizes recent developments and emerging trends, with particular attention to the accelerating adoption of digital twins and the deepening convergence between operational technologies (OT) and information technologies (IT) across multiple sectors. While notable progress has been made, significant challenges persist, especially in developing resilient integration architectures and fully capitalizing on emerging capabilities such as quantum computing and next-generation communication networks. Future research directions emphasize the need to advance semantic interoperability, promote human-centric integration paradigms, and strengthen secure, decentralized information infrastructures. Collectively, these directions highlight IIIE’s pivotal role in enabling intelligent, interconnected, and sustainable industrial ecosystems.

References 

  • 1.

    Xu, L.D. Enterprise Integration and Information Architecture: A Systems Perspective on Industrial Information Integration; CRC Press: Boca Raton, FLL, USA, 2015.

  • 2.

    Harrison, R.; Vera, D.A.; Ahmad, B. A Connective Framework to Support the Lifecycle of Cyber–Physical Production Systems. Proc. IEEE 2021, 109, 568–581. https://doi.org/10.1109/JPROC.2020.3046525.

  • 3.

    Smadi, A.A.; Ajao, B.T.; Johnson, B.K.; et al. A Comprehensive Survey on Cyber-Physical Smart Grid Testbed Architectures: Requirements and Challenges. Electronics 2021, 10, 1043. https://doi.org/10.3390/electronics10091043.

  • 4.

    Xu, L.D.; Lu, Y.; Li, L. Embedding Blockchain Technology into IoT for Security: A Survey. IEEE Internet Things J. 2021, 8, 10452–10473. https://doi.org/10.1109/JIOT.2021.3060508.

  • 5.

    Xu, L.D.; Xu, E.L.; Li, L. Industry 4.0: State of the Art and Future Trends. Int. J. Prod. Res. 2018, 56, 2941–2962.

  • 6.

    Xu, L.-d. Engineering Informatics: State of the Art and Future Trends. Front. Eng. Manag. 2014, 1, 270–282. https://doi.org/10.15302/J-FEM-2014038.

  • 7.

    Li, L. Education Supply Chain in the Era of Industry 4.0. Syst. Res. Behav. Sci. 2020, 37, 579–592. https://doi.org/10.1002/sres.2702.

  • 8.

    Li, L.; Duan, L. Human Centric Innovation at the Heart of Industry 5.0—Exploring Research Challenges and Opportunities. Int. J. Prod. Res. 2025, 1–33. https://doi.org/10.1080/00207543.2025.2462657.

  • 9.

    Sun, Y.; Li, L.; Yu, Z.; et al. Exploring AI Models and Applications within a System Framework. Syst. Res. Behav. Sci. 2025, 42, 1163–1180. https://doi.org/10.1002/sres.3036.

  • 10.

    Ustaoglu, E.; Aydınoglu, A.C. Suitability Evaluation of Urban Construction Land in Pendik District of Istanbul, Turkey. Land Use Policy 2020, 99, 104783. https://doi.org/10.1016/j.landusepol.2020.104783.

  • 11.

    Geng, R.; Li, M.; Hu, Z.; et al. Digital Twin in Smart Manufacturing: Remote Control and Virtual Machining Using VR and AR Technologies. Struct. Multidiscipl. Optim. 2022, 65, 321. https://doi.org/10.1007/s00158-022-03426-3.

  • 12.

    Patera, L.; Garbugli, A.; Bujari, A.; et al. A Layered Middleware for OT/IT Convergence to Empower Industry 5.0 Applications. Sensors 2021, 22, 190. https://doi.org/10.3390/s22010190.

  • 13.

    Chen, Y. A Survey on Industrial Information Integration 2016–2019. J. Ind. Integr. Manag. 2020, 5, 33–163. https://doi.org/10.1142/S2424862219500167.

  • 14.

    Falagas, M.E.; Kouranos, V.D.; Arencibia-Jorge, R.; et al. Comparison of SCImago Journal Rank Indicator with Journal Impact Factor. FASEB J. 2008, 22, 2623–2628. https://doi.org/10.1096/fj.08-107938.

  • 15.

    González-Pereira, B.; Guerrero-Bote, V.P.; Moya-Anegón, F. A New Approach to the Metric of Journals' Scientific Prestige: The SJR Indicator. J. Informetr. 2010, 4, 379–391. https://doi.org/10.1016/j.joi.2010.03.002.

  • 16.

    Shen, B.; Xu, L.D.; Cai, H.; et al. Enhancing Context-Aware Reactive Planning for Unexpected Situations of On-Orbit Spacecraft. IEEE Trans. Aerosp. Electron. Syst. 2022, 58, 4965–4983. https://doi.org/10.1109/TAES.2022.3172022.

  • 17.

    Wong, K.K.L.; Chipusu, K.; Ashraf, M.A.; et al. In-Space Cybernetical Intelligence Perspective on Informatics, Manufacturing and Integrated Control for the Space Exploration Industry. J. Ind. Inf. Integr. 2024, 42, 100724. https://doi.org/10.1016/j.jii.2024.100724.

  • 18.

    Tang, Y.M.; Ip, W.H.; Yung, K.L.; et al. Industrial Information Integration in Deep Space Exploration and Exploitation: Architecture and Technology. J. Ind. Inf. Integr. 2024, 42, 100721. https://doi.org/10.1016/j.jii.2024.100721.

  • 19.

    Ye, H.; Wang, Y.; Zhang, Y.; et al. Digital Transformation of Agriculture: A New Integrated Modeling Framework for Arable Farm Enterprises. Comput. Electron. Agric. 2023, 212, 108041. https://doi.org/10.1016/j.compag.2023.108041.

  • 20.

    Alabugin, A.; Osintsev, K.; Aliukov, S. IIoT Based Multimodal Communication Model for Agriculture and Agro-Industries. IEEE Access 2021, 9, 10070–10088. https://doi.org/10.1109/ACCESS.2021.3050391.

  • 21.

    Chen, J.; Chen, T.; Cao, Y.; et al. Information-Integration-Based Optimal Coverage Path Planning of Agricultural Unmanned Systems Formations: From Theory to Practice. J. Ind. Inf. Integr. 2024, 40, 100617. https://doi.org/10.1016/j.jii.2024.100617.

  • 22.

    Fuentes-Peñailillo, F.; Silva, G.C.; Guzmán, R.P.; et al. Automating Seedling Counts in Horticulture Using Computer Vision and AI. Horticulturae 2023, 9, 1134. https://doi.org/10.3390/horticulturae9101134.

  • 23.

    Milella, A.; Rilling, S.; Rana, A.; et al. Robot-as-a-Service as a New Paradigm in Precision Farming. IEEE Access 2024, 12, 47942–47949. https://doi.org/10.1109/ACCESS.2024.3381511.

  • 24.

    Chaves, M.E.D.; Alves, M.D.C.; Sáfadi, T.; et al. Time-Weighted Dynamic Time Warping Analysis for Mapping Interannual Cropping Practices Changes in Large-Scale Agro-Industrial Farms in Brazilian Cerrado. Sci. Remote Sens. 2021, 3, 100021. https://doi.org/10.1016/j.srs.2021.100021.

  • 25.

    Victor, N.; Maddikunta, P.K.R.; Mary, D.R.K.; et al. Remote Sensing for Agriculture in the Era of Industry 5.0—A Survey. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2024, 17, 5920–5945. https://doi.org/10.1109/JSTARS.2024.3370508.

  • 26.

    Barbosa, V.; Sabino, A.; Lima, L.N.; et al. Performance Evaluation of IoT-Based Industrial Automation Using Edge, Fog, and Cloud Architectures. J. Netw. Syst. Manag. 2025, 33, 15. https://doi.org/10.1007/s10922-024-09893-x.

  • 27.

    Sharma, R.; Kamble, S.; Mani, V.; et al. An Empirical Investigation of the Influence of Industry 4.0 Technology Capabilities on Agriculture Supply Chain Integration and Sustainable Performance. IEEE Trans. Eng. Manag. 2024, 71, 12364–12384. https://doi.org/10.1109/TEM.2022.3192537.

  • 28.

    Ameri, F.; Wallace, E.; Yoder, R.; et al. Enabling Traceability in Agri-Food Supply Chains Using an Ontological Approach. J. Comput. Inf. Sci. Eng. 2022, 22, 051002. https://doi.org/10.1115/1.4054092.

  • 29.

    Hualpa Zúñiga, A.M.; Rangel Díaz, J.E. Trazabilidad En El Sector Agrícola: Una Revisión Para El Periodo 2017–2022. Agron. Mesoam. 2023, 34, 51828. https://doi.org/10.15517/am.v34i2.51828.

  • 30.

    Figorilli, S.; Violino, S.; Moscovini, L.; et al. Olive Fruit Selection through AI Algorithms and RGB Imaging. Foods 2022, 11, 3391. https://doi.org/10.3390/foods11213391.

  • 31.

    Yao, H.; Shu, L.; Lin, W.; et al. Pests Phototactic Rhythm Driven Solar Insecticidal Lamp Device Evolution: Mathematical Model Preliminary Result and Future Directions. IEEE Open J. Ind. Electron. Soc. 2024, 5, 236–250. https://doi.org/10.1109/OJIES.2024.3372577.

  • 32.

    Krupitzer, C.; Stein, A. Food Informatics—Review of the Current State-of-the-Art, Revised Definition, and Classification into the Research Landscape. Foods 2021, 10, 2889. https://doi.org/10.3390/foods10112889.

  • 33.

    Xu, H.; Liu, X.; Yu, W.; et al. Reinforcement Learning-Based Control and Networking Co-Design for Industrial Internet of Things. IEEE J. Sel. Areas Commun. 2020, 38, 885–898. https://doi.org/10.1109/JSAC.2020.2980909.

  • 34.

    Liu, X.; Zhu, J.; Zhu, Z.; et al. CBS-YOLOv5: Fault Detection Algorithm of Electrolyzer Plate with Low-Resolution Infrared Images Based on Improved YOLOv5. Meas. Sci. Technol. 2025, 36, 016202. https://doi.org/10.1088/1361-6501/ad8254.

  • 35.

    Cheng, S.; Han, Y.; Wang, Z.; et al. An Underwater Object Recognition System Based on Improved YOLOv11. Electronics 2025, 14, 201. https://doi.org/10.3390/electronics14010201.

  • 36.

    Zhou, S.; Zhao, J.; Shi, Y.S.; et al. Research on Improving YOLOv5s Algorithm for Fabric Defect Detection. Int. J. Cloth. Sci. Technol. 2023, 35, 88–106. https://doi.org/10.1108/IJCST-11-2021-0165.

  • 37.

    Vengaloor, R.; Muralidhar, R. Deep Learning Based Feature Discriminability Boosted Concurrent Metal Surface Defect Detection System Using YOLOv-5s-FRN. Int. Arab J. Inf. Technol. 2024, 21. https://doi.org/10.34028/iajit/21/1/9.

  • 38.

    Xiao, Q.; Huang, J.; Huang, Z.; et al. Transparent Component Defect Detection Method Based on Improved YOLOv7 Algorithm. Int. J. Pattern Recognit. Artif. Intell. 2023, 37, 2350030. https://doi.org/10.1142/S0218001423500301.

  • 39.

    Chen, Y.; Zhou, Z.; Liu, Y.; et al. Dynamic Grouping with a Self-Aware Computational Resource Allocation for Large-Scale Multi-Objective Optimization. IEEE Trans. Evol. Comput. 2025. https://doi.org/10.1109/TEVC.2025.3564335.

  • 40.

    Guan, Y.; Chen, Y.; Liu, Y.; et al. Real-Time Scheduling Framework for Multiagent Cooperative Logistics with Dynamic Supply Demands. IEEE Trans. Ind. Inform. 2025, 21, 3007–3016. https://doi.org/10.1109/TII.2024.3516131.

  • 41.

    Geda, M.W.; Tang, Y.M. Adaptive Hybrid Quantum-Classical Computing Framework for Deep Space Exploration Mission Applications. J. Ind. Inf. Integr. 2025, 44, 100803. https://doi.org/10.1016/j.jii.2025.100803.

  • 42.

    Xu, Y.; Yan, X.; Sun, B.; et al. Multireceptive Field Denoising Residual Convolutional Networks for Fault Diagnosis. IEEE Trans. Ind. Electron. 2022, 69, 11686–11696. https://doi.org/10.1109/TIE.2021.3125666.

  • 43.

    Niu, W.; Tan, W.; Jia, W.; et al. Method Toward Network Embedding Within Homogeneous Attributed Network Using Influential Node Diffusion-Aware. IEEE Trans. Comput. Soc. Syst. 2024, 11, 2620–2631. https://doi.org/10.1109/TCSS.2023.3276159.

  • 44.

    Miao, D.; Xu, R.; Dai, Y.; et al. Adaptive Scheduling of Robots in the Mixed Flow Workshop of Industrial Internet of Things. Int. J. Adv. Comput. Sci. Appl. 2024, 15. https://doi.org/10.14569/IJACSA.2024.0150511.

  • 45.

    Tan, W.; Niu, W.; Jia, W.; et al. TNSAR: Temporal Evolution Network Embedding Based on Structural and Attribute Retention. IEEE Trans. Serv. Comput. 2023, 16, 4418–4431. https://doi.org/10.1109/TSC.2023.3322588.

  • 46.

    Zou, B.; Wang, H.; Li, H.; et al. Predicting Stock Index Movement Using Twin Support Vector Machine as an Integral Part of Enterprise System. Syst. Res. Behav. Sci. 2022, 39, 428–439. https://doi.org/10.1002/sres.2862.

  • 47.

    Zhao, L.; Li, B.; Tan, W.; et al. Joint Coverage-Reliability for Budgeted Edge Application Deployment in Mobile Edge Computing Environment. IEEE Trans. Parallel Distrib. Syst. 2022, 33, 3760–3771. https://doi.org/10.1109/TPDS.2022.3166163.

  • 48.

    Younan, M.; Elhoseny, M.; Ali, A.E.-M.A.; et al. Data Reduction Model for Balancing Indexing and Securing Resources in the Internet-of-Things Applications. IEEE Internet Things J. 2021, 8, 5953–5972. https://doi.org/10.1109/JIOT.2020.3035248.

  • 49.

    Zhao, Y.; Liu, Q.; Xu, W.; et al. An Ontology Self-Learning Approach for CNC Machine Capability Information Integration and Representation in Cloud Manufacturing. J. Ind. Inf. Integr. 2022, 25, 100300. https://doi.org/10.1016/j.jii.2021.100300.

  • 50.

    Tian, Y.; Wang, H. Communication Information Exchange and Transmission Method of Industrial Internet of Things Based on Audio Information Hiding. Web Intell. 2023, 21, 127–137. https://doi.org/10.3233/WEB-220044.

  • 51.

    Zhao, L.; Tan, W.; Li, B.; et al. Joint Shareability and Interference for Multiple Edge Application Deployment in Mobile-Edge Computing Environment. IEEE Internet Things J. 2022, 9, 1762–1774. https://doi.org/10.1109/JIOT.2021.3088493.

  • 52.

    Villegas-Ch, W.; Govea, J.; Jaramillo-Alcazar, A. Tamper Detection in Industrial Sensors: An Approach Based on Anomaly Detection. Sensors 2023, 23, 8908. https://doi.org/10.3390/s23218908.

  • 53.

    Kumar, S.; Kumar, A.; Panda, B.S. Identifying Influential Nodes for Smart Enterprises Using Community Structure With Integrated Feature Ranking. IEEE Trans. Ind. Inform. 2023, 19, 703–711. https://doi.org/10.1109/TII.2022.3203059.

  • 54.

    Zhao, L.; Tan, W.; Li, B.; et al. Multiple Cooperative Task Assignment on Reliability-Oriented Social Crowdsourcing. IEEE Trans. Serv. Comput. 2022, 15, 3402–3416. https://doi.org/10.1109/TSC.2021.3103636.

  • 55.

    Wang, S.; Wang, Y.; Yang, B.; et al. Variational Bayesian Learning with Reliable Likelihood Approximation for Accurate Process Quality Evaluation. IEEE Trans. Ind. Inform. 2024, 20, 815–823. https://doi.org/10.1109/TII.2023.3264288.

  • 56.

    Mohedas, S.D.; Alcarria, F.J. La Integración de Inteligencia Artificial Generativa En El Flujo de Trabajo de Postproducción Audiovisual: El Caso de La Mesías (Movistar Plus+, 2023). Prisma Soc. 2025, 48, 96–121.

  • 57.

    Yi, Y.; Yan, Y.; Liu, X.; et al. Digital Twin-Based Smart Assembly Process Design and Application Framework for Complex Products and Its Case Study. J. Manuf. Syst. 2021, 58, 94–107. https://doi.org/10.1016/j.jmsy.2020.04.013.

  • 58.

    Mei, S.; Xie, Y.; Liu, J.; et al. Physics-Based Modeling and Intelligent Optimal Decision Method for Digital Twin System towards Sustainable CNC Equipment. Robot. Comput. Integr. Manuf. 2025, 95, 103028. https://doi.org/10.1016/j.rcim.2025.103028.

  • 59.

    Li, L.; Lei, B.; Mao, C. Digital Twin in Smart Manufacturing. J. Ind. Inf. Integr. 2022, 26, 100289. https://doi.org/10.1016/j.jii.2021.100289.

  • 60.

    Zhang, C.; Li, J.; Zhou, G.; et al. A Multi-Level Modelling and Fidelity Evaluation Method of Digital Twins for Creating Smart Production Equipment in Industry 4.0. Int. J. Prod. Res. 2024, 62, 3671–3689. https://doi.org/10.1080/00207543.2023.2246161.

  • 61.

    Maia, E.; Wannous, S.; Dias, T.; et al. Holistic Security and Safety for Factories of the Future. Sensors 2022, 22, 9915. https://doi.org/10.3390/s22249915.

  • 62.

    Zhang, S.; Yao, Z.; Liao, H.; et al. Endogenous Security-Aware Resource Management for Digital Twin and 6G Edge Intelligence Integrated Smart Park. China Commun. 2023, 20, 46–60. https://doi.org/10.23919/JCC.2023.02.004.

  • 63.

    Li, Y.; Huang, L.; Yu, Q.; et al. Optimization of Synchronization Frequencies and Offloading Strategies in MEC-Assisted Digital Twin Networks. IEEE Internet Things J. 2025, 12, 29203–29216. https://doi.org/10.1109/JIOT.2025.3563858.

  • 64.

    Hung, M.-H.; Lin, Y.-C.; Hsiao, H.-C.; et al. A Novel Implementation Framework of Digital Twins for Intelligent Manufacturing Based on Container Technology and Cloud Manufacturing Services. IEEE Trans. Autom. Sci. Eng. 2022, 19, 1614–1630. https://doi.org/10.1109/TASE.2022.3143832.

  • 65.

    Shi, Z.; Xie, Y.; Xue, W.; et al. Smart Factory in Industry 4.0. Syst. Res. Behav. Sci. 2020, 37, 607–617. https://doi.org/10.1002/sres.2704.

  • 66.

    Runji, J.M.; Lin, C.-Y. Markerless Cooperative Augmented Reality-Based Smart Manufacturing Double-Check System: Case of Safe PCBA Inspection Following Automatic Optical Inspection. Robot. Comput. Integr. Manuf. 2020, 64, 101957. https://doi.org/10.1016/j.rcim.2020.101957.

  • 67.

    Zhang, N. A Cloud-Based Platform for Big Data-Driven CPS Modeling of Robots. IEEE Access 2021, 9, 34667–34680. https://doi.org/10.1109/ACCESS.2021.3061477.

  • 68.

    Shahbazi, Z.; Byun, Y.-C. Improving Transactional Data System Based on an Edge Computing–Blockchain–Machine Learning Integrated Framework. Processes 2021, 9, 92. https://doi.org/10.3390/pr9010092.

  • 69.

    Seiger, R.; Malburg, L.; Weber, B.; et al. Integrating Process Management and Event Processing in Smart Factories: A Systems Architecture and Use Cases. J. Manuf. Syst. 2022, 63, 575–592. https://doi.org/10.1016/j.jmsy.2022.05.012.

  • 70.

    Mourad, M.H.; Nassehi, A.; Schaefer, D.; et al. Assessment of Interoperability in Cloud Manufacturing. Robot. Comput. Integr. Manuf. 2020, 61, 101832. https://doi.org/10.1016/j.rcim.2019.101832.

  • 71.

    Manogaran, G.; Alazab, M.; Shakeel, P.M.; et al. Blockchain Assisted Secure Data Sharing Model for Internet of Things Based Smart Industries. IEEE Trans. Reliab. 2022, 71, 348–358. https://doi.org/10.1109/TR.2020.3047833.

  • 72.

    Kim, H.; Lee, J.; Park, J.-G. SITRAN: Self-Supervised IDS With Transferable Techniques for 5G Industrial Environments. IEEE Internet Things J. 2024, 11, 35465–35476. https://doi.org/10.1109/JIOT.2024.3437448.

  • 73.

    Godor, I.; Luvisotto, M.; Ruffini, S.; et al. A Look Inside 5G Standards to Support Time Synchronization for Smart Manufacturing. IEEE Commun. Stand. Mag. 2020, 4, 14–21. https://doi.org/10.1109/MCOMSTD.001.2000010.

  • 74.

    Boudagdigue, C.; Benslimane, A.; Kobbane, A.; et al. Trust Management in Industrial Internet of Things. IEEE Trans. Inf. Forensics Secur. 2020, 15, 3667–3682. https://doi.org/10.1109/TIFS.2020.2997179.

  • 75.

    Volpe, G.; Mangini, A.M.; Fanti, M.P. An Architecture Combining Blockchain, Docker and Cloud Storage for Improving Digital Processes in Cloud Manufacturing. IEEE Access 2022, 10, 79141–79151. https://doi.org/10.1109/ACCESS.2022.3194264.

  • 76.

    Xiao, R.; Zhang, Y.; Cui, X.H.; et al. A Hybrid Task Crash Recovery Solution for Edge Computing in IoT-Based Manufacturing. IEEE Access 2021, 9, 106220–106231. https://doi.org/10.1109/ACCESS.2021.3068471.

  • 77.

    Bi, J.; Wu, R.; Yuan, H.; et al. Ontology-Based Semantic Reasoning for Multisource Heterogeneous Industrial Devices Using OPC UA. IEEE Internet Things J. 2025, 12, 25020–25032. https://doi.org/10.1109/JIOT.2025.3556934.

  • 78.

    Shirbazo, A.; Li, B.; Ata, S.; et al. A Guideline for the Standardization of Smart Manufacturing and the Role of RAMI 4.0 in Digitising the Industrial Sector. IEEE Internet Things J. 2025, 12, 19090–19118. https://doi.org/10.1109/JIOT.2025.3559929.

  • 79.

    Marek, L.; Granja, C.; Jakubek, J.; et al. Data Processing Engine (DPE): Data Analysis Tool for Particle Tracking and Mixed Radiation Field Characterization with Pixel Detectors Timepix. J. Instrum. 2024, 19, C04026. https://doi.org/10.1088/1748-0221/19/04/C04026.

  • 80.

    Park, H.; Shin, M.; Choi, G.; et al. Integration of an Exoskeleton Robotic System into a Digital Twin for Industrial Manufacturing Applications. Robot. Comput. Integr. Manuf. 2024, 89, 102746. https://doi.org/10.1016/j.rcim.2024.102746.

  • 81.

    Xia, K.; Saidy, C.; Kirkpatrick, M.; et al. Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding. Sensors 2021, 21, 4276. https://doi.org/10.3390/s21134276.

  • 82.

    Tsanousa, A.; Bektsis, E.; Kyriakopoulos, C.; et al. A Review of Multisensor Data Fusion Solutions in Smart Manufacturing: Systems and Trends. Sensors 2022, 22, 1734. https://doi.org/10.3390/s22051734.

  • 83.

    Huang, K.; Jia, G.; Jiao, Z.; et al. MSTAN: Multi-Scale Spatiotemporal Attention Network with Adaptive Relationship Mining for Remaining Useful Life Prediction in Complex Systems. Meas. Sci. Technol. 2024, 35, 125019. https://doi.org/10.1088/1361-6501/ad78f5.

  • 84.

    Sandini, G.; Sciutti, A.; Morasso, P. Artificial Cognition vs. Artificial Intelligence for next-Generation Autonomous Robotic Agents. Front. Comput. Neurosci. 2024, 18, 1349408. https://doi.org/10.3389/fncom.2024.1349408.

  • 85.

    Wan, J.; Li, X.; Dai, H.-N.; et al. Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges. Proc. IEEE 2021, 109, 377–398. https://doi.org/10.1109/JPROC.2020.3034808.

  • 86.

    Kwon, S.; Monnier, L.V.; Barbau, R.; et al. Enriching Standards-Based Digital Thread by Fusing as-Designed and as-Inspected Data Using Knowledge Graphs. Adv. Eng. Inform. 2020, 46, 101102. https://doi.org/10.1016/j.aei.2020.101102.

  • 87.

    Cao, H.; Yang, X.; Deng, R. Ontology-Based Holonic Event-Driven Architecture for Autonomous Networked Manufacturing Systems. IEEE Trans. Autom. Sci. Eng. 2021, 18, 205–215. https://doi.org/10.1109/TASE.2020.3025784.

  • 88.

    Ge, J.; Wang, F.; Sun, H.; et al. Research on the Maturity of Big Data Management Capability of Intelligent Manufacturing Enterprise. Syst. Res. Behav. Sci. 2020, 37, 646–662. https://doi.org/10.1002/sres.2707.

  • 89.

    Lee, C.K.M.; Huo, Y.Z.; Zhang, S.Z.; et al. Design of a Smart Manufacturing System with the Application of Multi-Access Edge Computing and Blockchain Technology. IEEE Access 2020, 8, 28659–28667. https://doi.org/10.1109/ACCESS.2020.2972284.

  • 90.

    Feidl, F.; Vogg, S.; Wolf, M.; et al. Process-wide Control and Automation of an Integrated Continuous Manufacturing Platform for Antibodies. Biotechnol. Bioeng. 2020, 117, 1367–1380. https://doi.org/10.1002/bit.27296.

  • 91.

    Barberi, G.; Benedetti, A.; Diaz-Fernandez, P.; et al. Integrating Metabolome Dynamics and Process Data to Guide Cell Line Selection in Biopharmaceutical Process Development. Metab. Eng. 2022, 72, 353–364. https://doi.org/10.1016/j.ymben.2022.03.015.

  • 92.

    Madani, M.; Lin, K.; Tarakanova, A. DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks. Int. J. Mol. Sci. 2021, 22, 13555. https://doi.org/10.3390/ijms222413555.

  • 93.

    Sharma, S.; Verma, P.; Agrawal, K. Harnessing the Potential of Fungal Xylanases: An Insight into Its Application and Technological Advancements. Ind. Crops Prod. 2024, 222, 119967. https://doi.org/10.1016/j.indcrop.2024.119967.

  • 94.

    Pearcy, N.; Garavaglia, M.; Millat, T.; et al. A Genome-Scale Metabolic Model of Cupriavidus Necator H16 Integrated with TraDIS and Transcriptomic Data Reveals Metabolic Insights for Biotechnological Applications. PLOS Comput. Biol. 2022, 18, e1010106. https://doi.org/10.1371/journal.pcbi.1010106.

  • 95.

    Saranya, S.; Thamanna, L.; Chellapandi, P. Unveiling the Potential of Systems Biology in Biotechnology and Biomedical Research. Syst. Microbiol. Biomanuf. 2024, 4, 1217–1238. https://doi.org/10.1007/s43393-024-00286-4.

  • 96.

    Mansueto, L.; Kretzschmar, T.; Mauleon, R.; et al. Building a Community-Driven Bioinformatics Platform to Facilitate Cannabis Sativa Multi-Omics Research. Gigabyte 2024, 2024, gigabyte137. https://doi.org/10.46471/gigabyte.137.

  • 97.

    Soares, R.; Azevedo, L.; Vasconcelos, V.; et al. Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation. J. Chem. Inf. Model. 2024, 64, 9576–9593. https://doi.org/10.1021/acs.jcim.4c00995.

  • 98.

    Neves, P.; McClure, K.; Verhoeven, J.; et al. Global Reactivity Models Are Impactful in Industrial Synthesis Applications. J. Cheminform. 2023, 15, 20. https://doi.org/10.1186/s13321-023-00685-0.

  • 99.

    Amirian, H.; Dalvand, K.; Ghiasvand, A. Seamless Integration of Internet of Things, Miniaturization, and Environmental Chemical Surveillance. Environ. Monit. Assess. 2024, 196, 582. https://doi.org/10.1007/s10661-024-12698-9.

  • 100.

    Moshood, T.D.; Rotimi, J.O.; Shahzad, W.; et al. Infrastructure Digital Twin Technology: A New Paradigm for Future Construction Industry. Technol. Soc. 2024, 77, 102519. https://doi.org/10.1016/j.techsoc.2024.102519.

  • 101.

    Madubuike, O.C.; Anumba, C.J.; Khallaf, R. A Review of Digital Twin Applications in Construction. J. Inf. Technol. Constr. 2022, 27, 145–172. https://doi.org/10.36680/j.itcon.2022.008.

  • 102.

    Jiang, Y.; Li, M.; Wu, W.; et al. Multi-Domain Ubiquitous Digital Twin Model for Information Management of Complex Infrastructure Systems. Adv. Eng. Inform. 2023, 56, 101951. https://doi.org/10.1016/j.aei.2023.101951.

  • 103.

    Sepasgozar, S.M.E. Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment. Buildings 2021, 11, 151. https://doi.org/10.3390/buildings11040151.

  • 104.

    Yitmen, I.; Almusaed, A.; Hussein, M.; et al. AI-Driven Digital Twins for Enhancing Indoor Environmental Quality and Energy Efficiency in Smart Building Systems. Buildings 2025, 15, 1030. https://doi.org/10.3390/buildings15071030.

  • 105.

    Wimmer, J.; Braml, T.; Kaiser, M. Digitale Zwillinge Für Brücken Mittlerer Stützweite—Pilotprojekt Brücke Schwindegg—Teil 2: Verwaltungsschale. Beton Stahlbetonbau 2024, 119, 160–168. https://doi.org/10.1002/best.202300096.

  • 106.

    Gourlis, G.; Kovacic, I. A Holistic Digital Twin Simulation Framework for Industrial Facilities: BIM-Based Data Acquisition for Building Energy Modeling. Front. Built Environ. 2022, 8, 918821. https://doi.org/10.3389/fbuil.2022.918821.

  • 107.

    Badenko, V.L.; Bolshakov, N.S.; Tishchenko, E.B.; et al. Integration of Digital Twin and BIM Technologies within Factories of the Future. Mag. Civ. Eng. 2021, 1, 10114. https://doi.org/10.34910/MCE.101.14.

  • 108.

    Yoon, S. Virtual Building Models in Built Environments. Dev. Built Environ. 2024, 18, 100453. https://doi.org/10.1016/j.dibe.2024.100453.

  • 109.

    Amin, K.; Mills, G.; Wilson, D. Key Functions in BIM-Based AR Platforms. Autom. Constr. 2023, 150, 104816. https://doi.org/10.1016/j.autcon.2023.104816.

  • 110.

    Brandín, R.; Abrishami, S. Information Traceability Platforms for Asset Data Lifecycle: Blockchain-Based Technologies. Smart Sustain. Built Environ. 2021, 10, 364–386. https://doi.org/10.1108/SASBE-03-2021-0042.

  • 111.

    Selvanesan, H.; Satanarachchi, N. Potential for Synergetic Integration of Building Information Modelling, Blockchain and Supply Chain Management in Construction Industry. J. Inf. Technol. Constr. 2023, 28, 662–691. https://doi.org/10.36680/j.itcon.2023.035.

  • 112.

    Khan, A.; Sepasgozar, S.; Liu, T.; et al. Integration of BIM and Immersive Technologies for AEC: A Scientometric-SWOT Analysis and Critical Content Review. Buildings 2021, 11, 126. https://doi.org/10.3390/buildings11030126.

  • 113.

    Kang, K.-Y.; Wang, X.; Wang, J.; et al. Utility of BIM-CFD Integration in the Design and Performance Analysis for Buildings and Infrastructures of Architecture, Engineering and Construction Industry. Buildings 2022, 12, 651. https://doi.org/10.3390/buildings12050651.

  • 114.

    Fonsati, A.; Cosentini, R.M.; Tundo, C.; et al. From Geotechnical Data to GeoBIM Models: Testing Strategies for an Ex-Industrial Site in Turin. Buildings 2023, 13, 2343. https://doi.org/10.3390/buildings13092343.

  • 115.

    Pan, M.; Wong, M.O.; Lam, C.C.; et al. Integrating Extended Reality and Robotics in Construction: A Critical Review. Adv. Eng. Inform. 2024, 62, 102795. https://doi.org/10.1016/j.aei.2024.102795.

  • 116.

    Nassereddine, H.; Veeramani, D.; Hanna, A.S. Design, Development, and Validation of an Augmented Reality-Enabled Production Strategy Process. Front. Built Environ. 2022, 8, 730098. https://doi.org/10.3389/fbuil.2022.730098.

  • 117.

    You, Z.; Feng, L. Integration of Industry 4.0 Related Technologies in Construction Industry: A Framework of Cyber-Physical System. IEEE Access 2020, 8, 122908–122922. https://doi.org/10.1109/ACCESS.2020.3007206.

  • 118.

    Zhang, S.; Li, Z.; Li, T.; et al. A Holistic Literature Review of Building Information Modeling for Prefabricated Construction. J. Civ. Eng. Manag. 2021, 27, 485–499. https://doi.org/10.3846/jcem.2021.15600.

  • 119.

    Vrana, J. The Core of the Fourth Revolutions: Industrial Internet of Things, Digital Twin, and Cyber-Physical Loops. J. Nondestruct. Eval. 2021, 40, 46. https://doi.org/10.1007/s10921-021-00777-7.

  • 120.

    Wang, S.; Chen, Q. Development of a Cloud-Based Building Information Modeling Design Configurator to Auto-Link Material Catalogs with Code-Compliant Designs of Residential Buildings. Buildings 2024, 14, 2084. https://doi.org/10.3390/buildings14072084.

  • 121.

    Aburumman, M.O.; Sweis, R.; Sweis, G.J. Investigating Building Information Modelling (BIM) and Lean Construction: The Potential BIM-Lean Interactions Synergy and Integration in the Jordanian Construction Industry. Int. J. Lean Six Sigma 2024, 15, 400–438. https://doi.org/10.1108/IJLSS-01-2023-0013.

  • 122.

    Yu, Y.; Yazan, D.M.; Junjan, V.; et al. Circular Economy in the Construction Industry: A Review of Decision Support Tools Based on Information & Communication Technologies. J. Clean. Prod. 2022, 349, 131335. https://doi.org/10.1016/j.jclepro.2022.131335.

  • 123.

    Miatto, A.; Sartori, C.; Bianchi, M.; et al. Tracking the Material Cycle of Italian Bricks with the Aid of Building Information Modeling. J. Ind. Ecol. 2022, 26, 609–626. https://doi.org/10.1111/jiec.13208.

  • 124.

    Bellon, F.G.; Martins, A.C.P.; de Carvalho, J.M.F.; et al. IFC Framework for Inspection and Maintenance Representation in Facility Management. Autom. Constr. 2025, 174, 106157. https://doi.org/10.1016/j.autcon.2025.106157.

  • 125.

    Junussova, T.; Nadeem, A.; Kim, J.R.; et al. Sustainable Construction through Resource Planning Systems Incorporation into Building Information Modelling. Buildings 2022, 12, 1761. https://doi.org/10.3390/buildings12101761.

  • 126.

    Yilmaz, S.; Kumar, D.; Hada, S.; et al. A PMBOK-Based Construction Cost Management Framework for BIM Integration in Construction Projects. Int. J. Constr. Manag. 2025, 25, 861–875. https://doi.org/10.1080/15623599.2024.2371626.

  • 127.

    Guowei, Z.; Su, Y.; Guoqing, Z.; et al. Smart Firefighting Construction in China: Status, Problems, and Reflections. Fire Mater. 2020, 44, 479–486. https://doi.org/10.1002/fam.2800.

  • 128.

    Tong, W.; Wang, Y.; Su, Q.; et al. Digital Twin Campus with a Novel Double-Layer Collaborative Filtering Recommendation Algorithm Framework. Educ. Inf. Technol. 2022, 27, 11901–11917. https://doi.org/10.1007/s10639-022-11077-6.

  • 129.

    Paulauskas, L.; Paulauskas, A.; Blažauskas, T.; et al. Reconstruction of Industrial and Historical Heritage for Cultural Enrichment Using Virtual and Augmented Reality. Technologies 2023, 11, 36. https://doi.org/10.3390/technologies11020036.

  • 130.

    Izquierdo-Domenech, J.; Linares-Pellicer, J.; Ferri-Molla, I. Virtual Reality and Language Models, a New Frontier in Learning. Int. J. Interact. Multimed. Artif. Intell. 2024, 8, 46–54. https://doi.org/10.9781/ijimai.2024.02.007.

  • 131.

    Xiao, Y.; Yu, J.; Wen, J. Reform of Four-Dimensional Integration Curriculum System of ‘on-the-Job Competition Certificate’ for New Engineering Electronic Information Majors. J. Comput. Methods Sci. Eng. 2024, 24, 2343–2355. https://doi.org/10.3233/JCM-247455.

  • 132.

    He, Z.; Chen, L.; Zhu, L. A Study of Inter-Technology Information Management (ITIM) System for Industry-Education Integration. Heliyon 2023, 9, e19928. https://doi.org/10.1016/j.heliyon.2023.e19928.

  • 133.

    Hu, Y.; Panyadee, C. LogTODIM-PROMETHEE Technique for Development Evaluation of School-Enterprise Cooperation from the Perspective of Collaborative Education Based on the Probabilistic Linguistic Group Decision-Making. Heliyon 2024, 10, e33391. https://doi.org/10.1016/j.heliyon.2024.e33391.

  • 134.

    Rostoka, M.; Kuzmenko, O. EСO-Environment of the Information—Analytical System of Scientific Personnel Training as a Means of Open Science. Int. J. Eng. Pedagog. 2023, 13, 94–101. https://doi.org/10.3991/ijep.v13i1.36111.

  • 135.

    Inchan, S.; Akatimagool, S. A Blended Laboratory-Based Learning Model for Embedded Systems Education. J. Tech. Educ. Train. 2025, 17, 1–13.

  • 136.

    Chen, W.; Bohloul, S.M.; Ma, Y.; et al. A Blockchain-Based Information Management System for Academic Institutions: A Case Study of International Students' Workflow. Inf. Discov. Deliv. 2022, 50, 343–352. https://doi.org/10.1108/IDD-01-2021-0010.

  • 137.

    Rahman, M.A.; Abuludin, M.S.; Yuan, L.X.; et al. EduChain: CIA-Compliant Blockchain for Intelligent Cyber Defense of Microservices in Education Industry 4.0. IEEE Trans. Ind. Inform. 2022, 18, 1930–1938. https://doi.org/10.1109/TII.2021.3093475.

  • 138.

    Hui, H.; Bao, M.; Ding, Y.; et al. Incorporating Multi-Energy Industrial Parks into Power System Operations: A High-Dimensional Flexible Region Method. IEEE Trans. Smart Grid 2025, 16, 463–477. https://doi.org/10.1109/TSG.2024.3426997.

  • 139.

    Cui, K.; Chi, M.; Zhao, Y.; et al. Bilevel Optimization Framework for Multiregional Integrated Energy Systems Considering 6G Network Slicing and Battery Energy Storage Capacity Sharing. IEEE Open J. Ind. Electron. Soc. 2025, 6, 396–414. https://doi.org/10.1109/OJIES.2025.3542262.

  • 140.

    Peng, Y.; Jolfaei, A.; Yu, K. A Novel Real-Time Deterministic Scheduling Mechanism in Industrial Cyber-Physical Systems for Energy Internet. IEEE Trans. Ind. Inform. 2022, 18, 5670–5680. https://doi.org/10.1109/TII.2021.3139357.

  • 141.

    Wu, H.; Chai, L.; Tian, Y.-C. Distributed Multirate Control of Battery Energy Storage Systems for Power Allocation. IEEE Trans. Ind. Inform. 2022, 18, 8745–8754. https://doi.org/10.1109/TII.2022.3153055.

  • 142.

    Gharibi, R.; Khalili, R.; Vahidi, B.; et al. Enhancing Energy Hub Performance: A Comprehensive Model for Efficient Integration of Hydrogen Energy and Renewable Sources with Advanced Uncertainty Management Strategies. J. Energy Storage 2025, 107, 114948. https://doi.org/10.1016/j.est.2024.114948.

  • 143.

    Liu, N.; Tan, L.; Sun, H.; et al. Bilevel Heat–Electricity Energy Sharing for Integrated Energy Systems with Energy Hubs and Prosumers. IEEE Trans. Ind. Inform. 2022, 18, 3754–3765. https://doi.org/10.1109/TII.2021.3112095.

  • 144.

    Zhong, W.; Su, W.; Huang, X.; et al. Joint Energy-Computation Management for Electric Vehicles Under Coordination of Power Distribution Networks and Computing Power Networks. IEEE Trans. Smart Grid 2025, 16, 1549–1561. https://doi.org/10.1109/TSG.2024.3498945.

  • 145.

    Zhu, Z.; Gao, X.; Bu, S.; et al. Cooperative Dispatch of Renewable-Penetrated Microgrids Alliances Using Risk-Sensitive Reinforcement Learning. IEEE Trans. Sustain. Energy 2024, 15, 2194–2208. https://doi.org/10.1109/TSTE.2024.3406590.

  • 146.

    Li, S.; Hu, W.; Cao, D.; et al. Coordinated Operation of Multiple Microgrids with Heat–Electricity Energy Based on Graph Surrogate Model-Enabled Robust Multiagent Deep Reinforcement Learning. IEEE Trans. Ind. Inform. 2025, 21, 248–257. https://doi.org/10.1109/TII.2024.3452192.

  • 147.

    Jadidi, S.; Badihi, H.; Zhang, Y. Hybrid Fault-Tolerant and Attack-Resilient Cooperative Control in an Offshore Wind Farm. IEEE Trans. Sustain. Energy 2024, 15, 1365–1379. https://doi.org/10.1109/TSTE.2023.3344749.

  • 148.

    Zhou, H.; He, Y.; Liu, T.; et al. NEST: Network-Energy-Stress Threat Against Thermal Energy Equipment. IEEE Trans. Autom. Sci. Eng. 2025, 22, 9622–9635. https://doi.org/10.1109/TASE.2024.3510336.

  • 149.

    Park, K.; Lee, J.Y.; Das, A.K.; et al. BPPS:Blockchain-Enabled Privacy-Preserving Scheme for Demand-Response Management in Smart Grid Environments. IEEE Trans. Dependable Secure Comput. 2023, 20, 1719–1729. https://doi.org/10.1109/TDSC.2022.3163138.

  • 150.

    Fu, X.; Chang, F.; Sun, H.; et al. Knowledge-Integrated GAN Model for Stochastic Time-Series Simulation of Year-Round Weather for Photovoltaic Integration Analysis. IEEE Trans. Power Syst. 2025, 40, 5289–5301. https://doi.org/10.1109/TPWRS.2025.3559455.

  • 151.

    Fathabad, A.M.; Cheng, J.; Pan, K.; et al. Data-Driven Planning for Renewable Distributed Generation Integration. IEEE Trans. Power Syst. 2020, 35, 4357–4368. https://doi.org/10.1109/TPWRS.2020.3001235.

  • 152.

    Yang, S.; Zhu, M.N.; Yu, H. Are Artificial Intelligence and Blockchain the Key to Unlocking the Box of Clean Energy? Energy Econ. 2024, 134, 107616. https://doi.org/10.1016/j.eneco.2024.107616.

  • 153.

    Yang, X.; Cui, T.; Wang, H.; et al. Multiagent Deep Reinforcement Learning for Electric Vehicle Fast Charging Station Pricing Game in Electricity-Transportation Nexus. IEEE Trans. Ind. Inform. 2024, 20, 6345–6355. https://doi.org/10.1109/TII.2023.3345457.

  • 154.

    Afsher, P.A.; Kumar, M.V.M.; Sooraj, S.K. Power Management Control of a PV-Battery Grid-Tied System Based on the Energy Price. IEEE Trans. Ind. Inform. 2025, 21, 5997–6005. https://doi.org/10.1109/TII.2025.3557497.

  • 155.

    Tran, M.; De Luis, A.; Liao, H.; et al. S3Former: A Deep Learning Approach to High Resolution Solar PV Profiling. IEEE Trans. Smart Grid 2025, 16, 2611–2623. https://doi.org/10.1109/TSG.2025.3531764.

  • 156.

    Komala, C.R.; Jeyakumar, S.; Deepika, G.; et al. IoT Integration With CMPA-PINN for Islanding Detection Through Microgrid Hierarchical Control. Int. J. Commun. Syst. 2025, 38, e6087. https://doi.org/10.1002/dac.6087.

  • 157.

    Liu, L.; Mao, Z. Hybrid Deep LSTM-GAT Network with Mechanism Information for Prediction of Mach Number. IEEE Trans. Instrum. Meas. 2025, 74, 2514813. https://doi.org/10.1109/TIM.2025.3548222.

  • 158.

    Lu, N.; Li, L.; Qin, J. PV Identifier: Extraction of Small-Scale Distributed Photovoltaics in Complex Environments from High Spatial Resolution Remote Sensing Images. Appl. Energy 2024, 365, 123311. https://doi.org/10.1016/j.apenergy.2024.123311.

  • 159.

    Getie, E.M.; Gessesse, B.B.; Workneh, T.G. Photovoltaic Generation Integration with Radial Feeders Using GA and GIS. Int. J. Photoenergy 2020, 2020, 8854711. https://doi.org/10.1155/2020/8854711.

  • 160.

    Romero-Ramos, J.A.; Arias, I.; Escobar, R.A.; et al. Assessing Green Hydrogen Production Potential Using Photovoltaic Solar Energy in Industrial Buildings of Southeastern Spain. Int. J. Hydrogen Energy 2025, 112, 418–432. https://doi.org/10.1016/j.ijhydene.2025.01.496.

  • 161.

    Pelda, J.; Stelter, F.; Holler, S. Potential of Integrating Industrial Waste Heat and Solar Thermal Energy into District Heating Networks in Germany. Energy 2020, 203, 117812. https://doi.org/10.1016/j.energy.2020.117812.

  • 162.

    Fu, X.; Zhang, C.; Xu, Y.; et al. Statistical Machine Learning for Power Flow Analysis Considering the Influence of Weather Factors on Photovoltaic Power Generation. IEEE Trans. Neural Netw. Learn. Syst. 2025, 36, 5348–5362. https://doi.org/10.1109/TNNLS.2024.3382763.

  • 163.

    Velazquez Medina, S.; Portero Ajenjo, U. Performance Improvement of Artificial Neural Network Model in Short-Term Forecasting of Wind Farm Power Output. J. Mod. Power Syst. Clean Energy 2020, 8, 484–490. https://doi.org/10.35833/MPCE.2018.000792.

  • 164.

    Saadi, M.; Djalel, D.; Meghni, B.; et al. Intelligent Energy Management Strategy and Sizing Methodology for Hybrid Systems in Isolated Regions. Chin. J. Electr. Eng. 2024, 10, 50–62. https://doi.org/10.23919/CJEE.2024.000091.

  • 165.

    Zhang, M. Enhanced Estimation of Thermodynamic Parameters: A Hybrid Approach Integrating Rough Set Theory and Deep Learning. Int. J. Heat Technol. 2023, 41, 1587–1595. https://doi.org/10.18280/ijht.410621.

  • 166.

    Alabugin, A.; Osintsev, K.; Aliukov, S. Methodological Foundations for Modeling the Processes of Combining Organic Fuel Generation Systems and Photovoltaic Cells into a Single Energy Technology Complex. Energies 2021, 14, 2816. https://doi.org/10.3390/en14102816.

  • 167.

    Angizeh, F.; Bae, J.; Chen, J.; et al. Impact Assessment Framework for Grid Integration of Energy Storage Systems and Renewable Energy Sources Toward Clean Energy Transition. IEEE Access 2023, 11, 134995–135005. https://doi.org/10.1109/ACCESS.2023.3337133.

  • 168.

    Raza, M.H.; Rind, Y.M.; Javed, I.; et al. Smart Meters for Smart Energy: A Review of Business Intelligence Applications. IEEE Access 2023, 11, 120001–120022. https://doi.org/10.1109/ACCESS.2023.3326724.

  • 169.

    Zhu, K.; Teng, Z.; Qiu, W.; et al. Aiming to Complex Power Quality Disturbances: A Novel Decomposition and Detection Framework. IEEE Trans. Ind. Inform. 2024, 20, 4317–4326. https://doi.org/10.1109/TII.2023.3321024.

  • 170.

    Abazari, A.; Sarieddine, K.; Ghafouri, M.; et al. Electric Vehicle Switching Attacks against Subsynchronous Stability of Power Systems IEEE Trans. Ind. Inform. 2025, 21, 475–486. https://doi.org/10.1109/TII.2024.3453190.

  • 171.

    Priyanka, E.B.; Thangavel, S.; Gao, X.-Z.; et al. Digital Twin for Oil Pipeline Risk Estimation Using Prognostic and Machine Learning Techniques. J. Ind. Inf. Integr. 2022, 26, 100272. https://doi.org/10.1016/j.jii.2021.100272.

  • 172.

    Fernandes, T.L.; Baldo, C.R.; Donatelli, G.D. The Concept of Digital Twin Used to Investigate Geometrical Variations in the Production of Pipe Spools. Adv. Ind. Manuf. Eng. 2021, 3, 100054 https://doi.org/10.1016/j.aime.2021.100054.

  • 173.

    Arraño-Vargas, F.; Konstantinou, G. Modular Design and Real-Time Simulators Toward Power System Digital Twins Implementation. IEEE Trans. Ind. Inform. 2023, 19, 52–61. https://doi.org/10.1109/TII.2022.3178713.

  • 174.

    Yan, W.; Shi, Y.; Ji, Z.; et al. Intelligent Predictive Maintenance of Hydraulic Systems Based on Virtual Knowledge Graph. Eng. Appl. Artif. Intell. 2023, 126, 106798. https://doi.org/10.1016/j.engappai.2023.106798.

  • 175.

    Mishra, M.; Biswal, M.; Bansal, R.C.; et al. Intelligent Computing in Electrical Utility Industry 4.0: Concept, Key Technologies, Applications and Future Directions. IEEE Access 2022, 10, 100312–100336. https://doi.org/10.1109/ACCESS.2022.3205031.

  • 176.

    Zhang, X.; Shen, J.; Saini, P.K.; et al. Digital Twin for Accelerating Sustainability in Positive Energy District: A Review of Simulation Tools and Applications. Front. Sustain. Cities 2021, 3, 663269. https://doi.org/10.3389/frsc.2021.663269.

  • 177.

    Qin, C.; Srivastava, A.K.; Davies, K.L. Unbundling Smart Meter Services Through Spatiotemporal Decomposition Agents in DER-Rich Environment. IEEE Trans. Ind. Inform. 2022, 18, 666–676. https://doi.org/10.1109/TII.2021.3060870.

  • 178.

    Zhao, X. Research on Management Informatization Construction of Electric Power Enterprise Based on Big Data Technology. Energy Rep. 2022, 8, 535–545. https://doi.org/10.1016/j.egyr.2022.05.122.

  • 179.

    Collado-Mariscal, D.; Cortés-Pérez, J.P.; Cortés-Pérez, A.; et al. Proposal for the Integration of the Assessment and Management of Electrical Risk from Overhead Power Lines in BIM for Road Projects. Int. J. Environ. Res. Public Health 2022, 19, 13064. https://doi.org/10.3390/ijerph192013064.

  • 180.

    Marino, D.L.; Wickramasinghe, C.S.; Singh, V.K.; et al. The Virtualized Cyber-Physical Testbed for Machine Learning Anomaly Detection: A Wind Powered Grid Case Study. IEEE Access 2021, 9, 159475–159494. https://doi.org/10.1109/ACCESS.2021.3127169.

  • 181.

    Abdel-Basset, M.; Moustafa, N.; Hawash, H. Privacy-Preserved Generative Network for Trustworthy Anomaly Detection in Smart Grids: A Federated Semisupervised Approach. IEEE Trans. Ind. Inform. 2023, 19, 995–1005. https://doi.org/10.1109/TII.2022.3165869.

  • 182.

    Nasiri, S.; Seifi, H.; Delkhosh, H. A Secure Power System Distributed State Estimation via a Consensus-Based Mechanism and a Cooperative Trust Management Strategy. IEEE Trans. Ind. Inform. 2024, 20, 3002–3014. https://doi.org/10.1109/TII.2023.3299385.

  • 183.

    Jogunola, O.; Adebisi, B.; Ikpehai, A.; et al. Consensus Algorithms and Deep Reinforcement Learning in Energy Market: A Review. IEEE Internet Things J. 2021, 8, 4211–4227. https://doi.org/10.1109/JIOT.2020.3032162.

  • 184.

    Al-Abri, T.; Onen, A.; Al-Abri, R.; et al. Review on Energy Application Using Blockchain Technology with an Introductions in the Pricing Infrastructure. IEEE Access 2022, 10, 80119–80137. https://doi.org/10.1109/ACCESS.2022.3194161.

  • 185.

    Verma, P.; Chakraborty, C. Load Redistribution Attacks against Smart Grids–Models, Impacts, and Defense: A Review. IEEE Trans. Ind. Inform. 2024, 20, 10192–10208. https://doi.org/10.1109/TII.2024.3393005.

  • 186.

    Mokhtari, S.; Yen, K.K. False Data Injection Attack Detection, Isolation, and Identification in Industrial Control Systems Based on Machine Learning: Application in Load Frequency Control. Electronics 2024, 13, 3239. https://doi.org/10.3390/electronics13163239.

  • 187.

    Yu, Y.; Liu, C.; Xiong, L.; et al. Localization of False Data Injection Attacks in Smart Grids with Renewable Energy Integration via Spatiotemporal Network. IEEE Internet Things J. 2024, 11, 37571–37581. https://doi.org/10.1109/JIOT.2024.3436520.

  • 188.

    Zhou, Y.; Tang, Z.; Nikmehr, N.; et al. Quantum Computing in Power Systems. iEnergy 2022, 1, 170–187. https://doi.org/10.23919/IEN.2022.0021.

  • 189.

    Zhao, S.; Zhao, S.H. Intelligent Power Equipment for Autonomous Situational Awareness and Active Operation and Maintenance. J. Mod. Power Syst. Clean Energy 2024, 12, 2081–2090. https://doi.org/10.35833/MPCE.2023.000697.

  • 190.

    Rimal, B.; Kong, C.; Poudel, B.; et al. Smart Electric Vehicle Charging in the Era of Internet of Vehicles, Emerging Trends, and Open Issues. Energies 2022, 15, 1908. https://doi.org/10.3390/en15051908.

  • 191.

    Haces-Fernandez, F. Assessment of the Financial Benefits from Wind Farms in US Rural Locations. J. Risk Financ. Manag. 2022, 15, 423. https://doi.org/10.3390/jrfm15100423.

  • 192.

    Cesari, D.; Merico, E.; Grasso, F.M.; et al. Analysis of the Contribution to PM10 Concentrations of the Largest Coal-Fired Power Plant of Italy in Four Different Sites. Atmos. Pollut. Res. 2021, 12, 101135. https://doi.org/10.1016/j.apr.2021.101135.

  • 193.

    Zou, T.; Aljohani, N.; Wang, P.; et al. Distributed Nonlinear State Estimation Using Adaptive Penalty Parameters with Load Characteristics in the Electricity Reliability Council of Texas. J. Ind. Inf. Integr. 2021, 24, 100223. https://doi.org/10.1016/j.jii.2021.100223.

  • 194.

    Mujeeb, S.; Javaid, N. Deep Learning Based Carbon Emissions Forecasting and Renewable Energy’s Impact Quantification. IET Renew. Power Gener. 2023, 17, 873–884. https://doi.org/10.1049/rpg2.12641.

  • 195.

    Liu, H.; Yan, S.; Huang, M.; et al. A Fault Diagnosis Method for Hydraulic System Based on Multi-Branch Neural Networks. Eng. Appl. Artif. Intell. 2024, 137, 109188. https://doi.org/10.1016/j.engappai.2024.109188.

  • 196.

    Li, K.; Fan, H.; Yao, P. Estimating Carbon Emissions from Thermal Power Plants Based on Thermal Characteristics. Int. J. Appl. Earth Obs. Geoinf. 2024, 128, 103768. https://doi.org/10.1016/j.jag.2024.103768.

  • 197.

    Babaei, F.; Boozarjomehry, R.B.; Ravandi, Z.K.; et al. An Information Integration Framework toward Cross-Organizational Management of Integrated Energy Systems. J. Ind. Inf. Integr. 2025, 44, 100791. https://doi.org/10.1016/j.jii.2025.100791.

  • 198.

    Banerjee, A.; Choppella, V. A Knowledge-Driven Approach for Dynamic Reconfiguration of Control Design in Internet of Things and Cyber–Physical Systems. IEEE Internet Things J. 2024, 12, 5615–5641. https://doi.org/10.1109/JIOT.2024.3487578.

  • 199.

    Fan, Y.; Dai, C.; Huang, S.; et al. A Life-Cycle Digital-Twin Collaboration Framework Based on the Industrial Internet Identification and Resolution. Int. J. Adv. Manuf. Technol. 2022, 123, 2883–2911. https://doi.org/10.1007/s00170-022-10269-1.

  • 200.

    Fraga, A.L.; Vegetti, M.; Leone, H.P. Ontology-Based Solutions for Interoperability among Product Lifecycle Management Systems: A Systematic Literature Review. J. Ind. Inf. Integr. 2020, 20, 100176. https://doi.org/10.1016/j.jii.2020.100176.

  • 201.

    Guo, Z.; Zhou, D.; Yu, D.; et al. An Ontology-Based Method for Knowledge Reuse in the Design for Maintenance of Complex Products. Comput. Ind. 2024, 161, 104124. https://doi.org/10.1016/j.compind.2024.104124.

  • 202.

    Li, X.; Zhang, S.; Jiang, P.; et al. Knowledge Graph Based OPC UA Information Model Automatic Construction Method for Heterogeneous Devices Integration. Robot. Comput. Integr. Manuf. 2024, 88, 102736. https://doi.org/10.1016/j.rcim.2024.102736.

  • 203.

    López, A.; Casquero, O.; Estévez, E.; et al. An Industrial Agent-Based Customizable Platform for I4.0 Manufacturing Systems. Comput. Ind. 2023, 146, 103859. https://doi.org/10.1016/j.compind.2023.103859.

  • 204.

    Miny, T.; Thies, M.; Lukic, L.; et al. Overview and Comparison of Asset Information Model Standards. IEEE Access 2023, 11, 99189–99221. https://doi.org/10.1109/ACCESS.2023.3312286.

  • 205.

    Polenghi, A.; Roda, I.; Macchi, M.; et al. Knowledge Reuse for Ontology Modelling in Maintenance and Industrial Asset Management. J. Ind. Inf. Integr. 2022, 27, 100298. https://doi.org/10.1016/j.jii.2021.100298.

  • 206.

    Sousa, J.; Mendonça, J.P.; Machado, J. A Generic Interface and a Framework Designed for Industrial Metrology Integration for the Internet of Things. Comput. Ind. 2022, 138, 103632. https://doi.org/10.1016/j.compind.2022.103632.

  • 207.

    Xiao, J.; Anwer, N.; Huang, H.; et al. Information Exchange and Knowledge Discovery for Additive Manufacturing Digital Thread: A Comprehensive Literature Review. Int. J. Comput. Integr. Manuf. 2024, 38, 1052–1077. https://doi.org/10.1080/0951192X.2024.2387768.

  • 208.

    Yang, C.; Zheng, Y.; Tu, X.; et al. Ontology-Based Knowledge Representation of Industrial Production Workflow. Adv. Eng. Inform. 2023, 58, 102185. https://doi.org/10.1016/j.aei.2023.102185.

  • 209.

    Zhang, F.; Chen, L.; Zhang, B.; et al. An Integrated System Theoretic Process Analysis with Multilevel Flow Modeling for the Identification of Cyber-physical Hazards in a Process Industry. Process Saf. Prog. 2024, 43, 587–596. https://doi.org/10.1002/prs.12604.

  • 210.

    Holasova, E.; Fujdiak, R.; Misurec, J. Comparative Analysis of Classification Methods and Suitable Datasets for Protocol Recognition in Operational Technologies. Algorithms 2024, 17, 208. https://doi.org/10.3390/a17050208.

  • 211.

    Reinpold, L.M.; Wagner, L.P.; Gehlhoff, F.; et al. Systematic Comparison of Software Agents and Digital Twins: Differences, Similarities, and Synergies in Industrial Production. J. Intell. Manuf. 2025, 36, 765–800. https://doi.org/10.1007/s10845-023-02278-y.

  • 212.

    Wang, J.; Xu, C.; Zhang, J.; et al. A Collaborative Architecture of the Industrial Internet Platform for Manufacturing Systems. Robot. Comput. Integr. Manuf. 2020, 61, 101854. https://doi.org/10.1016/j.rcim.2019.101854.

  • 213.

    Edrisi, F.; Perez-Palacin, D.; Caporuscio, M.; et al. Developing and Evolving a Digital Twin of the Organization. IEEE Access 2024, 12, 45475–45494. https://doi.org/10.1109/ACCESS.2024.3381778.

  • 214.

    Rolle, R.; Martucci, V.; Godoy, E. Architecture for Digital Twin Implementation Focusing on Industry 4.0. IEEE Lat. Am. Trans. 2020, 18, 889–898. https://doi.org/10.1109/TLA.2020.9082917.

  • 215.

    Bruno, G.; Faveto, A.; Traini, E. An open source framework for the storage and reuse of industrial knowledge through the integration of PLM and MES. Manag. Prod. Eng. Rev. 2020, 11. https://doi.org/10.24425/mper.2020.133729.

  • 216.

    Liu, L.; Zeng, N.; Liu, Y.; et al. Multi-Domain Data Integration and Management for Enhancing Service-Oriented Digital Twin for Infrastructure Operation and Maintenance. Dev. Built Environ. 2024, 18, 100475. https://doi.org/10.1016/j.dibe.2024.100475.

  • 217.

    Pontarolli, R.P.; Bigheti, J.A.; De Sá, L.B.R.; et al. Microservice-Oriented Architecture for Industry 4.0. Eng 2023, 4, 1179–1197. https://doi.org/10.3390/eng4020069.

  • 218.

    Park, S.; Huh, J.-H. A Study on Big Data Collecting and Utilizing Smart Factory Based Grid Networking Big Data Using Apache Kafka. IEEE Access 2023, 11, 96131–96142. https://doi.org/10.1109/ACCESS.2023.3305586.

  • 219.

    Zhang, Y.; Tang, D.; Zhu, H.; et al. An Efficient IIoT Gateway for Cloud–Edge Collaboration in Cloud Manufacturing. Mach. 2022, 10, 850. https://doi.org/10.3390/machines10100850.

  • 220.

    Kannisto, P.; Hästbacka, D.; Marttinen, A. Information Exchange Architecture for Collaborative Industrial Ecosystem. Inf. Syst. Front. 2020, 22, 655–670. https://doi.org/10.1007/s10796-018-9877-0.

  • 221.

    Trakadas, P.; Simoens, P.; Gkonis, P.; et al. An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications. Sensors 2020, 20, 5480. https://doi.org/10.3390/s20195480.

  • 222.

    Nateghi̇, A.; Mosharraf, M. Architecting the Future: A Model for Enterprise Integration in the Metaverse. J. Metaverse 2023, 3, 190–199. https://doi.org/10.57019/jmv.1355500.

  • 223.

    Giao, J.; Nazarenko, A.A.; Luis-Ferreira, F.; et al. A Framework for Service-Oriented Architecture (SOA)-Based IoT Application Development. Processes 2022, 10, 1782. https://doi.org/10.3390/pr10091782.

  • 224.

    Wei, J.; Shi, S.; Xia, H. The Integration of Building Information Modeling (BIM) and Integrated Project Delivery (IPD) in Industrial Buildings: Evidence from China. J. Comput. Methods Sci. Eng. 2021, 21, 1711–1726. https://doi.org/10.3233/JCM-215392.

  • 225.

    Abdellatif, S.; Berthou, P.; Villemur, T.; et al. Management of Industrial Communications Slices: Towards the Application Driven Networking Concept Comput. Commun. 2020, 155, 104–116. https://doi.org/10.1016/j.comcom.2020.02.057.

  • 226.

    Azad, S.M.A.K.; Srinivasan, K. A Computational Scheme for Data Scheduling in Industrial Enterprise Network Using Linear Mixed Model Approach. Int. J. Comput. Integr. Manuf. 2024, 37, 572–588. https://doi.org/10.1080/0951192X.2023.2228269.

  • 227.

    Bagozi, A.; Bianchini, D.; Rula, A. Multi-Perspective Data Modelling in Cyber Physical Production Networks: Data, Services and Actors. Data Sci. Eng. 2022, 7, 193–212. https://doi.org/10.1007/s41019-022-00194-4.

  • 228.

    Jiang, D.; Tong, Y.; Song, Y.; et al. Industrial Federated Topic Modeling. ACM Trans. Intell. Syst. Technol. 2021, 12, 1–22. https://doi.org/10.1145/3418283.

  • 229.

    Krupitzer, C.; Temizer, T.; Prantl, T.; et al. An Overview of Design Patterns for Self-Adaptive Systems in the Context of the Internet of Things. IEEE Access 2020, 8, 187384–187399. https://doi.org/10.1109/ACCESS.2020.3031189.

  • 230.

    Qiu, K.; Yang, J.; Rong, B.; et al. Semantic Reconstruction of Multimodal Process Data With Dual Latent Space Constraints. IEEE Sens. J. 2024, 24, 32782–32791. https://doi.org/10.1109/JSEN.2024.3451190.

  • 231.

    Semenov, V.A.; Arishin, S.V.; Semenov, G.V. Formal Rules to Produce Object Notation for EXPRESS Schema-Driven Data. Program. Comput. Softw. 2022, 48, 455–468. https://doi.org/10.1134/S0361768822070076.

  • 232.

    Yu, J.; Ruan, H.; Li, Z.; et al. Bidirectional Heterogeneous Synergistic Fault Detection Using Multiple Local Data in Large-Scale Industrial Systems. Control Eng. Pract. 2025, 157, 106251. https://doi.org/10.1016/j.conengprac.2025.106251.

  • 233.

    Ding, H.; Zhao, L.; Yan, J.; et al. Implementation of Digital Twin in Actual Production: Intelligent Assembly Paradigm for Large-Scale Industrial Equipment. Machines 2023, 11, 1031. https://doi.org/10.3390/machines11111031.

  • 234.

    Frizziero, L.; Leon-Cardenas, C.; Galiè, G.; et al. Industrial Design Structure: A Straightforward Organizational Integration of DFSS and QFD in a New Industry and Market Reality. TQM J. 2023, 35, 2413–2435. https://doi.org/10.1108/TQM-11-2021-0314.

  • 235.

    Ghodsian, N.; Benfriha, K.; Olabi, A.; et al. A Framework to Integrate Mobile Manipulators as Cyber–Physical Systems into Existing Production Systems in the Context of Industry 4.0. Robot. Auton. Syst. 2023, 169, 104526. https://doi.org/10.1016/j.robot.2023.104526.

  • 236.

    Jagatheesaperumal, S.K.; Yang, Z.; Yang, Q.; et al. Semantic-Aware Digital Twin for Metaverse: A Comprehensive Review. IEEE Wirel. Commun. 2023, 30, 38–46. https://doi.org/10.1109/MWC.003.2200616.

  • 237.

    Kamali, M.; Atazadeh, B.; Rajabifard, A.; et al. Advancements in 3D Digital Model Generation for Digital Twins in Industrial Environments: Knowledge Gaps and Future Directions. Adv. Eng. Inform. 2024, 62, 102929. https://doi.org/10.1016/j.aei.2024.102929.

  • 238.

    Latsou, C.; Ariansyah, D.; Salome, L.; et al. A Unified Framework for Digital Twin Development in Manufacturing. Adv. Eng. Inform. 2024, 62, 102567. https://doi.org/10.1016/j.aei.2024.102567.

  • 239.

    Li, P.; Cheng, Y.; Song, W.; et al. Manufacturing Services Collaboration: Connotation, Framework, Key Technologies, and Research Issues. Int. J. Adv. Manuf. Technol. 2020, 110, 2573–2589. https://doi.org/10.1007/s00170-020-06042-x.

  • 240.

    Liu, M.; Meng, M.; Shin, J.G.; et al. Task-Centric Method for Shipyard Hoisting Process Modelling and Its Application in CAPP. J. Mar. Sci. Technol. 2021, 26, 792–811. https://doi.org/10.1007/s00773-020-00772-z.

  • 241.

    Meng, Z.; Wu, Z.; Gray, J. Architecting Ubiquitous Communication and Collaborative-Automation-Based Machine Network Systems for Flexible Manufacturing. IEEE Syst. J. 2020, 14, 113–123. https://doi.org/10.1109/JSYST.2019.2918542.

  • 242.

    Monti, F.; Silo, L.; Favorito, M.; et al. Orchestration of Services in Smart Manufacturing Through Automated Synthesis. IEEE Trans. Serv. Comput. 2024, 17, 4069–4082. https://doi.org/10.1109/TSC.2024.3495521.

  • 243.

    Strimovskaya, A.; Barykin, S. A Multidimensional Approach to the Resource Allocation Problem (RAP) through the Prism of Industrial Information Integration (III). J. Ind. Inf. Integr. 2023, 34, 100473. https://doi.org/10.1016/j.jii.2023.100473.

  • 244.

    Sukhomlinov, A.I. An Integrated Production Management System for an Industrial Enterprise. J. Mach. Manuf. Reliab. 2024, 53, 1003–1013. https://doi.org/10.1134/S105261882470078X.

  • 245.

    Van Stiphoudt, C.; Menci, S.P.; Kaymakci, C.; et al. The Energy Synchronization Platform Concept in the Model Region Augsburg to Enable and Streamline Automated Industrial Demand Response. Appl. Energy 2025, 388, 125455. https://doi.org/10.1016/j.apenergy.2025.125455.

  • 246.

    Wu, S.; Wang, G.; Lu, J.; et al. Design Ontology for Cognitive Thread Supporting Traceability Management in Model-Based Systems Engineering. J. Ind. Inf. Integr. 2024, 40, 100619. https://doi.org/10.1016/j.jii.2024.100619.

  • 247.

    Zhou, X.; Xiong, H.; He, F. Hybrid Partition- and Network-Level Scheduling Design for Distributed Integrated Modular Avionics Systems. Chin. J. Aeronaut. 2020, 33, 308–323. https://doi.org/10.1016/j.cja.2019.08.027.

  • 248.

    Guo, K.; Liang, Y.; Niu, M.; et al. Integrated Optimization of Process Planning and Scheduling Problems Based on Complex Networks. J. Ind. Inf. Integr. 2023, 36, 100533. https://doi.org/10.1016/j.jii.2023.100533.

  • 249.

    Hollerer, S.; Sauter, T.; Kastner, W. A Survey of Ontologies Considering General Safety, Security, and Operation Aspects in OT. IEEE Open J. Ind. Electron. Soc. 2024, 5, 861–885. https://doi.org/10.1109/OJIES.2024.3441112.

  • 250.

    Ji, F.; Vogel-Heuser, B.; Schypula, R.; et al. Ontology Versioning for Managing Inconsistencies in Engineering Models Arising from Model Changes in the Design of Intralogistics Systems. IEEE Trans. Autom. Sci. Eng. 2025, 22, 1249–1261. https://doi.org/10.1109/TASE.2024.3362599.

  • 251.

    Montero Jiménez, J.J.; Vingerhoeds, R.; Grabot, B.; et al. An Ontology Model for Maintenance Strategy Selection and Assessment. J. Intell. Manuf. 2023, 34, 1369–1387. https://doi.org/10.1007/s10845-021-01855-3.

  • 252.

    Pokojski, J.; Szustakiewicz, K.; Woźnicki, L.; et al. Industrial Application of Knowledge-Based Engineering in Commercial CAD/CAE Systems. J. Ind. Inf. Integr. 2022, 25, 100255. https://doi.org/10.1016/j.jii.2021.100255.

  • 253.

    Zhang, C.; Lu, Y. Study on Artificial Intelligence: The State of the Art and Future Prospects. J. Ind. Inf. Integr. 2021, 23, 100224. https://doi.org/10.1016/j.jii.2021.100224.

  • 254.

    Zhao, Q.; Taniguchi, I.; Onoye, T. Novel Object Motion Estimation Method for Industrial Vision Systems in Aligning Machines. J. Ind. Inf. Integr. 2022, 25, 100295. https://doi.org/10.1016/j.jii.2021.100295.

  • 255.

    Colli, M.; Uhrenholt, J.N.; Madsen, O.; et al. Translating Transparency into Value: An Approach to Design IoT Solutions. J. Manuf. Technol. Manag. 2021, 32, 1515–1532. https://doi.org/10.1108/JMTM-06-2020-0225.

  • 256.

    Dedousis, P.; Stergiopoulos, G.; Arampatzis, G.; et al. A Security-Aware Framework for Designing Industrial Engineering Processes. IEEE Access 2021, 9, 163065–163085. https://doi.org/10.1109/ACCESS.2021.3134759.

  • 257.

    Fayos, T.; Calderón, H.; Cotarelo, M.; et al. The Contribution of Digitalisation, Channel Integration and Sustainability to the International Performance of Industrial SMEs. Manag. Environ. Qual. Int. J. 2023, 34, 624–646. https://doi.org/10.1108/MEQ-06-2022-0159.

  • 258.

    Khorasani, M.; Loy, J.; Ghasemi, A.H.; et al. A Review of Industry 4.0 and Additive Manufacturing Synergy. Rapid Prototyp. J. 2022, 28, 1462–1475. https://doi.org/10.1108/RPJ-08-2021-0194.

  • 259.

    Kozlovska, M.; Petkanic, S.; Vranay, F.; et al. Enhancing Energy Efficiency and Building Performance through BEMS-BIM Integration. Energies 2023, 16, 6327. https://doi.org/10.3390/en16176327.

  • 260.

    Liu, L.; Zhao, P. Manufacturing Service Innovation and Foreign Trade Upgrade Model Based on Internet of Things and Industry 4.0. Math. Probl. Eng. 2022, 2022, 4148713. https://doi.org/10.1155/2022/4148713.

  • 261.

    Pérez-Lara, M.; Saucedo-Martínez, J.A.; Marmolejo-Saucedo, J.A.; et al. Vertical and Horizontal Integration Systems in Industry 4.0. Wirel. Netw. 2020, 26, 4767–4775. https://doi.org/10.1007/s11276-018-1873-2.

  • 262.

    Wang, Z.; Feng, W.; Ye, J.; et al. A Study on Intelligent Manufacturing Industrial Internet for Injection Molding Industry Based on Digital Twin. Complexity 2021, 2021, 8838914. https://doi.org/10.1155/2021/8838914.

  • 263.

    Mantravadi, S.; Srai, J.S.; Møller, C. Application of MES/MOM for Industry 4.0 Supply Chains: A Cross-Case Analysis. Comput. Ind. 2023, 148, 103907. https://doi.org/10.1016/j.compind.2023.103907.

  • 264.

    Zhang, C.; Peng, K.; Dong, J.; et al. KPI-Related Operating Performance Assessment Based on Distributed ImRMR-KOCTA for Hot Strip Mill Process. Expert Syst. Appl. 2022, 209, 118273. https://doi.org/10.1016/j.eswa.2022.118273.

  • 265.

    Bajagain, S.; Qin, C.; Pannala, S.; et al. Integrating Solar Resources and Topology Estimation Modules in Industrial ADMS Environment. IEEE Trans. Ind. Appl. 2024, 60, 1508–1518. https://doi.org/10.1109/TIA.2023.3322117.

  • 266.

    Bi, Z.; Yung, K.L.; Ip, A.W.H.; et al. The State of the Art of Information Integration in Space Applications. IEEE Access 2022, 10, 110110–110135. https://doi.org/10.1109/ACCESS.2022.3215154.

  • 267.

    Dong, S.; Qi, L. Model Analysis and Simulation of Equipment-Manufacturing Value Chain Integration Process. Complexity 2020, 2020, 6620679. https://doi.org/10.1155/2020/6620679.

  • 268.

    Hu, F.; Liu, Y.; Li, Y.; et al. Task-Driven Data Fusion for Additive Manufacturing: Framework, Approaches, and Case Studies. J. Ind. Inf. Integr. 2023, 34, 100484. https://doi.org/10.1016/j.jii.2023.100484.

  • 269.

    Joanna, M.; Marek, G.; Władysław, M. The Concept of the Qualitology and Grey System Theory Application in Marketing Information Quality Cognition and Assessment. Cent. Eur. J. Oper. Res. 2020, 28, 817–840. https://doi.org/10.1007/s10100-019-00635-y.

  • 270.

    Lu, Y.; Sigov, A.; Ratkin, L.; et al. Quantum Computing and Industrial Information Integration: A Review. J. Ind. Inf. Integr. 2023, 35, 100511. https://doi.org/10.1016/j.jii.2023.100511.

  • 271.

    Ma, N. Analysis of Industry Convergence Based on Improved Neural Network. Soft Comput. 2022, 26, 7437–7448. https://doi.org/10.1007/s00500-021-06439-0.

  • 272.

    Nafei, A.; Chen, S.-C.; Garg, H.; et al. Improving Industrial Automation Selection with Dynamic Exponential Distance in Neutrosophic Group Decision-Making Framework. J. Oper. Res. Soc. 2025, 1-20. https://doi.org/10.1080/01605682.2025.2477665.

  • 273.

    Peng, C.; Tang, Z.; Gui, W.; et al. Review of Key Technologies and Progress in Industrial Equipment Health Management. IEEE Access 2020, 8, 151764–151776. https://doi.org/10.1109/ACCESS.2020.3017626.

  • 274.

    Stoykova, S.; Shakev, N. Artificial Intelligence for Management Information Systems: Opportunities, Challenges, and Future Directions. Algorithms 2023, 16, 357. https://doi.org/10.3390/a16080357.

  • 275.

    Yamada, S.; Komatsu, Y.; Bracke, S.; et al. Product Architecture Derivation Methodology Based on Multi-Objective Design Structure Matrix Integration and Supply Chain Evaluation. Int. J. Autom. Technol. 2025, 19, 88–99. https://doi.org/10.20965/ijat.2025.p0088.

  • 276.

    Yin, S.; Zhang, N.; Dong, H. Preventing COVID-19 from the Perspective of Industrial Information Integration: Evaluation and Continuous Improvement of Information Networks for Sustainable Epidemic Prevention. J. Ind. Inf. Integr. 2020, 19, 100157. https://doi.org/10.1016/j.jii.2020.100157.

  • 277.

    Bimpizas-Pinis, M.; Calzolari, T.; Genovese, A. Exploring the Transition towards Circular Supply Chains through the Arcs of Integration. Int. J. Prod. Econ. 2022, 250, 108666. https://doi.org/10.1016/j.ijpe.2022.108666.

  • 278.

    Fabregas, A.D.; Crawford, P.; Mesa, R.; et al. Parametric Evaluation of Internet of Things Applications to Freight Transportation Using Model-Based Systems Engineering. Transp. Res. Rec. 2022, 2676, 38–48. https://doi.org/10.1177/03611981211049134.

  • 279.

    Slim, R.; Houssin, R.; Coulibaly, A.; et al. Analysis of Industrial Expectations for the Integration of Human Factors from the Early Design Phase. Sci. Iran. 2022, 31, 2128–2138. https://doi.org/10.24200/sci.2022.56606.4807.

  • 280.

    Karakoltzidis, A.; Battistelli, C.L.; Bossa, C.; et al. The FAIR Principles as a Key Enabler to Operationalize Safe and Sustainable by Design Approaches. RSC Sustain. 2024, 2, 3464–3477. https://doi.org/10.1039/D4SU00171K.

  • 281.

    Zhang, H.; Li, H.; Li, N. MeshLink: A Surface Structured Mesh Generation Framework to Facilitate Automated Data Linkage. Adv. Eng. Softw. 2024, 194, 103661. https://doi.org/10.1016/j.advengsoft.2024.103661.

  • 282.

    Nakhal A., A.J.; Patriarca, R.; De Carlo, F.; et al. A System-Theoretic Fuzzy Analysis (STheFA) for Systemic Safety Assessment. Process Saf. Environ. Prot. 2023, 177, 1181–1196. https://doi.org/10.1016/j.psep.2023.07.014.

  • 283.

    Saba, T.; Haseeb, K.; Rehman, A.; et al. Blockchain-Enabled Intelligent IoT Protocol for High-Performance and Secured Big Financial Data Transaction. IEEE Trans. Comput. Soc. Syst. 2024, 11, 1667–1674. https://doi.org/10.1109/TCSS.2023.3268592.

  • 284.

    Jun, X.; Ai, J.; Zheng, L.; et al. Impact of Information Technology and Industrial Development on Corporate ESG Practices: Evidence from a Pilot Program in China. Econ. Model. 2024, 139, 106806. https://doi.org/10.1016/j.econmod.2024.106806.

  • 285.

    Nair, T.M.B.; Sarma, V.; Lotliker, A.A.; et al. An Integrated Buoy-Satellite Based Coastal Water Quality Nowcasting System: India’s Pioneering Efforts towards Addressing UN Ocean Decade Challenges. J. Environ. Manag. 2024, 354, 120477. https://doi.org/10.1016/j.jenvman.2024.120477.

  • 286.

    Yang, Y.; Elsinghorst, R.; Martinez, J.J.; et al. A Real-Time Underwater Acoustic Telemetry Receiver with Edge Computing for Studying Fish Behavior and Environmental Sensing. IEEE Internet Things J. 2022, 9, 17821–17831. https://doi.org/10.1109/JIOT.2022.3164092.

  • 287.

    Quevy, Q.; Lamrini, M.; Chkouri, M.; et al. Open Sensing System for Long Term, Low Cost Water Quality Monitoring. IEEE Open J. Ind. Electron. Soc. 2023, 4, 27–41. https://doi.org/10.1109/OJIES.2022.3233919.

  • 288.

    Kristiani, E.; Yang, C.-T.; Huang, C.-Y.; et al. On Construction of Sensors, Edge, and Cloud (iSEC) Framework for Smart System Integration and Applications. IEEE Internet Things J. 2021, 8, 309–319. https://doi.org/10.1109/JIOT.2020.3004244.

  • 289.

    Samuel, D.J.; Sermet, Y.; Cwiertny, D.; et al. Integrating Vision-based AI and Large Language Models for Real-time Water Pollution Surveillance. Water Environ. Res. 2024, 96, e11092. https://doi.org/10.1002/wer.11092.

  • 290.

    Chen, J.-C.; Chen, C.-C.; Shen, C.-H.; et al. User Integration in Two IoT Sustainable Services by Evaluation Grid Method. IEEE Internet Things J. 2022, 9, 2242–2252. https://doi.org/10.1109/JIOT.2021.3091688.

  • 291.

    Bandara, R.M.P.N.S.; Jayasignhe, A.B.; Retscher, G. The Integration of IoT (Internet of Things) Sensors and Location-Based Services for Water Quality Monitoring: A Systematic Literature Review. Sensors 2025, 25, 1918. https://doi.org/10.3390/s25061918.

  • 292.

    Mutunga, T.; Sinanovic, S.; Harrison, C.S. Integrating Wireless Remote Sensing and Sensors for Monitoring Pesticide Pollution in Surface and Groundwater. Sensors 2024, 24, 3191. https://doi.org/10.3390/s24103191.

  • 293.

    Górka-Kostrubiec, B.; Werner, T.; Karasiński, G. Measuring Magnetic Susceptibility of Particulate Matter Collected on Filters. Environ. Sci. Pollut. Res. 2023, 31, 4733–4746. https://doi.org/10.1007/s11356-023-31416-5.

  • 294.

    Amri, E.; Dardouillet, P.; Benoit, A.; et al. Offshore Oil Slick Detection: From Photo-Interpreter to Explainable Multi-Modal Deep Learning Models Using SAR Images and Contextual Data. Remote Sens. 2022, 14, 3565. https://doi.org/10.3390/rs14153565.

  • 295.

    Manikandan, S.; Kaviya, R.S.; Shreeharan, D.H.; et al. Artificial Intelligence-driven Sustainability: Enhancing Carbon Capture for Sustainable Development Goals—A Review. Sustain. Dev. 2025, 33, 2004–2029. https://doi.org/10.1002/sd.3222.

  • 296.

    Zaman, N.A.F.K.; Kanniah, K.D.; Kaskaoutis, D.G.; et al. Improving the Quantification of Fine Particulates (PM2.5) Concentrations in Malaysia Using Simplified and Computationally Efficient Models. J. Clean. Prod. 2024, 448, 141559. https://doi.org/10.1016/j.jclepro.2024.141559.

  • 297.

    Lu, Y.; Wang, J.; Liu, T.; et al. Integration of Dynamic Slow Feature Analysis and Deep Neural Networks for Subway Indoor PM2.5 Prediction. IEEE Trans. Instrum. Meas. 2024, 73, 2533209. https://doi.org/10.1109/TIM.2024.3476591.

  • 298.

    Etemadfard, H.; Sadeghi, V.; Ali, F.H.; et al. CO Emissions Modeling and Prediction Using ANN and GIS. Pollution 2021, 7, 739–747. https://doi.org/10.22059/poll.2021.323244.1077.

  • 299.

    Radman, A.; Mahdianpari, M.; Varon, D.J.; et al. S2MetNet: A Novel Dataset and Deep Learning Benchmark for Methane Point Source Quantification Using Sentinel-2 Satellite Imagery. Remote Sens. Environ. 2023, 295, 113708. https://doi.org/10.1016/j.rse.2023.113708.

  • 300.

    Li, F.; Su, Z.; Wang, G.-M. An Effective Integrated Control with Intelligent Optimization for Wastewater Treatment Process. J. Ind. Inf. Integr. 2021, 24, 100237. https://doi.org/10.1016/j.jii.2021.100237.

  • 301.

    Polizzi, C.; Falcioni, S.; Mannucci, A.; et al. Integrating Online Differential Titrimetry and Dynamic Modelling as Innovative Energy Saving Strategy in a Large Industrial WWTP. Clean Technol. Environ. Policy 2022, 24, 1771–1780. https://doi.org/10.1007/s10098-022-02285-2.

  • 302.

    Chakrabortty, R.; Kumar, A.; Mishuk, S.R.; et al. Assessment of Urban Environment Quality Using Analytical Hierarchical Process and Multi-Dimensional Decomposition Analysis for Mumbai Metropolitan Region, India. Adv. Space Res. 2025, 75, 2792–2809. https://doi.org/10.1016/j.asr.2024.11.058.

  • 303.

    Nguyen, D.-T.; Truong, M.-H.; Ngo, T.-P.-U.; et al. GIS-Based Simulation for Landfill Site Selection in Mekong Delta: A Specific Application in Ben Tre Province. Remote Sens. 2022, 14, 5704. https://doi.org/10.3390/rs14225704.

  • 304.

    Sadhasivam, N.; Mohideen, A.R.S.; Alankar, B. Optimisation of Landfill Sites for Solid Waste Disposal in Thiruverumbur Taluk of Tiruchirappalli District, India. Environ. Earth Sci. 2020, 79, 522. https://doi.org/10.1007/s12665-020-09264-0.

  • 305.

    Sinha, S. Appropriate Solid Waste Dumping Site Selection for Patna City by Using GIS-TOPSIS Method. J. Earth Syst. Sci. 2024, 133, 98. https://doi.org/10.1007/s12040-024-02296-1.

  • 306.

    Wu, Y.; Li, Y. Digital Twin-Driven Performance Optimization for Hazardous Waste Landfill Systems. Math. Probl. Eng. 2022, 2022, 7778952. https://doi.org/10.1155/2022/7778952.

  • 307.

    Wang, L.; Wang, H.; Li, Y.; et al. The Design and Implementation of an Intelligent Carbon Data Management Platform for Digital Twin Industrial Parks. Energies 2024, 17, 5972. https://doi.org/10.3390/en17235972.

  • 308.

    Feng, X.; Wei, C.; Xue, X.; et al. RST-DeepLabv3+: Multi-Scale Attention for Tailings Pond Identification with DeepLab. Remote Sens. 2025, 17, 411. https://doi.org/10.3390/rs17030411.

  • 309.

    Tang, L.; Chen, W.; Luo, X.; et al. Multi-Technological Integration in a Smelting Site: Visualizing Pollution Characteristics and Migration Pattern. J. Hazard. Mater. 2023, 459, 132135. https://doi.org/10.1016/j.jhazmat.2023.132135.

  • 310.

    Zhang, S.; Cheng, Q. Data Analysis and Management System Design of Contaminated Site Based on Intelligent Data Acquisition Vehicle and 5G Communication. Int. J. Commun. Syst. 2021, 34, e4555. https://doi.org/10.1002/dac.4555.

  • 311.

    Bulbula, S.T.; Serur, A.B. Groundwater Potential and Recharge Zone Mapping Using GIS and Remote Sensing Techniques: The Melka Kunture Watershed in Ethiopia. Discov. Sustain. 2024, 5, 328. https://doi.org/10.1007/s43621-024-00521-x.

  • 312.

    Amponsah, T.Y.; Danuor, S.K.; Wemegah, D.D.; et al. Groundwater Potential Characterisation over the Voltaian Basin Using Geophysical, Geological, Hydrological and Topographical Datasets. J. Afr. Earth Sci. 2022, 192, 104558. https://doi.org/10.1016/j.jafrearsci.2022.104558.

  • 313.

    Saro, L.; Fetemeh, R. Status of Groundwater Potential Mapping Research Using GIS and Machine Learning. Korean J. Remote Sens. 2020, 36, 1277–1290.

  • 314.

    Elmorsi, R.R.; El-Alfy, M.A.; Abou-El-Sherbini, K.S. Evaluation of the Trophic State Predicted from Lab and Landsat Data of Western Coastal Water of Suez Bay, Egypt. GeoJournal 2021, 86, 2809–2821. https://doi.org/10.1007/s10708-020-10230-3.

  • 315.

    Wang, X.; Slawinska, J.; Giannakis, D. Extended-Range Statistical ENSO Prediction through Operator-Theoretic Techniques for Nonlinear Dynamics. Sci. Rep. 2020, 10, 2636. https://doi.org/10.1038/s41598-020-59128-7.

  • 316.

    Kaiser, S.; Boike, J.; Grosse, G.; et al. Multisource Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in AlaSka (SIRIUS). Earth Syst. Sci. Data 2024, 16, 3719–3753. https://doi.org/10.5194/essd-16-3719-2024.

  • 317.

    Wu, H.; Duan, H.-F.; Lai, W.W.L.; et al. Leveraging Optical Communication Fiber and AI for Distributed Water Pipe Leak Detection. IEEE Commun. Mag. 2024, 62, 126–132. https://doi.org/10.1109/MCOM.003.2200643.

  • 318.

    Liu, C.; Muravskyi, V.; Wei, W. Evolution of Blockchain Accounting Literature from the Perspective of CiteSpace (2013–2023). Heliyon 2024, 10, e32097. https://doi.org/10.1016/j.heliyon.2024.e32097.

  • 319.

    Li, X.; Sigov, A.; Ratkin, L.; et al. Artificial Intelligence Applications in Finance: A Survey. J. Manag. Anal. 2023, 10, 676–692. https://doi.org/10.1080/23270012.2023.2244503.

  • 320.

    Yen, C.-Y.; Ju, Y.-L.; Sung, S.-F.; et al. Prediction of Sovereign Credit Risk Rating Using Sensing Technology. Sens. Mater. 2021, 33, 3053. https://doi.org/10.18494/SAM.2021.3244.

  • 321.

    Lei, M.; Xu, L.; Liu, T.; et al. Integration of Privacy Protection and Blockchain-Based Food Safety Traceability: Potential and Challenges. Foods 2022, 11, 2262. https://doi.org/10.3390/foods11152262.

  • 322.

    Li, Y.; Wang, P.; Feng, Q.; et al. Landslide Detection Based on Shipborne Images and Deep Learning Models: A Case Study in the Three Gorges Reservoir Area in China. Landslides 2023, 20, 547–558. https://doi.org/10.1007/s10346-022-01997-2.

  • 323.

    Tomás, R.; Díaz, E.; Szeibert, W.T.; et al. Geomorphological Characterization, Remote Sensing Monitoring, and Modeling of a Slow-Moving Landslide in Alcoy (Southern Spain). Landslides 2023, 20, 1293–1301. https://doi.org/10.1007/s10346-023-02032-8.

  • 324.

    Li, B.; Zhang, H.; Zhang, H.; et al. Development of a Robot for in Situ Detection of Loess Geological Information Based on Machine Vision. Acta Geophys. 2025, 73, 2523–2549. https://doi.org/10.1007/s11600-024-01497-y.

  • 325.

    Hu, X.; Cai, H.; Alazab, M.; et al. Federated Learning in Industrial IoT: A Privacy-Preserving Solution That Enables Sharing of Data in Hydrocarbon Explorations. IEEE Trans. Ind. Inform. 2024, 20, 4337–4346. https://doi.org/10.1109/TII.2023.3306931.

  • 326.

    Luo, C.; Wang, C.; Lu, G.; et al. High-Resolution Isochronous Stratigraphic Framework through Well-Seismic Integration in Tight Sandstone: A Case Study of Luodai Gas Field, Sichuan, China. Front. Earth Sci. 2024, 12, 1445770. https://doi.org/10.3389/feart.2024.1445770.

  • 327.

    Oguntoyinbo, P.O.; Iyiola, O.M.; Komolafe, A.A. Integrated GIS-Based and Geophysical Techniques in Groundwater Potential Zonation: A Case Study of Jos North Local Government Area. Sustain. Water Resour. Manag. 2024, 10, 58. https://doi.org/10.1007/s40899-023-01028-5.

  • 328.

    Pérez-Hernández, E.; Ferrer-Valero, N.; Hernández-Calvento, L. Lost and Preserved Coastal Landforms after Urban Growth. The Case of Las Palmas de Gran Canaria City (Canary Islands, Spain). J. Coast. Conserv. 2020, 24, 26. https://doi.org/10.1007/s11852-020-00743-x.

  • 329.

    Firoozi, A.A.; Firoozi, A.A.; Aati, K.; et al. Integrating the Fourth Industrial Revolution into Geotechnical Engineering: Transformations, Challenges, and Future Directions. Ecol. Quest. 2025, 36, 1–41. https://doi.org/10.12775/EQ.2025.012.

  • 330.

    Huang, Q.; Xia, H.; Zhang, Z. Clustering Analysis of Integrated Rural Land for Three Industries Using Deep Learning and Artificial Intelligence. IEEE Access 2023, 11, 110530–110543. https://doi.org/10.1109/ACCESS.2023.3321894.

  • 331.

    Ganasegeran, K.; Manaf, M.R.A.; Safian, N.; et al. GIS-Based Assessments of Neighborhood Food Environments and Chronic Conditions: An Overview of Methodologies. Annu. Rev. Public Health 2024, 45, 109–132. https://doi.org/10.1146/annurev-publhealth-101322-031206.

  • 332.

    Jianjia, H.; Gang, L.; Xiaojun, T.; et al. Research on Collaborative Recommendation of Dynamic Medical Services Based on Cloud Platforms in the Industrial Interconnection Environment. Technol. Forecast. Soc. Chang. 2021, 170, 120895. https://doi.org/10.1016/j.techfore.2021.120895.

  • 333.

    Uysal, M.P. Machine Learning-Enabled Healthcare Information Systems in View of Industrial Information Integration Engineering. J. Ind. Inf. Integr. 2022, 30, 100382. https://doi.org/10.1016/j.jii.2022.100382.

  • 334.

    Karatas, M.; Eriskin, L.; Deveci, M.; et al. Big Data for Healthcare Industry 4.0: Applications, Challenges and Future Perspectives. Expert Syst. Appl. 2022, 200, 116912. https://doi.org/10.1016/j.eswa.2022.116912.

  • 335.

    Fang, Z.; Tang, K.; Lou, L.; et al. A Silicon-Based Radio Platform for Integrated Edge Sensing and Communication Toward Sustainable Healthcare. IEEE Trans. Microw. Theory Tech. 2023, 71, 1296–1311. https://doi.org/10.1109/TMTT.2022.3222216.

  • 336.

    Lee, K.H.; Urtnasan, E.; Hwang, S.; et al. Concept and Proof of the Lifelog Bigdata Platform for Digital Healthcare and Precision Medicine on the Cloud. Yonsei Med. J. 2022, 63, S84. https://doi.org/10.3349/ymj.2022.63.S84.

  • 337.

    Li, J. Evaluating the Trend of Digital Technology Adoption in Health Industry: The Industrial Integration and Adaptation to the HITECH Act. Int. J. Health Plan. Manag. 2025, 40, 948–960. https://doi.org/10.1002/hpm.3935.

  • 338.

    Fu, L.; Li, L.; Li, L.; et al. Impact of Hospital Size on Healthcare Information System Effectiveness: Evidence from Healthcare Data Analytics. J. Manag. Anal. 2022, 9, 211–231. https://doi.org/10.1080/23270012.2022.2036647.

  • 339.

    Ha, T.; Kang, S.; Yeo, N.Y.; et al. Status of My Health Way and Suggestions for Widespread Implementation, Emphasizing the Utilization and Practical Use of Personal Medical Data. Healthc. Inform. Res. 2024, 30, 103–112. https://doi.org/10.4258/hir.2024.30.2.103.

  • 340.

    Malik, S.; Rouf, R.; Mazur, K.; et al. The Industry Internet of Things (IIoT) as a Methodology for Autonomous Diagnostics in Aerospace Structural Health Monitoring. Aerospace 2020, 7, 64. https://doi.org/10.3390/aerospace7050064.

  • 341.

    Epizitone, A. Framework to Develop a Resilient and Sustainable Integrated Information System for Health Care Applications: A Review. Int. J. Adv. Comput. Sci. Appl. 2022, 13. https://doi.org/10.14569/IJACSA.2022.0130758.

  • 342.

    Kim, H.; Park, C.; Kim, J.H.; et al. Multimodal Reinforcement Learning for Embedding Networks and Medication Recommendation in Parkinson’s Disease. IEEE Access 2024, 12, 74251–74267. https://doi.org/10.1109/ACCESS.2024.3405009.

  • 343.

    Gómez-Bocanegra, V.; García-de La Torre, G.S.; Pantoja Meléndez, C.A.; et al. Blockchain aplicado en afecciones mamarias: Desafíos y consideraciones éticas. Rev. Senol. Patol. Mamaria 2025, 38, 100652. https://doi.org/10.1016/j.senol.2024.100652.

  • 344.

    Liu, S.; Li, X.; Jiang, Y.; et al. Integrated Learning Approach Based on Fused Segmentation Information for Skeletal Fluorosis Diagnosis and Severity Grading. IEEE Trans. Ind. Inform. 2021, 17, 7554–7563. https://doi.org/10.1109/TII.2021.3055397.

  • 345.

    Guo, C.; Tian, P.; Choo, K.K.R. Enabling Privacy-Assured Fog-Based Data Aggregation in E-Healthcare Systems. IEEE Trans. Ind. Inform. 2021, 17, 1948–1957. https://doi.org/10.1109/TII.2020.2995228.

  • 346.

    Ge, X.; Yu, J.; Hao, R.; et al. Verifiable Keyword Search Supporting Sensitive Information Hiding for the Cloud-Based Healthcare Sharing System. IEEE Trans. Ind. Inform. 2022, 18, 5573–5583. https://doi.org/10.1109/TII.2021.3126611.

  • 347.

    Al-Turjman, F.; Deebak, B.D. A Proxy-Authorized Public Auditing Scheme for Cyber-Medical Systems Using AI-IoT. IEEE Trans. Ind. Inform. 2022, 18, 5371–5382. https://doi.org/10.1109/TII.2021.3126316.

  • 348.

    Kumar, A.; Krishnamurthi, R.; Nayyar, A.; et al. A Novel Smart Healthcare Design, Simulation, and Implementation Using Healthcare 4.0 Processes. IEEE Access 2020, 8, 118433–118471. https://doi.org/10.1109/ACCESS.2020.3004790.

  • 349.

    Das, P.; Kumar, N.; Jain, C.; et al. Intelligent IoT-Enabled Healthcare Solutions Implementing Federated Meta-Learning with Blockchain. J. Ind. Inf. Integr. 2025, 45, 100797. https://doi.org/10.1016/j.jii.2025.100797.

  • 350.

    Khan, A.A.; Bourouis, S.; Kamruzzaman, M.M.; et al. Data Security in Healthcare Industrial Internet of Things with Blockchain. IEEE Sens. J. 2023, 23, 25144–25151. https://doi.org/10.1109/JSEN.2023.3273851.

  • 351.

    Khan, N.; Ma, Z.; Ullah, A.; et al. DCA-IoMT: Knowledge-Graph-Embedding-Enhanced Deep Collaborative Alert Recommendation Against COVID-19. IEEE Trans. Ind. Inform. 2022, 18, 8924–8935. https://doi.org/10.1109/TII.2022.3159710.

  • 352.

    Adil, M.; Attique, M.; Jadoon, M.M.; et al. HOPCTP: A Robust Channel Categorization Data Preservation Scheme for Industrial Healthcare Internet of Things. IEEE Trans. Ind. Inform. 2022, 18, 7151–7161. https://doi.org/10.1109/TII.2022.3148287.

  • 353.

    Rajput, A.S.; Raman, B. Privacy-Preserving Distribution and Access Control of Personalized Healthcare Data. IEEE Trans. Ind. Inform. 2022, 18, 5584–5591. https://doi.org/10.1109/TII.2021.3138993.

  • 354.

    Stephanie, V.; Khalil, I.; Atiquzzaman, M.; et al. Trustworthy Privacy-Preserving Hierarchical Ensemble and Federated Learning in Healthcare 4.0 With Blockchain. IEEE Trans. Ind. Inform. 2023, 19, 7936–7945. https://doi.org/10.1109/TII.2022.3214998.

  • 355.

    Wang, X.; Hu, J.; Lin, H.; et al. Federated Learning-Empowered Disease Diagnosis Mechanism in the Internet of Medical Things: From the Privacy-Preservation Perspective. IEEE Trans. Ind. Inform. 2023, 19, 7905–7913. https://doi.org/10.1109/TII.2022.3210597.

  • 356.

    Gu, Y.; Kalibatseva, Z.; Song, X.; et al. Effective Use of Online COVID-19 Information and eHealth Information Literacy among US University Students. J. Am. Coll. Health 2024, 72, 1458–1465. https://doi.org/10.1080/07448481.2022.2080505.

  • 357.

    Reddi, S.; Rao, P.M.; Saraswathi, P.; et al. Privacy-Preserving Electronic Medical Record Sharing for IoT-Enabled Healthcare System Using Fully Homomorphic Encryption, IOTA, and Masked Authenticated Messaging. IEEE Trans. Ind. Inform. 2024, 20, 10802–10813. https://doi.org/10.1109/TII.2024.3397343.

  • 358.

    Gao, Y.; Lin, H.; Chen, Y.; et al. Blockchain and SGX-Enabled Edge-Computing-Empowered Secure IoMT Data Analysis. IEEE Internet Things J. 2021, 8, 15785–15795. https://doi.org/10.1109/JIOT.2021.3052604.

  • 359.

    Ghayvat, H.; Zuhair, M.; Shukla, N.; et al. Healthcare-CT: Solid PoD and Blockchain-Enabled Cyber Twin Approach for Healthcare 5.0 Ecosystems. IEEE Internet Things J. 2024, 11, 6119–6130. https://doi.org/10.1109/JIOT.2023.3312448.

  • 360.

    Dhingra, S.; Raut, R.; Naik, K.; et al. Blockchain Technology Applications in Healthcare Supply Chains—A Review. IEEE Access 2024, 12, 11230–11257. https://doi.org/10.1109/ACCESS.2023.3348813.

  • 361.

    Kim, H.; Lee, S.; Kwon, H.; et al. Design and Implementation of a Personal Health Record Platform Based on Patient-Consent Blockchain Technology. KSII Trans. Internet Inf. Syst. 2021, 15. https://doi.org/10.3837/tiis.2021.12.008.

  • 362.

    Jeon, E.; Sohn, S.Y. Digital Therapeutics Product Repositioning via Multiplex Link Prediction Based on Genomic, Chemical, and Technological Information. IEEE J. Biomed. Health Inform. 2023, 27, 2660–2669. https://doi.org/10.1109/JBHI.2022.3200692.

  • 363.

    Sagdic, K.; Eş, I.; Sitti, M.; et al. Smart Materials: Rational Design in Biosystems via Artificial Intelligence. Trends Biotechnol. 2022, 40, 987–1003. https://doi.org/10.1016/j.tibtech.2022.01.005.

  • 364.

    Guedj, M.; Swindle, J.; Hamon, A.; et al. Industrializing AI-Powered Drug Discovery: Lessons Learned from the Patrimony Computing Platform. Expert Opin. Drug Discov. 2022, 17, 815–824. https://doi.org/10.1080/17460441.2022.2095368.

  • 365.

    Tanveer, M.; Sajid, M.; Akhtar, M.; et al. Fuzzy Deep Learning for the Diagnosis of Alzheimer’s Disease: Approaches and Challenges. IEEE Trans. Fuzzy Syst. 2024, 32, 5477–5492. https://doi.org/10.1109/TFUZZ.2024.3409412.

  • 366.

    Wu, E.Q.; Peng, X.-Y.; Chen, S.-D.; et al. Detecting Alzheimer’s Dementia Degree. IEEE Trans. Cogn. Dev. Syst. 2022, 14, 116–125. https://doi.org/10.1109/TCDS.2020.3015131.

  • 367.

    Tai, Y.; Qian, K.; Huang, X.; et al. Intelligent Intraoperative Haptic-AR Navigation for COVID-19 Lung Biopsy Using Deep Hybrid Model. IEEE Trans. Ind. Inform. 2021, 17, 6519–6527. https://doi.org/10.1109/TII.2021.3052788.

  • 368.

    Nie, Y.; Sommella, P.; Carratu, M.; et al. Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning. IEEE Access 2022, 10, 95716–95747. https://doi.org/10.1109/ACCESS.2022.3199613.

  • 369.

    Du, R.F.; Carbonell, E.L.; Huang, J.; et al. Ethics of Foundation Models in Computational Pathology: Overview of Contemporary Issues and Future Implications. IEEE Trans. Med. Imaging 2025. https://doi.org/10.1109/TMI.2025.3551913.

  • 370.

    Siddique, A.A.; Boulila, W.; Alshehri, M.S.; et al. Privacy-Enhanced Pneumonia Diagnosis: IoT-Enabled Federated Multi-Party Computation in Industry 5.0. IEEE Trans. Consum. Electron. 2024, 70, 1923–1939. https://doi.org/10.1109/TCE.2023.3319565.

  • 371.

    Xames, M.D.; Topcu, T.G. A Systematic Literature Review of Digital Twin Research for Healthcare Systems: Research Trends, Gaps, and Realization Challenges. IEEE Access 2024, 12, 4099–4126. https://doi.org/10.1109/ACCESS.2023.3349379.

  • 372.

    Deng, W.; Nguyen, K.T.P.; Gogu, C.; et al. Enhancing Prognostics for Sparse Labeled Data Using Advanced Contrastive Self-Supervised Learning with Downstream Integration. Eng. Appl. Artif. Intell. 2024, 138, 109268. https://doi.org/10.1016/j.engappai.2024.109268.

  • 373.

    Palmal, S.; Saha, S.; Arya, N.; et al. CAGCL: Predicting Short- and Long-Term Breast Cancer Survival With Cross-Modal Attention and Graph Contrastive Learning. IEEE J. Biomed. Health Inform. 2024, 28, 7382–7391. https://doi.org/10.1109/JBHI.2024.3449756.

  • 374.

    Chen, Q.; Liu, Y.; Tan, B.; et al. Respiration and Activity Detection Based on Passive Radio Sensing in Home Environments. IEEE Access 2020, 8, 12426–12437. https://doi.org/10.1109/ACCESS.2020.2966126.

  • 375.

    Zhang, B.; Zhu, L.; Pei, Z.; et al. A Framework for Remote Interaction and Management of Home Care Elderly Adults. IEEE Sens. J. 2022, 22, 11034–11044. https://doi.org/10.1109/JSEN.2022.3170295.

  • 376.

    Kotzias, K.; Bukhsh, F.A.; Arachchige, J.J.; et al. Industry 4.0 and Healthcare: Context, Applications, Benefits and Challenges. IET Softw. 2023, 17, 195–248. https://doi.org/10.1049/sfw2.12074.

  • 377.

    Son, H.; Vora, A.; Pandey, G.; et al. Infrastructure Enabled Guided Navigation for Visually Impaired. IEEE Trans. Intell. Transp. Syst. 2025, 26, 6764–6777. https://doi.org/10.1109/TITS.2025.3538317.

  • 378.

    Privitera, D.; Bellissima, S.; Bartolini, S. Adaptive Dosing Control System Through ARIMA Model for Peristaltic Pumps. IEEE Access 2023, 11, 99558–99572. https://doi.org/10.1109/ACCESS.2023.3314379.

  • 379.

    Kavasidis, I.; Lallas, E.; Mountzouris, G.; et al. A Federated Learning Framework for Enforcing Traceability in Manufacturing Processes. IEEE Access 2023, 11, 57585–57597. https://doi.org/10.1109/ACCESS.2023.3282316.

  • 380.

    Gaikwad, V.D.; Ananthakumaran, S. A Review: Security and Privacy for Health Care Application in Wireless Body Area Networks. Wirel. Pers. Commun. 2023, 130, 673–691. https://doi.org/10.1007/s11277-023-10305-7.

  • 381.

    Dobson, R.; Stowell, M.; Warren, J.; et al. Use of Consumer Wearables in Health Research: Issues and Considerations. J. Med. Internet Res. 2023, 25, e52444. https://doi.org/10.2196/52444.

  • 382.

    Fawad, M.; Salamak, M.; Hanif, M.U.; et al. Integration of Bridge Health Monitoring System with Augmented Reality Application Developed Using 3D Game Engine–Case Study. IEEE Access 2024, 12, 16963–16974. https://doi.org/10.1109/ACCESS.2024.3358843.

  • 383.

    Iman, U.R.; Zada, M.; Basir, A.; et al. IoT-Enabled Real-Time Health Monitoring via Smart Textile Integration with LoRa Technology Across Diverse Environments. IEEE Trans. Ind. Inform. 2024, 20, 12803–12813. https://doi.org/10.1109/TII.2024.3424517.

  • 384.

    Gigli, L.; Zyrianoff, I.; Zonzini, F.; et al. Next Generation Edge-Cloud Continuum Architecture for Structural Health Monitoring. IEEE Trans. Ind. Inform. 2024, 20, 5874–5887. https://doi.org/10.1109/TII.2023.3337391.

  • 385.

    Yazdannik, S.; Sanisales, S.; Tayefi, M. A Novel Quadrotor Carrying Payload Concept via PID with Feedforward Terms. Int. J. Intell. Unmanned Syst. 2024, 12, 331–347. https://doi.org/10.1108/IJIUS-10-2023-0141.

  • 386.

    Wang, W.; He, Y.; Li, F.; et al. Digital Twin Rehabilitation System Based on Self-Balancing Lower Limb Exoskeleton. Technol. Health Care 2023, 31, 103–115. https://doi.org/10.3233/THC-220087.

  • 387.

    Wang, Z.; Tang, S.; Guo, G.; et al. Adaptive Quality Control with Uncertainty for a Pharmaceutical Cyber-Physical System Based on Data and Knowledge Integration. IEEE Trans. Ind. Inform. 2024, 20, 3339–3350. https://doi.org/10.1109/TII.2023.3306355.

  • 388.

    Putra, K.T.; Arrayyan, A.Z.; Hayati, N.; et al. A Review on the Application of Internet of Medical Things in Wearable Personal Health Monitoring: A Cloud-Edge Artificial Intelligence Approach. IEEE Access 2024, 12, 21437–21452. https://doi.org/10.1109/ACCESS.2024.3358827.

  • 389.

    Li, S.; Wang, R.; Zheng, P.; et al. Towards Proactive Human–Robot Collaboration: A Foreseeable Cognitive Manufacturing Paradigm. J. Manuf. Syst. 2021, 60, 547–552. https://doi.org/10.1016/j.jmsy.2021.07.017.

  • 390.

    Yang, X.; Weng, C.; Jiao, L.; et al. ATD-GCN: A Human Activity Recognition Approach for Human-Robot Collaboration Based on Adaptive Skeleton Tree-Decomposition. Robot. Comput. Integr. Manuf. 2025, 95, 103019. https://doi.org/10.1016/j.rcim.2025.103019.

  • 391.

    Yang, Y.; Zhang, Y. Design of Human-Machine Integration System to Meet Diverse Interactive Tasks. Int. J. Hum. Comput. Interact. 2024, 1–14. https://doi.org/10.1080/10447318.2024.2307689.

  • 392.

    Wang, Z.; Li, X.; Zhao, H.; et al. Geometry and Force Guided Robotic Assembly with Large Initial Deviations for Electrical Connectors. IEEE Trans. Autom. Sci. Eng. 2025, 22, 8095–8107. https://doi.org/10.1109/TASE.2024.3477918.

  • 393.

    Aslam, S.; Kumar, K.; Zhou, P.; et al. DartBot: Overhand Throwing of Deformable Objects with Tactile Sensing and Reinforcement Learning. IEEE Trans. Autom. Sci. Eng. 2025, 22, 13644–136661. https://doi.org/10.1109/TASE.2025.3556875.

  • 394.

    Grigore, L.S.; Priescu, I.; Joita, D.; et al. The Integration of Collaborative Robot Systems and Their Environmental Impacts. Processes 2020, 8, 494. https://doi.org/10.3390/pr8040494.

  • 395.

    Dai, W.; Zhang, Y.; Zhang, Y.; et al. Automatic Information Model Generation for Industrial Edge Applications Based on IEC 61499 and OPC UA. IEEE Trans. Ind. Inform. 2023, 19, 6093–6104. https://doi.org/10.1109/TII.2022.3191365.

  • 396.

    Hu, G.; Tong, C.; Zeng, J.; et al. Joint Time-Serial Variation Analysis for Fault Monitoring of Chemical Processes. Process Saf. Environ. Prot. 2025, 196, 106867. https://doi.org/10.1016/j.psep.2025.106867.

  • 397.

    Baek, S. System Integration for Predictive Process Adjustment and Cloud Computing-Based Real-Time Condition Monitoring of Vibration Sensor Signals in Automated Storage and Retrieval Systems. Int. J. Adv. Manuf. Technol. 2021, 113, 955–966. https://doi.org/10.1007/s00170-021-06652-z.

  • 398.

    Korodi, A.; Nicolae, A.; Brisc, D.; et al. Long Short-Term Memory-Based Prediction Solution Inside a Decentralized Proactive Historian for Water Industry 4.0. IEEE Access 2024, 12, 99526–99536. https://doi.org/10.1109/ACCESS.2024.3428866.

  • 399.

    Liu, X.; Yu, W.; Liang, F.; et al. On Deep Reinforcement Learning Security for Industrial Internet of Things. Comput. Commun. 2021, 168, 20–32. https://doi.org/10.1016/j.comcom.2020.12.013.

  • 400.

    Kuts, V.; Marvel, J.A.; Aksu, M.; et al. Digital Twin as Industrial Robots Manipulation Validation Tool. Robotics 2022, 11, 113. https://doi.org/10.3390/robotics11050113.

  • 401.

    Sartaj, H.; Iqbal, M.Z.; Khan, M.U. Testing Cockpit Display Systems of Aircraft Using a Model-Based Approach. Softw. Syst. Model. 2021, 20, 1977–2002. https://doi.org/10.1007/s10270-020-00844-z.

  • 402.

    Elser, A.; Lechler, A.; Verl, A. Process-Integrated Computerized Numerical Control: An Analysis on Process-Machine Coupling and Feed Scheduling. Int. J. Adv. Manuf. Technol. 2024, 135, 1291–1301. https://doi.org/10.1007/s00170-024-14437-3.

  • 403.

    Santhosh, R.; Sut, D.J.; Uma, M.; et al. Optimizing IRB1410 Industrial Robot Painting Processes through Taguchi Method and Fuzzy Logic Integration with Machine Learning. Int. J. Intell. Robot. Appl. 2024, 8, 380–400. https://doi.org/10.1007/s41315-024-00325-2.

  • 404.

    Mirbod, M.; Ghatari, A.R.; Saati, S.; et al. Industrial Parts Change Recognition Model Using Machine Vision, Image Processing in the Framework of Industrial Information Integration. J. Ind. Inf. Integr. 2022, 26, 100277. https://doi.org/10.1016/j.jii.2021.100277.

  • 405.

    Zhang, G.; Lu, Y.; Jiang, X.; et al. LGGFormer: A Dual-Branch Local-Guided Global Self-Attention Network for Surface Defect Segmentation. Adv. Eng. Inform. 2025, 64, 103099. https://doi.org/10.1016/j.aei.2024.103099.

  • 406.

    Fu, G.; Le, W.; Zhang, Z.; et al. A Surface Defect Inspection Model via Rich Feature Extraction and Residual-Based Progressive Integration CNN. Machines 2023, 11, 124. https://doi.org/10.3390/machines11010124.

  • 407.

    Yang, Z.-Y.; Chou, C.-W.; Lin, W.-C.; et al. A Novel Environmental Monitoring Strategy for Industrial Safety and Disaster Prevention Management Applications. Sens. Mater. 2020, 32, 2247. https://doi.org/10.18494/SAM.2020.2882.

  • 408.

    Gu, T.; Zhang, Y.; Wang, L.; et al. A Comprehensive Analysis of Multi-Strategic RIME Algorithm for UAV Path Planning in Varied Terrains. J. Ind. Inf. Integr. 2025, 43, 100742. https://doi.org/10.1016/j.jii.2024.100742.

  • 409.

    Li, S.; Gu, J.; Li, Z.; et al. A Visual SLAM-Based Lightweight Multi-Modal Semantic Framework for an Intelligent Substation Robot. Robotica 2024, 42, 2169–2183. https://doi.org/10.1017/S0263574724000511.

  • 410.

    Zheng, Z.; Li, S.; Deng, X.; et al. A Process-Oriented and Coalescent Analysis Method for Safety and Security in Railway Systems. IEEE Intell. Transp. Syst. Mag. 2025, 17, 38–54. https://doi.org/10.1109/MITS.2025.3550868.

  • 411.

    Wang, Y.; Li, D.; Li, L.; et al. A Novel Deep Learning Framework for Rolling Bearing Fault Diagnosis Enhancement Using VAE-Augmented CNN Model. Heliyon 2024, 10, e35407. https://doi.org/10.1016/j.heliyon.2024.35407.

  • 412.

    Hu, F.; Wang, W.; Zhou, J. Petri Nets-Based Digital Twin Drives Dual-Arm Cooperative Manipulation. Comput. Ind. 2023, 147, 103880. https://doi.org/10.1016/j.compind.2023.103880.

Share this article:
How to Cite
Li, J. Part I: Industrial Information Integration Review 2020–2025. Journal of Emerging Technologies With Industrial Applications 2026, 1 (1), 1.
RIS
BibTex
Copyright & License
article copyright Image
Copyright (c) 2026 by the authors.