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1
Ihimoyan, Moral Kuve, Akan Ime Ibokette, Felix Olugbenga Olumide, Onuh Matthew Ijiga, and Adeshina Akin Ajayi. "The Role of AI-Enabled Digital Twins in Managing Financial Data Risks for Small-Scale Business Projects in the United States." International Journal of Scientific Research and Modern Technology 3, no. 6 (2024): 12–39. https://doi.org/10.5281/zenodo.14598498.
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The rise of financial data risks in small-scale business projects poses significant challenges, particularly in ensuring data accuracy, security, and resilience against evolving cyber threats. This review examines the transformative role of AI-enabled digital twins as an innovative solution for managing these risks in the United States. Digital twins, virtual replicas of financial systems, use AI to simulate, monitor, and predict potential vulnerabilities in real-time, enabling proactive risk mitigation and enhanced decision-making. The paper explores the integration of AI technologies such as machine learning and natural language processing within digital twins, emphasizing their capabilities in anomaly detection, data validation, and predictive analytics. Furthermore, it highlights case studies demonstrating the practical implementation of AI-enabled digital twins in financial risk management for small businesses. By addressing regulatory compliance and scalability concerns, this paper outlines a pathway for adopting digital twin technology to foster robust financial data governance in small-scale business environments.
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Phillips, Ibukun, and C. Robert Kenley. "Validation Framework of a Digital Twin: A System Identification Approach." INCOSE International Symposium 34, no. 1 (2024): 249–67. http://dx.doi.org/10.1002/iis2.13145.
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AbstractThe constant improvement and developments in Artificial Intelligence/Machine learning models coupled with increased computing power have led to the incorporation of AI/ML for simulating learning and problem‐solving in simple and complex engineering systems. This latent uncertainty and unpredictable characteristics of AI‐enabled systems challenges engineers and industry stakeholders who care about ensuring the right systems are built (system validation). Digital Twins are an excellent example of such AI‐enabled systems due to their data‐dependent nature when tasked with replicating, monitoring, and updating physical assets for structural health monitoring and control. However, Digital Twins' system validation has not been well‐researched. This study delves into existing research and frameworks for validating Digital Twins and proposes a novel model‐centric validation framework based on system identification techniques. As a case study, we apply this model‐centric validation framework towards partially validating a Digital Twin for a single‐heat‐pipe test article for a Microre‐actor Agile Non‐nuclear Experimental Testbed.
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Subhasis, Kundu. "IoT-Enabled Digital Twins for Personalized Healthcare: Real-Time AI Models for Predictive Health and Targeted Treatment." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 6 (2020): 1–6. https://doi.org/10.5281/zenodo.15086790.
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This study examines the incorporation of Internet of Things (IoT) devices and digital twin technology in personalized healthcare. It explores how artificial intelligence models can leverage continuous sensor data in real-time to create precise digital representations of patients. This research also delves into the potential applications of these digital twins for early disease identification, health prediction analysis, and tailored treatment strategies. This paper presents a framework for deploying IoT-enabled digital twins in medical environments while also discussing the challenges and opportunities associated with this technology. Additionally, the study addresses ethical considerations and privacy concerns related to the use of digital twins in healthcare. The paper concludes by providing case studies that showcase the successful implementation of this technology in various medical contexts and outlines future research directions in this rapidly advancing field.
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Li, Yankun. "AI-Enhanced Digital Twins for Energy Efficiency and Carbon Footprint Reduction in Smart City Infrastructure." Applied and Computational Engineering 118, no. 1 (2025): 42–47. https://doi.org/10.54254/2755-2721/2025.20569.
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The combination of Artificial Intelligence (AI) and Digital Twin technology provides disruptive potential to increase energy efficiency and carbon footprint in smart city infrastructure. Digital Twins virtual copies of the real-world systems are augmented by AI algorithms that enable continuous monitoring, predictive analysis and optimization. In this paper, we explore the use of AI-based Digital Twins on smart buildings, transport networks and smart grids to save significant amounts of energy and drive sustainability. This is done through machine learning and reinforcement learning algorithms which identify patterns of energy use with high precision and helps to reduce the energy usage in smart buildings by 25-30%. For transportation, AI-enabled traffic infrastructure reduced carbon emissions by 20% and enhanced EV infrastructure efficiency by 18%. The smart grids were better served by predictive energy distribution, which allowed for a 15% decrease in losses and a 20% rise in the use of renewable energy. All of these results point towards the potential of AI-augmented Digital Twins to reshape city planning, optimise resource consumption and play a key role in achieving global sustainability targets. This research underscores the need to embrace high-tech solutions for the next smart city projects to combat climate change and promote sustainable development.
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Hornik, Jacob, and Matti Rachamim. "Leveraging AI-Enabled Digital Twins for Smart Advertising Design and Testing." Journal of Advertising and Public Relations 5, no. 1 (2025): 5–26. https://doi.org/10.22259/2639-1953.0501002.
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Nwadiokwu, Obiajuru Triumph. "AI Augmented Digital Twins for IoT-Enabled Smart Infrastructure: Big Data Analytics for Real-time Optimization." International Journal of Research Publication and Reviews 6, no. 2 (2025): 1871–86. https://doi.org/10.55248/gengpi.6.0225.0901.
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Miguel Santos, Emilia Martinez, and Carlos Hernandez. "The Role Of Artificial Intelligence in Modernizing Mechanical Engineering and Industrial Innovation." International Journal of Industrial Innovation and Mechanical Engineering 1, no. 2 (2024): 13–17. http://dx.doi.org/10.61132/ijiime.v1i2.54.
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Artificial Intelligence (AI) is transforming mechanical engineering and industrial processes by introducing unprecedented levels of efficiency, precision, and innovation. From predictive maintenance and autonomous robotics to material optimization and digital twins, AI-enabled systems are reshaping the industry landscape. This article examines key applications of AI in mechanical engineering, exploring how they contribute to sustainable industrial innovation, improve productivity, and pave the way for future advancements.
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Carlos Hernandez, Miguel Santos, and Emilia Martinez. "The Role Of Artificial Intelligence in Modernizing Mechanical Engineering and Industrial Innovation." International Journal of Mechanical, Electrical and Civil Engineering 1, no. 2 (2024): 27–31. http://dx.doi.org/10.61132/ijmecie.v1i2.73.
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Artificial Intelligence (AI) is transforming mechanical engineering and industrial processes by introducing unprecedented levels of efficiency, precision, and innovation. From predictive maintenance and autonomous robotics to material optimization and digital twins, AI-enabled systems are reshaping the industry landscape. This article examines key applications of AI in mechanical engineering, exploring how they contribute to sustainable industrial innovation, improve productivity, and pave the way for future advancements.
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Mirwais, Musazai, Muhammad Adeel, Ahmad Walid Rahmani, and Ahmad Nesaar Rahmani. "AI-Driven Generative Design for Next-Generation 3D Concrete Printing in Architecture: State of the Art." European Journal of Applied Science, Engineering and Technology 3, no. 2 (2025): 225–32. https://doi.org/10.59324/ejaset.2025.3(2).19.
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The convergence of artificial intelligence (AI) and 3D concrete printing (3DCP) heralds a paradigm shift in architectural design and construction, blending computational innovation with sustainable practices. This study synthesizes the state of the art in AI-driven generative design for 3DCP, examining cutting-edge methodologies such as generative adversarial networks (GANs), topology optimization, reinforcement learning (RL), and digital twins. These technologies collectively address longstanding challenges in material efficiency, structural robustness, and ecological impact by automating design exploration, real-time process control, and lifecycle management. The integration of AI with 3DCP enables unprecedented geometric complexity, adaptive fabrication, and smart city alignment, yet barriers in scalability, regulatory frameworks, and material standardization persist. By critically evaluating advancements in ChatGPT-aided ideation, physics-informed simulations, and IoT-enabled digital twins, this research maps a holistic framework for AI-augmented 3DCP. The paper underscores the transformative potential of AI in redefining architectural workflows, advocating for interdisciplinary collaboration to bridge computational creativity, ethical governance, and sustainable urban development.
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Mirwais, Musazai, Muhammad Adeel, Ahmad Walid Rahmani, and Ahmad Nesaar Rahmani. "AI-Driven Generative Design for Next-Generation 3D Concrete Printing in Architecture: State of the Art." European Journal of Applied Science, Engineering and Technology 3, no. 2 (2025): 225–32. https://doi.org/10.59324/ejaset.2025.3(2).19.
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The convergence of artificial intelligence (AI) and 3D concrete printing (3DCP) heralds a paradigm shift in architectural design and construction, blending computational innovation with sustainable practices. This study synthesizes the state of the art in AI-driven generative design for 3DCP, examining cutting-edge methodologies such as generative adversarial networks (GANs), topology optimization, reinforcement learning (RL), and digital twins. These technologies collectively address longstanding challenges in material efficiency, structural robustness, and ecological impact by automating design exploration, real-time process control, and lifecycle management. The integration of AI with 3DCP enables unprecedented geometric complexity, adaptive fabrication, and smart city alignment, yet barriers in scalability, regulatory frameworks, and material standardization persist. By critically evaluating advancements in ChatGPT-aided ideation, physics-informed simulations, and IoT-enabled digital twins, this research maps a holistic framework for AI-augmented 3DCP. The paper underscores the transformative potential of AI in redefining architectural workflows, advocating for interdisciplinary collaboration to bridge computational creativity, ethical governance, and sustainable urban development.
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Olayinka Akinbolajo. "Digital twin technology for sustainable industrial operations." World Journal of Advanced Research and Reviews 22, no. 2 (2024): 2347–53. https://doi.org/10.30574/wjarr.2024.22.2.1342.
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Digital Twin Technology (DTT) is revolutionizing the industrial landscape by enabling real-time virtual representations of physical systems. As industries pursue sustainability goals, the integration of digital twins into smart factories presents a transformative solution for enhancing energy efficiency, minimizing waste reduction, and optimizing lifecycle management. This article explores the foundational principles of DTT, its implementation in industrial automation, and its role in advancing sustainable manufacturing. Through an analysis of Industry 4.0 applications, predictive maintenance, and IoT-enabled systems, we highlight how digital twin solutions improve operational efficiency and resource optimization. By examining case studies and emerging technologies, this study demonstrates how DTT drives the transition toward intelligent factories, circular economy practices, and eco-conscious production. The findings underscore the potential of AI-driven simulations, cyber-physical systems, and data-driven decision-making in shaping the future of green manufacturing and industrial sustainability.
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Elgebaly, Hamdy, Basma Elhariry, Amr Noureldin, and Doaa Stohy. "Digital Twin for Maintenance and Smart Manufacturing: The Mediating Role of Replacement Maintenance in the Saudi Industrial Sector." Journal of Lifestyle and SDGs Review 5, no. 4 (2025): 06107. https://doi.org/10.47172/2965-730x.sdgsreview.v5.n04.pe06107.
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Objectives: This study examines the role of digital twin technology in optimizing maintenance within smart manufacturing. It explores the mediating effect of replacement maintenance on the relationship between digital twins and smart manufacturing outcomes in the Saudi industrial sector, focusing on energy efficiency, waste reduction, and operational performance. Theoretical Framework: Grounded in Industry 4.0 principles, this research highlights how digital twin technology enhances predictive maintenance and resource optimization. It aligns with Saudi Arabia’s Vision 2030 by promoting advanced manufacturing solutions. Method: A quantitative approach is employed using structural equation modeling (SEM) to analyze data from 21 leading Saudi food industry companies, including Almarai, Savola, Al Mangal Foods, and Wataniya Poultry. The study evaluates digital twin-enabled maintenance strategies and their impact on manufacturing performance. Results and Discussion: The findings reveal that replacement maintenance partially mediates the relationship between digital twins and smart manufacturing. Digital twin technology enhances predictive maintenance, reduces downtime, and optimizes resource use, leading to improved energy efficiency and waste reduction. These outcomes support the broader adoption of Industry 4.0 technologies. Research Implications: The study underscores the need for further exploration of AI-driven analytics and deeper digital twin integration in smart manufacturing to improve efficiency and sustainability. Originality/Value: This research provides empirical evidence on the mediating role of replacement maintenance, offering valuable insights for businesses aiming to enhance production efficiency and align with Vision 2030 initiatives.
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Hartmann, Sabine, Raquel Valles, Annette Schmitt, et al. "Digital-Twin-Based Management of Sewer Systems: Research Strategy for the KaSyTwin Project." Water 17, no. 3 (2025): 299. https://doi.org/10.3390/w17030299.
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Sewer infrastructure is vital for flood prevention, environmental protection, and public health. As part of sewer infrastructure, sewer systems are prone to degradation. Traditional maintenance methods for sewer systems are largely manual and reactive and rely on inconsistent data, leading to inefficient maintenance. The KaSyTwin research project addresses the urgent need for efficient and resilient sewer system management methods in Germany, aiming to develop a methodology for the semi-automated development and utilization of digital twins of sewer systems to enhance data availability and operational resilience. Using advanced multi-sensor robotic platforms equipped with scanning and imaging systems, i.e., laser scanners and cameras, as well as artificial intelligence (AI), the KaSyTwin research project focuses on generating digital twin-enabled representations of sewer systems in real time. As a project report, this work outlines the research framework and proposed methodologies in the KaSyTwin research project. Digital twins of sewer systems integrated with AI technologies are expected to facilitate proactive maintenance, resilience forecasting against extreme weather events, and real-time damage detection. Furthermore, the KaSyTwin research project aspires to advance the digital management of sewer systems, ensuring long-term functionality and public welfare via on-demand structural health monitoring and non-destructive testing.
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Nativi, Stefano, Paolo Mazzetti, and Max Craglia. "Digital Ecosystems for Developing Digital Twins of the Earth: The Destination Earth Case." Remote Sensing 13, no. 11 (2021): 2119. http://dx.doi.org/10.3390/rs13112119.
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This manuscript discusses the key characteristics of the Digital Ecosystems (DEs) model, which, we argue, is particularly appropriate for connecting and orchestrating the many heterogeneous and autonomous online systems, infrastructures, and platforms that constitute the bedrock of a digitally transformed society. Big Data and AI systems have enabled the implementation of the Digital Twin paradigm (introduced first in the manufacturing sector) in all the sectors of society. DEs promise to be a flexible and operative framework that allow the development of local, national, and international Digital Twins. In particular, the “Digital Twins of the Earth” may generate the actionable intelligence that is necessary to address global change challenges, facilitate the European Green transition, and contribute to realizing the UN Sustainable Development Goals (SDG) agenda. The case of the Destination Earth initiative and system is discussed in the manuscript as an example to address the broader DE concepts. In respect to the more traditional data and information infrastructural philosophy, DE solutions present important advantages as to flexibility and viability. However, designing and implementing an effective collaborative DE is far more difficult than a traditional digital system. DEs require the definition and the governance of a metasystemic level, which is not necessary for a traditional information system. The manuscript discusses the principles, patterns, and architectural viewpoints characterizing a thriving DE supporting the generation and operation of “Digital Twins of the Earth”. The conclusions present a set of conditions, best practices, and base capabilities for building a knowledge framework, which makes use of the Digital Twin paradigm and the DE approach to support decision makers with the SDG agenda implementation.
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Imam, Niddal H. "Adversarial Examples on XAI-Enabled DT for Smart Healthcare Systems." Sensors 24, no. 21 (2024): 6891. http://dx.doi.org/10.3390/s24216891.
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There have recently been rapid developments in smart healthcare systems, such as precision diagnosis, smart diet management, and drug discovery. These systems require the integration of the Internet of Things (IoT) for data acquisition, Digital Twins (DT) for data representation into a digital replica and Artificial Intelligence (AI) for decision-making. DT is a digital copy or replica of physical entities (e.g., patients), one of the emerging technologies that enable the advancement of smart healthcare systems. AI and Machine Learning (ML) offer great benefits to DT-based smart healthcare systems. They also pose certain risks, including security risks, and bring up issues of fairness, trustworthiness, explainability, and interpretability. One of the challenges that still make the full adaptation of AI/ML in healthcare questionable is the explainability of AI (XAI) and interpretability of ML (IML). Although the study of the explainability and interpretability of AI/ML is now a trend, there is a lack of research on the security of XAI-enabled DT for smart healthcare systems. Existing studies limit their focus to either the security of XAI or DT. This paper provides a brief overview of the research on the security of XAI-enabled DT for smart healthcare systems. It also explores potential adversarial attacks against XAI-enabled DT for smart healthcare systems. Additionally, it proposes a framework for designing XAI-enabled DT for smart healthcare systems that are secure and trusted.
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Gomaa, Prof Dr Attia Hussien. "RCM 4.0: A Novel Digital Framework for Reliability-Centered Maintenance in Smart Industrial Systems." International Journal of Emerging Science and Engineering 13, no. 5 (2025): 32–43. https://doi.org/10.35940/ijese.e2595.13050425.
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Reliability-Centered Maintenance (RCM) 4.0 introduces an AI-driven digital framework that integrates Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), Digital Twins, and Big Data Analytics to enhance Reliability, Availability, Maintainability, and Safety (RAMS) in Smart Industrial Systems. As industrial environments grow increasingly complex and data-driven, traditional maintenance strategies struggle to deliver the agility and precision required for intelligent asset management. This study presents RCM 4.0 as a self-optimizing, predictive maintenance paradigm, transforming reactive and preventive approaches into autonomous, data-driven ecosystems that enhance operational efficiency and resilience. The proposed framework synergizes RCM principles with Lean Six Sigma’s DMAIC (Define-Measure-Analyze-Improve-Control) methodology, providing a structured, data-driven approach to failure mode classification, risk-based maintenance prioritization, and real-time performance optimization. By leveraging IIoT enabled condition monitoring, Digital Twin simulations, and machine learning-driven predictive analytics, RCM 4.0 enables real-time anomaly detection, intelligent diagnostics, and adaptive maintenance strategies. This shift eliminates inefficiencies, minimizes downtime, optimizes asset performance, and enhances cost-effective maintenance planning. This research establishes RCM 4.0 as a transformative approach to industrial maintenance, integrating cyber-physical intelligence to drive operational resilience, sustainability, and cost efficiency. Future research will explore 5G-enabled industrial communication, autonomous robotic maintenance, blockchain-secured predictive maintenance, and edge AI-powered diagnostics, further advancing next generation digitalized maintenance ecosystems' scalability, cybersecurity, and self-learning capabilities.
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Choi, Sebin, and Sungmin Yoon. "AI Agent-Based Intelligent Urban Digital Twin (I-UDT): Concept, Methodology, and Case Studies." Smart Cities 8, no. 1 (2025): 28. https://doi.org/10.3390/smartcities8010028.
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The concept of digital twins (DTs) has expanded to encompass buildings and cities, with urban building energy modeling (UBEM) playing a crucial role in predicting urban-scale energy consumption via modeling individual energy use and interactions. As a virtual model within urban digital twins (UDTs), UBEM offers the potential for managing energy in sustainable cities. However, UDTs face challenges with regard to integrating large-scale data and relying on bottom-up UBEM approaches. In this study, we propose an AI agent-based intelligent urban digital twin (I-UDT) to enhance DTs’ technical realization and UBEM’s service functionality. Integrating GPT within the UDT enabled the efficient integration of fragmented city-scale data and the extraction of building features, addressing the limitations of the service realization of traditional UBEM. This framework ensures continuous updates of the virtual urban model and the streamlined provision of updated information to users in future studies. This research establishes the concept of an I-UDT and lays a foundation for future implementations. The case studies include (1) data analysis, (2) prediction, (3) feature engineering, and (4) information services for 3500 buildings in Seoul. Through these case studies, the I-UDT was integrated and analyzed scattered data, predicted energy consumption, derived conditioned areas, and evaluated buildings on benchmark.
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Lin, Yuancheng, Junlong Tang, Jing Guo, Shidong Wu, and Zheng Li. "Advancing AI-Enabled Techniques in Energy System Modeling: A Review of Data-Driven, Mechanism-Driven, and Hybrid Modeling Approaches." Energies 18, no. 4 (2025): 845. https://doi.org/10.3390/en18040845.
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Artificial intelligence (AI) is increasingly essential for optimizing energy systems, addressing the growing complexity of energy management, and supporting the integration of diverse renewable sources. This study systematically reviews AI-enabled modeling approaches, highlighting their applications, limitations, and potential in advancing sustainable energy systems while offering insights and a framework for addressing real-world energy challenges. Data-driven models excel in energy demand prediction and resource optimization but face criticism for their “black-box” nature, while mechanism-driven models provide deeper system insights but require significant computation and domain expertise. To bridge the gap between these approaches, hybrid models combine the strengths of both, improving prediction accuracy, adaptability, and overall system optimization. This study discusses the policy background, modeling approaches, and key challenges in AI-enabled energy system modeling. Furthermore, this study highlights how AI-enabled techniques are paving the way for future energy system modeling, including integration and optimization for renewable energy systems, real-time optimization and predictive maintenance through digital twins, advanced demand-side management for optimal energy use, and hybrid simulation of energy markets and business behavior.
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Jaymin Pareshkumar Shah. "Integration of AI-based predictive maintenance for energy-efficient mechanical systems." World Journal of Advanced Engineering Technology and Sciences 11, no. 2 (2024): 664–73. https://doi.org/10.30574/wjaets.2024.11.2.0153.
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Predictive maintenance enabled by Artificial Intelligence (AI) transforms mechanical systems by improving their reliability levels as well as energy efficiency attributes. The conventional maintenance methods that include reactive and preventive measures repeatedly produce inefficient energy usage together with elevated operation expenses. Using AI alongside machine learning predictive maintenance transforms real-time sensor data into predictions which help maintainers schedule optimal maintenance times. The proactive system helps prevent downtime and cuts down energy loss and delivers improved operational results. Current industrial applications benefit from AI methods made up of deep learning and IoT-enabled data analytics and digital twins to anticipate anomalies and detect faults in HVAC systems and production facilities as well as power generation facilities. The ongoing implementation challenges involve poor quality data as well as cybersecurity threats together with difficult integration between systems. Self-learning AI models combined with edge computing and automated intelligent systems will enable better predictive maintenance through future advancements which will generate more sustainable and energy-efficient mechanical systems.
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Lifelo, Zita, Jianguo Ding, Huansheng Ning, Qurat-Ul-Ain, and Sahraoui Dhelim. "Artificial Intelligence-Enabled Metaverse for Sustainable Smart Cities: Technologies, Applications, Challenges, and Future Directions." Electronics 13, no. 24 (2024): 4874. https://doi.org/10.3390/electronics13244874.
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Rapid urbanisation has intensified the need for sustainable solutions to address challenges in urban infrastructure, climate change, and resource constraints. This study reveals that Artificial Intelligence (AI)-enabled metaverse offers transformative potential for developing sustainable smart cities. AI techniques, such as machine learning, deep learning, generative AI (GAI), and large language models (LLMs), enhance the metaverse’s capabilities in data analysis, urban decision making, and personalised user experiences. The study further examines how these advanced AI models facilitate key metaverse technologies such as big data analytics, natural language processing (NLP), computer vision, digital twins, Internet of Things (IoT), Edge AI, and 5G/6G networks. Applications across various smart city domains—environment, mobility, energy, health, governance, and economy, and real-world use cases of virtual cities like Singapore, Seoul, and Lisbon are presented, demonstrating AI’s effectiveness in the metaverse for smart cities. However, AI-enabled metaverse in smart cities presents challenges related to data acquisition and management, privacy, security, interoperability, scalability, and ethical considerations. These challenges’ societal and technological implications are discussed, highlighting the need for robust data governance frameworks and AI ethics guidelines. Future directions emphasise advancing AI model architectures and algorithms, enhancing privacy and security measures, promoting ethical AI practices, addressing performance measures, and fostering stakeholder collaboration. By addressing these challenges, the full potential of AI-enabled metaverse can be harnessed to enhance sustainability, adaptability, and livability in smart cities.
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Fatorachian, Hajar, Hadi Kazemi, and Kulwant Pawar. "Enhancing Smart City Logistics Through IoT-Enabled Predictive Analytics: A Digital Twin and Cybernetic Feedback Approach." Smart Cities 8, no. 2 (2025): 56. https://doi.org/10.3390/smartcities8020056.
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The increasing complexity of urban logistics in smart cities requires innovative solutions that leverage real-time data, predictive analytics, and adaptive learning to enhance efficiency. This study presents a predictive analytics framework integrating digital twin technology, IoT-enabled logistics data, and cybernetic feedback loops to improve last-mile delivery accuracy, congestion management, and sustainability in smart cities. Grounded in Systems Theory and Cybernetic Theory, the framework models urban logistics as an interconnected network, where real-time IoT data enable dynamic routing, demand forecasting, and self-regulating logistics operations. By incorporating machine learning-driven predictive analytics, the study demonstrates how AI-powered logistics optimization can enhance urban freight mobility. The cybernetic feedback mechanism further improves adaptive decision-making and operational resilience, allowing logistics networks to respond dynamically to changing urban conditions. The findings provide valuable insights for logistics managers, smart city policymakers, and urban planners, highlighting how AI-driven logistics strategies can reduce congestion, enhance sustainability, and optimize delivery performance. The study also contributes to logistics and smart city research by integrating digital twins with adaptive analytics, addressing gaps in dynamic, feedback-driven logistics models.
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Attia, Hussien Gomaa. "RCM 4.0: A Novel Digital Framework for Reliability-Centered Maintenance in Smart Industrial Systems." International Journal of Emerging Science and Engineering (IJESE) 13, no. 5 (2025): 32–43. https://doi.org/10.35940/ijese.E2595.13050425.
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<strong>Abstract: </strong>Reliability-Centered Maintenance (RCM) 4.0 introduces an AI-driven digital framework that integrates Artificial Intelligence (AI), the Industrial Internet of Things (IIoT), Digital Twins, and Big Data Analytics to enhance Reliability, Availability, Maintainability, and Safety (RAMS) in Smart Industrial Systems. As industrial environments grow increasingly complex and data-driven, traditional maintenance strategies struggle to deliver the agility and precision required for intelligent asset management. This study presents RCM 4.0 as a self-optimizing, predictive maintenance paradigm, transforming reactive and preventive approaches into autonomous, data-driven ecosystems that enhance operational efficiency and resilience. The proposed framework synergizes RCM principles with Lean Six Sigma’s DMAIC (Define-Measure-Analyze-Improve-Control) methodology, providing a structured, data-driven approach to failure mode classification, risk-based maintenance prioritization, and real-time performance optimization. By leveraging IIoTenabled condition monitoring, Digital Twin simulations, and machine learning-driven predictive analytics, RCM 4.0 enables real-time anomaly detection, intelligent diagnostics, and adaptive maintenance strategies. This shift eliminates inefficiencies, minimizes downtime, optimizes asset performance, and enhances cost-effective maintenance planning. This research establishes RCM 4.0 as a transformative approach to industrial maintenance, integrating cyber-physical intelligence to drive operational resilience, sustainability, and cost efficiency. Future research will explore 5G-enabled industrial communication, autonomous robotic maintenance, blockchain-secured predictive maintenance, and edge AI-powered diagnostics, further advancing nextgeneration digitalized maintenance ecosystems' scalability, cybersecurity, and self-learning capabilities.
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Wei, Jinhua. "Convergence of IoT and PLC in Industrial Automation: A Systematic Review of Emerging Trends, Technical Challenges, and Prospects." Applied and Computational Engineering 150, no. 1 (2025): 89–94. https://doi.org/10.54254/2755-2721/2025.22249.
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Integrating PLCs with emerging IoT technologies for industrial automation has transformed the manufacturing ecosystems towards a smarter and data-driven one. This systematic review explores the synergistic potential of IoT's connectivity and PLCs' reliability in modern industrial settings. It analyses trends such as edge computing, AI-driven analytics and digital twins for technical challenges and proposes future directions using blockchain integration and 5G-enabled automation to support them. This review synthesizes academic literature, industry case studies and technological frameworks to outline a roadmap for resilient, efficient and adaptable industrial systems.
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Hu, Fuwen, Chun Wang, and Xuefei Wu. "Generative Artificial Intelligence-Enabled Facility Layout Design Paradigm." Applied Sciences 15, no. 10 (2025): 5697. https://doi.org/10.3390/app15105697.
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Facility layout design (FLD) is critical for optimizing manufacturing efficiency, yet traditional approaches struggle with complexity, dynamic constraints, and fragmented data integration. This study proposes a generative-AI-enabled facility layout design, a novel paradigm aligning with Industry 4.0, to address these challenges by integrating generative artificial intelligence (AI), semantic models, and data-driven optimization. The proposed method evolves from three historical paradigms: experience-based methods, operations research, and simulation-based engineering. The metamodels supporting the generative-AI-enabled facility layout design is the Asset Administration Shell (AAS), which digitizes physical assets and their relationships, enabling interoperability across systems. Domain-specific knowledge graphs, constructed by parsing AAS metadata and enriched by large language models (LLMs), capture multifaceted relationships (e.g., spatial adjacency, process dependencies, safety constraints) to guide layout generation. The convolutional knowledge graph embedding (ConvE) method is employed for link prediction, converting entities and relationships into low-dimensional vectors to infer optimal spatial arrangements while addressing data sparsity through negative sampling. The proposed reference architecture for generative-AI-enabled facility layout design supports end-to-end layout design, featuring a 3D visualization engine, AI-driven optimization, and real-time digital twins. Prototype testing demonstrates the system’s end-to-end generation ability from requirement-driven contextual prompts and extensively reduced complexity of modeling, integration, and optimization. Key innovations include the fusion of AAS with LLM-derived contextual knowledge, dynamic adaptation via big data streams, and a hybrid optimization approach balancing competing objectives. The 3D layout generation results demonstrate a scalable, adaptive solution for storage workshops, bridging gaps between isolated data models and human–AI collaboration. This research establishes a foundational framework for AI-driven facility planning, offering actionable insights for AI-enabled facility layout design adoption and highlighting future directions in the generative design of complex engineering.
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De, Donato Lorenzo, Ruth Dirnfeld, Alessandra Somma, et al. "Towards AI-assisted Digital Twins for Smart Railways: Preliminary Guideline and Reference Architecture." Journal of Reliable Intelligent Environments 9 (June 12, 2023): 303–17. https://doi.org/10.1007/s40860-023-00208-6.
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<strong>Abstract</strong>: In the last years, there has been a growing interest in the emerging concept of digital twins (DTs) among software engineers and researchers. DTs not only represent a promising paradigm to improve product quality and optimize production processes, but they also may help enhance the predictability and resilience of cyber-physical systems operating in critical contexts. In this work, we investigate the adoption of DTs in the railway sector, focusing in particular on the role of artificial intelligence (AI) technologies as key enablers for building added-value services and applications related to smart decision-making. In this paper, in particular, we address predictive maintenance which represents one of the most promising services benefiting from the combination of DT and AI. To cope with the lack of mature DT development methodologies and standardized frameworks, we detail a workflow for DT design and development specifically tailored to a predictive maintenance scenario and propose a high-level architecture for AI-enabled DTs supporting such workflow. <strong>Fundings and Disclaimer</strong>: This research has received funding from the Shift2Rail Joint Undertaking (JU) under grant agreement No 881782 RAILS (Roadmaps for Artificial Intelligence (A.I.) integration in the raiL Sector). The JU receives support from the European Union’s Horizon 2020 research and innovation programme and the Shift2Rail JU members other than the Union. The information and views set out in this document are those of the author(s) and do not necessarily reflect the official opinion of Shift2Rail Joint Undertaking. The JU does not guarantee the accuracy of the data included in this document. Neither the JU nor any person acting on the JU’s behalf may be held responsible for the use which may be made of the information contained therein. <strong>Publication Notes</strong>: This Journal Article is available in Open Access at: https://link.springer.com/article/10.1007/s40860-023-00208-6
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Louis Owusu-Berko. "Advanced supply chain analytics: Leveraging digital twins, IoT and blockchain for resilient, data-driven business operations." World Journal of Advanced Research and Reviews 25, no. 2 (2025): 1777–99. https://doi.org/10.30574/wjarr.2025.25.2.0572.
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The evolution of supply chain analytics has been accelerated by digital transformation, with Digital Twins, the Internet of Things (IoT), and Blockchain emerging as pivotal technologies for creating resilient, data-driven business operations. Traditional supply chains face challenges such as demand fluctuations, logistical inefficiencies, supply disruptions, and lack of real-time visibility. Advanced supply chain analytics, powered by Artificial Intelligence (AI) and big data, enables organizations to transition from reactive to predictive and prescriptive decision-making, optimizing efficiency and mitigating risks. Digital Twins facilitate real-time simulations of physical supply chain processes, allowing businesses to model disruptions, optimize inventory, and improve logistics planning before implementing changes in the physical world. IoT-enabled devices, such as smart sensors and RFID trackers, provide continuous monitoring of goods in transit, ensuring visibility into factors like temperature, location, and shipment integrity. Meanwhile, Blockchain technology enhances supply chain transparency, traceability, and security, enabling secure data sharing and fraud prevention in multi-stakeholder ecosystems. These technologies collectively enable organizations to achieve real-time supply chain optimization, proactive risk management, and sustainable operations. However, challenges such as data standardization, integration complexity, and cybersecurity risks must be addressed for seamless implementation. This paper examines the methodologies, industry applications, and future potential of advanced supply chain analytics, providing strategic insights into how businesses can leverage Digital Twins, IoT, and Blockchain to enhance agility, reduce operational costs, and build resilient global supply networks.
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Ajayi, Ajibola Joshua, Experience Efeosa Akhigbe, Nnaemeka Stanley Egbuhuzor, and Oluwole Oluwadamilola Agbede. "Bridging Data and Decision-Making: AI-Enabled Analytics for Project Management in Oil and Gas Infrastructure." International Journal of Multidisciplinary Research and Growth Evaluation 2, no. 1 (2021): 567–80. https://doi.org/10.54660/.ijmrge.2021.2.1.567-580.
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The oil and gas industry is increasingly complex, requiring robust project management approaches to handle challenges such as cost overruns, delays, regulatory compliance, and risk management. Artificial Intelligence (AI)-enabled analytics has emerged as a transformative solution, offering real-time data-driven insights to enhance decision-making and improve project outcomes. This paper explores the integration of AI in project management for oil and gas infrastructure, emphasizing how predictive analytics, machine learning, and optimization algorithms bridge the gap between raw data and actionable decisions. Key challenges in oil and gas infrastructure projects include managing vast amounts of unstructured data, mitigating risks in dynamic operational environments, and aligning projects with sustainability goals. AI-enabled analytics addresses these challenges by automating data processing, identifying patterns, and generating actionable insights. This study proposes a comprehensive framework for implementing AI-driven analytics in project management, focusing on resource allocation, scheduling, and risk mitigation. The framework also incorporates predictive models to forecast potential delays, cost escalations, and equipment failures, enabling proactive interventions. Case studies highlight the successful application of AI-enabled analytics in major oil and gas projects, demonstrating significant improvements in operational efficiency, cost control, and safety compliance. The use of AI tools such as digital twins, natural language processing (NLP) for document management, and computer vision for site monitoring is discussed, showcasing tangible benefits in reducing downtime and optimizing resource utilization. This paper concludes by addressing future trends, including the integration of AI with the Internet of Things (IoT) for real-time project monitoring, the role of generative AI in designing project workflows, and advancements in autonomous decision-making systems. These developments have the potential to redefine project management in the oil and gas industry, enabling organizations to navigate challenges with greater agility and precision.
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Mondal, Surajit, Shankha Shubhra Goswami, Krishna Kumar Gupta, and Sushil Kumar Sahoo. "Synergistic Effects of Lean Manufacturing and Digitalization on Operational Effectiveness: A Comprehensive Review." Spectrum of Decision Making and Applications 3, no. 1 (2025): 21–39. https://doi.org/10.31181/sdmap31202634.
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In today’s industrial landscape, lean manufacturing and digitalization are key strategies for enhancing efficiency. Lean manufacturing minimizes waste and optimizes processes, while digitalization leverages IoT, AI, and big data to streamline operations and improve decision-making. This review explores their integration across industries like manufacturing, healthcare, and logistics. The combination of lean principles and digital tools enhances productivity, reduces costs, and improves efficiency. Organizations benefit from real-time monitoring, predictive maintenance, and automation, leading to smoother workflows and less downtime. AI-driven analytics help identify inefficiencies, while digital twins enable real-time simulations for optimizing processes. IoT-enabled smart factories support continuous data collection, providing insights that align with lean objectives. This synergy fosters continuous improvement, enabling businesses to adapt swiftly to market changes and customer demands. However, challenges such as cultural shifts, cybersecurity risks, and initial investment costs must be addressed. By analyzing successful case studies, this review highlights strategies to overcome these barriers, reinforcing the importance of integrating lean manufacturing and digitalization to remain competitive in a technology-driven environment.
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Zou, Shubin, Hanyu Ju, and Jingjie Zhang. "Water Quality Management in the Age of AI: Applications, Challenges, and Prospects." Water 17, no. 11 (2025): 1641. https://doi.org/10.3390/w17111641.
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Artificial intelligence (AI) is transforming water environment management, creating new opportunities for improved monitoring, prediction, and intelligent regulation of water quality. This review highlights the transformative impact of AI, particularly through hybrid modeling frameworks that integrate AI with technologies like the Internet of Things (IoT), Remote Sensing (RS), and Unmanned Monitoring Platforms (UMP). These advances have significantly enhanced real-time monitoring accuracy, expanded the scope of data acquisition, and enabled comprehensive analysis through multisource data fusion. Coupling AI models with process-based models (PBM) has notably enhanced predictive capabilities for simulating water quality dynamics. Additionally, AI facilitates dynamic early-warning systems, precise pollutant source tracking, and data-driven decision-making. However, significant challenges remain, including data quality and accessibility, model interpretability, monitoring of hard-to-measure pollutants, and the lack of system integration and standardization. To address these bottlenecks, future research should focus on: (1) constructing high-quality, standardized open-access datasets; (2) developing explainable AI (XAI) models; (3) strengthening integration with digital twins and next-generation sensors; (4) improving the monitoring of trace and emerging pollutants; and (5) coupling AI with PBM by optimizing input data, internal mechanisms, and correcting model outputs through validation against observations. Overcoming these challenges will position AI as a central pillar in advancing smart water quality governance, safeguarding water security, and achieving sustainable development goals.
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Gomaa, Prof Dr Attia Hussien. "SCM 4.0 Excellence: A Strategic Framework for Smart and Competitive Supply Chains." International Journal of Management and Humanities 11, no. 8 (2025): 24–44. https://doi.org/10.35940/ijmh.g1798.11080425.
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The rapid advancement of Industry 4.0 is transforming Supply Chain Management (SCM) through the integration of Artificial Intelligence (AI), the Internet of Things (IoT), Blockchain, Big Data, Cyber-Physical Systems (CPS), Digital Twins, and Autonomous Systems. These technologies are reshaping traditional supply chains into intelligent, interconnected, and self-optimizing ecosystems, enabling real-time visibility, predictive analytics, automation, and resilience—critical for navigating today’s volatile, uncertain, complex, and ambiguous (VUCA) business landscape. This paper presents a strategic framework for achieving SCM 4.0 excellence through the structured deployment of Industry 4.0 technologies. The framework integrates AI-driven demand forecasting for agility, blockchain-enabled transactions for transparency, digital twins for real-time process optimization, autonomous logistics for smart transportation, and predictive maintenance for asset reliability. To ensure structured and measurable transformation, it incorporates Lean and Agile principles, the DMAIC (Define, Measure, Analyze, Improve, Control) methodology, and Key Performance Indicators (KPIs)—enabling data-driven decision-making, risk mitigation, and continuous improvement. Beyond technological advancements, this study examines key adoption challenges, including systems interoperability, cybersecurity threats, workforce reskilling, data governance, and organizational resistance to change. It underscores the need for strategic alignment between digital transformation initiatives and business objectives to ensure seamless integration, adaptability, and longterm sustainability. By providing practical insights, implementation roadmaps, and real-world case studies, this research serves as a valuable resource for industry leaders, policymakers, and researchers. The findings demonstrate that SCM 4.0 enhances efficiency, reduces costs, optimizes inventory, strengthens resilience, and improves demand forecasting— securing a sustainable competitive advantage. As global supply chains become increasingly complex and interconnected, this study highlights the imperative for businesses to embrace digitalization, intelligent automation, and data-driven strategies to remain agile, resilient, and competitive in the next industrial era.
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Prof., Dr. Attia Hussien Gomaa. "SCM 4.0 Excellence: A Strategic Framework for Smart and Competitive Supply Chains." International Journal of Management and Humanities (IJMH) 11, no. 8 (2025): 24–44. https://doi.org/10.35940/ijmh.G1798.11080425.
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<strong>Abstract: </strong>The rapid advancement of Industry 4.0 is transforming Supply Chain Management (SCM) through the integration of Artificial Intelligence (AI), the Internet of Things (IoT), Blockchain, Big Data, Cyber-Physical Systems (CPS), Digital Twins, and Autonomous Systems. These technologies are reshaping traditional supply chains into intelligent, interconnected, and self-optimizing ecosystems, enabling real-time visibility, predictive analytics, automation, and resilience—critical for navigating today’s volatile, uncertain, complex, and ambiguous (VUCA) business landscape. This paper presents a strategic framework for achieving SCM 4.0 excellence through the structured deployment of Industry 4.0 technologies. The framework integrates AI-driven demand forecasting for agility, blockchain-enabled transactions for transparency, digital twins for real-time process optimization, autonomous logistics for smart transportation, and predictive maintenance for asset reliability. To ensure structured and measurable transformation, it incorporates Lean and Agile principles, the DMAIC (Define, Measure, Analyze, Improve, Control) methodology, and Key Performance Indicators (KPIs)—enabling data-driven decision-making, risk mitigation, and continuous improvement. Beyond technological advancements, this study examines key adoption challenges, including systems interoperability, cybersecurity threats, workforce reskilling, data governance, and organizational resistance to change. It underscores the need for strategic alignment between digital transformation initiatives and business objectives to ensure seamless integration, adaptability, and longterm sustainability. By providing practical insights, implementation roadmaps, and real-world case studies, this research serves as a valuable resource for industry leaders, policymakers, and researchers. The findings demonstrate that SCM 4.0 enhances efficiency, reduces costs, optimizes inventory, strengthens resilience, and improves demand forecasting— securing a sustainable competitive advantage. As global supply chains become increasingly complex and interconnected, this study highlights the imperative for businesses to embrace digitalization, intelligent automation, and data-driven strategies to remain agile, resilient, and competitive in the next industrial era.
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Nath, Narayan Chandra, and Omar Faruq. "The application of cybersecurity in smart city innovations: an AI aspect." Современные инновации, системы и технологии - Modern Innovations, Systems and Technologies 5, no. 2 (2025): 3025–39. https://doi.org/10.47813/2782-2818-2025-5-2-3025-3039.
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Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) have enabled "Smart Cities," opening up new municipal governance and civic participation prospects. Technological advances improve infrastructure, efficiency, and urban ecology. These complex systems pose cybersecurity risks that require new solutions for protecting information systems. This article creates digital twins using IoT, AI, and ML to study cybersecurity in Smart Cities (SCs). The IoT connects the city's transportation networks, services, and government offices to form a unified system. The system improves efficiency, decision-making, and robotics using AI and ML. This relationship boosts the economy, green behavior, and citizens' lives. Smart cities are subject to hacking, privacy breaches, and system disruptions due to their computer dependency. Modern urban areas face complicated safety problems due to IoT, AI, and ML. Most IoT devices lack security mechanisms, making them accessible to scammers. Biased ML and AI theories may affect results. This paper examines how virtual copies reduce supply chain cybersecurity risks. Simulations of actual commodities and infrastructure help model and assess supply chain vulnerabilities. These synthetic copies help us find weaknesses, simulate cyberattacks, and assess their effects. IoT, AI, and ML connected smart cities offer pros and cons. The paper recommends employing replicas to reduce cybersecurity risks and putting safety first in supply chain development and management.
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Mintoo, Abdul Awal, Abu Saleh Muhammad Saimon, Mohammed Majid Bakhsh, and Marjina Akter. "NATIONAL RESILIENCE THROUGH AI-DRIVEN DATA ANALYTICS AND CYBERSECURITY FOR REAL-TIME CRISIS RESPONSE AND INFRASTRUCTURE PROTECTION." American Journal of Scholarly Research and Innovation 1, no. 1 (2022): 137–69. https://doi.org/10.63125/sdz8km60.
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This study investigates the integration of artificial intelligence (AI) and cybersecurity frameworks in enhancing national resilience through real-time crisis response and critical infrastructure protection. Employing a qualitative case study approach, the research examines twelve carefully selected national and sectoral implementations across diverse contexts, including public health emergencies, smart grid monitoring, intelligent transportation systems, water management, and cyber-physical infrastructure defense. The study reveals that AI-driven data analytics significantly improve early warning capabilities, situational awareness, and decision-making speed in high-risk scenarios. It also demonstrates that the adoption of AI-enhanced cybersecurity tools—such as anomaly detection, behavioral analytics, and autonomous incident response—plays a crucial role in securing digital infrastructure against evolving cyber threats. Furthermore, the application of simulation models and digital twins was found to support real-time modeling, predictive planning, and operational testing, thereby strengthening the adaptability of critical systems. Multi-agent decision support systems and explainable AI interfaces facilitated better interagency coordination and user trust, while zero-trust architectures enabled granular control over access and threat containment. Despite these advancements, the study identified notable gaps in methodological integration, sectoral coverage (particularly in education and water sanitation), and inclusive system design. The findings emphasize the importance of interdisciplinary collaboration and governance alignment in developing comprehensive AI and cybersecurity strategies for national resilience. By synthesizing empirical evidence from twelve cross-sectoral case studies, this research contributes actionable insights into the design and implementation of intelligent, secure, and adaptive infrastructure systems in an era of complex and interconnected global threats.
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Rana, Sohel. "AI-DRIVEN FAULT DETECTION AND PREDICTIVE MAINTENANCE IN ELECTRICAL POWER SYSTEMS: A SYSTEMATIC REVIEW OF DATA-DRIVEN APPROACHES, DIGITAL TWINS, AND SELF-HEALING GRIDS." American Journal of Advanced Technology and Engineering Solutions 1, no. 01 (2025): 258–89. https://doi.org/10.63125/4p25x993.
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The increasing complexity of electrical power systems necessitates advanced fault detection and predictive maintenance strategies to enhance operational efficiency and grid reliability. Traditional maintenance approaches, such as reactive and preventive maintenance, have proven insufficient in mitigating unplanned outages and optimizing asset utilization. Recent advancements in artificial intelligence (AI) have introduced data-driven solutions that significantly improve fault classification, failure prediction, and automated recovery processes. This study conducts a systematic review of 180 high-quality peer-reviewed articles, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous and transparent research methodology. The findings reveal that AI-driven predictive maintenance methods, including machine learning, deep learning, digital twin technology, IoT-enabled sensor networks, and self-healing grids, have outperformed traditional fault detection techniques in terms of accuracy, adaptability, and cost-effectiveness. AI-based fault detection models achieve an average accuracy of 85% to 95%, reducing false alarms by 50% and minimizing power restoration times by up to 60%. The integration of IoT sensors with real-time analytics has improved anomaly detection rates by 28%, while digital twin technology has enhanced predictive maintenance efficiency, reducing unplanned outages by 35%. Additionally, self-healing grid mechanisms, powered by reinforcement learning algorithms, have demonstrated the ability to autonomously isolate faults and reconfigure energy distribution, preventing nearly 45% of potential service disruptions. Despite these advancements, challenges such as the black-box nature of deep learning models, cybersecurity vulnerabilities, and interoperability with legacy systems continue to pose barriers to large-scale AI adoption. The study highlights the need for explainable AI frameworks, standardized data governance policies, and enhanced cybersecurity measures to ensure the sustainable deployment of AI in power grid management. The findings provide valuable insights for researchers, utility companies, and policymakers seeking to enhance the resilience and efficiency of modern electrical power systems through AI-driven fault detection and predictive maintenance strategies.
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Monlezun, Dominique J., and Keir MacKay. "Artificial Intelligence and Health Inequities in Dietary Interventions on Atherosclerosis: A Narrative Review." Nutrients 16, no. 16 (2024): 2601. http://dx.doi.org/10.3390/nu16162601.
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Poor diet is the top modifiable mortality risk factor globally, accounting for 11 million deaths annually with half being from diet-linked atherosclerotic cardiovascular disease (ASCVD). Yet, most of the world cannot afford a healthy diet—as the hidden costs of the inadequate global food system total over USD 13 trillion annually—let alone the much more clinically, financially, and ecologically costly and resource-intensive medical interventions required to address the disease progression and acute complications of ASCVD. Yet, AI is increasingly understood as a force multiplying revolutionary technology which may catalyze multi-sector efforts in medicine and public health to better address these significant health challenges. This novel narrative review seeks to provide the first known overview of the state-of-the-art in clinical interventions and public health policies in healthy diets for ASCVD, accelerated by health equity-focused AI. It is written from the first-hand practitioner perspective to provide greater relevance and applicability for health professionals and data scientists. The review summarizes the emerging trends and leading use cases in population health risk stratification and precision public health, AI democratizing clinical diagnosis, digital twins in precision nutrition, and AI-enabled culinary medicine as medical education and treatment. This review may, therefore, help inform and advance the evidence-based foundation for more clinically effective, financially efficient, and societally equitable dietary and nutrition interventions for ASCVD.
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Chinthamu, Narender, Ashish, Mathiyalagan P, Devananda Rao B, Kannadhasan S, and Suganya M. "Data Mining Techniques for Predictive Maintenance in Manufacturing Industries a Comprehensive Review." ITM Web of Conferences 76 (2025): 05012. https://doi.org/10.1051/itmconf/20257605012.
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Predictive maintenance (PdM) is one of the major methods used in modern manufacturing to realize downtime minimization, lower the cost of maintenance and maximize machine service life by analyzing the collected data using data mining methodologies. However existing works mainly focus on conventional ML models without provide systems design real world applications systems and do not include any dimension related to network security dimension, cost and benefit analyzing dimension utility dimension and light weight A.I model for edge computing. In this paper, we contribute with a systematic literature review of state-of-the-art data-mining techniques for predictive maintenance with emphasis on hybrid AI frameworks, deep learning and online data processing approaches, as well as, privacy-aware methods. We contribute by providing a number of real-world industrial use case which differentiate us from previous researched; we discuss details of cybersecurity issues in IoT-enabled PdM; and we discuss use of XAI (Explainable AI) to build interpretable models. Moreover, this survey introduces marginal AI applications in edge computing, predictive maintenance frameworks with scalability, and AI-powered anomaly identification for enhancing predictions in industrial-scale production. It also covers a review of predictive maintenance methodologies in addition to a future research agenda, highlighting emerging patterns such as digital twins, Industry 5.0, and reinforcement learning in predictive maintenance. The current study aims to bridge critical gaps in the literature and support valuable direction for researchers, industry practitioners and policymakers for effective predictive maintenance strategies and task performance.
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Sah, Bhanu Prakash, MD Mahamudul Hasan, Shaikh Shofiullah, and Shown Ahmed Faysal. "AI-Driven IoT And Blockchain Integration In Industry 5.0 A Systematic Review Of Supply Chain Transformation." Innovatech Engineering Journal 1, no. 01 (2024): 99–116. https://doi.org/10.70937/itej.v1i01.12.
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The integration of Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain technologies in Industry 5.0 has revolutionized supply chain management, offering unprecedented opportunities for efficiency, transparency, and sustainability. This systematic review, which analyzed 120 peer-reviewed articles published between 2018 and 2024, provides a comprehensive exploration of the transformative potential of these technologies. The findings reveal that the convergence of AI-driven predictive analytics, IoT-enabled real-time monitoring, and Blockchain's decentralized ledger enhances decision-making, streamlines operations, and fosters trust across supply chain networks. Moreover, the study highlights the critical role of these technologies in achieving Environmental, Social, and Governance (ESG) compliance, with applications in sustainable sourcing, ethical practices, and carbon footprint reduction. However, challenges such as scalability, interoperability, and organizational resistance remain significant barriers to adoption, underscoring the need for innovative solutions and interdisciplinary approaches. The review also identifies emerging opportunities, including the integration of quantum computing and digital twins, which promise to address existing limitations and redefine supply chain capabilities. By synthesizing insights from over 120 studies with a cumulative citation count exceeding 30,000, this research provides valuable perspectives on the current state, challenges, and future directions of integrated supply chain technologies in Industry 5.0, offering actionable insights for researchers and practitioners aiming to advance the field.
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Parapalli, Shriprakashan L. "Evolution of MES in Autonomous Factories: From Reactive to Predictive Systems." International journal of data science and machine learning 05, no. 01 (2025): 127–36. https://doi.org/10.55640/ijdsml-05-01-15.
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Manufacturing Execution Systems (MES) have evolved significantly over the past few decades, serving as a critical link between shop-floor operations and enterprise resource planning. Initially focused on reactive strategies—offering real-time visibility and control based on immediate conditions—MES have transitioned toward predictive capabilities driven by Industry 4.0 technologies. The integration of big data analytics, the Internet of Things (IoT), machine learning, and cloud computing has enabled autonomous factories to leverage MES for proactive and adaptive decision-making. This paper explores the transformation of MES from reactive to predictive systems, detailing the technological enablers, including IoT sensor networks, machine learning algorithms, digital twins, and cyber-physical systems. A methodology for designing and implementing a predictive MES architecture is presented, supported by empirical findings from a pilot implementation. Results demonstrate improvements in production efficiency, reduced downtime, and optimized resource use. Challenges such as data security, integration complexities, and workforce training are discussed, alongside future directions involving cognitive MES and AI-driven manufacturing. The paper also highlights environmental sustainability benefits, positioning predictive MES as a cornerstone of modern autonomous factories.
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Olimat, Hosam, Zaid Alwashah, Osama Abudayyeh, and Hexu Liu. "Data-Driven Analysis of Construction Safety Dynamics: Regulatory Frameworks, Evolutionary Patterns, and Technological Innovations." Buildings 15, no. 10 (2025): 1680. https://doi.org/10.3390/buildings15101680.
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Construction remains one of the most hazardous industries, consistently reporting high rates of workplace injuries and fatalities. Despite advancements in safety regulations and technologies, significant risks persist due to hazardous tasks, including working at heights, operating heavy machinery, and exposure to harmful materials. The establishment of the Occupational Safety and Health Administration in 1971 marked a significant turning point in construction safety, resulting in a decline in workplace fatalities. However, evolving construction methodologies and digital transformations demand continuous research to enhance worker protection and mitigate emerging risks. This study conducts a longitudinal bibliometric analysis to examine the evolution of construction safety research from 1972 to 2025. Using a dataset of 14,174 journal publications from Scopus, the analysis identifies key research trends, technological advancements, and regulatory shifts that have shaped the field. Findings reveal a transition from basic safety regulations to AI-driven hazard detection, digital twins, and IoT-enabled safety monitoring. The study also identifies key contributors, including prominent countries. By tracing both historical and contemporary research trends, this study offers insights into knowledge gaps and provides guidance on future directions. The findings provide valuable insights for researchers, policymakers, and industry professionals, supporting the development of research-informed safety strategies and the integration of emerging technologies in an increasingly complex industry.
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Varzeshi, Shabnam, John Fien, and Leila Irajifar. "Smart Technologies for Resilient and Sustainable Cities: Comparing Tier 1 and Tier 2 Approaches in Australia." Sustainability 17, no. 12 (2025): 5485. https://doi.org/10.3390/su17125485.
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Smart city research often emphasises technology while neglecting how governance structures and resources influence outcomes. This study compares Tier 1 (Sydney, Melbourne, Brisbane, Adelaide) and Tier 2 (Geelong, Newcastle, Hobart, Sunshine Coast) Australian cities to evaluate how urban scale, economic capacity, governance complexity, and local priorities influence smart-enabled resilience. We analysed 22 official strategy documents using a two-phase qualitative approach: profiling each city and then synthesising patterns across technological integration, community engagement, resilience objectives and funding models. Tier 1 cities leverage extensive revenues and sophisticated infrastructure to implement ambitious initiatives such as digital twins and AI-driven services, but they encounter multi-agency delays and may overlook neighbourhood needs. Tier 2 cities deploy agile, low-cost solutions—sensor-based lighting and free public Wi-Fi—that deliver swift benefits but struggle to scale without sustained support. Across the eight cases, we identified four governance archetypes and six recurring implementation barriers—data silos, funding discontinuity, skills shortages, privacy concerns, evaluation gaps, and policy changes—which collectively influence smart-enabled resilience. The results indicate that aligning smart technologies with governance tiers, fiscal capacity, and demographic contexts is essential for achieving equitable and sustainable outcomes. We recommend tier-specific funding, mandatory co-design, and intergovernmental knowledge exchange to enable smaller cities to function as innovation labs while directing metropolitan centres towards inclusive, system-wide transformation.
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Stefko, Robert, Katarina Frajtova-Michalikova, Jarmila Strakova, and Andrej Novak. "Digital twin-based virtual factory and cyber-physical production systems, collaborative autonomous robotic and networked manufacturing technologies, and enterprise and business intelligence algorithms for industrial metaverse business intelligence algorithms for industrial metaverse." Equilibrium. Quarterly Journal of Economics and Economic Policy 20, no. 1 (2025): 389–425. https://doi.org/10.24136/eq.3557.
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Research background: Cognitive computing and robotic technologies, enterprise digital twin system modeling, and sensory perception algorithms optimize industrial big data exchange and production collaboration, production floor management, and smart device 3D simulation and visualization in the Industry 5.0 metaverse and virtual shop floor environments. Enterprise metaverse business operations, multi-granularity cognitive computing, and industrial big data fusion simulation integrate virtual and augmented reality technologies, collaborative robotic and industrial cyber-physical production systems, and artificial intelligence-enabled edge computing and Internet of Everything devices in mobile edge computing environments. Cloud-based production and digital twin Internet of Things networks, 3D immersive virtual reality and realistic 3D scene construction technologies, and cyber-physical production and business process management systems articulate smart production engineering and management, artificial intelligence-driven physics simulation, and Internet of Things-based robotic manufacturing in highly realistic industrial product representations and 3D virtual spaces with regard to big data-driven business decisions. Purpose of the article: We show that 3D immersive virtual reality and digital twin metaverse technologies, spatial scanning modeling, and autonomous robotic and virtual factory simulation systems are pivotal in immersive 3D process management, industrial manufacturing production value, and knowledge accumulation in synthetic simulated environments. 3D simulation-based industrial processes and immersive experiences can be attained through cognitive computing and robotic technologies, multi-modal information fusion, autonomous intelligence generation, and multiple production process management in immersive 3D metaverse environments. Immersive, multisensory, and augmented digital experiences can be attained through 3D factory simulation and immersive extended reality technologies, cognitive robotic process automation, autonomous robotic and industrial machine learning systems, and task allocation optimization in computer-generated 3D virtual environments. Methods: We analyzed and synthesized common operations for the first 60 companies in industrial metaverse on ensun (AI-based supplier sourcing tool’s) website in terms of key takeaway, working industry, type of company, and specialized areas, and identified three main topics. Findings & value added: The main value added derived from our research is that industrial metaverse 3D simulation and modeling, digital twin and remote fault diagnosis technologies, multiphysics simulation and predictive maintenance tools assist industrial big data monitoring and management, Internet of Things-based robotic manufacturing, and multiple processing tasks in 3D digital twin factories. Collaborative autonomous manufacturing operations, artificial intelligence-driven physics simulation, and smart industrial devices and processes necessitate industrial metaverse decentralized federated learning, cognitive computing and robotic technologies, and cognitive digital twins in virtual shop floor environments, generating economic value. 3D simulation and visualization technologies, business intelligence and digital twin-based cyber-physical production systems, and big data-driven forecasting and real-time collision detection algorithms can be harnessed in robotic automation processes, intelligent manufacturing upgrading, and sustainable industrial value creation across 3D digital twin factories and distributed computing environments.
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Toluwanimi Adenuga, Noah Ayanbode, Tolulope Ayobami, and Francess Chinyere Okolo. "Supporting AI in Logistics Optimization through Data Integration, Real-Time Analytics, and Autonomous Systems." International Journal of Scientific Research in Science, Engineering and Technology 11, no. 3 (2024): 511–46. https://doi.org/10.32628/ijsrset241487.
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The integration of artificial intelligence (AI) into logistics systems is reshaping the efficiency and agility of global supply chains. This paper explores the transformative role of AI in optimizing logistics operations through advanced data integration, real-time analytics, and autonomous systems. AI technologies are increasingly applied to enhance core logistics functions such as dynamic routing, intelligent scheduling, and capacity planning, enabling organizations to meet rising customer expectations while minimizing operational costs. The fusion of big data and IoT-enabled supply chains allows for continuous data flow across interconnected logistics networks, providing the foundation for real-time, data-driven decision-making. Key to this evolution is the deployment of digital twins, which create virtual replicas of physical logistics systems to simulate, monitor, and predict performance outcomes under varying conditions. These systems leverage predictive analytics and machine learning algorithms including reinforcement learning to improve resource allocation, identify anomalies, and adapt routing and inventory decisions in real-time. Demand sensing models, informed by structured and unstructured data, further support proactive forecasting and inventory balancing, thereby reducing lead times and avoiding stockouts or overstock situations. Moreover, the integration of predictive maintenance tools within logistics fleets ensures that asset health is continuously monitored, preventing unplanned downtimes and extending vehicle lifespan. Autonomous mobile robots and AI-powered drones are also emerging as vital components in last-mile delivery and warehouse management, offering enhanced speed, accuracy, and scalability. The study presents use cases from multinational logistics providers that have successfully implemented AI-powered platforms, resulting in significant gains in fuel efficiency, delivery accuracy, and supply chain resilience. It also addresses the technical and organizational challenges associated with adopting AI, including data interoperability, cybersecurity, workforce adaptation, and ethical governance. By synthesizing advancements in AI, IoT, and real-time analytics, this paper underscores how intelligent logistics systems are not only enhancing operational performance but also setting new standards for sustainability and customer-centricity in global trade. The findings advocate for continued investment in integrated AI infrastructures to ensure logistics networks are agile, responsive, and future-ready in the face of evolving market demands and global disruptions.
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Shuja, Naveed. "The Future of Personalized Medicine." DEVELOPMENTAL MEDICO-LIFE-SCIENCES 1, no. 7 (2024): 1–3. https://doi.org/10.69750/dmls.01.07.084.
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It is the era of personalized medicine, ushering us into a new healthcare era of treatment based on the individual characteristics of each. Using advances in genomics, artificial intelligence (AI), and multi-omics technologies, this revolutionary approach promises diagnosis, prevention, and treatment strategies that go far beyond the “one size fits all” model of the past[1]. From Genomics to Multi-Omics: The Precision Healthcare Foundation The completion of the Human Genome Project was a major step forward in modern medicine, unveiling the sequence of the genetic code that defines each of us. However, the human genome was not the end of the story. With the advent of personalized medicine, we define it through its multi-omics nature, which integrates genomics, transcriptomics, proteomics, metabolomics, and the microbiome. These layers of data give us an understanding of the biological mechanisms driving disease that allow targeted intervention[2]. For instance, genetic biomarkers have made a sea change in oncology. Targeted therapies improve the outcome in breast cancer (e.g. BRCA1/BRCA2) and lung cancer (e.g. mutant EGFR) by detecting such mutations in genomic profiling. Likewise, technologies such as liquid biopsy are advancing cancer care by providing real-time monitoring of circulating tumor DNA without invasive monitoring[3]. Artificial Intelligence and Digital Twins: Accelerating Progress AI and machine learning have become the new indispensable tools for personalized medicine. However, the vastness of datasets, such as genetic profiles, electronic health records (EHRs), and wearable device data, can be analyzed by AI algorithms to predict disease risks, advise treatments, and optimize clinical decision-making. For example, AI-driven models have shown themselves capable of detecting breast cancer with similar accuracy to radiologists, identifying new biomarkers for the prediction of disease, and personalizing pharmacotherapy[4]. A very exciting advance is digital twins. These are so-called virtual replicas of individual patients who are created using real-time health data, simulations, and predictive models. Digital twins enable healthcare providers to test treatment plans in a virtual environment before applying them in the real world. This innovation reduces risks, shortens clinical trial timelines, and paves the way for truly individualized care[5]. Personalized Medicine in Clinical Practice Personalized medicine is already being translated into the clinic, albeit at a slower pace. For example, pharmacogenomics helps clinicians optimize drug therapy for an individual’s genetic makeup. Examples include genetic testing-guided dosing of warfarin or the use of targeted therapies in cancers with defined molecular signatures. In addition, smart devices and digital health tools promote continuous health monitoring and allow patients to take an active role in managing their health[6]. Advances in genomics are allowing us to identify people at high risk for cardiovascular disorders, or diabetes, among other diseases, and intervene before the problems happen. For example, BRCA1 mutation carriers have taken proactive steps, like Angelina Jolie has, to mitigate breast and ovarian cancer risks[7]. Challenging Issues and Ethical Issues The promise of personalized medicine has not gone unchallenged. First, it is still expensive for many healthcare systems to perform multi-omics analysis, AI tools, and genetic testing. If we don’t address equity in access, health disparities will continue to widen[8]. Second, these massive amounts of data are problematic because of the problems those data create around privacy, security, and ethical use. Strong policies and regulations must cover the issue of informed consent and data ownership, as well as protection against the misuse of genetic information[9]. Clinicians and patients alike need to be educated and trained on the many facets of personalized medicine. Streamlined workflows, interoperable health systems, and clinical guidelines are needed for integration into routine care[10]. The Road Ahead: Personal, Predictive, Preventive Technology, as well as our increased knowledge of biology, is the future of personalized medicine. If you keep investing in genomics, AI, and digital tools we are about to enter a world where disease prevention, early detection, and targeted treatment are the norm[11]. This is really future enabled by truly personalized health, predictive health through advanced models to predict health outcomes, and preventive health to prevent before a disease strikes. The future holds the promise not only of improved individual health outcomes but a more efficient, less costly, more equitable healthcare system on a global scale[12]. Conclusion In a future of personalized medicine, we have enormous promise from technological advances and a greater understanding of human biology. The move towards more precise, efficient, and patient-centric healthcare is seeing these integrated with genomic variation, AI, and digital tools. However, to get to this future, there are issues of accessibility, ethical issues, and data security. As we stand on the cusp of a new era, we can not achieve personalized medicine without collaboration between researchers, clinicians, policymakers, and technologists. By doing this, not only will we improve individual health outcomes, but we will also change the global healthcare landscape for generations to come.
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Pedro Barros, Chijioke Paul Agupugo, Emmanuella Ejichukwu, Mario David Hayden, and Kehinde Adedapo Ogunmoye. "Smart grid security: Safeguarding sustainable energy systems from cyber threats." World Journal of Advanced Research and Reviews 26, no. 3 (2025): 1284–301. https://doi.org/10.30574/wjarr.2025.26.3.2233.
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The rapid advancement and integration of smart grid technologies have revolutionized energy systems by enabling real-time monitoring, enhanced efficiency, decentralized energy generation, and renewable energy integration. However, this increased digitization and connectivity have simultaneously exposed critical infrastructures to a growing array of sophisticated cyber threats. As smart grids evolve into complex, data-driven ecosystems, ensuring their cybersecurity becomes paramount to achieving sustainable and resilient energy systems. This paper explores the intersection of cybersecurity and smart grid sustainability, identifying vulnerabilities in advanced metering infrastructure (AMI), supervisory control and data acquisition (SCADA) systems, distributed energy resources (DERs), and communication protocols. It discusses real-world incidents and simulated attack scenarios to highlight the potential consequences of cyber intrusions on grid stability, data integrity, and energy availability. A comprehensive framework for smart grid security is proposed, focusing on proactive risk management, threat detection through artificial intelligence (AI) and machine learning (ML), blockchain-enabled data validation, and zero-trust architecture models. The framework emphasizes the importance of stakeholder collaboration, regulatory compliance, and continuous system auditing to reinforce cybersecurity postures. Additionally, this study investigates the role of digital twins in simulating cyber-physical interactions and enabling predictive threat modeling for proactive resilience. Furthermore, the paper examines policy gaps, standardization issues, and workforce capacity constraints that hinder effective implementation of cybersecurity measures across diverse energy infrastructures. Strategies for integrating cybersecurity into the lifecycle of smart grid components from design to deployment are also discussed. By aligning technological innovation with robust cybersecurity governance, the paper aims to support the development of secure, adaptive, and sustainable smart energy systems capable of withstanding emerging cyber threats. The insights provided are intended to guide policymakers, grid operators, technology developers, and researchers in fortifying energy systems against cyber vulnerabilities while ensuring the continued advancement of clean and intelligent energy solutions. Ultimately, safeguarding smart grids is not merely a technical imperative but a foundational element for achieving long-term energy sustainability and national security in the digital era.
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Oruganti, Yagna. "Technology Focus: Data Analytics (October 2022)." Journal of Petroleum Technology 74, no. 10 (2022): 89–90. http://dx.doi.org/10.2118/1022-0089-jpt.
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Oil and gas companies are starting to invest more in the energy transition and establish emissions-reduction goals, such as an end to routine gas venting and flaring, with some going as far as setting net-zero goals by mid-century. A wide range of climate technologies with varying degrees of maturity levels are needed to pave the road to net zero. These include electrification and grid decarbonization, advances in battery technology, blue and green hydrogen fuels, bioenergy, carbon capture use and storage, and mitigating emissions of potent greenhouse gases such as methane. The COVID-19 pandemic has helped accelerate the pace of the digital transformation in the oil and gas industry. Digitalization is, in effect, a part of the broader energy transition that is occurring in the industry. This includes a move from on-premises data centers to the cloud, building digital twins of physical assets, process automation using the Internet of Things (IoT), leveraging reams of data from oil and gas operations for artificial intelligence/machine leaning (AI/ML) applications, cloud-based high-performance computing for applications such as seismic imaging for carbon capture and storage, and predictive maintenance to fix leaky equipment to reduce the environmental footprint of operations. These technologies serve to improve productivity, increase operational efficiency, reduce downtime, increase cost savings, and reduce the carbon intensity of operations. The International Energy Agency report “The Oil and Gas Industry in Energy Transitions” states that reducing methane leaks to the atmosphere is the single most important and cost-effective way for the industry to bring down these emissions. This is a domain that can benefit significantly from the use of AI/ML techniques for leak detection and remediation (LDAR) efforts. ML techniques such as computer vision and anomaly detection can be used to identify both large and localized methane leaks from remote-sensing data and data streaming in from IoT sensors in the field. A combination of data modalities, with varying spatiotemporal resolutions, together with appropriate AI/ML technologies, would be essential for an effective methane LDAR program. As companies look to reduce their Scope 1, 2, and 3 emissions, breaking down data silos and establishing data standardization with common data models becomes critical in systems integration to develop an optimal emissions-reduction strategy. A seamless flow of information will enable the generation of high-fidelity data sets, which can be used to lower the operational footprint and drive business effects with AI solutions. With cloud-enabled technologies, driven by the application of ML and deep learning, companies can combine speed of implementation with scalability to accelerate their energy-transition efforts. I would like to invite readers to review the selection of papers to get an idea of various applications in the upstream oil and gas space where ML methods have been used. The highlighted papers cover the use of transformer-based models to predict oil production, the use of data analytics to study parent/child well relationships in shale plays, and the use of convolutional neural networks in core analysis. Recommended additional reading at OnePetro: www.onepetro.org. SPE 207744 Accelerating Subsurface Data Processing and Interpretation With Cloud-Based Full Waveform Inversion Systems by Sirivan Chaleunxay, Amazon Web Services, et al. SPE 205443 Natural-Language Processing and Text-Mining Approaches in Production-Shortfalls Analytics: Methodology, Case Study, and Value in the North Sea by Edgar Bernier, Total Denmark, et al. URTEC 208367 Real-Time Applications for Geological Operations: Repeatable AI Use Cases by Alfio Malossi, Eni, et al.
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Besigomwe, Kenneth. "Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems." Cognizance Journal of Multidisciplinary Studies 5, no. 1 (2025): 18–38. https://doi.org/10.47760/cognizance.2025.v05i01.002.
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This research provides an in-depth analysis of recent literature on Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems, focusing on the integration of Artificial Intelligence (AI) and Digital Twin technologies within modern manufacturing environments. Through a systematic literature review of studies sourced from leading academic databases such as Scopus, Web of Science, IEEE Xplore, Science Direct, Springer Link, and Google Scholar, this research examines how these advanced technologies are being used to optimize production processes, improve operational efficiency, and reduce costs. The review synthesizes key findings related to real-time data collection, predictive maintenance, and quality control, highlighting the role of AI in enabling self-regulating and self-improving manufacturing workflows. The application of AI and Digital Twin systems in closed-loop manufacturing facilitates enhanced decision-making through continuous feedback loops. These systems allow manufacturers to simulate, predict, and monitor production processes in real-time, enabling proactive maintenance, process optimization, and better-quality control. Moreover, the integration of AI allows for the dynamic adjustment of manufacturing parameters, reducing waste and improving resource utilization. This research identifies and highlights the potential of AI and Digital Twin technologies in driving sustainability and flexibility in manufacturing operations. However, the research also identifies several challenges in implementing AI-enabled Digital Twin systems, including data integration issues, high initial investment costs, and cybersecurity risks. The shortage of skilled professionals capable of managing these advanced systems further hinders widespread adoption. Based on the synthesis of current literature, the research concludes with recommendations for overcoming these obstacles, offering insights into the future of closed-loop manufacturing systems and their potential to transform industrial production processes.
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Kenneth, Besigomwe. "Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems." Cognizance Journal of Multidisciplinary Studies (CJMS) 5, no. 1 (2025): 18–38. https://doi.org/10.47760/cognizance.2025.v05i01.002.
Full textAbstract:
This research provides an in-depth analysis of recent literature on Closed-Loop Manufacturing with AI-Enabled Digital Twin Systems, focusing on the integration of Artificial Intelligence (AI) and Digital Twin technologies within modern manufacturing environments. Through a systematic literature review of studies sourced from leading academic databases such as Scopus, Web of Science, IEEE Xplore, Science Direct, Springer Link, and Google Scholar, this research examines how these advanced technologies are being used to optimize production processes, improve operational efficiency, and reduce costs. The review synthesizes key findings related to real-time data collection, predictive maintenance, and quality control, highlighting the role of AI in enabling self-regulating and self-improving manufacturing workflows. The application of AI and Digital Twin systems in closed-loop manufacturing facilitates enhanced decision-making through continuous feedback loops. These systems allow manufacturers to simulate, predict, and monitor production processes in real-time, enabling proactive maintenance, process optimization, and better-quality control. Moreover, the integration of AI allows for the dynamic adjustment of manufacturing parameters, reducing waste and improving resource utilization. This research identifies and highlights the potential of AI and Digital Twin technologies in driving sustainability and flexibility in manufacturing operations. However, the research also identifies several challenges in implementing AI-enabled Digital Twin systems, including data integration issues, high initial investment costs, and cybersecurity risks. The shortage of skilled professionals capable of managing these advanced systems further hinders widespread adoption. Based on the synthesis of current literature, the research concludes with recommendations for overcoming these obstacles, offering insights into the future of closed-loop manufacturing systems and their potential to transform industrial production processes.
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Moztarzadeh, Omid, Mohammad (Behdad) Jamshidi, Saleh Sargolzaei, et al. "Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer." Bioengineering 10, no. 4 (2023): 455. http://dx.doi.org/10.3390/bioengineering10040455.
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Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artificial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters.
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Blut, Christoph, Ralf Becker, Tristan Kinnen, et al. "Optimizing Building Energy Systems through BIM-enabled georeferenced Digital Twins." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W11-2024 (June 27, 2024): 1–8. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w11-2024-1-2024.
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Abstract. Building energy system management is critical for resource-saving approaches amid climate change-driven energy transitions. This paper presents a digital twin toolchain leveraging modern technologies such as Building Information Modeling (BIM), Artificial Intelligence (AI), Virtual Reality (VR), and Augmented Reality (AR). The toolchain automates the derivation of georeferenced digital twins during Technical Building Equipment (TBE) commissioning. Using a Scan vs. BIM process, discrepancies between as-planned and as-built TBE are identified, allowing automatic updates to the BIM model. Validation methods ensure both physical and functional aspects of the TBE are accurate. VR and AR facilitate off- and on-site commissioning, enabling immersive visualization and live sensor data access. An evaluation in small and large-scale demonstrators shows the toolchain's scalability and efficiency, with promising results in performance and accuracy. Future work aims to integrate more operational data, enhancing the digital twin's capabilities for building energy system management.
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Younesi Heravi, Moein, Israt Sharmin Dola, Youjin Jang, and Inbae Jeong. "Edge AI-Enabled Road Fixture Monitoring System." Buildings 14, no. 5 (2024): 1220. http://dx.doi.org/10.3390/buildings14051220.
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Effective monitoring of road fixtures is essential for urban safety and functionality. However, traditional inspections are time-consuming, costly, and error prone, while current automated solutions struggle with high initial setup costs, limited flexibility preventing wide adaptation, and reliance on centralized processing that can delay response times. This study introduces an edge AI-based remote road fixture monitoring system which automatically and continuously updates the information of the road digital twin (DT). The main component is a small-sized edge device consisting of a camera, GPS, and IMU sensors designed to be installed in typical cars. The device captures images, detects the fixture, and estimates their location by employing deep learning and feature matching. This information is transmitted to a dedicated cloud server and represented on a user-friendly user interface. Experiments were conducted to test the system’s performance. The results showed that the device could successfully detect the fixture and estimate their global coordinates. Outputs were marked and shown on the road DT, proving the integrated and smooth operation of the whole system. The proposed Edge AI device demonstrated that it could significantly reduce the data size by 80–84% compared to traditional methods. With a satisfactory object detection accuracy of 65%, the system effectively identifies traffic poles, stop signs, and streetlights, integrating these findings into a digital twin for real-time monitoring. The proposed system improves road monitoring by cutting down on maintenance and emergency response times, increasing the ease of data use, and offering a foundation for an overview of urban road fixtures’ current state. However, the system’s reliance on the quality of data collected under varying environmental conditions suggests potential improvements for consistent performance across diverse scenarios.