Resilient infrastructure management systems using real-time analytics and AI-driven disaster preparedness protocols

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.

The increasing complexity and vulnerability of modern energy systems underscore the urgent need for intelligent, resilient, and sustainable infrastructure solutions. However, challenges such as cybersecurity risks, ethical governance concerns, and interoperability barriers hinder progress. This study investigates how the integration of digital twin (DT) and artificial intelligence (AI) technologies addresses these challenges and transforms energy infrastructure management. Using empirical datasets from the International Energy Agency (IEA) Digitalisation and Energy Database, the OpenEI Digital Twin Case Studies, the NREL Grid Modernization Consortium, and bibliometric records from the Dimensions AI Scholarly Platform, the study applies descriptive statistics, multivariate frequency analysis, event sequence analysis, and citation network mapping. Findings reveal that North America leads global DT-AI adoption at 65%, with performance optimization accounting for 103 documented use cases. Real-time AI-driven interventions demonstrated action windows of 2–7 minutes, achieving efficiency gains of 30–60%, maintenance cost reductions of up to 40%, and false alarm rate improvements by 50%. Scholarly analysis identified 1,280 relevant publications, exhibiting a 21.3% annual growth rate, with growing emphasis on explainable AI (XAI) and federated digital twin architectures. The study emphasizes the necessity of harmonized global standards for interoperability and ethical AI governance to ensure secure and scalable deployments. It advocates for increased investment in underrepresented regions and strengthened academic-industry collaborations. By addressing both technological capabilities and systemic challenges, this research offers actionable insights to advance the resilience, sustainability, and operational intelligence of future energy infrastructures.

The burgeoning evolution of smart cities, characterized by the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML), heralds a transformative era in urban management and citizen engagement. These technological advancements promise enhanced efficiency in city operations, improved public services, and a sustainable urban environment. However, the complexity and interconnectedness inherent in these systems introduce significant cybersecurity challenges, necessitating innovative approaches to safeguard the digital infrastructure of smart cities. This paper aims to explore the cybersecurity landscape of smart cities from the perspective of integrating IoT, AI, and ML for the creation of digital twins, offering a comprehensive analysis of the opportunities and threats within this domain. Smart cities leverage IoT to connect various components of the urban infrastructure, including transportation systems, utilities, and public services, creating an integrated network of devices that communicate and share data. The incorporation of AI and ML into this framework facilitates intelligent decision-making, enabling the automation of services and the optimization of resources. This synergy enhances the quality of life for residents, promotes economic development, and supports sustainable environmental practices. However, the dependence on digital technologies also exposes smart cities to a range of cybersecurity risks, from data breaches and privacy violations to the disruption of critical infrastructure. The integration of IoT, AI, and ML in smart cities, while offering unprecedented opportunities for urban innovation, also amplifies the complexity of the cybersecurity landscape. IoT devices, often designed with minimal security features, become potential entry points for cyber attacks. The vast amount of data generated and processed by these devices, if compromised, could lead to significant privacy and security breaches. AI and ML models, for their part, are susceptible to manipulation and bias, which can undermine the integrity of decision-making processes. The interconnectivity of systems means that a breach in one sector could have cascading effects throughout the city's infrastructure. Against this backdrop, the paper investigates the role of digital twins in mitigating cybersecurity risks in smart cities. Digital twins, digital replicas of physical entities or systems, offer a powerful tool for simulating and analyzing smart city operations, including cybersecurity scenarios. By mirroring the city's infrastructure in a virtual environment, digital twins allow for the identification of vulnerabilities, the simulation of cyber attacks, and the evaluation of potential impacts. This proactive approach to cybersecurity enables city administrators to anticipate threats and implement protective measures before real-world systems are compromised. The research questions guiding this inquiry include: How can the integration of IoT, AI, and ML enhance the resilience of smart cities against cyber threats? What are the specific cybersecurity challenges presented by these technologies, and how can they be addressed? And, most crucially, what role can digital twins play in fortifying the cybersecurity defenses of smart cities? To address these questions, the paper begins with a review of the current state of smart city technology, focusing on the integration of IoT, AI, and ML. It then delves into the cybersecurity challenges unique to this technological landscape, drawing on recent examples of cyber incidents in smart cities. The analysis highlights the vulnerabilities introduced by the widespread use of IoT devices and the complexities of securing AI and ML systems. Following this, the discussion turns to the potential of digital twins as a cybersecurity tool, examining how they can be employed to detect vulnerabilities, simulate attacks, and plan responses. The paper argues that while the integration of IoT, AI, and ML in smart cities presents significant cybersecurity challenges, it also offers opportunities for innovative solutions. Digital twins emerge as a promising approach to enhancing the cybersecurity posture of smart cities, enabling a dynamic and proactive defense mechanism. By facilitating the simulation of cyber threats in a controlled environment, digital twins allow city administrators to identify weaknesses, test the efficacy of protective measures, and develop more resilient urban infrastructures. In conclusion, the integration of IoT, AI, and ML in smart cities represents a double-edged sword, offering both remarkable opportunities for urban innovation and formidable cybersecurity challenges. This paper underscores the critical importance of adopting a cybersecurity perspective in the development and management of smart cities, highlighting the potential of digital twins as a strategic tool in mitigating these risks. As smart cities continue to evolve, embracing these technologies in a secure and responsible manner will be paramount in realizing their full potential while safeguarding the digital and physical well-being of urban populations.

The integration of Artificial Intelligence (AI) in civil engineering is reshaping traditional practices and driving innovation across the field. This comprehensive review explores emerging AI methods, including machine learning, deep learning, natural language processing, computer vision, generative AI, and reinforcement learning, highlighting their applications in key civil engineering domains. AI is revolutionizing structural engineering through predictive maintenance and design optimization, enhancing construction management with intelligent scheduling and automation, and transforming geotechnical, transportation, environmental, and water resources engineering with advanced modeling and predictive analytics. Despite its transformative potential, the adoption of AI in civil engineering faces significant challenges, such as data standardization, model interpretability, integration with established practices, and computational demands. Addressing these challenges requires continued research, ethical governance, and collaboration among academia, industry, and policymakers. This review underscores the importance of integrating AI with emerging technologies, such as IoT, blockchain, and digital twins, to unlock new possibilities for sustainable and resilient infrastructure. By addressing existing limitations and embracing advancements in AI algorithms, civil engineering is poised to achieve unprecedented levels of efficiency, sustainability, and innovation. This paper concludes with a call for ongoing research and development to fully harness the transformative potential of AI in building the infrastructure of the future.

Artificial intelligence (AI) has become an essential force in modern civil engineering, reshaping conventional methods through intelligent data processing, predictive modeling, and automated decision-making. This review presents a comprehensive synthesis of AI applications across eight major civil engineering domains: structural analysis and design, concrete technology, geotechnical engineering, hydraulic and water systems, transportation and pavement engineering, construction management, building information modeling (BIM), and green infrastructure with sustainability assessment. The study systematically analyzes findings from 106 peer-reviewed sources, highlighting how AI models, especially machine learning (ML), deep learning (DL), and hybrid combinations such as ANN-PSO and CNN-XGBoost, have significantly improved performance in complex, nonlinear, and data-intensive scenarios. The review reveals that AI-based systems have enhanced the precision of strength predictions, classification of soil types, analysis of fluid dynamics, optimization of transport networks, and the integration of BIM environments with real-time data for advanced infrastructure management. In construction, AI supports safety monitoring, resource planning, and risk forecasting, while in sustainability, it aids in carbon footprint modeling and energy efficiency analysis. Moreover, the incorporation of Internet of Things (IoT) devices and sensor-based systems with AI has enabled real-time feedback and intelligent decision support in several infrastructure systems. Despite its transformative impact, AI integration in civil engineering still faces limitations. These include data scarcity, model transparency challenges, high computational costs, and lack of standardized benchmarks. To address these issues, the review outlines key research recommendations, including the use of physics-informed neural networks (PINNs), the adoption of transfer learning for small datasets, explainable AI (XAI) frameworks, and AI-enabled digital twins for infrastructure lifecycle management. This paper serves as a consolidated reference for researchers and professionals aiming to harness AI in the planning, design, operation, and sustainability of civil infrastructure systems. It not only maps the current state of AI in civil engineering but also offers a roadmap for future advancements toward smarter and more resilient infrastructure.

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.

While the field of hydrography is crucial for maritime navigation and other maritime applications, oceanography is the field that provides the relevant data and knowledge for predicting climate change, monitoring marine resources, and exploring marine life. Digital ocean twins combine these two exciting fields and combine ocean observations and ocean models to establish virtual representations of a real world system, in this case the ocean or an ocean area, as well as assets in the ocean and processes within ocean industries or the natural environment. They have the potential to play a critical role in optimising and supporting sustainable ocean development. Digital Twins are synchronised with their real-world counterparts at a specific frequency and fidelity. They can provide valuable insights into the ocean's state and its evolution over time, which can be used to support decision-making in ocean governance and various ocean-related industries. Digital ocean twins can transform human ocean interactions by accelerating holistic understanding, optimal decision-making, and effective interventions. Digital twins of the ocean use ocean observations, historical and forecast data to represent the past and present and simulate possible future scenarios. They are motivated by outcomes, tailored to use cases, powered by integration, built on data, guided by domain knowledge, and implemented in IT systems. In this article, we explore the benefits of digital twins for the ocean, the challenges in developing them, and the current state of the art in ocean digital twin technology. One of the main benefits of digital ocean twins is their ability to provide accurate predictions of ocean conditions under expected interventions. Their information can be used to support decision- making in various applications including ocean-related industries, such as fishing, shipping, and offshore energy production. Additionally, digital twins can help to improve our understanding of the ocean's complex processes and their interactions with human activities, such as climate change, pollution, resource extraction and overfishing. Researchers and IT companies are combining various technologies and data sources, such as the Internet of Things for ocean observations, state of the art data science, artificial intelligence and machine learning, data spaces and vocabularies into digital ocean twins to contextualise data, improve the accuracy of ocean models and make ocean knowledge more accessible to a wide range of users.

The integration of artificial intelligence (AI) with digital twin (DT) technology has revolutionised the industry by enabling the creation of autonomous, adaptive, and resilient systems that are beyond static digital replicas. AI-enhanced DTs facilitate real-time monitoring, predictive maintenance, proactive decision making, and operational efficiency, aligning with the human-centric objectives of Industry 5.0. In this study, an AI–DT Integration framework is introduced, AI is systematically incorporated into the DT lifecycle across virtualisation and synchronisation, monitoring and awareness, and decision-making and optimisation phases. By employing advanced techniques, such as generative design, predictive analytics, and scenario simulations, this framework enhances DT autonomy and resilience while addressing critical challenges such as interoperability, scalability, and data security. Case studies have demonstrated the transformative impact of AI on DT functionality, including self-optimisation, adaptive scheduling, and risk mitigation. These findings underscore the potential of AI-driven DTs to revolutionise industries and urban systems, highlighting the need for global standards and scalable architectures to realise their role as foundational tools in sustainable and adaptive Industry 5.0 ecosystems.

In recent years, innovative technologies for monitoring and managing civil infrastructure have been widely used by stakeholders and managing bodies, to ensure the correct and efficient maintenance of these critical elements. In this context, great attention is given to the environment surrounding these assets. Indeed, the environment plays a crucial role in altering the conditions of the civil infrastructures, either as a result of catastrophic natural events or due to the gradual morphological changes over time. Understanding the surrounding conditions of critical infrastructures is therefore essential to ensure accurate predictions regarding the changing conditions of the infrastructures over time, to prevent damages, and to intervene promptly and efficiently when necessary. In addition, comprehending the environmental conditions surrounding critical infrastructures is crucial for ensuring accurate long-term monitoring procedures, mitigating potential damages proactively, and facilitating efficient and timely interventions when necessary. Moreover, several international studies and research projects focused on these topics have been increasingly supported by industry and infrastructure managers in handling data from innovative monitoring technologies. Among these technologies, remote sensing techniques, including satellite analysis using MT-InSAR methods for assessing structural subsidence [1], and change detection techniques to evaluate temporal variations are gaining momentum. Furthermore, the use of Unmanned Aerial Vehicles (UAVs), to integrate information derived from other remote surveys, stands as a crucial topic to be more investigated. This research aims to identify a methodology for managing multi-sensor and multi-scale survey information integrating satellite remote sensing and ground-based Non-Destructive Testing for Digital Twin-based infrastructure monitoring. However, the interpretation of data derived from satellite remote sensing [1] and ground-based Non-Destructive Testing (NDT) [2] techniques remain an area awaiting comprehensive exploration within the realm of transport infrastructure monitoring. This approach is aimed at the definition of a Digital Twin of the analyzed infrastructure and the environment in which it is located. An experimental application was developed selecting a bridge, located in Italy, identified as a case study. Several data obtained from inspections performed by UAVs and satellite remote sensing were implemented, as well as a digital modeling process, specifically developed for integrating such database to create a Digital Twin of the bridge and the environment. This application stands as a starting point for defining a broader integrated monitoring methodology for the management of critical transport infrastructures. AcknowledgementsThis research is supported by the Project “SIMICOM”  accepted and funded by the Lazio Region, ItalyReferences[1] Gagliardi V., et Al. Digital twin implementation by multisensors data for smart evaluation of transport infrastructure. SPIE Optical Metrology. Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, Munich, 2023. [2] Tosti F., et Al "Integration of Remote Sensing and Ground-Based Non-Destructive Methods in Transport Infrastructure Monitoring: Advances, Challenges and Perspectives," 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS), Jakarta Pusat, Indonesia, 2021, pp. 1-7, 

Organisation theorists and practitioners alike have become greatly interested in high reliability in the management of large hazardous technical systems and society's critical service infrastructures. But much of the reliability analysis is centred in particular organisations that have command and control over their technical cores. Many technical systems, including electricity generation, water, telecommunications and other “critical infrastructures,” are not the exclusive domain of single organisations. Our essay is organised around the following research question: How do organisations, many with competing, if not conflicting goals and interests, provide highly reliable service in the absence of ongoing command and control and in the presence of rapidly changing task environments with highly consequential hazards?We analyse electricity restructuring in California as a specific case. Our conclusions have surprising and important implications both for high reliability theory and for the future management of critical infrastructures organised around large technical systems.

The structural integrity of transportation infrastructure is critical in ensuring public safety, economic stability and societal advancement. The demand for versatile, scalable and real-time monitoring solutions becomes exponential as these assets age, get used more and face environmental pressures. Conventional inspection methods, such as visual inspections and static evaluations, while valuable in localized applications, have significant limitations, including dependence on the expertise of specialized operators, time consumption, and an inability to provide dynamic insights across extensive networks [1]. In this regard, Digital Twin (DT) technology has emerged to provide a virtual replica of physical assets in real-time with data from multiple sources [2]. Supplementing DTs, remote sensing techniques including Multi-Temporal InSAR (MT-InSAR) and high-resolution satellite images can easily identify structural displacements in the millimeter scale region over extensive region.Satellite constellations, provide periodical updates with high spatial and temporal resolution, allowing a near real time monitoring of infrastructure without the need for ground-based instrumentation. These advancements are further enhanced by Building Information Modeling (BIM), which supports the creation of dynamic digital models encompassing all data relevant to the management, maintenance, and optimization of transportation infrastructure. This research presents a comprehensive approach to integrating Digital Twin technology with satellite remote sensing, BIM, and non-destructive testing methodologies. The study highlights the potential of combining near-real-time satellite data, field inspections, and advanced visualization techniques to develop a scalable, network-level monitoring system for critical assets such as bridges and viaducts. These results reiterate the value of high-resolution satellite missions along with next-generation technologies for enabled predictive maintenance and structural integrity management, supporting the sustainable and resilient transportation infrastructure development. AcknowledgementsThis research is supported by the Project “PIASTRE” accepted and funded by the Lazio Region, ItalyReferences[1] Napolitano A., et al., Integration of Satellite Monitoring data in a Digital Twin of Transport Infrastructure. Proceedings Volume 13197, Earth Resources and Environmental Remote Sensing/GIS Applications XV; 131970Y (2024) https://doi.org/10.1117/12.3034395 [2] Gagliardi V., et al., Digital twin implementation by multisensors data for smart evaluation of transport infrastructure. SPIE Optical Metrology. Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, Munich, 2023.

The integration of Artificial Intelligence (AI) into Business-to-Business (B2B) marketing decision systems is rapidly transforming how firms assess market dynamics, customer behavior, and campaign performance. Despite the growing interest, there remains a significant gap in systematically incorporating AI adoption metrics into B2B marketing frameworks. This proposes a conceptual framework designed to bridge this gap by integrating AI adoption metrics such as model maturity, data readiness, algorithmic transparency, and AI-driven customer insights into B2B marketing decision-making systems. The framework emphasizes a multi-layered approach, wherein AI adoption is not treated as a static investment, but rather as a dynamic capability evolving alongside organizational strategy, technological infrastructure, and market feedback. The proposed model is structured around four core pillars: (1) Organizational Readiness, focusing on leadership alignment, data governance, and talent capabilities; (2) Technological Integration, capturing AI tool deployment, system interoperability, and real-time analytics; (3) Market Responsiveness, addressing customer segmentation accuracy, lead scoring efficiency, and predictive performance; and (4) Ethical and Strategic Alignment, ensuring compliance, transparency, and long-term value creation. Each pillar encompasses specific metrics that allow B2B marketers to monitor, evaluate, and refine AI deployment in decision systems. By embedding these metrics within B2B marketing architectures, firms can enhance decision accuracy, automate routine tasks, and personalize communications at scale, ultimately improving return on marketing investment (ROMI). The framework also provides a foundation for benchmarking AI maturity across firms and industries, enabling continuous improvement and competitive advantage. This review contributes to the intersection of AI and marketing literature by offering a structured pathway for operationalizing AI metrics within strategic decision-making. It also sets the stage for future empirical validation and refinement of AI integration models in complex B2B environments. Future research directions include quantitative testing of the framework and sector-specific adaptations to account for differing AI adoption trajectories.

The primary objective of the author of this paper is to propose a preliminary and theoretical model of security management of maritime critical infrastructure under the conditions of the fourth industrial revolution. The proposed model in the definition is to be comprehensive in terms of shaping information security management. The article is also an attempt to capture the main threats in the sphere of information security management during the management of critical infrastructure. In addition, the author‘s intention is to provoke/open a discussion on this topic, because as is evident from numerous scientific studies, the awareness of the negative consequences of progress is essential to take correct actions to minimize their impact.

This paper aims to investigate the potential impact of Chinese digital twin technology and artificial intelligence (AI) in enhancing healthcare and medical systems in Belt and Road regions, with a specific focus on smart cities in the Middle East. In recent years, digital twin technology has garnered significant attention due to its ability to simulate and replicate complex systems and processes in a virtual environment. This paper focuses on the implementation of Chinese digital twin technology, which integrates AI advancements to enhance healthcare and medical systems in Belt and Road regions. By utilizing a combination of literature review, data analysis using CiteSpace software, and a case study approach, the potential collaboration and benefits of Chinese digital twin technology and AI in smart health-tech cities are explored. Through an extensive review of relevant literature, data analysis utilizing CiteSpace software, and a case study approach, this study offers valuable insights and recommendations for policymakers, healthcare systems, and stakeholders interested in utilizing advanced technologies to improve health outcomes and healthcare services in the regions covered by the Belt and Road Initiative, providing a comprehensive analysis of the potential impact of Chinese digital twin and AI in facilitating healthcare and medical systems in Belt and Road regions.

Value Engineering (VE), traditionally centered on optimizing the function-to-cost ratio, is being redefined by the transformative impact of Industry 4.0. Through the integration of cyber-physical systems, Artificial Intelligence (AI), Internet of Things (IoT), Digital Twins, and real-time analytics, VE 4.0 shifts from cost reduction to strategic, digitally enabled value creation. This evolution fosters smarter, more adaptive, and collaborative value optimization across complex industrial systems. This study critically analyzes the shortcomings of traditional VE, including limited digital integration, reactive implementation, and a narrow lifecycle perspective. In response, it introduces VE 4.0 as a function-driven, data-intelligent, and human-centric methodology that enables intelligent, real-time, and sustainable value creation. The proposed framework comprises six interrelated components: (1) foundational principles emphasizing digital integration, adaptability, sustainability, and organizational readiness; (2) digital transformation of VE processes through AI, IoT, digital twins, and data analytics to support predictive and connected decision-making; (3) enhancement of the VE Job Plan using advanced tools such as natural language processing (NLP), augmented/virtual reality (AR/VR), and blockchain to improve speed, accuracy, and lifecycle alignment; (4) a phased implementation roadmap including assessment, planning, piloting, scaling, and continuous improvement; (5) an enhanced Lean Six Sigma DMAIC framework embedded with smart technologies for ongoing, real-time optimization; and (6) enablers and mitigation strategies addressing challenges related to leadership, digital capabilities, infrastructure, and cybersecurity. By redefining VE as a digitally empowered, ethically guided, and sustainability-aligned methodology, this study positions VE 4.0 as a strategic enabler of innovation, resilience, and long-term value creation. It concludes by highlighting future research directions to support the evolution, validation, and cross-sector implementation of VE 4.0 in smart and sustainable industrial systems.