Figueiredo, Bernardo J. R., Ferreira, Marco P. M., Matos, João, Cova, Marco
A new approach to asset management and traceability emerges upon the integration of Non-Fungible Tokens (NFTs) and Digital Twin (DT) technology. While NFTs are widely used in digital art and gaming, their potential for securing real-world assets in DT simulations remains under-explored. A generic NFT-driven DT simulation platform could transform asset management by enhancing traceability, optimizing operations, and fostering sustainability within and across industries. In livestock management, DTs can model individual animals in real time, capturing data on health and growth. Linking this data to NFTs ensures ownership and provenance, improving traceability and accountability. Similarly, in manufacturing, DTs can identify inefficiencies, reducing waste and energy use. It is pressing to improve decision-making and operational efficiencies throughout distinct contexts. A Systematic Literature Review (SLR) was conducted following PRISMA. The goal was to build a solid, unbiased foundation for our research, contribute lasting value to the community, and identify where our work can make the most impact. From an initial set of 114 papers, the authors screened and classified the most relevant. This led to a final selection of 8 papers for full-text reading and in-depth analysis. They are presented in detail and compared in order to depict the current state of the art in the field. Results reveal a significant gap concerning the topic, particularly highlighting the absence of simulation environments that align with the previous proposal presented by the authors: a comprehensive NFT-driven and DT simulation platform that transforms NFT-based asset management from static ownership records to dynamic, provides simulation operational tools, enables real-time monitoring, predictive maintenance, and performance optimization for real-world assets.
Against the backdrop of the Industry 5.0 transformation, traditional centralized Manufacturing Execution Systems (MES) struggle to meet the complex demands of flexible and small-batch production. This paper proposes a four-level distributed architecture oriented toward human-centric manufacturing, encompassing Manufacturing Units (UMS), Production Lines (PL-MES), Workshops (MOM), and Factories (MOM), supporting dynamic reconfiguration and resource optimization from the unit level to cross-factory operations. Building on this, the study designs a smart factory operating system architecture based on ‘cloud-edge-end’ collaboration, achieving vertical integration of equipment, systems, and business processes through an industrial internet platform. Leveraging intelligent connectivity gateways and modular microservices, the architecture constructs a decentralized decision-making chain. Innovatively integrating multi-agent systems and federated learning, the architecture significantly enhances the autonomous decision-making capabilities of manufacturing units and cross-level collaboration efficiency. Additionally, the system supports rapid deployment for small and medium-sized enterprises through low-code toolchains, lowering technical barriers. This research provides a theoretical framework and technical pathway for human-machine collaborative manufacturing in the Industry 5.0 era, driving the paradigm shift of manufacturing systems from rigid control to ecological self-organization, laying a solid foundation for the sustainable development of future smart factories
Alexander Grünewald, Patrick Stuckmann-Blumenstein, Patrick Keitzl, Larissa Krämer
Additive manufacturing processes such as 3D printing have seen significant progress in the industry in recent years and have become an integral part of Industry 4.0. This fourth industrial revolution is characterized by the increasing networking and automation of production systems and the use of large amounts of data. In this context, distributed ledger technologies (DLT), which include blockchain technology, offer promising opportunities to change production fundamentally. Production processes can be more secure and efficient by creating trust and transparency in data storage and eliminating dependence on centralized instances. However, the full potential of blockchain technology is often not realized due to the perceived complexity of its implementation. Overcoming this skepticism requires a better understanding of the application possibilities and, more importantly, successful practical examples demonstrating blockchain technology’s transformative power in the industry. This study explores how blockchain can be effectively integrated into additive manufacturing processes and offers a structured overview of existing blockchain-based business models within this domain. Hence, a systematic literature interview, Crunchbase review, and Workshop are performed to examine specific use cases of blockchain in additive manufacturing and analyze how these technologies interact with existing business models. In order to provide an overview of existing blockchain-based business models in the context of additive manufacturing, a taxonomy is developed in the underlying paper to identify characteristic features. The taxonomy is further demonstrated along different existing business models.
As the U.S. healthcare system shifts toward value-based care, there is a growing need for patient-centered technologies that ensure data ownership, interoperability, and trust.This paper proposes a novel framework integrating neuro-symbolic artificial intelligence (AI) and zero-knowledge (ZK) blockchain to enable a secure, scalable, and ethically grounded digital-twin marketplace for patient data.The goal is to empower individuals to own and control their health digital twins-comprehensive, dynamic, AI-driven models representing real-time physiological, behavioral, and clinical states-while facilitating precision care and research collaborations.At a macro level, the framework leverages neuro-symbolic AI to enhance digital twin reasoning, enabling explainable predictions and treatment simulations across diverse datasets.This is paired with ZK-proof blockchain infrastructure to ensure privacy-preserving authentication, decentralized governance, and monetization of patient data without revealing sensitive health information.The integration addresses key challenges in trust, transparency, and consent in patient-provider and patient-researcher relationships.Zooming into operational layers, the paper outlines a decentralized application (dApp) architecture that supports smart contracts for patient-informed data sharing, automated payer-provider interactions, and regulatory compliance tracking.It also highlights how incentives within the marketplace can align with care quality metrics, promote social determinants of health inclusion, and advance equitable data access in underrepresented populations.Case scenarios in chronic disease management and clinical trials illustrate the feasibility of this patient-owned digital-twin ecosystem.Ethical considerations, including algorithmic fairness, data sovereignty, and digital consent protocols, are also critically examined.By combining symbolic logic, neural learning, and cryptographic assurance, this framework sets the foundation for a secure and equitable next-generation health economy.
Ruba Islayem, Ahmad Musamih, Khaled Salah, Raja Jayaraman · 5 authors
Medical digital twins (MDTs) are rapidly emerging as transformative tools in healthcare. They provide virtual representations of medical devices and systems that facilitate real-time analysis and enhance decision-making. However, challenges such as secure data management, access control, and the lack of immersive and intelligent patient interactions limit their effectiveness. In this paper, we propose a solution integrating blockchain technology, Non-Fungible Tokens (NFTs), and Large Language Models (LLMs) within a metaverse environment to enhance MDT functionality. Blockchain and NFTs ensure secure ownership and access control, while the metaverse offers an engaging platform for user interaction. An LLM-powered non-player character (NPC) enables intelligent real-time user interactions and personalized insights. We develop two blockchain smart contracts for user registration, NFT ownership, and access control, and utilize decentralized InterPlanetary File System (IPFS) storage for the metaverse, MDT metadata, and interaction logs. We present the system architecture, sequence diagrams, and algorithms, along with the implementation and testing details. We conduct cost, security, and response time analyses to evaluate the smart contracts and LLM performance and compare our solution with existing approaches. We discuss practical implications, as well as challenges and limitations of the proposed solution. Finally, we explore the generalization of our system for various applications. The smart contract code and metaverse files are publicly available on GitHub.
Financial disruptions, such as extended trade credit periods and reduced down payments, pose significant challenges to effective working capital management in supply chains. While the benefits of blockchain and digital twin technologies have been studied independently in supply chain finance, their combined potential to optimise working capital performance during disruptions remains underexplored. Our study addresses this research gap by proposing an integrated blockchain and digital twin framework to enhance financial resilience in disrupted supply chains. Blockchain facilitates secure, decentralised data sharing, providing visibility into product, order, and cash flows across supply chain stages. The digital twin complements blockchain by offering predictive capabilities and enabling dynamic adjustments to working capital policies in response to disruptions. Within this framework, discrete-event simulation assesses the impact of financial disruptions on working capital performance, while machine learning models generate decision rules for adaptive working capital management. The study highlights the critical role of inventory adjustments in mitigating financial disruptions and reducing working capital variability relative to demand fluctuations. This research provides actionable insights for supply chain managers seeking to improve working capital stability amid disruptions and offers a data-driven approach to financial resilience in supply chains.
The pharma supply chain has various issues, including counterfeiting, visibility, and product authenticity in the market. Some of the issues threatening the safety of the patients and the efficacy of the industry include those named above. Such issues are overcome by new technologies like Blockchain and Artificial Intelligence (AI) that are being offered. Blockchain enhances transparency since it is a distributed ledger technology that traces the drug through the supply chain and minimizes the risk of counterfeit drugs. In contrast, there is AI that extends beyond data acquisition and analytics to forecast supply chain disruptions, inventory controls, and even drug shortages to improve decision-making and performance. This paper seeks to focus on the issues that are overcome by Blockchain when integrated with Business Intelligence (BI) systems, such as drug authenticity and counterfeiting issues. This highlights the emphasis that there is a need to consider technology integration when there is a need to make a change, security considerations, and the integration of AI to make the supply chain in the pharma industries more efficient and reliable.
This article explores the transformative integration of generative AI capabilities with Data Mesh architecture to revolutionize enterprise analytics. Beginning with examining traditional data architectures' limitations, the discussion highlights how centralized proceeds towards creating bottlenecks that impede innovation and time-to-insight. The Data Mesh paradigm is presented as a fundamental shift that decentralizes data ownership while maintaining federated governance. The integration of generative AI within this framework enables natural language interfaces, synthetic data generation, automated documentation, and intelligent insight creation. Implementation strategies using Databricks platform capabilities demonstrate how organizations can balance domain autonomy with enterprise interoperability. The architecture delivers enhanced analytics through AutoML-powered data quality with generative explanations and event-driven processing that enables real-time, predictive intelligence. Together, these capabilities create a self-improving ecosystem that democratizes data access while ensuring governance, ultimately enabling organizations to move beyond traditional reporting toward autonomous, data-driven operations with cross-domain collaboration.
AI and DLT can play a transformative role when it comes to supply chain visibility and accountability. Artificial intelligence can be utilized to improve predictive analytics, demand forecasting, and automation of operations, resulting in real-time, data-driven decision-making. However, while DLT provides security, immutability, traceability, and decentralized control that enhance transparency, reduce the possible of fraud and build trust among diverse parties across the supply chain. This paper discusses the symbiosis of both technologies and will demonstrate how their emerging capabilities deliver end-to-end visibility, streamlining operations and improving goods traceability throughout supply chain logistics. Case studies from diverse industries including manufacturing, logistics, and retail highlight the tangible implementation and benefits of these technologies in real-world settings. The paper also emphasizes the difficulties and constraints encountered during the deployment of AI and DLT technologies, with specific mention to interoperability, scalability, and data privacy issues. Also it outlines a path for organizations to embrace such technologies and better integrate them into their supply chains, increasing efficiency, accountability, and competitive advantage.
Anniina Saari, Sarah Sinclair, Rebecca Leshinsky, Seppo Junnila
Digital transformation (DT) is complex and uncertain, particularly within traditional industries. Distributed ledger technology, such as blockchain, has driven DT across many global sectors. Despite its transformative potential, scaling blockchain solutions beyond pilot projects often fails due to technical, organisational and environmental barriers. Many failures stem from ineffective collaboration and strategic misalignment among ecosystem partners rather than from technology. This study examines collaborative processes and enabling capabilities required for successful DT by focusing on a longitudinal case study from Finland (2019–2023). The case follows a cross-industry, blockchain-driven DT initiative that achieved scale despite transitioning from private blockchain to centralised technologies after market entry. While blockchain's role in this success has been documented ( Saari et al., 2024 ), the ecosystem's collaboration mechanisms and strategies for tackling challenges remain unclear. Using the technology–organisation–environment (TOE) framework, this paper analyses how DT unfolded across these contexts, identifying best practice strategies and enabling capabilities. The study draws on 35 interviews with key stakeholders and thematic content analysis to identify critical scaling enablers. Our findings reveal that scaling blockchain-driven DT in cross-industry settings requires 1) industry-level mindset and shared vision, 2) formalised collaboration and public–private partnerships, 3) governance structure with a sustainable business model and funding, 4) complementary ecosystem roles and an early-stage champion, 5) stakeholder training and support and 6) flexibility in technology and agile implementation. These elements show that trust and strategic alignment can emerge even among direct competitors, offering valuable insights for enterprises exploring blockchain innovation in business-to-business contexts.
This study critically reviews the literature on metaverse technologies, developing an integrative framework to explore their sector-specific implications and transformative impact on business management. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and machine learning-based BERTopic modeling, the study identifies nine key themes, reflecting the diverse ways augmented reality (AR), virtual reality (VR), extended reality (XR), digital twins, and decentralized finance (DeFi) influence industries. These themes include the metaverse as a tool for economic and environmental policy experiments, navigating financial risk and regulatory dynamics, adapting human resource development to VR-driven environments, Industry 4.0 applications of VR and digital twins, digital twin applications in manufacturing and supply chain optimization, AR and VR in digital marketing and customer experience, AR in enhancing retail and consumer experiences, exploring user interaction and affordances in the metaverse, and VR and AR in tourism experience and engagement. The framework highlights drivers, constraints, and cross-sector linkages, addressing practical challenges such as high implementation costs, regulatory uncertainties, interoperability barriers, cybersecurity risks, and ethical concerns surrounding data privacy and inclusion. The study critically evaluates contradictions in metaverse adoption, such as the tension between sustainability goals and energy-intensive technologies like blockchain, the gap between immersive training potential and workforce adaptation challenges, and the disparity between metaverse-driven economic models and real-world policy implementation hurdles. Research propositions suggest integrating metaverse technologies into business operations while balancing ethical dimensions, psychological impacts, cost limitations, and accessibility barriers. Additionally, the study advocates for expanding theoretical frameworks such as the Resource-Based View (RBV), Technology Acceptance Model (TAM), and experiential learning to account for the dynamic capabilities, risks, and industry-specific constraints of metaverse adoption. Policymakers and practitioners are encouraged to address regulatory and ethical challenges, sectoral disparities, and the unintended consequences of metaverse-driven digital transformation, ensuring operational efficiency, resilience, and consumer engagement while fostering sustainable and inclusive adoption. This research offers actionable insights for strategic implementation, interdisciplinary theoretical expansion, and ethical progress in business management.
M. Ferreira, Bernardo J. R. Figueiredo, Alexandre Soares dos Santos, João Filipe Matos · 5 authors
Digital twins (DTs) are transforming industries by offering real-time virtual representations of physical assets, enabling smarter decision-making and optimization. However, as these systems become more complex and distributed, maintaining reliable traceability remains a significant challenge. To address this, we propose a multi-context, modular platform that leverages blockchain and distributed ledger technologies (DLTs) to enhance the traceability of digital twins. Our proposal will ensure secure, immutable, and transparent records of data interactions, fostering greater trust, accountability, and interoperability across various domains. The foundation of this work involved identifying the key Architecturally Significant Requirements (ASRs) that must be considered in the platform's design. Based on the first ASRs related to traceability and data flexibility, we developed a conceptual architecture supported by an ontology that addresses the multi-context traceability challenge. The proposed approach was validated through two distinct case studies: livestock management and shop-floor processes. Moving forward, our focus will be on addressing the remaining ASRs that were already identified, to further develop a high-performance, secure, and scalable modular platform capable of supporting diverse contexts and applications.
This article explores the impacts of digital transformations and new technologies in industrial sector (particularly through the Fourth Industrial Revolution) on optimizing production processes. Characterized by key technologies such as the Internet of Things (IoT), big data analytics, artificial intelligence (AI), blockchains, and advanced robotics, Industry 4.0 has significantly shaped modern manufacturing management. IoT enables autonomous communications between machines and equipment, providing real-time insights into production parameters and enabling predictive maintenance, and big data plays a vital role by analyzing the large volumes of data that are generated by these devices, thus supporting informed management decisions. AI and machine learning help automate complex tasks, optimize production schedules, and improve product quality through real-time adjustments. Blockchain enables decentralized and secure data recording, which is particularly useful in supply-chain management. Advanced robotics increases production speed and accuracy, thus reducing labor costs and mitigating any risks that are associated with hazardous tasks. Integrating these technologies requires strategic planning, including identifying key challenges, conducting pilot projects, integrating with existing IT and OT systems, and managing organizational change. Measuring the effectiveness of Industry 4.0 implementation should involve well-defined key performance indicators (KPIs) and return-on-investment (ROI) analysis. The primary challenges that are associated with adopting Industry 4.0 include the alignment of technology with specific business needs, employee resistance to change, and hidden costs of implementation. In summary, industrial transformation offers opportunities for companies to optimize production processes, reduce costs, and increase competitiveness in the global marketplace. However, a careful approach is necessary to maximize efficiency, foster innovation, and secure long-term success in an increasingly digitalized world.
This article explores the integration and innovation of NFT (Non-Fungible Token) and digital twin technologies within the realm of metaverse architecture. As the construction industry faces challenges such as limited resources, rising costs, and environmental pressures, the metaverse offers a new direction through digital modeling and twin representation technologies. These technologies are applied throughout the entire lifecycle of architectural projects, providing fundamental technical support and practical experience for the realization of metaverse architecture. The article highlights the unique features of metaverse architecture, such as virtuality, interactivity, and infinite possibilities, which allow for greater design freedom and new creative platforms for architects. The fusion of NFT and digital twin technologies brings new business models and market opportunities to the construction industry. The research method involves case studies, including the Mars House as an example of NFT architecture and the digital twin application of Beijing CBD. The main results show that this technological integration can enhance design efficiency, optimize asset management, and promote innovation and collaboration in the construction industry.
Marta Rinaldi, Mario Caterino, Stefano Riemma, Roberto Macchiaroli · 5 authors
Background: Emergency scenarios present unprecedented challenges for supply chains worldwide, particularly in the management and distribution of critical supplies, where timely delivery and maintaining integrity are crucial. Methods: This article explores an innovative approach to enhance the emergency management of supply chains using blockchain technology and simulation-based modelling. The proposed methodology aims to tackle issues such as transparency, efficiency, and security, which are vital for managing logistics during crises. A case study involving a vaccine rollout is used to demonstrate how blockchain can optimise supply chain operations, reduce bottlenecks, and ensure better traceability and accountability throughout the process. The case study is specifically developed based on the distribution of COVID-19 vaccines in Italy. Results: The integration of blockchain technology not only enhances data integrity and security but also facilitates real-time monitoring and decision-making. Conslusions: The findings suggest that the proposed blockchain-based model can significantly improve supply chain resilience in emergency situations compared to traditional methods, thereby offering valuable insights for policymakers and supply chain managers facing future crises.
By drawing from the Technology-Organization-Environment (TOE) theory, this research aims to develop a model to examine the adoption of a blockchain technology based on a survey from 362 manufacturers in Sichuan, China. A structural equation modeling method was adopted to analyze the effects of the TOE approach on the manufacturer’s adoption of blockchain technology. The results indicated that technology, organization, and the environment had positive effects on the manufacturer’s adoption of blockchain technology. Organizational factors are significant antecedents to a manufacturer’s adoption of blockchain technology, followed by technological and environmental factors. Specifically, this research found that environmental factors had a moderating effect and strengthened the influence of organizational factors on the manufacturer’s adoption of blockchain technology. Managerial and theoretical implications from the research findings were discussed to develop blockchain technology in the manufacturing industry.
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.
Mohammad Yaser Mofatteh, Ujjwal Khadka, Omid Fatahi Valilai
Energy management can be designed from different perspectives including production, distribution, and consumption. Focusing on consumption perspective, manufacturing systems can be enhanced by enabling smart machines as agents which operate with their own knowledge representation models in a shopfloor. These agents can benefit from industry 4.0 enablers like IoT including sensors, controllers, and actuators. This paper focuses on how these agents can interoperate with each other and exchange knowledge to optimize energy consumption. Since different knowledge models may not be capable of interacting with other ones based on their different provider semantics. This paper explores the application of blockchain technology for secure, decentralized storage and sharing knowledge models in smart energy systems. The research introduces EnerChain as a blockchain-integrated and a decentralized application (DApp) system prototype that employs smart contracts for access management and conflict resolution. It also incorporates the InterPlanetary File System (IPFS) for efficient off-chain storage, addressing scalability concerns. The feasibility and practicality of this approach are demonstrated through the development of EnerChain. The findings highlight the significant potential of blockchain technology in facilitating efficient knowledge model management for smart shopfloors. Additionally, an operational scenario has been evaluated as a case study for the proposed conceptual model to illustrate how it can solve energy conflicts in a smart environment. An impact analysis at the end of this research shows that EnerChain can make annual 27.5 TWh reduction in residential energy consumption which yields to annual 7.8 million tonnes reduction in CO 2 emissions and annual €8.25 billion financial benefits.
Digital product passports (DPPs) will become a reality for several regulated products in Europe. The topic is still in its infancy but will significantly impact product information and the infrastructure required by manufacturing in increasingly agile and circular supply chains. This paper presents the results of a two-year design science research to develop an end-to-end blockchain-based DPP prototype instantiated in the textile industry. On the one hand, the upstream supply chain involves physical product transformations from the early stages of production, requiring a robust traceability architecture. On the other hand, multiple events occur during downstream phases that need to be easily accessible by different stakeholders. Our results confirm the suitability of blockchain-based DPPs and define the information flow within the product life cycle. This paper advances the literature on sustainable product identification, ensuring tamper-proof and transparent information in the supply chain. Although aiming at the end consumers, the DPP will majorly impact production, requiring proper industry planning. For practitioners, this paper provides one of the first DPP instantiations in different segments of the textile supply chain, highlighting the requirements that the industry should be aware of for the coming years. Eleven design principles for blockchain-based DPPs are proposed.
Manufacturing sectors pursuing Industry 5.0 objectives increasingly require automation architectures capable of reasoning across heterogeneous cyber-physical domains while advancing sustainability targets. This paper synthesizes evidence from eighteen references spanning semantic web technologies, cyber-physical systems, digital twins, blockchain-enabled traceability, and multi-agent reinforcement learning to propose a Semantic AI-Orchestrated Cross-Domain Automation Framework for sustainable smart factories. The framework integrates a five-layer architecture, comprising physical sensing, cyber-physical integration, semantic reasoning, cross-domain orchestration, and sustainability decision layers, into a unified reasoning pipeline in which ontology-driven knowledge graphs mediate interoperability among heterogeneous equipment, enterprise systems, and human operators. Comparative synthesis indicates that semantic-based autonomous computing architectures reduce unplanned downtime by as much as 37 percent, while multi-agent reinforcement learning scheduling raises resource utilization to 88 percent relative to 58 percent under conventional rule-based scheduling. Interoperability standards such as OPC-UA demonstrate adoption rates near 78 percent among reviewed implementations, and hybrid semantic-blockchain ledgers achieve transaction throughput exceeding 2,100 transactions per second at latencies below 40 seconds, outperforming public proof-of-work ledgers by more than two orders of magnitude. Digital twin adoption trajectories synthesized from the references rose from approximately 8 percent in 2016 to 66 percent by 2024, correlating with a 24 percent reduction in energy consumption and a 31 percent reduction in material waste across reviewed sustainable manufacturing cases. The findings imply that semantic orchestration, combined with decentralized ledgers and human-centric digital twins, offers a scalable pathway toward resilient, low-carbon, and economically viable smart factory operations, while highlighting persistent challenges in ontology standardization, explainability, and cross-organizational governance.
In the past few years, "metaverse" and "non-fungible tokens (NFT)" have become buzzwords, and the prices of related assets have exhibited large fluctuations. Are those characteristic of a speculative bubble? In this paper, we attempt to answer this question, and better understand the underlying economic dynamics. We look at Decentraland, a virtual world platform where land parcels are sold as NFT collections. We find that initially, land prices followed traditional real estate pricing models - in particular, value decreased with distance from the most desirable areas - suggesting Decentraland behaved much like a virtual city. However, these real estate pricing models stopped applying when both the metaverse and NFTs gained increased popular attention and enthusiasm in 2021, suggesting a new driving force for the underlying asset prices. At that time, following a substantial rise in NFT market values, short-term holders of multiple parcels began to take major selling positions in the Decentraland market, which hints that, rather than building a metaverse community, early Decentraland investors preferred to cash out when land valuations became inflated. Our analysis also shows that while the majority of buyers are new entrants to the market (many of whom joined during the bubble), liquidity (i.e., parcels) was mostly provided by early adopters selling, which caused stark differences in monetary gains. Early adopters made money - more than 10,000 USD on average per parcel sold - but users who joined later typically made no profit or even incurred losses in the order of 1,000 USD per parcel. Unlike established markets such as financial and real estate markets, newly emergent digital marketplaces are mostly self-regulated. As a result, the significant financial risks we identify indicate a strong need for establishing appropriate standards of business conduct and improving user awareness.
To facilitate flexible manufacturing, modern industries have incorporated numerous modular operations such as multi-robot services which can be expediently arranged or offloaded to other production resources. However, complex manufacturing projects often consist of multiple tasks with fixed sequences, posing a significant challenge for smart factories in efficiently scheduling limited robot resources to complete specific tasks. Additionally, when projects span across factories, ensuring faithful execution of contracts becomes another challenge. In this paper, we propose a modified combinatorial auction method combined with blockchain and edge computing technologies to organize project scheduling. Firstly, we transform efficient resource scheduling into a resource-constrained multi-project scheduling problem (RCPSP). Subsequently, the solution integrates combinatorial auction with random sampling (CA-RS) into smart contracts. Alongside security analysis, simulations are conducted using real data sets. The results indicate that the suggested CA-RS approach significantly enhances efficiency and security in resource arrangement within the industrial Internet of Things compared to baseline algorithms.