The digital transformation of higher education creates new opportunities to enhance the effectiveness, inclusiveness, and sustainability of dual education systems. However, empirical evidence on the integration of emerging technologies into dual education remains limited in developing and post-Soviet countries. This study investigates stakeholder perceptions of digital transformation in dual higher education in Uzbekistan and explores the potential of Artificial Intelligence (AI), Virtual Reality (VR), and blockchain technologies to support inclusive and sustainable learning environments. A convergent mixed-methods design was used. Quantitative data were collected from 312 students and 80 industry representatives through structured surveys, while qualitative data were obtained from semi-structured interviews with 24 academic staff members involved in dual education programmes. Descriptive statistics, correlation analysis, and thematic analysis were used to examine stakeholder readiness, implementation barriers, and future development priorities. The findings indicate strong support for digital transformation by stakeholders. Most students perceived dual education as more effective than traditional instruction (81%), and 74% expressed interest in AI- and VR-supported learning environments. Employers demonstrated a high readiness to adopt digital assessment tools (85%) and blockchain-based credential verification systems (80%). However, major challenges were identified, including insufficient digital infrastructure, limited funding, inadequate professional development opportunities, and regulatory uncertainty. Only 31% of students considered the existing digital infrastructure sufficient for advanced technology integration.Based on these findings, this study proposes an integrated framework that combines AI-driven personalized learning, VR-based experiential training, and blockchain-enabled credential verification within the principles of Universal Design for Learning (UDL) and Sustainable Development Goal 4 (SDG 4). The framework aims to enhance educational accessibility, strengthen industry–university collaboration, and support equitable participation in dual higher education. This study contributes empirical evidence from a developing country context and offers practical recommendations for policymakers and higher education institutions seeking to implement inclusive and sustainable digital transformation strategies in dual education systems.
This article presents the DigInTraCE Blockchain Module, a secure and scalable framework for managing Digital Product Passports (DPPs) and traceability data across industrial supply chains. Built on Hyperledger Fabric, the solution combines distributed ledger technology, cloud-native infrastructure, smart contracts, and standardized EPCIS 2.0 traceability to enable trusted collaboration among multiple stakeholders. The technical article describes the platform architecture, governance mechanisms, secure API integration, identity management, and blockchain-based validation processes that support transparent, interoperable, and auditable product lifecycle information. The proposed framework provides a robust foundation for future Digital Product Passport implementations and circular industrial value chains.
Industrial supply chains involve multiple stakeholders, complex logistics operations, and financial transactions that require transparency, traceability, and secure coordination.Traditional supply chain systems suffer from limited transparency, the risk of data manipulation, and insufficient trust among participants.To address these challenges, this paper proposes a decentralized industrial supply chain management system implemented on an Ethereum-compatible blockchain network.The proposed architecture integrates smart contracts to automate workflows, including stakeholder registration and verification, multi-item order processing, shipment tracking, simulated delivery verification (SDV), and escrow-based conditional payment settlement.The system adopts a hybrid on-chain/off-chain storage architecture in which transactional records are maintained on-chain, while raw material and product images are stored off-chain using the InterPlanetary File System (IPFS).This design reduces blockchain storage overhead while preserving data integrity through cryptographic hash references.To improve operational efficiency and reduce overhead from repeated transactions, the proposed system supports multi-item batch transactions during procurement and ordering, while the logistics and settlement stages maintain per-item execution to preserve traceability and accountability.Experimental evaluation was conducted on the Celo Sepolia network to measure gas consumption and transaction fees for both batch-based and functionally equivalent per-item execution workflows under controlled conditions.The evaluation included multiple predefined workload configurations, and statistical analysis using mean and standard deviation was performed to assess execution stability.The results indicate that transaction aggregation reduces gas consumption by approximately 40-43% for raw material order creation and by 40-48% for raw material operations (addToMultipleCart).Product aggregation workflows also demonstrated measurable gas-efficiency improvements.These findings demonstrate the efficiency benefits of multi-item transaction aggregation within the proposed implementation while preserving lifecycle traceability and escrow-enabled settlement correctness.The reported results represent controlled implementation-level efficiency measurements within the proposed blockchain-based supply chain architecture.
Recent innovation theories on economics remain largely grounded in assumptions of hierarchical firms and closed organizational boundaries, offering limited insight into how innovation unfolds within decentralized, digitally native organizations. Decentralized Autonomous Organizations (DAOs) represent an emerging form of innovation ecosystem characterized by blockchain-based transparency, open participation, and token-driven governance, in which sustainability can be embedded directly into organizational design. This study compares two standards, ERC-8004 and Google A2A, who address the same agent interoperability question, while the former is governed by DAO and the latter by corporation consortium. They are examined through an LLM-powered comparative pipeline for large-scale governance discourse analysis, integrating automated annotation, neural topic modeling, and multi-layer network analysis to study socio-technical power structures. The study provides evidence-based insights for scholars, policymakers, and designers seeking to align innovation, technological governance, and sustainability in future organizational forms.
Rouwaida Abdallah, Guillermo Toyos Marfurt, Sara Tucci-Piergiovanni
This work has been accepted for publication in the proceedings of 3SCEA 2026 conference. The deposited manuscript corresponds to the author-accepted version presented at the conference. The final published version will appear in the official conference proceedings. Abstract: Traceability remains a critical challenge in modern supply chains, particularly as industries transition towards sustainability and circular economy models. The Digital Product Passport (DPP) emerges as a vital tool to consolidate and share comprehensive product information across its lifecycle. In this paper, we propose a decentralized, customizable, and self-deployable DPP system, leveraging blockchain technology and an extension of the Fractional Non-Fungible Token (F-NFT) model. This approach enables fine-grained traceability of individual product components and events across the supply chain, ensuring transparency and verifiability. A key strength of our system lies in its flexibility, enabling businesses to deploy tailored solutions without reliance on centralized service providers. The proposed system empowers stakeholders with greater control over product data while supporting selective information sharing. We present a functional implementation of the system and discuss the crucial design decisions that support its real-world applicability.
Operations and supply chains have witnessed spectacular transformations through Industry 4.0 and Industry 5.0. Is the next industrial revolution – Industry 6.0 – unfolding in the context of artificial intelligence (AI) and human-AI collaboration? And, possibly, even Industry 7.0 and superintelligence (SI) are just around the corner? In this paper, we conceptualize the transition toward Industry 6.0 as the Ecosystem Age that builds upon technologies developed in Industry 4.0 and viability-centric socio-ecological principles proposed in Industry 5.0, emerging into a cyber-socio-technical-ecological industrial revolution. We create a taxonomy of industrial revolutions based on the types of work that have been replaced/transformed by machines over time, and utilize it to delineate Industry 6.0 and forecast Industry 7.0 framed in technology (e.g., generative AI, agentic AI, edge AI, and humanoid robots), organization (i.e., decentralized, autonomous, agentic-driven planning and control), and modelling (OR-AI symbiosis) dimensions. Second, we discuss potential impacts of the transition toward Industry 6.0 on Operations Research (OR) with associated challenges and chances, outlining a 7-layer architecture of OR-AI symbiosis in digital twins. We elaborate on the technology and viability principles that frame Industry 6.0 and discuss scenarios for further transitioning toward next industrial revolutions and cyber-virtual, AI-driven networks that learn, adapt, self-organize, and regenerate. We conclude by outlining research opportunities for OR in the new era of supply chain and operations management in the AI and superintelligence age.
This article aims to examine how the metaverse is reshaping business and management by providing a review of existing literature, identifying critical research gaps, and proposing a novel conceptual framework—the Metaverse Ecosystem Model—that integrates technological, human, and sustainability dimensions with strategic business outcomes in the Web3 era. The article will embrace a conceptual knowledge and literature review that articulates conceptual underpinnings, marketing and consumer behaviour, sectoral uses, and sustainability/workforce/boundaryless futures. This was synthesised directly into the creation of the Metaverse Ecosystem Model that connects three pillars (technological infrastructure, workforce skills, and energy and sustainability) to the business opportunities, challenges, and quantifiable results. The review shows that, although the metaverse can be used to conduct immersive marketing, operational efficiency via digital twins, sustainable industrial use, and inclusive development in emerging economies, the studies are disjointed and siloed. Among the critical areas of gaps, there are the lack of integrated frameworks between the foundational enablers and outcomes and the scarcity of empirical focus on long-term sustainability and workforce readiness. The suggested Metaverse Ecosystem Model fills these gaps by showing causal relationships between the three pillars via opportunities and constraints to innovation, new business models, and high customer engagement. It represents the first comprehensive framework of the ecosystem, specific to business and management, which provides managers and policymakers with a useful roadmap to responsible adoption.
Manuel Lagos Rodríguez, Hilda Romero Velo, Álvaro Leitao Rodríguez, Javier Pereira Loureiro · 5 authors
Ethereum use as a decentralized platform for executing smart contracts has driven the adoption of standards that optimize interoperability in industrial environments. Ethereum Requests for Comments (ERC) establish uniform patterns for smart contracts, facilitating their integration and operation in network nodes, which are essential for industrial applications such as supply chain management or process automation. However, this standardization can propagate vulnerabilities or inefficiencies in critical systems if the contracts are not optimized, affecting the reliability of industrial processes. Since operations in Ethereum consume computational resources (measured in gas) with associated economic costs, poor design can lead to significant losses in industrial settings. This paper evaluates the efficiency and security of ERC standards by examining their functional diversity and technical complexity. Thus, it analyzes existing implementations to identify common errors and proposes improvements to enhance the robustness and optimization of three of the most popular standards: ERC-20, ERC-1400 and ERC-3643. The ultimate goal is to support the effective adoption of ERCs in industrial applications. Considering the Ethereum network incentives on lower complexity logic and the obtained results, it is advisable to use simple standards, which also reduce error risks and ease maintainability.
Umar Yeni Suyanto, Ratna Rosita Pangestika, Kinanti Puja Prameswari, Heni Setiyaningsih
The integration of Artificial Intelligence (AI) into Small and Medium Enterprises (SMEs) has become a critical lever for achieving resilience, efficiency, and long-term sustainability in the digital era. However, despite AI’s transformative potential, empirical evidence suggests a persistent gap between technological capabilities and actual adoption within the SME sector. This study employs a bibliometric analysis using VOSviewer with the keywords "artificial intelligence" OR "AI" AND "Small and medium enterprises" OR "SMEs" AND "digital", encompassing 150 Scopus indexed articles from 2017 to 2025. The visualizations reveal six prominent thematic clusters, including AI based adaptive strategies, post-pandemic digital transformation, decentralized finance, digital literacy, and emerging concepts such as green cybersecurity. Notably, overlay visualizations indicate that sustainability-oriented digital practices are gaining scholarly momentum, signaling a future research trajectory focused on inclusive, secure, and environmentally conscious AI applications in SMEs. This article proposes a conceptual model SDRAIS (SME Digital Resilience through AI and Sustainability) that integrates three strategic dimensions: Strategic AI Integration, Digital Capabilities, and Sustainability Orientation. The model advances theoretical development by aligning with the Dynamic Capabilities and TOE (Technology Organization Environment) frameworks, while also responding to gaps in Triple Bottom Line (TBL)-driven technology adoption. The findings offer new perspectives for policymakers, SME stakeholders, and researchers by emphasizing the importance of interdisciplinary approaches to foster AI-driven innovation ecosystems that are both competitive and sustainable. This study contributes to the evolving discourse on digital transformation in SMEs and sets a robust foundation for future empirical exploration.
This research paper examines the structural and philosophical changes in global commerce and management required because of the "Third Wave" of digital transformation (DT). While earlier versions of DT dealt with the digitization of analog records and the uptake of cloud computing, the modern age of Agentic AI, Industrial Metaverse and Decentralized Autonomous Organizations (DAOs) is demanding a fundamental re-engineering of the firm. Through a methodical approach of analyzing current technological trajectories and management frameworks this study identifies the existence of a critical "Agility Gap" between legacy led organizations and the digital native enterprises. The research proposes Integrated Digital-Managerial Framework (IDMF) as strategic roadmap of 2026 and onwards. Key findings suggest that "Digital Maturity" is no longer a technology benchmark but comes instead as a cultural and structural imperative, one determining whether a company survives in the marketplace in an increasingly automated commerce landscape.
Tsvetelina Ivanova, L Koleva, Idilia Batchkova, G Kolev
Abstract Reliable vacuum control in high-precision installations such as the Electron Beam Melting and Refining (EBMR) plant requires an intelligent architecture that integrates physical subsystems and cyber entities under an adaptive control framework. This paper proposes a multi-agent system (MAS) representation of the EBMR vacuum creation subsystem, developed using the Organizational Multi-Agent Systems Engineering (O-MaSE) methodology. The model unites the IEC 61512 (S88) batch-process standard with the IEC 61499 distributed-control architecture to form a modular and interoperable cyber-physical system (CPS). Each pump, valve, and sensor is modelled as an autonomous agent with defined goals, roles, and communication protocols. The O-MaSE-based design enhances scalability, fault tolerance, and system adaptability, enabling decentralized decision-making and efficient vacuum regulation. The integrated case study demonstrates that MAS-based CPS design substantially improves the responsiveness and resilience of EBMR operations, supporting the principles of Industry 4.0.
This work presents a concept and implementation for the secure storage and transfer of quality-relevant data of milled workpieces from online-quality assurance processes enabled by real-time simulation models. It utilises Non-Fungible Tokens (NFT) to securely and interoperably store quality data in the form of an Asset Administration Shell (AAS) on a public Ethereum blockchain. Minted by a custom smart contract, the NFTs reference the metadata saved in the Interplanetary File System (IPFS), allowing new data from additional processing steps to be added in a flexible yet secure manner. The concept enables automated traceability throughout the value chain, minimising the need for time-consuming and costly repetitive manual quality checks.
Open access
3 source records
Digital Transformation in Industry
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Abstract Product life cycle management (PLM) in large supply chains still suffers from limited transparency, manual record-keeping, and weak traceability of provenance and expiry, which often results in counterfeit products, delayed recalls, and unsafe items reaching consumers. After the advancements in blockchain technologies, immutable, decentralised and auto-generated smart contracts provide a secure, safe, and organised solution for consent to the agreement between two or more parties and help digital assets and transactions to occur efficiently This work proposes a Blockchain–Internet of Things (B-IoT) based smart-contract framework that automates three key phases of the product life cycle: purchase order creation, invoice generation at delivery, and expiry-driven discard management. The proposed system will auto-trigger the smart contract through a program. It will generate the smart contract for a product when the purchase order is placed, generate an invoice, and handle the expired and discarded products. IoT devices will record important parameters such as product ID scanning, recording storage temperature, GPS trackers during transportation, etc. These parameters will help to auto-trigger the smart contract. To tune threshold parameters (e.g., temperature bounds, delay limits) and minimize cost–latency trade-offs, we build a lightweight regression model whose hyper-parameters are optimized using nature-inspired Mayfly (MFA) and Honey Badger (HBA) algorithms. The model predicts gas usage and latency per phase, achieving an RMSE of 0.15 and R 2 ≈ 0.9 on simulated transaction logs, while the final configuration yields an average execution efficiency of 92.3% and accuracy of 94.9% in correctly auto-triggering contract phases. The prototype is implemented using Ganache Truffle Suite, Remix, and MyEtherWallet, and evaluated in terms of gas consumption, functional correctness, and automation benefits over traditional manual contracts. Results demonstrate that the proposed B-IoT smart-contract framework provides transparent, tamper-resistant, and fine-grained product life management suitable for industrial deployments.
The Fourth Industrial Revolution, commonly referred to as Industry 4.0, represents a fundamental transformation of manufacturing systems through the integration of advanced digital technologies such as the Industrial Internet of Things (IIoT), artificial intelligence, big data analytics, cloud computing, and autonomous robotics. This paradigm shift enables the development of cyber-physical systems and smart factories characterized by real-time connectivity, decentralized decision-making, and data-driven optimization. The present study examines the conceptual foundations, technological pillars, and operational impacts of Industry 4.0, with particular emphasis on automation, productivity enhancement, and sustainability outcomes. Using a synthesis of recent empirical studies, global market data, and evidence from World Economic Forum “Lighthouse” factories, the paper evaluates how digital transformation influences manufacturing efficiency, energy use, emissions reduction, and workforce dynamics. The findings indicate that Industry 4.0 adoption significantly improves labor productivity, operational flexibility, and resource efficiency, while also presenting challenges related to cybersecurity, legacy system integration, and skills gaps. The study concludes that Industry 4.0 is not merely a technological upgrade but a strategic and organizational transformation essential for achieving competitive advantage and sustainable industrial development in an increasingly volatile global economy.
Amer A. Hijazi, Ali Alashwal, Milad Baghalzadeh Shishehgarkhaneh, Rodrigo N. Calheiros
. The integration of Blockchain with Building Information Modelling (BIM) addresses persistent challenges of transparency, accountability, interoperability, and trust in construction. Yet, systematic insights into Blockchain–BIM implementation remain limited. This study conducts a Systematic Literature Review (SLR), identifying six lifecycle stages where integration occurs, supported by 29 workflows and 30 technical methods. Applications include blockchain-secured design reviews, provenance tracking, automated procurement, smart payments, and immutable handover records. BIM data types—design metadata, cost and schedule data, IoT evidence, and compliance records—are mapped to smart contract functions and blockchain platforms. Key mechanisms such as hybrid on/off-chain storage, cryptographic hashing, access controls, and watermarking are comparatively analyzed. Results show permissioned platforms (e.g., Hyperledger Fabric) enable controlled collaboration, while public ones (e.g., Ethereum) support transparency and tokenization. The review provides a structured taxonomy and conceptual framework to advance theory and guide Blockchain–BIM adoption in complex construction projects.
Martin Brennecke, Simon Mertel, Tobias Guggenberger, Johannes Sedlmeir · 6 authors
Zusammenfassung Auf dem Weg zu einer kreislauffähigen Wertschöpfung nimmt die lückenlose Dokumentation von Produktionsketten eine elementare Rolle ein: Sie erlaubt es, eingesetzte Ressourcen und Schritte im Wertschöpfungsprozess nachzuvollziehen und nachhaltigkeitsbezogene Angaben überprüfbar und somit vermarktbar zu machen. In diesem Kontext wird immer wieder über die Blockchain-Technologie diskutiert. Neben den Chancen, die eine Blockchain für eine verifizierbare Dokumentation und Interaktionen über Organisationsgrenzen hinweg bietet, werden in diesem Beitrag die Herausforderungen ihrer Nutzung aufgezeigt. Dabei wird auch auf komplementäre Technologien, insbesondere kryptographische Ansätze für digitales Identitätsmanagement und Zero-Knowledge Proofs, eingegangen und gezeigt, wie diese zur Bewältigung der Herausforderungen genutzt werden können.
Traditional electronic Kanban (eKanban) systems depend on manual scans and offer only discrete material visibility, limiting responsiveness and automation in lean manufacturing environments. These operational bottlenecks are magnified in high-mix contexts, where delayed replenishment signals degrade flow stability, increase work-in-progress, and hinder sustainable material handling. Furthermore, vendor-specific systems lack interoperability for scalable automation, constraining the development of intelligent manufacturing solutions. This work investigates whether zone-based replenishment automation can be enabled through real-time locating systems (RTLS) using open interoperability standards, addressing a gap in empirical validation of such approaches. A middleware architecture was developed that integrates ultra-wideband (UWB) positioning, an Omlox-compliant location middleware (DeepHub), and a cloud-based eKanban system to replace manual triggers with geofence-driven order creation. The novelty of this study lies in demonstrating a fully automated Kanban signaling loop built on the open Omlox standard, providing vendor-independent RTLS interoperability and eliminating human intervention in replenishment signaling. This contributes new knowledge on how continuous location data can be converted into actionable replenishment events in a standards-based, modular manner, enabling more intelligent and autonomous material-flow control. A controlled proof-of-concept experiment simulating shop-floor conditions showed that the system achieved a 100% detection success rate, zero duplicate orders, and an average trigger-to-action latency of 2.7 s, while automatically recovering from authentication and WebSocket failures. These results provide the first empirical evidence that Omlox-compliant RTLS middleware can reliably support zone-based eKanban automation. The findings have direct implications for intelligent and sustainable manufacturing by demonstrating a scalable pathway toward interoperable, real-time material-flow systems that reduce manual intervention, avoid unnecessary handling, and lower work-in-progress. More broadly, the work addresses the current lack of empirical validation of open-standard RTLS integration within lean and sustainable production environments.
Barbara Bigliardi, Virginia Dolci, Alberto Petroni, Benedetta Pini
How are digital technologies transforming public sector supply chains, and what factors condition their effectiveness? Despite the growing interest in this domain, the literature remains fragmented, with a lack of longitudinal studies, citizen-centered evaluations, and cross-country comparisons. This study addresses these gaps through a systematic review of 71 Scopus-indexed articles, combining descriptive mapping with a keyword-based bibliometric analysis. The approach identifies consolidated and emerging themes, particularly within the “Business, Management and Accounting” subject area, where methodological heterogeneity and limited generalizability persist. Findings reveal increasing scholarly attention to technologies such as blockchain, AI, and e-procurement, highlighting both operational modernization and newer concerns such as sustainability, digital governance, and decentralized finance. The paper contributes by structuring dispersed knowledge into a coherent framework, offering a roadmap for research and practical guidance for public administrators seeking value-driven digital transformation.
The swift expansion of IoT devices in smart cities demands decentralized and open systems of attentive exchange of assets in automotive supply chains. Nevertheless, the majority of the available blockchain-based solutions are focused on traceability and ignore scalability, conditional payment automation, and real-time IoT verification. To overcome those pitfalls, the research proposes a Blockchain-based framework implemented on Hyperledger Fabric, incorporating Non-Fungible Tokens, and escrow-based smart contracts, to facilitate verifiable, automated vehicle transactions. The payment is conditionally released, and the vehicle is represented as a discrete NFT that undergoes authenticated release under Fabric Certificate Authority with escrow verification. Sub-millisecond latency (0.0003 s), constant throughput, and minimal computational cost experimentally verify the effectiveness of the framework in terms of its efficiency, privacy, and scalability in the efficient and autonomous exchange of assets in next-generation smart cities.
The EU Deforestation Regulation (EUDR), Regulation (EU) 2023/1115, requires operators and traders placing cattle, cocoa, coffee, palm oil, soya, wood, rubber, charcoal and their derived products on the EU market to demonstrate that the underlying commodities are deforestation-free after 31 December 2020, are legally produced, and are covered by a due diligence statement. While deforestation detection is often treated as the technically dominant requirement, EUDR compliance is broader: it also requires traceability across supply chains, provenance of production, country-specific legal context, and auditable due diligence processes. Translating these obligations into an inspectable, reproducible, software-supported workflow raises three coupled problems: (i) the regulation itself is a moving artefact whose definitions, country risk classifications and implementing acts evolve; (ii) the geospatial evidence used to satisfy Article 3(a) depends on upstream datasets-primarily the Hansen Global Forest Change product-whose versions, tile schemes and methodological conventions also change; and (iii) the resulting compliance interpretations cannot be ethically delegated to a fully autonomous agent because they affect market access, livelihoods and the legal exposure of operators. This paper describes an architecture that addresses these problems jointly through a closed feedback loop linking regulation, data dependencies, implementation, validation, and governance. We separate authoritative deterministic generation of evidence from a public, non-authoritative Digital Twin portal that exposes system state, dependencies, and example outputs for inspection. A procedural Decentralized Autonomous Organization (DAO)implemented in this work as a file-grounded YAML proposal workflow, but compatible with optional blockchain anchoring of evidence digests and proposal records-closes the governance loop between stakeholders, developers, and evolving regulatory interpretation. The procedural design is deliberate: as we discuss below, the kind of DAO appropriate for governing truth claims about the physical world differs in object, voting subject and failure mode from the protocol-governance DAOs commonly associated with the term, and the choice to run the governance layer off-chain reflects that difference rather than a rejection of distributed-ledger technology as such. An LLM-based Digital Twin Engineer (DTE) agent supports inspection and proposal drafting under strict grounding rules, but never executes code or makes compliance determinations. We describe the multi-repository implementation, the deterministic evidence bundle contract, the public/private trust-zone separation that protects per-operator plot data while allowing example reports to be inspected publicly, and the regulation-as-dependency feedback loop that forces reruns of impacted methods when upstream artefacts change. Although the current implementation focuses primarily on geospatial deforestation evidence, the proposed pipeline is intended as a practical starting point for progressive enrichment as new forms of land intelligence, supply-chain transparency, legal provenance data, and business-network evidence become available. We argue that this design is a generalisable pattern for compliance domains in which regulatory requirements evolve over time and implementations must remain inspectable, reproducible, extensible, and corrigible by humans.
While traditional AI and data-driven facilities management approaches have improved building operational efficiency, they remain constrained by centralized organizational structures that are vulnerable to cyber attacks, limited contextual understanding, and decision-making processes that exclude key stakeholders from governance. This paper introduces a novel AI- and data-driven distributed governance framework for smart building management that integrates decentralized autonomous organizations (DAOs), digital twins, large language models (LLMs), and blockchain technology. The framework enables transparent collective decision-making through a DAO governance platform, implements data-driven management using IoT and digital twins, incorporates LLM-based virtual assistants for enhanced decision support, and utilizes blockchain for secure building automation. A full-stack decentralized application was developed to facilitate user interaction with these integrated components. The system was evaluated for cost efficiency, scalability, data security, and usability using the System Usability Scale (SUS). Expert interviews were also conducted to assess its practical benefits and implementation challenges.
Recently, it has been noted that the convergence of blockchain technology presents a promising paradigm for secure, privacy-preserving, and transparent healthcare systems. Moreover, Digital Twins enable real-time replication of patients, hospital operations, and medical devices, and their dependence on continuous sensitive data streams introduces the latest trust and Cybersecurity challenges. A systematic literature review aims to investigate how distributed ledger and blockchain technologies have been applied to secure healthcare digital twins from 2020 to 2025. Furthermore, the review addresses the proposed architecture of blockchain, the security objectives targeted, integration approaches within digital twins, and evaluation methods with limitations. The study follows PRISMA 2020 guidelines. Web of Sciences, IEEE Xplore, PubMed, Scopus, and ACM Digital Library were searched from January 2020 to October 2025 by using defined Boolean queries. Also, the focus of the inclusion criteria is on peer-reviewed studies that discussed blockchain for DT security in healthcare. Data extraction captured blockchain type, metadata, security mechanisms, DT domain, and evaluation methods. From the 487 identified records, only 20 successfully met the inclusion criteria. The fact behind it is that most studies only employed permissioned blockchains like Quorum and Hyperledger integrated with digital twins for monitoring patients, device lifecycle tracking, and data provenance. Some main security objectives include provenance assurance, access control, and integrity. Moreover, only some studies provide formal threat analysis or real-world deployment. Blockchain technology is reliable because it increases digital twin security through immutability, smart-contract-based governance, and decentralized trust. However, interoperability, scalability, and privacy-preserving computation remain the main barriers for clinical adoption.
The digital economy is rapidly transforming the global landscape by integrating technology, entrepreneurship, and innovation across every sector. Startups have become the key drivers of this transformation, enabling new models of production, finance, and governance. By 2047, the digital economy is expected to evolve into a deeply interconnected system powered by artificial intelligence, blockchain, decentralized finance, and sustainable technologies. These advancements will reshape industries, empower small enterprises, and foster inclusive growth. This paper explores how startups will act as engines of innovation, leveraging digital tools to solve complex social and economic challenges. It highlights emerging trends such as AI-driven decision-making, edge computing, green technologies, and decentralized governance models that will redefine the global business environment. At the same time, the paper acknowledges the challenges of data privacy, cybersecurity, skill development, and environmental sustainability. Through policy analysis and strategic recommendations, the study emphasizes the importance of strong digital infrastructure, ethical data practices, and inclusive innovation ecosystems to ensure balanced growth. By 2047, success in the digital economy will depend not only on technological advancement but also on human creativity, collaboration, and sustainable practices.