IntroductionCitiverse deployments and smart-city systems (e.g., converging artificial intelligence, digital twins, extended reality, IoT, and blockchain within urban governance) are scaling rapidly, yet the equity architecture embedded in their design remains unexamined. Whereas sustainability governance scholarship has treated equity chiefly as a normative outcome, this paper theorizes it instead as a structural design property: a performance multiplier embedded in socio-technical architecture. It extends the System Design and Technology Equity Nexus frameworks to the Citiverse and asks whether equity design quality is independently associated with sustainability performance, across dimensions formally independent of equity by design.MethodsThe paper draws on the ITU/UNICC/Digital Dubai Citiverse Use Case Taxonomy, the leading United Nations initiative in this domain. It develops two original instruments: the Citiverse Sustainability Impact Index (CSII) and the Equity Design Quality (EDQ) rubric. These are applied across 44 use cases, with their relationship tested through frequentist OLS using a three-specification identification strategy, together with a Monte Carlo measurement-uncertainty analysis.ResultsThe results offer conditional proof-of-concept support for the central hypothesis. Equity design quality carries independent predictive power over non-equity sustainability performance. This relationship is multiplicative rather than additive once technology richness is accounted for. At this sample size, implementation maturity is the dominant structural predictor.DiscussionThese findings are proof-of-concept rather than causal; causal inference will require longitudinal data. The measurement infrastructure introduced here, the CSII and EDQ rubric, is designed to scale with future documentation and to serve as a replicable basis for tracking the equity design gap over time.
Electricity procurement in post-conflict settings faces serious legal and financial pressure, especially when markets operate under sanctions and weak institutional control. Reconstruction requires fast contracting for fuel supply, repair works, and grid recovery, yet these processes often lack strong oversight and trusted payment systems. Existing studies discuss blockchain applications in energy, but they give less attention to how legal recognition and regulatory assurance can be supported in procurement affected by jurisdiction-specific sanctions regimes. This gap creates uncertainty for investors, regulators, and contractors, where compliance risks and payment delays can stop essential projects. The study is motivated by this challenge, focusing on how a structured legal framework can support secure and accountable procurement processes. The aim of this research is to develop a framework that connects blockchain records with legal and regulatory systems so that procurement actions and contract milestones can be documented, reviewed, and more readily relied upon in oversight and dispute processes. The study explains how ledger entries can function as evidence, how responsibilities are assigned, and how dispute processes can rely on these records. This research is important because it offers a clear model to support trust, reduce risks, and improve financial flows in electricity markets recovering from conflict while respecting legal limits, confidentiality requirements, and data-protection concerns. As a conceptual and jurisdiction-neutral framework, the study does not claim uniform legal recognition or empirical effectiveness across jurisdictions; rather, its application requires validation against jurisdiction-specific legal rules, institutional capacities, and sanctions regimes.
IntroductionBlockchain is increasingly discussed in higher education both as curriculum content and as infrastructure for digital credentials and student records. However, empirical evidence from emerging higher education systems remains limited. This study investigates the factors shaping blockchain adoption in Jordanian higher education and develops an empirically informed Technology–Organization–Environment (TOE)-based framework.MethodsA cross-sectional survey was conducted among 170 faculty members from public and private universities in Jordan. The survey examined current and planned blockchain-related teaching, perceived adoption barriers, and institutional support priorities. The resulting framework was subjected to initial content validation by an expert panel of 12 participants.ResultsOnly 11.8% of respondents reported currently teaching blockchain-related content, whereas 64.7% planned to introduce it during the following academic year. The principal barriers were a lack of high-quality textbooks and case studies (41.2%), limited time for specialized training (23.5%), and inadequate laboratory or software infrastructure (17.6%). Faculty most frequently prioritized pedagogical workshops and industry partnerships, with each selected by 35.3% of respondents. Based on these findings, the study developed a five-dimensional TOE-based framework encompassing strategy, governance, infrastructure, curriculum integration, and stakeholder capacity. Expert validation identified governance and regulatory issues, together with stakeholder capacity, as the main near-term bottlenecks.DiscussionThe findings indicate substantial faculty interest in blockchain education but limited institutional readiness to support its adoption. The proposed framework provides an evidence-informed roadmap for aligning faculty development, curriculum planning, infrastructure investment, governance, and industry engagement in Jordanian higher education.
The rapid growth of Internet of Things (IoT)-based healthcare systems has raised significant concerns regarding data privacy and security. Ensuring privacy while maintaining the utility of healthcare data remains a major challenge in IoT-enabled healthcare environments. This article proposes a novel data anonymization framework based on federated learning and adaptive differential privacy. Initially, IoT healthcare data are processed using a residual bidirectional gated recurrent unit (Res-BiGRU) model to capture contextual and privacy-related features. Subsequently, an adaptive differential privacy mechanism is applied to minimize information loss while ensuring strong privacy protection. To improve optimization performance, a modified Resilient Adaptive Apiary Organizational Optimization Algorithm (RAOOA) incorporating a fitness-based adaptive factor is introduced to enhance convergence stability and solution quality. The effectiveness of the proposed framework is evaluated using a healthcare dataset. The proposed approach demonstrates superior performance compared with existing methods in terms of computation time, information loss, and privacy risk. The results indicate that the proposed framework provides an efficient and scalable solution for privacy-preserving IoT healthcare data management while achieving an improved balance between data utility, computational efficiency, and privacy protection.
IntroductionDecentralized finance (DeFi) applications depend on external oracle networks to obtain asset prices that drive core financial operations, including lending, collateral management, and automated liquidations. Oracle systems remain vulnerable to manipulation through coordinated strategies such as flash loan attacks and temporary price distortions in low-liquidity markets. Although several studies have addressed oracle manipulation, most existing approaches rely on single-source validation or detect attacks only after malicious activity has occurred, and few provide a continuous quantitative measure of oracle data trustworthiness.MethodsThis study proposes TOAD-ML, a trust-aware machine learning framework that evaluates the reliability of decentralized oracle price feeds using multi-source validation. TOAD-ML integrates data from five sources: labeled oracle attack events from DeFiHackLabs, CoinGecko historical market prices, Chainlink on-chain oracle rounds, Uniswap V3 TWAP pool data, and DeFiLlama multi-token aggregated prices. An XGBoost classifier is trained on engineered cross-source deviation and temporal features, augmented by a trust-calibration layer that produces a binary anomaly label and a continuous trust score bounded in [0,1]. The model is trained on 19 months of oracle price data encompassing four distinct, well-documented oracle hacks covering approximately 60 days of attack periods.ResultsExtensive empirical evaluation demonstrates 96.72% accuracy in identifying oracle price anomalies, with an AUC-ROC of 0.97. Ablation studies show performance reductions of 5.27% when multi-source fusion is removed and 3.84% when the trust-scoring layer is removed. TOAD-ML outperforms the baseline models: SVM (88.42%), Random Forest (91.76%), and standalone XGBoost (93.85%). These values are consistent with the reported experimental results.DiscussionIn case-study evaluation on documented oracle attacks, In the documented case studies, TOAD-ML signalled suspicious oracle behaviour before corresponding token price crashes. The framework provides early-warning capability, continuous trust quantification, and interpretable anomaly detection, supporting reliability-aware monitoring of DeFi oracle price feeds. The manuscript does not report an independent quantitative detection-latency benchmark; latency evaluation is identified as future work.
IntroductionThe optimization of gas consumption in Ethereum smart contracts is critical for enhancing the economic viability, scalability, and security of blockchain applications. Existing tools, however, are largely limited to static heuristics or local pattern matching, failing to capture the complex, non-local dependencies that drive gas inefficiency.MethodsTo address this limitation, this paper introduces GasGAT, a deep learning framework that models smart contracts as semantic graphs and leverages a Graph Attention Network (GAT) to detect gas-intensive code patterns. By capturing intricate dependencies between functions, state variables, and control flows, GasGAT is able to identify non-local and inter-procedural inefficiencies that are often missed by rule-based or purely local static analysis tools. Unlike existing approaches, which focus primarily on local code patterns or rule-based detection, GasGAT explicitly reasons about long-range execution paths within smart contracts. We evaluate our method on a dataset of 40,000 verified Ethereum smart contracts (Solidity ≥0.8.0, labeled via Slither static analysis), including both a polarized subset (excluding ambiguous cases) and a full, non-filtered setting.ResultsUnder 5-fold cross-validation, GasGAT achieves an accuracy of 94.92% (±1.04%) and a macro F1-score of 88.69% (±2.70%), with a McNemar statistic of 633.12 (p ≪ 0.05) confirming statistical significance. Crucially, GasGAT is the only model whose performance remains stable when correlated node features are removed (Δ = 0.00%), demonstrating that its attention mechanism learns genuine structural patterns rather than label-feature correlations.DiscussionThe primary contribution of GasGAT lies not in marginal accuracy improvements, but in its ability to provide interpretable, structural insights through attention mechanisms. This represents a paradigm shift from traditional detection methods toward explainable, graph-based reasoning for gas optimization, enabling developers to design more efficient, scalable, and secure decentralized applications.
Blockchain technology has transformed international trade. Despite its beneficial potential in promoting international trade, blockchain technology is not a universal solution for all nations, as it affects global governance of trade activities. It is vital to know how blockchain technology affect international trade in developing nations. Therefore, the main objective of this study is to investigate the impact of blockchain technology on international trade in South Africa using quarterly time series data from 2013Q1 to 2024Q4. The autoregressive distributed lag (ARDL) and nonlinear autoregressive distributed lag (NARDL) approaches were used to analyse. The study found that blockchain technology positively affects international trade. This demonstrates that blockchain technology is effective in facilitating international trade transactions in South Africa. Regarding the nonlinear effects, the study reveals that positive change in blockchain technology has a positive impact on international trade, while negative change has a negative insignificant impact. Moreover, the study found that these effects are nonlinear in the long run. The study also found that inflation, FDI, exchange rate and GDP have a positive impact on international trade in South Africa. This study recommends that policymakers should promote the implementation of blockchain technology in international trade by creating supportive regulatory policies and investments in blockchain infrastructure. The implementation of supportive regulatory policies and investment in blockchain infrastructure could improve the level of South African international trade. The major contribution of this study was to provide a data-driven empirical analysis on the impact of blockchain technology on international trade at the country-specific level.
The purpose of this paper is to design and evaluate a reproducible single-chain proof-of-concept protocol for optimizer-driven portfolio intents over a real-world asset (RWA) proxy basket. The system combines an off-chain mean-risk optimization engine with a minimal on-chain intent registry, thereby linking portfolio analytics to a verifiable machine-readable format. In particular, the off-chain module converts optimized weights into a deterministic JSON paper-reproduction artifact and an API response while the on-chain contract validates authorization, vector consistency, epoch monotonicity and expiry freshness before publishing the artifact. The novel contribution of this paper is a reproducible interface between risk-aware portfolio optimization for an RWA-oriented proxy basket and verifiable on-chain publication. The experiment consists of seven exchange-traded fund (ETF) proxies for RWA-related sectors: gold, silver, oil, real estate, U.S. Treasuries, energy infrastructure, and agriculture. Using a fixed walk-forward design from 2021–03–01 to 2026–03–03, with 504 training days, 63-day test windows, 63-day rebalance steps, and a fixed 10 bps transaction cost, several risk-aware allocation rules were compared, including conditional value at risk (CVaR), semivariance, variance, entropic value at risk (EVaR), worst realization, maximum drawdown, and an equal-weight baseline. The result in the final fixed-window baseline experiment showed that CVaR produced the highest point-estimate total return under the chosen specification, yielding a final portfolio value of $1,637,035 from a $1,000,000 start, corresponding to a 63.7% total return, versus 55.4% for equal weight, while also achieving a slightly lower maximum drawdown (−9.7% versus −10.3%) and a higher Sharpe ratio (1.18 versus 1.08). However, the sensitivity analysis also showed that strategy rankings changed when different walk-forward schedules and weight bounds were used. This means the baseline results depend on the chosen settings. The main contributions are a deterministic, machine-readable interface that connects risk-aware portfolio optimization to verifiable on-chain smart contract publication, a validated intent-registry architecture, and a fully reproducible empirical comparison of risk-aware allocation rules against an equal-weight benchmark.
BackgroundIn precision medicine, the convergence of ionizing radiation, algorithmic bias, and carbon emissions creates a triad of “invisible risks” that stalls AI adoption. Traditional centralized validation fails to bridge this “trust gap” due to data silos and opacity. A new paradigm is required to audit these risks without compromising privacy.MethodsThis paper proposes the Invisible Risk Trust Framework, a conceptual decentralized architecture serving as a complementary audit layer to regulatory bodies. The framework integrates three core components: (1) Privacy-Preserving Audit utilizing Federated Evaluation and Zero-Knowledge Proofs (ZKPs) to validate models without moving data; (2) Distributed Expert Consensus by board-certified specialists; and (3) Cryptographic Incentives featuring an “Eco-priority Queue” for sustainable compute.ResultsApplied to a theoretical nuclear medicine use case (PET/CT denoising), architectural analysis suggests that this protocol is projected to substantially reduce the redundancy of centralized data warehousing and partially automate audit workflows. This is supposed to offer a scalable, cost-efficient validation pathway, providing an immutable audit trail of model performance, bias, and carbon footprint.ConclusionAs healthcare transitions to AI-augmented decision-making, trust must be engineered into the digital infrastructure. By rendering invisible risks visible and verifiable, this conceptual framework generates testable hypotheses for a scalable, privacy-preserving, and sustainable validation pathway.
Transparency in public affairs interactions between companies and governments is critical to democratic legitimacy, yet existing lobby registers suffer from fragmented reporting, weak record integrity, limited traceability, and compliance gaps. This paper reports a Design Science Research (DSR) study that develops and evaluates a permissioned blockchain architecture for mandatory Public Affairs transparency. Two stakeholder surveys provided empirical grounding: Survey 1 (N = 61 domain professionals) elicited functional, non-functional, and GDPR compliance requirements, while Survey 2 (N = 14 practitioner evaluators) assessed a proof-of-concept implementation on Hyperledger Fabric following a live demonstration. Findings reveal widespread concerns over non-repudiation and auditability in current systems — 87% rated existing record integrity as weak—alongside strong endorsement for blockchain’s immutability, versioned audit trails, and hybrid on-/off-chain design to ensure GDPR-aligned traceability. Post-demonstration evaluation achieved a mean score of 4.6/5 for traceability and integrity, and 86% of evaluators recommended real-world piloting. The study makes three contributions: (i) an empirically derived requirements model and information-lifecycle framework; (ii) a hybrid permissioned-blockchain blueprint implemented on Hyperledger Fabric; and (iii) a replicable stakeholder-centric DSR methodology for sociotechnical artefact design in regulated governance contexts.
IntroductionBlockchain technology (BCT) has been widely discussed as a potentially valuable technology for advancing sustainable development in the public sector. Its core features, including transparency, immutability and decentralisation, may contribute to more accountable, efficient and inclusive public services. However, limited empirical evidence exists on how BCT-based public sector initiatives align with the United Nations Sustainable Development Goals (SDGs).MethodsThis study examines 306 public sector BCT-based use cases across the EU, compiled by the Public Sector Tech Watch observatory. We apply a GPT-4o-based AI text classification pipeline to assess the degree of alignment between project descriptions and the 17 SDGs. The pipeline combines refined SDG descriptors, structured prompting and documented model parameters. Its outputs are benchmarked against a human-coded subset to assess validity.ResultsThe results show strong alignment with SDG 9 (Industry, Innovation and Infrastructure) and SDG 17 (Partnerships for the Goals), followed by more moderate alignment with SDG 8 (Decent Work and Economic Growth). By contrast, goals such as SDG 2, SDG 6 and SDG 14 remain weakly represented. These findings provide an empirical overview of how BCT applications in EU public administrations are framed in relation to the SDGs.DiscussionBy highlighting patterns of alignment between BCT adoption and the SDGs, this study offers evidence to inform policymakers, practitioners and future research on sustainability-oriented public sector innovation. It also demonstrates the value of AI-assisted classification for mapping large corpora of digital government initiatives, while recognising that the results capture stated or perceived alignment rather than verified sustainability impacts.
Due to the rapid development of new generation information technology worldwide, universities’ cultivation of digital and intelligent talents has become a necessity of the times. To address the common challenges in traditional practice-oriented teaching, including outdated course design, insufficient authenticity of experimental environments, and cross-institutional trust deficits in student evaluation, this study takes the ‘Digital-Intelligence Workshop’ as the implementation context. Guided by the principles of emerging engineering education, a student-centered philosophy, and an OBE- and project-driven teaching framework, the study systematically improves the curriculum system. By introducing a blockchain-enabled data governance layer, this platform ensures the tamper-resistance of learning records, enables secure data sharing between universities and enterprises, and facilitates transparent smart-contract-based assessments. This article shows that by carrying out interdisciplinary collaboration and real enterprise case projects on the trusted training platform, students’ digital thinking and algorithm practice abilities have significantly improved, and teachers have also made progress in achievement transformation. Relevant teaching practices indicate that the blockchain-enabled ‘Digital Intelligence Workspace’ effectively promotes the improvement of talent cultivation quality in universities, providing a feasible new approach and practical reference for undergraduate education reform in the era of intelligence.
IntroductionAI drives hyper personalization in digital marketing while blockchain offers privacy and security. This review addresses the tension between consumer demand for customized experiences and growing concern over data safety.MethodsThis study applies the PRISMA framework to systematically review 56 peer reviewed papers published between 2015 and 2024 addressing AI personalization, blockchain privacy, and consumer trade offs in digital marketing.ResultsThree central themes emerged: (1) AI drives hyper personalization and ROI strategies, (2) blockchain enhances data security, trust, and GDPR compliance, (3) consumers face trade offs between convenience and privacy. Consumers accept personalized marketing when the mechanism is transparent and under their control. Blockchain reduces certain ethical issues linked to AI, including data exploitation and lack of auditability, but does not resolve algorithmic bias or scalability challenges. Twenty five percent of the analyzed research originates from India, showing regional concentration, while Africa and Latin America remain under represented.DiscussionMarketers should adopt blockchain audited AI systems, such as transparent recommendation engines and decentralized data marketplaces, to build consumer trust. Policymakers should establish hybrid regulatory ecosystems that balance innovation with ethical compliance, including GDPR consistent consent mechanisms and global interoperability standards. Cross discipline collaboration remains necessary to align technology with consumer centric values and ensure equitable adoption of AI and blockchain across markets.
Token-governance decentralization is not established by launch allocation, raw holder counts, or token-inequality metrics. It is an auditable current-control condition: who holds governance-relevant tokens after protocol-controlled addresses (PCAs) are removed, who retains insider positions, and how voting mechanisms transform holdings into rule-making power. Token allocation is a launch document. Governance concentration is a live institutional state. We turn that standard into a method across a 52-protocol cross-section spanning DePIN, DeFi, infrastructure, and social tokens, computing Herfindahl-Hirschman Index (HHI) concentration after PCA exclusion. Under audit, launch-design and protocol-financial covariates (insider, team, and investor allocation; maturity; circulating float; valuation ratios) are each uninformative about steady-state concentration (insider allocation Pearson r = 0.09, p = 0.55, N = 50). Current insider retention is the holder-side correlate that survives: protocols with more insider wallets among top holders are more concentrated (Spearman rho = 0.44, p = 0.005, N = 39, surviving a non-insider HHI tautology check at rho = 0.54). Voting mechanisms then separate rule-making power from holdings: delegation amplifies voting power above token holdings in thirteen of eighteen protocols with sufficient governance data, with five design-driven exceptions (ENS, GMX, HNT, JUP, LPT). A subsidy-to-concentration association appears only through a single outlier (Pearson r = 0.62 including Livepeer, r = 0.07 excluding it). Applied across sectors, the audit also distinguishes DePIN from DeFi: DePIN governance is more concentrated than DeFi after correction, a directionally robust medium effect (Cohen’s d = 0.65, Mann-Whitney p = 0.028), reported as a descriptive sector contrast, not as the central claim. Holder-list concentration is meaningless until the unit of control is identified. The five-class PCA-exclusion typology is control attribution, not data cleaning, correcting systematic inflation in prior holder-list studies (median factor 2.3×, maximum approximately 18×); once control is attributed, Gini and HHI capture distinct properties of the same holder set (r = 0.52), and inequality metrics cannot substitute for direct concentration measurement. All findings are descriptive associations from a single 2026 cross-section (holder snapshots collected March to May 2026), not causal claims; the audit standard and the current-control thesis are general, while the specific point estimates are bounded to that sample. The paper specifies five forward predictions with falsification thresholds and commits to Open Science Framework pre-registration before any panel or event-study extension. The practical implication is a changed audit default: a decentralization claim requires a live-governance audit of PCA-corrected holdings, insider retention, and voting power, not launch allocation or raw holder lists. Concentration of this kind bears on the legitimacy of decentralized governance, not only its efficiency: where a small set of holders or delegates commands decisive voting weight, the broad participation in rule modification that the commons self-governance ideal presumes is nominal rather than operative.
Byzantine Fault Tolerance consensus protocols are widely used in permissioned blockchain systems to ensure data consistency and reliability. However, traditional Asynchronous Byzantine Fault Tolerance (ABFT) protocols are typically designed for static membership, where the consensus node set remains fixed. This static design makes dynamic node admission, departure, and exclusion difficult, since membership changes usually require costly system-wide reconfiguration and may interrupt normal consensus operation. Moreover, existing ABFT protocols provide limited support for evidence-based handling of suspected inactive or faulty nodes, which restricts their applicability in dynamic permissioned environments.To address these challenges, this paper presents Anole, a reconfiguration-based protocol for dynamic Asynchronous Byzantine Fault Tolerance. Anole supports certificate-based Join, Leave, and Complain operations while preserving the ordinary transaction-consensus path under the current membership. Instead of relying on an external single trusted third party, Anole uses a replicated Byzantine-fault-tolerant Supervisor Committee (SC) to handle certificate issuance and key-management requests. The ordinary transaction-consensus path combines dynamic threshold public-key encryption with Bracha’s local-coin ABA under an asynchronous network model, while the current PBFT-based Supervisor Committee implementation is treated as a low-frequency management path whose liveness relies on partial synchrony. We evaluate Anole against HoneyBadgerBFT, Dumbo, and HotStuff in LAN and WAN deployments of up to 31 nodes, reporting both consensus latency and derived throughput under a fixed batch size. The results show that Anole provides dynamic-membership support with competitive consensus performance.
Decentralized autonomous organizations (DAOs) represent one of the most consequential experiments in organizational design to emerge from blockchain technology. By encoding governance rules into smart contracts and recording every vote, proposal, and treasury decision immutably on-chain, DAOs offer globally distributed communities a high degree of transparency and accountability in collective decision-making. This study examines governance design and participatory innovation across three DAOs: RARI DAO, Arbitrum DAO, and Optimism DAO. Each has taken a distinct structural approach to the problem of collective decision-making at scale. Using a qualitative comparative case study method, the research draws on governance forum discussions, proposal records, and official documentation, analyzed through thematic coding and cross-case comparison. The theoretical frame draws primarily from Ostrom’s (1990) commons governance principles, with Scott’s (1995, 2014) institutional theory and Donaldson’s (2001) contingency theory applied as supplementary analytical lenses. Across all three cases, the findings indicate the emergence of increasingly formalized governance architectures designed to balance decentralization, coordination efficiency, and operational security. Communities building governance infrastructure from scratch, iterating rapidly in response to community feedback, and developing structural solutions: delegate incentive programs, participation incentive mechanisms, bicameral legitimacy systems, constitutional frameworks, and dedicated legal entities that represent an emerging configuration of governance mechanisms. Two cross-case findings are particularly notable. First, all three DAOs independently converged on a three-body governance architecture comprising a legal foundation, a security council, and token-holder governance — suggesting that similar governance problems, encountered in similar technical and legal environments, tend to produce similar structural solutions. Second, while these architectures are structurally similar, they differ significantly in how governance processes are implemented in practice, reflecting differences in scale, formalization, and community context. These findings contribute to the literature by providing a structured cross-case analysis of DAO governance design and offering practical insights into programmable institutional design and blockchain-enabled coordination systems.
The convergence of blockchain technology and the Metaverse is redefining digital media ownership and distribution. Drawing on survey data from 613 digital media professionals and a qualitative synthesis of literature (2023–2026), this study examines how blockchain-based mechanisms—specifically non-fungible tokens (NFTs), smart contracts, and decentralized identity (DID) solutions—are associated with creator sovereignty and platform interoperability. Using a moderated chain mediation model within a socio-technical systems framework, the analysis shows that blockchain integration is associated with lower perceptions of platform dependency. This association is linked to a sequential pathway whereby higher decentralized governance is associated with lower intermediary control, which in turn is associated with higher creator monetization autonomy. Connectedness to decentralized protocols differentially shaped these processes: at the technical level, stronger protocol integration strengthened the negative association between blockchain adoption and intermediary dependence; however, at the governance level, a paradoxical pattern emerged, whereby stronger decentralization was associated with higher perceived governance overload in the context of algorithmic decision-making. By disentangling the technical and governance pathways, this study extends current understanding of digital media ecosystems beyond simple use-outcome associations. The findings highlight the importance of considering individual differences in digital literacy and institutional trust when designing blockchain governance frameworks. We conclude that blockchain is not merely an incremental improvement but a necessary architectural requirement for a resilient and equitable Metaverse, contingent upon addressing the risks of surveillance federalism and the digital divide.
Blockchain meets the needs of data tamper-proofness, anticounterfeiting, and traceability in fields such as the Internet of Things, supply chain management, and industrial control. However, these fields often collect large amounts of data in real-time and require processing within a specified time frame. Existing research mainly focuses on achieving high throughput and low latency in blockchain without considering the timeliness characteristics of transactions, which cannot provide timely guarantees for applications in these fields. Ensuring that transactions from such applications can be processed within the specified time is an urgent issue that blockchain needs to address. In the current study, we design RTChain, a prototype for permissioned blockchains that supports real-time transactions. We design a transaction processing framework that is scheduled first and then executed to process transactions based on timeliness. Specifically, RTChain proposes a priority assignment strategy to transition transaction deadlines and guide transactions throughout their entire lifecycle. We propose a Priority-based Deterministic Concurrency Control (PDCC) algorithm to execute transactions, where conflicts among transactions are resolved by detecting read-write sets and aborting transactions with lower priorities. To enhance the efficiency of transaction execution, we design an address-based check table and introduce an Optimized Priority-based Deterministic Concurrency Control (OPDCC) algorithm. Numerous experiments show that RTChain exhibits stable and excellent performance within various conflict rates and deadline ranges, meeting the timeliness requirements of transactions.
Counterfeiting in the agricultural sector remains a critical challenge that causes substantial economic losses and erodes consumer confidence. Existing QR code-based solutions are inadequate because public codes can be easily copied and reused. Moreover, their reliance on centralized databases creates a risk of collusion, enabling administrators to issue counterfeit labels that appear legitimate. Advanced technologies such as RFID and hardware security modules offer stronger protection but are prohibitively expensive and impractical for most agricultural applications. This paper presents a dual-QR code authentication mechanism integrated with blockchain to ensure data integrity and prevent code reuse. Each product is assigned a concealed private QR code, revealed only upon package opening, enabling verification after purchase and immediate detection of counterfeit goods. The system combines the high-throughput, low-latency data handling of centralized databases with the immutability of blockchain, offering a cost-effective and easily deployable solution. Experimental results demonstrate the feasibility and practicality of the proposed method for large-scale anti-counterfeiting in agricultural products.
