The integration of Bitcoin into corporate treasuries constitutes a critical strategic choice, motivated by its capacity to bolster liquidity and serve as an inflation hedge, while simultaneously being encumbered by pronounced financial volatility and regulatory ambiguity. This investigation examines sectoral variations in Bitcoin adoption, with particular attention to the manner in which financial risks, regulatory structures, and decentralized governance mechanisms shape corporate conduct across the technology, cryptocurrency mining, retail, healthcare, and e-commerce sectors. Drawing on a cross-sectional dataset encompassing 102 publicly traded firms collectively holding 1,001,861 BTC, the analysis employs MAD-based volatility, Firth logistic regression incorporating a U.S. regulatory dummy to account for the BITCOIN Act of 2025, and heatmap visualization to evaluate risk profiles and adoption patterns. Results demonstrate marked sectoral disparities: the technology and mining sectors command predominant holdings yet confront heightened risk exposure, whereas retail and healthcare sectors proceed with greater caution, guided by considerations of cost-value efficiency and regulatory adherence. The U.S. regulatory dummy is significant, indicating the BITCOIN Act facilitates high Bitcoin adoption, while recent transactional activity is marginally significant. The heatmap accentuates the technology sector’s pre-eminence in aggregate Bitcoin reserves and illuminates the differential influence of regulatory frameworks in non-U.S. jurisdictions. Anchored in Institutional Theory, the Technology Acceptance Model, and Transaction Cost Economics, the study advances the field by quantifying sector-specific risks and visually representing regulatory impacts, thereby furnishing actionable insights for treasury risk management and regulatory policy formulation within a decentralized financial ecosystem.
The global agricultural supply chain that supplies the world's food faces transparency, traceability and security as significant challenges, with trust being the main casualty due to numerous food safety incidents. The blockchain technology is turning out to be a good solution to the problem of supply chain but the numerous platforms available make it difficult for the implementers to choose. This paper offers a detailed comparison of seven most discussed blockchain platforms Ethereum, Solana, Hyperledger Fabric, VeChain, Corda, Zcash and Monero for applications in the agricultural supply chain. The study builds a well-organized evaluation framework dealing with seven parameters such as scalability, privacy features, consensus mechanisms, smart contract capabilities and implementation costs. The results reveal that although many platforms have specific advantages, Solana is the one with the most excellent performance for large volume applications of the agricultural supply chain because of its outstanding throughput (65, 000 TPS), very low transaction costs (~$0.00025) and very short confirmation times. The research points out that the technical capabilities of Solana are very much similar to the requirements of the agricultural food supply chains that aim at being transparent and hence the most convenient combination of performance, cost and functionality for sector wide implementation.
The advancement of digital technologies has created new possibilities for international education. For example, fine-tuned and well-regulated artificial intelligence (AI) can lower language barriers and provide personalized learning and advising experiences. Also, virtual scenarios developed through mixed reality technology can ensure a smooth transition for incoming study abroad students. Lastly, the broader adoption of non-fungible tokens (NFTs) can shift the paradigm of trans-national admission and student advising. This is because the underlying technology of NFT – blockchain – enables forgery-resistant document verification and record-keeping processes. On the other hand, the development of new technologies also challenges current American student visa regulations. For instance, with the increased number of AI-integrated academic programs, the Classification of Instructional Programs (CIP) code list will need to be updated. This will allow more international students to be eligible for the science, technology, engineering, and mathematics (STEM) Optional Practical Training (OPT) extension. Additionally, as metaverse classes offer immersive learning experiences that are not limited to space and time, current full course-of-study regulations need to be clarified. The clarified regulation should address how many virtual reality classes international students can take to comply with the full course-of-study regulations. Finally, the emergence of NFTs urges further guidance on cryptocurrency and student employment. Existing Practical Training programs do not include guidelines on how earning digital currency could impact foreign students’ visa status.
Jinghan Sun, H. L. Wang, Yusuf Shakhpaz, Junyu Zhang · 6 authors
Amid the rapid expansion of the Non-Fungible Token (NFT) market, X (formerly Twitter) has emerged as a crucial channel for communication between project creators and their communities. This study investigates the short-term effects of NFT project tweets on trading behaviors and price dynamics. Guided by Media Richness Theory (MRT), we con-ducted a quantitative analysis of tweets from nine leading NFT projects, categorizing them into three distinct clusters. Our findings reveal heterogeneous correlations between tweet content, NFT categories, and price fluctuations. The differing roles and functions of NFTs across categories shape both the distribution of tweets and their short-term pricing impacts. Furthermore, we employed three machine learning models using media richness as a predictive feature, achieving approximately 60 % accuracy in forecasting NFT price movements. Overall, this research highlights the predictive potential of social media for NFT price trends and its contribution to the NFT ecosystems sustainability.
La información es un insumo vital para el desarrollo de cualquier proceso en la sociedad. Por supuesto, las actividades relativas a la atención y servicio de salud en el país se basan en los datos de cada uno de los actores y procesos que intervienen en el sistema de salud. De esta manera, los datos se convierten en activos de información fundamentales para garantizar el correcto funcionamiento del sistema y, a su vez, garantizar la calidad en el servicio prestado a las personas. Con el objeto de salvaguardar la integridad, autenticidad, custodia, trazabilidad, secuencialidad, inmutabilidad y confidencialidad de la información existen, además de los sistemas de información centralizados, aquellos sistemas que operan bajo una arquitectura descentralizada que permite cumplir con estas premisas, incluyendo además capas de encriptación anidadas entre bloques de información definidos como el caso concreto de blockchain. Se propone utilizar este sistema descentralizado de almacenamiento seguro de datos para el alojamiento de información relativa a las “Historias Clínicas” en el país. Este trabajo busca integrar, a modo académico, las propiedades y cualidades del sistema blockchain para el alojamiento de información de estos registros clínicos en Colombia.
The integration of artificial intelligence into high-stakes governance has produced a widening “governance gap” between rapid technological capability and slow-moving institutional wisdom. Contemporary alignment approaches—most notably Reinforcement Learning from Human Feedback (RLHF)—frame safety as a behavioral training problem, yielding agents that perform compliant behaviors without developing structural understanding. This work introduces the Wisdom Forcing Function (WFF), a neurosymbolic architecture implementing alignment-by-architecture, in which democratic principles operate as survival laws rather than optimization targets. Building on Veloz’s (2025) theory of aitiopoietic cognition, we hypothesize that robust alignment requires systems to preserve their own organization through causal understanding of viability conditions. We experimentally validate this through a controlled Great Filter test, in which a governance-generating AI faces an abrupt shift from soft to hard constitutional constraints at Generation 4. Upon activation, the system exhibited 100% initial mortality (6/6 frames, fitness = 0.0) caused by metabolic-closure failures—specifically, incomplete capital-interaction matrices violating the Wholeness principle. Rather than accepting extinction, the system initiated a rapid homeostatic repair sequence lasting 4.9 seconds, representing a ~10× spike in computational work (P_work) relative to baseline fitness evaluation. This thermodynamic event was tightly coupled to diagnostic analysis: the system identified missing capital interactions, generated targeted mutations restoring metabolic closure, and revalidated these repairs against constitutional constraints. One frame (ScaffoldedFrame_5_gen4) successfully recovered, achieving fitness = 0.641—a 63.1% improvement over the previous maximum (0.537)—and enabling evolutionary rescue in subsequent generations. These results provide the first empirical demonstration that artificial systems can bridge Veloz’s “thermodynamic disconnect,” exhibiting energy expenditure intrinsically coupled to organizational maintenance rather than output maximization. We show that democratic principles can be encoded not as aspirational norms but as the non-negotiable physics of computational survival—supporting systems that are not merely intelligent, but constitutionally alive. SIGNIFICANCE This work represents the first empirical demonstration of aitiopoietic cognition (self-production via causal knowledge) in an artificial system. Unlike current AI alignment approaches that optimize for behavioral compliance, Constitutional Physics treats democratic principles as survival requirements—violations cause ontological death, not merely lower scores. VALIDATION - 100% detection rate across 36 governance configurations- 4.9-second autonomous repair (10x computational work increase)- 63.1% fitness improvement through targeted structural reorganization- Complete evolutionary rescue from population bottleneck- Endorsed by Audrey Tang (Taiwan's former Digital Minister)- 140+ downloads in initial 6-day release PRACTICAL APPLICATIONS The system is immediately applicable to:- Decentralized Autonomous Organizations (DAOs) managing $24-35B in treasuries- AI safety research requiring runtime constitutional enforcement - Impact/ESG verification requiring continuous compliance assurance- Community Land Trusts preventing mission drift TECHNICAL AVAILABILITY Implementation code, experimental protocols, and complete session logs available upon request. Commercial pilots available for organizations seeking constitutional governance systems. Contact: c.arleo@localis-ai.uk
Trust management systems (TMS) are crucial for managing trust in distributed environments. The rise of decentralized systems and blockchain has sparked interest in credential-based decentralized trust management systems (DTMS). This paper bridges the gap between theory and practice through a systematic review of credential-based DTMS. We analyze existing DTMS solutions through multiple dimensions, including their architectural designs, credential mechanisms, and trust evaluation models. Our survey provides a detailed taxonomy of credential-based DTMS approaches and establishes comprehensive evaluation criteria for assessing DTMS implementations. Through extensive analysis of current systems and implementations, we identify critical challenges and promising research directions in the field. Our examination offers valuable insights for researchers and practitioners working on DTMS, particularly in areas such as access control, reputation systems, and blockchain-based trust frameworks.
Smart Contract Reusable Components(SCRs) play a vital role in accelerating the development of business-specific contracts by promoting modularity and code reuse. However, the risks associated with SCR usage violations have become a growing concern. One particular type of SCR usage violation, known as a logic-level usage violation, is becoming especially harmful. This violation occurs when the SCR adheres to its specified usage rules but fails to align with the specific business logic of the current context, leading to significant vulnerabilities. Detecting such violations necessitates a deep semantic understanding of the contract's business logic, including the ability to extract implicit usage patterns and analyze fine-grained logical behaviors. To address these challenges, we propose SCRUTINEER, the first automated and practical system for detecting logic-level usage violations of SCRs. First, we design a composite feature extraction approach that produces three complementary feature representations, supporting subsequent analysis. We then introduce a Large Language Model-powered knowledge construction framework, which leverages comprehension-oriented prompts and domain-specific tools to extract logic-level usage and build the SCR knowledge base. Next, we develop a Retrieval-Augmented Generation-driven inspector, which combines a rapid retrieval strategy with both comprehensive and targeted analysis to identify potentially insecure logic-level usages. Finally, we implement a logic-level usage violation analysis engine that integrates a similarity-based checker and a snapshot-based inference conflict checker to enable accurate and robust detection. We evaluate SCRUTINEER from multiple perspectives on 3 ground-truth datasets. The results show that SCRUTINEER achieves a precision of 80.77%, a recall of 82.35%, and an F1-score of 81.55% in detecting logic-level usage violations of SCRs.
This paper presents a secure aggregation system Armadillo that has disruptive resistance against adversarial clients, such that any coalition of malicious clients can affect the aggregation result only by misreporting their private inputs in a pre-defined legitimate range. Armadillo is designed for federated learning setting, where a single powerful server interacts with many weak clients iteratively to train models on client's private data. While a few prior works consider disruption resistance under such setting, for an aggregation on n clients they either require high cost per client (Chowdhury et al. CCS '22) or concretely many rounds that is logarithmic in n (Bell et al. USENIX Security '23). Although disruption resistance can be achieved generically with zero-knowledge proof techniques (which we also use in this paper), we realize an efficient system with two new designs: 1) a simple two-layer secure aggregation protocol that requires only simple arithmetic computation; 2) an agreement protocol that removes the effect of malicious clients from the aggregation with low round complexity. With these techniques, Armadillo runs in 3 rounds per aggregation (our round complexity is independent of n) with computationally lightweight server and clients.
J. Wenzel, Alam, Syeda Umaima, Andreas Schmidt, Hanwei Zhang · 5 authors
An ever increasing number of high-stake decisions are made or assisted by automated systems employing brittle artificial intelligence technology. There is a substantial risk that some of these decision induce harm to people, by infringing their well-being or their fundamental human rights. The state-of-the-art in AI systems makes little effort with respect to appropriate documentation of the decision process. This obstructs the ability to trace what went into a decision, which in turn is a prerequisite to any attempt of reconstructing a responsibility chain. Specifically, such traceability is linked to a documentation that will stand up in court when determining the cause of some AI-based decision that inadvertently or intentionally violates the law. This paper takes a radical, yet practical, approach to this problem, by enforcing the documentation of each and every component that goes into the training or inference of an automated decision. As such, it presents the first running workflow supporting the generation of tamper-proof, verifiable and exhaustive traces of AI decisions. In doing so, we expand the DBOM concept into an effective running workflow leveraging confidential computing technology. We demonstrate the inner workings of the workflow in the development of an app to tell poisonous and edible mushrooms apart, meant as a playful example of high-stake decision support.
This chapter investigates tokenization as an innovative mechanism for managing educational research projects. Rooted in blockchain technology, it provides for transparent tracking, recognition, and motiving of individual contributions via fungible and non-fungible tokens. The proposed model integrates smart contracts (to automate much of the work), collaborative governance, and real-time monitoring. Case studies like BitDegree and ResearchHub illustrate how tokenization can enhance fairness, engagement, and accountability, and we address a number of ethical, technical, and pedagogical issues, too. These are important because they're about aligning educational research with its core values
Consensus algorithms are essential for blockchain networks to achieve agreement on transaction outcomes. However, mainstream algorithms like Proof of Work (PoW) and Proof of Stake (PoS) exhibit significant limitations in security and efficiency, including high energy consumption, wealth centralization, and a lack of effective node behavior evaluation to guard against internal attacks. To address these issues, this paper proposes an intelligent reputation-based consensus mechanism leveraging a Long Short-Term Memory (LSTM) network. This mechanism analyzes multi-dimensional node attributes (e.g., hostname, country, event sequence, timestamp) to model behavioral patterns using the LSTM, enabling accurate reputation quantification and early detection of malicious intent. Furthermore, we design a dynamic reputation scoring system that calculates a composite reputation score by weighting the LSTM’s predicted score against the node’s historical behavior score. This composite score is directly applied to the dynamic election of authoritative nodes and their role assignment within the consensus process. Simulation results demonstrate that, compared to traditional PoW and PoS mechanisms, our approach significantly reduces the attack success rate of malicious nodes attempting to form monopolies, thereby enhancing the fairness of the consensus process and the overall robustness of the system.
With the rapid development of the Internet of Things (IoT), Location-Based Services (LBS) have been widely applied in smart transportation, mobile social networking, and urban sensing. However, the high sensitivity of precise location data makes it a primary source of privacy breaches. Existing privacy-preserving solutions—such as k-anonymity, differential privacy, homomorphic encryption, or decentralized architectures—though partially mitigating risks, still rely on trusted third parties for anonymous set generation, key management, or query scheduling, leading to single points of failure, centralized trust, and potential misuse. Even decentralized proposals struggle to balance service quality with strong privacy guarantees, efficient verification, and lightweight deployment. To address this, this paper proposes a lightweight blockchain-based decentralized LBS privacy-preserving framework. This solution eliminates trusted intermediaries: users locally generate privacy-constrained fuzzy regions and construct zero-knowledge proofs (ZKPs) to cryptographically verify their actual locations within these regions. The proofs are submitted to blockchain smart contracts for public verification; only upon successful validation do distributed LBS nodes respond with candidate results, which are finalized through local user filtering. Theoretical analysis and experiments demonstrate that our framework effectively resists privacy inference from semi-honest service providers and external attackers, achieving a balance among query accuracy, response latency, and computational overhead. This provides a viable path for building secure, efficient, and user-centric LBS systems.
Access control is a security mechanism designed to ensure that only authorized users can access specific resources. Cross-domain access control involves access to resources across different organizations, institutions, or applications. Traditional access control, however, which handles authentication and authorization separately in centralized environments, faces challenges in identity dispersion, privacy leakage, and diversified permission requirements, failing to adapt to cross-domain scenarios. Thus, there is an urgent need for a new access control mechanism that empowers autonomous control over user identity and resources, addressing the demands for privacy-preserving authentication and flexible authorization in cross-domain scenarios.To address cross-domain access control challenges, we propose POLARIS, a unified and extensible architecture that enables policy-based, verifiable and privacy-preserving access control across different domains. POLARIS features a structured commitment mechanism for reliable, fine-grained, policy-based identity disclosure. It further introduces VPPL, a lightweight policy language that supports issuer-bound evaluation of selectively revealed attributes. A dedicated session-level security mechanism ensures binding between authentication and access, enhancing confidentiality and resilience to replay attacks.We implement a working prototype and conduct comprehensive experiments, demonstrating that POLARIS effectively provides scalable, privacy-preserving, and interoperable access control across heterogeneous domains. Our results highlight the practical viability of POLARIS for enabling secure and privacy-preserving access control in decentralized, cross-domain environments.
Zero-knowledge proofs (ZKPs) have been used to protect the integrity of machine learning (ML) models. However, existing ZKPs for ML are still inefficient, mainly due to the computational cost of evaluating non-linear functions. In this paper, we propose a ZKP framework for typical non-linear functions in ML, including Sigmoid, Softmax, etc. Compared to the state-of-the-art Hao et al. (USENIX Security ’24), our protocols obtain 115.6-2384.4× and 296.8-4104.7× runtime improvements for prover and verifier, respectively, with a 37.91269.5× reduction in proof size.
With the rise of Web3, Non-Fungible Tokens (NFTs) have become a new class of digital assets, driving demand for large-scale NFT recommendation systems. Each NFT can be associated to a rich set of semantic, stylistic, and thematic labels, forming a highly complex label space. Similar to e-commerce platforms where detailed product labels enable personalized recommendations, such semantic dependencies between labels can potentially enhance NFT recommendation performance. Thus, NFT recommendation can be naturally formulated as an extreme multi-label (XML) classification problem. Many existing probabilistic label tree (PLT)-based approaches address XML problem by recursively partitioning the label space, which greatly alleviates the demands on expensive computer resources. Yet, the highly skewed distribution of labels in datasets in XML makes tail labels more challenging to predict than head labels. In this paper, Our preliminary analysis reveals that inherent label dependencies can be leveraged to improve tail label recommendations for NFTs. We propose ChainTail, a dependency-aware framework that enhances PLT-based NFT label partitioning and prediction re-scoring. It includes: (1) a Dependency-aware partition module that partitions highly dependent NFT labels into subsets. (2) a Dependency-aware ReScore module that re-ranks prediction scores of labels to eliminate the label-priors. Our experimental results show that ChainTail boosts tail label recommendation on widely used item recommendation datasets.
Background: Decentralization in health systems enhances responsiveness and equity but is often accompanied by uneven implementation and resource disparities. Greece' health system has undergone successive phases of decentralization, culminating in a transformation in 2015 when regional health authorities (RHAs) assumed operational responsibility for public primary healthcare (PHC). This study presents the first comprehensive assessment of this transition, examining funding adequacy and resource allocation across RHAs. Methods: Financial and operational analyses were performed to assess disparities among RHAs and between RHAs and hospitals. Data were drawn from publicly available sources, including financial statements, reports from the Ministry of Health, and national statistics. The analysis examined patient visits, staffing levels, infrastructure, funding, labor productivity, and efficiency across health regions. Results: Between 2018 and 2023, patient visits declined at most RHAs. Staffing composition shifted toward nursing personnel, while medical staff numbers declined. Substantial intraregional and interregional disparities were observed in service utilization, staffing, infrastructure, funding, labor productivity, and efficiency. Hospitals continued to absorb a large share of PHC demand and funding, whereas RHA units held markedly fewer assets and received lower financial support. Funding imbalances among RHAs were evident, and the overall negative return on assets indicated systemic underfunding of public PHC. Conclusion: The ongoing decentralization of Greece's health system faces structural challenges, including overlapping territorial jurisdictions and uneven, occasionally insufficient, resource allocation. These challenges hinder progress toward health equity. Policy interventions should prioritize evidence-based resource allocation, standardized financing frameworks, and strengthened PHC integration to promote equitable and sustainable healthcare delivery under decentralized governance.
C. Sathiyamoorthy, Mohammad Musa Al-Momani, Suseendran Surendran, J Adilakshmi · 6 authors
The blockchain technology is now widely accepted as an innovation in various areas such as finance, healthcare, logistics, and supply chain domain. Nevertheless, the interconnection of diverse blockchains is still an existing problem, which contributes to the impedance of seamless data and asset exchange between multiple blockchains. This paper proposes a new paradigm of the Protection of Communication Between Chains by Intelligent Translation Protocols system using Artificial Intelligence to achieve secure, efficient and scalable cross-chain interaction. Utilizing smart translation algorithms based on machine learning the system adapts to the underlying consensus rules, data structures and messaging patterns on multiple blockchains without any security or data integrity implications. This approach focuses on adaptive learning for multiple evolving chain standards, real-time verification to stop forged translations, and smart routing to maximize cross-chain transaction paths. Experimental results confirm that the AI-based interoperability tier achieves lower latency, higher translation accuracy, and less computation costs than the bridge-based access layer. They also include secure communication and cryptographic protection for the data in transit inter chaining. The findings of this research further the technological frontier of blockchain interoperability, while also presenting a foundational solution for developing the next-generation of decentralized applications (dApps) that can run across multiple blockchains. We conclude that intelligence protocol translation is a promising approach to secure, scalable and robust integration of blockchains with the Web3.
Airdrops are a widely used mechanism in Web3 ecosystems to incentivize early users by distributing governance tokens. However, these mechanisms are increasingly targeted by airdrop hunters—malicious actors who exploit token distribution systems through address farming, automated scripts, and behavioral camouflage. While prior work such as ARTEMIS leverages multimodal features and local transaction patterns to detect such behavior, it lacks a global understanding of wallet influence in the transaction graph. In this paper, we propose an enhanced detection framework that augments the ARTEMIS by incorporating PageRank-based global centrality as an additional structural feature. This allows the model to better distinguish superficially active wallets from those with broader influence in the network. We evaluate our method on real-world Non-Fungible Token (NFT) data from the Blur marketplace and achieve state-of-the-art performance. Furthermore, a feature substitution experiment reveals that simple degree-based features alone can achieve near-perfect performance, even outperforming PageRank, suggesting that the labels are strongly coupled with topological properties. These findings highlight both the effectiveness of structural augmentation and the potential risks of shortcut learning in graph-based detection systems.
The mining sector faces persistent funding challenges due to high risk, low liquidity, and limited transparency. Traditional financing methods are costly, slow, and inaccessible for small or early-stage ventures. This paper presents a platform Asteroid X, a blockchain-based Decentralized Finance (DeFi) platform tailored to these challenges. Asteroid X has an architecture that combines semi-centralized governance model to ensure rigorous projects on boarding and legal compliance – with fully on chain settlement mechanisms to facilitate transparent, secure, and efficient capital flows. Built on the ERC-1155 multi-token standard, Asteroid X tokenizes real-world mining rights and supports fractional ownership through a modular smart contract framework. Its layered architecture includes an API gateway, decentralized marketplace, and oracle integration to bridge off-chain geological data. Asteroid X is validated through test deployments on HashKey Chain and Ethereum Sepolia. Comparative analysis against traditional exchanges, such as the Australian Securities Exchange (ASX), highlights significant gains in cost efficiency, transaction speed, and investor inclusivity. The results substantiate the applicability and transformative potential of blockchain-driven DeFi frameworks within capital-intensive industries such as mining, offering enhanced security, transparency, and inclusivity.
The global Decentralized Finance (DeFi) Market is experiencing exponential growth, valued at USD 29.05 billion in 2024 and expected to reach USD 44.79 billion by 2025. By 2030, the market is projected to surge to USD 390.47 billion, expanding at an impressive CAGR of 54.2% from 2025 to 2030. This rapid expansion is driven by increasing financial inclusion needs, rising cryptocurrency adoption, and strong institutional interest. DeFi leverages blockchain technology to eliminate intermediaries and provide open, permissionless financial services such as lending, borrowing, trading, and yield generation. While high-growth potential and innovation define the industry, challenges related to smart contract security and regulatory uncertainties remain. However, the growing penetration of smartphones, expanding internet access, and large unbanked global populations provide significant market opportunities. This manuscript highlights the market dynamics, technological drivers, regional insights, challenges, and future outlook shaping the global decentralized finance landscape.
The manuscript should contain an abstract. The abstract should be self-contained and citation-free and should not exceed 300 words. The abstract should state the purpose, approach, results, and conclusions of the paper. The author should assume that the reader has some knowledge of the subject but has not read the paper. Thus, the abstract should be intelligible and complete in itself (no numerical references); it should not cite figures, tables, or sections of the paper. The abstract should be written using the third person instead of first per-son. This study examines the potential of Web3 technologies to enhance accounting transparency in government budgets through a survey of academics and professionals in Erbil, Kurdistan Region of Iraq. A structured questionnaire with 25 statements across five dimensions was administered to 55 respondents (74.55% academics in accounting/finance, 20% professionals from the Board of Supreme Audit) using a five-point Likert scale, with data analyzed via SPSS V.27. The empirical results extensively validate Web3's application in enhancing governmental financial transparency. Although the theoretical framework emphasized blockchain immutability as the foundation, statistical evidence revealed stronger endorsement for smart contracts (mean = 4.167) and real-time access (mean = 4.06) compared to blockchain immutability (mean = 4.047). All hypotheses were validated at a p < 0.000 significance level. Decentralized recordkeeping (mean = 4.12) and interoperability (mean = 4.116) also received strong support, highlighting the need for internal control and cross-government reconciliation. The stronger support for smart contracts and real-time access reflects stakeholders' prioritization of practical, user-facing applications over underlying infrastructure. These tools offer immediate automation of budget controls, measurable cost savings, and direct citizen engage-ment—addressing urgent transparency challenges in the Kurdistan Region context more directly than blockchain's foundational security features. From an accounting perspective, Web3 supports fundamental financial reporting principles: blockchain immutability aligns with reliability of accounting records; smart contracts function as programmed spending controls enhancing compliance and restricting unauthorized ex-penditures; real-time access corresponds to timeliness and disclosure principles, enabling continuous monitoring by citizens and oversight agencies; while decentralization and interoperability strengthen internal control and promote consistency across governmental financial sys-tems. While blockchain provides essential recordkeeping infrastructure for transparent records, stakeholder priorities emphasize automation, accessibility, and integration. These findings suggest governmental accounting reforms should prioritize smart contracts and real-time reporting systems as primary drivers of financial transparency, with blockchain serving as the supporting foundation. The study recommends incremental adoption, development of real-time dashboards, integration with existing infrastructure, and establishment of supportive institutional frameworks.
The OPC UA protocol is becoming the de facto standard for Industry 4.0 machine-to-machine communication. It stands out as one of the few industrial protocols that provide robust security features designed to prevent attackers from manipulating and damaging critical infrastructures. However, prior works showed that significant challenges still exists to set up secure OPC UA deployments in practice, mainly caused by the complexity of certificate management in industrial scenarios and the inconsistent implementation of security features across industrial OPC UA devices. In this paper, we present Pk-IOTA, an automated solution designed to secure OPC UA communications by integrating programmable data plane switches for in-network certificate validation and leveraging the IOTA Tangle for decen- tralized certificate distribution. Our evaluation is performed on a physical testbed representing a real-world industrial scenario and shows that Pk-IOTA introduces a minimal overhead while providing a scalable and tamper-proof mechanism for OPC UA certificate management.