Arthur Ramos, Anjolina Grisi de Oliveira, Ruy de Queiroz, Tiago M. L. de Veras
We present Metatheory, a comprehensive library for programming language foundations in Lean 4. The library features a modular framework for proving confluence of abstract rewriting systems using three classical proof techniques: the diamond property, Newmans lemma, and the Hindley-Rosen lemma. These are instantiated across six case studies including untyped lambda calculus, combinatory logic, term rewriting, simply typed lambda calculus, and STLC with products and sums. All theorems are fully mechanized with zero axioms or sorry statements. We provide complete proofs of de Bruijn substitution infrastructure and demonstrate strong normalization via logical relations. To our knowledge, this is the first comprehensive confluence and normalization framework for Lean 4.
Despite the recognition of Blockchain Technology’s disruptive potential, there is ongoing debate about its ontological and axiomatic foundations. This study develops a theoretical framework to explain the underline structural principles of blockchain technology through the lens of Arthur’s theory of technology, and the framework is developed through adopting Narrative Literature Review. By integrating conceptual analysis with a structural examination of Ethereum, this study reveals that blockchain technology is not a single invention but a composite technological system developed through recursive interactions among sub-technologies. The proposed framework identifies three interrelated structural patterns—the Combinatorial Pattern of Components elucidating blockchain technology’s structural ontology, the Capturing Pattern of Algorithms revealing the operational source of its innovation, and the Recursive Pattern of Technologies characterizing its inner logical structure of components—that together explain blockchain technology’s generative and evolving nature. The study extends Arthur’s theory by clarifying the “technology within technology” dynamic that underlies blockchain technology innovation. The Ethereum case confirms the framework’s applicability and generalizability, showing that blockchain systems, despite their diversity, share a consistent structural logic. Beyond its theoretical contribution, the framework offers practical guidance for sustainable technological innovation. It provides analytical support for designing blockchain-based applications’ architectures that enhance transparency, efficiency, and adaptability, contributing to the sustainable evolution of digital technologies.
Artificial intelligence increasingly governs access to credit, employment, and identity verification, raising questions of rights protection when deployed across borders. This paper develops a computable framework for Fundamental Rights Impact Assessment (FRIA) that transforms the legal principles of necessity and proportionality into quantifiable metrics. By embedding these standards into algorithmic pipelines, the framework enables verifiable auditing of high-risk AI systems. Simulations were conducted in two domains, credit scoring and biometric authentication, using synthetic datasets modeled on European and non-European jurisdictions. The necessity audits reduced the average input set by 24.6 ± 2.3 variables while sustaining predictive accuracy, while proportionality assessments exposed heavy reliance on sensitive features in 39%* of credit scoring models and significant subgroup disparities in biometric authentication. Distributed verification protocols preserved results on blockchain ledgers, ensuring transparency and cross-border accountability. The findings demonstrate that computable FRIAs can operationalize fundamental rights obligations, producing results that can be inspected by regulators and reviewed in courts. The study concludes that computable methods offer a practical bridge between jurisprudential principles and algorithmic implementation, though persistent divergences in cross-border proportionality standards remain a major challenge for harmonized enforcement.
In the Aether Physics Model (APM), each elementary particle is a distributed-charge excitation of an Aether unit with two electrostatic spheres and four magnetic loxodromes in five dimensions. The Quantum Measurement Units (QMU) system expresses all ledgers in terms of the base atoms $m_e$, $\lambda_C$, $F_q$, $e^2$, and ${e_\mathrm{emax}}^2$, with the Aether unit $A_u$ and the curl unit $\mathrm{curl}$ satisfying the rotational identity\[A_u \cdot \mathrm{curl} = {F_q}^2 {\lambda_C}^2.\] This article develops a complete QMU ledger for the superconducting electron pair. A superconducting pair is modeled as two electrons mutually occupying each other's positive electrostatic spheres, with their magnetic loxodromes polarly aligned so that south poles are adjacent and the chronovibrational Singularity lies between them. This configuration traps torsion internally, strongly suppresses the external curl, and leaves the Aether rotational identity intact. The QMU enrg unit and temp unit are defined by the electron rest-enrg and the Aether rotational ledger,\[\mathrm{enrg} = m_e {\lambda_C}^2 {F_q}^2,\qquad\mathrm{temp} = {F_q}^2 {\lambda_C}^2,\]so that all superconducting observables can be written without reference to SI/MKS units. For the superconducting pair we obtain\[m_{\mathrm{pair}} \approx 2 m_e,\qquadQ_{\mathrm{pair}} = 2 e^2,\]and introduce the magnetic cancellation parameter $\eta_{\mathrm{pair}}$ via\[\mathrm{curl}_{\mathrm{ext}}^{(\mathrm{pair})}= (1 - \eta_{\mathrm{pair}})\,\mathrm{curl}_{\mathrm{int}},\]with $\mathrm{curl}_{\mathrm{int}} \approx \mathrm{curl}$ for the combined Aether unit. This leads to an effective external magnetic charge\[e_{\mathrm{eff}}^2 = 2 (1 - \eta_{\mathrm{pair}})\, e_{\mathrm{emax}}^2,\]and a pair flux unit\[\mathrm{mflx}_{\mathrm{pair}}= \frac{m_{\mathrm{pair}} \lambda_C^2 F_q}{e_{\mathrm{eff}}^2}= \frac{\mathrm{mflx}}{1 - \eta_{\mathrm{pair}}},\]so that the pair becomes magnetically ``invisible'' as $\eta_{\mathrm{pair}} \to 1$. The pair binding enrg $E_{\mathrm{bind}}$ is written in units of $\mathrm{enrg}$,\[E_{\mathrm{bind}} = \beta_{\mathrm{pair}}\,\mathrm{enrg},\qquad0 < \beta_{\mathrm{pair}} \ll 1,\]and the superconducting transition is expressed as a ledger equality between the binding ledger and a chronovibrational thermal ledger,\[E_{\mathrm{th}}(\theta_c) = f_{\mathrm{th}}(\theta_c)\,\mathrm{enrg}\approx E_{\mathrm{bind}},\qquad\theta_c = T_c / \mathrm{temp},\]so that $f_{\mathrm{th}}(\theta_c) \approx \beta_{\mathrm{pair}}$ defines the critical temp in pure QMU. Material dependence is encoded in three dimensionless parameters:\[\eta_{\mathrm{pair}},\qquad\beta_{\mathrm{pair}},\qquad\Xi_{\mathrm{Aether}},\]where $\Xi_{\mathrm{Aether}}$ is an Aether–lattice coupling index that measures how well the lattice geometry supports positive-sphere mutual occupation, south–south loxodrome alignment, and chronovibrational phase locking along conduction paths. Using penetration-depth and gap-ratio data from conventional superconductivity experiments, the paper constructs two complementary QMU maps: 1. A superconductivity engineering map in terms of a composite pair exponent $\alpha_{\mathrm{pair}}(\eta_{\mathrm{pair}},\beta_{\mathrm{pair}})$, showing that A15 compounds, cuprates, and hydrides occupy distinct but ordered regions of the $(\eta_{\mathrm{pair}},\beta_{\mathrm{pair}})$ ledger space. 2. A superconductivity parameters plot in the plane\[\left(\sigma_{\mathrm{pair}}^{(T)},\, \sigma_{\mathrm{pair}}^{(\mathrm{iso})}\right),\]where $\sigma_{\mathrm{pair}}^{(T)}$ is a critical-temp–scaled pairing parameter and $\sigma_{\mathrm{pair}}^{(\mathrm{iso})}$ is an isotope-effect parameter. All known materials fall close to a universal straight line\[\sigma_{\mathrm{pair}}^{(\mathrm{iso})}\approx \sigma_A - \sigma_{\mathrm{pair}}^{(T)},\]with $\sigma_A$ a dimensionless constant. In the QMU interpretation this line is the shared superconducting pair ledger relating the projections of $\eta_{\mathrm{pair}}$, $\beta_{\mathrm{pair}}$, and $\Xi_{\mathrm{Aether}}$. The article closes with an experimental outlook formulated entirely in QMU, including chronovibrational sensitivity tests, Aether–lattice design heuristics, and a program for extracting $(\eta_{\mathrm{pair}},\beta_{\mathrm{pair}},\Xi_{\mathrm{Aether}})$ from future superconductivity data sets. All results are presented in QMU-only form, with SI/MKS appearing only as a secondary cross-check in the appendix.
The aim of this thesis is to examine the pricing and efficiency of Bitcoin options. It reviews theories of market efficiency and considers how effectively these frameworks apply to cryptocurrency markets. The thesis examines multiple option pricing models by comparing their performance for pricing Bitcoin options. Bitcoin’s high volatility and the relatively young age of its market development highlight the need to analyze how these characteristics influence both option pricing and overall market efficiency. In addition, the thesis examines the characteristics of Bitcoin options. The study provides guidelines for future research and market development, helping to build trust and support the integration of cryptocurrency derivatives into the broader financial system. Tämän opinnäytetyön tavoitteena on tarkastella Bitcoin-optioiden hinnoittelua ja markkinoiden tehokkuutta. Työssä käydään läpi markkinatehokkuuden teorioita ja arvioidaan, kuinka hyvin nämä viitekehykset soveltuvat kryptovaluuttamarkkinoihin. Opinnäytetyössä tarkastellaan useita optioiden hinnoittelumalleja vertailemalla niiden toimi- vuutta Bitcoin-optioiden hinnoittelussa. Bitcoinin korkea volatiliteetti ja sen markkinoiden suhteellisen varhaisessa kehitysvaiheessa oleva tila korostavat tarvetta analysoida, miten nämä ominaisuudet vaikuttavat sekä optioiden hinnoitteluun että markkinoiden yleiseen tehokkuuteen. Lisäksi työssä tarkastellaan Bitcoin-optioiden erityispiirteitä. Tutkimus tarjoaa suuntaviivoja tu- levalle tutkimukselle ja markkinoiden kehittämiselle, ja sen tavoitteena on lisätä luottamusta sekä tukea kryptovaluuttajohdannaisten integroitumista laajempaan finanssijärjestelmään.
Mohammad Sharif Karimi, Omar Esqueda, Naveen Mahasen Weerasinghe
This study employs a quantile-on-quantile connectedness approach to analyze the asymmetric, distribution-dependent, and time-varying spillovers between FinTech indices and traditional financial markets. The results show that spillovers are concentrated in the distribution tails, with FinTech indices exhibiting strong co-movements with equities and Bitcoin under extreme conditions, while linkages with U.S. Treasury bonds are weaker and often inverse. Net connectedness analysis reveals that the S&P 500 and Bitcoin act as the primary transmitters of shocks into FinTech indices, whereas Treasuries generally serve as receivers, except during stress episodes when safe-haven flows or heightened credit risk reverse the direction of spillovers. The dynamic ∆TCI (Difference between the total direct connectedness and the reverse total connectedness) further demonstrates that FinTech indices serve as net transmitters in stable markets but become receivers during crises such as the COVID-19 pandemic, the Federal Reserve’s tightening cycle of 2022–2023, and the FTX-driven crypto collapse. Segmental heterogeneity is also evident: distributed ledger firms are highly sensitive to cryptocurrency dynamics, alternative finance providers respond strongly to both equity and bond markets, and digital payments firms are primarily influenced by equity spillovers. Overall, the findings underscore FinTech’s dual role—transmitting shocks during tranquil periods but amplifying systemic vulnerabilities during crises. For investors, diversification benefits are state-dependent and largely disappear under adverse conditions. For regulators and policymakers, the results highlight the systemic importance of FinTech–equity and crypto–ledger linkages and the need to integrate FinTech exposures into macroprudential surveillance to contain volatility spillovers and safeguard financial stability.
During the first decade of cryptocurrencies (2008–2017) there were few connections established between crypto and the conventional finance sector, but in the US in 2025 the integration of these two sectors is proceeding at speed. This paper examines one part of this integration – the centralisation of cryptocurrency trading inside of large, digital platformed exchanges, which is theorised as a shift from cryptocurrency to cryptofinance. Furthermore, the paper shows how this shift to cryptofinance has been aided by an emergent crypto-state nexus. The novel contribution of the paper is explaining how the US crypto markets have progressed from niche, relatively decentralised and blockchain-based, with little association with or regulation by nation-states, into what is now competition between FinTech-fuelled, digital platform firms that provide suites of financial services and instruments and collect fees for mediating access to the underlying blockchain markets. Empirically, the paper traces the rise of Sam Bankman-Fried’s firm, FTX, as it evolved from a small, California-based start-up running arbitrage trades in 2017 into one of the world’s largest crypto exchanges servicing over a million customers in 2022. In light of the FTX story, the paper analyses the geographical political economy of platformed cryptofinance as it struggles with both the incumbent financial sector and the US state.
Topicality. Fraudulent activities on the Ethereum blockchain pose a substantial risk to decentralized finance and require capable models not only to respond to already detected abuses but also to identify suspicious accounts proactively before losses escalate. The subject of study is the application of graph and temporal neural models to the task of classifying Ethereum accounts as benign or fraudulent, considering the structural relationships between addresses and the temporal dynamics of transactions. The purpose of this article is to develop and experimentally evaluate a neural architecture based on a multilayer perceptron as a baseline component for the subsequent integration of graph and temporal mechanisms, and to analyze its performance on the open Ethereum Fraud Detection dataset, which features a high-class imbalance. The following results were obtained. A baseline deep model for binary account classification was constructed using feature preprocessing, stratified data splitting, class weight balancing, L2 regularization, Dropout, and early stopping, which enabled the achievement of an ROC AUC value of approximately 0.98 under conditions of a pronounced dominance of the safe class. A detailed analysis of the confusion matrix and the precision, recall, and F1 metrics demonstrated an acceptable trade-off between reducing false positives and minimizing the proportion of missed fraudulent accounts, which is critical for real-world financial scenarios. Conclusion. The results indicate that a properly designed baseline neural model on tabular features can ensure high-quality proactive identification of fraudulent Ethereum accounts and serve as a starting point for further integration of graph and temporal architectures aimed at improving interpretability and robustness to the evolution of malicious behavior patterns.
Vladyslav Prosolov, Oleksandr Kushnerov, Vladyslav Sokol, Ruslan Trofymenko
Topicality. Fraudulent activities on the Ethereum blockchain pose a substantial risk to decentralized finance and require capable models not only to respond to already detected abuses but also to identify suspicious accounts proactively before losses escalate. The subject of study is the application of graph and temporal neural models to the task of classifying Ethereum accounts as benign or fraudulent, considering the structural relationships between addresses and the temporal dynamics of transactions. The purpose of this article is to develop and experimentally evaluate a neural architecture based on a multilayer perceptron as a baseline component for the subsequent integration of graph and temporal mechanisms, and to analyze its performance on the open Ethereum Fraud Detection dataset, which features a high-class imbalance. The following results were obtained. A baseline deep model for binary account classification was constructed using feature preprocessing, stratified data splitting, class weight balancing, L2 regularization, Dropout, and early stopping, which enabled the achievement of an ROC AUC value of approximately 0.98 under conditions of a pronounced dominance of the safe class. A detailed analysis of the confusion matrix and the precision, recall, and F1 metrics demonstrated an acceptable trade-off between reducing false positives and minimizing the proportion of missed fraudulent accounts, which is critical for real-world financial scenarios. Conclusion. The results indicate that a properly designed baseline neural model on tabular features can ensure high-quality proactive identification of fraudulent Ethereum accounts and serve as a starting point for further integration of graph and temporal architectures aimed at improving interpretability and robustness to the evolution of malicious behavior patterns.
Uisang Lee, Changhoon Chung, Junmo Lee, Sung Jun Moon
The rapid growth of Ethereum has made it more important to quickly and accurately detect smart contract vulnerabilities. While machine-learning-based methods have shown some promise, many still rely on rule-based preprocessing designed by domain experts. Rule-based preprocessing methods often discard crucial context from the source code, potentially causing certain vulnerabilities to be overlooked and limiting adaptability to newly emerging threats. We introduce BugSweeper, an end-to-end deep learning framework that detects vulnerabilities directly from the source code without manual engineering. BugSweeper represents each Solidity function as a Function-Level Abstract Syntax Graph (FLAG), a novel graph that combines its Abstract Syntax Tree (AST) with enriched control-flow and data-flow semantics. Then, our two-stage Graph Neural Network (GNN) analyzes these graphs. The first-stage GNN filters noise from the syntax graphs, while the second-stage GNN conducts high-level reasoning to detect diverse vulnerabilities. Extensive experiments on real-world contracts show that BugSweeper significantly outperforms all state-of-the-art detection methods. By removing the need for handcrafted rules, our approach offers a robust, automated, and scalable solution for securing smart contracts without any dependence on security experts.
Zero-knowledge proofs (ZKPs) are central to secure and privacy-preserving computation, with zk-SNARKs and zk-STARKs emerging as leading frameworks offering distinct trade-offs in efficiency, scalability, and trust assumptions. While their theoretical foundations are well studied, practical performance under real-world conditions remains less understood. In this work, we present a systematic, implementation-level comparison of zk-SNARKs (Groth16) and zk-STARKs using publicly available reference implementations on a consumer-grade ARM platform. Our empirical evaluation covers proof generation time, verification latency, proof size, and CPU profiling. Results show that zk-SNARKs generate proofs 68x faster with 123x smaller proof size, but verify slower and require trusted setup, whereas zk-STARKs, despite larger proofs and slower generation, verify faster and remain transparent and post-quantum secure. Profiling further identifies distinct computational bottlenecks across the two systems, underscoring how execution models and implementation details significantly affect real-world performance. These findings provide actionable insights for developers, protocol designers, and researchers in selecting and optimizing proof systems for applications such as privacy-preserving transactions, verifiable computation, and scalable rollups.
The convergence of Artificial Intelligence (AI) and Blockchain Technology (BCT) is transforming supply-chain ecosystems by enhancing transparency, intelligence, and automation. However, existing research lacks a unified theory explaining how these technologies jointly create resilience across organizational levels. This paper extends the Strategic–Decentralized Resilience Theory (SDRT), originally developed to guide effec-tive blockchain implementation, by integrating Agentic AI capabilities to form the SDRT–Agentic AI framework. The framework conceptualizes how predictive, adaptive, and agentic (autonomous) AI capabilities reinforce SDRT’s three pillars: Strategic, Or-ganizational, and Decentralized Resilience. The framework draws on three AI modali-ties—predictive AI for strategic foresight and agility, adaptive AI for organizational learning and flexibility, and agentic AI for self-governed, trustless coordination within blockchain ecosystems. Together, these mechanisms explain how intelligent and de-centralized systems co-evolve to generate dynamic, multi-level resilience. This con-ceptual paper develops a comprehensive model and propositions describing interac-tions between AI capabilities and blockchain-based organizational structures. It con-tributes to information systems and supply-chain research by unifying two fragmented domains, AI and blockchain, under a resilience-oriented mid-range theory. Practically, the framework provides managers with a roadmap to align AI investments with de-centralized governance mechanisms, enabling proactive decision-making, adaptability, and sustainable competitiveness in increasingly autonomous digital environments.
Gauhar Ali, Sajid Shah, Mohammed ElAffendi, Naveed Ahmad
Introduction Digital Twins (DT) have appeared as a significant tool in Industrial Internet of Things (IIoT) environments, allowing real-time monitoring, predictive maintenance, and maximizing device performance. However, integrating DTs with IIoT initiates serious security issues, specifically in the device’s authentication and authorization. The state-of-the-art mechanisms are exposed to insider threats, single points of failure, and privacy issues. Methods This study proposes a blockchain-based access control framework for cross-domain DTs. The blockchain (BC) integration eliminates reliance on the centralized authentication server. It uses platform verification from the manufacturer to validate IIoT device integrity and mitigate insider threats. Moreover, the authorization mechanism is implemented using smart contract and access control policies stored in BC. The proposed Non-Fungible Tokens enable role and permission delegation. Results and Discussion The integration of Hyperledger Fabric BC, platform hash verification, and NFT-based authorization in the proposed architecture enhanced its resilience against cyber-attacks i.e., replay, DoS/DDoS, insider, and spoofing attacks. Moreover, the proposed framework validates its viability with response times (approximately 300ms) for the authentication and authorization phases. Additionally, identity resolution attains 67 % depletion in latency compared to its counterpart.
Habib, Kyle, Vladislav Kapitsyn, Giovanni Mazzeo, Faisal Mehrban
Current blockchain consensus protocols -- notably, Proof of Work (PoW) and Proof of Stake (PoS) -- deliver global agreement but exhibit structural constraints. PoW anchors security in heavy computation, inflating energy use and imposing high confirmation latency. PoS improves efficiency but introduces stake concentration, long-range and "nothing-at-stake" vulnerabilities, and a hard performance ceiling shaped by slot times and multi-round committee voting. In this paper, we propose Proof of Trusted Execution (PoTE), a consensus paradigm where agreement emerges from verifiable execution rather than replicated re-execution. Validators operate inside heterogeneous VM-based TEEs, each running the same canonical program whose measurement is publicly recorded, and each producing vendor-backed attestations that bind the enclave code hash to the block contents. Because the execution is deterministic and the proposer is uniquely derived from public randomness, PoTE avoids forks, eliminates slot.time bottlenecks, and commits blocks in a single round of verification. We present the design of a PoTE consensus client, describe our reference implementation, and evaluate its performance against the stringent throughput requirements of the Trillion decentralized exchange.
G.B. Bhavana, R. S. Anand, J. Ramprabhakar, Josep M. Guerrero · 6 authors
Integration of renewable-energy (RE) sources of energy into local microgrids, decentralised energy markets using blockchain based peer-to-peer (P2P) energy trading is gaining more traction due to its ability to enable transparent, autonomous, and secure transactions among distributed energy resources (DERs). Due to the large variety in microgrid topologies and their respective operational constraints, a key challenge to obtaining the most effective trading framework is the choice of blockchain consensus protocol that suits a given microgrid type. Choice of consensus mechanism solely affects the reliability and security of a network. This paper presents a quantitative comprehensive evaluation of various blockchain consensus mechanisms such as Proof-of-Work (PoW), Proof-of-Stake (PoS), Delegated Proof-of-Stake (DPoS), Proof-of-Elapsed-Time (PoET), Practical Byzantine Fault Tolerance (PBFT), Raft, and Tendermint to determine their suitability for P2P energy trading in microgrids. Various metrics such as fault tolerance, energy efficiency, latency, throughput and consensus time were evaluated for multiple consensus mechanisms through simulations. This paper also studies node-scaling impact on the protocols and presents a final decision framework to match the suitable protocol with various microgrid topologies.
Deepika Dash, Bipin Raj C., B Jnyanadeep, Anala M R
The proliferation of decentralized finance (DeFi) has highlighted critical challenges in cross- chain oracle reliability and performance assessment. Traditional blockchain networks remain isolated from external data sources, creating the fundamental Oracle Problem that hinders institutional adoption of DeFi protocols. This paper presents DeFiLens, a comprehensive benchmarking framework that provides standard- ized performance metrics and real-time analytics across multiple blockchain ecosystems including Ethereum, Binance Smart Chain, Polygon, and Avalanche. Our framework addresses the gap between traditional finance’s seamless market data access and blockchain’s data isolation through systematic oracle assessment. DeFiLens implements a six-layer security scoring system encompassing cryptographic verification, attack detection, and network health monitoring. Through extensive evaluation of major oracle providers including Chainlink, Band Protocol, and Tellor, we demonstrate significant performance variations across chains, with response times ranging from 2.1 seconds to 8.7 seconds and reliability scores varying between 72% and 95%. Our statistical analysis reveals critical arbitrage opportunities with price discrepancies up to 2.3% across chains. The framework serves as a ‘‘Bloomberg Terminal’’ for oracle data, enabling financial institutions, DeFi protocols, and researchers to make data-driven decisions for oracle integration and risk management.
Decentralized autonomous organizations (DAOs) have emerged as significant governance models, prioritizing transparency and community participation. However, there remains a knowledge gap regarding the impact of online discussions on the decision-making processes within these organizations. This study aims to fill this gap by investigating the relationships between engagement metrics and proposal approval rates. We find a strong coherence between the most discussed topics in each DAO’s forums and their stated missions. Our analysis also reveals a nuanced but noticeable correlation between community engagement and voting outcomes. Furthermore, our paper explores the complexities of community coordination and collective governance within DAOs, highlighting existing challenges and providing design recommendations.
Quang Huy Duong, Carlos F.A. Arranz, Mao Xu, Li Zhou · 5 authors
The rapid transition to electric vehicles has intensified challenges in electric vehicle battery (EVB) closed-loop supply chains (CLSC), particularly regarding material traceability, supply chain transparency, and recycling efficiency. While decentralised technologies, particularly Web3 and Metaverse, offer promising solutions, their integration into EVB CLSC remains fragmented and insufficiently examined. We introduce an Operational Decentralisation Framework enabling a systematic analysis of centralised operations and a critical evaluation of decentralised alternatives as transformational forces. By adopting a holistic perspective, the framework equips firms with strategic guidance for transitioning from centralised structures to decentralised ecosystems. We analyse 588 academic articles and 1,168 industry documents through two advanced text mining techniques – Dynamic Latent Dirichlet Allocation and Burst Detection. Web3 and metaverse can potentially reconfigure the design, manufacturing, end-of-life diagnostics, procurement, waste management, load balancing, capacity planning, inventory management and service operations of two key areas: (1) EVB CLSC operations and (2) EVB circular energy/grid operations. We also found that while blockchain and digital twins show established applications, Web3 and Metaverse applications face significant barriers, including scalability, technology complexity, and expertise gaps, despite their great potentials. Therefore, we propose four visionary models integrating Web3, Metaverse, and AI technologies that have the potential to overcome existing barriers and enable transformative decentralisation. Extending the TOE framework, the study contributes to the theory by developing an integrated framework for evaluating decentralised technology adoption in EVB CLSCs. For practitioners, we provide actionable insights and pathways for technology implementation across different CLSC stages and guidance for addressing key adoption barriers.
Dinh C. Nguyen, Md Bokhtiar Al Zami, Ratun Rahman, Shaba Shaon · 6 authors
Quantum federated learning (QFL) is emerging as a key enabler for intelligent, secure, and privacy-preserving model training in next-generation 6G networks. By leveraging the computational advantages of quantum devices, QFL offers significant improvements in learning efficiency and resilience against quantum-era threats. However, future 6G environments are expected to be highly dynamic, decentralized, and data-intensive, which necessitates moving beyond traditional centralized federated learning frameworks. To meet this demand, blockchain technology provides a decentralized, tamper-resistant infrastructure capable of enabling trustless collaboration among distributed quantum edge devices. This paper presents QFLchain, a novel framework that integrates QFL with blockchain to support scalable and secure 6G intelligence. In this work, we investigate four key pillars of \textit{QFLchain} in the 6G context: (i) communication and consensus overhead, (ii) scalability and storage overhead, (iii) energy inefficiency, and (iv) security vulnerability. A case study is also presented, demonstrating potential advantages of QFLchain, based on simulation, over state-of-the-art approaches in terms of training performance.
The rise of space AI is reshaping government and industry through applications such as disaster detection, border surveillance, and climate monitoring, powered by massive data from commercial and governmental low Earth orbit (LEO) satellites. Federated satellite learning (FSL) enables joint model training without sharing raw data, but suffers from slow convergence due to intermittent connectivity and introduces critical trust challenges--where biased or falsified updates can arise across satellite constellations, including those injected through cyberattacks on inter-satellite or satellite-ground communication links. We propose OrbitChain, a blockchain-backed framework that empowers trustworthy multi-vendor collaboration in LEO networks. OrbitChain (i) offloads consensus to high-altitude platforms (HAPs) with greater computational capacity, (ii) ensures transparent, auditable provenance of model updates from different orbits owned by different vendors, and (iii) prevents manipulated or incomplete contributions from affecting global FSL model aggregation. Extensive simulations show that OrbitChain reduces computational and communication overhead while improving privacy, security, and global model accuracy. Its permissioned proof-of-authority ledger finalizes over 1000 blocks with sub-second latency (0.16,s, 0.26,s, 0.35,s for 1-of-5, 3-of-5, and 5-of-5 quorums). Moreover, OrbitChain reduces convergence time by up to 30 hours on real satellite datasets compared to single-vendor, demonstrating its effectiveness for real-time, multi-vendor learning. Our code is available at https://github.com/wsu-cyber-security-lab-ai/OrbitChain.git
This paper will propose a novel machine learning based portfolio management method in the context of the cryptocurrency market. Previous researchers mainly focus on the prediction of the movement for specific cryptocurrency such as the bitcoin(BTC) and then trade according to the prediction. In contrast to the previous work that treats the cryptocurrencies independently, this paper manages a group of cryptocurrencies by analyzing the relative relationship. Specifically, in each time step, we utilize the neural network to predict the rank of the future return of the managed cryptocurrencies and place weights accordingly. By incorporating such cross-sectional information, the proposed methods is shown to profitable based on the backtesting experiments on the real daily cryptocurrency market data from May, 2020 to Nov, 2023. During this 3.5 years, the market experiences the full cycle of bullish, bearish and stagnant market conditions. Despite under such complex market conditions, the proposed method outperforms the existing methods and achieves a Sharpe ratio of 1.01 and annualized return of 64.26%. Additionally, the proposed method is shown to be robust to the increase of transaction fee.
Formal verification of smart contracts is widely regarded as an effective method for ensuring correctness and security properties across all possible executions. Its practical relevance has been driven by the availability of automatic verification tools that discharge intricate proofs. Another area of growing interest is the integration of specification paradigms - for example, combining Hoare-logic–style specifications (pre/postconditions and invariants) with SMT and symbolic reasoning - so that each technique can precisely capture complementary aspects of contract behavior. In this article we present a comparative analysis of four leading Solidity verification tools - solc-verify, SMTChecker, VeriSmart and the Certora Prover - and define what is meant here by a formal verification tool: a system that provides mathematically rigorous proofs that stated properties hold for every possible execution of a contract. We also describe a consistent evaluation framework that considers the Solidity version support, the preservation of the original contract structure, the local execution capability, the verification time, and the modeling-language requirements, among other criteria. We used the ERC-20 token standard as a benchmark and applied this framework to obtain empirical evidence of each tool’s capabilities and limitations. Our results expose substantial variability in the tools performances that undermines their trustworthiness in practice and highlights a gap between an academic tool capabilities and the industrial requirements. Finally, we discuss how these findings can inform developers and researchers in selecting appropriate verification tools, thereby contributing to improved smart contract security and reliability.
Industry 4.0 and digital transformation have accelerated the emergence of virtual assets such as cryptocurrencies. Among them, Bitcoin, a virtual currency, has captured significant attention from both finance theorists and practitioners, achieving the highest market capitalization to date. The objective of this study is to examine the behavior and interrelationships between Bitcoin and several traditional financial assets within the framework of an international diversification strategy that combines conventional and crypto assets. In this context, Bitcoin is considered as a potential new asset class for portfolio diversification. To explore this relationship, we analyze the links between Bitcoin and a selection of major currencies—EUR, GBP, and JPY—as well as certain commodities. The study employs the Value at Risk (VaR) approach using three empirical methods, complemented by Conditional Value at Risk (CVaR) as a robustness measure, given its ability to capture tail risk more effectively than VaR. Using daily data from October 29, 2016, to October 23, 2020, the findings reveal that including Bitcoin in a diversified portfolio can significantly enhance risk–return characteristics. These results provide new insights for portfolio managers and investors seeking optimal diversification strategies in the context of digital finance.