Maxime L. D. Nicolas, François Sicard, Marion Laboure, Zixin Sun · 5 authors
This study investigates the transmission of monetary policy narratives to Bitcoin prices, distinguishing the impact of ex-ante expectations from ex-post interest rate implementation. We introduce a high-frequency Monetary Policy Expectations (MPE) index, using a Large Language Model (LLM)-based classification of 118,000+ market messages to achieve a precise hawkish/dovish decomposition. Results from a framework combining Long Short-Term Memory (LSTM) networks with SHapley Additive exPlanations (SHAP) indicate that Bitcoin functions as a sensitive barometer of central bank signaling; specifically, hawkish narratives consistently trigger negative price responses independently of actual Federal Funds Rate adjustments. We demonstrate that the MPE index Granger-causes Bitcoin returns at short-to-medium horizons, establishing linear predictive causality, while the LSTM-SHAP framework reveals pronounced non-linear, macroeconomic regime-dependent interactions. These findings highlight Bitcoin's structural sensitivity to global monetary discourse, establishing LLM-derived sentiment as a potent leading macroeconomic indicator for the digital asset landscape.
Marcin Bienkowski, Julien Dallot, Dominik Danelski, Maciej Pacut · 5 authors
Payment channel networks (PCNs) are a promising approach to making cryptocurrency transactions faster and more scalable. At their core, PCNs bypass the blockchain by routing transactions through intermediary channels. However, a channel can forward a transaction only if it has the necessary funds: the problem of keeping the channels balanced is a current bottleneck for the PCN's transaction throughput. This paper considers the problem of maximizing the number of transactions accepted by a channel in a PCN. Previous works either considered the associated optimization problem with all transactions known in advance or developed heuristics tested on particular transaction datasets. This work, however, considers the problem in its purely online form where the transactions are arbitrary and revealed one after the other. We show that the problem can be modeled as a new online knapsack variant where the items (transaction proposals) can be either positive or negative depending on the direction of the transaction. The main contribution of this paper is a deterministic online algorithm that is $O(\log B)$-competitive, where $B$ is the knapsack capacity (maximum allowed channel balance). We complement this result with an asymptotically matching lower bound of $Ω(\log B)$ which holds for any randomized algorithm, demonstrating our algorithm's optimality.
Jiahao Qi, Dian Ding, Jie Li, Jiannong Cao · 7 authors
Account migration in sharded blockchains presents a critical trade-off between optimization effectiveness and system availability. While dynamically reallocating accounts across shards can significantly reduce cross-shard transaction overhead, existing migration mechanisms cause service disruptions that intensify as state data volumes grow. To address this challenge, we propose BIND, a batch-wise account migration protocol that eliminates service interruptions by enabling continuous transaction processing throughout migration. BIND introduces a dual transaction pool architecture that isolates transactions involving migrating accounts while allowing non-migrating accounts to operate uninterrupted. To optimize migration efficiency, we design a reverse greedy heuristic algorithm that partitions accounts into batches based on community cohesion, maximizing intra-batch connectivity to front-load cross-shard communication reduction. We evaluate BIND using real Ethereum transactions, demonstrating superior performance over existing mechanisms. BIND achieves 12% higher overall throughput, reduces migration time to 23.6%-39.3% of the one-shot baseline (across 1-10Gbps bandwidth), and lowers cross-shard transaction rates by 24.1% compared to random batching. These results confirm BIND as a practical solution for large-scale, non-disruptive account migration in production sharded blockchains.
The cryptocurrency market represents a decentralized, 24/7 arena that is to a great measure free from regulatory constraints. Although this favors innovation and inclusion, it puts the market in a very vulnerable place due to manipulations with the help of technology. Social media increasingly shapes flows of information and sentiment, disrupting crypto price dynamics. This chapter will review market manipulation in crypto and how social media fuels speculative activity. Manipulation types to be covered include pump-and-dump, wash sales, spoofing, and DeFi-related manipulation. These concepts have been integrated with the author's work on behavioral finance, market microstructure, and blockchain research.
Although the digital media ecosystem has changed creation and sharing of content, existing copyright management systems suffer from inefficiencies, such as slow payment of royalties, a lack of transparency about how much an artist is owed, high administrative costs and difficulties in tracking cross-border use. One of the most promising methods for addressing these shortcomings is the use of smart contracts, which are self-executing applications that work off a public distributed ledger called a blockchain to automatically pay royalties at the time of use, based on preconfigured conditions that are based on a predetermined number of streams, downloads or views. Current study explores the technological architecture, relevant legal issues and practical implications for automated payment of royalties to content creators through the use of smart contracts in the context of music services, audiovisual works and digital publishing. The smart contracts allow peer-to-peer transactions without a third party, based on elements of the blockchain, like the principles of decentralized consensus and immutability (integrity). The legal issues related to smart contracts using code as a contract include whether smart contracts will be legally enforceable across different jurisdictions, whether a smart contract's code can be considered enforceable with moral rights, and concentration on complying with different data privacy laws, e.g., the General Data Protection Regulation (GDPR) in countries where blockchain is essentially immutable. While smart contracts can address a number of the core pain points associated with these areas (i.e., transparency gaps, fragmented ownership data, transactional friction), they must overcome various challenges to achieve broad acceptance. These challenges include scalability; the reliability of oracles for off-chain data; interoperability across disparate blockchains; regulatory uncertainty related to anti-money laundering/know-your-customer regulations, and taxation; and a lack of statutory recognition of smart contracts, standardized metadata for ownership rights, on/off-chain hybrid models, and international harmonization via treaties.
Abstract This study proposes a methodological strategy composed of econometric techniques and time series modelling to analyse the dynamic asynchrony between Bitcoin and a basket of traditional sustainable financial assets and emerging markets over a 10-year period marked by major economic and financial changes. The centrepiece of this proposal is the Relation Index that combines vector autoregression and detrended cross-correlation analysis to capture linear and nonlinear dependencies, causality, and time-scale sensitive correlations. Thus, this research fills existing gaps in understanding cross-market interdependencies by integrating cryptocurrencies, sustainability indices, and emerging economies into a rigorous multivariate time series framework. Sustainability indices, emerging markets and Bitcoin have shown a growing correlation since 2020, with both interest rates and Bitcoin having strong autoregressive components. The findings indicate that emerging market equities have undergone a structural shift towards synchronisation with global risk assets, with a correlation index that frequently exceeds 0.6 in periods of systemic stress. This evolution highlights the decline in the advantages offered by diversification in developed and developing economies in a complex and interrelated financial environment.
Sensors, wearables, implanted devices, and cloud platforms provide real-time monitoring, diagnosis, and clinical decision-making with the Internet of Medical Things (IoMT). Growth of IoMT infrastructure affects data privacy, cybersecurity, interoperability, and trust. IoMT systems, non-standard communication protocols, device capabilities, and medical network incursions are covered. We suggest data fusion, encryption, and decentralised trust enforcers for patient data. A decentralised, unchangeable, and secure blockchain. Blockchain-based solutions protect IoMT data flow, eliminate single-point-of-failure, and secure distributed medical device trust evaluation. Distributed ledger data integrity, authentication, and trust score storage improve active blockchain trust models. AI, data fusion, cloud/fog computing, smart hospitals, and blockchain secure IoMT. This chapter covers IoMT system design, cybersecurity, MULC frameworks, and blockchain-based health innovations. We discuss open research, regulation, and resilient, scalable, and dependable IoMT ecosystems.
The compatibility of faster and faster digitalization of higher education has exerted pressure on the necessity to have a secure, interoperable, and smart academic credentialing system. Traditional centralized record management models are prone to data editing, slowness in verification and inter-institutional identification. In this chapter, the author suggests a decentralized-trust semantic intelligence hybrid model of credentialing and academic data management on a block chain-artificial intelligence (AI) system.The findings show that the combination of AI and distributed ledger technology turns the traditional credentialing of storing records to an active, learner-focused system. The chapter provides a scalable and governance-conscious paradigm in the future of digital universities, enhanced lifelong learning, micro-credential portability and transparent academic data ecosystems.
The increasing complexity of global supply chains has intensified the need for stronger environmental, social, and governance (ESG) oversight and transparency. Traditional governance mechanisms often rely on fragmented information systems and periodic reporting processes that limit the credibility of sustainability monitoring across distributed supply networks. Emerging technologies such as blockchain and tokenization provide new opportunities to enhance transparency, traceability, and accountability in ESG governance. Drawing on the Natural Resource-Based View, Agency Theory, and Stakeholder Theory, this chapter explores how decentralized technologies can improve data verifiability, reduce information asymmetries, and support more reliable ESG governance in sustainable supply chains.
Ian M. Smith, The Institute for Relational Performatism
A Note Before We Start This paper is free. It is meant to be shared. The scientific paper this accompanies is precise, technical, and deliberately formal. It is written for researchers, security architects, and people who need to understand what the architecture actually does. This paper is for everyone else. And for them too, when they want the human version first. Every institution that holds information about you assumes one thing so consistently that it has never thought to question it. The information exists somewhere. The job is to protect it. Lock it down. Encrypt it. Control who can reach it. Kūn begins with a different question entirely. What if the information never existed in the space the attacker inhabits at all? If anything in here sounds like it matters, keep reading. That is exactly why it was written. Part One: The Problem in Plain English Right now, somewhere in the United Kingdom, a person's most sensitive information is being protected by a system that assumes the attacker is trying to break a lock. The lock might be very strong. The encryption might be sophisticated. The access controls might be well-designed. But the information is there, on the other side of the lock, waiting. And the history of information security is the history of locks being broken, eventually, by someone with enough time, enough resource, or enough computing power. This is not a criticism of the people who build those locks. They are doing exactly what the field has always done, and doing it well. The problem is not the quality of the locks. The problem is the assumption underneath them. Every cryptographic system ever built assumes that the information being protected exists in the same space as the person trying to reach it. Encryption transforms it. Access control restricts who can approach it. Zero-knowledge proofs allow you to prove you know something without revealing what you know. But in every case the information is present somewhere in the system. The security mechanism governs what happens to it next. Kūn asks: what if it were not present at all? Not hidden. Not encrypted. Not access-controlled. Structurally absent from the space in which an attacker operates. That is not a stronger lock. It is the architectural design of a space in which the door the attacker is looking for does not exist in their universe. What You Can Do Right Now Read the full architectural paper. It is free, published under open licence, and available at: https://doi.org/10.5281/zenodo.19474858 Read the companion papers in this series. The neurodevelopmental paper is at https://doi.org/10.5281/zenodo.19386155. The endometriosis paper is at https://doi.org/10.5281/zenodo.19461999. Share this paper. It is free. It is designed to travel.
Abuzar Khan, Ahmad Junaid, Abid Iqbal, Ghassan Husnain
This study proposes a Federated Cloud Intelligence for Privacy-Preserving AI, with new layered framework that can support secure and eco friendly learning across different cloud providers. Instead of centralizing data, our method trains models locally on varied client datasets and combines their updates using federated learning (FL) to stay compliant with data protection rules. The experiment have shown that the federated setup reached an average accuracy of 0.844 over five communication rounds, just slightly lower than the centralized baseline of 0.850. Meanwhile, the loss decreased from 0.367 to 0.285, coming close to the centralized value of 0.318. To build trust, a blockchain-based layer that permanently stored updates with little extra cost, adding blocks each round with an average consensus delay of 0.189 seconds. Tests showed that this consensus process reduced the impact of malicious client attacks, keeping accuracy stable around 0.827. Further it is then incorporated with zero-knowledge proofs (ZKM), where adds only 0.196 seconds of latency and 260–360 MB GPU memory overhead and showcases an accuracy up to 0.844. A reinforcement learning agent optimized workload scheduling by shifting the computation from AWS to GCP, reducing carbon scores by 20% with minimal accuracy trade-off. Finally, explainability analysis revealed balanced provider contributions from 0.021 to 0.023 and highlighted key features such as logPurchases and storePurchases.
Fraudulent activities on blockchain networks threaten the integrity and reliability of decentralized finance ecosystems. Accurately identifying malicious nodes such as phishing or ransomware addresses, within large-scale blockchain transaction graphs remains a critical challenge due to their dynamic, sparse, and continuously evolving topologies. Transfer learning offers a powerful paradigm for fraud detection because many fraudulent schemes, including ransomware and phishing, are often orchestrated by overlapping actor groups that share behavioral and structural patterns across networks. Leveraging these shared representations enables knowledge transfer from previously observed fraud types to emerging ones. However, the complex and multi-modal nature of digital financial systems introduces substantial challenges for graph-based transfer learning. Fraudulent activities are shaped by diverse modalities including graph structure, transaction sequences, temporal price dynamics, and textual metadata, while distributional shifts frequently occur across time and platforms. Existing graph transfer learning methods struggle to model such multi-modal dependencies and to align divergent feature distributions. To tackle these challenges, we develop a Multi-mOdal Enhanced Graph Transfer Learning (MOE-GTL) framework which incorporates graph, temporal, and textual modalities for fraudulent node detection. We further introduce Temporal-aware Maximum Mean Discrepancy (TMMD), a regularization mechanism that explicitly aligns multi-modal feature distributions between source and target graphs over time. Extensive experiments reveal that our MOE-GTL model notably improves the accuracy of fraudulent node classifications on Ethereum and Solana transaction graphs.
Description For two thousand years, Euclid's fifth postulate — that exactly one parallel line passes through any external point — was accepted as a truth about the structure of space. Gauss, Bolyai, and Lobachevsky demonstrated it was not a truth but a special case: the degenerate curvature-zero limit of a richer geometric framework. Riemann generalized this into a theory where flat space is the exception, not the rule. The Davis Non-Decoupling Theorem (2025) completed the picture: on any manifold with intrinsic curvature, parallel lines are excluded by the geometry itself. This paper applies the same structural logic to zero. We construct the geometric natural numbers G, a connection-based number system in which each natural number is a pair (G_n, G) consisting of an element count n >= 1 and a simple, undirected graph G on n vertices. For n >= 2, the graph must be connected — multiplicity without connection is excluded from the system. The pre-geometric seed (G_1, P_1), a single vertex with no edges, is retained as the irreducible element from which geometry can emerge but has not yet emerged. The void state G_0 (no elements, no graph, no base space) is excluded entirely: it is not a degenerate member of G but the dissolution of the conditions under which G is defined. Formal Results Theorem (Peano Embedding). The path-graph naturals P = {(G_n, P_n) : n >= 1}, where P_n is the undirected path graph on canonical vertex set {1, ..., n} with linear order inherited from the labeling, satisfy all five Peano axioms with (G_1, P_1) in the role of zero and S(G_n, P_n) = (G_{n+1}, P_{n+1}) as successor. The map phi: N -> P defined by phi(n) = (G_{n+1}, P_{n+1}) is an isomorphism of Peano systems. All five axioms are verified: distinguished element, closure, non-circularity, injectivity, and induction. Proposition (Addition Preservation). Path-graph addition, defined by canonical concatenation with reindexing — (G_a, P_a) + (G_b, P_b) = (G_{a+b-1}, P_{a+b-1}) — satisfies phi(a + b) = phi(a) + phi(b). Peano addition is preserved under the embedding. Proposition (Monoid Structure). The path-graph naturals (P, +, (G_1, P_1)) form a commutative monoid. Identity, associativity, and commutativity are proved on the nose via canonical reindexing, not merely up to isomorphism. Corollary (Proper Containment). The Peano naturals embed properly into the geometric naturals: N = P (proper subset of) G. The geometric system contains structures — cycles, complete graphs, trees, arbitrary connected graphs — that have no Peano representation. The embedding is strict: the triangle (G_3, C_3) is a member of G with no preimage in N. The Three-Tier Ontology The paper defines three formally distinct states: Void (G_0): Outside the domain of G. No elements, no vertices, no graph, no base space. Not a degenerate geometry but the absence of the conditions for geometry. Excluded from the geometric naturals by construction. Pre-geometric (G_1): In the domain of G but carrying no geometric content. One vertex, no edges. The connection map Gamma is undefined here (G_1 does not satisfy the domain predicate |V| >= 2). The Davis Field Equation C = tau/K is undefined — not zero, undefined — because reach tau = 0 and curvature K is statistically degenerate on a single observation. This is the irreducible seed: formally present, structurally inert. Under the Peano embedding, Peano's 0 maps here. Geometric (G_n, n >= 2, G connected): Nontrivial. Curvature is measurable. Capacity C = tau/K returns a positive real. The conservation law S + d^2 = 1 becomes a genuine constraint. The connection map Gamma(G_n, G) = |E| >= 1. The economy of the Davis Field Equations activates. This is where arithmetic has geometric content. The Connection Map The connection map Gamma: {(G_n, G) in G : |V| >= 2, G connected} -> Z_{>=1} returns the edge count |E|. Its domain is formally restricted to connected graphs with two or more vertices. For path graphs, Gamma = n - 1, and element-counting (Peano) and connection-counting (geometric) are interchangeable up to a constant offset. For non-path topologies, they diverge: G_1 (single vertex): Peano count 1, Gamma undefined (pre-geometric) G_2 (edge): Peano count 2, Gamma = 1, path P_2 G_3 (path): Peano count 3, Gamma = 2, path P_3 G_3 (triangle): Peano count 3, Gamma = 3, cycle C_3 G_4 (path): Peano count 4, Gamma = 3, path P_4 G_4 (complete): Peano count 4, Gamma = 6, complete graph K_4 Peano arithmetic is the path-graph restriction — the case where topology is invisible. The Davis Field Equation at n = 1 C = tau/K is not zero but undefined for a single unconnected element. Reach tau = 0 (no peer to reach). Curvature K = sigma/mu is statistically degenerate (sample size 1). Capacity C = 0/0+ is an indeterminate form. The field equation does not return zero — it refuses to produce a meaningful output. The distinction between "returns zero" (a measurement) and "undefined" (not a measurement) is central to the paper's ontology. The Structural Parallel The analogy between zero and parallel lines is not rhetorical but structural. Peano arithmetic is to the geometric naturals what Euclidean geometry is to Riemannian geometry: the curvature-zero, topology-blind, path-restricted special case of a richer framework. Euclidean geometry (K = 0) is a non-generic specialization of Riemannian geometry. Peano arithmetic (0 is primitive, topology is a path) is a non-generic specialization of geometric arithmetic. Prior Art and Novelty The debate over whether N starts at 0 or 1 is a convention dispute — nobody in that debate constructs an alternative formal system. Mathematical structuralism (Shapiro, Benacerraf, Resnik) holds that numbers are positions in structures defined by relations, but no structuralist has built a number system that properly contains Peano and excludes the void. The philosophy of zero (Barton et al., Synthese 2019) analyzes zero through absence perception but argues FOR zero's existence. The Greek opposition to the void ("How can not-being be?") anticipated the intuition but had no formal machinery. This paper is, to the author's knowledge, the first to: Construct a formal number system that properly contains the Peano naturals and excludes the void state, with a proved embedding theorem Make the parallel-postulate analogy precise as a structural correspondence between flat/curved geometry and flat/curved arithmetic Connect zero's exclusion to fiber bundle geometry and a field equation (C = tau/K) that is undefined at n = 1 Define a three-tier domain ontology (void / pre-geometric / geometric) with formal consequences for each tier Prove that the path-graph naturals form a commutative monoid under canonical concatenation, with addition preserved under the Peano embedding Scope The paper does not claim that ZFC is inconsistent or that Peano arithmetic is wrong. It claims they are flat — valid frameworks operating in the path-graph limit of a richer geometric arithmetic. Within the geometric naturals, the void is excluded from the domain, the singleton is retained as the pre-geometric seed, and nontrivial arithmetic content begins only with connection. That is the precise sense in which zero does not exist. We do not claim that ZFC is wrong. We claim it is flat. C = tau/K. Relation to the Davis Geometric Research Program This paper extends the Davis Field Equations into the foundations of arithmetic. Prior publications in the program include: The Davis Duality of Approximation and Obstruction: Why Machine Learning Works, Why the Vacuum Has Mass, and the Universal Law of Flat Failure (DOI: 10.5281/zenodo.19428406) — Proves the curvature sandwich inequality governing both ML scaling laws and the Yang-Mills mass gap. The duality theorem established there is the direct ancestor of this paper's claim: you cannot flatten a curved structure without error, and the error is the curvature. In the Zero paper, "flattening" is Peano's projection of the geometric naturals onto a path graph, and the "error" is the lost topological information. The Geometry of Delivery: A Uniqueness Theorem for Section Coherence over Stratified Barrier Bundles (DOI: 10.5281/zenodo.19321978) — Proves that C = tau/K is the unique coherence functional satisfying four axioms via the Cauchy functional equation. The uniqueness proof in that paper (harmonic series composition leading to the additive Cauchy equation) is the same proof structure used in this paper's Theorem 2.1 to derive the Davis Field Equation. The Zero paper's Axiom A3 (harmonic series composition) and the Delivery paper's Axiom A3 (inverse scaling for series impedance) are the same axiom in different notation. The Double Cover Principle (DOI: 10.5281/zenodo.18895462) — S + d^2 = 1 as a geometric constraint from fiber bundle structure No Parallel Lines: The Non-Decoupling Theorem (DOI: 10.5281/zenodo.18754646) — Exclusion of parallel geodesics on curved manifolds. The direct precedent for this paper's central claim: just as parallel lines are excluded from curved geometry, zero is excluded from connection-based arithmetic. The Bra Strap Principle (DOI: 10.5281/zenodo.18827805) — Fiber bundle gauge theory applied to structural topology Keywords foundations of mathematics, natural numbers, zero, Riemannian geometry, fiber bundles, Davis Field Equations, relational ontology, non-Euclidean arithmetic, geometric counting, Peano axioms, connection map, graph theory, commutative monoid, mathematical structuralism Files zero_paper.pdf — The paper (14 pages, LaTeX-compiled) zero_paper.tex — LaTeX source Citation Davis, B.R. (2026). Zero Does Not Exist: A Geometric Foundation for the Natural Numbers. Zenodo. DOI: [pending] License Creative Commons Attribution 4.0 International (CC BY 4.0)
Progressive Web Applications (PWAs) have emerged as a transformative paradigm in modern software engineering, combining the reach of the web with the capabilities of native applications. Simulta- neously, decentralized systems—anchored by blockchain technology, distributed ledger frameworks, and peer-to-peer networking protocols—are reshaping trust architectures across industries ranging from finance and healthcare to supply chain and digital identity. Despite the clear synergies between these two technological pillars, the intersection of PWAs and decentralized systems remains relatively underexplored in the academic literature. This survey addresses that gap by systematically reviewing and analyzing the convergence of PWA design principles with decentralized infrastructure paradigms. We examine how service workers, Web App Manifests, push notifications, and IndexedDB offline storage can be effectively integrated with blockchain nodes, smart contracts, IPFS-based content stor- age, and decentralized identity (DID) frameworks to produce resilient, censorship-resistant, and user- centric applications. We survey thirteen seminal works spanning cross-platform application devel- opment, blockchain architecture, decentralized identity management, IoT integration, and distributed application (DApp) design. Our analysis reveals recurring challenges including transaction latency, key management complexity, offline consistency under Byzantine fault conditions, and the tension between decentralization purity and user experience expectations. We further synthesize findings through a structured comparative analysis across six dimensions: focus area, PWA feature utilization, blockchain integration depth, reported performance metrics, and identified limitations. Based on this synthesis, we identify open research directions and propose guidelines for practitioners seeking to build production-grade PWA-based DApp frontends. This survey contributes a consolidated reference for researchers and engineers working at the intersection of web engineering and decentralized computing.
T.A. Krithika, Muniappan Muniappan M, Mayank Raj, Naveen T · 5 authors
Abstract—This paper defines the privacy focused alternative for popular cloud storage providers. It differentiates itself from conventional service providers by incorporating client side encryption and zero knowledge proofings. It proposes a secure, private and trust-less system for photo storage and sharing. Keywords—End-to-End Encryption(E2EE), Zero-Knowledge, Cloud Computing, Photo Storage, Cryptography, Key Management, Client-side Encryption
The metaverse presents the fashion industry with unprecedented commercial possibilities, yet its transnational, decentralized, and jurisdictionally indeterminate architecture demands measured and deliberate engagement from brands, consumers, and regulators alike. This thesis contends that a sustainable and equitable trajectory is contingent upon the principled alignment of intellectual property protections, regulatory frameworks, and consumer rights. Existing intellectual property doctrine proves structurally inadequate to govern digital goods, non-fungible tokens, and virtual assets within an environment defined by interoperability failures, traceability deficits, pseudonymous transactional infrastructure, and the foundational decentralization of blockchain-based platforms. The governance imperative extends well beyond the protection of incumbent commercial interests. Coherent metaversal intellectual property frameworks carry profound social, cultural, and institutional significance – safeguarding cultural communities from digital appropriation, redressing the informational asymmetries embedded in smart contract transactions, and cultivating the conditions under which independent digital creativity can flourish without systematic disadvantage. This thesis maintains that effective governance cannot merely analogize from conventional intellectual property frameworks to virtual environments, nor can it simply transpose the enforcement paradigms developed for the early internet onto a space that is architecturally, commercially, and experientially distinct. It must instead navigate the compounding doctrinal challenges of omniterritoriality, platform interoperability, pseudonymous traceability, and structural decentralization. The progressive blurring of physical and virtual extended realities will require genuine global multilateral partnership, coordinated intergovernmental engagement, and a willingness to treat the governance architecture of the metaverse as a problem of institutional design rather than doctrinal extrapolation. Most critically, the framework must be prospective rather than reactive, internationally coordinated rather than territorially fragmented, and constitutively embedded with values of equity, access, and transparency as foundational commitments from which the architecture of metaverse IP governance is built – and against which its legitimacy will ultimately be measured.
The problem of verifying the authenticity of academic and professional credentials has been one of the biggest challenges. The conventional methods such as resumes, certificates, and online profiles can be easily faked, and there is no trusted system for their verification. Even though blockchain with its transparency and immutability seems an attractive solution, but implementing it on a large scale is very expensive, too slow, and even complicated. We introduce X GENESIS, a hybrid framework that integrates blockchain with AI, to solve these problems. X GENESIS, through Layer-2 scaling, batch NFT minting, off-chain storage to store the metadata and hash on chain (Commit-Store Pattern) with event driven architecture, enables credential issuance to be done in a cheaper cost and more efficient manner. So we have build a Decentralized application which mints the SBT's (Soulbound tokens) and NFT's (Non- Fungible token) as per the usage and type of credential. Our main focus is on academic credentials, so here the users can hold NFT's and can list them to others. Our application also aim that real talent should be valued and also for the recruiters perspective they can get the real talent they want without wasting there time on the false profiles. So to make our application more secure we have build AI agents, which checks for plagiarism, fraud detection, skill extraction, and recommendation insight provision, so trust is further enhanced. As a result$\mathbf{X}$GENESIS can achieve higher throughput and lower operational costs than traditional blockchain methods existing. This framework ensures that credentials are verifiable, tamper-proof, and built to last, which can be used in universities, hiring platforms, and government bodies that issue official certifications.
Against the complex characteristics of the Ethereum transaction network and the limitations of existing graph embedding methods based on random walks, which fail to effectively capture transaction temporal dynamics and the flow of funds, we propose a fraud detection algorithm for Ethereum, ETX2Vec (Ethereum Transactions (TX) to Vector), which improves upon transaction subgraph construction and random walk strategies. First, in terms of transaction subgraph construction, we extract the first-order predecessor and successor neighboring nodes of the target node to reconstruct the transaction subgraph, enabling the random walk to effectively capture the complete flow of funds. Second, in the design of the random walk strategy, we introduce two key improvements: (1) the next node is selected based on the non-decreasing principle of transaction timestamps, effectively capturing the temporal dynamics of transactions within the network, and (2) a biased random walk strategy is designed based on both transaction timestamps and amounts, with a parameter α introduced to control the weighting of these factors when calculating transition probabilities. Experimental results show that ETX2Vec achieves an average performance of 96.04% in downstream node classification tasks, outperforming the best model in similar studies by 3.74%, and even surpassing neural network models such as GAT and GCN. This demonstrates that ETX2Vec is more effective at understanding and processing the Ethereum transaction network, leading to the learning of high-quality node embedding vectors.
ABSTRACT This paper explores how financial innovation and environmental sustainability intersect by analyzing spillovers between FinTech, blockchain energy use, and green finance. Using a Quantile Vector Autoregression (QVAR) framework, we examine weekly data from 2018 to 2024 across 11 digital, environmental, and macro‐financial indices. Our findings reveal a striking asymmetry: FinTech and equity markets consistently act as systemic shock transmitters, especially during crises and booms, while blockchain energy consumption behaves as a passive shock absorber. Notably, Ethereum's energy profile remains sensitive to market exuberance even after its transition to proof‐of‐stake. Connectedness weakens markedly in tranquil regimes but resurges sharply at market extremes, underscoring the fragility of digital–green linkages. These results advance the literature on climate‐FinTech integration by showing how digital finance volatility propagates to sustainability assets. We call for targeted policy interventions that align blockchain development with climate goals and promote transparency and resilience in digital financial markets.
Md Moniruzzaman, Shahroz Abbas, Ajmery Sultana, Georges Kaddoum
The increasing adoption of electric vehicles (EVs) and distributed energy resources has led to the rise of peer-to-peer (P2P) energy trading, in which participants exchange energy within local markets. Blockchain technology has emerged as a secure and transparent solution for managing these transactions. However, the advancement of quantum computing poses a significant threat to the traditional cryptographic mechanisms used in blockchain systems. This paper proposes a quantum-safe blockchain framework designed specifically to secure P2P energy trading networks. The proposed system integrates quantum-resistant cryptographic techniques, including lattice-based cryptography and quantum key distribution (QKD), to safeguard transactions against quantum attacks. Additionally, a quantum-safe consensus mechanism, Quantum Delegated Proof of Stake (QDPoS), is introduced to enhance network security and scalability. Experimental evaluations demonstrate that the proposed approach improves transaction security while maintaining efficiency and reducing computational overhead. The findings highlight the need to integrate quantum-safe solutions into blockchain systems to ensure long-term security in decentralized energy trading networks.
Money laundering enables malicious actors to integrate illegal profits into the legitimate economy and has long been a central concern in financial regulation. Blockchain systems introduce new channels for laundering through decentralized, pseudonymous, and cross-border asset transfers. In this context, blockchain exploiters often rely on laundering to conceal fund origins and enable cash-out.
Insider threats pose a persistent and evolving challenge to contemporary software ecosystems, where privileged users can exploit access for malicious purposes, often evading traditional perimeter-based defences. This paper introduces a novel hybrid framework that synergistically integrates zero-knowledge proofs (ZKPs) and behavioural analytics to detect and mitigate such threats with enhanced privacy and precision. ZKPs enable secure authentication and data verification without revealing sensitive information, ensuring compliance with privacy regulations like GDPR while thwarting unauthorized access. Complementarily, our behavioural analytics engine employs advanced machine learning models, including graph neural networks and unsupervised anomaly detection (e.g., isolation forests), to profile user behaviours across software pipelines, identifying deviations indicative of insider malice. The proposed architecture is deployed in a microservices-based ecosystem, demonstrating scalability via containerized components on Kubernetes. Extensive evaluations on benchmark datasets (e.g., CERT Insider Threat) and simulated enterprise environments yield a 95% detection accuracy, with 40% fewer false positives than state-of-the-art methods like UEBA systems. Latency remains under 50ms for real-time operations, preserving performance in high-throughput scenarios. Our framework outperforms baselines by 25% in F1-score, validated through rigorous ablation studies. By bridging cryptographic privacy with AI-driven intelligence, this work advances proactive security for modern software, offering deployable solutions against sophisticated insiders. Future extensions explore quantum-resistant ZKPs for post-quantum resilience.