M. Ganesh, Gaddam Richitha, B Sai Jagadeesh Goud, Gannarapu Ramani · 5 authors
Drug repurposing has gained significant attention as an efficient strategy for identifying new therapeutic applications of existing drugs, thereby reducing both development time and cost compared to traditional drug discovery processes. Current drug discovery approaches rely on experimental procedures, expert analysis, and extensive clinical trials, which are time-intensive and computationally inefficient when handling large-scale biomedical data. These methods often struggle to process complex and highdimensional datasets, resulting in slower analysis and limited predictive capability. Additionally, these systems lack robust mechanisms for secure data management, making clinical records and trial discussions susceptible to inconsistencies and unauthorized modifications. To overcome these limitations, this work proposes an intelligent drug repurposing framework that integrates Machine Learning (ML), Deep Learning (DL), and blockchain technologies. The system utilizes baseline models such as K-Nearest Neighbors (KNN) and Gaussian Naive Bayes (GNB) for comparative analysis, along with a hybrid DrugNet model that combines Convolutional Neural Networks (CNN) for feature extraction and Random Forest (RF) for classification. This hybrid approach enhances the ability to capture complex patterns in drug-related data and improves prediction accuracy. Furthermore, blockchain integration using Web3 ensures secure storage of user data, clinical interactions, and trial information, providing transparency, immutability, and data integrity. The proposed framework enables automated prediction of potential drugâdisease associations through a unified processing pipeline, supporting real-time analysis and decision-making. By combining advanced Artificial Intelligence (AI) techniques with decentralized data management, the system improves scalability, reliability, and efficiency in drug repurposing. This approach offers a practical and secure solution for accelerating pharmaceutical research and supporting data-driven medical innovation
The rapid advancement of digital technologies in healthcare has increased the need for secure, transparent, and efficient management of medical data. However, most existing systems rely on centralized architectures, where sensitive patient information is controlled by a single authority. This creates vulnerabilities such as data breaches, unauthorized access, and single points of failure, which can compromise data integrity and patient privacy. The core problem addressed in this research is the lack of a decentralized and tamper-resistant mechanism for managing Electronic Health Records (EHR). Current solutions often suffer from limited transparency, inefficient data sharing between patients and doctors, risks of data manipulation, dependency on intermediaries, and scalability issues due to large medical files like reports and prescriptions. To address these challenges, this research proposes a blockchain-based healthcare management system integrated with the Inter-Planetary File System (IPFS). Blockchain technology, implemented using Ethereum and Web3, ensures secure, immutable, and transparent transaction handling through smart contracts. IPFS is used for decentralized storage of medical files, with only cryptographic hashes stored on the blockchain to reduce storage overhead while maintaining data integrity. The system enables patients to book appointments, upload medical reports, and securely share them with doctors. Doctors can access records, provide diagnoses, and generate prescriptions, which are also stored via IPFS and linked to the blockchain. This ensures that data cannot be altered without detection, enhancing trust. The proposed system improves data security, privacy, reliability, and scalability in healthcare data management.
Immutable code and steep transaction fees make smart contract deployment uniquely unforgiving. While continuous integration (CI/CD) pipelines excel at catching standard software bugs, applying exhaustive security tests to Web3 applications severely bottlenecks development through massive computational overhead and gas consumption. This paper presents a testing architecture designed specifically to resolve this tension between security depth and execution speed. The system pipelines three core engines. First, an AI-driven pre-execution gate flags immediate vulnerabilities. Next, a structural reduction module applies the k + 1 symmetric pattern to strip out redundant test permutations. Finally, the system constrains the remaining test suite using the NSGA-II evolutionary algorithm. This multi-objective optimizer dynamically schedules execution to maximize fault detection against strict, predefined gas budgets. To evaluate the model empirically, I bridged a localized EVM sandbox with a Python optimization engine. Results confirm the framework collapses exponential test generation and throttlesexecution costs without sacrificing critical security coverage. Ultimately, it offers a highly scalable path forward for modern DevSecOps.
The emergence of decentralized autonomous organizations (DAOs) represents a significant shift in organizational design driven by blockchain technology. Unlike traditional hierarchical structures, DAOs operate through decentralized governance, algorithmic rules, and collective coordination embedded within distributed systems. Despite growing scholarly attention, existing research remains fragmented, with limited integration across technological, governance, and organizational dimensions. This paper addresses this gap by developing a multidimensional theoretical framework that explains the organizational logic of DAOs through the interaction of blockchain architecture, governance mechanisms, and coordination processes. Drawing on an integrative review of the literature, DAOs are conceptualized as socio-technical systems in which technological infrastructure enables decentralized governance, governance mechanisms shape participation and decision-making, and coordination processes support collective action and value creation. The framework highlights the interdependencies among these dimensions and advances a set of theoretical propositions to guide future research. By offering a more integrated perspective, this study contributes to DAO scholarship and extends organizational theory to better account for decentralized and algorithmic forms of organizing. The findings also provide insights for designing and governing DAOs in the evolving digital economy.
Lyudmila Kovalchuk, Mariia Rodinko, Roman Oliynykov, Volodymyr Artemchuk
This paper studies the probability of a double-spend attack in an Ouroboros-like Proof-of-Stake (PoS) setting when confirmation decisions must be made for a finite number of blocks. Existing security analyses of Ouroboros-family protocols are mainly asymptotic and therefore do not directly provide the attack probability for a fixed confirmation depth. We consider an analytically tractable model that allows empty slots and multiple slot leaders, and assumes fixed stake distribution within an epoch, one-block growth of the public longest chain in any slot containing at least one honest leader, and next-slot block visibility. These assumptions hold when the time slot length is much greater than the network delay, and are applicable to practical deployment scenarios such as Cardano. Under these assumptions, for the first time, an exact closed-form solution for the success probability of a double-spend attack considering a realistic model with multiple leaders and empty time slots. Numerical examples illustrate how the required confirmation depth depends on the adversarial stake ratio and the active slot coefficient. The results apply to the stated analytical model and do not yet cover delayed fork resolution or the full protocol-level fork-choice and finality mechanisms of Ouroboros Praos.
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.
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.
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.
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. KĆ«n 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. KĆ«n 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.
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 â that exactly one parallel line passes through any external point â 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 â 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 â (G_a, P_a) + (G_b, P_b) = (G_{a+b-1}, P_{a+b-1}) â 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 â cycles, complete graphs, trees, arbitrary connected graphs â 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 â not zero, undefined â 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 â 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 â 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 â 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 â 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) â 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) â 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) â S + d^2 = 1 as a geometric constraint from fiber bundle structure No Parallel Lines: The Non-Decoupling Theorem (DOI: 10.5281/zenodo.18754646) â 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) â 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 â The paper (14 pages, LaTeX-compiled) zero_paper.tex â 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 systemsâanchored by blockchain technology, distributed ledger frameworks, and peer-to-peer networking protocolsâare 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.
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 â 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 â and against which its legitimacy will ultimately be measured.
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 macroâfinancial 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 proofâofâstake. Connectedness weakens markedly in tranquil regimes but resurges sharply at market extremes, underscoring the fragility of digitalâgreen linkages. These results advance the literature on climateâFinTech 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.
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.
Accurate, transparent, and scalable Measurement, Reporting, and Verification (MRV) of greenhouse-gas emissions is foundational to credible climate governance, yet prevailing systems remain fragmented, low-frequency, and vulnerable to manipulation. This paper proposes a hybrid IoTâHadoopâblockchain architecture that reconceptualizes carbon data as a continuously governed digital asset rather than a static compliance artifact. High-frequency operational data are collected through IoT infrastructures, stored and pre-processed in Hadoop for scalability and data sovereignty, and anchored on a Hyperledger Fabric consortium blockchain using Merkle-tree commitments to ensure immutability and traceability. A Carbon Data Interface Standard (CDIS) harmonizes heterogeneous data sources, while Decentralized Autonomous Organization (DAO)-based governance distributes authority across individual and institutional stakeholders. A Dynamic Authority Selection Mechanism (DASM) aligns participation in the consensus process with verifiable performance, institutionalizing a coopetitive model of data stewardship. The architecture further integrates with a public-chain value layer, enabling tokenization pathways and interoperability with emerging Web3 and Real-World Asset (RWA) climate-finance mechanisms. The results demonstrate how decentralized infrastructure, cryptographic verification, and polycentric governance can jointly improve data integrity, transparency, and market relevance in MRV systems. The paper concludes by outlining empirical pilot pathways and future research directions in AI-assisted verification, dynamic standardization, and climate-linked digital finance.
As blockchain technology evolves from specialized financial tools to foundational infrastructure for Web3, the necessity for rigorous performance validation becomes paramount. Stress testingâdefined as the evaluation of system stability under extreme workloadsâis critical for identifying bottlenecks in consensus mechanisms and peer-to-peer communication. This survey provides an exhaustive analysis of web-based stress testing frameworks. Unlike traditional CLI-based tools, web-based frameworks provide real-time telemetry and distributed orchestration capabilities essential for modern decentralized applications. We categorize existing literature into three generations of benchmarking, evaluate ten prominent frameworks based on a multi-dimensional rubric, and identify significant research gaps including the lack of standardized cross-chain stress protocols and AI-integrated anomaly detection. This work aims to provide a roadmap for researchers and DevOps engineers to select and implement robust testing environments for enterprise-grade blockchain deployments.
In decentralized finance (DeFi), accidental cryptocurrency transfers to incorrect wallet addresses are a large usability and security issue, frequently causing permanent loss of funds. We present CryptoSafeSend, a smart contract-based safety protocol for transactions featuring a cryptographically secure passcode verification scheme supporting conditional transfers. This work addresses higher-level security issues by introducing a PBKDF2-based key derivation function, which generates strong encryption keys based on Firebase's Firebase Unique Identifier. Secret passcodes are encrypted in Advanced Encryption Standard operating in Galois/Counter Mode functions deployed underneath the Web Crypto API, and the initialization vector and ciphertext are Base64 encoded for off-chain reliable storage and Firebase Firestore-based messaging. The protocol prevents unauthorized exploitation by safely binding off-chain passcode transmission to a matching on-chain verification, reinforcing user trust without undermining decentralization. Also, to ensure recoverability, CryptoSafeSend integrates a 7-day on-chain escrow lock, after which unclaimed funds become permissionlessly refundable to the sender, preserving decentralization while eliminating reliance on trusted intermediaries. Testing on an Ethereum testnet confirms negligible gas overhead, immunity against double claims, and strong security guarantees, qualifying CryptoSafeSend as a valuable constituent in next-generation secure digital asset protocols.
This thesis, submitted at the Institute for Law and Finance at Goethe University Frankfurt, provides a critical legal and technological analysis of the effectiveness of the Financial Action Task Force framework in addressing money laundering risks arising from decentralized finance. It examines how decentralized blockchain-based systems fundamentally challenge the assumptions underlying traditional anti-money laundering regulation.The study argues that FATF Recommendations, originally designed for centralized financial systems, are structurally incompatible with decentralized architectures that operate without identifiable intermediaries such as Virtual Asset Service Providers. Through an integrated legal and technological assessment, the research demonstrates how privacy-enhancing tools, including non-custodial wallets, cryptocurrency mixers, zero-knowledge proof mechanisms, and cross-chain bridges, obscure ownership trails and significantly impair regulatory oversight.While these technologies are designed to enhance user privacy, they simultaneously enable sophisticated money laundering techniques, including chain hopping, transaction obfuscation, and the untraceable movement of assets across blockchain networks. The thesis further identifies critical regulatory gaps in the application of core FATF standards, particularly in relation to customer due diligence, beneficial ownership transparency, and the implementation of the Travel Rule.A case study of Bosnia and Herzegovina illustrates the practical consequences of fragmented regulatory implementation. Divergent adoption of FATF standards across its entities reflects the broader âSunrise Issue,â whereby asynchronous global implementation of the Travel Rule generates cross-border inconsistencies and enforcement challenges.To address these structural deficiencies, the thesis proposes a reinterpretation of FATF standards based on the principle of functional equivalence, extending AML obligations to any actor or protocol exercising effective control over financial transactions, irrespective of formal legal classification. It further advocates for the integration of RegTech, tokenization, and machine learning as tools to reconcile regulatory oversight with technological innovation.The research concludes that the current FATF framework remains fundamentally misaligned with the operational realities of decentralized finance. Ensuring the continued integrity of the global financial system will require the adoption of technologically adaptive, risk-based, and internationally coordinated regulatory approaches. Only through such innovation can AML enforcement remain effective in an increasingly decentralized digital economy.
Maximal Extractable Value, or MEV, remains a structural threat to blockchain fairness because a block producer can often observe pending transactions and unilaterally decide their ordering or inclusion. Existing mitigations hide transaction contents or outsource ordering, but they often leave two gaps unresolved. First, commitments are not authenticated by slashable identities. Second, inclusion obligations are not backed by transferable evidence that other validators can verify. This paper presents MEV ACE, a fair ordering protocol for proposer controlled ordering MEV. MEV ACE combines three mechanisms. First, it uses registered economic identities whose authentication keys are deterministically derived from the ACE GF framework and bonded on chain. Second, it uses authenticated commit and open messages with validator receipt thresholds, which make admissibility and inclusion obligations independently auditable. Third, it uses verifiable delay based randomness to determine transaction order only after the admissible commitment set is fixed. We formalize the protocol in a Byzantine fault tolerant validator model with threshold receipts and show three properties under standard assumptions: order unpredictability after the admissible set is locked, commitment authenticity under signature unforgeability, and accountable inclusion for transactions that obtain threshold commit and open receipts. Under these conditions, and when producer and user bonds exceed the one slot gain from invalid execution or selective non opening, MEV ACE removes unilateral proposer discretion over front running, sandwich attacks, and censorship against admitted transactions. The protocol remains single slot in structure, requires no threshold decryption committee, and is compatible with post quantum signature schemes such as ML DSA 44.