This paper examines whether blockchain-registered AI agents demonstrate operational readiness beyond identity registration. Using a dataset of ERC-8004 agents on Ethereum, we construct an agent-level feature table covering identity status, metadata, service declarations, reputation feedback, transfers, and cross-chain registration. We develop an operational readiness framework based on observable evidence layers and complement it with network analysis of owner-agent, feedback-client, wallet-transfer, and combined evidence relationships. The results show that early ERC-8004 adoption is registration-heavy but operationally shallow. While the identity layer is visible at scale, metadata availability, service exposure, reputation formation, and cross-chain evidence remain limited. Ownership and feedback activity are also highly concentrated, suggesting that early participation is shaped by a small number of high-activity wallets and clients. The network analysis further shows that richer operational evidence clusters around a small subset of agents rather than being broadly distributed across the ecosystem. The findings suggest that ERC-8004 provides an important identity layer for decentralized AI agents, but the transition from agent identity to agent economy remains incomplete.
State root computation dominates (78%) blockchain block processing time. Ethereum's canonical authenticated data structure, i.e., Merkle Patricia Trie (MPT), suffers from severe tree-height growth and is vulnerable to \textit{Nurgle attacks} (SP'24), where adversaries inflate path depth via hash collisions and degrade system performance at negligible cost. Existing defenses increase node fanout (span) to bound tree height, but higher span inflates proof size exponentially. Prior work mitigates this trade-off using vector commitments, at the cost of trusted setup or expensive verification. We present \textsc{Mhot}, a height-optimal authenticated data structure for blockchain state commitment that preserves standard hash-based verification without trusted setup. Unlike MPT's fixed-prefix indexing, which couples span and fanout exponentially, \textsc{Mhot} indexes by discriminative bits that actually distinguish keys, achieving adaptive span with linear fanout coupling and provably minimal height. To prevent high fanout from inflating proofs, we introduce hierarchical proofs, a two-layer Merkle construction that reduces per-node proof overhead from O(k) to O(log k). On Ethereum mainnet workloads, \textsc{Mhot} achieves up to 9X higher write throughput, 4X lower write amplification, and 2X smaller proofs than MPT. Under Nurgle attacks, even when the adversary consumes an entire block's gas budget, \textsc{Mhot} maintains a 0% attack success rate (v.s., 99.97% for MPT). Our results, somewhat surprisingly, show that height optimality (not new crypto primitives!) is the key abstraction for scalable and attack-resilient blockchain state commitment.
In the contemporary landscape of modernity, characterised by the evolving information age, cryptocurrencies have emerged as a decentralised mode of transaction, qualifying to be termed as liquid modernity (Bauman, 2012). The apparent fluidity, flexibility and the unrevealed potentially rigid tendencies inherent in cryptocurrencies; present it as a virgin domain to be researched with sociological perspectives. This paper aims to understand and outline the history of monetary systems starting from the ancient practice of barter to the establishment of national currencies, and up to the recent advent of cryptocurrency, in an evolutionary framework. As the second objective, this paper attempts to delineate the mechanism of construction and the causal explanations for the adoption and diffusion of cryptocurrency from a sociological lens. In view of the factual status of its legitimation and denial by different governing authorities, the third objective of this paper is to explore into the nuances pertaining to trust, governance and dynamics of power relations with a exploratory concern for rigidity within the claimed fluidity of the cryptocurrency and its utilisation. However, we are assuming one conclusion for our study and that is we are going to get stuck with more significant questions rather than the answers for our objectives.
Measuring and rewarding distributed productive contribution remains unsolved: GDP-era indicators miss digital commons, and existing token economies systematically reward speculation over contribution. We address this problem by formally specifying VitaChain, a protocol for verified productive value. Our contributions are threefold. (i)Theoretically, we introduce a quadratic-form asset scoring system with a diversity-corrected aggregate index (nvpvi), multi-source consensus-weighted attribution, and a four-class parameter governance system with constitutional invariants. (ii)Architecturally, we propose a dual-token design separating soulbound contribution records (pc{) from transferable value tokens (vt), governed by Proof-of-Productivity (PoP) with dual-track decay to prevent plutocracy. (iii)Empirically, we characterize six protocol properties through simulation-based consistency checks under synthetic ensembles ($n \geq 50$ trials per configuration): \pop{}-weighted voting reduces top-1\% governance influence 2.8$\times$ versus token-weighted voting (under the assumption that contribution is less concentrated than wealth); temporal decay illustrates how the chosen decay bands translate into a 39$\times$ long-horizon value gap between maintained and unmaintained assets at year 20; and Progressive Trust with $\sigma{=}50$ limits Sybil inclusion-pool capture to below 3.5\% of pool capacity. Sybil attribution shift remains within the theoretically derived bound across all configurations. The privacy architecture---blockchain hashes only, zero-knowledge verification bridges, federated off-chain storage---is designed to target GDPR Article~17 requirements, subject to legal interpretation. All experiments operate on synthetic asset ensembles; results characterize the behavior of the formalism rather than real-world deployment outcomes.
Kabilesh C M, Dr. B. Raja, Dr. S. Geetha, Dr. V. Cyrilraj
As decentralized finance (DeFi) continues to scale, traditional forensic methodologies often fail due to their retrospective, "post-mortem" nature, analyzing illicit activities only after they are permanently recorded on the ledger. This project proposes coinEth, a real-time institutional blockchain surveillance and autonomous defense system designed for the Ethereum Sepolia network. The framework operates across a four-layer architecture: a Data Acquisition Layer that intercepts pending transactions via Alchemy WebSockets (WSS); a Persistence and Forensic Engine that utilizes SQLite and Python-based heuristics to detect suspicious behavioral patterns such as "structuring" and "high velocity"; a Governance Layer that executes an autonomous enforcement loop via a Solidity-based "Gatekeeper" smart contract; and a Visualization Layer built with Streamlit and PyVis. By assigning dynamic risk scores—categorized as Safe (Level 0), Warning (Level 1), and Frozen (Level 2)—the system can automatically broadcast on-chain transactions to freeze illicit accounts before fund exfiltration occurs. Furthermore, coinEth reconstructs a chronological "money trail" through sequential path mapping (T0 → T1 → T2...), ensuring a verifiable digital chain of custody for investigative reporting. This proactive approach shifts blockchain security from passive observation to active, real-time intervention, significantly enhancing the defense mechanisms available to institutional stakeholders.
Blockchain technology solves central Internet of Things (IoT) forensics challenges—data volatility, device heterogeneity, and chain-of-custody integrity—via decentralized immutability, cryptographically secure systems, and smart contract automaton. Standard forensic software and hardware are not successful in distributed IoT contexts due to centralized reliance and evidence tampering vulnerabilities. Blockchain deployment uses layered architectures (device/edge/blockchain layers) where edge nodes process data in advance and on-chain hashes (e.g., using SHA-256) lock forensic logs onto immutable blockchains. Smart contracts make automation of evidence gathering and custody management tracking, and off-chain storage (e.g., InterPlanetary File System (IPFS)) support big data. Public blockchains like Ethereum and permissioned blockchains like Hyperledger Fabric support contextualized deployments, as seen in smart home, healthcare, and industrial IoT applications. Age-old problems are settling blockchain immutability with General Data Protection Regulation (GDPR) “right to erasure,” scalability for massive-volumes of IoT data with sharding/Layer 2 solutions, and zero-knowledge proofs for upholding privacy. New trends are artificial intelligence (AI)-based anomaly detection, quantum-resistant cryptography, and forensic admissibility standard frameworks (NIST/IEEE).
Blockchain technology has emerged as one of the most transformative innovations of the digital economy, extending far beyond cryptocurrencies into sectors such as finance, healthcare, logistics, governance, and intelligent automation. This study critically examines the role of blockchain technology in enhancing global economic growth through decentralization, transparency, cybersecurity, smart contracts, and digital trust mechanisms. Drawing upon contemporary literature and emerging industrial applications, the paper explores how blockchain contributes to economic resilience, operational efficiency, supply chain optimization, decentralized finance (DeFi), central bank digital currencies (CBDCs), and AI-integrated digital ecosystems. The study adopts a conceptual and analytical approach to evaluate blockchain’s macroeconomic implications and institutional challenges in the context of Industry 4.0. Findings suggest that blockchain has the potential to reduce transaction costs, enhance cross-border economic integration, improve governance transparency, and facilitate sustainable digital transformation. However, regulatory uncertainty, scalability limitations, cybersecurity concerns, and energy consumption remain significant barriers to global adoption. The paper contributes to the literature by proposing an integrated framework linking blockchain innovation with economic sustainability, digital governance, and technological resilience. Policy implications and future research directions are also discussed.
The metaverse is emerging as a complex digital ecosystem enabled by artificial intelligence, immersive technologies, blockchain, and advanced computing infrastructures. As global scientific and technological competition intensifies, this expansion introduces critical challenges related to security, privacy, and governance. This chapter examines how scientific rivalry shapes the security architecture, regulatory frameworks, and power structures governing metaverse environments. Key risks such as biometric data exposure, avatar impersonation, decentralized finance fraud, and emerging quantum-enabled cyber threats are analyzed alongside issues of digital sovereignty and platform control. This chapter proposes a multi-layered security and governance framework integrating decentralized identity systems, AI-driven threat intelligence, privacy-preserving design principles, and global interoperability standards.
Ethics and Social Impacts of AI
Law, AI, and Intellectual Property
Legal, Health, Environmental and COVID-19 Challenges
The Metaverse represents a paradigm shift from two-dimensional internet interaction to immersive, persistent, three-dimensional environments. As this ecosystem evolves, the attack surface expands exponentially, introducing novel vulnerabilities that traditional HTTPS and TLS protocols cannot adequately address. This chapter explores the future of secure communication within the Metaverse, moving beyond standard data encryption to address the protection of biometric data, haptic feedback integrity, and avatar identity. We will analyze the unique challenges posed by the convergence of Virtual Reality (VR), Augmented Reality (AR), and the Internet of Things (IoT). The chapter will propose a multi-layered security framework integrating Quantum-Resistant Cryptography, Zero-Knowledge Proofs (ZKPs) for identity verification without exposure, and AI-driven behavioral analysis to detect “Man-in-the-Avatar” attacks. Finally, we will discuss the regulatory and ethical implications of surveillance in a world where user movements and gaze are constantly tracked.
Promoting harmonious interaction between human beings and the ecological environment has become a key issue for achieving sustainable development. Given the cross-regional mobility of resources and production activities, a single region cannot merely rely on its own efforts to balance economic expansion and carbon reduction. In this context, Digital finance can play a key role in improving information connectivity, facilitating green capital allocation, and reducing transaction costs for cross-regional low-carbon collaboration. Against this background, this study introduces and quantifies regional coordinated emission reduction potential by integrating economic ties, geographical proximity, and interregional carbon emissions from a network perspective. By using the panel fixed effect model, the study explored how digital finance shapes this potential and identified the energy-related carbon consumption structure as a transmission mechanism. Findings reveal that the carbon emission spillover effect is most powerful under the combined influence of economic similarity and geographical proximity. Digital finance significantly enhances the potential for regional coordinated emission reduction, and the effect is even stronger in provinces with lower potential for coordinated emission reduction or weaker fiscal decentralization. The level of digitalization and the depth of usage have a greater influence than the breadth of coverage. In addition, the energy-related transmission channels exhibit clear heterogeneity. The coal-related emission channel provides relatively stronger evidence, whereas the gas-related channel shows a countervailing effect.
Amid intensifying climate change and rapid urbanization, urban ecosystems face unprecedented disturbances. Enhancing urban ecological resilience (UER) is thus a key governance issue. The explosive growth of digital finance (DF) has reshaped capital flows, but its ecological effects are dynamic and stage-specific. This study uses a dynamic nonlinear model to systematically explore the DF-UER relationship. The results reveal that: (1) There exists a U-shaped relationship between DF and UER. In the early stage, DF may negatively impact UER; however, Whereas as DF matures, it positively influences UER, with robust results from various tests. (2) Heterogeneity analysis indicates that the U-shaped relationship between DF and UER is more pronounced in smart city pilot cities, high government ecological attention cities, and cities with a high degree of market integration. (3) Mechanism tests reveal that DF has a U-shaped impact on green capital allocation, renewable energy technology innovation, and industrial structure optimization, consequently leading to a U-shaped effect on UER. (4) Fiscal decentralization and financial regulation strengthen the U-shaped relationship, whereas artificial intelligence tends to weaken it and may even reverse this trend. These findings point out the possible structural friction of DF, and provide valuable insights for supporting China and other economies to balance financial expansion and ecological security.
With the rapid development of digital finance, the mode of enterprise credit risk assessment has changed, and now also requires methods that can handle large-scale, diverse data and smart computation. The old system of credit rating has been based on the results of past financial reports and is no longer suitable for evaluating the changes and risks in modern corporate finance. This paper proposes a multi-dimensional model for mining credit reports of mining enterprises and combines structured financial data, transaction information, operating indicators, and other unstructured auxiliary data such as social media presence, online communication, supply chain dynamics, etc. By building a relatively detailed credit report, the bank can gain some information on the risk of a company's credit and its repayment ability for a loan. Algorithms that use machine learning, deep learning, ensemble models and predictive analysis are also known as intelligent default risk assessment algorithms that enhance the accuracy and flexibility of credit assessment. The following are ways to discover abnormal or complex patterns in a large amount of data early on for risk early warning, online credit assessment and dynamic portfolio management. Interoperability of digital finance platforms can support lifelong learning, automation and scalable high-frequency financial data, and maintain security, privacy and regulatory compliance. Although the above have been achieved, there are still deficiencies in the quality of data, interpretability of models, adherence to regulations, and sufficient computational resources, especially for small and medium-sized enterprises and new market institutions. Future research directions include building explainable AI systems, continuous learning, integrating multiple types of data (multimodality), and decentralized finance (DeFi) based on blockchains. At this point, the above technologies are expected to help enterprises strengthen credit risk management in the age of digital finance and provide more accurate and timely credit evaluations.
Ramis, Richard-Jung and Thomann (C. R. Math. 363 (2025), 1065–1081) introduced parity-separated spectral determinants for the negative (non-classical) part of the Connes–Moscovici prolate spectrum: entire functions D_even(μ) = y⁺{τ,μ}(0) and D_odd(μ) = (y⁺)'{τ,μ}(0) of order ≤ 1/2 whose zeros are, respectively, the negative even and negative odd CM eigenvalues; they conjectured that these zeros are simple. We prove this simplicity assertion for every fixed τ > 0 and prove in addition that the zeros of D_even and D_odd strictly interlace. The proof uses the imaginary-axis Sturm–Liouville reduction and identifies D_odd/D_even, up to a nonzero constant, with the Weyl m-function of the associated half-line problem. Its Herglotz property yields simplicity and strict interlacing. Consequently, the negative CM spectrum ordered by increasing |μ| alternates strictly in parity, beginning with the odd sector. No claim is made concerning the Weil quadratic form of the Connes–Consani–Moscovici program or the Riemann Hypothesis. Version 4 clarifies the regular-endpoint argument at t = 0. It defines AC_loc and H¹ = W¹,² explicitly, states the Neumann and Dirichlet form domains, and proves from the first representation theorem and integration by parts that every element of the Neumann operator domain, in particular its fundamental state, satisfies u′(0) = 0. The proof of ν₀ᴺ < ν₀ᴰ is rewritten to invoke this lemma explicitly. An acknowledgment to Professor Jean-Pierre Ramis has also been added. No theorem statement or conclusion is changed.
Abstract Non-fungible tokens (NFTs) have revolutionized digital ownership, offering unique provenance and value to digital assets. Existing text-to-image models do not have the incentive mechanisms to generate statistically rare features, even when they optimize for visual fidelity. This paper introduces DiffNFTGen, a new generative framework that is the first to combine a customized RarityReward measure derived from a Vision Transformer (ViT) with reinforcement learning. The suggested method ensures fidelity to NFT styles while explicitly maximizing the generation of rare features by fine-tuning Stable Diffusion using Proximal Policy Optimization (PPO) and Kullback-Leibler (KL) divergence regularization. DiNFTGen achieves a 2.4x greater rarity score than baseline models while keeping competitive visual quality, according to quantitative evaluation utilizing Frédechet Inception Distance (FID) and Rarity Score. In order to examine the trade-off between fidelity and rarity, we also perform ablation studies regarding reward weighting. The model’s capacity to generalize NFT styles to new domains is confirmed by qualitative evaluations. The datasets, analysis code, and suggested approach are accessible on https://github.com/seferlab/diffnftgen .
Open access
Cell Image Analysis Techniques
Generative Adversarial Networks and Image Synthesis
Website: https://manual.warondisease.org/knowledge/proof/wishonias-wager.html Abstract: There's a non-zero possibility that hell exists, and that you and everyone you love is going to die and burn in it for eternity. As it is very hot in hell, this would be unfortunate. There's also a non-zero possibility that biotechnology lets you feel very good for an indefinitely long period of time. Expected value is the chance of a thing multiplied by the size of it, and if you multiply infinity by any likelihood at all, even 0.0000001%, it's still infinity. So the expected value of doing nothing is infinity bad, and the expected value of acting is infinitely good, even if there's an extremely low probability that any of this is true. The cost of acting is finite: roughly one share of a company that makes missiles. This paper argues that when one outcome is infinitely terrible and the other is infinitely good, any finite action that shifts the odds from the first toward the second is rational, and that the cheapest such action available is redirecting the resources your governments waste being really good at killing the taxpayers who pay for them. (They currently spend 604 (95% CI: 453-894) times more on the military than on the clinical trials that would cure the diseases doing the killing.) It's Pascal's wager with the broken parts replaced: one hypothesis instead of a thousand gods, real evidence instead of none, an action that actually changes the outcome, and a stake of one share instead of your eternal soul. Summary: A reconstruction of Pascal's Wager with its defects removed. Two propositions cannot be disproven: that a conscious being may suffer without end, and that it may flourish without end. Both carry nonzero probability and infinite magnitude, so any finite action that shifts probability from the first toward the second has unbounded expected value. Unlike Pascal's, the wagered action is empirical, not theological: funding the clinical trials that extend healthy lifespan and modify conscious experience.
Open access
2 source records
Neuroethics, Human Enhancement, Biomedical Innovations
Peer-to-peer (P2P) energy trading requires network-aware coordination because transactions are physically realized through distribution networks. However, sensitivity-based coordination causes a confidentiality-verifiability tradeoff, as network sensitivities may reveal vulnerable components while undisclosed sensitivities prevent participants from verifying utility-provided transaction guides. This paper proposes a zero-knowledge-proof-based method for verifying the computational integrity of network-constrained transaction guides with respect to committed private network data, without exposing network-sensitivity information. The guide defines admissible injection and withdrawal volumes derived from sign-decomposed sensitivity matrices while satisfying balance, voltage, line-flow, and optimality conditions. These conditions are encoded in an arithmetic circuit, represented as R1CS constraints and a quadratic arithmetic program, and verified using a bilinear pairing. Blockchain commitments bind the approved circuit, public inputs, statement identifiers, proof, and verification result for tamper-evident auditability. The proposed proof certifies correct guide computation from committed network data; the authenticity of the committed network data is handled through an explicit registration and attestation assumption. Case studies on a modified IEEE 33-bus system show satisfaction of network constraints after clearing, rejection of public-input and witness-inconsistency attacks, and practical on-chain overhead, with an 806-byte proof.
Bilateral attribute-based access control for data trading must hide policies, provide cryptographic fairness, and avoid trusted third parties. Existing solutions either leak policy information, incur super-linear costs, or rely on trusted dispute resolution. We present PriME-Deal, a non-interactive protocol that simultaneously achieves policy-hiding bilateral matching, efficient threshold access control, and auditable fair exchange on public blockchains. The seller embeds a secret token under the buyer policy into an oblivious key-value store with pseudorandom masking; the buyer reconstructs the token locally via tag-based probing, eliminating combinatorial enumeration, and proves correctness in zero-knowledge. Fair exchange is enforced through a collateralized on-chain reveal with a cryptographic audit that penalizes misbehaviour without trusted parties. We prove security in the Universal Composability framework under standard assumptions. Compared with the state-of-the-art threshold fuzzy IB-ME scheme, the seller's publishing time is reduced by two orders of magnitude (e.g., 8.76s vs. 690s for a policy of 500 attributes). For a typical configuration of (200,20,5), the buyer completes token reconstruction and proof generation in 8.9s, with the zero-knowledge proof taking under 0.6s and remaining constant across all parameter scales. The on-chain cost is approximately 28.6M gas, well within Ethereum's block limit. PriME-Deal thus delivers the first practical privacy-preserving data trading protocol that combines linear seller overhead, bilateral policy hiding, and auditable fairness.
Abstract The increasing use of residential distributed energy resources (DERs), including photovoltaic generation, electric vehicles, stationary batteries, and flexible electric water heaters, is changing the operation of low-voltage distribution networks. These resources can reduce household electricity costs and provide demand-side flexibility; however, uncoordinated operation can result in voltage deviations, network congestion, line overloading, and stress on distribution transformers. This paper presents a code-grounded comparison of three DER coordination architectures: decentralized household-level optimization, centralized feeder-level optimal power flow (OPF), and hierarchical aggregator-based coordination. The benchmark is conducted on a three-phase low-voltage distribution network with 157 residential households over a 24-hour horizon. The decentralized architecture optimizes each household individually, and the resulting network behavior is validated in OpenDSS. In the centralized architecture, detailed household DER models are integrated into a nonlinear AC-OPF formulation. The hierarchical architecture coordinates prosumer flexibility using local flexibility estimation, network-level activation, allocation, household-level tracking, and final OpenDSS validation. For the studied feeder, the results indicate that the hierarchical architecture provides the most favorable physically validated trade-off between customer economics and network performance among the three architectures considered. Compared with decentralized control, it decreases the mean daily household electricity cost from 6.74 to 6.14 GBP/home/day (an 8.9% reduction), reduces network peak demand by 20.4%, reduces the worst line-loading ratio by 15.3%, and reduces transformer loading by 19.0%. In this case study, all 157 households obtain lower daily electricity bills than under the decentralized baseline, indicating that the incentive-based coordination mechanism can align network-level objectives with individual customer benefits under the modeled conditions. The centralized OPF achieves the strongest nominal network performance but relies on restrictive assumptions regarding full DER observability and controllability and does not provide the same customer-level autonomy, compensation, or implementation pathway as the hierarchical framework; it is therefore treated as an idealized network benchmark rather than a directly deployable controller. It should be noted that the hierarchical architecture reduces, but does not fully eliminate, network constraint violations and that these findings are obtained for a single feeder under deterministic daily input profiles. Overall, the results suggest that DER coordination strategies should be assessed using a holistic framework that considers customer cost, network security, flexibility realization, fairness, computational effort, and physical validation, and that, under this broader evaluation, hierarchical coordination offers a practical compromise between decentralized autonomy and centralized optimality for the studied network.
Nagwan AlQershi, T. Ramayah, Mohammed Alzoraiki, Gamal Abdualmajed Ali · 5 authors
Purpose This study examines how agentic leadership, conscious unbossing and AI-augmented ethical decision-making (AI-EDM) jointly shape crisis-resilient leadership among emerging global leaders. Addressing a key gap in leadership scholarship, the study conceptualizes artificial intelligence (AI) not merely as a technical tool but as a governance architecture that structures decision authority, accountability and ethical action under conditions of uncertainty. Design/methodology/approach A mixed-method design was employed. Survey data were collected from 423 early-career professionals across 12 countries. Partial least squares structural equation modeling using SmartPLS (version 4.1.1.6) tested a moderated mediation model in which agentic leadership predicts crisis-resilient leadership effectiveness (CR-LE) through AI-EDM, with conscious unbossing and crisis exposure as moderating conditions. Qualitative interviews with 24 Gen Z leaders operating in crisis-prone contexts were used to contextualize and interpret the quantitative findings. Findings Agentic leadership significantly predicted CR-LE, with this relationship mediated by AI-EDM. The indirect pathway was stronger among leaders exhibiting higher levels of conscious unbossing, indicating that leadership agency is conditioned by decentralized and algorithmically governed authority structures. Crisis exposure intensified the salience of ethical AI engagement, highlighting the heightened accountability pressures leaders face under volatility. Qualitative findings reinforced these results, illustrating how leaders navigate responsibility for outcomes shaped by AI-mediated decision systems they do not fully control. Research limitations/implications The cross-sectional design limits causal inference, and scalar measurement invariance was not tested. Future longitudinal and cross-contextual research is needed to examine how leadership agency and accountability operate across different institutional and regulatory environments. Practical implications The findings underscore the need to reconceptualize leadership development and organizational governance for AI-mediated contexts. Rather than focusing solely on ethical capability enhancement, organizations and policymakers must equip leaders to operate under algorithmic constraints by clarifying decision rights, escalation pathways and contestability mechanisms. Originality/value This study advances leadership theory by identifying accountability displacement under AI-mediated governance as a central mechanism reshaping leadership agency in crisis contexts. It reframes leadership not as autonomous discretion or technological mastery but as accountable action under constraint within socio-technical governance systems.
India's Unified Payments Interface (UPI) processes billions of transactions monthly across dozens of Payment Service Providers (PSPs). However, a structural architectural gap persists: no single entity maintains a unified, real-time, immutable view of an individual's complete financial transaction graph across competing platforms. This fragmentation has become the primary mechanism enabling complex UPI digital payment fraud and mule-account networks. Scammers easily spin up identities across multiple platforms, quickly cascade stolen funds across PSP boundaries, and discard the handles, leaving a broken trail that takes weeks for law enforcement to piece together. This white paper proposes the Universal Financial Identity (UFI) system—a sovereign, mobile-number-anchored financial identity layer that federates all of a user's UPI handles, bank accounts, and Central Bank Digital Currency (CBDC/e-Rupee) wallets into a single, immutable distributed ledger record. Key Architectural Features: Zero-Action Trigger Mechanism: The permanent UFI record (formatted as <mobilenumber>@ufi) is generated automatically in the background at the exact moment a user links any bank account to a UPI application, requiring zero user friction. Immutable Distributed Ledger Layer: Operates as a permissioned Hyperledger Fabric network managed by sovereign nodes (RBI, NPCI, and authorized banks), rendering transaction trails cryptographically non-erasable and resilient against app-side account deletions. Graph Intelligence Layer: Integrates native Neo4j graph analytics for real-time, cross-PSP fraud ring detection, rapid fund-movement velocity tracking, and automated fan-in pattern profiling. Programmable Money Integration: Leverages India's e-Rupee infrastructure to enable purpose-bound, geography-restricted, and expiry-enforced smart contract payments for public welfare disbursements (DBT) and B2B settlements. DPDP Act 2023 Compliance: Built with privacy-by-design principles, using salted SHA-256 identity tokenization and multi-tiered, consent-gated access frameworks via the Account Aggregator network. This protocol layer sits seamlessly below existing Third-Party Application Providers (TPAPs) and above core settlement switches, offering real-time cross-PSP fraud tracing and post-deletion identity recovery without introducing latency into synchronous payment pathways. This paper is released for open public community review, protocol exploration, and technical feedback prior to formal provisional patent filings with the Indian Patent Office (IPO).
Abstract. The accelerating development of Artificial General Intelligence (AGI) raises a fundamental question that Stuart Russell articulated with precision: how do we ensure that increasingly powerful AI systems remain aligned with human values? This paper argues that the answer lies not in constraining AGI, but in building parallel infrastructure that preserves human sovereign will-expression. BeTrueCore Modular System — built on Web3 Intuitive Symmetry Methodology (Web3-ISM) v1.2 — proposes a sociotechnical architecture where AI acts as notary, not judge. Drawing on Gödel's incompleteness theorems, wabi-sabi philosophy, Bayesian evolution, and cryptographic governance primitives (ZK-SNARKs, MACI, Lit Protocol), the system transforms collective human intuition into mathematically verifiable decisions. We argue that BeTrueCore does not compete with AGI — it provides the ethical infrastructure upon which AGI-era governance must be built. Keywords: AGI alignment, gorilla problem, assistive gaming, collective intelligence, digital sovereignty, cryptographic Voting, Weight Unit, voice of silence, self-awareness game, AI as a notary, participatory democracy.
Decentralized Autonomous Organizations (DAOs) enable blockchain-based collective governance, yet existing studies often evaluate DAO governance through isolated mechanisms, particularly voting systems. This narrow view does not sufficiently explain recurring problems such as governance capture, weak accountability, inadequate safeguards, and inefficient resource allocation. This paper proposes a Layered Governance Coverage Model that conceptualizes DAO governance as a system of seven interdependent institutional functions spanning participation, agenda formation, collective choice, safeguards, execution, incentives, and meta-governance. The model uses a four-level strength scale to assess not only whether governance functions are present, but also how strongly they are institutionalized. It is empirically applied to thirty-seven active DAOs through evidence-based coding of publicly available governance artifacts. The results show that governance breadth does not necessarily imply governance maturity: collective choice and execution mechanisms are more developed than accountability, safeguards, and meta-governance. Beyond DAO-native settings, the paper positions governance maturity as a trust and resilience regime for blockchain-based IoT and AI infrastructures, where governance affects security, reliability, data integrity, and risk oversight. The paper discusses AI-enabled governance analytics as a support mechanism for monitoring governance activity, detecting anomalies, and improving governance observability. The proposed framework contributes a structured approach for evaluating and designing resilient governance architectures in DAOs and blockchain-based IoT/AI systems.