Electronic voting has become an important digital governance mechanism for remote elections, institutional decision-making, shareholder voting, public consultations, and large-scale Internet-based democratic participation. Despite its growing relevance, secure electronic voting remains difficult to implement because a practical system must simultaneously preserve voter anonymity, verify voter eligibility, prevent double voting, ensure ballot integrity, support public auditability, and maintain acceptable transaction throughput. To address these challenges, this study proposes a post-quantum secure and privacy-preserving blockchain-based electronic voting framework that integrates Dilithium digital signatures, zero-knowledge proofs, nullifier-based double voting prevention, encrypted ballot submission, smart contract-based election rule enforcement, and a Byzantine fault-tolerant consensus mechanism. In the proposed architecture, Dilithium signatures are used for post-quantum authentication of voter transactions and validator messages, whereas the zero-knowledge proof layer is used separately to verify voter eligibility, candidate validity, credential ownership, and correct nullifier generation without revealing the voter identity or ballot choice. Dilithium verification is performed externally at the transaction authentication layer, while the zero-knowledge circuit handles privacy-preserving voting logic. Each voter locally generates a private credential and submits only a public commitment during registration, thereby reducing the risk of authority-based impersonation or identity-to-vote linkage. Smart contracts verify the proof, reject reused nullifiers, enforce voting rules, and record auditable election events on the permissioned blockchain ledger. Experimental evaluation demonstrates that the proposed prototype achieves an average throughput of 408 transactions per second and an average block finalization time of 2.18 s under stress testing. The results indicate that the framework can provide a practical balance between post-quantum security, privacy preservation, verifiability, and transaction efficiency in permissioned electronic voting environments.
Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance. Bitcoin exhibits fat-tailed returns, non-stationary dynamics, and a price discovery process influenced by social discourse on Reddit and Twitter. Conventional approaches fuse OHLCV technical features with sentiment via static concatenation, applying identical fusion weights regardless of market state. This is inconsistent with the behavioural finance literature, which shows that retail sentiment is most predictive during volatile periods and noisy during calm ones. This paper proposes Regime-Aware Multi-Modal Learning (RAML), which conditions fusion of sentiment and price features on a dynamically detected binary market regime. Rolling 24-hour volatility partitions observations into stable and volatile regimes; a learnable sigmoid gate adjusts the weight of the sentiment embedding relative to the price embedding, trusting sentiment more during volatility and price dynamics more during stable phases. The system is evaluated on 3,491 hourly observations (July 2024-September 2025), combining Bitcoin OHLCV data with Reddit /r/Bitcoin FinBERT sentiment. Four models are compared - price-only BiLSTM, sentiment-only classifier, static-concatenation BiLSTM, and RAML - across 3-hour and 6-hour horizons, with an ablation study isolating the sentiment branch, regime detection, and adaptive fusion. RAML achieves macro-F1 of 0.5474 (3h) and 0.5513 (6h), with the highest AUC at 3 hours (0.5084), indicating better calibration. Ablation confirms every component is necessary, and replacing adaptive weighting with concatenation causes recall collapse at 6 hours (F1: 0.14). These results establish regime-conditioned adaptive fusion as a necessary design principle for multi-modal financial forecasting.
Abstract: The aviation industry encounters difficulties with quality and safety because of fragmented and manual data management systems. This survey examines issues related to aviation data sharing within the aviation ecosystem. Data silos, transparency gaps, and security risks are discussed, along with issues concerning traceability. Blockchain and Distributed Ledger Technology (DLT) are reviewed as possible solutions. The review considers how these technologies enable secure, transparent, and permanent data exchange within the aviation ecosystem. Benefits include simpler regulatory compliance, improved supply chain traceability, and better trust among stakeholders. The paper also points out challenges like regulatory acceptance, interoperability, and scalability. Future research directions for a robust aviation quality data-sharing platform are outlined.
Abstract: Product traceability within supply chains is a critical concern that has gained increasing attention in recent years. One promising solution to this problem is the use of Public Blockchain (PBC) technology, which offers an immutable, transparent, and decentralized ledger for storing key information such as ownership transfers and distribution records. These blockchain-based systems significantly enhance traceability by ensuring that data, once recorded, cannot be altered. However, a key challenge arises from the fact that information stored on public blockchains is freely accessible to anyone, potentially exposing sensitive distribution data. In this paper, I introduce a novel method that preserves the privacy of distribution data while maintaining high traceability in supply chain systems leveraging PBC. The proposed method utilizes encryption to safeguard sensitive data and Zero-Knowledge Proofs (ZKPs) to allow supply chain participants to authenticate themselves without revealing private information, such as their blockchain addresses. The solution was implemented using Ethereum smart contracts and evaluated for its cost-effectiveness. The results show that the transaction fee per supply chain participant is capped at USD 2.6, demonstrating that the approach is practical for real-world deployment.
The paper addresses entity authentication in quantum key distribution (QKD) systems as a decisive condition of their practical security. It is shown that the information-theoretic security of quantum key agreement does not eliminate the need to authenticate the communicating parties: an unauthenticated classical channel leaves the system exposed to the man-in-the-middle attack, since the eavesdropper can run independent QKD instances with each party and reconcile two keys under full control. Existing authentication methods are analysed and classified by the underlying cryptographic primitive: symmetric schemes based on Wegman–Carter universal hashing, pre-shared and fixed keys, public-key infrastructure, two-way authentication, quantum entity/identity authentication, and zero-knowledge proofs. For each class the operating principle, advantages and limitations are determined, with emphasis on key management, scalability and trust distribution. It is established that symmetric and quantum-layer methods rely on pre-shared secrets with a quadratic growth of key material, public-key infrastructure introduces a single trust bottleneck and quantum-vulnerable primitives, while existing zero-knowledge authentication schemes are quantum and bound to the physical layer or solve network properties other than identity. A comparative analysis reveals an unresolved scientific gap: the absence of a scalable entity-authentication method that simultaneously provides non-disclosure of the secret, quantum resistance, sub-quadratic scalability and minimisation of trust assumptions. On this basis, a prospective research direction is substantiated – the construction of entity-authentication methods based on post-quantum zero-knowledge proofs operating over the classical control plane of scalable QKD networks. The requirements for such a method are formulated, and its compatibility with formal QKD security proofs is discussed.
The paper addresses entity authentication in quantum key distribution (QKD) systems as a decisive condition of their practical security. It is shown that the information-theoretic security of quantum key agreement does not eliminate the need to authenticate the communicating parties: an unauthenticated classical channel leaves the system exposed to the man-in-the-middle attack, since the eavesdropper can run independent QKD instances with each party and reconcile two keys under full control. Existing authentication methods are analysed and classified by the underlying cryptographic primitive: symmetric schemes based on Wegman–Carter universal hashing, pre-shared and fixed keys, public-key infrastructure, two-way authentication, quantum entity/identity authentication, and zero-knowledge proofs. For each class the operating principle, advantages and limitations are determined, with emphasis on key management, scalability and trust distribution. It is established that symmetric and quantum-layer methods rely on pre-shared secrets with a quadratic growth of key material, public-key infrastructure introduces a single trust bottleneck and quantum-vulnerable primitives, while existing zero-knowledge authentication schemes are quantum and bound to the physical layer or solve network properties other than identity. A comparative analysis reveals an unresolved scientific gap: the absence of a scalable entity-authentication method that simultaneously provides non-disclosure of the secret, quantum resistance, sub-quadratic scalability and minimisation of trust assumptions. On this basis, a prospective research direction is substantiated – the construction of entity-authentication methods based on post-quantum zero-knowledge proofs operating over the classical control plane of scalable QKD networks. The requirements for such a method are formulated, and its compatibility with formal QKD security proofs is discussed.
Hayder A. Nahi, Rusul A. Salman, Awring Falah Hassan, Ebtehal Akeel Hamed · 7 authors
Abstract The Internet of Things look out on growing security and privacy defies, principally in light of the up growth of quantum threats. To handle these defies, we suggest a unified security framework that merges post-quantum blockchain technologies and zero-knowledge proofs (ZKPs) to attain secure authentication, decentralized identity management, and advanced data protection. The provided system based on a power-weighted consensus mechanism, compressed and overlapping recursive ZKPs, and transaction batching to decrease on-chain load. The outcomes display that the suggested system outperforms conventional systems and state-of-the-art solutions, with response time reduced to 92 ms, transaction throughput increased to 735 tx/s, energy consumption reduced to 0.37 J/op, and authentication accuracy increased to 97.6%, achieving a privacy score of 0.91.These outcomes emphasize that the offered framework not only attains superior performance but as well supplies strong resistance to quantum attacks and high privacy warranties, making it a promising solution for securing future IoT environments.
Reshma D’Souza, S Sheela, H S Sameena, S Jyothi · 6 authors
Abstract: In this era where technology is used to create unidentical counterfeit products, Finding the original objects is a very tedious task for the users. These Counterfeit Products affect the health of the user in the case of medical and skin care products also. This project implements Blockchain, a new Technology which is used to overcome this problem. Blockchain technology is the distributed, and immutable technology that provides data consistency and security. Here a QR code is generated for each product that is linked to the database which in turn is mapped to the chain nodes. By scanning this QR code the user can detect the original products amongst the fake ones. It highlights the need of cryptocurrency in the broader vision of supply chain security, elaborating on how Blockchain network, particularly using Ethereum Framework, provides a decentralized and transparent ledger for tracking and validating products.
Muhammad Wahid, Shahzaib Khan, Mashhood Ali, Muhammad Hassan · 6 authors
The immutable nature of smart contracts makes it challenging to fix and patch bugs once they are deployed to a blockchain. This implies that security vulnerabilities may be exposed to possible exploitation for a longer period, necessitating comprehensive pre-deployment testing. Property-based testing combined with fuzzing has proven itself as a promising technique for uncovering vulnerabilities. Traditionally, system properties are written by human experts, which is time-consuming and consequently expensive.With the recent advancement in Large Language Models (LLMs) and their ability to 'understand' natural language and code semantics, it may be possible to generate effective properties. This study, leverages state-of-the-art LLMs to generate high-quality properties for Soliditybased smart contracts. We measure the quality of the generated properties using mutation testing. Our results show that LLMs have the potential to generate high-quality properties that are close to those written by human experts. We extensively evaluate LLMs using various prompting techniques (e.g., zero shot, few shot, and prompt chaining). Overall, we find that Gemini Pro 1.5, when combined with prompt chaining, achieves the highest average mutation score of 25.99% among all studied configurations, closely approaching the human written benchmark of 31.75%. However, our per contract analysis reveals notable variance, particularly for the LibBit contract, where Gemini Pro 1.5 under prompt chaining achieves a mutation score of 74.34%, which is on par with human written properties (74.83%). This highlights that while average performance is informative, individual contract level results demonstrate that LLMs can, in some cases, match expert level property generation.
AI now generates mathematics, code, and claims faster than anyone can review them; the limiting resource is no longer generation but trust. The honest response to "I don't trust it" is not "trust me" — it is "here is the check; run it." This deposit is a working demonstration of that response, run on the most scrutinized AI-math result of 2026: the July 2026 counterexample to the 87-year-old Jacobian Conjecture announced by Levent Alpöge with an AI as collaborator. A human directed Claude Code (Opus 4.8) as the proposer, with every mathematical claim compiled and machine-checked by Attestral, a verifier that signs an ed25519 certificate only when its own checker passes. The proposer cannot certify; the adjudicator has no stake in the proposer being right. The output is proof-carrying rather than model-asserted. Working only from the public polynomial list, the loop: independently verified the counterexample (det(JF) ≡ −2 exactly, a rational triple collision); reverse-engineered its mechanism (a non-nilpotent, degree-3 étale endomorphism — outside the classical nilpotent search space); found its hidden cubic (a three-cube-root Cardano fiber) and built an infinite tower of derived counterexamples; mapped the surrounding z-linear construction space (fold-parity obstruction, uniqueness skeleton); proved its natural four-dimensional generalization obstructed at every compensator degree in the Lean kernel; caught three of its own errors mid-run — including a finite-field prime silently collapsing to p = 3 — and discarded them; and reported an honest wall on the nilpotent normal form. Days later the same loop, unchanged, verified the counterexample to the Gaussian Moments Conjecture (Long, arXiv:2607.18186) posted in the same wave. Every claim carries a certificate any reader can re-verify offline: 20 Lean 4 kernel proofs (Mathlib, axiom-audited) for the load-bearing theorems and 23 exact-symbolic certificates (including the Gaussian-Moments companion) for the exploratory identities — two tiers, never blurred. The artifact bundle contains all 43 signed certificates, the Lean sources, the published verification key, and a standalone verifier needing only Python and pynacl: python verify_all.py → 43/43 certificates verified offline, ALL VALID. Scope, stated plainly: we verify and classify; the counterexample is Alpöge's. Certificates settle correctness only; one structural overlap is credited (Shaska, arXiv:2607.20210); no progress is claimed on the still-open plane (ℂ²) case. Interactive companion: https://simgen.dev/attestral/jacobian-counterexample/
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Polynomial and algebraic computation
Cryptography and Residue Arithmetic
Advanced Differential Equations and Dynamical Systems
The article presents a comprehensive analysis of the legal framework for public procurement in Ukraine and the European Union through the lens of balancing transparency requirements with the protection of confidential commercial information. The key principles of the Law of Ukraine “On Public Procurement” and Directive 2014/24/EU, which establish the foundations of equal treatment, non-discrimination, proportionality, and procedural openness, are examined. The practical outcomes of the Prozorro electronic procurement system are analyzed; since its launch, the system has saved over USD 8.7 billion in public funds and increased the number of bidding participants from 14,000 to 140,000. The main corruption risks at various stages of the procurement cycle are systematized – from planning and needs formulation to tender evaluation and contract execution. Based on an analysis of international studies using the Analytic Hierarchy Process (AHP) and principal-agent theory, it is established that tender evaluation is the most corruption-prone stage, with information asymmetry being the key factor. It is shown that under martial law conditions, simplified procurement mechanisms necessary for operational efficiency simultaneously expand the space for abuse. The feasibility of applying zero-knowledge proof (ZKP) protocols as a cryptographic instrument that enables combining public verification of participants’ compliance with qualification requirements while preserving the confidentiality of their commercial data is substantiated. The main classes of ZKP – interactive protocols, zk-SNARKs, zk-STARKs, and Bulletproofs – their properties, trade-offs, and practical applications in financial compliance, identity verification, and anonymous whistleblowing systems are examined in detail. Specific scenarios for integrating ZKP into public procurement procedures are considered: proof of financial capacity without disclosing reporting details, confirmation of the absence of conflicts of interest based on encrypted registry data, verification of the correctness of electronic auction results, and authentication of the supply chain. Key implementation barriers are identified: regulatory recognition of cryptographic proofs as equivalents to traditional documents, technical complexity and institutional capacity, performance and scalability concerns, legal liability for protocol errors, and compliance with GDPR requirements. A phased model for integrating ZKP into the Prozorro infrastructure is proposed, and recommendations for necessary legislative and institutional changes are formulated, including updating the Public Procurement Reform Strategy for 2024–2026, establishing independent cryptographic audit mechanisms, and developing methodological guidelines for contracting authorities.
The article presents a comprehensive analysis of the legal framework for public procurement in Ukraine and the European Union through the lens of balancing transparency requirements with the protection of confidential commercial information. The key principles of the Law of Ukraine “On Public Procurement” and Directive 2014/24/EU, which establish the foundations of equal treatment, non-discrimination, proportionality, and procedural openness, are examined. The practical outcomes of the Prozorro electronic procurement system are analyzed; since its launch, the system has saved over USD 8.7 billion in public funds and increased the number of bidding participants from 14,000 to 140,000. The main corruption risks at various stages of the procurement cycle are systematized – from planning and needs formulation to tender evaluation and contract execution. Based on an analysis of international studies using the Analytic Hierarchy Process (AHP) and principal-agent theory, it is established that tender evaluation is the most corruption-prone stage, with information asymmetry being the key factor. It is shown that under martial law conditions, simplified procurement mechanisms necessary for operational efficiency simultaneously expand the space for abuse. The feasibility of applying zero-knowledge proof (ZKP) protocols as a cryptographic instrument that enables combining public verification of participants’ compliance with qualification requirements while preserving the confidentiality of their commercial data is substantiated. The main classes of ZKP – interactive protocols, zk-SNARKs, zk-STARKs, and Bulletproofs – their properties, trade-offs, and practical applications in financial compliance, identity verification, and anonymous whistleblowing systems are examined in detail. Specific scenarios for integrating ZKP into public procurement procedures are considered: proof of financial capacity without disclosing reporting details, confirmation of the absence of conflicts of interest based on encrypted registry data, verification of the correctness of electronic auction results, and authentication of the supply chain. Key implementation barriers are identified: regulatory recognition of cryptographic proofs as equivalents to traditional documents, technical complexity and institutional capacity, performance and scalability concerns, legal liability for protocol errors, and compliance with GDPR requirements. A phased model for integrating ZKP into the Prozorro infrastructure is proposed, and recommendations for necessary legislative and institutional changes are formulated, including updating the Public Procurement Reform Strategy for 2024–2026, establishing independent cryptographic audit mechanisms, and developing methodological guidelines for contracting authorities.
Sourena Khanzadeh, Daniel Platnick, Marjan Alirezaie, Hossein Rahnama
Modern AI systems bring societal risks such as mass surveillance, extreme concentrations of power, and loss of user autonomy---calling into question a model where third-parties collect and control massive amounts of user data. Users require a sovereign system to securely own, govern, and disclose their context while remaining compliant across regulated domains with strict provenance, interpretability, and policy adherence. Perspective-aware AI approaches this by transforming a user's aggregated personal data into a structured identity model called a \emph{Chronicle}: a temporal knowledge graph that represents and grows with the user. Chronicles support the secure disclosure of context across federated networks. A Chronicle holder may expose a queryable, authorized view that a third-party agent may consult without centralizing anyone's data. This paper explores the problem of minimum-necessary disclosure across domain boundaries: when a requester's agent queries a Chronicle, how can the system constrain its response to release only what the requester's relationship, stated purpose, and specific task require? We propose \textbf{Provenance Preserving Chronicles} (PPC), a federated protocol that compiles each holder's Chronicle into a compact \emph{authorized evidence subgraph} governed by one rule: \emph{share no more than the request requires}. Holders keep local sovereignty; an access controller projects relationship-aware views over domain-expert ontologies; and a two-phase flow returns provenance-linked text first, releasing raw artifacts only after explicit holder approval. We frame the problem, map gaps in blockchain, P2P, and holder-sovereign designs, define the core constructs, and sketch the protocol with an explicit threat model.
Yifeng Ye, Rongji Huang, Gerui Wang, Mingchao Wan · 7 authors
In blockchain systems, peer-to-peer (P2P) overlay networks play a crucial role in providing reliable, scalable and efficient message-delivery services to upper layers. However, the consensus layer and the underlying P2P network remain mutually opaque in existing blockchains, waiving the opportunity for further improvement. In contrast to other P2P applications, blockchain can naturally be abstracted as a state machine. We therefore leverage this abstraction to record network topologies and latencies in a trusted and coordinated manner. With this support, we propose three improvements to rotating-leader consensus protocols and their underlying P2P networks: (1) accelerating leader rotation; (2) introducing a reliable-broadcast paradigm that employs tree-based dissemination in the normal case and falls back to gossip only when necessary; and (3) constructing latency-aware dissemination trees. We integrate the above ideas into Tendermint and libp2p, and conduct empirical evaluation on Amazon EC2 platform using up to 300 nodes distributed across 10 regions. The results demonstrate that, compared with gossip-based dissemination over the same topology, our prototype improves peak throughput by up to $7.26\times$.
Large-scale biometric identification in outsourced settings requires two properties simultaneously: biometric templates and queries must remain protected during computation, and the encrypted similarity outputs produced by an untrusted compute node must be verifiably correct before any application result is released. Existing FHE-based biometric systems primarily address confidentiality, while practical verifiability introduces two bottlenecks in the underlying encrypted 1:N matching layer: rotation- and bandwidth-heavy similarity evaluation and the high cost of proving repeated homomorphic similarity traces. We present BioZKFHE, a framework for scalable encrypted biometric identification via verifiable homomorphic similarity evaluation that combines BGV homomorphic computation with committee-mediated proof opening/decryption and smart-contract verification of opened proof batches. To reduce encrypted storage and avoid rotation-heavy encrypted 1:N matching, we propose Single-Coefficient Multi-Value (SCMV) packing, which binds multiple quantized embedding values into each plaintext entry through base-T expansion. To make proof generation practical, we propose Parallelizable and Verifiable Similarity Computation (PVSC), which exploits the Double-CRT execution structure of BGV to decompose each blockwise similarity trace into parallel proof instances that are opened and checked before result release. Under standard lattice assumptions and explicit committee/verifier assumptions, we analyze recoverability, noise growth, confidentiality, encrypted-output integrity, and finalized-result integrity. Experiments on FaceNet and MobileFaceNet show near-lossless biometric utility, up to 67 percent encrypted-storage reduction, and about 22 to 44 seconds end-to-end proof-verified runtime for 10k to 40k templates.
In Ethereum, transaction inclusion is rarely in question; what matters is the delay until inclusion. Currently, block builders could exercise censorship across consecutive blocks, threatening time-critical applications, such as on-chain auctions. To mitigate this risk, existing proposals such as FOCIL, scheduled for deployment in late 2026, assign a committee to list transactions for mandatory inclusion. However, no committee member is held accountable for the actual inclusion of the transactions: an adversary can bribe the entire committee to omit any transaction for less than 2 Euro per block under current conditions. We argue that accountability, i.e., requiring all exclusion decisions to be publicly disclosed and verifiably complete, with violations attributable to a specific party, substantially raises censorship costs. To this end, we propose Fair Forward Inclusion Lists (FairFIL) as an accountable censorship resistance mechanism for Ethereum. In FairFIL, every builder must publish all transactions the builder chooses to censor, subject to a protocol-anchored policy; a committee verifies the completeness and validity of this disclosure. The subsequent builder must include these transactions, forfeiting the full block reward upon any omission. Therefore, under FairFIL, extending censorship beyond a single slot requires an assembler to forfeit a full block reward. We show that compliance is rational for all participants within our behavior model. Our empirical evaluation on Ethereum mainnet indicates that multi-block censorship costs one order of magnitude more than under existing proposals, while leaving the builder's MEV extraction freedom largely intact. Initial measurements further suggest that the mempool consistency FairFIL requires is met in practice.
Intent-based decentralized exchanges delegate execution to a competitive class of agents -- solvers -- whose behavior is shaped by protocol-designed reward rules. We measure how a change to those rules reshapes who captures value, using a governance-dated natural experiment: CoW Protocol CIP-74 (effective 8 December 2025), which replaced a fixed solver-reward cap with one tied to protocol revenue and introduced an ad-valorem volume fee. Using daily solver shares over 395 days, we find the reform reallocated trading value by order size. The robust signature is a monotone size gradient: concentration fell in small orders and rose in large ones across four order-value buckets (Spearman rho=1.00, exact permutation p=0.042) -- a pattern that survives dropping the single largest solver. Aggregate concentration also rose (volume-weighted HHI 0.176->0.241), substantially carried by the incumbent top solver. By trade count the market de-concentrated (count-HHI -0.060). A simple solver-economics model rationalizes the pattern: an ad-valorem fee is competitively neutral, while a revenue-linked reward cap raises the marginal payoff to inventory-rich solvers on large orders -- consistent with restricted-entry predictions (Chitra et al. 2024). A control venue (UniswapX) shows no matching break. We detect no change in average execution quality (~7 bps bound). A triple-difference exploiting a February 2026 fee cut is directionally consistent but underpowered. Reward design measurably reallocates who captures value in intent markets, without moving the average price users receive.
Why do local elected representatives facing similar institutional constraints choose different strategies? This dissertation develops a theory of strategy choice under incomplete decentralization, where municipal councilors remain electorally accountable but depend on bureaucratic and higher-level political actors for implementation, finance, and approval. Administrative, fiscal, and political constraints define the institutional setting, while leverage, the capacity to induce response, and cover, protection from sanction, capture councilors’ unequal positions within it. Drawing on more than 450 interviews, 67 municipal council meeting transcripts, and an original survey of 506 current and former councilors in urban India, I distinguish collaborative strategies based on coordination and follow-up from combative strategies based on public pressure and cost-imposition. Collaboration overwhelmingly dominates. Perceived bureaucratic discretion is the strongest correlate of movement toward combativeness, while leverage and cover do not reliably predict the binary shift between strategies. Instead, leverage more clearly distinguishes procedural from discretionary forms of collaboration. These findings show that incomplete decentralization does not eliminate local representation. It channels representation through continued dependence on actors councilors do not command and shifts attention from whether councilors collaborate to how they collaborate and when they escalate.
Direct user-specified research topic: Autonomous agent wallets spend under English mandates like 'only stablecoin swaps under $200 daily, never bridge, never touch unaudited pools', yet deployed policy engines (Safe Transaction Guards, ERC-7579 modules, session-key allowlists) enforce only stateless numeric and selector limits and cannot express 'unaudited' or 'per day', while a naive base-model prompt over raw hex calldata cannot recover function, recipient or token flow and confabulates verdicts. Evaluate a tool-augmented structured-decoding LLM judge that fetches ABIs from Sourcify and Etherscan, decodes calldata including multicall and Permit2 payloads, simulates via eth_call state overrides for token-flow and approval deltas, attaches counterparty features (contract age, verification), and emits constrained JSON: in_policy, violated_clause quoted verbatim, offending_calldata_field. Read Ethereum and Base: ERC-20 Transfer/Approval logs, Uniswap/1inch routers, Across/Stargate bridges, Permit2 at 0x000000000022D473030F116dDEE9F6B43aC78BA3. Measure macro-F1 and clause-attribution precision on 600 hand-labeled mandate/transaction pairs plus replay accuracy on transactions whose approvals owners later revoked, beating a naive raw-hex prompt and a Safe Guard numeric-allowlist baseline. Deliver as the prototype a minimal runnable Python MCP server (stdio) exposing the priced AI tool screen_transaction_against_mandate that invokes a language or ML model over onchain data to produce its output, with a typed input/output schema, an x402-style pay-per-call metering stub that records a per-call price in USDT and emits a settlement receipt, and one smoke test that exercises the tool end to end.. Investigate this topic end-to-end: survey the state of the art, identify a concrete tractable research question within it, design and run an experiment, and report results.
Cryptocurrency's convenience is a convenient truth — granted here in full, with receipts. A permissionless ledger settles across borders without account approval, banking hours, or correspondent chains; Nakamoto designed exactly that, on purpose. The correction is that the convenience and the danger are the same property: what makes the transfer fast and unstoppable is that it is final — no chargeback, no administrator, no undo. Institutions can price that trade. A person cannot, and the proposal is that the rational personal policy is a wall, not a judgment call. Offered as a proposal, not a result.
Part 2: Enhanced Pure-Milk Green Finance Matrix – Toward Net-Positive Regenerative Dairy Systems builds upon the original prospectus (DOI: 10.5281/zenodo.21538664) by integrating abundant low-cost clean energy, advanced on-site CO₂ scrubbers, intelligent multi-functional greenbelts, and decentralized vertical hydroponics. This evolution transforms New Zealand dairy farms from environmentally sustainable operations into active net-positive regenerative systems that function as carbon sinks, biodiversity enhancers, and water quality producers, while maintaining or increasing economic output. By leveraging current technological convergence — including satellite virtual fencing, AI-driven optimization, renewable power, and closed-loop nutrient cycling — the model delivers accelerated ROI, greater resilience for smaller farms, and a scalable blueprint for global pastoral agriculture. The enhanced framework resolves long-standing tensions between productivity and environmental stewardship, positioning New Zealand as a leader in high-tech regenerative food systems for the 21st century. (Word count: 148 – suitable for presentations, funding proposals, or DOI metadata) Keywords (for search, tagging, academic indexing, or presentation metadata) Primary Keywords: Pure-Milk Green Finance Matrix Regenerative dairy farming Sustainable intensification Net-positive agriculture New Zealand dairy transformation Technical & Solution Keywords: Virtual fencing On-site Direct Air Capture (DAC) Decentralized vertical hydroponics Methane-scrubbing greenbelts Agritech closed-loop systems Renewable energy integration Carbon sequestration farming Strategic Keywords: Macroeconomic transformation Green finance KiwiSaver reinvestment Shared-equity sharemilking Global agritech IP export Climate-smart agriculture Net-zero dairy
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Agriculture Sustainability and Environmental Impact
ComputeGrid is a concept paper and feasibility framework for a utility-integrated network of provider-owned compute nodes hosted at commercial buildings, public facilities, and — through a heat-recovery variant — homes. Hosts receive full metered reimbursement of node electricity plus a separate credit; nodes are aggregated by an orchestration and settlement platform (ComputeGrid OS) into one secure, grid-aware, dispatchable compute layer beneath hyperscale data centers. Version 3.0 evaluates the concept against two structural facts: residential retail electricity is the most expensive power a compute operator can buy, and a decade of distributed-compute ventures (spot marketplaces, deploy-first token networks, and heat-recovery operators) shows which configurations survive. The paper therefore leads with commercial and public-building deployment, admits residential nodes only where waste heat displaces heating the host would otherwise purchase, and requires every site to pass at least one of four economic qualifiers — near-commercial power price, monetizable heat, a priced locality premium, or measurable grid-flexibility value — before hardware is committed. The paper includes a two-sided illustrative unit ledger, host consumer-protection and property-rights baselines, rules preventing host-purchased “income” hardware, a staged offtake-first pilot plan with published kill criteria, and a claim framework that treats all economics as illustrative pending Stage 0 diligence. It is an open concept paper, not peer reviewed; no field deployment or empirical dataset is reported. Version 1.0 drafting was assisted by OpenAI ChatGPT; Versions 2.0 and 3.0 critique and revision were assisted by Anthropic Claude under the author’s direction. The author is responsible for the final claims and release.
This study investigates critical success factors crucial for the effective implementation of blockchain-based smart contracts in supply chain management. Through qualitative content analysis of expert interviews, diverse perspectives from industry professionals and blockchain technologists were synthesized. The findings emphasize critical dimensions such as technological infrastructure, stakeholder collaboration, regulatory compliance, data privacy, security, organizational culture, and change management. These factors collectively form a comprehensive framework essential for successful adoption. This research offers valuable guidance for organizations aiming to integrate blockchain-based smart contracts into supply chain operations. The insights derived from eight expert interviews provide strategic direction for practitioners, policymakers, and academics navigating the complexities of blockchain and smart contract technology in supply chain ecosystems.
This article presents a novel, first-of-its-kind predictive RegTech solution to address this challenge using machine learning methods. The rapid global adoption of Real‑Time Payment Systems (RTPS) has created a significant “velocity gap” in regulatory compliance. While expanding financial accessibility, these systems introduce new vulnerabilities into existing AML frameworks. Static, rule-based systems and batch processing architectures cannot effectively counter money laundering in sub-second transaction environments. This limitation enables sophisticated activities such as digital layering and smurfing that move illicit financial flows across networks faster than regulatory systems can react. The core of our solution involves the use of graph neural networks (GNNs). This approach enables real-time, pre-settlement risk assessment, preventing illicit transactions before execution. Unlike traditional AML systems that evaluate transactions in isolation, this framework analyzes the entire transaction network to detect coordinated illicit behavior in real time. GNNs capture complex structures such as loops, funnels, and bridges that indicate illicit activity. To support efficient implementation, the framework integrates Event-Driven Architecture (EDA). The proposed architecture introduces the concept of the Zero-Knowledge Proof (ZKP) protocol layer in order to make risk-sharing possible in a secure manner across multiple institutions. This allows the banks to cooperate with each other in order to combat financial crimes while maintaining their data sovereignty. With predictive graph analytics, event-driven integration, and private cooperation, the proposed architecture enables proactive compliance in real-time payment environments, including real-time payment systems such as FedNow, against high-speed financial crime.