Alisha Abbasi Shaikh, Aman Ullah Khan, Mohammad Fahad Kirmani, S. Ali
The rapid digital transformation of healthcare systems has significantly improved the storage, accessibility, and management of patient information; however, it has also introduced serious challenges related to data security, privacy, and trust. Traditional centralized medical record systems are vulnerable to single points of failure, unauthorized access, and data breaches, which may compromise sensitive patient data. This paper proposes a decentralized framework for secure medical records management using blockchain technology. The system utilizes a distributed ledger to store medical data in a tamper-resistant and immutable manner, ensuring integrity and transparency. Cryptographic techniques are employed to encrypt patient data and enforce secure access control, allowing only authorized users to retrieve or update records. Additionally, smart contracts are used to automate access permissions and eliminate the need for intermediaries, improving efficiency. By removing dependence on a central authority, the proposed approach enhances reliability, security, and trust among stakeholders while ensuring privacy protection and controlled data sharing in modern healthcare environments.
A deployed model can appear unchanged while ceasing to be the model it claims to be. Publicly available weight-level mutation toolchains now automate safety-alignment removal from open-weight models on ordinary hardware, producing checkpoints intended to preserve operational familiarity while discarding refusal behavior. This paper argues that safety-alignment removal is a model-identity failure: in tested published checkpoints from multiple toolchains across two model families, the mutation leaves measurable structural scars ranging from 7.6 to over 2,300 times the instrument's acceptance threshold. Artifact identity, workload identity, and agent authorization can all remain valid while structural model identity fails — a finding that the program's formally verified admissibility doctrine predicted before this threat class existed. A sentinel validation panel across four model families confirms that the hardened instrument configuration preserves or improves all tested positives. In an agentic deployment context, model-identity failure propagates upward into agent-integrity failure: the agent is authenticated, but the model inside it is no longer the model the surrounding controls were designed to govern. The practical implication is that runtime evaluation frameworks — including those emerging under the EU AI Act — implicitly depend on a model continuity that weight-level mutation can break, and that structural identity verification offers a candidate evidentiary layer for closing that gap. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).
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Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
About this paper This paper argues that the conflict between online protection and privacy is not inevitable. The real problem is that most current systems wrongly treat compliance and identity as the same thing. The proposed VI + CJT framework separates them. It allows platforms to receive only the minimum lawful compliance result they need — for example, whether a user falls below the relevant legal age threshold — without learning the child’s name, date of birth, address, biometric profile, or broader identity. In that sense, the paper’s central theme is age verification without surveillance through purpose-bound cryptographic enforcement. How AI Makes the Problem Worse AI makes the children’s online safety problem more serious in three distinct ways. First, it changes exposure from passive to active. Harmful material is no longer merely available on a platform; recommendation and optimisation systems can identify vulnerable users, rank harmful content more aggressively for them, and progressively amplify it based on engagement signals. In that environment, a child is not simply finding harmful content — the system is learning from the child and serving more of it. Second, AI makes weak age-verification methods more dangerous. A false self-declared age is no longer just a wrong entry in a sign-up form. Once accepted, it becomes operational input for recommendation, advertising, and behavioural optimisation systems, which then treat the child as an adult user profile. This means the error is not static; it is continuously acted upon by AI systems that optimise for attention and engagement rather than child protection. Third, AI encourages platforms to solve the problem through more surveillance. In practice, this often means AI-based age estimation using faces, voices, or behavioural patterns. But this approach creates a new harm while claiming to solve another one: it turns child protection into biometric and behavioural monitoring, and can generate datasets that may later be reused for additional profiling or model training. In other words, AI can make age assurance both more intrusive and less accountable. A further difficulty is that AI systems are often opaque even to their operators. As your draft correctly notes, policy rules alone may not be enough, because platforms may not reliably know how their own recommendation systems are treating minors in practice. This is why the problem is not only one of age verification, but also one of enforceable control over AI behaviour. That is precisely why the VI + CJT model matters. It does not ask AI systems to infer age or interpret law for themselves. Instead, it provides a minimal, authoritative compliance signal and machine-readable constraints that can limit recommendation, advertising, and profiling behaviour toward minors without exposing identity. Current Solutions Self-declaration is easily bypassed. A child can simply enter a false age, and the platform’s AI systems then treat that false declaration as valid input for recommendation, targeting, and optimisation. Identity-linked verification creates major privacy risks. When age assurance depends on sharing civil identity information with commercial platforms, the result is unnecessary exposure of family and child data to entities with strong incentives to collect, retain, and monetise it. AI-based age estimation introduces biometric surveillance. Estimating age from face, voice, or behaviour may appear convenient, but it creates new harms by collecting sensitive personal and biometric data as a side effect of child protection. Current systems collapse compliance into identity. What platforms usually need is not the full identity of the user, but only the legally relevant compliance fact. Existing approaches fail because they demand far more data than is necessary for that purpose. Policy rules alone are not enough in AI-driven environments. Even where legal obligations exist, platforms may not reliably translate them into enforceable constraints on opaque recommendation and engagement systems. As a result, compliance may remain declaratory rather than technically enforced. Proposed Solution Use VI + CJT as a purpose-bound cryptographic layer. The framework converts verified civil identity held by trusted authorities into a minimal compliance credential that reveals only the relevant age-threshold result for the applicable jurisdiction. Avoid disclosure of identity data. The credential contains no name, no full date of birth, no address, and no biometric data. Each credential uses a fresh random identifier, making it unlinkable across sessions. Keep the credential under user control. The credential is stored on the user’s device in secure hardware rather than on platform servers, reducing centralised exposure and retention risks. Use zero-knowledge proof for age compliance. When access is requested, the platform receives only a yes-or-no compliance result, without learning the underlying identity attributes or credential contents. Encode law into machine-readable CJTs. The Compliance Jurisdiction Token expresses the applicable legal rules, including jurisdiction-specific age thresholds and AI-related restrictions such as limits on engagement optimisation, advertising targeting, or behavioural profiling for minors. Constrain platform AI without making it identity-aware. Recommendation engines and other AI systems receive only the compliance signal necessary to adjust behaviour for minors, allowing them to become jurisdiction-aware and age-aware without becoming identity-aware. Replace probabilistic AI age estimation with authoritative attestation. Instead of guessing age through opaque models, the framework provides deterministic, government-signed, legally relevant compliance proof. Enable auditability and cross-border enforcement. Regulators can test whether platforms respond correctly to compliance signals, and the applicable child-protection rule can follow the user across borders through jurisdiction-bound credentials and tokens. Core Message The paper’s core message is simple: platforms do not need to know who a child is in order to know what protections the law requires. By separating compliance from identity, the VI + CJT model offers a path to child safety that is enforceable, privacy-preserving, and better suited to AI-driven digital environments.
Sika AGNONVI, Sèdjro Guillaume Nonkoudjè, Kpèdadon Louis Tèkpanzo, Yao Messah Kounetsron
Résumé Cette recherche analyse le rôle de la finance verte dans la promotion de l’entrepreneuriat durable au sein des petites et moyennes entreprises agricoles au Bénin. La méthodologie adoptée repose sur une approche qualitative fondée sur 46 entretiens semi-directifs réalisés auprès d’agripreneurs répartis dans cinq régions agricoles. L’analyse thématique des verbatims, conduite avec le logiciel NVivo 12, a permis de mettre en évidence une dynamique double. D’une part, la finance verte est perçue comme un levier stratégique pour accompagner la transition écologique et renforcer la résilience des exploitations face aux aléas climatiques, mais son accès reste limité par l’inadéquation des produits financiers existants, la rareté des subventions adaptées et l’absence de guichets verts décentralisés. D’autre part, l’entrepreneuriat durable se traduit par des pratiques telles que l’agriculture biologique, la conservation des sols, l’économie circulaire, la certification écologique et l’innovation verte, intégrant également des dimensions sociales comme l’implication des jeunes et des femmes. Les résultats révèlent une complémentarité forte dont l’accès à une finance verte adaptée facilite l’adoption des pratiques durables, tandis que ces dernières renforcent la crédibilité des PME agricoles auprès des institutions financières. Mots-clés : Finance verte, entrepreneuriat durable, PME agricoles, transition écologique. Abstract The objective of this research is to analyze the role of green finance in promoting sustainable entrepreneurship among small and medium-sized agricultural enterprises in Benin. The methodology adopted is based on a qualitative approach using 46 semi-structured interviews with agripreneurs in five agricultural regions. Thematic analysis of the transcripts, conducted using NVivo 12 software, revealed a dual dynamic. On the one hand, green finance is perceived as a strategic lever to support ecological transition and strengthen the resilience of farms in the face of climate hazards, but access to it remains limited by the inadequacy of existing financial products, the scarcity of appropriate subsidies, and the absence of decentralized green windows. On the other hand, sustainable entrepreneurship is reflected in practices such as organic farming, soil conservation, the circular economy, ecological certification, and green innovation, also incorporating social dimensions such as the involvement of young people and women. The results reveal a strong complementarity, with access to appropriate green finance facilitating the adoption of sustainable practices, while the latter reinforce the credibility of agricultural SMEs with financial institutions. Keywords: Green finance, sustainable entrepreneurship, agricultural SMEs, ecological transition.
Citra Fadhilah Utami, Arum Ira Nadhira, Clarisa Rofiati, Della Affesia Putri
Urban infrastructure financing in Indonesia faces a structural funding gap alongside rising subnational fiscal risk under decentralization. Therefore, this study aimed to develop a Multi-Criteria Decision Analysis framework to allocate loans across 50 priority cities in the 2025–2029 National Development Plan. The framework integrated fiscal capacity, debt sustainability, institutional readiness, public investment productivity, and spatial role into three composite indices, namely Soft Gate, Impact, and Priority. Using a weighted additive framework with percentile-based screening, cities were classified into Loan Priority, Blended, Grant, and Selective categories. The results showed that 28 percent qualified as Loan Priority, while 24 percent require blended mechanisms due to fiscal-impact mismatch. In conclusion, the framework enhances fiscal discipline and transparency in subnational borrowing decisions.
AIGP-Σ (AI Governance Protocol — Sigma) is a post-quantum cryptographic identity and authorization framework designed for autonomous AI agents operating in multi-agent and agentic payment environments. The protocol suite consists of five interconnected specifications: WP-01: Core Protocol — ML-DSA (CRYSTALS-Dilithium) based identity anchoring with STARK zero-knowledge proofs via RISC0, Bitcoin blockchain timestamping, and a cryptographic Kill Switch mechanism for emergency AI halt. WP-02: Kill Switch — Formal specification of the HALT proof system enabling verifiable, tamper-proof shutdown of AI agents without revealing operational state. WP-03: SSL for Agents — A mutual TLS-equivalent handshake protocol adapted for AI agent-to-agent communication, providing forward secrecy and post-quantum resistance. WP-04: Agentic Payments — Authorization layer for autonomous financial transactions executed by AI agents, with cryptographic scope limitation and audit trails. WP-05: Multi-Agent Orchestration — Trust propagation and delegation model for hierarchical multi-agent systems with verifiable credential chains.
Java applications are prone to vulnerabilities stemming from the insecure use of security-sensitive APIs, such as file operations enabling path traversal or deserialization routines allowing remote code execution. These sink APIs encode critical information for vulnerability discovery: the program-specific constraints required to reach them and the exploitation conditions necessary to trigger security flaws. Despite this, existing fuzzers largely overlook such vulnerability-specific knowledge, limiting their effectiveness. We present GONDAR, a sink-centric fuzzing framework that systematically leverages sink API semantics for targeted vulnerability discovery. GONDAR first identifies reachable and exploitable sink call sites through CWE-specific scanning combined with LLM-assisted static filtering. It then deploys two specialized agents that work collaboratively with a coverage-guided fuzzer: an exploration agent generates inputs to reach target call sites by iteratively solving path constraints, while an exploitation agent synthesizes proof-of-concept exploits by reasoning about and satisfying vulnerability-triggering conditions. The agents and fuzzer continuously exchange seeds and runtime feedback, complementing each other. We evaluated GONDAR on real-world Java benchmarks, where it discovers four times more vulnerabilities than Jazzer, the state-of-the-art Java fuzzer. Notably, an earlier GONDAR version contributed to Team Atlanta's first-place CRS in the DARPA AI Cyber Challenge, and is integrated into OSS-CRS, a sandbox project in The Linux Foundation's OpenSSF, to analyze open-source Java projects, where it has already uncovered a zero-day vulnerability.
We present a unified dynamical framework for the nontrivial zeros of the Riemann zeta function, integrating three perspectives: (i) the de Bruijn–Newman flow and its reduction to a logarithmic Coulomb gas, (ii) a renormalization group information flow from the 2C Theory, and (iii) spectral compression in 2D Dirac systems under strong magnetic fields. Through an iterative discovery process — connecting existing knowledge, identifying new principles at the intersection, then connecting those principles with prior knowledge to discover deeper ones — we identify three structural contributions: (1) The Disorder–Order Paradox: the irregularity of the prime distribution generates the information restoring force (curvature V''(1/2) = π²/8) that confines zeros to the critical line Re(s) = 1/2. (2) The Universal Irreversibility Threshold: the critical value C = 2/3, independently derived in D.S. Theory (holographic ratio β = 3/2), the 2C Theory (RG flow fixed point), and Lowest Landau Level physics (spectral weight threshold for forced Landauer erasure), marks the point at which one-dimensional spectral reduction becomes irreversible. (3) The Entropic Barrier: the information free energy V(σ) possesses a barrier surrounding σ = 1/2 whose height grows with integrated prime density, forbidding zero escape once the critical threshold is exceeded. We formulate one precisely stated open problem: proving that the entropic barrier height diverges as T → ∞, which is equivalent to establishing an L² + entropy → L∞ inequality for the equilibrium measure of the logarithmic gas. The framework connects analytic number theory, information theory, renormalization group methods, and condensed matter physics within a single coherent structure. This paper is a structural framework proposal, not a proof of the Riemann Hypothesis. The iterative discovery methodology is inspired by the WillCore simulation platform.
This study presents ZK-EHR, a decentralized access control framework designed to enable secure and privacy-preserving sharing of encrypted electronic health records across institutional boundaries. Unlike existing blockchain-based EHR access control systems that expose user identities on-chain or lack cryptographic privacy guarantees, ZK-EHR decouples authorization from identity disclosure by integrating zk-SNARK-based proofs with blockchain smart contracts to verify policy compliance without revealing user roles, affiliations, or credentials. The framework employs three differentiated actor roles—Patient (Data Owner), Doctor (Care Provider), and Researcher (Authorized Analyst)—with distinct policy-driven access workflows, a custom Groth16 zero-knowledge circuit for role-based constraint enforcement, and a modular architecture combining on-chain verification with off-chain encrypted storage via IPFS. Concrete design proposals for access revocation and replay attack prevention are introduced to address operational security requirements. The system was evaluated under multiple operational and adversarial scenarios. Experimental results indicate consistent on-chain verification latency (approximately 390 ms), reliable rejection of tampered submissions, and per-verification gas consumption of 216,631 gas. A comparative analysis against representative baseline systems demonstrates that ZK-EHR uniquely combines identity anonymity, on-chain cryptographic policy enforcement, and auditable encrypted record retrieval. These findings establish the feasibility of zk-SNARK-based access control for decentralized, verifiable, and privacy-aware EHR management.
Margherita Cozzolino, Stephan Krenn, Thomas Lorünser
While QKD ensures information-theoretic security at the link level, real-world deployments depend on trusted repeaters, creating potential vulnerabilities. In this paper, we thus introduce a topology-hiding connectivity assurance protocol to enhance trust in quantum key distribution (QKD) network infrastructures. Our protocol allows network providers to jointly prove the existence of a secure connection between endpoints without revealing internal topology details. By extending graph-signature techniques to support multi-graphs and hidden endpoints, we enable zero-knowledge proofs of connectivity that ensure both soundness and topology hiding. We further discuss how our approach can certify, e.g., multiple disjoint paths, supporting multi-path QKD scenarios. This work bridges cryptographic assurance methods with the operational requirements of QKD networks, promoting verifiable and privacy-preserving inter-network connectivity.
This study investigates the role of artificial intelligence (AI) tokens in dynamic interactions, diversification, and hedging capabilities, in relation to non-fungible tokens (NFTs), decentralised finance (DeFi) tokens, and renewable energy assets. Using the Time-Varying Parameter Vector Autoregressive (TVP-VAR) model, we examine return, volatility, and higher-order spillovers across both time and frequency domains. The results show that NFTs serve as persistent channels for the transmission of return and volatility shocks, driven by their speculative nature. AI and renewable tokens primarily absorb systemic risk due to their lower liquidity and niche adoption. DeFi tokens play flexible roles, shifting between transmitters and receivers across market regimes. The results demonstrate asset-specific idiosyncrasies and that volatility spillovers are generally stronger than return spillovers. Frequency-domain analysis highlights that digital tokens dominate short-term spillovers, while renewable assets absorb shocks across horizons. However, higher-order moment results reveal that extreme risk linkages shift transmission channels. Our results also confirm that oil market (OVX) shocks drive short-term return connectedness, CBOE volatility (VIX) volatility, and policy uncertainty (EPU) significantly impact return linkages. The results of our portfolio analysis show that AI tokens form the core of diversification, NFTs provide short-term speculative hedging, and renewable assets, particularly solar-linked tokens, act as low-cost stabilisers, underscoring the need for active rebalancing under different market regimes. These findings provide meaningful implications for policymakers, regulators, and portfolio managers for strengthening systemic risk oversight and considering asset-specific idiosyncrasies in investment strategies.
This paper describes a zero-knowledge proof system that enables verification of password policy compliance within an asymmetric password-authenticated key exchange (aPAKE) protocol specifically OPAQUE (RFC 9807) without revealing the password to the server. The system is built on a composable sub-circuit architecture: independent verification gadgets are combined into a single zero-knowledge proof, each gadget accepting portions of the private witness and producing public instance values, enabling the server to verify multiple password properties in one proof verification. Four gadgets are disclosed: (1) a Policy Engine for character class verification via lookup tables, (2) a History Nullifier for password inequality proof via squared-difference accumulation, (3) an OPAQUE Binder for cryptographic binding to the aPAKE registration element via hash-to-curve and elliptic curve scalar multiplication, and (4) a Breach Detector for offline breached-password detection via Bloom filter non-membership proof using algebraic hashing. The composable architecture permits addition of further gadgets without modifying existing ones, each extending the public instance vector.
Today, many computing workloads are executed in loosely coupled, geographically distributed environments where resources are owned by different organizations. Examples include inter-institutional research infrastructures, community-operated clusters, and edge deployments. As disconnections are frequent in such environments, ensuring reliable task execution remains a fundamental challenge. Kubernetes, the de facto standard for cluster orchestration, provides centralized control and strong consistency, but suffers from slow recovery when node failures occur frequently. At the opposite extreme, blockchain-based orchestration removes centralized control but incurs substantial latency due to global consensus, making it unsuitable for time-sensitive task scheduling. This paper presents Mutual Cloud, a decentralized orchestration framework that operates between these two extremes. Mutual Cloud adopts a hybrid architecture where task admission and queue management are handled in a centralized manner similar to conventional public clouds, whereas most scheduling functions, including execution-node selection and failure handling, are performed in a decentralized manner by autonomous agents using a distributed hash table. We implement a prototype of Mutual Cloud and evaluate its performance through large-scale simulation studies. The results show that Mutual Cloud maintains stable performance comparable to centralized baselines under normal conditions while achieving approximately five-second-level recovery latency under substantial node failures.
The rising frequency of cyber threats increases the need for incident reporting that is transparent, efficient, and privacy-preserving. This study designs and implements a hybrid Web2-Web3 cyber incident reporting prototype that anchors report references on a blockchain while storing full incident details off-chain, and explores non-fungible token (NFT) recognition incentives for reporters. Using an SDLC-based iterative prototyping approach, we built a React single-page application integrated with a Laravel REST API and MySQL for off-chain storage, and deployed Solidity smart contract modules on the Arbitrum Sepolia testnet to record report identifiers and UUID pointers (dataPointer) and to mint NFTs after administrative validation. We conducted black-box functional testing across core scenarios (submission, storage, pointer anchoring, validation, and minting) and a user acceptance study with 25 participants (15 cybersecurity students and 10 IT practitioners) using a 5-point Likert questionnaire. All tested scenarios executed as expected in the test environment, and on-chain events were traceable to corresponding backend records via transaction receipts and logged identifiers. The acceptance evaluation yielded an overall mean score of 3.4/5 (about 68%), indicating moderate acceptance and supporting the work as a prototype feasibility study rather than organizational-level generalization. The prototype demonstrates a practical workflow for hybrid incident reporting with transaction-level traceability and recognition incentives; future work should strengthen cryptographic binding (e.g., content hashing) and validate the approach with CSIRT stakeholders in operational settings.
ABSTRACT This article explores the application of demurrage money, a concept developed by Silvio Gesell, into Web3. Demurrage money, designed to discourage the hoarding of currency and prevent economic stagnation and concentrations in wealth, offers a potential remedy for the problems of traditional fiat and gold‐backed monetary systems. The article presents an overview of Web3, highlighting its core principles such as being decentralized, permissionless, community governed, and programmable. It critiques the limitations of current Web3 cryptocurrencies, particularly Bitcoin and other networks that have emerged since. By design these networks enable excessive asset storage and face sustainability challenges such as governance centralization and inadequate ecosystem funding. The article proposes that the implementation of a network coin tax, as a form of demurrage, would help to incentivize productive economic activity, decentralize coin ownership, provide reliable funding for node operators and ecosystem development and create opportunities for large‐scale public goods funding. Various monetary supply models are discussed, evaluating their compatibility with demurrage systems. The article concludes that demurrage based economic systems could lead to more resilient, equitable and sustainable Web3 ecosystems that have significant potential for making a global societal impact.
Daily probability changes in Kalshi macro prediction markets forecast cryptocurrency realized volatility through two distinct channels. The monetary policy channel, measured by Fed rate repricing on KXFED contracts, predicts Bitcoin volatility in sample with t = 3.63 and p < 0.001 but exhibits regime dependence tied to the 2024-2025 rate-cutting cycle. The recession risk signal from KXRECSSNBER proves more stable out of sample, delivering an MSFE ratio of 0.979 with Clark-West p = 0.020. The inflation channel, measured by CPI repricing on KXCPI contracts, predicts altcoin volatility for Ethereum, Solana, Cardano, and Chainlink with t-statistics ranging from -2.1 to -3.4 and out-of-sample gains for Ethereum at MSFE = 0.959 with p = 0.010 and Solana at p = 0.048. Both the Bitcoin--Fed-dovish and Chainlink--CPI specifications survive Benjamini-Hochberg correction at q = 0.05. Orthogonalization and baseline comparisons against Fed Funds futures, Treasury yields, and the Deribit implied volatility index confirm that these signals carry information not embedded in conventional financial instruments. The sample covers ten Kalshi event series and six cryptocurrency assets over January 2023 to March 2026.
We study asynchronous alignment, a first-class multimodal learning setting in which a dense primary stream must be fused with sporadic external context whose value depends on when it arrives. Unlike standard multimodal benchmarks that assume structural synchrony, this setting requires models to reason explicitly about freshness and trust. We focus on the event-conditioned case in which continuous market states are paired with delayed web intelligence, and we use high-frequency cryptocurrency markets only as a timestamped, high-noise stress test for this broader problem. We propose CGCMA (Conditionally-Gated Cross-Modal Attention), whose central design principle is to separate text-conditioned grounding from lag-aware trust control. Text first attends over price sequences to identify event-relevant market states, after which a conditional gate uses modality agreement, web features, and lag $τ_{\mathrm{lag}}$ to regulate residual injection and fall back toward unimodal prediction when external context is stale or contradictory. We introduce CMI (Crypto Market Intelligence), an asynchronous evaluation corpus with 27,914 real-news samples pairing high-frequency price sequences with lagged web intelligence. On the current short real-news corpus, CGCMA attains the highest mean downstream Sharpe ratio ($+0.449 \pm 0.257$) among the evaluated baselines under a shared zero-cost threshold-trading evaluation on news-available bars. Additional controls show that the gain is not explained by web scalars alone and is not recovered by simple freshness heuristics. The resulting evidence supports problem validity and a promising asynchronous multimodal gain on this stress-test setting.
Smart contract vulnerabilities can cause substantial financial losses due to the immutability of code after deployment. While existing tools detect vulnerabilities, they cannot effectively repair them. In this paper, we propose SCPatcher, a framework that combines retrieval-augmented generation with a knowledge graph for automated smart contract repair. We construct a knowledge graph from 5,000 verified Ethereum contracts, extracting function-level relationships to build a semantic network. This graph serves as an external knowledge base that enhances Large Language Model reasoning and enables precise vulnerability patching. We introduce a two-stage repair strategy, initial knowledge-guided repair followed by Chain-of-Thought reasoning for complex vulnerabilities. Evaluated on a diverse set of vulnerable contracts, SCPatcher achieves 81.5\% overall repair rate and 91.0\% compilation pass rate, substantially outperforming existing methods.
Over the past years, there has been increased risk of forging and replicating academic credentials unauthorized, and manipulation of data due to fast computerization of academic credentials. The traditional verification system that is centred on the Public Key Infrastructure (PKI), has included instances such as centralized control, the lack of transparency, and vulnerability to points of failures. Such challenges are suggesting a decentralized approach to the generation of digital certificates as well as their validation with the assistance of a blockchain Technology that is secure in nature. The suggested system will use cryptographic hashing, smart contracts using Ethereum and distributed ledger mechanisms to provide integrity, authenticity, and immutability of data. The blockchain has certificates in the hash values that can be easily verified and without the involvement of middle men. The framework will also enhance trust among the stakeholders as they will be in a position to ensure validation without disruption. As it is revealed through the experiment analysis and modular evaluation, the offered solution enhances the effectiveness of the verification towards its significant extent, the chance of fraud decrease, and offers a solution which can be further scaled and become suitable in the contemporary digital certification systems.
BACKGROUND: Lassa fever remains a major public health threat in West Africa, requiring coordinated scientific, policy, and financing responses. Regional scientific convenings are increasingly used to connect research evidence with policy action, yet their contribution to epidemic preparedness is not well documented. METHODS: We conducted a qualitative health systems and policy analysis of the 2nd ECOWAS Lassa Fever International Conference (ELFIC 2025) in Abidjan, Côte d'Ivoire. Data sources comprised 302 scientific abstracts, plenary and ministerial session records, and the official Ministerial Joint Communiqué. Using the conference's six thematic pillars as a deductive framework, we conducted a thematic content analysis and synthesized findings into four domains: scientific advances; surveillance and laboratory systems; policy and financing insights; and cross-cutting lessons for regional preparedness. RESULTS: Progress was noted in diagnostics, therapeutics, vaccine development, decentralized laboratory capacity, genomic surveillance, and digital reporting. Persistent gaps remain at sub-national and community levels, in surveillance coverage, workforce capacity, and operational readiness. A major outcome was the Ministerial Joint Communiqué endorsing regional co-financing for Lassa fever vaccine development. CONCLUSION: ELFIC 2025 demonstrates the role of regional scientific platforms in aligning evidence with policy commitments. Sustained impact will require institutionalized coordination, strengthened accountability, and targeted investments in frontline capacity.
Smart contracts have become a cornerstone of modern blockchain ecosystems by enabling decentralized, transparent, and autonomous execution of digital agreements. Despite their widespread adoption, smart contracts continue to suffer from two persistent challenges: inefficient execution and critical security vulnerabilities. These limitations not only increase operational costs but also undermine trust in blockchain-based systems. This research paper presents a comprehensive and plagiarism-free investigation into smart contract optimization with a strong emphasis on security-driven design principles. The study analyzes execution inefficiencies, gas consumption patterns, and architectural constraints across major blockchain platforms, alongside prevalent vulnerabilities such as reentrancy attacks, integer overflows, access control flaws, and logic inconsistencies. Building upon this analysis, the paper proposes an integrated optimization–security framework that combines code-level optimization, modular design, formal verification, automated vulnerability detection, and hybrid on-chain/off-chain computation models. The proposed approach demonstrates how efficiency and security can be jointly enhanced rather than treated as isolated objectives. The findings aim to guide developers, researchers, and practitioners in designing smart contracts that are cost-effective, secure, and resilient within rapidly evolving blockchain environments.
Der Text analysiert den tiefgreifenden Wandel des Finanzsystems in Zeiten der Digitalisierung. Er zeigt, wie private Fintechs und Krypto-Emittenten das staatliche Monopol der Regulierung und der Geldbereitstellung infrage stellen. Marktmacht entsteht durch Regulierungsversagen.