Automated Market Makers based on concentrated liquidity, such as Uniswap v3, significantly improve capital efficiency but expose Liquidity Providers (LPs) to adverse selection costs, formalized as Loss-Versus-Rebalancing (LVR). While theoretical literature quantifies these costs, the interplay between realistic blockchain microstructure and endogenous pricing mechanisms remains under-explored. This paper develops a granular Agent-Based Model of a Uniswap v3 pool interacting with a stochastic reference market governed by Heston volatility dynamics. The framework incorporates discrete block propagation, mempool latency, and a heterogeneous population of agents, including latency-sensitive arbitrageurs, smart routers, Maximal Extractable Value searchers, and active LPs benchmarked against a frictionless rebalancing strategy. We propose and evaluate dynamic fee schedules driven by volatility and order-flow toxicity proxies intended to compensate LPs for adverse-selection losses. Our simulations investigate the conditions under which LPs can achieve positive hedged Profit and Loss (fees minus LVR). The analysis suggests that dynamic fee adjustments can improve hedged LP profitability mainly by increasing fee income in states associated with stale-price risk. Depending on the configuration, these rules may also affect realized LVR, but the current aggregate results support compensation for LVR more directly than a reduction of LVR itself.
Blockchain systems expose a large number of tunable parameters that significantly influence system performance. However, in practice, a single parameter configuration is often applied across different workloads, leaving substantial unexploited performance potential. To address this, we propose EchoFlow, a blockchain parameter tuning framework that adaptively adjusts parameter configurations based on workload characteristics, enabling continuous performance optimization. EchoFlow employs a distributed reinforcement learning approach in which multiple actors perform parallel sampling to mitigate the substantial time required for sample generation in blockchain environments. To further accelerate convergence, we introduce a genetic algorithm during the initial phase of training to generate high-quality samples. Extensive experimental evaluations demonstrate that EchoFlow consistently outperforms existing methods across diverse workload scenarios while also reducing training time, highlighting its effectiveness and practical value.
Blockchain has been recognized as a promising technology to improve transparency, authenticity and efficiency in supply chain (SC) traceability for food, pharmaceuticals, textiles etc. It is observed that recent literature have extensively studied blockchain based traceability systems in the domains, level of implementation maturity, technical architecture and sustainability dimensions. There have been experimental models to demonstrate that a practical, inexpensive and scalable blockchain has not yet been fulfilled. In textile and garment, numerous blockchain frameworks consider problems of information asymmetry and low visibility by providing secure sharing of data, smart contract based validation procedure, and transparent tracking (e.g., organic cotton supply chain). Decentralized types such as Hyperledger Fabric provide greater integrity, quality assurance and process traceability over traditional centralized models within the Agri-Food sector. Other possible works address enhancing efficiency, for which a parallel record-search is proposed by developing multi-chunk replication and maximum matching algorithms to lower time overhead by more than 85%. On the whole, the blockchain is promising but needs to be practically validated on a large scale.
This article examines the potential of blockchain-based state registries toenhance transparency and reduce corruption in public administration. The purpose of the researchis to evaluate how distributed ledger technology can address structural weaknesses inherent incentralized registry systems including data manipulation, lack of accountability and limitedauditability. The article employs a qualitative research design based on comparative analysis andcase study methodology drawing on international experiences and existing scholarly literature ondigital governance and blockchain implementation in the public sector. The findings indicate thatblockchain technology strengthens data integrity through immutability, decentralization andcryptographic verification mechanisms, thereby reducing opportunities for unauthorizedalterations and corrupt practices. Evidence from implemented registry reforms demonstratesimprovements in transparency, traceability and institutional trust. The article concludes thatblockchain-based state registries can serve as a foundational infrastructure for strengtheningpublic sector integrity, provided that legal frameworks, institutional readiness and technologicalcapacity are adequately developed to support sustainable implementation.
中文受人工智能自身能力局限,其易产生信息幻觉,且不擅长高精度数值运算。本文档内所有内容应严谨审核。EnglishDue to the inherent limitations of artificial intelligence, it is prone to generating hallucinations and performs poorly in high-precision numerical calculations. All contents in this document should be strictly reviewed. 5D几何统一,一切可计算。从夸克到文明,从DNA到意识。 DOI: 10.5281/zenodo.20798927 Black Hole & UVMM v4.0 CoreDOI: 10.5281/zenodo.20738759 Earth SystemDOI: 10.5281/zenodo.20285613 Cosmic BoundaryDOI: 10.5281/zenodo.20325710 Cosmic EvolutionDOI: 10.5281/zenodo.20677198 Information & Consciousness (Millennium Prize Problems)DOI: 10.5281/zenodo.20325710 UTFF Core (Atomic and Molecular Scale)DOI: 10.5281/zenodo.20343471 UVMM Core Axioms and Mathematical Proofs UVMM v4.0 CORE continue:https://doi.org/10.5281/zenodo.21500910 github.com A Topologically Designed Zero-Pressure Room-Temperature Superconductor _ First-Principles Derivation and CTP Verification这个超导方案可能更靠谱些 UVMM v4.0.15.01 High-Precision Global Calculation AI Knowledge Package.md UVMM v4.0.15 High-Precision Global Calculation AI Knowledge Package(6D‑Coordinate‑SuperKit‑v4.0 ).md UVMM v4.0.15 高精度计算适用领域(中英双语精简版)量子化学与分子化学 Quantum Chemistry & Molecular Chemistry中文:原子半径、键能、反应活化能全域计算,计算误差<0.1%。English: Global calculation of atomic radius, bond energy and reaction activation energy, calculation error < 0.1%.凝聚态材料物理 Condensed Matter & Material Physics中文:超导临界温度、拓扑能隙、合金力学性能预测,整体精度<2%。English: Prediction of superconducting critical temperature, topological band gap and mechanical properties of alloys, overall precision < 2%.生物大分子与意识神经科学 Biomacromolecules & Consciousness Neuroscience中文:蛋白折叠自由能求解,脑意识拓扑序参量精准判别,分类 AUC=1.000。English: Calculation of protein folding free energy, accurate discrimination of brain topological order parameter for consciousness, classification AUC = 1.000.核裂变 / 聚变与衰变物理 Nuclear Fission, Fusion & Decay Physics中文:各类核反应能量完整拓扑积分求解,全套 20 组核反应误差严格控制<2%。English: Complete topological integral solution for energy of various nuclear reactions, the error of 20 groups of nuclear reactions is strictly controlled below 3%.QED 与电弱粒子物理 QED & Electroweak Particle Physics中文:电子反常磁矩匹配标准模型10 −12量级精度,弱混合角偏差<0.01%。English: Electron anomalous magnetic moment matches the Standard Model with precision of 10 −12, the deviation of weak mixing angle is less than 0.01%.宇宙学与引力 Cosmology & Gravitation中文:CMB 功率谱、原初引力波偏振、暗物质暗能量密度推演,计算误差<2%。English: Deduction of CMB power spectrum, primordial gravitational wave polarization, dark matter & dark energy density, calculation error < 2%.量子精密计量 Quantum Precision Metrology中文:铯原子钟频率全环境修正拓扑闭式计算,频率偏差低于2×10 −10 Hz。English: Closed-form topological calculation of full environmental corrections for cesium atomic clock frequency, frequency deviation lower than 2×10 −10 Hz. ERROR edition: First-Principles Derivation of Light Speed as the Acoustic Velocity of Vacuum Superfluid Based on the UVMM Framework Abstract 摘要 English Based on two first-principles axioms—the Global Zero Angular Momentum Axiom (strict zero total cosmic angular momentum) and the Dynamic Möbius Projection Axiom (the fifth dimension constitutes a non-orientable Möbius manifold with curvature-dependent dynamic characteristic radius)—this work establishes a unified geometric framework for black holes within the Unified Vacuum Medium Model & Unified Topological Force Field (UVMM-UTFF). In this framework, black holes are no longer geometric singularities passively bending spacetime, but 5D topological solitons projected onto the 4D boundary. All energy release behaviors of black holes (jets, gravitational waves, electromagnetic radiation) essentially originate from topological phase transitions or steady pumping processes of prestressed vacuum medium. This paper systematically verifies the framework via four independent multi-beacon observational datasets: LIGO-Virgo-KAGRA gravitational-wave catalogs (GWTC-4.0/5.0, containing 390 binary black hole merger events), Event Horizon Telescope (EHT) polarization imaging of M87* and Sgr A*, LHAASO PeV ultra-high-energy gamma-ray observations of five microquasars, and GAIA DR3 Milky Way rotation curve data. The results demonstrate that theoretical predictions match observational values within a factor of 20 across 9 orders of magnitude, ranging from transient merger luminosity () to steady-state AGN jet power (). Dark matter effects are reduced to geometric prestress effects of vacuum medium, without introducing any exotic dark matter particles. 中文摘要 本文基于两条第一性公理 —— 全域角动量归零公理(宇宙总角动量严格为零)与动态莫比乌斯投影公理(第五维为非定向莫比乌斯流形,特征半径随局域曲率动态演化)—— 建立 UVMM-UTFF 框架下黑洞统一几何理论。本框架定义黑洞并非被动弯曲时空的几何奇点,而是 5 维拓扑孤子在 4 维时空边界的投影;黑洞全部能量释放行为(喷流、引力波、电磁辐射),本质为预应力真空介质的拓扑相变或稳态泵浦过程。依托四类独立多信标观测数据完成系统性核验:LIGO-Virgo-KAGRA 引力波目录(GWTC-4.0/5.0,共计 390 例黑洞并合事件)、事件视界望远镜 M87与 Sgr A黑洞阴影偏振成像、LHAASO 五组微类星体 PeV 超高能伽马射线观测、GAIA DR3 银河系旋转曲线观测。结果表明:在瞬态并合光度()至稳态活动星系核喷流功率()跨越 9 个数量级区间内,理论预测与观测值误差控制在 20 倍因子以内;暗物质观测效应被还原为真空介质几何预应力效应,无需引入任何未知暗物质粒子。 Revision of the Definition of the Three Universes: A Triple-Sector Unification Based on 5D Topological Superfluid Ontology twin‑prime conjecture; Goldbach’s conjecture; Kepler’s conjecture
The Universal Form of Historical-Genetic Logic: From the Propositional Matrix to the Computable Index DOI: 10.5281/zenodo.20799967 Author: Aikaterini Xenopoulou TyrokomouIndependent ResearcherORCID: 0009 0004 9057 7432Email: katerinaxenopoulou@gmail.com Theoretical Foundation: Epameinondas Xenopoulos †Based on the Historical Genetic Logic of Epameinondas Xenopoulos, Epistemology of Logic: Logic – Dialectic or Theory of Knowledge (posthumous 2nd ed., 2024) [1, 2]Independent ResearcherORCID: 0009 0000 1736 8555† In memoriam (1920–1994) METHODOLOGICAL NOTE The present work mathematizes and extends central ideas of the formal-dialectical logic of Epameinondas Xenopoulos [1,2], with the direct aim of creating a computable and applicable tool. The mathematical expression of concepts such as dialectical intensity, historical memory, and the critical threshold constitutes a fully explicit, functional, and deliberate interpretative choice. Other consistent mathematizations are equally possible; here we choose those that ensure computational stability, transparency, and broad applicability. The work introduces original mathematical elements (such as the historical memory functions τ(t) and paradox factor Π(t), the stochastic extension, and the explicit form of the synthesis operator). These elements are presented as proposals of the author and are not attributed to Xenopoulos. The theoretical background, the fundamental categories, the logical principles, and the overall architecture belong to the work of Xenopoulos. The systematic formalization, the mathematical analysis, the proofs of the index properties, and the computational applications constitute the original contribution of the present work. ABSTRACT This work introduces the XEPTQLRI index, a computable, domain-agnostic diagnostic tool for anticipating critical transitions in complex dynamical systems. The index is grounded in the formal-dialectical logic developed by the Greek philosopher Epameinondas Xenopoulos (1920–1994), which treats contradiction not as an error but as the driving force of qualitative change. The index quantifies the "dialectical pressure" building within a system prior to a bifurcation. It combines three components: (1) dialectical intensity T(t), expressed as the harmonic mean of opposing tendencies ("Being" B(t) and "Non-Being" N(t)); (2) historical memory τ(t), capturing the direction and momentum of change; and (3) a paradox factor Π(t), which registers whether the system has historically experienced extreme opposing states. The index is defined as: Ξ(t) = [ T(t) · τ(t) · (1 + Π(t)) ] / Θ₀ where Θ₀ is a system-specific critical threshold. We prove that for systems undergoing pitchfork, transcritical, or Hopf bifurcations, the condition Ξ(t) = 1 coincides exactly with the vanishing of the maximum Lyapunov exponent — the mathematical signature of impending instability. The index is invariant under affine transformations of the coherence function, computable in linear time, and provides quantifiable early warning signals. Empirical validation across seven diverse fields — stochastic differential equations, COVID-19 epidemiology, LSTM networks under extreme noise, composting kinetics, open thermodynamics, Lindblad quantum systems, and strategic decision-making — demonstrates that the index reliably detects imminent qualitative shifts, often months before observable regime changes. The XEPTQLRI index offers a rigorous, efficient, and broadly applicable framework for early warning in nonlinear and complex systems, bridging dialectical philosophy with modern dynamical systems theory. Keywords: Historical-Genetic Logic, Formal-Dialectical Logic, Propositional Matrix of the World, XEPTQLRI Index, Dialectical Intensity, Historical Memory, Paradox Factor, Aufhebung, Critical Transitions, Phase Transitions, Bifurcations, Early Warning Signals, Maximum Lyapunov Exponent, Nonlinear Dynamics, Complex Systems, COVID-19 Epidemiology, Quantum Systems, Lindblad Equation, LSTM Neural Networks, Stochastic Differential Equations, Structural Stability, Dual Temporality. Lead Paragraph Detecting critical transitions before they happen: A dialectical index for early warning in complex dynamical systems Predicting when a complex system is about to undergo a qualitative change—whether a pandemic wave, a financial collapse, or a quantum phase transition—remains one of the most challenging problems in nonlinear science. Conventional early-warning indicators often fail to capture the slow accumulation of internal contradiction that precedes a bifurcation. Drawing on the formal-dialectical logic of the Greek philosopher Epameinondas Xenopoulos, we introduce the XEPTQLRI index, a novel pre-transitional diagnostic tool that quantifies the "dialectical pressure" building within a dynamical system. The index combines three components: dialectical intensity (the harmonic mean of opposing tendencies), historical memory (the direction and momentum of change), and a paradox factor that registers whether the system has experienced extreme opposing states in its past. We prove that, for systems undergoing pitchfork, transcritical, or Hopf bifurcations, the index crossing unity coincides exactly with the vanishing of the maximum Lyapunov exponent—the mathematical signature of impending instability. Empirical validation across seven diverse domains—from stochastic differential equations and COVID-19 epidemiology to LSTM networks under extreme noise, composting kinetics, open thermodynamics, the Lindblad equation for open quantum systems, and strategic decision-making—demonstrates that the index provides reliable early warnings, often months in advance of observable regime shifts. The XEPTQLRI index offers a mathematically rigorous, computationally efficient, and domain-agnostic framework for anticipating critical transitions in nonlinear and complex systems. INTRODUCTION The study of change runs throughout the entire history of philosophy. From Heraclitus ("πάντα ῥεῖ" – "everything flows") to Hegel, Marx, and Piaget, thought recognizes reality as an uninterrupted process of genesis, contradiction, and transcendence. Formal logic, although an indispensable tool of science, is founded on the abstraction of time and the principle of non-contradiction (p · ¬p = 0). The Greek philosopher Epameinondas Xenopoulos (1920–1994) developed a Historical-Genetic Logic (or formal-dialectical logic) that incorporates contradiction as the driving force of knowledge, bridging the gap between static formal thought and the dynamic flow of reality. In his work "Epistemology of Logic" [1,2], Xenopoulos establishes three central structures: 1. The epistemological correspondence Sπ ↔ Y(L, B, Θ): knowledge is born from the practical interaction of the subject-in-action (Sπ) with the object (Y), which is analyzed into logical structure (L), material substrate (B), and concrete position (Θ). 2. The Propositional Matrix of the World: a formal structure where each proposition carries a truth value from a discrete fractional spectrum {0, ½v, ½²v, …, 1} and passes through dialectical stages: thesis (A), development of negation (B), rupture (Γ), and new synthesis (Δ). 3. The operator N[Fi(Gj)]: the logical engine that drives propositions from one stage to another, expressing the necessary synthesis of thesis and its negation. The present article mathematizes and operationalizes these structures, giving them an explicit, computable form. For each concept we propose specific mathematical expressions. These choices are functional, not theoretically unique. The present form was chosen for its computational stability, broad applicability, and clear philosophical correspondence. The resulting XEPTQLRI index is not a simple statistical method, but the computable implementation of the dialectical operator itself in a specific, explicit mathematical framework. The empirical verification of the index in seven diverse fields (from stochastic dynamics to neural networks and epidemiology) demonstrates the practical power of this mathematization. PART I – THEORETICAL FOUNDATION 1. The Epistemological Correspondence: Sπ ↔ Y(L, B, Θ) Every cognitive process begins from the practical relation of the subject with the world. Xenopoulos [1,2] conceives this relation as an epistemological correspondence between two poles: · Sπ (Subject-Action): the subject in its active, transformative activity. Sπ is process, not state. It changes the world through action. · Y(L, B, Θ) (Object): the object of knowledge analyzed into three components: o L (Logos): the logical structure, the regularity, the form. o B (Matter): the material substrate, the content. o Θ (Thesis): the concrete spatiotemporal existence. The action Sπ modifies the object Y. This modification, assimilated by the subject, produces knowledge Sα = f(Sπ, Y). The correspondence is dialectical: action transforms the object, the transformation transforms knowledge, and new knowledge guides new action. Knowledge is not a passive image, but a historical product of interaction. This fundamental correspondence constitutes the cornerstone of every formal-dialectical analysis. 2. The Propositional Matrix of the World The knowledge born from the correspondence Sπ ↔ Y crystallizes into a dynamic formal structure: the Propositional Matrix [1,2, pp. 247-249]. Definition 1 (Proposition). A proposition P is defined as: P(x, y, z, t) = [v, τ, σ] with: · v ∈ V = {0, ½v, ½²v, …, 1}: the truth value on a fractional scale. 0 marks complete contradiction ("zero identity"), 1 marks the new integrated synthesis. · τ ∈ {T, D, TD}: the type of proposition (Formal, Dialectical, Formal-Dialectical). · σ ∈ {A, B, Γ, Δ}: the dialectical stage: o A (Thesis): stable formal knowledge. o B (Development of Negation): emergence of internal contradiction. o Γ (Rupture): critical
Valerio Mandarino, Giuseppe Pappalardo, Emiliano Tramontana
Authentication is essential to hold users accountable across online services. Conventional authentication systems rely on centralized architectures or third-party identity providers, which, however, introduce single points of failure, privacy concerns, and limited user autonomy. Conversely, fully decentralized authentication frameworks often struggle to provide reliable identity attestation mechanisms. This makes them vulnerable to Sybil attacks and self-asserted claims, while limiting their interoperability with trust-based systems. This paper presents dAuth, a hybrid blockchain-based authentication architecture based on Ethereum smart contracts to provide cryptographic tokens that enable authentication to services. These tokens, anchored to the smart contract, are derived by users from institutionally certified base credentials issued by an accredited verifying authority and enable authentication to services without further involvement of the authority. Each token is cryptographically bound to a specific service, constrained in scope and duration, and verifiable off-chain through data and cryptographic commitments provided by the user. No plaintext personal information is published on-chain: identity attributes are committed as cryptographic digests, which anchor certified identity data on-chain while keeping the underlying personal information private and auditable. This design removes the verifying authority from the authentication process, as all authentication steps are assisted by the user-controlled smart contract. The verifying authority’s role is limited to initial identity certification and exceptional update procedures. The result is a privacy-preserving and verifiable hybrid authentication framework that leverages the cryptographic security properties of the underlying blockchain infrastructure and inherits its scalability characteristics. The proposed design has been implemented and experimentally evaluated on the Ethereum platform, addressing public blockchain-specific challenges such as scalability constraints and transaction costs to ensure practical deployment.
We propose cryptographic certificates of validity for agentic AI systems. The core idea is to formally specify a correctness or policy condition as a logical predicate, compile this predicate to a witness-checking problem over polynomial constraints, and use a succinct cryptographic proof system (and optionally zero-knowledge) to certify that the condition holds. This offers a middle ground between formal verification of source code, and cryptographic authentication. An agent's action can be accompanied by an independently checkable proof that it satisfies an agreed formal policy, without requiring the verifier to trust the agent or to re-execute computation. We outline the approach at a high level, give the core mathematical translation, relate the proposal to proof-carrying code, zkVMs, formal methods, and agent governance, and note the specification, auditing, and deployment questions that a full implementation must answer.
ABSTRACT This report examines the “Bitcoin as digital gold” narrative through the concept of the Retailization of Scarcity. It argues that Bitcoin’s mathematical supply limit does not automatically create value, and that scarcity becomes financially powerful only when it is narrated, packaged, institutionalized, traded, and repeatedly believed by market participants. Rather than claiming that Bitcoin is inherently fraudulent or meaningless, the report focuses on the structures surrounding Bitcoin: price prophecy, financial packaging, retail investor access, media amplification, and the asymmetry of forecast responsibility. It distinguishes Bitcoin as a protocol and speculative asset from the commercial narratives that transform limited code into a simplified investment myth. The report analyzes how the digital gold metaphor compresses technical, market, custody, liquidity, regulatory, and incentive risks into emotionally powerful language. It also examines how forecasts, exchange-traded products, custodial services, and public narratives can turn uncertainty into perceived inevitability for ordinary investors. The central argument is that the problem is not the existence of Bitcoin itself, but the hidden retailization of scarcity: the process through which mathematical scarcity is disguised as inevitable wealth, price prophecy as foresight, convenience as conviction, and monetization as monetary truth. This document is an analytical report and does not constitute investment advice.
The Trust Revolution in Digital Health (Comprehensive)The total digitization of health care demands a fundamental change in the practice of data management, because the existing Digital Health Records (DHR) systems involve insurmountable tension between the unconditional privacy of patients and the necessity to benefit large-scale medical data utility. Although it has been demonstrated that established decentralized solutions, especially those that are based on the permissioned Hyperledger Fabric (HLF) are effective in ensuring integrity of data and basic Role-Based Access Control (RBAC), they, structurally, fall short of three important, real-life shortcomings: the inherent threat of unlimited unauthorized access, the full traceability of user identities on the unalterable ledger and the ethical stalemate regarding collaborative use of data. This paper presents a very new, integrated DHR architectural design that greatly secures the HLF core by integrating three synergistic, state-of-the-art cryptographic and privacy enhancing pillars: 1) Time-Bound Keys (T-BK) a very innovative mechanism that substitutes current access policy flags with cryptographically enabled and self revoking key mechanism to enforce granular time-sensitive access control. 2) Zero-Knowledge Proofs (ZKP) and Fabrics Transient Fields, which guarantee assured transaction unlinkability and full.
Communication and networked systems rely heavily on cryptographic digital signatures to ensure message authenticity, integrity, and non-repudiation. However, rapid advancements in quantum computing and artificial intelligence (AI) have expanded the attack surface, posing significant threats to conventional public-key schemes such as RSA, DSA, and ECDSA. Quantum algorithms undermine their underlying hardness assumptions, while AI-driven techniques enable traffic analysis, side-channel inference, and behavioral pattern recognition. This review presents a structured analytical assessment of post-quantum signature schemes and privacy-preserving authentication mechanisms to address these dual threats. It evaluates lattice-based, hash-based, and zero-knowledge proof-based signatures, as well as anonymity-enhancing schemes such as ring and group signatures. A unified analytical framework is introduced to map cryptographic schemes to quantum and AI-assisted threat models, based on parameters such as security strength, anonymity, efficiency, and applicability. The analysis shows that lattice- and hash-based schemes provide strong quantum resistance, while privacy-preserving mechanisms enhance anonymity but introduce performance trade-offs. It also identifies a gap in integrating post-quantum cryptography with AI-resilient privacy mechanisms. The findings highlight the need for unified, future-ready cryptographic designs.
ABSTRACT Financial innovation profoundly reshapes the financing mechanisms, risk structures, and resilience of agricultural value chains. Based on 40 high‐quality English studies (2018–2026), this review identifies five core agricultural financial innovations: digital payments, digital credit, supply chain finance, blockchain, and decentralized finance, as well as climate derivatives and blended finance. These innovations enhance financial inclusion for smallholders and agribusinesses by mitigating information asymmetry and easing financing constraints. The integration of green finance and digital inclusive finance lifts agricultural green total factor productivity and promotes eco‐friendly technology adoption. The synergy of supply chain finance, AI, and blockchain strengthens the shock resistance, recovery, and adaptive transformation capabilities of agricultural value chains. Constraints include the digital divide, technological uncertainty, insufficient regulation, and unsustainable business models. This paper constructs a “technology–institution–value” framework to illustrate transmission pathways and puts forward future research directions and policy implications.
Abstract Privacy is a precondition of dignity, autonomy, and democratic legitimacy. This chapter reconceptualizes privacy in an AI‑saturated economy by tracing its philosophical roots and codification and by comparing regulatory models in the EU, United States, Canada, China, and Indigenous data sovereignty frameworks. We diagnose structural limits of consent‑heavy regimes, commodification of personal data, and private surveillance infrastructures that states increasingly co‑opt. We then outline a program for effective protection that shifts responsibility from individuals to accountable institutions through rights‑based law, privacy‑preserving technical design (e.g., Global Privacy Control, Self-Sovereign Identity, Zero-Knowledge Proofs), and coordinated international governance. Treating privacy as a public good anchors the proposal.
Centro Tecnolóxico de Telecomunicacións de Galicia
Este trabajo presenta una evaluación comparativa del rendimiento de tres tecnologías Zero Knowledge Proof (BBS, Longfellow, Crescent Credentials) integradas en la cartera digital NovaWallet. Se analizan métricas de tiempo de generación/verificación de pruebas y eficiencia de espacio en escenarios reales de demostraci´on selectiva de atributos. Los resultadosmuestran [incluir 1-2 hallazgos clave cuando se tengan los datos].
Belonging in eastern Democratic Republic of Congo is negotiated through everyday practices that link land, ancestry, displacement, and local authority. This article examines how ethnicized categories are produced in routine encounters and dispute arenas rather than only through elite politics or episodes of violence. Drawing on comparative ethnography in a periurban setting near Bukavu and a rural setting in Kalehe, based on 72 interviews, observation of 18 dispute and community forums, and a bounded discourse corpus of radio, public speech, and WhatsApp materials, it analyses how people assemble and contest claims to local membership. The study shows that local citizenship is produced through practical regimes of proof in which people must demonstrate credibility via relational anchors, witnesses, documents, and moral narratives about stewardship and suffering. Belonging emerges as graded and situational rather than binary: the same person may be recognized as a church member, tenant, in-law, displaced person, or stranger depending on the arena and resource at stake. Dispute resolution forums institutionalize these claims, while rumor and media infrastructures accelerate boundary hardening or enable restraint. The article contributes to scholarship on boundary making, autochthony, and everyday bordering by showing how local citizenship is assembled through ordinary evidentiary demands and partial forms of recognition.
Ethereum Layer 2 (L2) rollups improve scalability but expose a trade-off between fast sequencer soft finality and slow Layer 1 (L1) settlement finality, limiting latency-sensitive applications that require timely and durable guarantees on transaction ordering. We introduce a Byzantine Fault Tolerant (BFT) finality layer that extends existing rollup architectures without requiring L1 or rollup protocol changes. This layer provides deterministic transaction-level finality ahead of L1 settlement by committing to the sequencer's transaction order, bridging the gap between soft and hard finality. At its core, the layer uses a 1-chain variant of the Jolteon consensus protocol, adapted to the rollup setting where a single sequencer determines transaction ordering. Experiments show sub-second committee finalization latency and stable performance under Byzantine faults.
Ethereum account classification is essential for identifying individuals engaged in illicit transactions and analyzing behavioral patterns across various account types. This process serves as a critical mechanism for monitoring and regulating unlawful activities within transactional markets. However, the Ethereum network exhibits the characteristics of a complex heterophilic graph which poses significant challenges to the effectiveness and performance of conventional graph neural networks (GNNs). To address this challenge, the present study proposes FSGCN(Fourier-Sage GCN), a novel architecture for heterophilic graph neural networks (GNNs) that integrates Kolmogorov–Arnold Networks (KANs) with GraphSAGE. FSGCN is specifically designed to adapt efficiently to the structural complexity of heterophilic graphs. By leveraging KANs to extract high-order neighborhood information and employing GraphSAGE to capture low-order neighborhood patterns, FSGCN effectively aggregates both homophilic and heterophilic features, thereby improving classification performance. Furthermore, to improve training efficiency and generalization, we propose the MLPInit weight initialization scheme and the DropEdge graph augmentation technique. Experiments on a large-scale Ethereum transaction dataset show that FSGCN achieves an F1-score of 91.8% and a classification accuracy of 91.6%, significantly outperforming traditional homophilic and heterophilic GNN baselines. Additionally, FSGCN demonstrates high training efficiency, completing each epoch in just 2.302 s per epoch and improving overall training speed by 130.4% compared to conventional GraphSAGE.
Federated Learning (FL) enables privacy-preserving collaborative learning for Internet of Vehicles (IoV) scenarios, but extreme heterogeneity of vehicular-edge-cloud resources severely limits system efficiency. Dynamic scheduling strategies mitigate this issue but introduce new trust concerns: verifying fair scheduling decisions and faithful client execution of compression instructions without privacy leakage remains an open challenge. We propose Nautilus, a verifiable efficient federated learning framework. First, a multi-dimensional resource-aware scheduling algorithm dynamically allocates compression ratios and training tasks based on vehicle bandwidth, latency and computing power, improving training efficiency. Second, a Zero-Knowledge Proof (ZKP) mechanism ensures scheduling fairness and execution compliance while preserving privacy. Experiments show the framework reduces communication overhead and accelerates convergence with guaranteed system integrity.
Protecting patient privacy in clinical trials and healthcare data management is becoming more difficult due to the growing amount of medical data. This study introduces a novel hybrid framework that combines fully homomorphic encryption (FHE) and zero-knowledge proofs (ZKPs), building on previous work that used blockchain and homomorphic encryption for secure cohort selection. By assigning result verification to effective ZKP constructs, the hybrid approach overcomes the main drawbacks of FHE, including high computational overhead and precision loss. Our tests show that this integrated approach maintains patient privacy while greatly increasing computational efficiency. We wrap up by talking about future research directions and possible healthcare applications.
Modern representative democracies are increasingly vulnerable to systemic structural failure modes, including special-interest capture, asymmetric foreign intelligence leverage, and informational noise saturation (astroturfing/botnets). This paper introduces the Cryptographic Agora, a novel institutional framework that transitions governance from representative mediation to a scientifically audited, direct epistocracy. The model synthesizes three core architectural components: (1) state-verified biometric identity mapping coupled with Zero-Knowledge Proofs (ZKPs) to guarantee non-traceable, un-hackable civic participation; (2) a dynamic reputation engine utilizing Quadratic Weighting to mitigate the concentration of charismatic authority; and (3) a double-blind, retrospective peer-review protocol modeled on the scientific method to vet policy proposals. We evaluate the structural resilience of this framework against traditional threats, detailing its capacity to achieve a self-correcting equilibrium while maintaining individual voter safety and systemic legitimacy.
Proof-of-Work blockchains secure consensus through hash puzzles, producing no external value. In this research, we propose a decentralized AI economy where nodes are rewarded for useful machine-learning work, i.e., inference and training, instead of ineffective hashing method. Our proposed three-layer architecture separates compute, validation, and economic coordination. We formalize it via a $(θ_c, θ_w, W)$-closed-loop token economy and derive a sufficient-stake condition for honest participation. While existing Grover's algorithm provides only a quadratic speedup against hash puzzles, it does not accelerate ML-native linear algebra. On the other hand, Shor's algorithm threatens classical blockchain signatures. Post-quantum migration to lattice-based and hash-based standards can address the signature layer. Therefore, useful-work consensus thus offers both economic and quantum-security advantages over classical proof-of-work.