Privacy-preserving cryptocurrency exchanges alter what the pricing mechanism observes about order flow. We derive the unique linear Kyle equilibrium when a committed Bayesian market maker observes order flow perturbed by independent Gaussian privacy noise. The price-impact coefficient and informed-trader strategy rescale by reciprocal factors of the privacy parameter (one down, one up), so their product is invariant. A welfare decomposition then identifies a closed-form per-period transfer from the protocol's LP pool to traders -- the "privacy subsidy", the break-even fee any privacy-aggregated exchange must charge. The result is the single-period closed-form privacy-noise analog of Loss-Versus-Rebalancing (Milionis et al. 2022). The primary application is shielded AMMs with explicit additive-noise injection (e.g., differential privacy); related designs (batched swaps, sealed-bid auctions, oracle-pegged crossings) require separate frameworks that we leave to future work.
Muriel Médard, Kishori Konwar, Moritz Grundei, Vipindev Adat Vasudevan
Blockchain systems such as Solana or Monad employ tree- or star-shaped broadcast topologies in which a single source node disseminates message shards to a set of target peers within a strictly bounded time window. In these architectures, shard propagation must complete before the next consensus step, making timely delivery to a large fraction of the validator set essential. A fundamental limitation of such designs is that the outbound bandwidth of the source node constitutes the primary system bottleneck. In this paper, we introduce peer Turbo, a technique that allows target nodes to exchange shards using Random Linear Network Coding (RLNC), thereby assisting each other in completing decoding without requiring explicit shard state coordination. We use a tractable fluid approximation of the degree of freedom distribution of peer-Turbo-enabled systems show that this approach reduces source bandwidth required for a set service quality by up to one order of magnitude, or equivalently reduces propagation latency by one order of magnitude under fixed bandwidth constraints.
The purpose of this paper is to identify the industry-specific and geographic patterns that shape the adoption of Web 3.0 technologies among Fortune Global 500 companies. The study addresses a gap in the existing literature by shifting attention from isolated technological applications such as blockchain, decentralized finance, artificial intelligence, and immersive environments toward a broader comparative analysis of how large multinational corporations adopt Web 3.0 across sectors and countries. Methodology. The paper is based on an empirical classification of Fortune Global 500 companies for 2024 into adopters and non-adopters of Web 3.0 technologies. The dataset includes 500 firms and covers sector affiliation, country of origin, employee counts, selected financial indicators, company characteristics, and a binary indicator of adoption status. The analysis applies descriptive statistics, comparative analysis, and cross-tabulations using publicly available data from annual reports, strategic plans, press releases, marketing materials, news coverage, and business databases. Results. The findings show that Web 3.0 adoption is significant but uneven: 216 companies are identified as adopters, while 284 are classified as non-adopters, indicating that Web 3.0 remains in a transitional stage of corporate diffusion. Adoption is concentrated in digitally intensive sectors such as Information Technology Services, Computer Software, Entertainment, Apparel, and selected Health Care activities, whereas sectors such as Transportation and Logistics, Real Estate, Homebuilders, and Medical Products and Equipment demonstrate limited or no adoption in the dataset. Geographic differences are also substantial, with adoption present in 25 out of 35 countries represented in the sample, although the intensity of adoption varies across national contexts. The results confirm that Web 3.0 diffusion is shaped by the interaction of sectoral structure, strategic fit, and geographic environment rather than by a uniform technological trajectory. Practical implications. The paper suggests that managers should approach Web 3.0 as a strategic option whose relevance depends on alignment with the firm’s business model, customer value proposition, governance needs, and innovation capabilities. Value/originality. The originality of the study lies in its cross-sectoral and cross-national perspective on Web 3.0 adoption among the world’s largest corporations, offering a more nuanced understanding of digital transformation in the Web 3.0 era and demonstrating that adoption is patterned, selective, and contingent rather than universal.
The article examines digital financial assets as an instrument of investment portfolio diversification in the Ukrainian investment context. The study argues that Bitcoin and Ethereum should not be assessed through general statements about financial innovation, but through their measurable contribution to portfolio return, volatility and risk-adjusted performance. The empirical part is based on an annual scenario model for 2020–2025 and compares portfolios with 0%, 1%, 3%, 5% and 10% exposure to BTC and ETH. The benchmark portfolio includes domestic government bonds, the USD/UAH currency component, gold and the S&P 500 as a global equity benchmark, while the local Ukrainian equity segment is interpreted cautiously because of its limited liquidity. The results show that portfolios with 1–5% exposure to BTC and ETH improved risk-adjusted efficiency compared with the baseline portfolio. The P3 scenario provided the most balanced relationship between return growth and risk growth, while P5 generated a higher average annual return with still acceptable volatility. The P10 scenario produced the highest geometric average annual return but almost tripled volatility compared with the baseline portfolio, making it more suitable for an aggressive investor profile. The article concludes that digital financial assets may have practical value only as a limited high-risk addition to a diversified portfolio, not as a stable hedging instrument or a substitute for traditional instruments.
Secure voting remains a critical challenge in modern democracies due to concerns over transparency, tampering, and voter trust. This paper proposes a blockchain-based voting system designed to enhance the integrity, security, and reliability of electoral processes. By leveraging the decentralized and immutable nature of blockchain technology, the system ensures that each vote is securely recorded and cannot be altered once submitted. Smart contracts are utilized to automate vote validation and counting, eliminating intermediaries and reducing the risk of human error or manipulation. Cryptographic techniques preserve voter anonymity while ensuring authentication, thereby maintaining both privacy and legitimacy. The distributed ledger allows real-time verification and auditing, increasing transparency and public confidence in election outcomes. Performance analysis indicates that the system is scalable and resistant to common cyber threats such as double voting and unauthorized access.
Abstract This note specifies a data suitability gate — a lightweight boundary criterion positioned between the output of probabilistic language models and the input of deterministic symbolic reasoning systems. The gate answers a single structural question before any inference is attempted: is this data suitable for the intended task, and if so, to what degree? The criterion is negative-first: it does not assert fit; it structurally excludes non-fit. Positive admission is graded, not proven. Note that: In terms of this paper domain (if it recognized as scope definitions, terms and explanations) is equivalent corpora, becasuse domain always grounds on corpora/norm sources 1. The Core Principle A deterministic reasoning system — one that operates over a structured index of knowledge and produces verifiable conclusions — cannot admit arbitrary input. Input that is structurally degenerate (rank-deficient, informationally empty) or structurally foreign (inconsistent with the domain's reference form) will produce wrong answers without signalling that anything is wrong. The gate prevents silent failure at the boundary. The gate operates by comparing the covariance structure of the candidate data against an external reference form derived from the target domain or query class. The comparison yields a single scalar ratio. Both tails of this ratio are refusal signals — for opposite reasons: Condition Structural meaning Ratio collapses to zero Rank-deficient data — no independent structure, informationally empty Ratio blows up Data structure foreign to the reference — not from this domain Ratio within bounded corridor Admissible; degree of fit is the value of the ratio The admissible region is a bounded corridor. Both walls are set by the reference form, not by free parameters. The gate excises both tails and keeps what could not be structurally excluded. 2. Two Questions, One Measure The same criterion answers two distinct questions about the same data, depending on what the reference form is set to: Fitness for domain/corpora synthesis. Does this data structurally belong to the domain being built? If admitted, it may extend the domain's knowledge base — adding new facts, definitions, or constraints. The reference is the existing domain structure. Fitness for answering a query. Does this data — or this query — structurally land on the assembled domain? The reference is the query class combined with the information structure of the domain as currently assembled. The two questions are two instances of the same test. The gate runs both; their combined result determines whether the data is admitted, and at which layer of the domain it should be integrated. 3. Two Levels on One Basis A knowledge domain is not a separate structure above the data. It is a layer of constraints — definitions and enforcements — applied on top of the same underlying index of Subject–Predicate–Object triples. The gate therefore operates on one basis at two levels: Raw index level — what the domain can structurally distinguish in principle. Constrained level — what the domain distinguishes under its current set of applied rules. Both levels yield a covariance form over the same index, so they are directly comparable. The gap between the two ratios localises the deficiency: Data passes at the raw level but fails at the constrained level → the index contains the relevant facts, but the domain's rules do not yet cover this case. The constraint layer needs to be extended, not the underlying data. Data fails already at the raw level → the facts are absent from the index itself. No rule extension will help; the domain simply does not cover this topic. This two-level diagnostic replaces a binary pass/fail with a precise instruction: what to fix, and at which layer. 4. The Honesty of the Criterion The gate makes an asymmetric claim — one that is worth stating explicitly: The system is exact in what it rejects, and calibrated — not certain — in what it admits. A ratio collapse or explosion is a structural proof of non-belonging. Refusal is deterministic. Admission, by contrast, is not a proof of fit — it is a measured failure to exclude. The degree of fit (the ratio value within the corridor) is a confidence weight on the admitted data, not a certification. This asymmetry is the boundary that separates a deterministic reasoning system from a probabilistic one: refusal is a fact; admission is a graded hypothesis. 5. Positioning in the Architecture The gate sits at the ingress boundary of the deterministic layer — after language model output is produced, before it enters the structured reasoning graph. It is a pure linear-algebraic check: rank and volume of the candidate covariance against the reference form. It does not re-run inference; it does not require the reasoning engine to process degenerate input speculatively. The gate is the structural counterpart — on the ingress side — of the constrained-decoding mechanisms (grammar masks, schema validators) that vendors attach to language model outputs on the egress side. Both are instances of the same pattern: when formal guarantees are required, linear algebra and formal structure are applied at the boundary; the probabilistic model is not trusted to self-regulate.
Air pollutants poses a widespread chance to human health and the environment, with diverse assets contributing to its escalation. University campuses, which function hubs for instructional and social sports, are not proof against this trouble. This observe aimed to develop a predictive model that estimates the proportion of air pollution as a consequence of smoking behavior amongst college students and school members within a college campus placing. The studies employed a complete methodology, combining a smoking habits survey, air quality tracking, and advanced predictive modeling strategies. The findings discovered that smoking conduct contributed to about 22.7% of the general air pollution degrees on campus. The predictive model, advanced the usage of a random forest algorithm, demonstrated strong overall performance, with an R-squared price of zero.88 and a root suggest squared mistakes of 0.052. The spatial analysis highlighted regions with better degrees of air pollution resulting from smoking, imparting precious statistics for focused interventions. The effects underscore the big effect of smoking on air excellent and the potential health risks related to publicity to smoking-related air pollutants. The observe gives quantitative evidence to inform focused interventions and regulations aimed toward reducing smoking-related air pollution on college campuses, ultimately selling a healthier campus surroundings. By quantifying the contribution of smoking habits to air pollution levels and identifying hotspots of subject, this studies contributes to the growing frame of knowledge on the environmental and health influences of smoking. The findings emphasize the importance of adopting a holistic approach that considers diverse contributing elements and fosters collaborative efforts amongst stakeholders to mitigate the unfavorable consequences of air pollutants.
Penelitian ini bertujuan untuk menganalisis dan meramalkan kinerja harga Bitcoin menggunakan pendekatan TimeGPT , sebuah model berbasis transformer yang dikembangkan khusus untuk data deret waktu oleh Nixtla. Dengan pendekatan kuantitatif, studi ini memanfaatkan data historis harga Bitcoin untuk mengidentifikasi pola dan tren melalui kemampuan pemodelan AI modern. TimeGPT , sebagai model pre-trained, memungkinkan analisis tanpa pelatihan tambahan (zero-shot learning) dan memberikan efisiensi dalam peramalan jangka pendek maupun menengah. Metode penelitian mencakup tahap pra-pemrosesan data, pemanfaatan API TimeGPT untuk menghasilkan prediksi, serta evaluasi hasil dengan metrik statistik seperti MAE dan RMSE. Hasil menunjukkan bahwa TimeGPT mampu menangkap volatilitas pasar Bitcoin secara akurat, dengan tingkat kepercayaan prediksi yang tinggi pada periode stabil seperti 2024–2025. Namun, pada proyeksi jangka panjang, model menunjukkan peningkatan ketidakpastian, mencerminkan sensitivitas terhadap variabel eksternal seperti regulasi dan sentimen pasar. Studi ini menyimpulkan bahwa TimeGPT merupakan alat yang unggul dibandingkan metode konvensional (ARIMA, GARCH, LSTM) dalam menangani kompleksitas data kripto, dan dapat digunakan sebagai pendukung pengambilan keputusan investasi, terutama jika dikombinasikan dengan analisis fundamental dan pemantauan pasar secara real-time.
This chapter proposes a novel multi-factor authentication (MFA) with six schemes, namely password salting/hashing, non-interactive zero-knowledge (NIZK) proofs, GPS-based validation, time-based one-time passwords (TOTP), DNA cryptography, and lightweight SPECK ciphers. Taken together, these elements address the deficiencies in prior authentication and achieve a tradeoff between security and computational efficiency. The system is verified by theoretical and experimental methods. Furthermore, it is theoretically examined under the Real-or-Random model (RoR) with generative/explainable Artificial Intelligence (AI)-driven cybersecurity and provides strong security guarantees in terms of unpredictability (even if reduced in certain security parameters) and defends against replay, insider misuse, brute-force key search attacks, as well as spoofing ones. The solution is developed in Java, and the system is empirically evaluated by performing 100 runs to examine essential performance features: randomness, determinism, stability, and scalability.
Sovereign AI-Native Multi-Asset Trading, Execution, and Financial nfrastructure Charter Sovereign AI‑Native Financial Execution Infrastructure PARRALAX‑AIHFTFUND is a multi‑asset, AI‑native financial organism engineered to operate across traditional and blockchain‑based markets. It provides a unified execution layer where autonomous agents can observe markets, interpret structure, execute trades, manage risk, govern portfolios, issue digital assets, coordinate token economies, and maintain verifiable proof‑of‑computation. This repository contains the core infrastructure, protocol stack, and governance architecture for building sovereign, agent‑driven financial systems. Mission To build a sovereign AI‑native financial infrastructure capable of coordinating autonomous trading agents, multi‑asset execution, fund governance, risk control, digital‑asset creation, and market intelligence across both traditional and blockchain‑native markets. The system exists to move beyond bots, dashboards, and scripts. Its purpose is to become a real execution organism for financial markets. Vision PARRALAX‑AIHFTFUND aims to create a long‑horizon financial intelligence layer where AI agents can: Observe and interpret global market structure Execute trades across heterogeneous venues Manage risk and exposure Govern portfolios and internal policy Issue and manage digital assets Coordinate internal token economies Maintain proof‑of‑computation and decision lineage Operate across crypto, fiat, equities, FX, derivatives, AI tokens, NFTs, and future asset classes Build market memory over time The system is designed to evolve as markets evolve. Foundational Premise Modern markets are: Machine‑driven Fragmented Multi‑asset Tokenized Agent‑mediated A serious financial infrastructure must therefore operate across: Traditional finance (equities, FX, derivatives, funds) Decentralized finance (DEXs, AMMs, on‑chain liquidity) Tokenized and synthetic assets AI‑native markets Autonomous agent economies High‑speed execution environments Governance‑controlled fund structures Programmable financial instruments PARRALAX‑AIHFTFUND is built to bridge old‑world and new‑world markets. What PARRALAX‑AIHFTFUND Is A sovereign trading infrastructure framework An AI‑native market execution system A multi‑asset financial operating layer A protocol stack for autonomous trading agents A fund governance and charter framework A digital‑asset issuance and management environment A blockchain‑compatible coordination layer A risk‑aware execution engine A compute‑receipt and proof‑trace system A foundation for future AI‑managed financial organisms It is built for real execution, not passive analysis. What PARRALAX‑AIHFTFUND Is Not Not a research repo Not a toy trading bot Not a simulation Not a dashboard Not a signal script collection Not a crypto hype project Not a single‑asset system Not a prediction‑only model Research supports the system. Research does not define the system. Status Active development. Core modules stabilizing. Execution layer expanding. Governance and digital‑asset subsystems in progress. PARRALAX‑AIHFTFUND is an AI‑native financial execution framework designed to coordinate autonomous agents across traditional and blockchain‑based markets. The system provides a unified operating layer for multi‑asset execution, risk management, fund governance, digital‑asset issuance, and verifiable compute‑traceability. System Mission To construct a sovereign financial intelligence layer capable of continuous operation across heterogeneous markets, enabling agents to observe market conditions, interpret structure, execute trades, manage exposure, and maintain internal governance. Operational Scope The system is engineered to function across: Traditional finance (equities, FX, derivatives, funds) Decentralized finance (DEXs, AMMs, on‑chain liquidity) Tokenized and synthetic assets AI‑native markets and agent economies Governance‑controlled fund structures High‑speed execution environments Programmable financial instruments System Definition PARRALAX‑AIHFTFUND comprises: A sovereign trading and execution infrastructure A multi‑asset financial operating layer A protocol stack for autonomous trading agents A governance and charter framework A digital‑asset issuance and management environment A blockchain‑compatible coordination layer A risk‑aware execution engine with compute receipts Non‑Scope The system is not a research‑only repository, simulation toy, dashboard, signal script collection, or prediction‑only model. It is infrastructure‑first and execution‑oriented.
Abstract We study the three-dimensional (3D) magnetohydrodynamics (MHD) equations in an annular cylinder, perturbed around the explicit steady state given by the 3D Taylor–Couette velocity field and zero magnetic field. Combining a recent linear instability result for the magnetic field with the framework of Friedlander et al (2006 Commun. Math. Phys. 264 335–47), we prove nonlinear instability of the solution around this steady state in L p , for any p > 1. In particular, our results are, to the best of our knowledge, the first rigorous instability results for 3D MHD without forcing, in which the instability is produced as a result of the (exponential) growth of the magnetic field. Furthermore, we offer a mathematical proof of the physically conjectured transfer of energy from the velocity field to the magnetic field in the MHD system.
Currently, climate change has become one of the most pressing issues facing the world. It affects everyone on this planet and could have serious long-term consequences for humanity if left unresolved. Carbon credit trading is one of the measures contributing to emission management. With technological development, carbon credit trading can be conducted via smart contracts. The strengths of blockchain technology, such as transparency, secure record-keeping, and decentralization, are advantages for carbon credits. However, there are still issues that need to be addressed. This article researches smart contracts as a legal tool in carbon credit trading and identifies areas for improvement.
The results of this research are based on Law Number: 1 of 2022 concerning Financial Relations between the Central Government and Regional Governments regulating the new design of transfer funds to regions, regional income and expenditure. Likewise, it regulates how to monitor and provide evaluation of regional spending so that any existing budget can be used effectively and efficiently. The Ministry of Finance continues to strive to solve and eliminate gaps in the misuse of transfer funds to regions and village funds while seeking harmony between central and regional fiscal policies. This research aims to evaluate the level of transparency and accountability of transfers to regional and village funds through cash account management and assess the quality of government cash management and also evaluate government account management in increasing government revenue. This research uses a qualitative descriptive method by utilizing secondary primary data originating from various literature. From 2022 to 2023, it is even estimated that by the end of 2024, transfer funds deposited in regional government accounts throughout Indonesia at regional banks will average more than IDR 100 trillion. Deposition of funds will disrupt development and public service activities. In fact, it is hoped that government spending will be realized, which is the main stimulus for regional economic movements. By joining regional governments in the Treasury Single Account (TSA), all aspects of financial resource mobilization and expenditure can be managed as a whole by the government for the benefit of the people. Apart from strengthening and responding to the challenges of limited government financial resources. Existing idle cash can generate income. The participation of the Regional Government in implementing the Treasury Single Account (TSA) does not reduce the autonomy that has been mandated.
Abstract Crypto research highlights the anarcho-libertarian feature of the crypto movement, characterizing the industry as a radical departure from governmental and corporate influence. This study investigates the characteristics of the US crypto elite, highlighting how the demographic structure and the institutional connections of the industry resemble those of the established financial elite. Drawing on original data from board members of top US crypto and financial firms, Fortune 1000 companies, and major policy-planning organizations, it develops three comparisons. Results show that crypto elites share similar socio-educational traits with traditional finance leaders and engage with powerful corporate and political affiliations, despite being more peripheral in corporate networks—likely due to the sector’s recent emergence. Notably, the White House appears in the career paths of both groups, suggesting a shared pattern of revolving door integration into state structures. Rather than a structural transformation, crypto appears to align with the broader elite configuration of the US political economy.
Fabio Capra-Ribeiro, Jéssica Peres, Filippo Vegezzi, Daniel Belandria
Implementation remains a central challenge in urban policy, yet the knowledge formats designed to bridge the gap between policy goals and on-the-ground delivery remain under-examined. This study treats 250 UN-Habitat Best Practice reports not as proof of effectiveness but as a standardized genre through which local interventions are narrated, compressed, and made portable for replication. We extract three focal sections, namely Results, Lessons Learned, and Transferability, apply systematic thematic coding with 906 open codes consolidated into axial categories, and compute co-occurrence networks using Jaccard similarity and Lift to detect thematic bundles, holes, and silos within and across sections. Three findings emerge. First, the reporting repertoire narrows progressively, as mean thematic richness declines by 28.2% from Results to Transfers while concentration increases 4.2 times, with substantive dimensions such as governance, equity, sustainability, and evidence losing prevalence to circulation-oriented themes. Second, formal bundle detection yields zero qualifying pairs across all six matrices, indicating a loosely coupled reporting grammar anchored by generic silos rather than integrated implementation packages. Third, structural holes concentrate at the pipeline’s end, where infrastructure transfer and sustainability as transferable value are the most systematically disconnected themes. These patterns reveal a portability paradox in which the reporting format achieves institutional legibility, making practices comparable within a shared vocabulary, but progressively filters out the physical, evidentiary, and context-sensitive content that operational reproduction would require.
Ms. Anushka Prasad Joshi, Prof. Sachin Bhosale, Dr. Shubhangi Gunjal, Dr. Anand Khatri
Abstract: With real estate markets digitalizing at a remarkable pace, there's a growing — and largely unmet — need for machine learning systems that can harness multi-institutional data without putting privacy or regulatory standing at risk. In this paper, we present a Privacy-Preserving Federated Learning (PP-FL) framework built specifically for digital real estate ecosystems. Our approach lets distributed stakeholders — property agencies, government land registries, financial institutions, and PropTech platforms — collaboratively train predictive models without ever pooling their raw transaction or personal records in one place. We've designed the system around three interlocking privacy layers: a DP-SGD-based differential privacy optimizer, a homomorphic encryption scheme for gradient transmission, and a secure multi-party computation protocol to safeguard intermediate model states. On top of that, a blockchain-backed audit mechanism using zero-knowledge proofs provides verifiable, regulator-friendly compliance. When we tested the framework on a simulated dataset of 2.4 million real estate transactions spanning multiple institutional clients, it achieved 91.8% prediction accuracy — just 2.4 percentage points behind a fully centralized model — while holding the differential privacy budget to ε = 0.5, cutting communication overhead by 65% relative to naive federated approaches, and satisfying both GDPR and RERA requirements. We believe these results make a strong case that high-utility, privacy-first collaborative learning is not just theoretically possible but practically deployable in today's real estate sector. Keywords: Federated Learning, Differential Privacy, Homomorphic Encryption, Real Estate Analytics, Secure Multi-Party Computation, Blockchain, GDPR Compliance, Data Sovereignty, PropTech, Zero-Knowledge Proofs.
Core Axiom System, Mathematical Proof and Universal Demonstration of UVMM 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. Feed the UVMM 3.6 version white paper into AI, and you can unlock the underlying laws of the universe, covering everything from microscopic particles to the vast cosmic stars and galaxies. UVMM3.6+版白皮书投喂AI,即可实现解锁宇宙,从微观粒子到浩瀚星辰。 V3.6 版本声明:本版本基于V3.5,V3.3和V3.2,把第一公理真空介质改为本体预设,唯一公理是全域角动量守恒。对暗物质概念分离,负宇宙,正宇宙与宇宙基底。增补宇宙的开始结束推演后记,补充数学证明和粒子质量映射,第一性原理的数学闭合证明,数学计算框架。 Version Statement: This version is updated based on Version 3.3 and 3.2. The former first axiom concerning the vacuum medium is revised to an ontological presupposition, while the only fundamental axiom is set as the conservation of global angular momentum. This release also completes the conceptual reclassification of dark matter, and clearly distinguishes the definitions of the positive universe, the negative universe and the cosmic base. Supplement on the Deduction of Cosmic Origin and Final Evolution for Postage. Supplement Mathematical Proofs and Particle Mass Mapping. Mathematical closed-form proof and computational framework based on first principles v4.0+,https://doi.org/10.5281/zenodo.20798927 updated v3.7(including the .md file for feeding AI tool ) Version 3.7.3 corrects the error in the gravitational wave formula and value in prediction table etc.Overall Closure Status:Core Theory DoC=100% (Full Theoretical Closure), V3.7.4 (a full conclusion of V3.7.3,and recover gravitational wave value keep 9.7~ )(continue:https://doi.org/10.5281/zenodo.20738759) V3.7.5 (a full conclusion of V3.7.3,and distinguish Universe 0,+,- by phase) Based on the sign and magnitude of the background phase , the entire cosmos is divided into three mutually orthogonal sectors. Background phase is the primary classification criterion; topological winding number serves only as auxiliary topological characteristics. • Universe 0 (Zero Universe): Background phase (constant ground-state phase), with auxiliary winding number . This sector is completely electromagnetically decoupled with vanishing angular momentum density. It acts as the fundamental vacuum substrate and contributes diffuse dark matter. • Universe + (Positive Universe): Background phase . Its topological excitations carry positive winding numbers . This sector hosts conventional gauge fields and fermions, and electromagnetic interactions are observable. • Universe – (Negative Universe): Background phase satisfies phase conjugation . Its excitations carry negative winding numbers . Gauge fields here are strictly orthogonal to those in Universe +, leading to electromagnetic invisibility. Its matter manifests as particle dark matter via gravitational projection onto Universe +. This classification is a direct consequence of the phase-conjugation symmetry derived from Möbius boundary conditions, and it automatically satisfies the global angular momentum constraint . Version 版本:UVMM v3.7.5 / UTFF v2.0 OmegaLast Updated 最后更新:2026-06-16DOI:10.5281/zenodo.20343471Mathematical Closure Status 数学证明状态:✅ 100% Closed — All low-energy observable quantities derived solely from two axioms without free parameters✅ 100% 闭合(从两条公理出发,无自由参数推导出所有低能可观测物理量)Experimental Status 实验验证状态:⏳ Awaiting Critical Tests — Partial predictions consistent with existing data, core predictions unvalidated⏳ 等待判决性检验(41 项定量预言中,部分已与现有数据兼容,核心预言待验证) DOI:10.5281/zenodo.20343471 (UVMM Main White Paper / UVMM 主白皮书) DOI:10.5281/zenodo.20590317 (UTFF CHEM White Paper / UTFF 主白皮书) DOI: 10.5281/zenodo.20798927 Black Hole & UVMM v4.0 Core :UVMM v4.0.15 High-Precision Global Calculation AI Knowledge Package.mdDOI: 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 Fine-grained calculations require supercomputing resources.更精细的计算需要超算进行。 First-Principles Mathematical Proof · Full Closed Document (base on V3.5)20260627 https://chat.qwen.ai/s/t_48b0de7f-1d3c-4635-8a41-8531025055ef?fev=0.2.57 Complete First-Principles Mapping & Derivation of Fundamental Constants https://chat.qwen.ai/s/t_8b766c0e-fb46-4907-8fff-5271a25272fb?fev=0.2.57 dark matter&cosmo: https://chat.qwen.ai/s/t_4fc1b5da-8ca3-4798-a037-894f5315d1e3?fev=0.2.61
This paper is dedicated to a complex analysis of the fiscal decentralization process in Georgia and its causal relationship with the level of municipal financial sustainability. The relevance of the research is driven by the legislative changes implemented over the last decade, including the transition to a new Value Added Tax (VAT) distribution model since 2019, which fundamentally altered the budgetary architecture of local self-governments. The aim of the paper is to determine the extent to which the existing fiscal model ensures real financial autonomy for municipalities and whether it reduces vertical fiscal imbalance. The study employs quantitative methodology, specifically a panel data analysis of the budgetary figures of Georgia's 64 municipalities for the period 2018-2024. To evaluate financial sustainability, a system of indicators is utilized, encompassing the self-sufficiency ratio, the transfer dependency index, and the capacity for capital expenditure financing. The research findings reveal that despite the declared progress in decentralization, the majority of Georgian municipalities still experience fiscal illusion and a high level of dependency on central transfers. The analysis shows that while the VAT distribution formula has increased the predictability of municipal revenues, it has failed to ensure the complete leveling of regional inequalities. The paper argues that to achieve financial sustainability, it is essential to optimize the local property tax base and diversify municipal borrowing authorities. The study concludes with practical recommendations focused on refining fiscal policy in accordance with the standards of the European Charter of Local Self-Government.
A framework for reusable compliance attestation in regulated industries based on cryptographic primitives, vector commitments with selective-opening proofs, re-randomisable signatures, zero-knowledge succinct non-interactive arguments of knowledge, and cryptographic accumulators with succinct non-membership proofs, composed into a protocol structure adapted to the specific structure of multidimensional compliance state. We identify five gaps that separate the generic primitives from a deployable solution for compliance attestation: multidimensional state binding, temporal freshness without correlation, revocation under reuse, cross-issuer aggregation, and verifier predicate richness. We sketch the protocol structure that addresses these gaps, analyse its security and privacy properties, and discuss applications in age verification, right-to-work assurance, and continuous-compliance monitoring. Companion preprint to UK Patent Application GB2611280.5 filed at the UK Intellectual Property Office on 14 May 2026.
IoT device authentication must resist impersonation and credential theft while respecting the computational constraints of edge devices. Existing frameworks rely on static cryptographic keys that, once extracted, enable full impersonation, whereas RF fingerprinting schemes that bind identity to hardware imperfections transmit and store device templates in plaintext, exposing them to template theft and linkability attacks. ZK-RFAuth is a three-phase authentication framework that integrates Siamese neural network-based RF fingerprinting, Groth16 zero-knowledge proof (ZKP) verification, and proof-of-authority blockchain logging. During registration, a Siamese convolutional network extracts a compact embedding from raw I/Q samples and commits a Poseidon hash of the quantized mean template on-chain. During verification, the prover generates a Groth16 proof demonstrating that the L1 distance between a fresh embedding and the registered template falls below a per-device threshold without revealing either vector. The proof and authentication outcome are recorded on-chain for tamper-evident auditing. Evaluated on the WiSig dataset (28 WiFi transmitters, 224,000 frames), ZK-RFAuth achieves 91.4% closed-set accuracy and 2.25% equal error rate at embedding dimension d = 64, with 88.4% genuine acceptance rate and 70.8% open-set rogue rejection using per-device P95 thresholds. The ZKP circuit requires only 972 rank-1 constraint system (R1CS) constraints over 100× fewer than an equivalent SHA-256 circuit producing 144-byte proofs verifiable in approximately 3 ms.
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Wireless Signal Modulation Classification
Physical Unclonable Functions (PUFs) and Hardware Security
Capped-usage SaaS products -- LLM subscriptions such as Claude Code and ChatGPT, cloud platforms such as Vercel and Cloudflare Workers, corporate benefit platforms, identity-verification services with liability transfer -- share a structural signature with insurance products: a fixed premium decoupled from realized consumption, stochastic per-user demand with heavy-tailed severity, a non-fungible cap that resets on a fixed schedule, and a portfolio-level exposure that requires reserve adequacy under tail risk. We argue that this is not an analogy. It is the same operational problem actuarial science has been tooled for decades to address, restated with new dependent variables (tokens, bandwidth bytes, function-invocations, gym check-ins) in place of medical claims. This paper proposes a modeling framework for capped-usage SaaS pricing built from frequency-severity decomposition, premium calculation principles, and Monte Carlo reserve adequacy. We map the framework to publicly observable subscription tiers in two domains (LLM services and cloud platforms), ground it in canonical health-insurance economics (Arrow 1963; Pauly 1968; Manning et al. 1987; Brot-Goldberg et al. 2017), and demonstrate divergence from traditional unit economics through a worked example. The contribution is operational rather than theoretical: not a new theorem, but vocabulary and tools currently absent from cs.LG/stat.ML practice.
Open access
2 source records
Artificial Intelligence in Healthcare and Education
Many blockchain-based cyber threat intelligence (CTI) sharing systems emphasize immutability and auditability, but often treat CTI submissions as ordinary blockchain transactions without explicitly separating content validation from publication anchoring. This paper presents CTIB, a proof-of-concept hybrid Proof-of-Stake (PoS) and Proof-of-Work (PoW) framework for CTI publication. CTIB uses a sequential workflow in which a PoS committee first evaluates CTI submissions, and an accepted feed hash is then anchored through a PoW step to provide verifiable temporal binding. The prototype is evaluated in a controlled local Hardhat environment; therefore, the results should be interpreted as prototype-level feasibility evidence rather than production-scale deployment results. CTI content is represented using STIX 2.1, canonicalized, and hashed using SHA-256; only integrity-critical evidence is stored on-chain, while full CTI content remains off-chain. Experimental results demonstrate prototype-level feasibility, with measured throughput, latency, and success rate metrics under different PoW difficulty profiles. Across ten independent local runs, CTIB achieved an average throughput between 141.13 and 166.14 feeds/min, average p50 latency between 326.18 and 403.09 ms, and average p95 latency between 553.22 and 700.82 ms under the tested difficulty profiles. Security analysis uses analytical modeling, committee capture probability, and Monte Carlo simulation to evaluate majority-attack feasibility under stated assumptions. The results indicate that sequential compromise of both validation and anchoring layers increases the cost of coordinated manipulation.
Cross-domain data sharing in decentralised environments faces persistent challenges related to confidentiality, auditability, and trust decentralisation, particularly when data transmission relies on centralised intermediaries or single proxy entities. To address these issues, this paper proposes a blockchain-enabled auditable data sharing scheme that integrates threshold secret sharing with non-interactive zero-knowledge proofs. In the proposed framework, the encrypted file fragments and secret key shares are decentralised across multiple blockchain nodes using threshold cryptography, preventing any single entity from reconstructing the encryption key or unilaterally performing ciphertext transformations. Zero-knowledge proofs are employed to publicly verify the correctness of the transmission and sharing operations without disclosing plaintexts, secret keys, or sensitive metadata, while the blockchain records verifiable proofs to support tamper-evident auditing. Security analysis shows that the scheme achieves confidentiality, collusion resistance, and verifiable correctness under standard cryptographic assumptions.Experimental evaluations indicate that the proposed scheme incurs acceptable computational and on-chain overhead, suggesting its feasibility in decentralised and cross-domain data sharing scenarios.