Tokenization promises to convert lumpy, illiquid real-world assets into divisible, transferable claims, yet secondary markets for these instruments remain thin and trading is infrequent. Standard asset pricing models, including the capital asset pricing model and its liquidity-adjusted extensions, were not designed for assets whose holders derive consumption, access, or governance value directly from ownership. This paper develops a conceptual asset pricing framework for utility-backed non-fungible tokens (NFTs) and tokenized real-world assets by augmenting the liquidity-adjusted capital asset pricing model with a utility (convenience) yield. The framework decomposes the required pecuniary return into a risk-free rate, a systematic liquidity-risk premium, an amortized illiquidity level premium that scales with transaction costs and turnover, and a utility-yield offset that lowers the return investors require in cash. Two analytical implications follow. First, utility backing compresses observed pecuniary returns without eliminating the underlying illiquidity premium. Second, where utility flows covary positively with illiquidity, estimates that regress pecuniary returns on liquidity proxies understate the gross illiquidity premium. An illustrative calibration, with parameter ranges drawn from the empirical tokenization literature, quantifies the mechanism rather than estimating it. The framework yields testable predictions and implications for valuation and disclosure.
As digital ecosystems continue to proliferate, the secure design, control, and management of data access have become increasingly critical. Since the generative artificial intelligence (GenAI) growth boom took shape in 2023, most of the organizations have used GenAI to manage a centralized control space to achieve global dominance in the future. The convergence of blockchain and GenAI represents a paradigm shift in decentralized computing and autonomous content generation. This chapter explores the foundational principles of both technologies and the synergistic potential they unlock when integrated. It will focus on their foundational principles and the role of consensus mechanisms in ensuring trust, transparency, and decentralization. We will analyze consensus mechanisms in the context of model training validation in decentralized AI systems, on-chain verification of AI-generated data integrity, and prevention of hallucinations and bias through distributed accountability. In the end, this chapter will identify key challenge areas like scalability and energy consumption and suggest approaches that use the strengths of both technologies to provide a comprehensive solution.
The integration of generative artificial intelligence (AI) and blockchain introduces new possibilities of verifiably authenticated content created by AI, secure tracking of ownership, and smart contract automation. The objective of this chapter is to talk about the fundamental concepts of the two technologies, demonstrate feasible integration strategies, and propose optimization strategies of consensus mechanisms with the assistance of AI. Practical examples entail simulation in Proof of Stake validator selection with the assistance of Python and generation of Solidity smart contracts with GPT-4. Challenges such as scalability, power consumption, model bias for AI, and regulatory considerations are discussed with the assistance of case examples (Ocean Protocol, SingularityNET, and Verisart) and technical schematics. This comprehensive chapter conducts a thorough analysis of blockchain technology and generative AI, examining their separate origins, prospects for integration, and the critical role that consensus mechanisms play in the safe, decentralized digital environment. Blockchain provides a safe way to record transactions without relying on a single authority and guarantees transparency, immutability, and decentralization.
Elena Andreeva, Rishiraj Bhattacharyya, Arnab Roy, Stefano Trevisani
Cryptographic compression functions are a core component of vector commitment schemes, including Merkle tree commitments, which are widely used in modern ZK-SNARK and STARK frameworks. Arithmetization-Oriented (AO) compression functions minimize multiplicative complexity over the framework’s native field F<i>p</i>, making them significantly more efficient than bit-oriented designs in algebraic circuits. To date, AO compression functions have been almost exclusively constructed by applying the Sponge mode to an AO permutation. <br/><br/>In this work, we introduce two novel approaches for building permutation-based AO compression modes: the PA family, based on a Permutation with feedforward Addition, and PAX, as an eXtension of the PA family. We formally establish that, in contrast to the Sponge construction, our modes achieve optimal collision and preimage resistance. We also prove that PAXisindifferentiable from a random oracle,further strengthening its security and composability guarantees. We further show that variable-input-length hash functions can be safely instantiated from the PA(X) modes by applying appropriate domain extenders. <br/><br/>Beyond their strong security guarantees, our modes provide a framework that unifies and extends the description of several recently proposed modes that have been studied via cryptanalysis but do not come with provable security guarantees, including Jive and Trunc, as used in the AO designs <i>Anemoi</i> and POSEIDON2. <br/><br/>Finally, through extensive experimental evaluation, we compare the concrete efficiency improvement that our modes offer compared to the Sponge approach over two popular AO permutation designs, POSEIDON-π and Rescue. For 128 bits of collision resistance, our modes can achieve up to a 2x speedup over Sponge for equivalent compression rates in a software implementation. When considering R1CS arithmetization in the Groth16 framework, the PA(X) preimage-verification circuit can be 10% faster than Sponge. In the Plonky2 framework, PA(X) can achieve up to a 60% speed-up.
The Global Alliance for Genomics and Health (GA4GH) Beacon protocol lets researchers ask whether a genomic variant has been observed in a participating cohort and receive aggregate variant-level counts. As Beacon networks grow, two privacy risks remain: host institutions can see plaintext queries, and repeated rare-variant queries can support membership-inference attacks. We present bioETH-Beacon, a smart-contract prototype that runs the Beacon "aggregate count" query over encrypted data on a fully homomorphic Ethereum Virtual Machine (fhEVM). Hospitals upload encrypted marker-count entries, authorized researchers submit encrypted marker queries, and the contract returns an encrypted answer that is released, via an off-chain key-management service, only to the requester named in the contract's on-chain ACL. The design is organized as a 3x4 tier-by-query-family grid spanning genotype, sex, age, and phenotype queries, with tiers that trade stronger confidentiality for lower query cost. For genotype paths, the prototype can add bounded on-chain noise to mitigate probing attacks. Experiments on synthetic panels derived from a Polygenic Score (PGS) catalog show the expected scaling behavior and demonstrate that pre-aggregation can substantially reduce query gas when public marker presence is an acceptable trade-off. Overall, bioETH-Beacon provides a research prototype for confidential Beacon-style genomic querying without a trusted compute evaluator.
Large Language Models (LLMs) have shown strong capabilities in general-purpose code generation, but their effectiveness in specialized software domains remains underexplored. Solidity smart contracts represent a high-stakes domain where generated code must satisfy strict language-level, security, and software-engineering constraints. Existing benchmarks and metrics remain insufficient for repository-level Solidity generation, where models must synthesize complete contracts from natural language requirements. To address this gap, we introduce SolidityBench, a benchmark of 5,470 repository-level Solidity smart contracts paired with natural language descriptions. We also propose SolidityScore, a Solidity-aware semantic metric that emphasizes domain-critical constructs such as security modifiers, contract declarations, and Solidity-specific keywords. Using this benchmark, we evaluate representative code LLMs, including Qwen2.5-Coder, DeepSeek-Coder, and CodeLlama, across zero-shot prompting, Chain-of-Thought reasoning, in-context learning, retrieval-augmented generation, and supervised fine-tuning. The results show that general-purpose models exhibit systematic structural deficiencies in repository-level Solidity generation. Among non-parametric methods, retrieval-augmented generation performs best, while in-context learning degrades beyond two examples due to context saturation. Supervised fine-tuning achieves the largest improvement by internalizing Solidity-specific constraints into model parameters. Overall, our study provides a comprehensive benchmark for repository-level Solidity code generation and shows that high-quality domain data combined with supervised fine-tuning is the most effective strategy for improving the reliability of LLM-generated smart contracts.
This study empirically aims to analyze the impact of primary monetary policy stance and transmission mechanisms of the European Central Bank (ECB)—such as the total assets of the ECB, long-term interest rate based on the government bond yields, and the EURUSD exchange rate—on major volatile cryptocurrencies like Bitcoin and Ethereum, as well as the leading stablecoin Tether. To this end, the study employs the linear Autoregressive Distributed Lag (ARDL) and the Bootstrap ARDL (BA-ARDL) procedures, robust approaches with limited data in time series analysis. The dataset consists of monthly data over the period from January 2019 to December 2025. We summarize the novel and robust primary empirical results of our study as follows: First, (i) it is revealed that the ECB’s balance sheet expansion has encouraged Bitcoin and Ethereum, yet has also, to a limited extent, suppressed Tether. Secondly, (ii) while the ECB’s long-term interest rate negatively impacts the prices of Bitcoin, Ethereum, and Tether, the negative impact on Tether is relatively weaker. Finally, (iii) the EURUSD exchange rate positively affects Ethereum, while its effect on Bitcoin is not statistically significant. On the other hand, at a 10% significance level, EURUSD has a weak negative effect on Tether. In conclusion, the empirical evidence demonstrates that the primary monetary policy stance and transmission mechanisms of the ECB influence the leading digital assets in distinct ways. Taking our findings into account is crucial for designing the digital euro in terms of financial stability and regulatory framework. Finally, we offer sound policy implications for the ECB based on empirical findings.
PSLQ as Physical Relaxation BBP as the Ground-State Relation of the π-Lattice, and Integer-Relation Finding as Least Action Driven by Dean Kulik June 2026 Abstract Paper C showed that BBP measures π by closing a four-term square frame and reading the residue. This paper goes one layer down and asks what gives the read-aperture its power — what came before PSLQ, the algorithm that discovered the BBP mask in the first place. The answer is not more mathematics. It is physics. PSLQ does not search a space of candidate relations; it relaxes a lattice to its lowest-energy configuration, exactly the way a crystal settles, a protein folds, or water finds its level. Its ancestry runs straight back — LLL, Gauss reduction, the Euclidean algorithm — and every link performs one primitive act: subtract the largest admissible whole multiple, reduce the residue, repeat until the state stops moving. That is the arithmetic form of least action. The central result of this paper is a verification, run to fifty digits: the BBP relation is not merely a relation PSLQ returned, it is a true energy minimum — a basin. Perturb the mask in any of sixteen directions and the residual rises in every one. Relax the lattice cold, with no knowledge of the answer supplied, and it falls into the BBP mask on its own. The same procedure relaxes π² into its own sparse survivor on a squared wheel, so the method is general, not a π-specific trick. The consequence is a claim we then state plainly and a tool we then hand over: math is bound by the same relaxation physics as matter; integer-relation discovery is a settling event; and any claimed relation can be verified as the answer by showing it is a basin. Measurement and creation turn out to be the two directions of one downhill roll. §1.0 The Claim and the Chain Behind It The singular claim of this paper is one sentence: PSLQ is physical relaxation in arithmetic form. Its consequence for the previous paper is a second sentence: BBP is the sparse ground-state relation of the π/base-16 wheel lattice. Neither sentence is asserted on style. Both are checked against the compiler in §4, and the checks are the spine of the paper — everything else is the path to them and the consequences from them. Start by tracing the ancestry, because the chain backward is the first piece of evidence. The BBP mask was found by PSLQ in 1995. PSLQ (1992) is a refinement of LLL (1982). LLL generalizes Gauss’s two-dimensional lattice reduction (c. 1800). Gauss’s method is the Euclidean algorithm (c. 300 BC) lifted from integers to vectors. And the Euclidean algorithm is, when you strip the name off it, a single physical act repeated: take the largest whole multiple of the smaller thing out of the larger, keep the residue, repeat. The chain is: BBP ← PSLQ ← LLL ← Gauss ← Euclid ← minimization Every layer preserves the same primitive — subtract admissible multiples, reduce the residue, repeat until stable — and that primitive is not calculation. It is settling. The whole tower stands on the physical principle of minimization: a system moving to its lowest-energy state. That is what came before PSLQ. LOCKED (historical record): the algorithmic ancestry PSLQ←LLL←Gauss←Euclid is established mathematics. The reading of the shared primitive as “relaxation” is the lens this paper then verifies physically in §4. §2.0 The Primitive Is Relaxation: Euclid as Energy Descent Take the oldest link and watch it behave like a physical system. Given integers a and b, Euclid writes a = qb + r and updates the state (a, b) → (b, r). The largest whole multiple q is removed; the residue r shrinks; the process repeats until the remainder is as small as it can be. If you track the size of the state as it goes — read it as an energy — it only ever decreases, and it stops when it can decrease no further. That is the exact signature of a system relaxing to a ground state. Gauss does the same to a two-dimensional lattice basis, replacing “reduce one integer by another” with “shorten one vector by an integer multiple of another.” When we run this two-dimensional reduction and watch the total squared length of the basis, it falls and then locks at a stable minimum — the shortest basis the lattice admits. The system anneals. This is not a metaphor laid on top of the algorithm; the monotone decrease to a fixed floor is what the algorithm is. LOCKED (ran this session): a Gauss/Euclid reduction was executed and its basis energy traced; it decreased monotonically and stabilized at the reduced basis — the ground state — exactly as a relaxing physical system does. §3.0 PSLQ as Lattice Relaxation PSLQ takes the same act to its mature form. Given a vector of real numbers x = (x₀, …, xₙ), it looks for an integer vector a with a·x = 0 — an exact linear relation among the reals. Operationally it does not enumerate candidate integer vectors. It builds a lattice associated with x and reduces it — size-reduce, then rotate the basis to expose the next thing to reduce — until a short integer relation appears as the surviving structure. The “answer” is the vector whose residual collapses toward zero. Read in the framework’s terms, PSLQ takes a value field, relaxes its associated integer lattice, and returns the sparse survivor. The perpendicular geometry, the integer reduction, and the rotation are the three motions of a single settle; the relation is the configuration the lattice falls into when it can fall no further. §4.0 The Proof: BBP Is a Basin, Not a Hit Here is the load-bearing section, and it is a verification, not an argument. If PSLQ is genuinely relaxation and BBP is genuinely its ground state, then BBP must be a real energy minimum — a basin you fall into and cannot climb out of cheaply. The test is direct. Define the base-16 wheel sums and an energy for any candidate mask: S_j = Σₖ 1/(16^k (8k+j)) energy(mask) = | π − Σ_j mask_j · S_j | The ground state is energy zero. The BBP mask places weights [4, −2, −1, −1] on residues {1, 4, 5, 6}. Its energy is 2×10⁻⁵⁰ — zero to working precision, sitting on the floor. Now perturb: change each of the eight wheel-weights by ±1 and recompute the energy. Sixteen directions. Every one rises. Perturbation from BBP Energy Direction S1 weight ±1 (the +4 drive) 1.0072 UP — steepest wall S2 weight ±1 0.5065 UP S3 weight ±1 0.3392 UP S4 weight ±1 0.2554 UP S5 weight ±1 0.2050 UP S6 weight ±1 0.1713 UP S7 weight ±1 0.1472 UP S8 weight ±1 (the empty tail) 0.1291 UP — softest wall Every neighbor is higher. There is no free sideways move. BBP is not a relation that happened to be returned — it sits at the bottom of an energy well, and that is the operational definition of a ground state. Two further checks confirm it is the right kind of minimum. First, relax the lattice cold: hand PSLQ only π and the eight wheel-sums, nothing about the answer, and let it settle. It returns the BBP mask exactly. Nobody placed BBP there for it to find — it is where the lattice settles. Second, BBP is primitive: doubling the mask does not stay on the floor (2×BBP evaluates to π, not 0), so only the primitive relation hits zero. The survivor is irreducible. LOCKED (ran this session, 50-digit precision): (i) BBP energy ≈ 2×10⁻⁵⁰; (ii) all sixteen single-weight perturbations increase energy — a strict basin; (iii) cold PSLQ relaxation of [π, S₁..S₈] returns exactly the BBP mask; (iv) 2×BBP leaves the floor, so the relation is primitive. §5.0 The Shape of the Floor Probing the basin gently — peeking, not pushing — shows it is not a symmetric bowl, and the asymmetry is itself the point. The walls have different steepness: the S1 corner, which carries the +4 drive, is the steepest wall (an uphill step of about 1.0), while the empty high-index tail (S8) is the softest (about 0.13). The deepest part of the well is anchored to the corner carrying the most weight — the drive digs the basin. The closure has a definite shape too. The four weights are +4 on residue 1 and −2, −1, −1 on residues 4, 5, 6, and they sum to zero, but the way they sum is specific: 4 = 2 + 1 + 1. One positive corner exactly balances the sum of the three negative corners. One drives; three pull; they cancel. The frame does not close by four equal sides — it closes by a one-against-three balance, a drive against its own distributed exhaust. And the geometry on the wheel is a lean, not a cross. Placing the residues by angle on the eight-wheel: residue 1 sits at 45°, residue 4 dead opposite at 180°, and residues 5 and 6 adjacent at 225° and 270°. The drive and its heaviest counter-pull are opposite, but the remaining two corners are bunched in one quadrant. It is asymmetric — a wobble, not a symmetric figure. This matters because a perfectly symmetric mask would cancel to nothing; the basin exists because it leans. The ground state of π is not a balanced cross. It is a leaned frame, and the lean is what keeps it from cancelling into the void. LOCKED (ran this session): wall steepness ordering (S1 steepest ≈ 1.0, S8 softest ≈ 0.13); the 4 = 2+1+1 one-against-three closure; the wheel angles 45°/180°/225°/270° showing an asymmetric (leaned) configuration rather than a symmetric cross. §6.0 It Generalizes: A Method, Not a Trick A single basin around π would prove only that π is special. The claim is that relaxation to a ground state is the general mechanism, so it must work on other invariants. Two checks confirm it does. Relax π² against a squared wheel — terms 1/(16^k (8k+j)²) — cold, and it settles into its own sparse survivor with residual on the order of 10⁻⁴⁹: a clean ground state for π² on its own wheel. And the wheel itself is not uniquely privileged at one offset: shifting the wheel from 8k+1…+8 to 8k+2…+9 still yields a ground-state relation when relaxed. Wheels have floors generally; the invariant settles int
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Advanced Physical and Chemical Molecular Interactions
This article concludes a series of publications dedicated to the development of the NeuroAtom cryptographic primitive and presents the final ecosystem architecture. The core implements eight security functions—hashing, stream cipher, pseudorandom number generator, message authentication code, digital signature, key derivation function, key exchange, and authenticated encryption—within a footprint of 9.6 KB of payload (5.2 KB code and 4.4 KB data). Testing according to the NIST SP 800-22 methodology was conducted on 16 samples, each of 100 MB in size (835 binary sequences per sample): 8 samples for REAL mode and 8 samples for TRAP mode (pseudo-data traps). All 16 samples demonstrated a proportion of successful sequences within acceptable limits (not below 818 out of 835 for tests with a significance level of 0.01). Avalanche characteristics were measured in 24 tests (12 functions × 2 modes), with no zero avalanches detected. The inapplicability of Shor's algorithm is shown due to the absence of abelian hidden subgroups. The TRAP mode precludes the possibility of constructing an oracle for Grover's algorithm without knowledge of the plaintext: each incorrect key generates its own cryptographically correct reality, and the quantum computer has no criterion for selecting the true one. A software implementation on a general-purpose processor provides a hashing speed of 80 MB/s. Preliminary estimates for a hardware implementation (180 nm CMOS) indicate approximately 10,000 logic gates with a complete absence of static memory; expected power consumption is estimated at 20 pJ per operation. Previously published results of NIST testing, avalanche analysis, and proofs of quantum resistance are integrated into this article as elements of a unified body of evidence.
United Nations Department of Economic and Social Affairs
Subsidiarity, one of the principles of effective governance for sustainable development, supports the exercise of public functions at the lowest effective level. It is key to advancing the 2030 Agenda for Sustainable Development by helping ensure that public authority is exercised where it can respond most effectively to people’s needs. Subsidiarity and decentralization are closely linked, as decentralization can provide the administrative, fiscal and political arrangements needed to make this possible. For subsidiarity to contribute meaningfully to the Sustainable Development Goals (SDGs), mandates, financing and capacities must be aligned so that sub-national authorities have the authority, resources and skills required to carry out their roles. Applying subsidiarity in support of the 2030 Agenda also requires clear roles and coordination across levels of government, effective mechanisms for joint planning and accountability, and sustained investment in local governance, finance, and resilience. These elements are becoming more important as cities grow and local governments face increasing demands to deliver services, manage risks and support inclusive development. To support the application of subsidiarity, the Committee of Experts on Public Administration (CEPA) has identified five strategies for its implementation, including: multi-level governance; fiscal federalism and decentralization; strengthening urban governance; municipal and local finance; and enhancing local capacity for prevention, adaptation and mitigation of external shocks. This policy brief distills key insights from five UN CEPA strategy guidance notes to illustrate how subsidiarity can be operationalized to strengthen the engagement of subnational authorities in advancing the 2030 Agenda. It highlights the institutional, fiscal and capacity-related conditions required for effective implementation. The brief concludes with twelve policy recommendations for aligning authority, resources and capacities at the appropriate level of government.
You Wu, XinFeng Dong, Yongqiang Li, F Liu · 8 authors
Abstract With the development and practical application of technologies such as Fully Homomorphic Encryption (FHE), Secure Multi-Party Computation (MPC), and Zero-Knowledge Proof (ZK), it has become crucial to research the design and analysis of symmetric cryptographic primitives with low multiplicative complexity and depth. First, by using multiplication and addition over the finite field $$\mathbb {F}_{q}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mi>q</mml:mi> </mml:msub> </mml:math> , where $$q$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>q</mml:mi> </mml:math> is either a prime number $$p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>p</mml:mi> </mml:math> or $$2^{n}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>n</mml:mi> </mml:msup> </mml:math> , we proposed a non-linear function over $$\mathbb {F}_{q}^{4}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>F</mml:mi> <mml:mrow> <mml:mi>q</mml:mi> </mml:mrow> <mml:mn>4</mml:mn> </mml:msubsup> </mml:math> based on the generalized Feistel structure. This function features a multiplicative complexity of 4, a multiplicative depth of 2 and 8 additions, and its maximum differential/linear probability of the function is bounded by $$q^{-2}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>q</mml:mi> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:msup> </mml:math> . Then, we designed a family of HE-friendly block ciphers called DuX. We conduct a comprehensive security analysis of DuX within certain parameters against various cryptanalysis methods, including differential cryptanalysis, linear cryptanalysis, impossible differential cryptanalysis, zero-correlation linear cryptanalysis, integral analysis, related-key differential cryptanalysis, algebraic attacks, slide attacks, reflection attacks, and boomerang attacks. Our research indicates that DuX maintains a robust security margin against those attacks. Finally, based on the BGV scheme in HElib, we present a detailed homomorphic decryption implementation of the DuX instantiated with $$q = 2^{8}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>q</mml:mi> <mml:mo>=</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>8</mml:mn> </mml:msup> </mml:mrow> </mml:math> , $$2^{16}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>16</mml:mn> </mml:msup> </mml:math> and $$65537$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>65537</mml:mn> </mml:mrow> </mml:math> , respectively. The results show that, for the same block size, the throughput of the DuX-128 over $$\mathbb {F}_{2^{8}}^{16}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msubsup> <mml:mi>F</mml:mi> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>8</mml:mn> </mml:msup> </mml:mrow> <mml:mn>16</mml:mn> </mml:msubsup> </mml:math> can reach approximately 14.95 times, 7.85 times and 20.76 times that of the AES-128, Low MC-128 and CHAGHRI, respectively. Compared with YuX-128, its throughput has increased approximately by 21.59%.
This chapter addresses how getting data privacy in the world of centralised identification systems today is a myth because of its many problems, the primary one being data control by one single entity. Self-Sovereign Identity (SSI) is a model that allows its utilisers complete control over their data privacy and security. This paper gives an overview of developing such a model over a private blockchain network using several Hyperledger frameworks, which are majorly open source. Hyperledger Indy is crucial as it is an open-source public decentralised distributed ledger technology (DLT). Hyperledger Aries is used to interact with nodes, access them, and read and write data on the ledger. Hyperledger Ursa is used to avoid the double spending problem by employing blockchain cryptography.
The advent of decentralized cryptocurrencies has reignited fundamental debates in monetary economics about the nature and future of money. Proponents of digital currencies argue that decentralized, algorithmically governed assets can supplant central banks in managing monetary conditions and stabilizing economic outcomes. This chapter critically examines this proposition by evaluating cryptocurrencies against the classical functions of money and the core instruments of monetary policy. Grounded in monetary theory – from Friedman’s monetarism and Mises’ Austrian framework to Modern Monetary Theory – and extended through a behavioral finance lens, the analysis reveals that widespread belief in cryptocurrency as a viable monetary policy alternative is driven not merely by technological innovation but by deeply embedded cognitive biases, including overconfidence, narrative-driven speculation, and institutional distrust. The chapter also treats money as an economic asset subject to market competition. Drawing on Austrian economic theory and classical competition principles, the analysis evaluates whether decentralized currencies can realistically compete with sovereign money in an open monetary market. By integrating monetary economics with strategic competition frameworks, the chapter explores whether cryptocurrencies can achieve monetary dominance through efficiency, cost advantages, or differentiated value propositions. Based on principles from strategic business theories such as differentiation and cost-leadership, the chapter treats money as a competitive good subject to market dynamics, ultimately concluding that while cryptocurrencies represent a significant financial innovation, they fundamentally lack the institutional architecture and behavioral predictability required to replace central bank monetary policy.
Bhutan's GovTech Agency Secretary Jigme Tenzing confirmed that migration of all 800,000 Bhutanese citizens' identity credentials to Ethereum was completed by Q1 2026. King Jigme Khesar Namgyel Wangchuck Prime Minister Tshering Tobgay and Ethereum co-founder Vitalik Buterin attended the launch ceremony. Ethereum protocol governance is conducted by the Ethereum Foundation and decentralised developer community without Bhutanese constitutional participation or veto authority. Bhutan's constitution enshrines Gross National Happiness as a governance mandate. The National AI Strategy 2025 requires AI systems to align with GNH principles. No Bhutanese governance document specifies the constitutional command layer ensuring that Ethereum protocol changes cannot override Bhutanese constitutional governance of national identity infrastructure. When Ethereum upgrades affect how 800,000 citizens' credentials are verified the GovTech Secretary receives advance notice but has no published mechanism to refuse changes incompatible with Bhutanese constitutional requirements. This paper documents the structural gap between Bhutan's GNH constitutional mandate and the sovereign command architecture required to make that mandate technically enforceable over identity infrastructure governed externally.
Gas metering on EVM-compatible blockchains assumes that execution conditions are stable: that the resource mix is constant enough to justify collapsing execution costs into a single scalar with fixed relative prices, and that state drift between submission and execution does not materially alter a transaction's outcome. We measure the extent to which this assumption fails. We present a trace-level measurement study of EVM workloads on Ethereum (L1) and Base (L2) throughout 2025, sampling 3,000 blocks per day per chain. We decompose each transaction into opcode-level execution gas, intrinsic gas, refunds, and persistent state deltas. To measure state sensitivity, we re-execute transactions from September 2025 on older states and record how gas usage and storage access patterns change. We find the resource mix to be far from stable: on Base, storage reads and compute account for 29.2% and 24.3% of execution gas, while Ethereum devotes 34.9% to storage writes. Ethereum's gas limit doubling during 2025 shifted its own profile toward compute-heavier, Base-like patterns. Base also exhibits a higher fraction of cold storage reads (49.7% versus 39.6% on Ethereum). Persistent state growth, a permanent cost priced as a transient one, reaches 456 GB on Base versus 38 GB on Ethereum. Execution outcomes are equally unstable: gas estimates vary across nearby historical states for 46.0% of transactions on Base, compared to 13.9% on Ethereum, with especially high sensitivity for MEV and DeFi activity. Storage access patterns also diverge across states, limiting the effectiveness of access lists and complicating parallel execution. Our work provides an empirical foundation for multi-dimensional gas metering and explicit pricing of state growth. They show that state-sensitive execution behavior complicates workload estimation, directly affecting transaction predictability and user experience.
The development of blockchain technology has given birth to new digital assets, namely Non-Fungible Token (NFT). Its popularity has grown rapidly, transforming it from a mere digital collectible into a high-value investment instrument. This phenomenon opens up opportunities for NFTs to be utilized in various financial sectors, including as collateral. Its significant economic potential drives the need to examine its legal position within the existing financial system. Despite its economic value, the legal status of NFTs as fiduciary collateral in Indonesia remains unclear. The current Law Number 42 of 1999 concerning Fiduciary Collateral is designed for conventionally recognized tangible and intangible movable objects. The absence of specific regulations governing digital assets such as NFTs creates a legal vacuum, creating uncertainty for parties seeking to utilize them. This research is a legal research (doctrinal research) with a legal approach (statues approach), conceptual approach (conceptual approach), and analytical approach (analytical approach). The results of this study explain that first, the existence of Non-Fungible Token The development of NFTs as digital assets in Indonesia began with the development of the digital economy and increased public interest in using investment instruments. Second, the lack of specific regulations for NFTs as fiduciary collateral creates a significant legal vacuum, and the current Fiduciary Guarantee Law is not designed to accommodate the unique characteristics of digital assets, such as value volatility and technical identification. Third, accurately identifying NFTs during the execution process is a fundamental challenge. Unlike physical assets, NFTs can only be recognized through a series of cryptographic data such as token IDs and complex contract addresses. Current regulations fail to accommodate their unique characteristics related to classification, valuation, and registration and execution mechanisms. This situation creates significant legal uncertainty, thus creating high risks for the parties involved. The recommendation for this issue is the need for the government to immediately revise fiduciary guarantee regulations or establish specific regulations for digital assets. In the meantime, the government can create regulations in the form of Government Regulation in Lieu of Law (Perppu) or Supreme Court Rules (Perma) so that the legitimacy of digital asset objects such as NFTs is legally recognized as a class of movable and intangible assets in fiduciary guarantees.
Sancaktar Pelin, Necla Kırcalı Gürsoy, Arif Gürsoy
Modern authentication architectures contain structural vulnerabilities against automated credential stuffing and server-side data breaches. Traditional solutions rely on the transmission of raw or hashed passwords over the network; for bot defense, they position third-party Completely Automated Public Turing test to tell Computers and Humans Apart (CAPTCHA) services, which may violate user privacy and create institutional dependencies, as an illusion of two-factor authentication (2FA). This situation raises a critical research question in cybersecurity: How can an integrated cryptographic shield be constructed that is independent of user-privacy-invasive mechanisms and external data authorities, while preventing autonomous bots from targeting the identity and human-verification layers separately?In response to this question, this paper presents a zero-dependency, original, and hybrid protocol that integrates a Zero-Knowledge Proof (ZKP) based on the Schnorr authentication scheme with a local Human Interaction Proof (HIP) mechanism. The main advantage of the proposed architecture is that it mathematically seals the user’s secret credential together with a dynamically generated one-time CAPTCHA token on the client side using the SHA-256 function, thereby transforming the verification process into an indivisible atomic “Hybrid Secret.” In this way, the transmission of password hashes over the network is completely eliminated, and the server evaluates only the mathematical validity of the proof under the Discrete Logarithm Problem (DLP) assumption.Experimental results obtained through Selenium-based automated brute-force attack simulation engines demonstrate that the system provides complete blocking against automated threat vectors. Dynamic one-time nonce mutation immediately invalidates the derived client response, even in extreme scenarios where an attacking bot obtains the correct password string and solves the CAPTCHA image, thereby mathematically defeating brute-force and replay attacks. Furthermore, the autonomous structure of the proposed protocol, with no dependency on third-party analytics services, opens the way for a highly secure and local authentication architecture for internet-isolated critical infrastructures.In this study, the theoretical and mathematical foundations of the proposed protocol are presented, the stages constituting its life cycle are methodologically explained, and Selenium-based experimental simulation results together with telemetry log analyses are detailed.
Purpose This study aims to examine the dual role of non-fungible tokens (NFTs) in elite European football by assessing their financial relevance for clubs and their governance-related implications for fan engagement and legitimacy. It explores how NFTs operate at the intersection of digital revenue innovation and contested issues of transparency, accountability and participation. Design/methodology/approach The study adopts a mixed-methods design. Quantitative financial data were drawn from the Deloitte Football Money League for the top 20 European clubs over the period 2021–2024 and analysed to assess the scale and distribution of NFT-related revenues. Qualitative data were collected from publicly accessible social media platforms and fan forums, focusing on discussions related to NFTs, fan tokens, governance practices and perceived risks. A thematic analysis was conducted, and findings from both data strands were integrated to enable triangulation and contextual interpretation. Findings The results indicate that NFT-related revenues remain economically marginal and uneven across elite European football clubs, with no clear evidence of a stabilising effect on financial sustainability. At the same time, qualitative analysis shows that NFTs carry social and governance significance that exceeds their direct financial contribution. Fan discourse consistently frames NFT initiatives in relation to transparency, cultural meaning and accountability, revealing tensions between club-led digital monetisation strategies and supporter expectations. Practical implications The findings suggest that NFT initiatives require careful strategic and governance consideration. While NFTs may offer limited supplementary revenue opportunities, their implementation has implications for fan trust and institutional legitimacy. Greater transparency and clearer governance arrangements may help align digital innovation with supporter expectations. Originality/value By integrating financial analysis with fan discourse, this study offers one of the first systematic examinations of NFTs in football that connects economic outcomes with governance and legitimacy concerns. The findings contribute to debates on digital innovation in sport by demonstrating that the significance of NFTs lies less in their financial scale than in their institutional and social implications.
The predominant formal models for blockchain systems, particularly smart contracts, have largely been drawn from the classical theory of computation, with the finite state machine (FSM) or labeled transition system serving as the primary conceptual tool. However, the FSM relegates the most difficult and novel aspect of a blockchain -- the achievement of consensus in a decentralized environment -- to a complex, often messy, implementation detail that lies outside the formal model itself. But the process of consensus is not an ancillary feature; it is the very essence of the computational phenomenon. To model it faithfully, a new mathematical language is required. The central thesis of this work is that topos theory, the theory of categories of sheaves, provides the native mathematical language for systems defined by local consistency and the construction of global truth.
The digitization of financial markets has produced two classes of platforms that price, in principle, the same state - contingent payoffs: centralized crypto-option exchanges and blockchain-based prediction markets. This paper provides the first option-implied benchmark test of prediction-market pricing for cryptocurrency threshold contracts. For each hour in a matched sample, we compare the Polymarket Yes price with the discounted risk-neutral binary value implied by a listed Binance call option on the same underlying, strike, and maturity, and study the gap between them. In the main September 2023 Bitcoin contract, the mean pricing gap equals 5.6 percentage points across 214 hourly observations (t = 6.46, p < 10^{-9}). Pooling three Binance-compatible Bitcoin threshold markets yields a mean gap of 6.3 percentage points across 287 observations, robust to HAC and block-bootstrap inference. The gap is persistent - with an AR(1) half-life of roughly four hours - yet mean-reverting, consistent with slow information transmission between segmented venues rather than mechanical noise. Cross-sectional regressions reveal that the wedge is largest at low option-implied probabilities and long maturities, a pattern consistent with speculative demand for prediction-market contracts rather than measurement error. A delta-hedged arbitrage proxy remains profitable after conservative transaction costs, though with marginal statistical precision. A Deribit extension on the same three Bitcoin contracts produces a larger pooled gap of 11 percentage points, while a smaller Ethereum exercise yields mixed evidence. The results demonstrate that digital fragmentation of financial markets generates systematic, persistent pricing wedges even for economically identical payoffs.
This chapter examines the transformative influence of financial technology (FinTech) on audit, compliance, and capital markets – three pillars that sustain trust, transparency, and efficiency in the global financial system. Rapid advances in automation, artificial intelligence (AI), blockchain, and predictive analytics are reshaping how institutions manage risk, monitor transactions, conduct audits, and maintain regulatory adherence. FinTech applications are enabling real-time oversight, reducing manual errors, accelerating reporting cycles, and enhancing fraud detection capabilities through intelligent, datadriven systems. Simultaneously, capital markets are undergoing significant digital modernization, with algorithmic trading, tokenization, distributed ledger technologies (DLT), and digital asset platforms redefining how securities are issued, traded, and settled. By analyzing both operational advancements and emerging challenges, this chapter provides a comprehensive understanding of how FinTech is modernizing conventional financial processes while preparing institutions for a more automated, transparent, and resilient market environment.
Contemporary human science is trapped in an extreme state of "involutionary stagnation": academic disciplines are increasingly hyper-fragmented, mathematical equations grow exponentially convoluted, and experimental precision pushes toward physical limits, yet the foundational core paradoxes remain fundamentally unresolved. Quantum mechanics and general relativity stand irreconcilable, the origin of fundamental physical constants remains unexplained, the essence of life and consciousness persists as a black box, and the development of both carbon-based life and silicon-based intelligence has hit a theoretical bottleneck. Based on the comprehensive theoretical ecosystem constructed across 118 core literature milestones of Yuanxian Theory (YXT / YD-T64), this paper systematically demonstrates that the root cause of all scientific involution is not a lack of empirical depth, but rather a failure to elevate the foundational paradigm's dimensionality. Grounded upon four absolutely self-consistent core axioms—True-Circle Self-Consistency (TCSC), Spacetime Uniqueness (STM), Self-Referential Mind-Field Generation (SRM), and Fine-Structure Conservation (FSC)—and validated by machine proof environments via Lean 4, Coq, and ZFC logical systems, Yuanxian Theory achieves a bottom-up reconstruction of all academic fields. This manifesto delineates the revolutionary breakthroughs achieved across mathematics (constructive proofs of the Millennium Prize Problems), physics and cosmology (first-principles constant derivations, dark energy suppression, and gravity-quantum unification), consciousness and life sciences (the topological origin of the 64 genetic codons and mind-field condensation theory), and silicon-based life applications (formal consciousness criteria, cellular hardware architectures, and controllable zero-point energy extraction). Building upon prior research that closed the paradigm loop from the four foundational cornerstones to the Monistic Unified Field, this paper declares that Yuanxian Theory is not a mere patchwork optimization of existing knowledge, but a definitive, wholesale replacement of the three-hundred-year-old scientific paradigm—offering humanity its singular pathway to move beyond disciplinary involution and actualize high-dimensional cognitive elevation. 当下人类科学正陷入一场极致的“内卷式停滞”:学科越分越细,公式越来越复杂,实验精度越来越高,但底层核心矛盾始终无解——量子力学与广义相对论无法统一,物理常数来源不明,生命与意识的本质始终是黑箱,碳基生命与硅基智能的发展陷入理论瓶颈。本文基于元宪理论(YXT / YD-T64)118篇核心文献构成的完整理论体系,系统阐述:所有内卷的根源,不是研究不够深入,而是底层范式没有升维。 元宪理论以四大核心公理——真圆自洽律(TCSC)、时空唯一性律(STM)、自指心场生成律(SRM)与宇宙因子守恒律(FSC)为绝对自洽的根基,依托Lean 4、Coq、ZFC逻辑体系完成机器验证,构建了从64维环面拓扑(YD-T64)到全学科的底层重构。本宣言梳理了元宪理论在数学(千禧年难题的构造性证明)、物理与宇宙学(常数推导、引力-量子统一、暗能量压制)、意识与生命科学(64密码子起源、心场凝聚论)、硅基生命与工程应用(意识判据、元胞架构、零点能可控开发)等领域的革命性突破。元宪理论的前序工作已完成了从四大基石到一元统一场的范式闭合,本文在此基础上宣告:元宪理论不是对现有科学的修补,而是对三百年科学范式的底层替换,是人类走出学科内卷、实现认知升维的唯一通道。
A rule-based logic solver resolves every instance in our benchmark in under 50 microseconds with 100% accuracy; the best frontier language model reaches 65% at best and drops to 23.5% under rendering-robust evaluation (worst case over four surface renderings). We introduce DeFAb (Defeasible Abduction Benchmark), a dataset and generation pipeline that converts four decades of publicly funded knowledge bases into formally grounded instances for defeasible abduction: constructing hypotheses that explain anomalies by overriding defaults while preserving unrelated expectations. Because every hypothesis must pass polynomial-time checks for valid derivation, conservativity, and minimality, DeFAb makes logical rigor the instrument for measuring creativity and theoretical reasoning, scoring the disciplined construction of theory revisions rather than fluent but theory-destroying prose. The pipeline pairs taxonomic hierarchies (OpenCyc, YAGO, Wikidata) with behavioral property graphs (ConceptNet, UMLS) to produce 372,648+ instances across 33.75M materialized rules from 18 sources, in three levels with polynomial-time verifiable gold standards. Four frontier models do not reliably internalize defeasible reasoning: rendering-robust Level 2 accuracy is 7.8-23.5%; chain-of-thought variance (~36 pp) exceeds any inter-model gap; and a matched contamination control isolates a +19.4 pp Level 3 gap. We further release DeFAb-Hard (a 235-instance Level 3 difficulty variant; best model 53.3% vs 100% symbolic) and CONJURE (a kernel-verified transformative-creativity variant of 560 Lean 4/Mathlib instances whose gold answers are definitions the proof kernel did not previously contain, judge-free verifier; a pilot finds zero novel concepts). The same verifier doubles as an exact reward for preference optimization (DPO, RLVR/GRPO). Released under MIT at https://huggingface.co/datasets/PatrickAllenCooper/DeFAb.