In 2021, Masson, Sanso, and Zhang introduced the Bandersnatch curve associated to the BLS12-381 pairing-friendly curve, an elliptic curve designed for zero-knowledge proofs requiring circuits with a curve arithmetic. This type of curve is useful for privacy-preserving protocols, and more generally for succinct validity proof using pairing-based SNARKs. An embedded curve is defined over a field whose order is the group order of its associated curve. In this way, the pairing-friendly curve is used to express a zero-knowledge proof (such as a SNARK) of a statement taking place on the embedded curve. Contrary to the previous embedded curves (such as CØCØ, JubJub), Bandersnatch was built with the complex multiplication (CM) method, in order to ensure a very small discriminant (-8, whose magnitude is small), and thus efficient scalar multiplication thanks to the GLV technique. The algorithm provided by Masson, Sanso, and Zhang for searching this type of curves requires computation of Hilbert class polynomials, making the search of curve slow. It was not known whether Bandersnatch was an exceptional curve or whether comparable curves exist, of larger discriminants. This paper highlights the technicalities of the CM method already in use in the 90s to generate curve parameters of chosen order. This old technique allows revisiting the curve search of Bandersnatch, providing a dramatic speed-up improvement. This paper presents two algorithms: one to generate embedded elliptic curves of SNARK-friendly elliptic curves, with a variable discriminant; a second to generate families (parameterized by polynomials) with a fixed discriminant. When the (negative) discriminant is -3 modulo 4, it is possible to obtain a prime-order curve, and form a cycle. To illustrate this, we apply the technique first to generate more embedded curves like Bandersnatch with BLS12-381, such as a curve of discriminant -6673027, defining a plain twist-secure cycle. We also comment on the scarcity of Bandersnatch-like CM curves, and recall that with this generic algorithm, it is only a question of core-hours to find them. Second, we show the link between a paper of Ben Smith in 2015 and the work of Dai, Lin, Zhao, and Zhou in 2023, obtaining prime-order parameterized families of embedded curves of fixed discriminant, such as -3 for BLS and KSS18 curves. With KSS16 curves, the discriminant -4 is also possible (the curve has an even order). The technique can work with any KSS, Scott–Guillevic, Gasnier–Guillevic, or other fixed-discriminant parameterized family of pairing-friendly curves. This paper provides a more general point of view on embedded curves such as Bandersnatch, putting into perspective the works of Masson, Sanso, and Zhang, and Sanso and El Housni. The Python/SageMath scripts are available at https://gitlab.inria.fr/zk-curves/cm-embedded-curves/.
Cryptographic protocols are evaluated not only by the security properties they achieve, but also by the resources required to execute them. Unlike conventional algorithm analysis, where a single running-time function may be sufficient, protocol analysis usually separates computational complexity, bit complexity, communication complexity, storage complexity, and round complexity. This article develops a systematic methodology for such analysis through three representative case studies: the Schnorr zero-knowledge proof of knowledge, a Diffie–Hellman-based one-out-of-two oblivious-transfer protocol, and Regev-style public-key encryption based on the Learning With Errors problem. For each construction, the protocol is stated formally, correctness is derived, and the dominant computational, communication, and memory costs are calculated step by step. The examples illustrate three qualitatively different bottlenecks: group exponentiation in discrete-logarithm protocols, amortized public-key cost in oblivious transfer, and dense matrix–vector arithmetic in lattice-based cryptography.
Shahla Atapoor, Cyprien Delpech de Saint Guilhem, Al Kindi
This work describes a digital signature scheme constructed from a zero-knowledge proof of knowledge of a pre-image of the Rescue Prime Optimized (RPO) permutation. The proof of knowledge is instantiated using the DEEP-ALI interactive oracle proof and made non-interactive via the Ben-Sasson–Chiesa–Spooner (BCS) transformation in the random oracle model. The resulting construction yields a signature scheme with transparent setup. Our design is motivated by recursive zero-knowledge applications, in which signature verification must itself be efficiently provable inside larger proof systems. To this end, the choice of the RPO permutation, the use of a simple algebraic intermediate representation (AIR), and working over the Goldilocks field are made with the goal of enabling efficient recursive verification and aggregation. The implementation of the scheme computes signatures in 4.6–7.2 ms and verifies them in 0.46–0.52 ms when the BCS transform is implemented with Blake3. When the BCS transform is instead instantiated with the RPO permutation itself, the configuration required when signature verification is to be proven recursively inside a proof system, signing takes 20.9–30.4 ms with Metal acceleration and 59.2–229.2 ms on CPU, while verification takes 5.09–5.79 ms. We validate the recursion-friendliness claim end to end by proving one signature verification inside the Miden zkVM and reporting the recursive prover time and proof size. These speeds are obtained with parameters achieving 113 or 122 bits of average-case security, depending on the chosen preset, against adversaries that can obtain up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mn>64</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> signatures.
Let $\xi(s)=\frac12s(s-1)\pi^{-s/2}\Gamma(s/2)\zeta(s),\qquad$ $\frac{\xi'}{\xi}\!\left(\frac1{1-x}\right)=\sum_{m\ge0}f_mx^m,$ and define $g_{ij}=f_{|i-j|}-f_{i+j+1}+\delta_{ij}f_0,\qquad$ $M_n=\begin{pmatrix}g_{nn}&g_{n,n+1}\\g_{n,n+1}&g_{n+1,n+1}\end{pmatrix}.$ The condition $M_n\succeq0$ for every $n\ge0$ is a previously established criterion equivalent to the Riemann hypothesis. We prove an unconditional finite-range extension for this family without scanning individual zero ordinates or individual Christoffel--Darboux values. Writing $q=n+1$, a critical-line zero $\frac12+i\gamma$ contributes a rank-one atom generated by a two-dimensional polynomial vector. In diagonal and anti-diagonal coordinates its exact phase is controlled by $x=2q\arctan\frac1{2\gamma}.$ We use two level-dependent ordinate windows $(q,5q/4],\qquad (2q/5,q/2],$ whose phase slopes have opposite signs. Every cross-window pair has wedge at least $c_*/q^3$, while an explicit zero-counting estimate supplies at least $q\log q/100$ zeros in each window. The resulting moving Gram core satisfies $\lambda_{\min}(A_{q-1}^{\rm mov}) \ge \frac{c_*^2}{10400}\frac{\log q}{q^3}.$ Combining this with the Platt--Trudgian verification height $H=3{,}000{,}175{,}332{,}800$ and an unrestricted high-zero tail estimate gives $M_n\succ0\qquad(0\le n\le2{,}030{,}956).$ Thus the first $2{,}030{,}957$ local inequalities are proved unconditionally. We also show that every fixed finite zero core has smallest eigenvalue with liminf zero, explaining why level adaptation is structurally necessary for this method. To the best of our knowledge, the moving-window frame transfer and this finite-range theorem are new. The result is not a proof of the Riemann hypothesis.
Zero-knowledge proofs enable a prover to convince a verifier that a statement is true, without revealing the underlying witness data. This primitive naturally lends itself to privacypreserving systems, where hiding the witness prevents the verifier from learning sensitive information. That said, zero-knowledge proofs can also be used in systems where the witness is not necessarily confidential but is not readily available to the verifier. One such use case is image provenance, where signed images are transformed before being distributed. Since the original image is not available to the user, the digital signature cannot be verified without a zero-knowledge proof. In this use case, zeroknowledge proofs enable verification of the authenticity of the image’s source, the integrity of the image contents, and that only permitted transformations were applied. In this work we present an end-to-end prototype system that implements this provenance framework and several optimizations. One of our key optimizations is a packing scheme for reducing the number of Poseidon sponge absorb and permutation operations by ≈31×. We also show that this packing scheme reduces the median prover runtime by ≈40× and the median verifier runtime by ≈22×. We also introduce a chain of trust that removes digital signature verification from the circuit. Finally, we introduce custom PNG chunks that embed the required information in the captured images.
Autonomous artificial intelligence agents increasingly act across decentralized systems, yet existing authorization models provide limited mechanisms for constraining delegated authority, proving policy compliance, and assigning accountability for machine-initiated actions. This paper presents a policy-enforced identity and accountability framework for cryptographic governance of autonomous AI agents. The proposed architecture binds each agent to a verifiable decentralized identity, machine-readable authorization policies, delegated capability constraints, and tamper-evident action records. Before an action is executed, the framework evaluates identity validity, policy scope, contextual conditions, delegation depth, expiration, and revocation status. Approved actions generate cryptographically verifiable receipts that link the agent, authorizing principal, applicable policy, execution context, and resulting state transition without requiring disclosure of unnecessary sensitive information. The framework also supports attenuated delegation, enabling subordinate agents to receive narrower permissions than their parent agents while preventing privilege amplification. A formal threat model evaluates impersonation, policy substitution, replay attacks, unauthorized delegation, audit-log manipulation, and compromised agent behavior. Security analysis indicates that the architecture strengthens provenance, non-repudiation, least-privilege enforcement, and post-execution auditability across heterogeneous decentralized environments. The proposed approach provides a foundation for governing autonomous agents in blockchain networks, distributed applications, machine-to-machine systems, and multi-agent infrastructures where conventional access control is insufficient. It shifts AI governance from trust-based supervision toward verifiable, policy-bound, and cryptographically accountable execution.
Saskia Hufnagel, Colin King, Alina-Theresa Schnedl, Milind Tiwari
Non-fungible tokens (NFTs) bring many opportunities for artists, investors, and creators, but they also have a dark side with significant potential for use in financial crimes. Drawing on relevant caselaw, a systematic review and topic modeling of literature, we map common examples of NFT-related crime, including fraud, money laundering, theft, and market-related offenses. This empirical review lays the groundwork for the core contribution of this article, that is, application of the “crime triangle” to NFT-related crime. Recognizing heterogeneity in NFT-related crime, we detail five scenarios where such crime can occur and analyze these in the context of the crime triangle (inner and outer). This enables us to identify potential gaps and vulnerabilities in current crime prevention strategies. Given challenges in policing cybercrime, and specifically NFT-related crime, we argue that the crime triangle provides a useful heuristic tool for understanding the nature of NFT-related crime and for preventing such crime from happening.
A rival has arrived on the unitarity triangle, and the confrontation is unusually clean. Arkani-Hamed, Figueiredo, Hall and Manzari (AHFHM, arXiv:2607.27315) observe that the CKM unitarity-triangle angles lie close to simple fractions of π — (α, β, γ) ≈ (π/2, π/8, 3π/8) — and propose sparse “9-link” Yukawa textures with a spontaneously broken CP phase quantized in multiples of π/8. This note shows the One-Octonion Brane-Bulk (OOB) framework already contains the pattern, with a different and sharper origin. (i) The flat OOB triangle is exactly right-angled. With apex (ρ̅, η̅) = (1/7, √6/7) fixed by the √7 Dehn twist, the apex sides are exactly orthogonal (−6/49 + 6/49 = 0): αflat = π/2 identically, with the right angle split as tan γflat = √6, so γflat = arccos(1/√7) = 67.79° = 3π/8 + 0.29°. The proximity to the π/8 lattice needs no discrete symmetry: an irrational Gudermannian angle grazes it. (ii) One bulk-transit depth warps the observed triangle. α0 = 0.10673 gives (α, β, γ) = (92.01°, 22.63°, 65.36°) — each within 0.4σ of PDG 2026, in a statistical dead heat with the AHFHM anchors today. (iii) The texture-level cross-match. Written in AHFHM's own parametrization (up-frame diagonal; down sector sparsified with the right-handed U(3)dR freedom to a canonical RQ chart), the OOB flavor point lands exactly on their 3π/8-family texture #29 — identical zero pattern and phase slot Yd12 — as its canonical hierarchical representative. The single rephasing invariant is φ = 65.409° = γ + 0.045°: it tracks γ exactly as their leading-order theorem requires, with the next-order texture correction computed here. The same texture chart thus carries two incompatible phase laws: AHFHM-quantized #29 predicts γ ≈ 3π/8 − 0.045° = 67.455°; OOB predicts γ = 65.364°. The mirrored (lower-triangular) gauge reproduces AHFHM's anomalous π/4 histogram peak. Next-generation LHCb/Belle II determinations of γ (sub-degree, 2030s) decide. Framing (stated honestly): the texture identification is a canonical-coordinate statement — the RQ chart's loop phase is a determined, rephasing-invariant function of YdYd† — not a dynamical derivation of the texture; the group-theoretic (Ursa-Major S4 Clebsch-Gordan) route to the magnitudes remains open. No new parameter and no new prediction number is introduced; the note sharpens the framework's long-standing γ stake (Papers CXLIX, CCCII, CCCIII) to the texture level. Verification: one Python gate script and 19 independent Wolfram gates (including symbolic proofs of the exact right angle and tan γ = √6), all passing (supplementary files); one Fugu cross-model pre-publication audit with all confirmed findings repaired. The results are strictly contingent on the established OOB framework — the G2 = Aut(O) reduction, the √7 Ursa-Major twist and apex closure, the Wolfenstein closures λ = √3/(ea*+6) and A = 8/π2, and the Class-I transit depth α0 — none of which is re-derived here. Full symbolic proofs and postulates are consolidated in the BraneBulk Omnibus (concept DOI 10.5281/zenodo.19185171).
As the blockchain and decentralized finance (DeFi) ecosystems continue to expand and mature, rug pull scams involving meme coins are occurring with increasing frequency, posing a threat to the security of investors' assets and the healthy development of the industry. Rug Pull scams are characterized by extremely low deployment costs, covert execution, rapid fund transfers, and high detection difficulty. Traditional manual reviews or fixed rules struggle to meet real-time early warning requirements, and existing detection methods generally suffer from issues such as a single feature dimension, inadequate handling of class imbalance, and weak model generalization and interpretability. To address these shortcomings, this paper focuses on the detection of Ethereum-based rug pull scams. First, we clarify their definitions, types, and harm mechanisms, and construct a multi-dimensional feature system based on dimensions such as malicious smart contract design, on-chain transaction anomalies, liquidity manipulation, and social media disclosures. Next, using the "Second Uncle Coin"(token symbol: BOBU) case as an example, we reconstruct the attack process and derive quantitative detection metrics. Subsequently, a risk detection model based on a Multi-Layer Perceptron (MLP) is designed. We employ a combined strategy of SMOTE oversampling and Focal Loss to address the issue of sample imbalance, dynamically search for optimal thresholds to balance precision and recall, and incorporate gradient pruning and early stopping to enhance training stability. Experiments show that the model achieves an accuracy of 0.927, an F1 score of 0.787, and an AUC-ROC of 0.952 on the test set, outperforming traditional methods. Finally, a visualizable web-based detection system is developed using the Flask framework, enabling batch risk assessment, high-risk ranking display, and result export functions.
ABSTRACT With the retirement of 21GW of legacy coal fleet looming, the task to replace Australia's thermal capacity with alternative firmed renewable ‘bulk energy’ solutions is non‐trivial. Indeed, calibrating necessary capital investment in new plant stock with efficient economic outcomes, vis‐à‐vis cost and reliability, represents a fundamental challenge to the ‘dynamic efficiency’ ideal for energy‐only markets. Recently, a major review of Australia's National Electricity Market (NEM) has proposed that underwriting of new energy generation utilise fungible derivative contracts instead of the canonical run‐of‐plant (RoP) revenue contract design. The proposition is based upon re‐linking medium‐ and long‐term energy markets, while mitigating some of the worst externalities attributed to off‐market underwriting policies when implemented at‐scale. However, sound economic theory can collide with applied corporate finance in practice. The very basis of the RoP design has been to address the needs of capital markets vis‐à‐vis revenue quality for new‐entrant plant, making bankability a threshold issue for contract viability. This article examines a set of proposed fungible contract designs, leveraging 100 years of synthetic, stochastic market data to draw conclusions on the compatibility of contract fungibility and bankability. Findings indicate that eliminating incentive incompatibilities between plant dispatch and market signals (which are inherent in the RoP design) appears adequate to counteract ‘shape’ risk—both idiosyncratic and systematic—within the confines of a typical project finance structure. In other words, bankability and fungibility do not appear to be mutually exclusive.
Large language model (LLM) inference services convert request arrivals into coupled input-token prefill and output-token decoding workloads. Capacity planning therefore depends on token volume, temporal bursts, service mix, queueing behavior, and reliability signals rather than request counts alone. This study evaluates an integrated planning pipeline on BurstGPT v1.1, comprising 5,288,173 raw requests over 121 trace days and 5,188,507 completed requests. Strictly chronological experiments aggregate demand, forecast hourly completed tokens, detect minute-level burst pressure, estimate zero-response risk, simulate capacity policies, and replay representative test hours in Vidur. Random forest, selected on the validation interval, achieved 64.73% weighted absolute percentage error (WAPE) on the locked test interval; XGBoost achieved the lowest test WAPE (64.57%), while the last-hour baseline reached 67.42%, indicating limited forecastability under a pronounced level shift. A seasonal-residual burst detector achieved F1 = 0.653, although burst prevalence was sensitive to rolling-horizon and quantile settings. For minute-level zero-response risk, raw XGBoost achieved ROC-AUC = 0.813 and average precision = 0.116; isotonic calibration improved the Brier score (0.0230–0.0204) and 10-bin expected calibration error (0.0262–0.0122), despite low F1. Static P90/P95 capacity eliminated under-provisioned test hours at cost indices of 13.10 and 18.09. More economical dynamic baselines achieved 12.65% under-provisioned hours at a cost index of 2.37 and 13.63% at 2.43. The validation-selected random-forest policy was cheaper but less reliable (34.31% at 1.23). Vidur replay linked normalized demand to A100/H100 GPU counts, latency, batching, and memory pressure. The results support conservative interpretation of point forecasts and validation of reserve rules under distribution shift, rare-event calibration, and serving-stack constraints.
Abstract This reply addresses a recent comment concerning the proof of the Nernst theorem. I clarify how a Carnot engine can consistently operate at $$T=0$$ T = 0 through a continuous deformation of a cycle operating at $$T>0$$ T > 0 . By examining the limit where heat exchange with the cold reservoir vanishes, I show that the Nernst theorem ensures that the concept of temperature remains physically consistent at the absolute zero limit.
PARI is a recent SNARK based on equifficient polynomial commitments, giving an exceptionally compact proof of just 1280 bits over the BLS12-381 curve, which is the smallest among all the known SNARKs in the literature. However, PARI does not achieve the zero-knowledge property; despite being very efficient, it is therefore less suitable for applications requiring witness privacy. In this work, we propose a zero-knowledge extension of PARI making it ideal for privacy-centric applications yet keeping the proof size compact. We prove perfect completeness, perfect zero-knowledge in the random-oracle model with challenge space <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>𝔽</mml:mi> <mml:mi>⧵</mml:mi> <mml:mi>K</mml:mi> </mml:mrow> </mml:math> , and knowledge soundness in the algebraic group model with random oracles under the SDH assumption.
Zero-knowledge proofs (ZKPs) are a fundamental building block in cryptography, enabling powerful privacy-preserving and verifiable computations. In the post-quantum era, hash-based ZKPs have emerged as a promising direction due to their conjectured resistance to quantum attacks, along with their simplicity and efficiency. In this work, we introduce SmallWood, a hash-based polynomial commitment scheme (PCS) and zero-knowledge argument system optimized for relatively small instances. Building on the recent degree-enforcing commitment scheme (DECS) from the Threshold-Computation-in-the-Head (TCitH) framework, we refine its formalization and combine it with techniques from Brakedown. This results in a new hash-based PCS that is particularly efficient for polynomials of relatively small degree –typically up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mn>16</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> – outperforming existing approaches in this range. Leveraging this new PCS, we design a hash-based zero-knowledge argument system that outperforms the state-of-the-art in terms of proof sizes for witness sizes ranging from <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>6</mml:mn> </mml:msup> </mml:mrow> </mml:math> to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mn>16</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> . Additionally, we present exact zero-knowledge arguments for lattice-based problems using SmallWood, demonstrating highly competitive performance: our scheme yields proof sizes under 25 KB across a wide range of lattice parameters, including Kyber and Dilithium instances.
Ryan Anthony, Jechenthia Maria Taso, Stephen Yohanes Christopher, Ridhwan Ardiyansyah
This study aims to analyze and compare the performance of three algorithms, namely Support Vector Regression (SVR) with a linear kernel, XGBoost, and LightGBM, in predicting the Price of Ethereum cryptocurrency based on daily historical data. The study uses Ethereum Price data in USD for the last five years obtained from the investing.com website. The variables used are Close, Open, High, and Low Prices. The study uses two data splitting scenarios: 80% training data and 20% testing data, and 70% training data and 30% testing data. This study also uses time step variations to test the effect of time dependency on algorithm performance. The results indicate that the LightGBM algorithm has the best performance compared to the other two algorithms with an average MAE value for High Price of 75.486, SVR has a value of 115.590, and XGBoost has a value of 77.314 in the 80% training data and 20% testing data split. In the 70% training data and 30% testing data split, the LightGBM algorithm still excels with an average MAE value for High Price of 78.228, SVR of 104.356, and XGBoost of 83.573. Other evaluations such as RMSE and R2 also show the superiority of the LightGBM algorithm. For the required computation time, the SVR algorithm outperforms the other two algorithms.
The efficiency and performance of logistics, and thereby business operations, increasingly depend on decentralized and tamper-proof records for managing transactions and data flows. For these reasons, the use and professional implementation of blockchain technology (BCT) has become essential to preserving the competitiveness of the digital and globalized economy. My aim was to examine the collaboration networks of articles on blockchain in logistics published in recent years, and to identify the key studies that may prove fundamental. Data was collected from the Web of Science scientific database in accordance with the PRISMA guidelines. The results were visualized using the VOSviewer bibliometric analysis software. As a result of the screening process, I analyzed 32 relevant studies in detail. I found that the United Kingdom and China play leading roles in this field, with several research groups contributing simultaneously to the scientific outcomes. Based on keyword analysis, a total of five clusters were identified, each representing distinct research areas.
Autonomous AI agents, increasingly empowered by large language models, are becoming important components of human-machine systems for high-stakes decision support in digital twin ecosystems. However, existing multi-agent systems often lack robust verification for identity, capability, and policy compliance, especially in decentralized environments spanning multiple institutions. This paper proposes a neuro-symbolic decentralized governance framework for verifiable agents in collaborative digital twin environments. By representing agents through multi-layer semantic profiles, the framework bridges probabilistic neural reasoning with deterministic institutional governance, thereby supporting trustworthy human-AI collaboration and meaningful human oversight. Capabilities are grounded in formal domain ontologies to enable machine-interpretable, policy-aware, and context-sensitive participation. These credentials, issued by organizational authorities, are validated via blockchain-based smart contracts, ensuring auditable participation without exposing sensitive data. We demonstrate the framework using a decision-support prototype with clinic, digital twin, and wearable provider agents effectively prevents unauthorized interaction and enforces institutional policies with manageable overhead. Our findings suggest that neuro-symbolic decentralized governance provides a scalable and trustworthy pathway for safe human-machine collaboration across institutional boundaries.
Supplementary appendix to the article "Digital Cash and the Governance of Payment Finality." It contains material displaced from the main text for length, referenced at the corresponding points in the article, and forming no part of the manuscript word count. The appendix documents in full the evidence that claims for digital cash treat technical irreversibility as legal finality, with the passages and page references from Böhme, Christin, Edelman and Moore (2015), Cong and He (2018), De Filippi and Hassan (2016), Kiviat (2015), Atzori (2017) and Politou, Casino, Alepis and Patsakis (2019), together with the passages in which several of those authors qualify or abandon the claim. It further sets out the argument that append-only recording does not entail unrecoverable entitlement: the regulatory history of write-once, read-many electronic recordkeeping under SEC Rule 17a-4 and its 2022 amendment; the accounting mechanics by which a chargeback operates as a contra entry rather than an erasure; the equivalence between that mechanism and reversal by subsequent transaction on a distributed ledger; and two limits on the argument, being value irretrievable because no person holds the key, and records whose own existence is the wrong, as with personal data subject to an erasure right.
This study examined blockchain technology's potential to enhance audit efficiency in selected service firms. Traditional auditing is often hindered by data manipulation, limited transparency, time-consuming verification, and high costs challenges that blockchain's decentralised, immutable, and transparent ledger system can plausibly address. The research assessed blockchain's role in improving audit efficiency, focusing on automation and realtime auditing, distributed ledger effects, and consensus mechanisms. Data was gathered through questionnaires, observation, and a technical readiness survey, and analysed using descriptive statistics, inferential statistics, and multiple regression. The study evaluated current auditing practices to identify the benefits and barriers of blockchain implementation, examining existing audit challenges, blockchain's capacity to resolve them, and the implications for service firms. Respondents were drawn from service firms with interest or prospects in adopting blockchain for audit activities. Findings showed marked improvements in audit accuracy, transparency, and overall efficiency, though adoption barriers, cost, and the need for regulatory structures were also identified. The study contributes to the growing body of knowledge on blockchain's practical application in auditing, offering guidance to service firms, audit practitioners, and policymakers on successful implementation. By addressing these challenges and leveraging blockchain's opportunities, service firms can achieve cleaner, safer, and more efficient audit practices. The research confirms that blockchain technology plays a significant role in enhancing audit efficiency within service firms.
Open access
2 source records
Blockchain Technology Applications and Security
Innovations and Analysis in Business and Education
Ensuring transparency and traceability in horticultural supply chains is difficult due to complex logistics, seasonal variability, and multiple intermediaries.We propose a framework that couples b-local irregular vertex coloring (b-LIVC) with a blockchain architecture to enable end-to-end verification of production and trade.On the Jember Regency subdistrict graph, we compute the b-local irregular chromatic number and obtain χ b-lis (J) = 6, yielding six planting color classes that schedule sowing and harvests to distribute output across the year.The local irregularity induces distinct neighborhood weights, which we use as cryptographic features for unique, verifiable batch identifiers.We implement the pipeline on a public blockchain: harvest lots are tokenized as video NFTs with QR links to a verification page and on-chain records.The integration of discrete mathematics and distributed ledgers provides auditable provenance and transaction history, practical scheduling that reduces harvest clustering, and a low-overhead mechanism for farmer-level transparency.
Let $\xi(s)=\frac12 s(s-1)\pi^{-s/2}\Gamma(s/2)\zeta(s)$ be the completed Riemann xi-function, and let $F(x)=\frac{\xi'}{\xi}\!\left(\frac{1}{1-x}\right)=\sum_{n\ge 0} f_nx^n$ initially denote its germ at the origin. We introduce the real symmetric Toeplitz--Hankel matrix $c_{ij}=f_{|i-j|}-f_{i+j+1}+\delta_{ij}f_0, \qquad i,j\ge 0.$ We prove that the Riemann hypothesis is equivalent to positive semidefiniteness of every finite leading principal block of this matrix. More strongly, the full matrix condition is equivalent, for the purpose of testing the Riemann hypothesis, to only the adjacent local inequalities $2f_0-f_{2n+1}\ge 0,$ $(2f_0-f_{2n+1})(2f_0-f_{2n+3})\ge (f_1-f_{2n+2})^2 \qquad(n\ge 0).$ The converse implication uses a Pringsheim bootstrap: these local inequalities force the germ of $F$ to have Taylor radius at least one, hence exclude zeros of $\xi$ from the half-plane $\Re s>1/2$. If $\lambda_n$ are the Keiper--Li coefficients, then $f_n=\lambda_{n+1}-2\lambda_n+\lambda_{n-1}$, so the criterion is a local quadratic condition on their second finite differences. This is an equivalent reformulation, not a proof of the Riemann hypothesis. To the best of our knowledge, the exact Toeplitz--Hankel matrix and its reduction to adjacent $2\times2$ conditions have not appeared previously.