We estimate the causal price elasticity of gas demand on Ethereum mainnet (L1) and Arbitrum One (L2), a quantity necessary for calibrating fee mechanism simulations, evaluating resource pricing reforms, and explaining observed usage patterns. A two-way fixed effects panel regression instrumented by each wallet's own lagged base fee removes the congestion-driven endogeneity that causes naive regressions to substantially underestimate demand sensitivity. On Ethereum mainnet (full year 2025), the pooled IV elasticity is -0.006***, near-inelastic: a 10% fee increase reduces total gas demand by approximately 0.06%. On Arbitrum One (October 2025--April 2026), the pooled IV elasticity is -0.036**. Both chains are inelastic in the aggregate, with L2 measurably more responsive than L1. A per-resource decomposition of L2 demand reveals elasticities ranging from modestly elastic computation (-0.027*) to -0.27*** for refunds, with storage growth (-0.15***) and calldata (-0.06*) in between. Behavioral clustering identifies always-on protocol wallets as near-inelastic and high-volume operators as substantially more responsive, with cluster-level elasticities up to roughly 6x the pooled estimate. These results establish an empirical foundation for downstream simulations and for evaluating fee mechanism designs.
Thomas Bakaysa, Ahmet Kurt, Abdul-Salem Beibitkhan, J E Hernández Leon · 9 authors
Bitcoin's Lightning Network (LN) can be exploited as a covert, low-cost command-and-control (C&C) channel for botnets, as demonstrated by the LNBot and D-LNBot designs. However, both remain proof-of-concept prototypes evaluated only through simulation, leaving key questions about real-world topology formation, propagation complexity, and resilience to takedowns unanswered. We present LNTest, the first reusable testbed for LN-based botnets, built from Core Lightning nodes containerized with Docker over a shared Bitcoin Core regtest chain. LNTest supports three overlay topology modes (a deterministic chain, autonomous peer discovery, and user-supplied graphs), enabling controlled experiments across different botnet structures. Using LNTest, we report three main findings. First, D-LNBot's autonomous formation protocol does not produce the uniform chain from its design; instead, it creates a clustered chain in which cliques are linked by bridge nodes whose removal fragments the network. Second, command propagation scales linearly with botnet size ($Θ(n)$), not the $O(m \log n)$ previously claimed, and gains nothing from higher neighbor connectivity. Third, the overlay topology determines the effectiveness of takedown strategies: uniform-degree chains resist targeted removal but fragment under random failure, scale-free topologies show the opposite pattern, and the autonomous clustered chain is fragile under both, making it the most vulnerable of the three. LNTest is released as open source, with a script that reproduces all our experiments, to support reproducible research on LN-based botnet defenses.
Luisa von Albedyll, Robert Ricker, Frank Kauker, Daniel Krogmann · 5 authors
Abstract. Arctic sea ice thickness has declined rapidly over recent decades, yet the relative roles of thermodynamic growth and dynamic redistribution in driving this change remain poorly constrained at basin scale. We quantify thermodynamic and dynamic contributions to sea-ice thickness change together with their uncertainties across the Arctic from 2002 to 2020 by combining satellite-derived thickness with sea-ice model simulations (Icepack) along trajectories. Separating dynamical thickening (30%), dynamical thinning (−24%), and lead-ice growth (10.2%) shows that dynamic processes contribute nearly as much to the average winter ice growth of 0.21 m per month as thermodynamic processes (35.8%). Regional, seasonal, and thickness-dependent variability is consistent with large-scale dynamic patterns and the ice-growth feedback. We quantify the effects of the overly smooth deformation forcing, which leads to an underestimation of large dynamic events and a substantial noise floor during dynamically quiet periods, and relate their magnitude to other sources of uncertainty. Analyzing the long-term trend from 2002–2020, we resolve a weak increase in median sea ice deformation (1.5% per year) and net dynamic thickness change (12% per year) within the limits of our study setup. Overall, our results suggest that increasing deformation in the Arctic enhances net dynamic thickness change and acts as a negative feedback in the pan-Arctic winter thickness budget.
Abstract Four independent fields—physics, biology, economics, and cultural evolution—have converged on the same mathematical machinery for describing persistence-conditioned dynamics. The convergence is not metaphorical but literal: the same fitness landscapes, selection operators, and transmission kernels appear independently. We synthesize these into the Replicator-Optimization Mechanism (ROM): a unified apparatus instantiable at any scale. Key Contributions Cross-field synthesis: Physics, biology, economics, and cultural evolution share identical formal structure Political application: ROM instantiated with friction from stake-voice mismatch as primitive, legitimacy as survival probability Machine-checked proofs: Core algebraic results verified in Lean 4 with Mathlib (28 theorems, zero sorry placeholders) Key results: Simplex preservation, survival monotonicity, moving equilibrium existence, impossibility of static equilibrium under varying friction Links arXiv: arXiv:2601.06363 Lean 4 proofs: github.com/studiofarzulla/lean-formalizations ASCRI: systems.ac/4/DAI-2503 Research Lab: Dissensus AI v3.0.0 (2026-07-11): Matches arXiv v3 (69pp). Keystone-legitimacy example corrected; a coarse-graining citation that could not be verified was removed from the bibliography; the Δ→σ step is now disclosed as an explicit worst-case identification; total-variation legitimacy remark added, aligning the measurement form with the level-form dynamics used in companion papers; Lean 4 formalization tree included in the arXiv source.
The transformation of the copyright institution in the context of the intensive development of digital technologies and the globalization of the information space is studied. The legal nature of objects created with the help of artificial intelligence systems is analyzed, and the challenges facing the traditional anthropocentric model of authorship are identified. The features of non-fungible token technology (NFT) as a tool for monetizing digital art are identified
Abstract: This paper explores the changing legal framework surrounding virtual property and digital land ownership in metaverse environments. While blockchain technology provides immutability and provenance through non-fungible tokens (NFT), the rights it provides are still merely technologically symbolic, not legally certain. Virtual land ownership is shown to be contingent, contractual and revocable - more like a licence of access rather than legally enforceable proprietary ownership. The Indian system recognizes virtual assets tax policy as property, however do not provide ownership protection, leaving clients with the paradox of economic recognition without legal ownership. This research proposes a sui generis legal framework- Lex Metaversi – that streamlines digital property regulation and deals with the tension that exists between ownership of code and unenforceable legal control. Keywords: Virtual Property Rights, Metaverse Law, Non-Fungible Tokens (NFTs), Lex Metaversi, Digital Asset Regulation
Traditional distributed consensus mechanisms rely on probabilistic assumptions, economic weighting (Proof-of-Stake), or arbitrary computational work (Proof-of-Work) to secure ledger state transitions. These models leave the application layer inherently vulnerable to Man-in-the-Middle (MITM) attacks, Maximal Extractable Value (MEV) extraction, and semantic exploits against critical infrastructure (SCADA/PLC). This manuscript introduces Proof-of-Rigidity (PoR), a deterministic state-validation framework that locks the consensus machine within a continuous 150-decimal-place geometric manifold ($G_{24}$ volume space). The paper formalizes three core components: The Brittle Acceptance Predicate: A Coq-verified mathematical boundary that enforces an absolute $10^{-80}$ validation tolerance, structurally denying unauthorized state mutations. Mantissa Tail Parity (The MEV Sieve): A mechanism utilizing Canonical Decimal Arithmetic ($\mathbb{D}_{150}$) to mathematically neutralize routing interception and front-running. Capability-Constrained Semantic Policies: A bipartite matrix that structurally subordinates LLM-based ontological analysis to strict cryptographic Role-Based Access Control (RBAC), preventing adversarial paraphrasing against industrial endpoints. By enforcing strict geometric determinism, PoR transforms network security from probabilistic difficulty into mathematical brittleness. Included in this deposit are the Coq formal verification proofs, a Python reference implementation of the Layer-1 substrate, and a computational benchmarking harness demonstrating throughput scalability. LEGAL, ETHICAL, AND SAFE HARBOR DISCLAIMER The mathematical models, formal Coq proofs, and Python reference implementations contained within this deposit are published strictly for academic research, cryptographic peer review, and educational purposes. The architectures described herein represent a theoretical substrate and an experimental prototype. They have not undergone formal, independent security auditing for production deployment. No Warranty (As-Is): The mathematical models and reference code are provided "AS IS", without warranty of any kind, express or implied. The continuous geometric bounds and mechanisms detailed herein are theoretical thresholds; physical hardware limitations, truncation errors, or implementation flaws may affect real-world execution. Limitation of Liability: Under no circumstances shall the author, contributors, or affiliated research entities be held liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, loss of use, data, stablecoin assets, or profits; business interruption; or industrial infrastructure failure) arising in any way out of the use, deployment, or misconfiguration of this protocol. Assumption of Risk: Any entity choosing to implement the $G_{24}$ volume space boundaries, the Topological Shatter mechanics, or any variant of the PoR consensus layer within a live environment does so entirely at their own risk, and is solely responsible for ensuring compliance with all applicable cybersecurity and financial regulations.
يتناول هذا البحث مسألة حماية الهوية الثقافية للأسرة في ظل التحولات التي أفرزتها البيئة الرقمية اللامركزية في عصر الشابكة اللامركزية (Web3). ولم يعد أثر التطور الرقمي مقتصراً على الجوانب التقنية البحتة، بل امتد إلى المجالات القيمية والتربوية التي تمارس الأسرة من خلالها وظيفتها في التنشئة الاجتماعية، ونقل الموروثات، وترسيخ المرجعيات الثقافية بين الأجيال. ويهدف البحث إلى بيان طبيعة الأثر الذي تمارسه هذه البيئة الرقمية في إعادة تشكيل المجال الثقافي داخل الأسرة، مع تحليل الإشكالات القانونية التي يثيرها هذا التحول، ولا سيما ما يتصل بمدى كفاية التشريعات التقليدية لمواكبة هذه التغيرات. وتعتمد الدراسة على المنهج الوصفي التحليلي، مع الاستفادة من لمحات مقارنة محدودة، لتقويم فعالية الأطر القانونية القائمة. وقد خلصت إلى أن الأدوات القانونية التقليدية لم تعد كافية، بمفردها، لضمان حماية الهوية الثقافية للأسرة، وأن المرحلة الراهنة تقتضي مقاربة قانونية متوازنة تقوم على الوقاية المسبقة، وتقييم المخاطر، والتصميم الآمن للمنصات الرقمية، مع توزيع واضح للمسؤوليات بين مختلف الفاعلين في الفضاء الرقمي، ومراعاة المصلحة الفضلى للطفل، وتحقيق التوازن بين الانفتاح الرقمي، والحفاظ على البعد الثقافي للأسرة، بما يعزز قدرتها على صون قيمها، وتماسكها في وجه التحولات المتسارعة. This study examines the protection of the family’s cultural identity in light of the transformations produced by the decentralized digital environment in the era of Web3. Digital influence is no longer confined to purely technical aspects; rather, it now extends to the value-based and educational spheres through which the family carries out its role in socialization, transmitting heritage, and consolidating cultural references across generations. The study aims to clarify the nature of this environment’s impact on reshaping the family’s cultural sphere while analyzing the legal challenges arising from this transformation, particularly those related to the adequacy of traditional legal frameworks. It adopts a descriptive-analytical approach, supported by limited comparative insights, to assess the effectiveness of existing legal frameworks. The study concludes that traditional legal tools are no longer sufficient on their own to effectively protect the family’s cultural identity. Instead, the current stage requires a balanced legal approach grounded in prevention, risk assessment, secure-by-design principles, clear allocation of responsibilities among digital actors, consideration of the child’s best interests, and preservation of the family’s cultural dimension.
Abstract A Non-Fungible Token (NFT) is a digital asset representing ownership or proof of authenticity of a unique digital item. NFTs are used for various purposes, including digital art, collectibles, virtual real estate, and tokenizing unique digital or physical items, and have introduced new dimensions to digital ownership and enabled individuals to tokenize unique digital assets using blockchain technology. Although NFTs offer exciting opportunities, they suffer from interoperability, high energy consumption, piracy, ownership control, and security issues. In this paper, an idea has been proposed in which any image, pdf file, or video file can be converted to an NFT and owned. We have used the ERC-721 standards, the PoS consensus protocol, and a smart contract to address the challenges. The proposed framework provides a step-by-step guide to create, list NFTs and maintain secure ownership where metadata are stored on IPFS, which generates a unique URL. This URL is then logged on the blockchain, saving time and costs. The created NFTs are interoperable among various applications and frameworks. Smart contract has been formally verified using Slither and tested against vulnerability using Smart Contract Weakness Classification (SWC) standards. Performance of the proposed system has been measured in terms of execution cost, latency, and throughput including statistical indicators like variance and confidence intervals. Minting cost has been compared with the similar network condition.
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
Trust in climate data remains a significant barrier to effective climate action. Skepticism about data manipulation and politicization reduces confidence and hinders evidence-based policy. Existing climate data systems lack transparent verification and accessible analytical tools, limiting accountability and stakeholder engagement. This study presents a reproducible framework that applies blockchain technology to provide transparent verification, analysis, and governance of climate data. The architecture includes three layers: a data ingestion layer that standardizes verified observations, a blockchain layer that ensures immutability and provenance through proof-of-stake consensus, and a statistical analysis layer that uses deterministic methods for anomaly detection and trend evaluation. The framework was tested using 8,403 hours of temperature data from the Manila, Philippines monitoring station during 2024. Analysis identified 33 temperature anomalies ranging from 36.9 to 38.0 °C that aligned with documented April–May 2024 heat waves, confirming the ability to detect genuine meteorological extremes. Estimated transaction latency was 1–2 seconds per observation, with on-chain storage requirements of about 138 kilobytes and off-chain storage requirements of 2.1 megabytes for a 90-day deployment. Estimated energy use for the same period was approximately 0.06 kilowatt-hours, representing a 97–99 percent reduction compared with proof-of-work systems. These findings demonstrate that the proposed framework can securely record, verify, and analyze climate data while consuming very little energy. By combining blockchain immutability with transparent statistical methods, this approach directly addresses the trust deficit in climate science and provides a foundation for verifiable, reproducible, and efficient climate information systems.
Federated Learning(FL) is predominantly deployed in enterprise environments, where limited transparency and restricted auditability hinder broader adoption. Existing FL systems often suffer from opaque aggregation processes, making it unclear which model updates are accepted or discarded. Current mitigation strategies typically rely on external validators introducing additional computational and communication overhead. In this paper, we propose a novel FL framework that leverages existing Web3 technologies to enhance transparency, trust and auditability throughout the training process. The framework adopts a hierarchical architecture in which delegated managers orchestrate the FL training process within their respective federations. To mitigate adversarial and poisoning attacks, a combination of novelty detection and consensus mechanisms were employed. Model updates are encoded and broad casted to all managers, who independently evaluate their validity and those model updates that are approved by the consensus are incorporated into the global model. Additionally, a reputation score based backup mechanism is employed to ensure model generation. Extensive experiments conducted under real world scenarios demonstrate the effectiveness, resilience of the proposed framework, highlighting its potential to enable transparent FL beyond traditional enterprise setting.
This paper examines whether blockchain-registered AI agents demonstrate operational readiness beyond identity registration. Using a dataset of ERC-8004 agents on Ethereum, we construct an agent-level feature table covering identity status, metadata, service declarations, reputation feedback, transfers, and cross-chain registration. We develop an operational readiness framework based on observable evidence layers and complement it with network analysis of owner-agent, feedback-client, wallet-transfer, and combined evidence relationships. The results show that early ERC-8004 adoption is registration-heavy but operationally shallow. While the identity layer is visible at scale, metadata availability, service exposure, reputation formation, and cross-chain evidence remain limited. Ownership and feedback activity are also highly concentrated, suggesting that early participation is shaped by a small number of high-activity wallets and clients. The network analysis further shows that richer operational evidence clusters around a small subset of agents rather than being broadly distributed across the ecosystem. The findings suggest that ERC-8004 provides an important identity layer for decentralized AI agents, but the transition from agent identity to agent economy remains incomplete.
State root computation dominates (78%) blockchain block processing time. Ethereum's canonical authenticated data structure, i.e., Merkle Patricia Trie (MPT), suffers from severe tree-height growth and is vulnerable to \textit{Nurgle attacks} (SP'24), where adversaries inflate path depth via hash collisions and degrade system performance at negligible cost. Existing defenses increase node fanout (span) to bound tree height, but higher span inflates proof size exponentially. Prior work mitigates this trade-off using vector commitments, at the cost of trusted setup or expensive verification. We present \textsc{Mhot}, a height-optimal authenticated data structure for blockchain state commitment that preserves standard hash-based verification without trusted setup. Unlike MPT's fixed-prefix indexing, which couples span and fanout exponentially, \textsc{Mhot} indexes by discriminative bits that actually distinguish keys, achieving adaptive span with linear fanout coupling and provably minimal height. To prevent high fanout from inflating proofs, we introduce hierarchical proofs, a two-layer Merkle construction that reduces per-node proof overhead from O(k) to O(log k). On Ethereum mainnet workloads, \textsc{Mhot} achieves up to 9X higher write throughput, 4X lower write amplification, and 2X smaller proofs than MPT. Under Nurgle attacks, even when the adversary consumes an entire block's gas budget, \textsc{Mhot} maintains a 0% attack success rate (v.s., 99.97% for MPT). Our results, somewhat surprisingly, show that height optimality (not new crypto primitives!) is the key abstraction for scalable and attack-resilient blockchain state commitment.
In the contemporary landscape of modernity, characterised by the evolving information age, cryptocurrencies have emerged as a decentralised mode of transaction, qualifying to be termed as liquid modernity (Bauman, 2012). The apparent fluidity, flexibility and the unrevealed potentially rigid tendencies inherent in cryptocurrencies; present it as a virgin domain to be researched with sociological perspectives. This paper aims to understand and outline the history of monetary systems starting from the ancient practice of barter to the establishment of national currencies, and up to the recent advent of cryptocurrency, in an evolutionary framework. As the second objective, this paper attempts to delineate the mechanism of construction and the causal explanations for the adoption and diffusion of cryptocurrency from a sociological lens. In view of the factual status of its legitimation and denial by different governing authorities, the third objective of this paper is to explore into the nuances pertaining to trust, governance and dynamics of power relations with a exploratory concern for rigidity within the claimed fluidity of the cryptocurrency and its utilisation. However, we are assuming one conclusion for our study and that is we are going to get stuck with more significant questions rather than the answers for our objectives.
Measuring and rewarding distributed productive contribution remains unsolved: GDP-era indicators miss digital commons, and existing token economies systematically reward speculation over contribution. We address this problem by formally specifying VitaChain, a protocol for verified productive value. Our contributions are threefold. (i)Theoretically, we introduce a quadratic-form asset scoring system with a diversity-corrected aggregate index (nvpvi), multi-source consensus-weighted attribution, and a four-class parameter governance system with constitutional invariants. (ii)Architecturally, we propose a dual-token design separating soulbound contribution records (pc{) from transferable value tokens (vt), governed by Proof-of-Productivity (PoP) with dual-track decay to prevent plutocracy. (iii)Empirically, we characterize six protocol properties through simulation-based consistency checks under synthetic ensembles ($n \geq 50$ trials per configuration): \pop{}-weighted voting reduces top-1\% governance influence 2.8$\times$ versus token-weighted voting (under the assumption that contribution is less concentrated than wealth); temporal decay illustrates how the chosen decay bands translate into a 39$\times$ long-horizon value gap between maintained and unmaintained assets at year 20; and Progressive Trust with $\sigma{=}50$ limits Sybil inclusion-pool capture to below 3.5\% of pool capacity. Sybil attribution shift remains within the theoretically derived bound across all configurations. The privacy architecture---blockchain hashes only, zero-knowledge verification bridges, federated off-chain storage---is designed to target GDPR Article~17 requirements, subject to legal interpretation. All experiments operate on synthetic asset ensembles; results characterize the behavior of the formalism rather than real-world deployment outcomes.
Kabilesh C M, Dr. B. Raja, Dr. S. Geetha, Dr. V. Cyrilraj
As decentralized finance (DeFi) continues to scale, traditional forensic methodologies often fail due to their retrospective, "post-mortem" nature, analyzing illicit activities only after they are permanently recorded on the ledger. This project proposes coinEth, a real-time institutional blockchain surveillance and autonomous defense system designed for the Ethereum Sepolia network. The framework operates across a four-layer architecture: a Data Acquisition Layer that intercepts pending transactions via Alchemy WebSockets (WSS); a Persistence and Forensic Engine that utilizes SQLite and Python-based heuristics to detect suspicious behavioral patterns such as "structuring" and "high velocity"; a Governance Layer that executes an autonomous enforcement loop via a Solidity-based "Gatekeeper" smart contract; and a Visualization Layer built with Streamlit and PyVis. By assigning dynamic risk scores—categorized as Safe (Level 0), Warning (Level 1), and Frozen (Level 2)—the system can automatically broadcast on-chain transactions to freeze illicit accounts before fund exfiltration occurs. Furthermore, coinEth reconstructs a chronological "money trail" through sequential path mapping (T0 → T1 → T2...), ensuring a verifiable digital chain of custody for investigative reporting. This proactive approach shifts blockchain security from passive observation to active, real-time intervention, significantly enhancing the defense mechanisms available to institutional stakeholders.
Blockchain technology has emerged as one of the most transformative innovations of the digital economy, extending far beyond cryptocurrencies into sectors such as finance, healthcare, logistics, governance, and intelligent automation. This study critically examines the role of blockchain technology in enhancing global economic growth through decentralization, transparency, cybersecurity, smart contracts, and digital trust mechanisms. Drawing upon contemporary literature and emerging industrial applications, the paper explores how blockchain contributes to economic resilience, operational efficiency, supply chain optimization, decentralized finance (DeFi), central bank digital currencies (CBDCs), and AI-integrated digital ecosystems. The study adopts a conceptual and analytical approach to evaluate blockchain’s macroeconomic implications and institutional challenges in the context of Industry 4.0. Findings suggest that blockchain has the potential to reduce transaction costs, enhance cross-border economic integration, improve governance transparency, and facilitate sustainable digital transformation. However, regulatory uncertainty, scalability limitations, cybersecurity concerns, and energy consumption remain significant barriers to global adoption. The paper contributes to the literature by proposing an integrated framework linking blockchain innovation with economic sustainability, digital governance, and technological resilience. Policy implications and future research directions are also discussed.
Promoting harmonious interaction between human beings and the ecological environment has become a key issue for achieving sustainable development. Given the cross-regional mobility of resources and production activities, a single region cannot merely rely on its own efforts to balance economic expansion and carbon reduction. In this context, Digital finance can play a key role in improving information connectivity, facilitating green capital allocation, and reducing transaction costs for cross-regional low-carbon collaboration. Against this background, this study introduces and quantifies regional coordinated emission reduction potential by integrating economic ties, geographical proximity, and interregional carbon emissions from a network perspective. By using the panel fixed effect model, the study explored how digital finance shapes this potential and identified the energy-related carbon consumption structure as a transmission mechanism. Findings reveal that the carbon emission spillover effect is most powerful under the combined influence of economic similarity and geographical proximity. Digital finance significantly enhances the potential for regional coordinated emission reduction, and the effect is even stronger in provinces with lower potential for coordinated emission reduction or weaker fiscal decentralization. The level of digitalization and the depth of usage have a greater influence than the breadth of coverage. In addition, the energy-related transmission channels exhibit clear heterogeneity. The coal-related emission channel provides relatively stronger evidence, whereas the gas-related channel shows a countervailing effect.
Amid intensifying climate change and rapid urbanization, urban ecosystems face unprecedented disturbances. Enhancing urban ecological resilience (UER) is thus a key governance issue. The explosive growth of digital finance (DF) has reshaped capital flows, but its ecological effects are dynamic and stage-specific. This study uses a dynamic nonlinear model to systematically explore the DF-UER relationship. The results reveal that: (1) There exists a U-shaped relationship between DF and UER. In the early stage, DF may negatively impact UER; however, Whereas as DF matures, it positively influences UER, with robust results from various tests. (2) Heterogeneity analysis indicates that the U-shaped relationship between DF and UER is more pronounced in smart city pilot cities, high government ecological attention cities, and cities with a high degree of market integration. (3) Mechanism tests reveal that DF has a U-shaped impact on green capital allocation, renewable energy technology innovation, and industrial structure optimization, consequently leading to a U-shaped effect on UER. (4) Fiscal decentralization and financial regulation strengthen the U-shaped relationship, whereas artificial intelligence tends to weaken it and may even reverse this trend. These findings point out the possible structural friction of DF, and provide valuable insights for supporting China and other economies to balance financial expansion and ecological security.
With the rapid development of digital finance, the mode of enterprise credit risk assessment has changed, and now also requires methods that can handle large-scale, diverse data and smart computation. The old system of credit rating has been based on the results of past financial reports and is no longer suitable for evaluating the changes and risks in modern corporate finance. This paper proposes a multi-dimensional model for mining credit reports of mining enterprises and combines structured financial data, transaction information, operating indicators, and other unstructured auxiliary data such as social media presence, online communication, supply chain dynamics, etc. By building a relatively detailed credit report, the bank can gain some information on the risk of a company's credit and its repayment ability for a loan. Algorithms that use machine learning, deep learning, ensemble models and predictive analysis are also known as intelligent default risk assessment algorithms that enhance the accuracy and flexibility of credit assessment. The following are ways to discover abnormal or complex patterns in a large amount of data early on for risk early warning, online credit assessment and dynamic portfolio management. Interoperability of digital finance platforms can support lifelong learning, automation and scalable high-frequency financial data, and maintain security, privacy and regulatory compliance. Although the above have been achieved, there are still deficiencies in the quality of data, interpretability of models, adherence to regulations, and sufficient computational resources, especially for small and medium-sized enterprises and new market institutions. Future research directions include building explainable AI systems, continuous learning, integrating multiple types of data (multimodality), and decentralized finance (DeFi) based on blockchains. At this point, the above technologies are expected to help enterprises strengthen credit risk management in the age of digital finance and provide more accurate and timely credit evaluations.
Ramis, Richard-Jung and Thomann (C. R. Math. 363 (2025), 1065–1081) introduced parity-separated spectral determinants for the negative (non-classical) part of the Connes–Moscovici prolate spectrum: entire functions D_even(μ) = y⁺{τ,μ}(0) and D_odd(μ) = (y⁺)'{τ,μ}(0) of order ≤ 1/2 whose zeros are, respectively, the negative even and negative odd CM eigenvalues; they conjectured that these zeros are simple. We prove this simplicity assertion for every fixed τ > 0 and prove in addition that the zeros of D_even and D_odd strictly interlace. The proof uses the imaginary-axis Sturm–Liouville reduction and identifies D_odd/D_even, up to a nonzero constant, with the Weyl m-function of the associated half-line problem. Its Herglotz property yields simplicity and strict interlacing. Consequently, the negative CM spectrum ordered by increasing |μ| alternates strictly in parity, beginning with the odd sector. No claim is made concerning the Weil quadratic form of the Connes–Consani–Moscovici program or the Riemann Hypothesis. Version 4 clarifies the regular-endpoint argument at t = 0. It defines AC_loc and H¹ = W¹,² explicitly, states the Neumann and Dirichlet form domains, and proves from the first representation theorem and integration by parts that every element of the Neumann operator domain, in particular its fundamental state, satisfies u′(0) = 0. The proof of ν₀ᴺ < ν₀ᴰ is rewritten to invoke this lemma explicitly. An acknowledgment to Professor Jean-Pierre Ramis has also been added. No theorem statement or conclusion is changed.
Abstract Non-fungible tokens (NFTs) have revolutionized digital ownership, offering unique provenance and value to digital assets. Existing text-to-image models do not have the incentive mechanisms to generate statistically rare features, even when they optimize for visual fidelity. This paper introduces DiffNFTGen, a new generative framework that is the first to combine a customized RarityReward measure derived from a Vision Transformer (ViT) with reinforcement learning. The suggested method ensures fidelity to NFT styles while explicitly maximizing the generation of rare features by fine-tuning Stable Diffusion using Proximal Policy Optimization (PPO) and Kullback-Leibler (KL) divergence regularization. DiNFTGen achieves a 2.4x greater rarity score than baseline models while keeping competitive visual quality, according to quantitative evaluation utilizing Frédechet Inception Distance (FID) and Rarity Score. In order to examine the trade-off between fidelity and rarity, we also perform ablation studies regarding reward weighting. The model’s capacity to generalize NFT styles to new domains is confirmed by qualitative evaluations. The datasets, analysis code, and suggested approach are accessible on https://github.com/seferlab/diffnftgen .
Open access
Cell Image Analysis Techniques
Generative Adversarial Networks and Image Synthesis