This paper proposes a reinforcement learning--based framework for cryptocurrency portfolio management using the Soft Actor--Critic (SAC) and Deep Deterministic Policy Gradient (DDPG) algorithms. Traditional portfolio optimization methods often struggle to adapt to the highly volatile and nonlinear dynamics of cryptocurrency markets. To address this, we design an agent that learns continuous trading actions directly from historical market data through interaction with a simulated trading environment. The agent optimizes portfolio weights to maximize cumulative returns while minimizing downside risk and transaction costs. Experimental evaluations on multiple cryptocurrencies demonstrate that the SAC and DDPG agents outperform baseline strategies such as equal-weighted and mean--variance portfolios. The SAC algorithm, with its entropy-regularized objective, shows greater stability and robustness in noisy market conditions compared to DDPG. These results highlight the potential of deep reinforcement learning for adaptive and data-driven portfolio management in cryptocurrency markets.
A long road trip is fun for drivers. However, a long drive for days can be tedious for a driver to accommodate stringent deadlines to reach distant destinations. Such a scenario forces drivers to drive extra miles, utilizing extra hours daily without sufficient rest and breaks. Once a driver undergoes such a scenario, it occasionally triggers drowsiness during driving. Drowsiness in driving can be life-threatening to any individual and can affect other drivers' safety; therefore, a real-time detection system is needed. To identify fatigued facial characteristics in drivers and trigger the alarm immediately, this research develops a real-time driver drowsiness detection system utilizing deep convolutional neural networks (DCNNs) and OpenCV.Our proposed and implemented model takes real- time facial images of a driver using a live camera and utilizes a Python-based library named OpenCV to examine the facial images for facial landmarks like sufficient eye openings and yawn-like mouth movements. The DCNNs framework then gathers the data and utilizes a per-trained model to detect the drowsiness of a driver using facial landmarks. If the driver is identified as drowsy, the system issues a continuous alert in real time, embedded in the Smart Car technology.By potentially saving innocent lives on the roadways, the proposed technique offers a non-invasive, inexpensive, and cost-effective way to identify drowsiness. Our proposed and implemented DCNNs embedded drowsiness detection model successfully react with NTHU-DDD dataset and Yawn-Eye-Dataset with drowsiness detection classification accuracy of 99.6% and 97% respectively.
Smart contract vulnerabilities continue to cause significant financial losses, despite the implementation of security measures such as manual audits and bug bounty platforms. A critical component often required by these security measures is the proof-of-concept (PoC) exploit, which validates vulnerability exploitability, assesses impact severity, and guides developers in fixes. Existing tools have explored automated PoC generation with techniques like symbolic execution, fuzzing, and program synthesis. However, these approaches frequently fail to generate PoCs for vulnerabilities exploited in real-world incidents, primarily due to their limitations in handling complex transaction dependencies, navigating vast on-chain state spaces, or requiring extensive manual specifications. Our migration-based approach extracts critical information from documented security incidents and applies it to generate PoCs for similar vulnerable code. This approach leverages proven exploit patterns rather than generating PoCs from scratch. This approach is motivated by two key observations: the prevalence of code reuse in smart contracts (up to 90% at the function level) and the increasing availability of documented PoCs for real-world incidents. Our approach operates in three phases: (1) abstracting essential components (i.e., environment properties, attack logic, and verification checks) from existing PoCs into templates, (2) given a new target contract, selecting suitable templates with adapted values through clone-detection and property-feasibility analysis, and (3) generating and validating PoCs in simulated environments. Our evaluation demonstrates effectiveness and efficiency across multiple scales. Our approach successfully generates valid PoCs for 62 out of 67 manually validated cases without false positives and completes analysis in 3.8 hours compared to 133.2 and 210.5 hours required by existing tools. Large-scale evaluation on 979,512 contracts identifies 256 vulnerable contracts across blockchain networks with 64 cross-chain cases, demonstrating real-world applicability.
Blockchain technology has recently undergone substantial investigation into the prospect of integrating it with several service sectors, having originally been designed for the Peer-to-Peer cryptocurrency network, Bitcoin Database security could be an expensive and time-consuming operation. When discussing a legally binding contract, the phrase "automated transaction protocol that, executes the terms of the agreement" is used. The Internet of Things (IoT), big data artificial intelligence technologies, and blockchain technology into the supply chain may help solve the transparency and traceability issue stated in the literature.
Smart contracts have become a foundational component of blockchain systems, enabling decentralized, transparent, and autonomous execution of application logic across various domains, including decentralized finance (DeFi), gaming, and digital identity. Due to their immutable and trustless nature, smart contracts often manage and transfer substantial amounts of assets without human intervention. However, vulnerabilities in smart contracts can lead to substantial financial losses. Among these, access control vulnerabilities are particularly critical, typically originating from inadequately designed or incorrectly implemented permission mechanisms. Most existing methods for detecting access control vulnerabilities are based on static analysis, which heavily relies on manually defined rules and pattern matching. While these methods are efficient at identifying certain classes of known vulnerabilities, they are inherently limited in scope and generalization. In particular, they often fail to capture the underlying business logic of smart contracts.In this paper, we propose an LLM-based multi-agent system, named ACTaint, for detecting access control vulnerabilities in Solidity smart contracts. ACTaint first performs static analysis to guide the sink agent in identifying potential sinks. Then, based on the identified sinks, the taint agent conducts taint analysis to determine whether a data flow exists from untrusted sources to these sinks. We evaluate our approach on three datasets: known CVE cases, a set of 624 real-world smart contracts, and another set of 93 real-world smart contracts. The results demonstrate that our method outperforms existing tools in both datasets. On the first dataset, our approach outperforms state-of-the-art tools, including AChecker and GPTLens, achieving higher recall and F1-score. On the second dataset, our method surpasses the leading static analysis tool AChecker, with a 8.3% improvement in precision and an 9.7% improvement in F1-score.
Early diagnosis of cardiac abnormalities depends on accurate classification of heart sounds, but centralized training methods run the danger of violating patient privacy. We thus propose a privacy-preserving and reliable heart sound abnormality detection system combining Blockchain Technology with Federated Learning (FL). Training is spread among seven clients, each simulating an independent data source, using a preprocessed dataset from the PhysioNet Challenge 2016 to enable distributed learning without sharing raw data. CNN-LSTM model using FedAvg achieved the best performance: 94\% accuracy, 0.90 precision, 0.96 recall, and an AUC of 0.98 among five deep learning architectures evaluated with FedAvg and FedProx strategies. Along with metadata including client ID and round number, SHA-256 hashes of local and global model weights were recorded on a local Ethereum blockchain following every communication round to guarantee model integrity. The hash of the final model is revalidated against the blockchain to confirm authenticity prior to deployment. It then guarantees safe, distributed, clinically valuable AI-based diagnostics by real-time classification of heart sounds as normal or abnormal.
Decentralized finance (DeFi) uses smart contracts to automate payments, lending, and asset management, but current blockchains often suffer from slow, expensive, and energy-hungry execution. In this project, I explore a quantum-enhanced optimization framework for smart contract–based financial services. The main idea is to treat gas use, transaction ordering, and resource allocation as optimization problems that can be tackled by hybrid quantum–classical algorithms. Using a conceptual model, I map smart contract execution to cost functions suitable for the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE). I then compare, at a qualitative level, how these quantum-inspired approaches differ from classical heuristics in terms of expected throughput, latency, and cost. A focused literature review on quantum computing, blockchain scalability, and quantum-safe cryptography provides context for these ideas. The results suggest that quantum-enhanced optimization could reduce gas fees, improve transaction scheduling, and support more efficient consensus under heavy load. The project also discusses the need for post-quantum security so that future quantum computers do not undermine blockchain trust. Overall, the work outlines how quantum computing might contribute to faster, safer, and more sustainable automated financial systems.
Han Liu, Daoyuan Wu, Yi Sun, Shuai Wang · 6 authors
OpenZeppelin is a building block for many smart contracts on Ethereum-compatible blockchains. It provides mod-ular and reusable libraries for various Ethereum standards (e.g., ERC20 and ERC721) and common functionalities such as upgradeable contracts. Little research has been done on Open-Zeppelin security except for a recent study, which focused only on the misuse of OpenZeppelin code, assuming OpenZeppelin itself is secure but contract developers may not follow OpenZeppelin’s function checks appropriately. We argue that, despite appearing robust, OpenZeppelin itself could have many vulnerabilities, and these library-level vulnerabilities could inadvertently affect third-party smart contracts, even without misuse from developers.We present ZepCompare, the first end-to-end system for demystifying OpenZeppelin’s own vulnerabilities and analyzing their propagation in third-party smart contracts. ZepCompare incorporates a manual analysis stage where we review OpenZeppelin’s 64 historical releases, identifying 109 vulnerable-fixed code pairs, exposing flaws in cryptographic utilities, access control, etc. Leveraging these pairs, ZepCompare introduces facts of changes, a novel structure capturing vulnerable and fixed code contexts for flexible matching. Evaluated across 88,605 contracts from three Ethereum-compatible chains, ZepCompare detects 4,708 instances of OpenZeppelin-derived vulnerabilities. Manual sampling and a ground-truth experiment confirm that ZepCompare achieves 86.7% precision and 77.1% recall. Our findings reveal significant security risks in both historical and the latest versions of OpenZeppelin libraries, underscoring the urgent need for systematic auditing of foundational contracts components.
Blockchain has emerged as a robust foundation for decentralized trust, secure data sharing, and immutable record keeping. However, its inherently transparent architecture creates significant privacy challenges when applied in sensitive domains such as healthcare, finance, identity management, and IoT. Although privacy-preserving techniques including Zero-Knowledge Proofs (ZKPs), Attribute-Based Encryption (ABE), homomorphic encryption, ring signatures, mixers, and hybrid off-chain storage mechanisms have demonstrated partial effectiveness, they remain limited by high computational overhead, poor scalability, interoperability constraints, and regulatory complications. These challenges hinder the practical deployment of blockchain in real-world, data-intensive environments. This review examines key blockchain privacy issues and synthesizes major research contributions from contemporary literature. It further emphasizes the importance of hybrid privacy-preserving models to balance transparency, confidentiality, and storage efficiency. The analysis reinforces the relevance of solutions such as ChainGuard, a dual-chain architecture that maintains sensitive data on a private blockchain while using a public chain to store verifiable hash references. This approach directly mitigates the transparency–privacy conflict, storage inefficiencies, and cryptographic performance limitations identified across existing studies. The paper concludes by outlining research gaps and proposing future directions for scalable, interoperable, and regulation-aligned blockchain privacy systems.
Zero-knowledge proof (ZKP) circuits implemented in programming languages like Circom are fundamental to blockchain and privacy-preserving applications. These code often suffer from constraint-related issues where constraints fail to accurately specify intended computations. While existing analysis tools have been proposed, they struggle with large-scale circuits containing complex template embeddings. We present ScaleCirc, a novel framework that addresses such limitations through: 1) systematic management of analysis redundancy via circuit deduplication strategies; 2) constrainedness propagation methods leveraging source code semantic information; and 3) a generalizable framework for different circuit analysis tasks. Evaluation on 691 real-world circuits shows ScaleCirc demonstrates higher efficiency, and successfully analyzes many Circom programs that existing works failed on.
Physical Unclonable Functions (PUFs) and Hardware Security
Solana has rapidly evolved into a leading next generation platform for supporting decentralized applications due to its high performance and low transaction costs. Its new contract execution model, which decouples code logic from states, gives rise to new vulnerability threats that can result in significant financial losses for users within the ecosystem. However, existing studies towards detecting vulnerabilities are predominantly tailored for Ethereum smart contracts, which are unsuitable for Solana platform because of the variations in implementation languages and runtime semantics. In this paper, we propose Soleker, a novel approach that leverages learning-based techniques to automatically identifying potential vulnerabilities in Solana smart contract bytecode. More specifically, Soleker captures runtime semantic information from instructions that are associated with blockchain interactions and extracts vulnerability-specific localized features. Then, a prefix-guided graph learning model is introduced to learn and integrate extracted features, enabling effective vulnerability detection. We conduct experiments on a newly constructed contract dataset and the results demonstrate that Soleker significantly outperforms the baseline methods, achieving an average effectiveness improvement of 126.4% and a 335× boost in efficiency.
Han Liu, Daoyuan Wu, Yi Sun, Shuai Wang · 5 authors
Access control (AC) vulnerabilities are among the most critical security threats to smart contracts. Despite extensive research, they remain widespread and damaging in the Ethereum ecosystem. To understand and advance the current state-of-the-art (SOTA) in AC vulnerability detection, we first curate a diverse dataset of 180 real-world AC vulnerabilities from CVE entries, DeFiHackLabs incidents, and Code4rena audit reports.Using this dataset, we conduct a systematic benchmark study along three dimensions. First, we develop a cause-based taxonomy and analyze the prevalence and evolution of AC vulnerabilities. Second, we evaluate six SOTA tools, including two from industry and four from academia, revealing low recall (3% to 8%) and significant blind spots. To understand these failures, we examine 1.2 million deployed contracts and uncover practical gaps in AC protection mechanisms overlooked by existing tools. Finally, we assess the potential of large language models (LLMs) for AC vulnerability detection and show that LLMs detect 53–75% of vulnerabilities, outperforming traditional tools but facing challenges such as hallucinations and scalability. Our findings highlight the need for hybrid approaches that combine static analysis with LLM-based semantic reasoning to address the complexity of modern AC vulnerabilities.
While Large Language Models (LLMs) have demonstrated remarkable progress in generating functionally correct Solidity code, they continue to face critical challenges in producing gas-efficient and secure code, which are critical requirements for real-world smart contract deployment. Although recent advances leverage Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) for code preference alignment, existing approaches treat functional correctness, gas optimization, and security as independent objectives, resulting in contracts that may achieve operational soundness but suffer from prohibitive execution costs or dangerous vulnerabilities. To address these limitations, we propose PrefGen, a novel framework that extends standard DPO beyond human preferences to incorporate quantifiable blockchain-specific metrics, enabling holistic multi-objective optimization specifically tailored for smart contract generation. Our framework introduces a comprehensive evaluation methodology with four complementary metrics: Pass@k (functional correctness), Compile@k (syntactic correctness), Gas@k (gas efficiency), and Secure@k (security assessment), providing rigorous multi-dimensional contract evaluation. Through extensive experimentation, we demonstrate that PrefGen significantly outperforms existing approaches across all critical dimensions, achieving 66.7% Pass@5, 58.9% Gas@5, and 62.5% Secure@5, while generating production-ready smart contracts that are functionally correct, cost-efficient, and secure.
The article is devoted to the study of the current legal regulation of virtual assets in the French Republic. The author analyzes the advantages and disadvantages of the relevant regulatory framework, decisions taken to harmonize legislation in accordance with the new Regulation of the European Parliament and of the Council, as well as the possibility and expediency of implementing the most successful decisions into Ukrainian legislation. Due to the lack of in-depth research that would combine the main regulatory norms and definitions, as well as provide a general overview of this regulatory system, there was a need to study in detail the current regulatory framework of the French Republic in this sector, which is characterized by simple and clear requirements. Below is a list of responsible regulators, as well as the legally established definition of virtual assets and their classification. The French Republic has developed an original classification system, which currently continues to operate within limits that do not contradict the MiCA classification. Currently, not all objects created on the basis of blockchain technology are subject to regulation, for instance, non-fungible tokens or central bank digital currencies, which complies with the provisions of the MiCA Regulation. The licensing system for service providers in the field of virtual asset circulation, the specifics of the transition period, and new provisions in accordance with MiCA were also examined. An analysis of the requirements for initial coin offerings (ICOs) in accordance with the legislation of the French Republic and MiCA was conducted. The issues of virtual asset mining regulation and taxation regime were examined. It is concluded that the French Republic has managed to regulate the circulation of most known types of virtual assets, create a clear system for all participants in this market, and be able to easily implement new European Union legislation if necessary. Therefore, Ukrainian legislation should adopt an approach to building such an adaptive regulatory system that can be seamlessly harmonised with European Union legislation.
The article is devoted to the study of the current legal regulation of virtual assets in the French Republic. The author analyzes the advantages and disadvantages of the relevant regulatory framework, decisions taken to harmonize legislation in accordance with the new Regulation of the European Parliament and of the Council, as well as the possibility and expediency of implementing the most successful decisions into Ukrainian legislation. Due to the lack of in-depth research that would combine the main regulatory norms and definitions, as well as provide a general overview of this regulatory system, there was a need to study in detail the current regulatory framework of the French Republic in this sector, which is characterized by simple and clear requirements. Below is a list of responsible regulators, as well as the legally established definition of virtual assets and their classification. The French Republic has developed an original classification system, which currently continues to operate within limits that do not contradict the MiCA classification. Currently, not all objects created on the basis of blockchain technology are subject to regulation, for instance, non-fungible tokens or central bank digital currencies, which complies with the provisions of the MiCA Regulation. The licensing system for service providers in the field of virtual asset circulation, the specifics of the transition period, and new provisions in accordance with MiCA were also examined. An analysis of the requirements for initial coin offerings (ICOs) in accordance with the legislation of the French Republic and MiCA was conducted. The issues of virtual asset mining regulation and taxation regime were examined. It is concluded that the French Republic has managed to regulate the circulation of most known types of virtual assets, create a clear system for all participants in this market, and be able to easily implement new European Union legislation if necessary. Therefore, Ukrainian legislation should adopt an approach to building such an adaptive regulatory system that can be seamlessly harmonised with European Union legislation.
Smart contracts are a core component of blockchain ecosystems, but their transparency and immutability make them vulnerable to attacks, leading to significant financial losses. Thus, repairing vulnerabilities in smart contracts is crucial for establishing a trustworthy blockchain environment. Existing smart contract vulnerability repair methods suffer from a critical "one-for-all" design limitation, where a single model is tasked with fixing diverse vulnerability types, leading to suboptimal performance due to insufficient specialization. To address this, we propose MoEFix, a novel framework leveraging a Mixture-of-Experts (MoE) architecture tailored for smart contract characteristics. MoEFix partitions vulnerabilities into subspaces, trains specialized experts for each type (e.g., reentrancy, integer overflow), and employs a vulnerability-aware router to dynamically allocate repairs. We further redesign the repair workflow to align with large language models, enabling end-to-end secure contract generation instead of partial patches, and to achieve this, we curated a dataset of 1,391 contracts covering five critical vulnerability types.To validate our approach, we extend the benchmark PVD test suite. Experiments demonstrate that MoEFix outperforms state-of-the-art methods by 21.64% in overall accuracy, achieving improvements of 26.19% (reentrancy) and 23.08% (delegatecall) for specific vulnerabilities.
As Ethereum smart contracts grow in complexity, upgrades are necessary but challenging due to their immutable nature. Proxy contracts enable upgrades without changing contract state, but current detection approaches often rely on source code or transaction history and fail to detect inactive proxies. Detecting these proxies is critical because dormant upgrade paths can be reactivated, introducing risks and potential attacks. We introduce PROXiFY, a lightweight bytecode-based tool that detects and classifies proxy contracts, including inactive ones, without requiring Ethereum nodes, source code, or customized EVMs. PROXiFY achieves a precision of 98.6% and recall of 97.1% on a high-confidence benchmark dataset. A demonstration of PROXiFY can be viewed at https://youtu.be/FuYs22_vosk.
Maximal Extractable Value (MEV) activities pose critical operational challenges for blockchain enterprises, requiring automated detection systems to maintain platform integrity and regulatory compliance. Current industrial practices rely on heuristic rule-based methods with substantial accuracy limitations and inability to adapt to evolving MEV strategies. This paper presents an automated software engineering solution for large-scale MEV detection, introducing a novel graph-based profitability identification algorithm that replaces inflexible heuristic rules with adaptive mechanisms. Our automated system achieves 0.6% false positive rates for arbitrage detection and 2.4% false negative rates, significant improvements over existing methods with much higher error rates. We validate our approach on 21 million Ethereum blocks containing 2.5 billion transactions, covering critical infrastructure transitions including The Merge and Proposer-Builder Separation. Our automated pipeline identifies 12.1 million MEV activities, including 1.2 million previously undetectable advanced variants that pose emerging risks to platform operators. Key findings provide actionable insights for blockchain enterprises: private transaction architectures protect 71.4% of low-yield MEV opportunities rather than harming participants, contradicting previous assumptions. However, we identify concerning builder-searcher collusion involving 2,000+ transactions worth 350 ETH, highlighting compliance risks. Additionally, intensifying centralization trends show a single oligopoly controlling 43.1% of MEV activities in 2024, presenting systemic risks. Our automated detection framework provides blockchain enterprises with production-ready tools for MEV monitoring, risk assessment, and compliance management while offering critical insights for infrastructure design decisions in rapidly evolving DeFi environments.
Decentralized Finance (DeFi) staking is one of the most prominent applications within the DeFi ecosystem, where DeFi projects enable users to stake tokens on the platform and reward participants with additional tokens. However, logical defects in DeFi staking could enable attackers to claim unwarranted rewards by manipulating reward amounts, repeatedly claiming rewards, or engaging in other malicious actions. To mitigate these threats, we conducted the first study focused on defining and detecting logical defects in DeFi staking. Through the analysis of 64 security incidents and 144 audit reports, we identified six distinct types of logical defects, each accompanied by detailed descriptions and code examples. Building on this empirical research, we developed SSR (Safeguarding Staking Reward), a static analysis tool designed to detect logical defects in DeFi staking contracts. SSR utilizes a large language model (LLM) to extract fundamental information about staking logic and constructs a DeFi staking model. It then identifies logical defects by analyzing the model and the associated semantic features. We constructed a ground truth dataset based on known security incidents and audit reports to evaluate the effectiveness of SSR. The results indicate that SSR achieves an overall precision of 92.31%, a recall of 87.92%, and an F1-score of 88.85%. Additionally, to assess the prevalence of logical defects in real-world smart contracts, we compiled a large-scale dataset of 15,992 DeFi staking contracts. SSR detected that 3,557 (22.24%) of these contracts contained at least one logical defect.
The NFT ecosystem represents an interconnected, decentralized environment that encompasses the creation, distribution, and trading of Non-Fungible Tokens (NFTs), where key actors, such as marketplaces, sellers, and buyers, utilize smart contracts to facilitate secure, transparent, and trustless transactions. Scam tokens are deliberately created to mislead users and facilitate financial exploitation, posing significant risks in the NFT ecosystem. Prior work has explored the NFT ecosystem from various perspectives, including security challenges, actor behaviors, and risks from scams and wash trading, leaving a gap in understanding the semantics and interactions of smart contracts during transactions, and how the risks associated with scam tokens manifest in relation to the semantics and interactions of contracts. To bridge this gap, we conducted a large-scale empirical study on smart contract semantics and interactions in the NFT ecosystem, using a curated dataset of nearly 100 million transactions across 20 million blocks on Ethereum. We observe a limited semantic diversity among smart contracts in the NFT ecosystem, dominated by proxy, token, and DeFi contracts. Marketplace and proxy registry contracts are the most frequently involved in smart contract interactions during transactions, engaging with a broad spectrum of contracts in the ecosystem. Token contracts exhibit bytecode-level diversity, whereas scam tokens exhibit bytecode convergence. Certain interaction patterns between smart contracts are common to both risky and non-risky transactions, while others are predominantly associated with risky transactions. Based on our findings, we provide recommendations to mitigate risks in the blockchain ecosystem, and outline future research directions.
This paper constructs a complete, species-indexed translation ledger between the Quantum Measurement Unit (QMU) system and SI, and generalizes the Aether Physics Model (APM) metrology framework from an electron-only sector to electrons, protons, and neutrons. The QMU bases are taken as the set\[\{ m_e,\;\lambda_C,\;F_q,\;e^{2},\;{e_\mathrm{xmax}}^{2} \},\]where $\lambda_C$ is the Compton wavelength, $F_q$ is the chronovibration frequency satisfying $c = \lambda_C F_q$, and ${e_\mathrm{xmax}}^{2}$ is the distributed magnetic charge associated with particle species $x\in\{e,p,n\}$. For each species the fine-structure parameter is\[\alpha_x = \frac{e^{2}}{8\pi\,{e_\mathrm{xmax}}^{2}},\]so that\[{e_\mathrm{emax}}^{2} = \frac{e^{2}}{8\pi\alpha_e},\qquad{e_\mathrm{pmax}}^{2} = \frac{e^{2}}{8\pi p},\qquad{e_\mathrm{nmax}}^{2} = \frac{e^{2}}{8\pi n}.\] Angular momentum is likewise species-indexed:\[h_x = m_x\,{\lambda_C}^{2}\,F_q,\]so that for the electron one has $h = m_e {\lambda_C}^{2} F_q$, while the proton and neutron satisfy $h_p = m_p {\lambda_C}^{2} F_q$ and $h_n = m_n {\lambda_C}^{2} F_q$. These relations make explicit that all particle species share the same Aether substrate $(\lambda_C, F_q)$ and differ only by $(m_x, {e_\mathrm{xmax}}^{2}, \alpha_x)$. The paper reviews the QMU unit grid and the dynamic/substrate dual ontology. Dynamic units place mass in the numerator and distributed charge in the denominator, while substrate units invert this ratio. The Aether rotating-field unit $A_u$, the curl exposure, the Coulomb-geometry factor $k_C$, the Aether Gforce, and the Aether mass scale $m_a$ are treated as primary derived ledger quantities. Their defining closures,\[A_u\,\mathrm{curl} = {F_q}^{2}{\lambda_C}^{2},\qquad\frac{A_u}{k_C} = 16\pi^{2},\qquad\mathrm{Gforce} = \lambda_C {F_q}^{2} m_a,\]follow directly from the QMU base definitions. To connect QMU with SI, which uses the singular charge $e$, the paper introduces a species-anchored charge conversion factor (CCF),\[\mathrm{ccf}_x = \frac{{e_\mathrm{xmax}}^{2}}{e} = \frac{e}{8\pi\alpha_x},\]allowing consistent translation between distributed-charge expressions and singular-charge legacy formulas. Unified rules are provided for CCF application, distinguishing charge in the numerator versus denominator (Rule~A), dynamic versus substrate units (Rule~B), and squared impedance-like classes (MFR/MFF). Five special ledger units (cond, capc, indc, perm, ptty) already incorporate distributed charge and therefore do not receive additional CCF factors. Using these rules, the paper derives benchmark identities in the electron sector:\[\begin{aligned}1\,\mathrm{potn}\cdot\mathrm{ccf}_e &\;\longleftrightarrow\; \frac{m_e c^{2}}{e},\\1\,\mathrm{mflx}\cdot\mathrm{ccf}_e &\;\longleftrightarrow\; \frac{h}{e},\\1\,A_u\cdot\mathrm{ccf}_e &\;\longleftrightarrow\; \frac{h c}{e},\\1\,\mathrm{mchg}\cdot\mathrm{ccf}_e &\;\longleftrightarrow\; \frac{m_e}{e},\\1\,\mathrm{expr}\cdot\mathrm{ccf}_e^{-1} &\;\longleftrightarrow\; \frac{e}{m_e}.\end{aligned}\]These recover well-known SI identities such as the electron rest-energy per charge, the flux-quantum scale, the photon energy–wavelength relation per charge, and the mass/charge ratios. The result is an empirical validation of the QMU ledger. A major conceptual advance is the generalization to proton and neutron sectors.A species-labeled Aether bookkeeping template\[A_{u,x} = \frac{m_x\,{\lambda_C}^{3} {F_q}^{2}}{{e_\mathrm{xmax}}^{2}}\]tracks how each particle species couples to the same Aether substrate. This yields proton and neutron benchmark chains completely analogous to the electron sector once $(m_x, {e_\mathrm{xmax}}^{2}, \alpha_x)$ are specified. The paper includes a TikZ diagram showing how the base electrostatic charge $e^{2}$ branches into species-specific distributed charges ${e_\mathrm{xmax}}^{2}$ through the fine-structure parameters $\alpha_x$, as well as a summary table of species-indexed quantities and corresponding benchmark identities. Appendix~A contains proton and neutron benchmark derivations in QMU form, and Appendix~B provides an optional SI numerical map for readers who require legacy-unit comparison. Together these elements transform the work into a complete, species-indexed metrology ledger for the QMU system, with internal coherence, clear translation rules, and direct links to measurable SI combinations for each particle species.
Web3 applications, particularly decentralized finance (DeFi) protocols, have grown rapidly with over $100 billion locked in smart contracts, attracting sophisticated attacks causing billions in losses. When attack occur, security analysts need to perform fault localization to identify vulnerable functions and understand attack vectors. This critical process currently requires an average of 16.7 analyst hours per incident due to complex blockchain execution models, rapidly evolving protocol interactions, and multi-contract attack patterns that exceed existing analytical capabilities. Despite its critical importance, blockchain fault localization has received limited attention due to fundamental challenges requiring semantic understanding of economic models and protocol-specific logic. Existing blockchain-specific tools target only single vulnerability types, while the only comprehensive solution, DAppFL, relies on machine learning model that may miss sophisticated exploits and lacks interpretability in results. Recent advances in large language models (LLMs) demonstrate remarkable code comprehension capabilities, but existing applications focus on proactive vulnerability detection with minimal exploration of post-incident fault localization.We present FaultSeeker, an LLM-empowered framework for blockchain transaction fault localization. Our two-stage architecture combines transaction-level forensics for strategic scoping with coordinated specialist agents for sustained reasoning. This design provides long-term memory management via orchestrator agents and specialized attention allocation through coordinated workers, enabling comprehensive analysis across complex multi-contract transactions without context loss. We evaluate Fault-Seeker on a compiled dataset of 115 real-world malicious transactions with expert-validated annotations spanning diverse attack patterns and complexity levels. Results demonstrate that FaultSeeker significantly outperforms existing approaches, including DAppFL and leading native LLMs (GPT-4o, Claude 3.7 Sonnet, DeepSeek R1), while maintaining practical efficiency (4.4- 8.6 minutes) and cost-effectiveness ($1.55-$4.53 per transaction).
Web3 applications, built on blockchain technology, manage billions of dollars in digital assets through decentralized applications (dApps) and smart contracts. These systems rely on complex, software supply chains that introduce significant security vulnerabilities. This paper examines the software supply chain security challenges unique to the Web3 ecosystem, where traditional Web2 software supply chain problems intersect with the immutable and high-stakes nature of blockchain technology. We analyze the threat landscape and propose mitigation strategies to strengthen the security posture of Web3 systems.
Mining blocks in a blockchain using the \textit{Proof-of-Work} consensus protocol involves significant risk, as network participants face continuous operational costs while earning infrequent capital gains upon successfully mining a block. A common risk mitigation strategy is to join a mining pool, which combines the computing resources of multiple miners to provide a more stable income. This article examines a Pay-per-Share (PPS) reward system, where the pool manager can adjust both the share difficulty and the management fee. Using a simplified wealth model for miners, we explore how miners should allocate their computing resources among different mining pools, considering the trade-off between risk transfer to the manager and management fees.