Zhuo Chen, Gaoqiang Ji, He Yun, Lei Wu · 5 authors
Decentralized finance (DeFi) is experiencing rapid expansion. However, prevalent code reuse and limited open-source contributions have introduced significant challenges to the blockchain ecosystem, including plagiarism and the propagation of vulnerable code. Consequently, an effective and accurate similarity detection method for EVM bytecode is urgently needed to identify similar contracts. Traditional binary similarity detection methods are typically based on instruction stream or control flow graph (CFG), which have limitations on EVM bytecode due to specific features like low-level EVM bytecode and heavily-reused basic blocks. Moreover, the highly-diverse Solidity Compiler (Solc) versions further complicate accurate similarity detection. Motivated by these challenges, we propose a novel EVM bytecode representation called Stable-Semantic Graph (SSG), which captures relationships between 'stable instructions' (special instructions identified by our study). Moreover, we implement a prototype, Esim, which embeds SSG into matrices for similarity detection using a heterogeneous graph neural network. Esim demonstrates high accuracy in SSG construction, achieving F1-scores of 100% for control flow and 95.16% for data flow, and its similarity detection performance reaches 96.3% AUC, surpassing traditional approaches. Our large-scale study, analyzing 2,675,573 smart contracts on six EVM-compatible chains over a one-year period, also demonstrates that Esim outperforms the SOTA tool Etherscan in vulnerability search.
This work presents the CTC–E₈ Integrated Architecture, a unified thermodynamic–informational control system designed for large-scale, autonomous off-world industrial civilizations. The framework combines the CTC nuclear architecture—a passive-safe, thorium–carbon high-temperature power system capable of tritium generation—with the QSOL-IMC E₈ Qutrit Power Module, an informational control layer that uses qutrit-based node governance and E₈ symmetry for global synchronization. The architecture establishes a dual-layer system: 1. Thermodynamic Power (CTC Architecture):Provides meltdown-proof, long-cycle thorium–carbon reactor output, high-temperature industrial heat, tritium production, and in-situ manufacturability suitable for distributed lunar, Martian, or orbital industry. 2. Informational Control (E₈ Qutrit Module):Implements computational-resonance control using qutrit-state vectors for local process management and an E₈ Cartan torus for global phase, load balancing, and industrial rhythm coordination. E₈ root-vector coupling provides decentralized, symmetry-enforced coherence across hundreds of reactors without a central controller. Together, these layers create a resonance-governed industrial ecosystem that delivers predictable harmonics, smooth power distribution, self-organizing energy networks, and fractal scalability from a single reactor outpost to a full city-state colony. The system minimizes computational overhead while maximizing stability, autonomy, and expansion capability. This submission includes the full technical manuscript and accompanying architecture documentation for researchers, engineers, and organizations exploring next-generation off-world industrial infrastructure.
This paper presents a comprehensive comparative analysis of two dominant blockchain consensus mechanisms, Proof of Work (PoW) and Proof of Stake (PoS), evaluated across seven critical metrics: energy use, security, transaction speed, scalability, centralization risk, environmental impact, and transaction fees. Utilizing recent academic research and real-world blockchain data, the study highlights that PoW offers robust, time-tested security but suffers from high energy consumption, slower throughput, and centralization through mining pools. In contrast, PoS demonstrates improved scalability and efficiency, significantly reduced environmental impact, and more stable transaction fees, however it raises concerns over validator centralization and long-term security maturity. The findings underscore the trade-offs inherent in each mechanism and suggest hybrid designs may combine PoW's security with PoS's efficiency and sustainability. The study aims to inform future blockchain infrastructure development by striking a balance between decentralization, performance, and ecological responsibility.
Audio piracy detection is increasingly complex in decentralised distribution settings, where mainstream approaches fail to ensure robustness, verifiability, or computational efficiency. Conventional Digital Rights Management (DRM) systems mainly enforce licensed access, but once content is copied or redistributed outside their control they offer little protection. Classical fingerprinting approaches such as MFCC based hashes can detect near-exact duplicates, yet they often fail under signal edits like pitch shifting, time stretching or equalisation. Deep learning embeddings improve robustness but demand heavy computation and centralised resources, making them less suitable for edge or decentralised deployments. These limitations call for a solution that is both edit resilient and verifiable. We propose HashWave, a blockchain-integrated perceptual hashing framework that combines robust audio fingerprinting with tamper-proof verification. The system fuses MFCC, chroma and chroma CENS, CQT, spectral contrast, and lightweight tempo/energy cues, applying operation-aware weighting via [Formula: see text] and constrained DTW for time-scale edits. Evaluated across GTZAN, FMA-A Dataset for Music Analysis, and MUSAN (SLR17) with over twenty signal-processing transformations, HashWave achieves AUC 0.957 and TPR@1%FPR 0.952, outperforming MFCC-only baselines and approaching deep embeddings at lower CPU cost. The blockchain layer, built on Ethereum and IPFS, ensures decentralised hash storage, duplication control, and verifiable authorship with average upload and contract execution times of 0.017 s and 0.044 s. Together, these results establish HashWave as a practical, scalable, and secure framework for piracy detection across streaming, podcasting, and Web3 ecosystems.
Open access
Advanced Steganography and Watermarking Techniques
Blockchain technology has emerged as a revolutionary paradigm for secure, transparent, and tamper-resistant data management. It offers a decentralized ledger where transactions are validated and recorded across a distributed network of nodes, eliminating the need for centralized authorities. Despite its widespread adoption across diverse domains—such as finance, supply chain, healthcare, and digital identity—blockchain still faces significant challenges in ensuring complete security and privacy. This paper addresses these challenges by proposing a novel security and privacy algorithm designed specifically to enhance blockchain resilience against evolving threats. The proposed approach integrates hybrid cryptography, pseudonymous identifiers, and an optimized consensus mechanism to achieve a balanced trade-off between security, privacy, and computational efficiency. The hybrid cryptographic model combines symmetric and asymmetric encryption techniques to safeguard transaction data at multiple layers. Symmetric encryption ensures fast and secure data exchange, while asymmetric keys are used for identity verification and secure key distribution. To further strengthen user anonymity, the algorithm incorporates pseudonymous identity management, which replaces permanent public keys with dynamically generated pseudonyms. These pseudonyms are refreshed periodically to prevent link ability between consecutive transactions, ensuring that individual identities remain hidden even if certain nodes or data patterns are compromised. Additionally, the optimized consensus protocol enhances transaction validation efficiency by reducing redundant computations and improving synchronization among nodes. This approach minimizes latency and energy consumption while maintaining strong resistance against consensus-based attacks such as 51% or Sybil attacks. Extensive simulations and experimental evaluations were conducted to measure the algorithm’s performance under various network conditions and adversarial scenarios. The results demonstrate that the proposed model significantly improves transaction validation speed and reduces cryptographic overhead compared to traditional Proof-of-Work and Proof-of-Stake systems.
Recent advances in large language models (LLMs) have enabled the emergence of intelligent agents capable of performing complex multi-step tasks across various domains. In parallel, the growth of Web3 has introduced a decentralized web infrastructure, yet remains largely inaccessible to non-technical users due to operational complexity, fragmented information, and security risks. In this article, we present Web3Agent , an AI agent system that integrates LLM-based interaction with blockchain environments to enable language-driven on-chain operations. Web3Agent automatically decomposes user instructions into structured workflows, dynamically queries blockchain data and APIs, and performs multi-step operations such as asset transfers, token swaps, and smart contract execution. Web3Agent incorporates real-time inspection, error handling, and interaction transparency across its operation log, and flow visualization components. We evaluate the system and perform ablation study with customized dataset in a simulated environment, demonstrating its feasibility in orchestrating complex Web3 tasks and highlighting implications for agent-based abstraction in decentralized systems.
The rapid ascent of Non-Fungible Tokens (NFTs) has fundamentally disrupted the art market and the broader visual culture landscape. While much scholarly and popular attention has been focused on their economic impact and speculative nature, this article explores a less examined dimension: the potential of blockchain technology, as manifested in NFTs, to redefine the paradigms of digital heritage preservation. Digital art and born-digital cultural artifacts face an existential threat from technological obsolescence, format degradation, and the inherent fragility of digital media. Traditional preservation institutions, such as museums and archives, have struggled to develop scalable, sustainable models for conserving these ephemeral works. This article argues that NFTs, through their core properties of decentralized ownership verification, immutability of provenance, and programmable permanence, offer a novel, albeit complex, framework for safeguarding our collective digital visual heritage. By analyzing the technical architecture of NFTs, the challenges of preserving the digital asset separate from its token, and the emergent models of decentralized autonomous organizations (DAOs) and community-led preservation, this paper posits that we are witnessing the nascent stages of a new preservation ecology. This study synthesizes literature from digital humanities, media studies, conservation science, and computer science to critically assess both the promises and perils of this convergence. It concludes that while NFTs are not a panacea, they introduce powerful tools that, if ethically and thoughtfully integrated, can significantly bolster the resilience and longevity of digital visual culture for future generations.
Reddit is in the minority of mainstream social platforms that permit posting content that may be considered to be at the edge of what is permissible, including so-called Not Safe For Work (NSFW) content. However, NSFW is becoming more common on mainstream platforms, with X now allowing such material. We examine the top 15 NSFW-restricted subreddits by size to explore the complexities of responsibly sharing adult content, aiming to balance ethical and legal considerations with monetization opportunities. We find that users often use NSFW subreddits as a social springboard, redirecting readers to private or specialized adult social platforms such as Telegram, Kik or OnlyFans for further interactions. They also directly negotiate image "trades" through credit cards or payment platforms such as PayPal, Bitcoin or Venmo. Disturbingly, we also find linguistic cues linked to non-consensual content sharing. To help platforms moderate such behavior, we trained a RoBERTa-based classification model, which outperforms GPT-4 and traditional classifiers such as logistic regression and random forest in identifying non-consensual content sharing, showing better performance in this specific task. The source code and model weights are publicly available at https://github.com/socsys/15NSFWsubreddits.
Partha S. Dey, Aditya S. Gopalan, Vijay G. Subramanian
We investigate the time to consensus in Nakamoto blockchains. Specifically, we consider two competing growth processes, labeled \emph{honest} and \emph{adversarial}, and determine the time after which the honest process permananetly exceeds the adversarial process. This is done via queueing techniques. The predominant difficulty is that the honest growth process is subject to \emph{random delays}. In a stylized Bitcoin model, we compute the Laplace transform for the time to consensus and verify it via simulation.
The security of autonomous vehicle networks is facing major challenges, owing to the complexity of sensor integration, real-time performance demands, and distributed communication protocols that expose vast attack surfaces around both individual and network-wide safety. Existing security schemes are unable to provide sub-10 ms (milliseconds) anomaly detection and distributed coordination of large-scale networks of vehicles within an acceptable safety/privacy framework. This paper introduces a three-tier hybrid security architecture HAVEN (Hierarchical Autonomous Vehicle Enhanced Network), which decouples real-time local threat detection and distributed coordination operations. It incorporates a light ensemble anomaly detection model on the edge (first layer), Byzantine-fault-tolerant federated learning to aggregate threat intelligence at a regional scale (middle layer), and selected blockchain mechanisms (top layer) to ensure critical security coordination. Extensive experimentation is done on a real-world autonomous driving dataset. Large-scale simulations with the number of vehicles ranging between 100 and 1000 and different attack types, such as sensor spoofing, jamming, and adversarial model poisoning, are conducted to test the scalability and resiliency of HAVEN. Experimental findings show sub-10 ms detection latency with an accuracy of 94% and F1-score of 92% across multimodal sensor data, Byzantine fault tolerance validated with 20\% compromised nodes, and a reduced blockchain storage overhead, guaranteeing sufficient differential privacy. The proposed framework overcomes the important trade-off between real-time safety obligation and distributed security coordination with novel three-tiered processing. The scalable architecture of HAVEN is shown to provide great improvement in detection accuracy as well as network resilience over other methods.
Smart contracts, the stateful programs running on blockchains, often rely on reports. Publishers are paid to publish these reports on the blockchain. Designing protocols that incentivize timely reporting is the prevalent reporting problem. But existing solutions face a security-performance trade-off: Relying on a small set of trusted publishers introduces centralization risks, while allowing open publication results in an excessive number of reports on the blockchain. We identify the root cause of this trade-off to be the standard symmetric reward design, which treats all reports equally. We prove that no symmetric-reward mechanism can overcome the trade-off. We present Personal Random Rewards for Reporting (Prrr), a protocol that assigns random heterogeneous values to reports. We call this novel mechanism-design concept Ex-Ante Synthetic Asymmetry. To the best of our knowledge, Prrr is the first game-theoretic mechanism (in any context) that deliberately forms participant asymmetry. Prrr employs a second-price-style settlement to allocate rewards, ensuring incentive compatibility and achieving both security and efficiency. Following the protocol constitutes a Subgame-Perfect Nash Equilibrium, robust against collusion and Sybil attacks. Prrr is applicable to numerous smart contracts that rely on timely reports.
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.
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.
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.
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.
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.
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, 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.
The Verification and Validation of Certificate Using Blockchain system is designed to provide a secure, transparent, and tamper-proof mechanism for issuing and verifying educational and professional certificates. Traditional verification methods are often prone to forgery, delays, and administrative inefficiencies due to centralized databases and manual validation. This system leverages blockchain technology to store certificate data in an immutable distributed ledger, ensuring authenticity and preventing manipulation. Additionally, the integration of the InterPlanetary File System (IPFS) provides decentralized, lowcost storage for certificates, while an Android-based interface simplifies issuance and verification processes. By enabling decentralized trust, rapid verification, and cross-border accessibility, this system enhances transparency, reduces fraudulent activities, and establishes a reliable digital framework for secure credential management