With the improvement of data technology advances and the sharp addition of web customers number since the 90s, numerous computerized monetary standards are presented. the most popular among them is Bitcoin. It was decided to investigate the possible relations between the most popular cryptocurrency Bitcoin price dynamics and global Nasdaq index dynamics using Mathematical and Statistical methods. The main question is: Are the Bitcoin prices somehow related with Nasdaq Composite Index? We use both, Quantitative and Qualitative data analysis methods to answer this question: Namely, the Regression model and NonÂ-Parametric testing. According to Quantitative methods, it was found that there exists a correlation and the regression equation is not bed: it seems that it is possible to explain about 60% of changes in Bitcoin Prices by changes in the Nasdaq Index. According to Qualitative methods, it was found that these two variables are independent. In this case, the Qualitative conclusion is more likely to be right, and the correlation is most likely because of coincidence.
Paying online often means sharing card details with merchants, advertising platforms, software providers, and payment processors. For freelancers, agencies, online sellers, and small teams, that can create unnecessary exposure: a compromised merchant account, an unexpected renewal, or a card number reused across several services may turn into a difficult cleanup project. A virtual card funded through a USDT top up offers another way to separate online spending from a primary bank account while keeping budgets easier to manage. This approach is not a promise of anonymity, approval, or freedom from verification. A responsible provider may still require identity checks, transaction monitoring, and information about the source of funds. The practical benefit is financial separation and control. Instead of giving every website direct access to a bank-linked card, you can use a dedicated card for approved online purchases, review the conversion terms, and keep records for accounting and compliance. Why use USDT to fund a virtual card USDT is a dollar-pegged digital asset commonly used to move value between supported wallets and platforms. When a card provider accepts USDT, it may convert the deposited amount into the card's spending balance, subject to its network, supported blockchain, confirmation requirements, fees, and compliance procedures. This can be useful for users who already hold USDT and want to pay merchants that accept ordinary card payments rather than cryptocurrency directly. The main operational advantage is separation. A dedicated virtual card can be assigned to advertising, SaaS subscriptions, supplier purchases, or a single project. If the card must be frozen or replaced, the issue may be contained to that spending channel instead of requiring changes across a personal bank account and every recurring payment connected to it. How the funding process usually works A typical flow has three stages: you create or select a card, send USDT to a deposit addre Full article attached as Markdown. Published for vccbusiness.com.
A decentralized system faces a fundamental governance tension: its governancerules are themselves amendable, which means that the metaârules stipulating howrules are modified are also at risk of being revised. Starting from the paradox ofselfâamendment uncovered by legal philosopher Peter Suber, this paper argues thatthis logical dilemma is not a purely philosophical speculation but a structural difficulty that repeatedly arises in the practice of blockchain constitutionalism. Underthe tenet thatâcode is law,âcodeâbased rules bear the metaâgovernance functionsthat in a constitutional structure ought to be carried by constitutional provisions,yet code logically cannot set an insurmountable boundary for its own amendmentauthority. In response, this paper proposes a layered metaâconstraint security architecture: metaâconstraints are divided into an unmodifiable layer of logical constants, a layer of cognitive virtues formulated through community constitutionalprocedures, and a layer of value homeostasis adjusted through public deliberationand evolution; the trustworthiness of metaâconstraints is anchored in the logicalphysical isolation provided by trusted hardware roots. Through the institutionalization of procedures for identifying and attributing metaâconstraints, this paperdemonstrates how forkâexitâbased social verification, cognitionâtesting through independent auditing, and physical anchoring through multiâkey witness mechanismstogether constitute a mutually independent multiâlayered defense system. By examining the 21âmillionâcoin supply cap of Bitcoin, the Ethereum EIP governanceprocess, and the constitutional crisis of The DAO incident as case studies, thispaper reveals the partial instantiation patterns of the threeâtier metaâconstraintarchitecture in existing systems and their failure boundaries. The paper concludesthat the longâterm security of a decentralized system ultimately depends not on theByzantineâfaultâtolerance strength of its consensus algorithm, but on the completeness of its metaâconstraint architectureâthat is, the existence of a set of boundariesthat are hierarchically protected in procedure, isolated and verified in hardware,and socially anchored in consensus, such that the combined cost of breaching themis raised to a level that no actor can afford within the expected life cycle of thesystem.
Financial settlement systems rely heavily on institutional trust: intermediaries maintain ledgers, certifycompliance, and prevent unauthorized creation or movement of value. Zero-knowledge (ZK) techniques make itpossible to replace part of that trust with verifiable properties. This paper presents a minimal ZK settlementlayer designed around a simple principle: prove what must be true, disclose only what must be seen, anddeclare remaining trust explicitly.We describe an architecture in which transfers preserve value, spending authority is proven without sharingspending keys with the operator, double-spending is prevented, and supervisors can verify balance bands orthresholds without receiving the full ledger. We also map the residual trust surface: the operator of a singlenode can still see balances, order transactions, and censor. The contribution is not a claim of full sovereignty ordecentralization. It is a precise shift from opaque institutional faith toward a smaller, named set of trustassumptions, with cryptographic checks covering the rest.We compare this model conceptually with core banking systems and permissioned blockchains, and argue thatthe main institutional value of ZK settlement is not âtrustlessness,â but trust minimization with honestresidual boundaries.This revision subjects that claim to its own standard. An audit pass against the reference implementation foundresidual dependencies the first version of this paper had not named: a confidentiality leak toward thecounterparty rather than the operator, three quantified capacity bounds, and a privilege that is counted butnever expires. We report them in §4.4 and §4.5, because a paper whose contribution is naming residual trust isfalsified by the trust it failed to name.
Digital image steganography has evolved from traditional rule-based techniques to advanced data-driven frameworks enabled by deep learning. However, existing surveys remain fragmented, often focusing on limited aspects while overlooking emerging paradigms such as blockchain-integrated and quantum-based approaches. This paper presents a comprehensive and systematic review of digital image steganography following the PRISMA 2020 guidelines, covering studies published between January 2015 and April 2026 across six major scientific databases. From an initial pool of 26,539 records, 83 relevant studies were selected through a rigorous two-stage screening process. The review provides a unified analysis of steganographic techniques by examining five dimensions: structural evolution and taxonomy, algorithmic modifications and hybridisation, application domain mapping, integration of emerging technologies, and future research trends. Comparative evaluation indicates that deep learning-based methods achieve 18â23% higher steganalysis resistance than classical approaches, whereas classical methods retain a 5â8 dB PSNR advantage. The quantitative synthesis further confirms the inherent capacityâimperceptibilityâsecurity trilemma, wherein no reviewed technique simultaneously achieves $$\text {PSNR} > 42$$ dB, embedding capacity $$> 4$$ bpp, and detection error rate $$> 0.48$$ . Six open challenges and seven future research directions are identified and grounded in evidence from the included studies, with explainable steganography, quantum-resistant frameworks, and latent diffusion model integration emerging as the most critical priorities for advancing the field toward practical and secure deployment.
Open access
Advanced Steganography and Watermarking Techniques
The Technology-Organization-Environment (TOE) framework is widely applied in organizational technology adoption research, yet its measurement practices remain fragmented. Across studies of EDI, cloud computing, blockchain, AI, and other contexts, researchers routinely rename, adapt, or recombine constructs without documenting how their operationalizations relate to prior work, producing a literature that is empirically rich but difficult to accumulate. This study addresses that problem by developing a measurement catalog of 14 reusable TOE constructs drawn from 45 empirical anchor studies. Using a targeted construct-selection approach, the study retained constructs that were peer-reviewed, tested at the firm level, statistically validated, and generalizable across technology domains. Related aliases were consolidated under canonical names through three documented rules based on shared theoretical mechanisms, item-level overlap, and functional equivalence. The catalog organizes constructs across the technological, organizational, and environmental contexts, provides core definitions with recommended measurement facets, and includes representative survey items with reported reliability coefficients. Beyond consolidation, this study identifies persistent gaps, including limited post-adoption measurement, weak readiness-capability differentiation, and underdeveloped governance constructs for emerging technologies. The catalog serves as a practical starting point for researchers designing TOE-based survey instruments and conceptual models, strengthening construct consistency while preserving the frameworkâs flexibility.
Proof-of-work blockchains purchase their security through the expenditure of compute and energy â yet the work performed is itself discarded entirely. Decentralized AI networks provide useful compute but secure no ledger. Myelin unifies both functions: miners jointly operate a large agentic language model (the network model) via pipeline parallelism, and the same cryptographically attested inference work (âProof of Inferenceâ, PoI) determines compensation and feeds the voting weight of consensus. The native coin MYL closes the value cycle: users burn MYL for inference credits, and miners receive newly minted MYL in proportion to verified work (burn-and-mint equilibrium). We specify (i) a layered architecture that decouples consensus latency from inference latency, (ii) a three-tier verification model combining deterministic redundancy, optimistic sampling with a bisection game, and optional zkML anchors, (iii) a token economy with a quantifiable security condition (S_min = g/pÂČ), and (iv) core data types and reference algorithms of an open-source implementation. We name the open core problems â deterministic cross-hardware inference, the latencyâcollusion trade-off of pod formation, and the 50% redundancy overhead â explicitly and propose measurement procedures. Bilingual release: this record contains the English and German editions of the whitepaper (PDF + Markdown each). In case of discrepancies, the German original prevails.
Layer 2 scaling solutionsâincluding payment-channel-based Lightning Networksand rollup-based off-chain execution environmentsâare commonly understood aslinear scaling projects for blockchain transaction throughput. This paper proposesan alternative structural interpretation: the emergence of Layer 2 is not a continuous increase in system capacity, but a percolation phase transition that occurswhen the density of off-chain channels or cross-rollup connections crosses a critical threshold. During this phase transition, the system shifts from a fragmentedlocally connected state to a globally routable giant connected state. The paperanalyzes the Lightning Network and the rollup ecosystem as comparative cases.Empirical studies of the Lightning Network show that its scale-free topology forcescritical hub nodes to bear a disproportionate connection load, thereby binding thenetworkâs global connectivity to the survival of a few high-centrality nodes. Therollup ecosystem faces the structural predicament of liquidity fragmentation, and itsevolution toward cross-rollup interoperability likewise exhibits a phase-transitionlogic from quantitative change to qualitative change in network effects. Based onthe above analysis, this paper distills three design principles for Layer 2 scalability:facilitating the institutionalization of cross-domain connections, avoiding overlyhomogenized cognitive convergence, and implementing differentiated verificationrouting among tasks with different security requirements.
Byzantine Fault Tolerance (BFT) consensus is a foundational achievement indistributed systems theory, providing dual guarantees of safety and liveness forasynchronous networks with malicious nodes. However, this theoretical frameworkimplicitly relies on a presupposition that has not been sufficiently examined: allhonest nodes are homogeneous in their cognition of the protocolâsobjectives. Whena decentralized system evolves from a closed task-oriented network into an opengovernance ecosystem, the functional differentiation of nodes in storage strategies,verification preferences, and governance commitments deprives this presuppositionof descriptive validity. This paper does not deny the security contributions of BFT,but argues that security alone is insufficient to constitute a complete consensus.The full logic of consensus requires a complementary dimension: the capacity toaccommodate functional differentiation. Integrating recent empirical classificationstudies of blockchain nodes, protocol architecture design experiences that acknowledge functional differentiation, and Ostromâs polycentric governance theory, thispaper proposesâCognitive Niche Equilibriumâ(CNE) as an extension of the consensus concept. System stability does not require all nodes to be isomorphic inevery function; rather, it requires the simultaneous satisfaction of three stabilityconditions: feedback anchoring, cross-validation, and evolutionary stability. Using Bitcoin and Ethereum as comparative cases, this paper translates these threeconditions into a layered implementation architecture symbiotic with existing BFTprotocol stacks, and discusses the security engineering principles and trade-offsunder this framework.
Blockchain can secure and verify electronic health records (EHRs) for multi-institution healthcare systems, but Layer-1 storage costs and throughput limitations make full on-chain EHR storage impractical. A new model is proposed named FZRP (Federated-ZK-Rollup Pipeline). It is a hybrid methodology combining Federated Learning (FL), off-chain storage (IPFS), zk-rollup batching with adaptive batch sizing, and parallel proof pipelines to minimize per-record transaction cost while preserving auditability and privacy. Using a synthetic dataset of 50,000 EHRs, it quantifies cost reductions under realistic assumptions and demonstrate orders-of-magnitude per-record savings. A formal cost model, latency and security analyses, and sensitivity studies are provided. The experimental evaluation demonstrates that adaptive batching significantly reduces per-record transaction cost under conservative Layer-1 cost assumptions to as low as $0.000024, achieving over 99.999% cost reduction while maintaining scalability and privacy. The limitations, regulatory considerations, and paths for future work are discussed.
Suman Bijapur, Shilpa Patil, Parimala, Shantala P H
With unparalleled threats to the integrity of digital information, democratic practices, and public confidence in media, deepfake technology comprises a new class of harm. Deep generative models can be used to generate realistic looking (and sounding) fake human faces and voices, which is great news for bad actors who seek to spread misinformation, commit crimes and ruin journalism. CyberLink Fights Deepfakes with New AI Model That Uses Neural Network Traditional methods used to identify deepfakes have depended on centralised AI systems that can't be trusted at face value and there is little or no way of proving a piece of content's authenticity. In this work, we have presented a solution that involves multi-modal deepfake detection and has utilized learning-based forgery detection framework to be deployed on blockchain for evidence tamper resistance. The proposed framework employs a hybrid CNN-RNN architecture that computes facial, audio and metadata feature in parallel to detect unseen deepfakes with accuracy of 94.2%, compared to the single-modal baselines (CNN only: 81.3%, and audio only: 67.4%). Novelty: Blockchain timestamping with cryptographically secured certificates of authenticity for third-party verification while protected proprietary detection logic is not revealed. At the computational efficiency and bandwidth threshold required for edge deployment, video processing at 30 FPS and only 2.1 MBs makes this applicable on any average mobile device. It holds for 12k synthetic videos (celebrities, politicians, newscasters) and diverse deepfake generation methods (FaceSwap, DeepFaceLab, StyleGAN). Societal impact: framework mitigates $1.2T annual disinformation damage and champions digital rights through decentralized verification. Via mashable.com Framework addresses the convergence of deepfake detection, blockchain authentication and the case for sustainable cybersecurity: As a global community grapples with synthetic media in ways we've never seen before, support online safety experts to respond.
Open access
2 source records
Generative Adversarial Networks and Image Synthesis
The increasing adoption of blockchain technology has transformed digital transaction systems by providing secure, decentralized, and transparent data management. The vehicle procurement process, however, still relies heavily on conventional procedures involving multiple intermediaries, manual documentation, and lengthy verification mechanisms that often increase operational costs and expose transactions to fraudulent activities. This paper presents a blockchain-enabled smart vehicle procurement framework that modernizes the complete purchasing lifecycle while preserving transaction integrity and user trust. The proposed system utilizes blockchain technology as an immutable distributed ledger for securely storing vehicle records, ownership history, buyer credentials, and transaction information. Smart contracts are employed to automate critical activities including buyer verification, ownership transfer, payment authorization, and regulatory validation without requiring manual intervention. The decentralized architecture minimizes dependency on third-party agencies while improving transparency, reducing processing delays, and enhancing security against data manipulation. Since every transaction is permanently recorded on the blockchain, both buyers and sellers can independently verify the authenticity of vehicle records before completing a purchase. The proposed framework maintains the same operational workflow and implementation strategy as the reference system while offering improved documentation quality and technical presentation. Experimental observations demonstrate that blockchain-assisted procurement significantly improves transaction efficiency, strengthens security, simplifies ownership transfer, and establishes a reliable digital marketplace for modern automotive commerce. The framework represents a scalable solution capable of supporting future intelligent transportation systems and smart mobility applications.
Counterfeit and substandard medicines remain a major global public health threat, with the World Health Organization estimating that up to 10% of medicines in low- and middle-income countries are falsified or substandard, exceeding 20% for some therapeutic classes in sub-Saharan Africa. Blockchain technology, with its decentralised, immutable, and transparent digital ledger architecture, has been proposed as a promising solution for strengthening pharmaceutical supply chain traceability, yet little is known about the readiness of community pharmacies in Nigeria to adopt it. This study assessed the knowledge, current practices, and implementation readiness of blockchain-based traceability for counterfeit medicine prevention among community pharmacists in Bayelsa State, Nigeria, and examined the barriers and enablers influencing adoption. A descriptive cross-sectional survey design was employed, using a structured, validated questionnaire administered electronically through the Association of Community Pharmacists of Nigeria (ACPN), Bayelsa State Chapter. A total of 119 valid responses were obtained, exceeding the minimum sample size derived from Yamane's and Cochran's formulae, representing a 79.3% response rate. Data were analysed using descriptive and inferential statistics, including Chi-square tests. Findings revealed that although 71.4% of respondents had heard of blockchain technology, mean knowledge scores across core blockchain concepts were uniformly low (1.26-1.72 on a 5-point scale), reflecting a substantial awareness-comprehension gap. Authentication practices were overwhelmingly manual, with 77.3% relying on visual inspection of NAFDAC numbers and only 9.2% using digital scanning; 68.9% of pharmacists had encountered suspected counterfeit medicines in the past year. Implementation readiness was below average (Mean = 2.70/5.0), driven by strong training willingness (Mean = 3.74) but deficient infrastructure (Mean = 1.80). Resistance to change and high cost were the leading barriers, while user-friendly applications and mandatory regulation were the strongest enablers. The study concludes that community pharmacies in Bayelsa State are not yet structurally ready for blockchain-based traceability, although a receptive attitudinal environment and strong professional motivation exist to support a phased transition. It is recommended that NAFDAC and the Pharmacists Council of Nigeria develop a phased regulatory framework and integrate blockchain literacy into continuing professional development, that government prioritise infrastructure investment, and that technology developers design mobile-first, user-friendly, offline-capable systems, to enable a coordinated transition toward blockchain-enabled pharmaceutical traceability in Nigeria.
The advent of fault-tolerant quantum computing represents the most significant and schedulable threat to the cryptographic foundations of blockchain infrastructure. Over $3.2 trillion in digital assets are currently secured by RSA, Elliptic Curve Cryptography (ECC), and ECDSA: algorithms provably broken by Shor's algorithm running on a Cryptographically Relevant Quantum Computer (CRQC). The Harvest Now, Decrypt Later (HNDL) threat means this risk is not future-dated. Adversaries with archival capability are already harvesting public blockchain data for retrospective decryption. In August 2024, NIST published three finalized post-quantum cryptographic standards: FIPS 203 (ML-KEM), FIPS 204 (ML-DSA), and FIPS 205 (SLH-DSA). In 2025, NIST standardized HQC, providing code-based cryptographic diversity alongside the lattice-based primary algorithms. These standards are mandated for U.S. national security systems under NSA CNSA 2.0 and for high-risk sector operators in the EU under the EU PQC Roadmap. This paper introduces QubitChain.io: a natively quantum-safe Layer 1 blockchain implementing all four NIST post-quantum standards from genesis block. The protocol employs hardware Quantum Random Number Generator (QRNG) entropy at both key generation and consensus randomness levels, and introduces Proof of Quantum Entropy (PoQE), a novel consensus mechanism whose validator selection cannot be predicted or manipulated by any adversary regardless of computational capability. The paper provides the complete technical, economic, and governance specification for the QubitChain.io protocol, covering cryptographic architecture, QRNG system design, consensus mechanism, network protocol, tokenomics, governance, and regulatory compliance.
Version control systems (VCS), including central VCS (CVCS) and distributed VCS (DVCS), are widely adopted to manage changes to software code and various types of documents. Unlike CVCS, where entities obtain data from a central server, each entity in DVCS stores the entire repository and shares it independently. In VCS, existing access control schemes require the participation of a central server and cannot be deployed in a completely distributed scenario. Additionally, these schemes often fail to enforce fine-grained access control for write permissions, which is crucial for collaborative work in a distributed environment. In this paper, we propose a distributed version control system access control scheme (named DVAC), which enforces cryptographic access control on distributed user nodes based on attribute-based encryption (ABE) and attribute-based signature (ABS). DVAC is designed to enforce a cryptographic access control protocol for DVCS, which enables file granularity read and write separation access control without the support of a central server. To ensure the integrity of the core version control functions in DVCS while protecting data security, DVAC incorporates a version control adaptation protocol. Additionally, DVAC leverages Ethereum smart contracts to maintain access control policies, ensuring distributed storage and trusted management of access policies. The architecture of DVAC is designed to seamlessly integrate with existing mature DVCS, such as Git, with minimal modifications. We have implemented a prototype of DVAC and integrated it with Git. A comprehensive performance evaluation was conducted to assess the overhead introduced by DVAC, and it was demonstrated that the overhead is modest.
Agentic AI networking (AgentNet) systems rely heavily on third-party skillset implementations and distributed multi-agent collaboration, yet they face major claim-to-capability inconsistencies and security vulnerabilities under trust-by-declaration assumptions. To bridge this gap, this paper proposes TrustAgentNet, a dual-tier blockchain-secured zero-trust framework. Specifically, a global Chain of Skillsets (CoS) governs the lifecycle of skillset metadata with protocols empowered by specialized agents to enforce off-chain auditing while maintaining lightweight on-chain cryptographic consensus. Furthermore, transient, task-oriented Chains of Collaboration (CoC) are dynamically established to enable trustless distributed multi-agent collaboration. Theoretical analysis of the three-way trade-off among security level, task performance, and resource overhead is provided and empirically validated. Experimental results on a hardware prototype demonstrate that compared with no-blockchain trust-by-default baselines, the zero-trust overhead of TrustAgentNet is dominated by off-chain inference, while the blockchain layer incurs minor ledger costs via the ledger-IPFS storage and on/off-chain integration design. Crucially, the proposed verification pipeline achieves a flawless 100% accuracy across 50 AI models, correctly validating 40 honest skillsets and intercepting 10 adversarial ones, and generalizes to non-AI domains with an 83.91% accuracy and a 0.85 F1-score across 1478 features from 171 ClawHub skills. Adversarial experiments further show that TrustAgentNet enables autonomous skillset self-recovery against various malicious attacks.
Rukhsar Zaka, Faiza Irfan, Sidra Rehman, Muhammad Ahsan Hayat
Cryptocurrency markets are highly volatile, nonlinear, and affected by several internal and external market factors, making price forecasting a challenging task. Accurate cryptocurrency price forecasting can support investors, traders, and financial analysts in making informed decisions. This research paper presents a comparative analysis of machine learning and deep learning models for cryptocurrency price forecasting using historical Aave (AAVE) cryptocurrency data. The dataset consists of 275 records and 10 features, including Date, High, Low, Open, Close, Volume, and Marketcap. The Close price is selected as the target variable, while High, Low, Open, Volume, and Marketcap are used as predictor variables. Five models are implemented and compared: Linear Regression, Support Vector Regression, Random Forest Regressor, XGBoost Regressor, and Long Short-Term Memory. The models are evaluated using Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, R-squared score, and directional accuracy. Experimental results show that the LSTM model achieved the best performance with the lowest RMSE of 2.74, MAE of 1.78, MAPE of 3.91%, and R-squared score of 0.965. The results indicate that deep learning models, especially LSTM, are more suitable for capturing temporal dependencies and nonlinear patterns in cryptocurrency price data.
This chapter explores the transformative potential of blockchain and artificial intelligence (AI) in revolutionizing green finance. It begins by examining the role of digital transformation in driving sustainable financial practices, highlighting the integration of blockchain and AI. The chapter delves into blockchain's applications in enhancing transparency, traceability, and security within green finance, particularly through smart contracts and decentralized finance solutions. It further discusses AI's contributions to improving risk assessment, ESG evaluation, and combating greenwashing. The synergies between blockchain and AI are explored, showing how their combined use optimizes sustainability-focused investments. Additionally, the chapter addresses regulatory and ethical considerations surrounding these technologies. Finally, it discusses emerging trends and opportunities in green finance, providing insights into the future of sustainable financial systems driven by technological innovation.