Blockchain technology is considered a transformative innovation, offering decentralized, secure, and transparent solutions to various industries, with cryptocurrencies being its most famous application. The volatility and non-linear behavior of cryptocurrency markets pose significant challenges for predicting their prices accurately. Predicting cryptocurrencies prices based on traditional statistical methods often fail to capture the market complex dynamics. Therefore, the recent developments in Artificial Intelligence, especially in deep learning and ensemble-based approaches have presented promising results. This study delivers a comprehensive literature review focusing on applying deep learning and ensemble deep learning algorithms in cryptocurrency time series price prediction. The main deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) are examined with a variety of time intervals and cryptocurrency types. The findings present that deep learning models, especially when used in hybrid or ensemble configurations, have obtained promising results. This review highlights the efficacy and significant potential of ensemble deep learning and its capabilities in cryptocurrencies price trend forecasting offering valuable insights for investors and researchers.
Purpose : Investors sometimes have difficulties in making appropriate decisions during unpredictable times such as the COVID-19 pandemic. Based on the extended theory of planned behavior (TPB), the objective of this study was to validate the psychological antecedents of willingness to invest in cryptocurrency during the outbreak of the COVID - 19 pandemic. Design/Methodology/Approach : This study was conducted by collecting primary data from 204 respondents using a structured online questionnaire, which was further analyzed using SPSS Amos 23.0. The current research explored the association between variables named “subjective norms,” “perceived self-efficacy,” and “attitude to invest.” Findings : The findings suggested that “subjective norms” and “perceived self-efficacy” are the major factors influencing investors’ attitudes toward cryptocurrency investment, directly impacting their intentions for the same. The pandemic underscored the dual nature of cryptocurrencies, demonstrating advantages such as enabling remote transactions and providing a hedge against economic instability, while simultaneously displaying difficulties, including environmental implications and market volatility. Practical Implications : The results have important reference significance for future investors who intend to invest in the crypto-assets market. The findings of the study provided important insights to investors and financial planners on which psychological factors affect whether to make a cryptocurrency investment during volatile periods, such as the COVID-19 era. Learning these key aspects will provide scientists with tools to navigate choppy waters, which will ultimately make them better decision-makers. Originality : This research enhanced the existing body of knowledge by uniquely incorporating the extended theory of planned behavior into the context of cryptocurrency investments during a global crisis. It offered an extensive comprehension of investor psychology during periods of uncertainty, essential for both scholarly study and pragmatic investment strategies.
The metaverse, a hyper-interactive digital environment where people work, play, socialise, and shop, is gaining attention as a potential opportunity for the fashion industry to engage Generation Z and other technology-savvy, consumers. Metaverse fashion is a new and exciting way to create, market, and sell fashion products in the virtual space and offers brands opportunities to distinguish themselves in an increasingly crowded marketplace and engage customers more interactively. It allows brands to create immersive experiences, interactively highlight their products, and reach a wider audience. The use of non-fungible tokens (NFTs), gaming, and virtual fashion could play a significant role in the future of the fashion industry. This paper aims to explore how the metaverse can revolutionise the fashion industry and what fashion executives need to know to tap into this new market. By leveraging this powerful innovative technology, brands have the potential to reach a new generation of consumers and create an unforgettable experience for them but like previous technologies before them they may invest heavily to see lower than expected returns as consumers move elsewhere. <br/>This positioning paper will consider and evaluate if, when and how the metaverse is currently marketing fashion and if these approaches are successful and which issues they may face going forward and what future creative opportunities it offers for fashion brands. The paper will also discuss the key technologies and projects most influential and areas of development within fashion marketing in the metaverse<br/>
High-mobility Internet of Things (IoT) for Vehicle-to-Grid (V2G) Demand Response (DR), including roaming between Charge Point Operators (CPOs), requires privacy-preserving authentication with sub-1 ms responses and cross-domain scalability as devices exceed 200km/h. Mechanisms must run on constrained hardware while remaining compatible with EV-charging message flows such as ISO 15118–20 and OCPP 2.0.1. Many deployed schemes re-authenticate from scratch, which inflates computation and airtime; static credentials also ignore trajectory context and struggle with rapid mobility. We present a Zero-Knowledge Proof-based Authentication Scheme (ZKPAS) for V2G/DR that proves possession without disclosure and replaces heavy handshakes with compact, mobility-aware proofs, targeting latencyLO(n) toO(logn). (iii) Predictive token generation with Long Short-Term Memory (LSTM) models trained on GeoLife and T-Drive pre-computes material, yielding 84.7% token reuse along trajectories. (iv) Cross-domain authentication employs (t,n)-threshold cryptography for Byzantine-tolerant roaming across operators. We prove resistance to impersonation, replay, man-in-the-middle, and trajectory inference; under the Computational Diffie–Hellman Problem (CDHP), the adversary’s success probability satisfies Pr[break] ≤ 2−λ. On real transportation topologies, ZKPAS cuts computation by 71.8%, authentication latency by 93.9%, and energy by 69.5%, while interfacing with V2G/DR control flows. The protocol sustains a 98.5% authentication success rate at 250km/h.
We study non-interactive zero-knowledge proofs (NIZKs) for NP satisfying: 1) statistical soundness, 2) computational zero-knowledge and 3) certified-everlasting zero-knowledge (CE-ZK). The CE-ZK property allows a verifier of a quantum proof to revoke the proof in a way that can be checked (certified) by the prover. Conditioned on successful certification, the verifier's state can be efficiently simulated with only the statement, in a statistically indistinguishable way. Our contributions regarding these certified-everlasting NIZKs (CE-NIZKs) are as follows: - We identify a barrier to obtaining CE-NIZKs in the CRS model via generalizations of known interactive zero-knowledge proofs that satisfy CE-ZK. - We circumvent this by constructing CE-NIZK from black-box use of NIZK for NP satisfying certain properties, along with OWFs. As a result, we obtain CE-NIZKs for NP in the CRS model, based on polynomial hardness of the learning with errors (LWE) assumption. - In addition, we observe that the aforementioned barrier does not apply to the shared EPR model. We leverage this fact to construct a CE-NIZK for NP in this model based on any statistical binding hidden-bits generator, which can be based on LWE. The only quantum computation in this protocol involves single-qubit measurements of the shared EPR pairs.
This study asks whether Ethereum’s proof-of-stake (PoS) incentives not only make economic sense on paper but also feel attractive to real validators who may be loss-averse and sensitive to risk. We take a canonical Eth2 slot-level model of rewards, penalties, costs, and proposer-conditional maximal extractable value (MEV) and overlay a prospect-theoretic valuation that captures reference dependence, loss aversion, diminishing sensitivity, and probability weighting. This Prospect-Theoretic Incentive Mechanism (PT-IM) separates the “money edge” (expected accounting return) from the “felt edge” (behavioral value) by mapping monetary outcomes through a prospect value function and comparing the two across parameter ranges. The mechanism is parametric and modular, allowing different MEV, cost, and penalty profiles to plug in without altering the base PoS model. Using stylized numerical examples, we identify regions where cooperation that pays in expectation can remain unattractive under plausible loss-averse preferences, especially when penalties are salient or MEV is volatile. We discuss how these distortions may affect validator participation, economic security, and the tuning of rewards and penalties in Ethereum’s PoS. Integrating behavioral valuation into crypto-economic design thus provides a practical diagnostic for adjusting protocol parameters when economics and perception diverge.
Blue carbon ecosystems are vital for atmospheric CO2sequestration, yet measurement, reporting, and verification (MRV) methodologies remain fragmented and opaque, hindering scalable climate finance. This paper introduces a unified, standards-driven that integrates blockchain smart contracts, decentralized storage, IoT sensor arrays, and machine learning to digitize and automate the carbon credit lifecycle. The architecture anchors project design documents, geospatial layers, and laboratory datasets immutably on-chain via ERC-1155 and IPFS, enabling tamper-evident provenance, real-time evidence linkage, and transparent buffer pool enforcement. Gasless EIP-712 meta-transactions ensure inclusive stakeholder participation, while ML-enabled MRV modules yield accurate, conservative credit estimation from diverse soil and spectral features. Prototype deployment demonstrates automated verification, auditability, and cost-effective credit tokenization, significantly advancing market integrity, efficiency, and accessibility. Current constraints include limited deployment scale and sensor diversity, with ongoing work targeting mainnet expansion, advanced ML pipelines, and collaboration for robust real-world adoption and policy integration.
Dan Alexandru Mitrea, Constantin Viorel Marian, Rareş Alexandru Manolescu
In many jurisdictions, property registration and transfers remain constrained by inefficient, paper-based processes that depend on multiple intermediaries and bureaucratic approvals. This paper proposes a decentralized, blockchain-based property platform designed to streamline these processes using Non-Fungible Tokens (NFTs) and artificial intelligence (AI) agents to modernize public-sector asset management. The work addresses the persistent inefficiencies of paper-based property registration and ownership transfer by embedding legal and administrative logic within smart contracts and automating compliance through an intelligent conversational interface. The system was implemented using Ethereum-based ERC-721 standards, React for the user interface, and Langfuse-powered AI integration for guided user interaction. The pilot implementation presents secure, transparent, and auditable property-transfer transactions executed entirely on-chain, while hybrid IPFS-based storage and decentralized identifiers preserve privacy and legal validity. Comparative analysis against existing national initiatives indicates that the proposed architecture delivers decentralization, citizen control, and interoperability without compromising regulatory requirements. The system reduces bureaucratic overhead, simplifies transaction workflows, and lowers user error risk, thereby strengthening accountability and public trust. Overall, the paper outlines a viable foundation for legally aligned, AI-assisted digital property registries and offers a policy-oriented roadmap for integrating blockchain-enabled systems into public-sector governance infrastructures.
Based on distributed ledger technology, a new type of arbitration courts has been emerging in the world for the last five years. Their task is to resolve disputes using blockchain and smart contracts. Did the creators of the idea of “distributed justice” really invent a new way to effectively and fairly resolve disputes in the 21st century? Blockchain arbitration involves resolving disputes using the theory of multi-person games, the concept of Schelling point, the idea of decentralized autonomous organizations (DAO), tokens and crowdsourcing. The article attempts to answer the question of whether arbitration decisions made on the basis of economic incentives can be considered to meet the criteria of Aristotelian rectificatory justice. The article is analytical in nature, addressing a topic that has only become relevant in the world a few years ago. The analysis uses theses from cryptoeconomics and game theory. The work initially outlines the problems. Due to the small number of experiences of digital arbitration in the world, the theses and hypotheses of the text, written from the perspective of theory and philosophy of law, require further in-depth analyses.
This paper explores the role social imaginaries play in the organization of the Ethereum blockchain community and its associated economy. Specifically, it focuses on the developers, researchers and organizers responsible for Ethereum’s maintenance and upgrades, known as the Core Devs. Using a Grounded Theory approach, we investigate the decentralized decision-making processes inherent in Ethereum’s governance mechanisms. Through interviews with seven Ethereum Core Devs, we examine the presence of previously known social imaginaries and analyze their function in contemporary governance, including Infrastructural Mutualism. We also discuss a previously unexamined social imaginary, the Infinite Garden, which captures the values-inflected work of maintaining the protocol in light of the pressures of corporate institutionalization and economic pragmatism. We discover how in the absence of a formal centralized hierarchy, these social imaginaries set boundaries on the legitimacy of decision-making in Ethereum protocol governance. Further, we comment on the contemporary understanding of sociotechnical imaginaries as institutionally stabilized, finding in Ethereum’s case that culture is the condition that enables stability, whilst also acting as a bulwark against external institutional capture.
Blockchain networks have revolutionized decentralized applications but remain vulnerable to evolving security threats due to their reliance on static consensus mechanisms that cannot adapt to changing threat landscapes. This paper addresses this critical security gap by proposing Autonomous Defense-Adaptive Consensus Optimisation for Blockchain Networks (ADACON), an original framework for the dynamic adjustment of consensus mechanisms based on Bayesian threat detection. The research investigates how real-time adaptation between multiple consensus protocols can enhance blockchain resilience while maintaining performance. The approach integrates a Bayesian Threat Detector, Consensus Adapter, and Network State monitor in a modular architecture that continuously assesses network conditions and switches between five consensus mechanisms (PoW, PoS, PBFT, PoA, DPoS) as threats emerge. The framework was evaluated through comprehensive simulations involving 1,000 nodes, testing response to six distinct attack vectors, including Sybil, DoS, Byzantine, Eclipse, Majority, and Routing attacks. Results demonstrate that ADACON effectively identifies and responds to varied attacks with a latency of 29.7 ms and throughput of 833 TPS). Statistical validation across five independent simulation runs (seeds 5-9) confirmed framework reliability with consistent performance metrics (CV < 7.1% for latency, 5.4% for throughput). Delegated Proof of Stake emerged as the most frequently selected mechanism (23.2%) due to its balanced performance across multiple security dimensions. Significantly, the system exhibited greater adaptability and attack coverage than existing hybrid approaches. The previous high switching frequency was reduced by using hysteresis, i.e., by providing dwell time and an improved threshold that avoids unnecessary switching. The study concludes that dynamic consensus adaptation offers substantial security advantages for blockchain networks, particularly in high-security environments like financial systems and critical infrastructure. However, further research must focus on optimizing switching frequency and developing secure transition protocols to maximize effectiveness. ADACON represents an incremental extension tested toward more resilient blockchain systems that can autonomously respond to emerging threats while balancing security, performance, and resource utilization.
Zia Ullah, Zia Ullah, Sanam Shahla Rizvi, Ibrar Ali Shah · 5 authors
Vehicular Ad Hoc Networks (VANETs) are essential for the success of Intelligent Transportation Systems (ITS), providing real-time communication between vehicles and infrastructure. However, the highly dynamic and decentralized nature of VANETs introduces significant challenges in ensuring trust and security across the network, including security threats, communication overhead, and energy inefficiencies. This paper presents a novel blockchain-based trust management framework that addresses these issues by incorporating lightweight consensus mechanisms, optimized data propagation strategies, and energy-aware protocols. Our approach reduces communication overhead by selectively propagating trust updates, leading to a 35% decrease in overall network traffic compared to traditional broadcast-based systems. In terms of trust accuracy, our model achieves over 95% accuracy in detecting malicious nodes, significantly outperforming existing solutions. The proposed system demonstrates the identification and penalization of malicious behaviors such as Sybil attacks and false reporting with a 25% improvement in detection rate, while maintaining low latency (an average reduction of 30% compared to PoW-based systems) and efficient energy consumption, reducing energy use by up to 40%. The proposed model also incorporates a hybrid Proof of Stake (PoS) and Practical Byzantine Fault Tolerance (PBFT) consensus mechanism, which further enhances its scalability and fault tolerance. Simulation results show that our framework converges to accurate trust values faster than traditional methods, ensuring that reliable trust evaluations are made in real-time, even under high mobility conditions. The combination of these optimizations ensures that our framework is not only secure but also highly efficient, capable of supporting scalable and resilient VANET deployments. Furthermore, our decentralized approach ensures that trust decisions are made in real-time without the need for a centralized authority, making the system more adaptable to the high-mobility conditions of VANETs. This research offers a comprehensive solution for VANETs trust management, significantly improving communication efficiency, trust accuracy, and energy consumption while maintaining robust security and scalability. Our proposed blockchain-based trust management system provides a secure, energy-efficient, and scalable solution for VANETs, setting the stage for future developments in secure vehicular communication networks.
Massive growth of the Chinese carbon-credit market has been characterized by endemic data obscurity, certification latency, and fraud, specifically in the Passenger Cars Corporate Average Fuel Consumption and New Energy Vehicle Credit Regulation (PCFN) scheme in the automotive industry. Its present centralized management system is not transparent and has information asymmetries and is very prone to manipulation results in erroneous carbon-credit determination, inefficient dealings, and the deteriorating stakeholder confidence. This paper offers a federated blockchain-IoT information infrastructure to deal with these severe inadequacies, namely, the provision of end-to-end transparency, tamper-resistance, and autonomous functionality in managing carbon-credit. The framework uses radio frequency identification (RFID) to capture real-time emission data, delegated proof-of-stake (DPoS) consensus to provide scalable verification and uses smart contracts to provide a decentralized credit assessment and trading. Besides, an AI-based predictive analytics control is incorporated to dynamically predict credit prices and identify anomalies on distributed nodes. Experimental comparison with national automotive carbon datasets reveals that the 72.6% latency of credit verification is reduced, the 38.2% transparency of audit is increased, and the 93.5% accuracy of fraud detection is achieved rather significantly in comparison to the traditional centralized model. The suggested framework will offer a platform on which the cross-sector carbon-credit markets of China can be scaled and verified to speed up the process of the country achieving its carbon neutrality targets of 3060.
Sangita Lade, Muhammad Parkar, Shreyas Nagarkar, Om Shintre · 5 authors
This paper focuses on the design and development of a decentralized blogging platform aimed at overcoming the limitations of existing centralized content publishing systems through the use of blockchain technology. Most mainstream blogging platforms remain vulnerable to censorship, data breaches, content manipulation, and arbitrary content removal. To address these challenges, the proposed system leverages Ethereum smart contracts for transaction validation and content management, along with the Interplanetary File System (IPFS) for secure, reliable, and distributed storage. The integration of these technologies ensures immutability, author verification, and strong resistance to censorship. Additionally, the platform incorporates an incentive mechanism that rewards content creators through a token-based economy and wallet-driven identity management. The system was evaluated on the Ethereum testnet and demonstrated significant improvements in content persistence, ownership verification, and censorship resistance compared to traditional platforms. Performance analysis indicates stable transaction confirmation times and efficient storage utilization, with potential for further optimization of gas consumption. User interface testing further confirms that the platform effectively abstracts blockchain complexity, offering a user-friendly experience. Overall, this work presents a practical Web3-based content publishing solution that supports creator empowerment and content integrity in a trustless digital environment.
This study examines budget management in the Decentralized Autonomous Governments (GADs) at the parish level, emphasizing the importance of transparency in public resource administration as a mechanism to promote accountability and prevent corruption. The main objective was to apply budgetary indicators to the Tacamoros Parish GAD, located in Sozoranga, Loja Province, during the fiscal periods 2021–2022, in order to evaluate the efficiency and effectiveness of budget allocations. A quantitative and descriptive approach was adopted, employing data collection and analysis tools based on the guidelines of the Organic Code of Territorial Organization, Autonomy, and Decentralization (COOTAD) and the Organic Code of Planning and Public Finance (COPFP). The results reveal that the budget cycle achieved a confidence level of 82.93%, with an associated risk of 17.07%. Likewise, revenue execution compared to the programmed figures reached 49.13% in 2021 and 69.15% in 2022, while expenditure execution was 59.47% in 2021 and 50.33% in 2022. The study concludes that strengthening long-term strategic planning and promoting greater financial autonomy in parish-level GADs are essential to ensure more efficient budget management for the benefit of the community.
Digital forensic investigation in 2025 faces unprecedented challenges posed by the convergence of decentralized web technologies (Web3), adversarial generative AI systems, and darknet infrastructure. Traditional attribution and evidence preservation methodologies prove in-sufficient when adversaries exploit blockchain immutability, synthetic media generation, and privacy-enhancing technologies to obscure malicious intent. This paper in-traduces SHARD (Shadowed and Silicon Hybrid Attribution and Reconstruction Diagnostic), a multi-modal forensic framework designed to recover, correlate, and at-tribute malicious artifacts across distributed ledger systems, synthetic content generators, and anonymized net-works. Through systematic analysis of 47 real-world cybercriminal cases and forensic evaluation against 12 at-tack vectors, SHARD achieves 89.2% attribution accuracy while reducing investigative timelines by 64% com-pared to conventional methods. We present novel techniques for blockchain temporal analysis, deepfake prove-nance tracking, and Tor-exit node correlation. The frame-work integrates machine learning-based anomaly detection with cryptographic verification to distinguish legitimate decentralized activity from adversarial manipulation. Our contributions include: (1) a formal threat model encompassing Web3 forensics; (2) a hybrid architecture combining on-chain and off-chain analysis; (3) algorithmic innovations for synthetic media fingerprinting; and (4) extensive empirical validation against contemporary attack scenarios. This work addresses a critical gap in digital forensics as investigative techniques must evolve alongside the technological infrastructure that criminals exploit.
Total Value Locked (TVL) explicitly reflects the total asset users deposit in Decentralized Finance (DeFi) protocols, similarly to the Asset Under Management (AUM) in traditional finance. This exposes liquidity providers to the risk of short-term liquidity depletion and highlight the urgent need for quantifiable and predictive risk management tools. As its short-term fluctuations can be effectively characterized by on-chain static features (e.g., fee tier, volatility), dynamic features (e.g., token balance changes, slippage), and technical indicators (e.g., MA), and since posterior calibration methods based on high-accuracy point forecasting models can provide more reliable estimations of downside risk boundaries, this study focuses on the USD Coin - Ethereum pool (0.3 % fee tier) of Uniswap V3. The model is constructed using 17 features, with eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine employed for TVL growth-rate regression forecasting. The results are compared with those from Long Short-Term Memory, Gated Recurrent Unit, and Naïve baselines. Furthermore, the research introduces the Liquidity-at-Risk (LaR95) metric to estimate downside risk through both residual-based and quantile regression approaches, and conduct interpretability analysis using SHAP values. XGBoost obviously outperforms Deep learning models on directional accuracy. XGBoost demonstrates a significantly superior performance to deep learning models in predicting the direction of TVL changes. The residual-based LaR95(liquidity-at-risk at the 95% confidence level) derived from its point forecasts exhibits a coverage rate closely aligned with the theoretical level, validating the effectiveness, robustness, and interpretability of the “high-accuracy prediction and residual calibration” framework in DeFi risk management.
This thesis examines whether Decentralized Autonomous Organizations (DAOs) can resolve the fundamental trilemma of global public goods provision, specifically addressing the seemingly impossible simultaneous achievement of effective climate action, responsible AI governance, and equitable international coordination. Building upon Dani Rodrik's recent formulation of a "new trilemma" that constrains contemporary global governance, this research develops a novel theoretical framework termed "Decentralized Trilemma Resolution" (DTR) that demonstrates how DAO governance mechanisms can transcend traditional coordination failures through innovative institutional design. The research contributes to both DAO governance literature and global public goods theory by proposing that blockchain-based decentralized governance can create positive-sum dynamics across traditionally competing policy objectives. Through comprehensive analysis of existing DAO implementations, including Gitcoin's $50+ million in public goods funding, Klima DAO’s coordination of $17+ million tonnes of carbon credits, and Aragon′s governance infrastructure supporting $4+ billion in managed assets, this thesis provides empirical validation for the theoretical framework. The DTR framework introduces four core mechanisms that enable trilemma resolution: Multi-Stakeholder Token Governance (MSTG), Algorithmic Transparency and Accountability (ATA), Modular Governance Architecture (MGA), and Incentive Alignment Mechanisms (IAM). Mathematical formalization demonstrates how these mechanisms create superadditive utility functions where coordination across climate action, AI governance, and equitable development generates synergistic rather than competitive outcomes. Key findings indicate that DAOs demonstrate 6-20x faster decision-making, 3-5x higher stakeholder participation, and 2-5x lower administrative costs compared to traditional governance mechanisms. However, challenges remain in enforcement capabilities, regulatory recognition, and scaling to nation-state level coordination. The thesis concludes that hybrid models combining DAO governance with traditional institutional structures offer the most promising pathway for near-term trilemma resolution, while pure DAO governance may emerge as viable for global coordination as technical and regulatory infrastructure matures. This research provides the first systematic framework for understanding how decentralized governance can address the fundamental coordination challenges of the 21st century, offering both theoretical insights and practical implementation pathways for policymakers, technologists, and global governance practitioners.
The security of a patient’s clinical records and their confidentiality is a crucial responsibility for any medical orga- nization seeking to operate optimally and protect the privacy of its patients. The loss of a patient’s clinical record in a data breach can have disastrous consequences. According to the 2022 Data Breaches Investigations Report by Verizon, human error contributed to 82% of data breaches. To address this issue, this research paper proposes a system that integrates user- side inspection software and blockchain technology to prevent data breaches by leveraging blockchain’s transparency and immutability. Our proposed system can prevent data breaches by protecting sensitive files from unauthorized access within the organization as well as outside the organization while also ensuring the secure transmission of cryptographically-secured files. The system utilizes a Proof of Stake (PoS) consensus algorithm to enhance security, scalability, and efficiency so that it can even be used by organizations without considerable computer architecture.
The user-ownership model of Web3 commerce is widely viewed as a potential paradigm shift for the digital economy, yet its macroeconomic implications remain under-quantified within a unified, dynamic, and parameterized framework. This paper develops a tractable dynamic macroeconomic model of a “wealth flywheel” featuring two feedback channels. The income loop operates through profit-backed user rebates that raise income-equivalent purchasing capacity and stimulate consumption. The asset loop operates through consumption-driven profit and valuation growth, which expands household wealth under user ownership and feeds back into consumption via wealth effects. In a static setting, the paper derives a closed-form consumption multiplier and a corresponding stability condition. Aggregate consumption responds proportionally to an exogenous income impulse, and the system is stable if the combined strength of rebate-induced consumption feedback and wealth-effect amplification remains below unity. The static mechanism is then embedded into a global multi-period simulation framework with time-varying Web3 penetration, finite-horizon household deposit reallocation into consumption, and endogenous valuation paths. Using illustrative parameterizations, the paper simulates trajectories for global real GDP, equity market capitalization, household wealth, and inflation under neutral and aggressive adoption scenarios. The analysis further examines distributional implications when capitalization gains are directed toward user cohorts with higher marginal propensities to consume. The framework provides a parsimonious diagnostic for stability in mechanism design and contributes to macro-prudential discussions of self-reinforcing growth dynamics. Importantly, the analysis abstracts from collateralized borrowing, leverage, rehypothecation, and other financial intermediation channels. All amplification effects in the model arise from ownership structure and wealth effects rather than from credit-driven financial accelerators.
Yihan Hong, Hengxiang Feng, Yinghan Wang, Boxuan Li
The approval of the Bitcoin Spot ETF in January 2024 marked a transformative event in cryptocurrency markets, signaling increased institutional adoption and integration into traditional finance. This study examines Bitcoin's changing relationships with traditional assets, including equities, gold, and fiat currencies, following this milestone. Using rolling correlation analysis, Chow tests, and DCC-GARCH models, we found that Bitcoin's correlation with the S\&P 500 increased significantly post-ETF approval, indicating stronger alignment with equities. Its relationship with gold stabilized near zero, while its correlation with the U.S. Dollar Index remained consistently negative, reflecting its continued independence from fiat currencies. These findings offer insights into Bitcoin's evolving role in portfolios, implications for market stability, and future research opportunities on cryptocurrency integration into traditional financial systems.
Decentralized federated learning (DFL) enables collaborative model training without centralized trust, but it remains vulnerable to Byzantine clients that poison gradients under heterogeneous (Non-IID) data. Existing defenses face a scalability trilemma: distance-based filtering (e.g., Krum) can reject legitimate Non-IID updates, geometric-median methods incur prohibitive $O(n^2 d)$ cost, and many certified defenses are evaluated only on models below 100M parameters. We propose Spectral Sentinel, a Byzantine detection and aggregation framework that leverages a random-matrix-theoretic signature: honest Non-IID gradients produce covariance eigenspectra whose bulk follows the Marchenko-Pastur law, while Byzantine perturbations induce detectable tail anomalies. Our algorithm combines Frequent Directions sketching with data-dependent MP tracking, enabling detection on models up to 1.5B parameters using $O(k^2)$ memory with $k \ll d$. Under a $(σ,f)$ threat model with coordinate-wise honest variance bounded by $σ^2$ and $f < 1/2$ adversaries, we prove $(ε,δ)$-Byzantine resilience with convergence rate $O(σf / \sqrt{T} + f^2 / T)$, and we provide a matching information-theoretic lower bound $Ω(σf / \sqrt{T})$, establishing minimax optimality. We implement the full system with blockchain integration on Polygon networks and validate it across 144 attack-aggregator configurations, achieving 78.4 percent average accuracy versus 48-63 percent for baseline methods.
Erdhi Widyarto Nugroho, R. Rizal Isnanto, Luhur Bayuaji
The Federated Byzantine Agreement (FBA) achieves rapid consensus by relying on overlapping quorum slices. But this architecture leads to a high dependence on the availability of validators when about one fourth of validators go down, the classical FBA can lose liveness or fail to reach agreement. We thus come up with an Adaptive FBA architecture that can reconfigure quorum slices intelligently based on real time validator reputation to overcome this drawback. Our model includes trust scores computed from EigenTrust and a sliding window behavioral assessment to determine the reliability of validators. We have built the intelligent adaptive FBA model and conducted tests in a Stellar based setting. Results of real life experiments reveal that the system is stable enough to keep consensus when more than half of the validators (up to 62 percent) are disconnected, which is a great extension of the failure threshold of a classical FBA. A fallback mode allows the network to be functional with as few as three validators, thus showing a significant robustness enhancement. Besides, a comparative study with the existing consensus protocols shows that Adaptive FBA can be an excellent choice for the next generation of blockchain systems, especially for constructing a resilient blockchain infrastructure.