On-chain lending has expanded across multiple distributed ledgers as DeFi becomes increasingly multi-chain. This environment introduces novel technical and financial mechanisms, particularly cross-blockchain communication and asset transfer protocols, yet cross-chain elements remain understudied in lending protocol risk management. To address this gap, we applied panel regression fixed effects and OLS models to empirically analyze cross-blockchain interoperability solutions, using TVL and total revenue as performance proxies from October 2022 to January 2025. Our data set covers 15 decentralized lending protocols and 53 cross-chain bridges across 9 EVM-compatible blockchains, categorized as Ethereum, alternative layer-1s, and Ethereum layer-2 networks. Results reveal that cross-chain activity impacts on protocol performance. Bridge volume emerges as a critical driver, exerts a significant effect on TVL and revenue across different categories, though the direction of this effect varies heterogeneously. Increased bridge integrations are associated with decreased TVL and protocol revenue across categories, indicating liquidity escapes from those lending ecosystems. Liquidations produce heterogeneous effects across categories. New network launches do not have as significant relationships with TVL and revenue while bridge hacks show a significant and positive relationship. High R-squared values confirm meaningful explanatory power. We further show Ethereum attracts large depositors, while layer-2s skew toward retail participation. We conclude that effective DeFi risk models should incorporate cross-chain metrics and adopt a layer-aware approach to accurately reflect the evolving multi-chain landscape.
Youssef Said, Al Mahdi Khaddar, Lahcen Hassine, Ahmed Eddaoui · 5 authors
Gas Gas consumption is a critical factor influencing the efficiency, scalability, and operational cost of Ethereum smart contracts.As contract complexity grows, identifying structurally gas-inefficient patterns becomes essential for improving development workflows and preventing costly deployment decisions.This study presents a graph-based deep learning framework for detecting gas-inefficiency risk patterns at the function level, leveraging multi-relational Graph Attention Networks (GAT) applied to function-level contract graphs.By modeling call dependencies, control-flow interactions, and storage-based data dependencies, the model learns structural indicators associated with excessive gas consumption while explicitly excluding direct gas metrics from the feature space to prevent data leakage.Experimental results under a strict contract-level data split protocol demonstrate strong classification performance and stable generalization across held-out contracts under the main split protocol, and consistent behavior under an additional time-forward temporal robustness check.Ablation analysis confirms the contribution of dependency-aware edges and semantic features to predictive accuracy, highlighting the importance of modeling cross-function interactions rather than isolated code metrics.Beyond predictive performance, the proposed approach provides interpretable attention weights that identify structurally influential functions, supporting predeployment analysis and developer-guided manual refactoring decisions.By framing gas inefficiency as a global structural property emerging from function interactions, this work contributes aa scalable and explainable methodology for structural gas-inefficiency detection in smart contracts.The proposed model performs structural detection only and does not automatically modify or optimize smart contract code.
In this research, the energy consumption models of Bitcoin, Ethereum, and Dogecoin are analyzed using Explainable Artificial Intelligence (XAI) models aided by the three stages of analysis involving Digiconomist data from 2022 to 2025: (1) exploratory data analysis for the nature of energy consumption, (2) model identification of influential variables using Random Forest models enhanced with SHAP values, and (3) an LSTM transfer learning method for predicting the energy consumption of Ethereum and Dogecoin using a model developed with Bitcoin data. The initial results show that while both assets vary largely when it comes to their normal usage level, Ethereum sees a sharp drop after the changeover from Proof-of-Work to Proof-of-Stake as a mechanism. The XAI analysis indicates that energy use is largely a consequence of past use, seasonality, and annual patterns. In addition to this, the models show a high level of accuracy for Dogecoin (R²: 88.4%, MAPE: 13.45%) and Ethereum (R²: 86.2%, MAPE: 11.47%) when it comes to predicting energy usage using the concepts of transfer learning.
The centralization of digital content creation and credentialing platforms has resulted in opaque monetization structures, monopolistic data silos, and a persistent absence of verifiable user sovereignty over intellectual contributions. This paper introduces Metaplay, a decentralized content marketplace architecture engineered to disintermediate the content creation and talent development lifecycle. Leveraging a modular blockchain framework, Metaplay utilizes Zero-Knowledge Rollups (zkEVM) for high-throughput, low-latency execution, and EIP-4844 blob-carrying transactions to minimize data availability costs. We introduce a privacy-preserving credentialing mechanism utilizing Soulbound Tokens (SBTs) and zk-SNARKs, enabling non-transferable, cryptographically verifiable proof of skill acquisition without compromising user privacy. Platform moderation employs a Decentralized Autonomous Organization with Identity-Gated Quadratic Voting to mitigate plutocratic governance capture. A dual-token incentive model (PLAY utility token and CRED reputation token) aligns creator economic incentives with verifiable content quality. Comparative benchmarks demonstrate transaction cost reductions exceeding 95% relative to Ethereum Layer-1 baselines.
K. Sundara Krishnan, R. Chithra Devi, Christo Ananth, D. Easwaramoorthy · 8 authors
E-commerce platforms incorporate reviews and reputation systems, allowing retailers and customers to manage and track their financial transactions. Consequently, it is crucial to design a reliable reputation system for the e-commerce environment, as it faces well-documented threats, including sybil attacks, feedback collusion, impersonation, review tampering, and whitewashing attacks. Current centralized systems are vulnerable to impersonation attacks, feedback manipulation, and lack automated verification against collusion-based reputation distortion. These unwanted ratings and reviews are highly correlated with abnormal cyber-attacks that damage both seller reputations and buyer experiences. To address these challenges, we propose a Blockchain-based Two-Level E-Commerce Trustable Reputation Framework (BTL-ETRF) utilizing deep learning-embedded transformers and redactable blockchain systems. Initially, we implement Multi-Factor Authentication for e-commerce users, utilizing three factors: PIN, OTP, and biometric fingerprint, to mitigate impersonation attacks. Only authenticated users are allowed to proceed to the reputation verification stage, where the proposed work considers five major metrics to classify user reputation using the Residual Dilated Convolution Transformer. To automate the reputation verification process, we design and employ two smart contracts, the Authentication Smart Contract and the Reputation Smart Contract which trigger automated actions based on the BTL-ETRF results. All transactions include reputation classification, and triggered actions are stored in the redactable blockchain, which can modify the stored transactions if needed. Finally, we demonstrate the performance of the proposed BTL-ETRF using Python and Ethereum Solidity, and conduct a formal analysis that shows the proposed model outperforms the compared works.
Cloud storage systems have become an essential component of modern data management, enabling users to store and access data remotely. However, traditional cloud storage architectures rely on centralized servers, which introduce critical challenges such as single-point failure, redundant data storage, high storage costs, and security vulnerabilities. In earlier systems, data was stored in centralized data centers where duplicate files were often saved multiple times, leading to inefficient utilization of storage resources. Although basic deduplication techniques were used, they frequently compromised data confidentiality and lacked transparency in metadata management. Moreover, failure of the central server could result in permanent data loss. To overcome these limitations, this research system integrates blockchain technology, InterPlanetary File System (IPFS), Convergent Encryption (CE), and heuristic-based chunking techniques to create a secure and decentralized storage framework, hereafter named Blockchain-enabled Heuristic Optimized Deduplication Model (BHODM). In this system, files are divided into optimized chunks using a heuristic method based on file size. Each chunk undergoes CE, where the encryption key is derived from the hash of the data itself, enabling secure deduplication without exposing plaintext information. Duplicate chunks are identified using hash comparison, ensuring that only unique data is stored. The encrypted chunks are stored in IPFS, a decentralized peer-to-peer storage network that eliminates reliance on a single server. Metadata such as file names, block numbers, and hash values are securely stored in an Ethereum blockchain smart contract, ensuring immutability and transparency. The system is implemented using Django for the web application, Web3 for blockchain interaction, IPFS Application Program Interface (API) for distributed storage, and Advanced Encryption Standard in Counter Mode (AES-CTR) encryption for security. By combining decentralized storage, blockchainbased metadata management, and secure deduplication, the proposed model effectively reduces storage overhead, enhances data integrity, and mitigates single-point failures. The system is further evaluated using storage utilization and computation time analysis, demonstrating improved efficiency compared to traditional approaches
We study Arbitrum's Timeboost mechanism following the adoption of Kairos by its main users -- Wintermute and Selini Capital -- to understand how the emergence of a just-in-time secondary market affects the dynamics of an ahead-of-time primary auction. We find that competition in the primary auction declines significantly and that Arbitrum captures a smaller share of the value generated around Timeboost. After the transition, paid bids in the primary auction correspond to only 14.8\% of the highest bid (compared with nearly 62.7\% in the \textit{Pre-Kairos} era) and to a smaller share of searcher profit-and-loss (PnL), even though total PnL remains of similar magnitude across regimes. While the exact distribution of the remaining surplus between searchers and Kairos remains unclear, the evidence suggests that a substantial share is no longer captured through the primary auction. More broadly, our findings suggest that ahead-of-time allocation mechanisms may be particularly vulnerable to secondary-market intermediation when competition among dominant participants is weak. We conclude by outlining possible ways for Arbitrum to improve revenue capture and discussing how these lessons may apply to Ethereum L1.
Jiahao Pei, Ning Duan, Gang Du, Kejia Zhang · 5 authors
Smart contracts are self-executing programs running on blockchain networks. Once deployed, they are immutable, making their security critically important. Reentrancy vulnerability is one of the most notorious security vulnerabilities in smart contracts, which allows attackers to repeatedly invoke target functions before the execution of contract functions is completed, thereby stealing funds or corrupting contract states, resulting in severe economic losses in recent years. Existing detection tools often suffer from insufficient path coverage and oversimplified detection rules. This paper proposes a static analysis approach based on smart contract bytecode that recovers execution paths by constructing a control flow graph (CFG), identifies all potential vulnerability paths using taint analysis, and detects reentrancy vulnerabilities through path matching rules. To validate the approach’s effectiveness, we compare it with mainstream detection tools on an annotated smart contract dataset. Experimental results demonstrate that the approach achieves a precision of 93.2%, outperforming other tools overall. Additionally, through analysis of 2023 real-world smart contracts deployed on Ethereum, 21 contracts are found to contain reentrancy vulnerabilities.
This study examines short-term return forecasting for Bitcoin, Ethereum, and Litecoin over 2020–2024, comparing autoregressive benchmarks with Kitchen Sink and VARX-type models using point and density accuracy measures supported by Diebold–Mariano and Model Confidence Set inference. The results demonstrate that the AR(1) benchmark and parsimonious specifications incorporating cryptocurrency-specific variables consistently outperform the more elaborate linear frameworks considered, while the inclusion of macro-financial predictors offers limited benefits. Findings highlight the robustness of autoregressive dynamics for short-term cryptocurrency forecasting and underscore the importance of parsimony over model complexity. These results are consistent with a market environment characterised by high structural uncertainty, sentiment-driven trading and rapidly shifting regimes, in which additional macro-financial information contributes little to forecastability beyond short-run return momentum and crypto-specific volatility.
This paper develops a deep reinforcement learning (DRL) framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation (PPO) agent is trained on a reward function derived from non-equilibrium thermodynamics: the free-energy Bellman equation, in which (i) transaction costs are the geodesic slippage S∗ on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and (ii) regime-transition costs are the Wasserstein-2 distance Wt between the calm and turbulent return distributions. The agent is embedded in the WOW-E-W quadrilogy, a four-paper research programme that integrates statistical mechanics, fluid dynamics, Riemannian information geometry, and thermodynamic control into a unified cryptocurrency risk architecture. The PPO agent observes an 11-dimensional state vector ot that combines turbulent-regime probabilities \( \hat{\xi}_t(2) \) and parameter estimates \( \hat{\theta}_t \) from a maximum-entropy Markov-switching GARCH model, a viscosity-filtered velocity signal ht and gate states zt, rt from a GRU viscosity filter, and the Fisher curvature Gt, Ricci scalar κt, Betti numbers β0,t, β1,t,Wasserstein dissipation Wt, and topological alarm dI(t) from the Riemannian execution geometry layer. The framework establishes a thermodynamic Carnot bound on portfolio efficiency: η ≤ 1 − Hturb/Hcalm, where Hturb and Hcalm are the maximum-entropy values of the turbulent and calm regime distributions. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026: the geometric-cost PPO agent achieves higher Sharpe ratio than Buy-and-Hold, Greedy signal-following, and flat-fee PPO baselines (bootstrap p < 0.05 for four of five assets); portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ranges from 0.6 percent (Bitcoin) to 1.8 percent (Ethereum), ordered by turbulent half-life (Spearman ρ = 0.94, p = 0.017); a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of ot contributes a statistically significant performance gain (Diebold-Mariano p < 0.05 for at least four of five assets per component). The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration and is an explicitly bounded limitation.
The rapid evolution of cloud computing has revolutionized digital data storage and sharing, enabling users to access information anytime and anywhere. Despite these advantages, cloud-based systems face major challenges related to data security, privacy protection, and trust management, particularly when handling sensitive user information. Conventional cloud storage solutions operate on centralized architectures, where a single cloud service provider manages and controls the stored data. This centralized model introduces significant risks, including single points of failure, unauthorized data access, data manipulation, and limited visibility into data-sharing activities. In many traditional systems, data protection mechanisms rely on basic encryption methods without strong auditing or verification features, leaving them vulnerable to insider attacks and external cyber threats. Furthermore, the absence of immutable transaction records and robust key management practices reduces accountability and weakens user confidence in cloud environments. To address these shortcomings, the proposed system presents a secure cloud data sharing framework that combines Elliptic Curve Cryptography (ECC) with blockchain technology. In this approach, user files are encrypted using ECC before being uploaded to the cloud, ensuring strong data confidentiality and protection against unauthorized access. Simultaneously, blockchain technology is employed to record file metadata and transaction details in a decentralized and tamper-resistant ledger, enabling transparent and verifiable audit trails. The decentralized architecture eliminates reliance on a single authority, enhances trust, and prevents unauthorized modification of stored records. Additionally, secure authentication and controlled access mechanisms further reinforce system security. By integrating advanced cryptographic encryption with decentralized verification, the proposed solution enhances data integrity, improves transparency, and establishes a reliable and accountable framework for secure cloud data sharing.
International Journal of Technology, Leadership and Sciences
Food and agriculture supply chain transparency is growing in importance for both consumers and states. The fast expansion of blockchain technology's use is being propelled by its inherent trustworthiness and immutability. This technology can offer safe traceability for the management of the agri-food chain, prevent food fraud, and provide information like a food product's provenance. It is far more difficult than in other businesses to create smart contracts that are suitable for certain use cases. Although many agri-food chain management systems based on smart contracts and blockchain have been developed, they are all quite ad hoc and not easily adaptable to different products or production processes. A new method for quickly adapting and developing universal smart contracts for the agri-food business based on Ethereum is presented in this research. We can automate the process and reuse modules and code using this strategy, which shortens development times without sacrificing dependability and safety. In order to set up a semi-automatic system, we want to start with the production process and build the smart contracts that control the system and the user interfaces that automatically connect with them. To further illustrate how our method works, we provide a case research on honey production. The primary goal of future studies will be to find ways to apply the method to different types of supply chains. Even though Ethereum is now in use, our technology can be simply adapted to other blockchain systems.
Andrew Kim, Jarrett Bobrin, David Weinstein, Isabelle Kim
Non-fungible Tokens (NFTs) in Diagnostic ImagingAndrew Kim1, Jarrett Bobrin1, David Weinstein1, Isabelle G. Kim.Temple University Hospital1, Department of Radiology, Philadelphia, PA.Non-fungible tokens (NFTs) have garnered significant media attention in recent years, largely due to the astronomical prices fetched by some digital artworks. They have emerged as a popular medium for buying and selling digital art. Most people associate NFTs with high-profile examples such as the Bored Ape Yacht Club or Beeple’s digital artwork, the latter of which famously sold for over $69 million. Even the world’s first SMS text message was converted into an NFT and sold for over 100,000 euros. In 2021, the NFT market was valued at approximately $41 billion USD, and the term “NFT” ranked among the most popular search terms on Google during both 2021 and early 2022.However, NFTs are more than just digital collectibles; they hold significant untapped potential, particularly in the medical field, including diagnostic imaging. While blockchain technology has been widely explored in healthcare, the specific role of NFTs in diagnostic imaging remains largely unexplored. Although there has been extensive discussion on the use of blockchain in medicine, the application of NFTs in this space is still in its infancy.So, what exactly is an NFT? A non-fungible token is a unique digital asset representing ownership of a specific item or piece of data—whether that be digital artwork, music, or in more recent applications, items in video games or medical records. NFTs are built using the same blockchain technology as cryptocurrencies like Ethereum. However, unlike cryptocurrencies or fiat currencies, NFTs are non-fungible, meaning they are not interchangeable, and each holds a distinct value. Both NFTs and cryptocurrencies rely on blockchain transactions to validate authenticity and ownership. NFTs serve as a digital certificate of ownership, and each time an NFT changes hands, the transaction is recorded on the blockchain decentralized, public ledger.NFTs also incorporate smart contract technology, which is particularly relevant to the field of medicine. For instance, in the art world, the original artist may receive royalty every time their artwork is resold. This same mechanism can be applied to healthcare data, offering both security and potential financial benefits to patients.In the U.S., it is estimated that each patient generates approximately 80 megabytes of health data annually. Utilizing NFTs to manage medical data would allow patients to enhance the confidentiality of their personal health information. Through smart contracts, patients could control and define who has access to their data—whether it’s their primary care physician, an emergency room doctor, a radiologist, or a specialist at a cancer center. Once recorded on a public, decentralized blockchain, this data becomes immutable and highly secure, preventing tampering or unauthorized access.This model empowers patients and shifts control away from commercial or nonprofit institutions that often manage and monetize patient data without individual input. As Dr. Kristin Kostick-Quenet has pointed out, once health information is digitized, it typically falls out of the patient’s control and is commodified by companies for profit. NFTs offer a solution: patients could maintain ownership over their data and even receive financial compensation when it is accessed or utilized.The digital contracts associated with NFTs also allow patients to trace the use of their data—who accessed it, when, how, and why. According to an article from Cointelegraph, the healthcare platform Aimedis plans to tokenize anonymized patient data into NFTs, which can then be sold to pharmaceutical companies. In return, patients may receive revenue from the sales of these NFT tokens. However, a key challenge remains, healthcare IT systems are currently fragmented and not yet optimized for this level of integration. In an ideal future, patients would use a single login interface to manage all their health data.Importantly, NFTs can enhance the quality and accessibility of medical data, making it more suitable for artificial intelligence applications and data mining. Aimedis aims to revolutionize global exchange and monetize de-identified health data using blockchain and NFT technologies.NFTs also have direct applications in radiology. Patients could predefine which radiologists or physicians can access their imaging studies and reports. They could also track who views their data and under what circumstances. If their imaging is later sold or used by a commercial entity—such as a medical center or pharmaceutical company—for research or drug development, the patient could receive royalty payments each time it is used. For example, if a cancer patient undergoes a PET/CT scan and the resulting data is converted into an NFT, a pharmaceutical company using that data in drug research might owe compensation to the patient.Moreover, NFTs could enhance the information available to radiologists. For example, they could include important historical details, such as previous reactions to gadolinium contrast, a history of renal insufficiency, or retained metal that could affect MRI compatibility. Such centralized and accessible data would aid in ensuring patient safety and improving diagnostic accuracy.With the rise of telemedicine, NFTs could also play a key role in verifying transactions between the physical and digital healthcare environments. For example, a doctor’s prescription or imaging order could be tokenized, eliminating any ambiguity regarding its origin or intent. In radiology, this could clarify whether a referring physician wanted a CT scan with or without contrast or preferred a two-view chest X-ray over a portable study—ultimately improving communication between referring clinicians and radiology departments.Teleradiology images could also be tokenized, giving patients visibility over who has accessed their reports and to whom results were sent. In addition, NFTs could be used to verify the credentials of radiologists, such as medical degrees and certifications. Since this information would be recorded on an immutable blockchain, it would be secure and tamper-proof. This could reduce administrative burdens, such as those placed on radiology file rooms by repeated requests for copies of reports or credentials.Tokenized radiology data may also serve as a valuable audit trail, allowing radiologists to confirm that their reports were viewed and used appropriately by referring clinicians.While numerous challenges remain, including legal considerations, government regulations, and the environmental impact of blockchain technology, NFTs are poised to play a growing role in healthcare. Diagnostic imaging, often at the forefront of technological innovation in medicine, is well positioned to benefit from the adoption of blockchain-based NFT applications.References:Conti, R. (2022, August 16). What is an NFT? non-fungible tokens explained. Forbes. Retrieved August 29, 2022, from https://www.forbes.com/advisor/investing/cryptocurrency/nft-non-fungible-token/Culbertson, N. (2021, August 6). Council post: The Skyrocketing Volume of Healthcare Data Makes Privacy Imperative. Forbes. Retrieved August 29, 2022, from https://www.forbes.com/sites/forbestechcouncil/2021/08/06/the-skyrocketing-volume-of-healthcare-data-makes-privacy-imperative/?sh=327ba8536555Diaz, N. (n.d.). What nfts need to achieve before healthcare implementation. Becker’s Hospital Review. Retrieved August 29, 2022, from https://www.beckershospitalreview.com/healthcare-information-technology/what-nfts-need-to-achieve-before-healthcare-implementation.htmlHarrison, S. (2022, April 13). Some medical ethicists endorse nfts-here’s why. Scientific American. Retrieved August 29, 2022, from https://www.scientificamerican.com/article/some-medical-ethicists-endorse-nfts-heres-why/HHMGlobal, C. T. (2022, April 11). Content team HHMGlobal. HHM Global B2B Online Platform Magazine. Retrieved August 29, 2022, from https://www.hhmglobal.com/knowledge-bank/news/can-nfts-be-repurposed-for-the-healthcare-industryJones, C. (2021, September 13). Why nfts, crypto and blockchain can help e-health thrive. Cointelegraph. Retrieved August 29, 2022, from https://cointelegraph.com/news/why-nfts-crypto-and-blockchain-can-help-e-health-thriveKhatri, N. (2021, December 8). Beyond Trendy Investments: Three applications of nfts in healthcare and Pharma Marketing. PM360. Retrieved August 29, 2022, from https://www.pm360online.com/beyond-trendy-investments-three-applications-of-nfts-in-healthcare-and-pharma-marketing/Kostick-Quenet, K., Mandl, K. D., Minssen, T., Cohen, I. G., Gasser, U., Kohane, I., & McGuire, A. L. (2022). How nfts could transform Health Information Exchange. Science, 375(6580), 500–502. https://doi.org/10.1126/science.abm2004Limited, V. M. P. (n.d.). AIMEDIS announces the NFT Healthcare Platform. Newsfile. Retrieved August 29, 2022, from https://www.newsfilecorp.com/release/103552/Aimedis-Announces-the-NFT-Healthcare-PlatformMcGuire, A. (2022, February 4). Can NFT technology benefit healthcare? in. Retrieved August 29, 2022, from https://healthcare-in-europe.com/en/news/can-nft-technology-benefit-healthcare.htmlShyam Sabat MD, M. B. A. (2021, April 27). Blockchain - promises for academic radiology. LinkedIn. Retrieved August 29, 2022, from https://www.linkedin.com/pulse/blockchain-promises-academic-radiology-shyam-sabat-md-sabat-mdTagliafico AS, Campi C, Bianca B, et al. Blockchain in radiology research and clinical practice: current trends and future directions. La Radiologia Medica. 2022 Apr;127(4):391-397.YouTube. (2021, September 20). How nfts will revolutionize medicine. YouTube. Retrieved August 29, 2022, from https://www.youtube.com/watch?v=TnhmUltTGo
Background: Blockchain and Multi-Agent System (MAS) are increasingly combined to support decentralized, secure, and autonomous peer-to-peer energy trading in microgrid environments. Objectives: This systematic review investigates how blockchain and MAS are integrated to support microgrid energy trading, identifies architectural and operational models, examines real-world implementations, and highlights technical, regulatory, and security challenges. Unlike prior reviews that focus on blockchain or MAS in isolation, this study provides a unified and comparative analysis of their joint integration. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted in IEEE Xplore, ACM Digital Library, and ScienceDirect, with the last search performed on 10 January 2025. Eligible studies focused on blockchain–MAS integration in microgrid energy trading; non-energy and non-microgrid applications were excluded. Study selection was performed independently by two reviewers, and methodological quality was assessed using an adapted Joanna Briggs Institute (JBI) checklist. A narrative synthesis categorized integration levels, blockchain platforms, MAS roles, and implementation contexts. Results: A total of 104 studies were included. Three dominant integration levels were identified—basic, intermediate, and advanced—distinguished by how decision-making responsibilities are distributed between MAS and smart contracts. Ethereum and Hyperledger Fabric were the most commonly used platforms. MAS agents perform concrete operational functions such as bid and offer generation, price negotiation, matching, and local energy optimization, fundamentally transforming control and monitoring processes. By enabling distributed, intelligent agents to perform real-time sensing, analysis, and response, an MAS enhances system resilience and adaptability. This architecture allows for proactive fault detection, dynamic resource allocation, and coherent, large-scale operations without centralized bottlenecks. Blockchain ensured transparency, trust, and secure transaction execution. Major challenges include scalability constraints, interoperability limitations with legacy grids, regulatory uncertainty, and real-time performance issues. Limitations: Most included studies were simulation-based, with limited real-world deployment and substantial heterogeneity in evaluation metrics. Conclusions: Blockchain–MAS integration shows strong potential for secure, transparent, and decentralized microgrid energy trading. Addressing scalability, regulatory frameworks, and interoperability is essential for large-scale adoption. Future research should emphasize real-world validation, standardized integration architectures, and AI-enabled MAS optimization. Funding: No external funding. Registration: This systematic review was not registered.
Ethereum Inscriptions (Ethscriptions) repurpose Ethereum calldata into a persistent inscription channel by embedding \texttt{data:}~URI payloads. These transactions typically target externally owned accounts, allowing the payload to bypass EVM execution while remaining permanently replicated across full nodes. Although calldata was originally designed for compact smart-contract parameters, this repurposing enables structured data embedding with long-term storage consequences. We present the first large-scale empirical study of Ethscriptions, treating them as a distinct \emph{calldata-resident workload} rather than merely a subset of general calldata usage. Our analysis focuses on the \textit{Ethscription} operational subset, which consists of payloads that decode to JSON and conform to a token-operation grammar (e.g., \texttt{p}, \texttt{op}, \texttt{tick}, \texttt{amt}). From $6.27$ million Ethscription candidates (\Uone), we extract $4.75$ million Ethscription operations (\Utwo, $75.8\%$ of \Uone). This result shows that structured token-like activity dominates the ecosystem. Our measurements further reveal (i) a complete workload lifecycle compressed into nine months (bootstrap, expansion, saturation), (ii) proliferation of $30$+ competing protocols without convergence toward a dominant standard, (iii) a lifecycle funnel exhibiting $201\times$ deploy-to-mint amplification and a $57.6{:}1$ mint-to-transfer collapse indicative of speculative minting, (iv) extreme participation inequality (Gini~$0.86$), and (v) a measurable permanent data footprint imposed on the Ethereum network.
Decentralized finance has experienced phenomenal growth, revolutionizing the landscape of financial transactions and asset management via blockchain. Yet, this swift growth brings with it substantial challenges, notably the surge in scam tokens, imposing significant security threats on cryptocurrency investments and trading. Existing detection methods of scam token, primarily relying on analyzing contract codes or transaction patterns, struggle to catch increasingly sophisticated tactics employed by scammers. For example, contract-based analysis are unable to identify scams lacking overt malicious code, e.g., most rugpulls, while transaction-based methods generally lack the foresight to early-detect potential risks. In this paper, we present TOKENSCOUT, the first temporal GNN-based framework for scam token early detection. TOKEN SCOUT formulates token transfer data as a dynamic temporal attributed multigraph and leverages the temporal graph learning model to learn graph representations. It also builds a graph rep resentation refining model based on contrastive learning to learn a more discriminative representation space for risk identification. We evaluated TOKENSCOUT using a comprehensive dataset of 214,084 standard ERC20 tokens from 2015 to February 2023. TOKENSCOUT achieves a balanced accuracy of 98.41%. Additionally, from March to May 2023, deploying TOKENSCOUT on Ethereum effectively identified 706 rugpulls, 174 honeypots, and 90 Ponzi schemes, thereby alerting to potential risks exceeding $240 million.
Jose Almarcha-Sanchez, Maria-Jesus Alba-Baena, Volodymyr Dubetskyy, Maria‐Dolores Cano
Abstract Open-source simulators let engineers stress-test blockchain ideas long before field deployment, yet few studies compare tools side-by-side. This tutorial article benchmarks two research-grade simulators, namely, BlockSim and Simewu, and the production-grade IOTA Hornet node under an identical traffic harness that runs on laptop-class hardware. Results show that consensus style dominates capacity. A DAG ledger that finalizes one milestone per second (≈ 6 tx s⁻¹) surpasses the 10 Transactions Per Second (TPS) ceiling of a six-node Bitcoin simulation, while Ethereum-style 12 s blocks lift the same mesh to approximately ~ 20TPS.BlockSim reproduces proof-of-work fairness within ± 3% of theoretical expectations, and a ten-fold increase in propagation delay cuts a miner’s reward roughly in half despite equal hash power. Hornet delivers protocol-truth execution, but at noticeably higher CPU, memory and bandwidth cost than the simulators. All scripts, Docker files and raw logs are released under an open license, providing a one-click baseline for future benchmarking of new distributed-ledger technologies.
Smart contracts, serving as self-executing programs on blockchain platforms, have emerged as a key innovation for enhancing data security. Despite advancements in both blockchain technology and smart contracts (SCs), Ethereum-based SCs remain vulnerable to security breaches. Exploitation of these vulnerabilities can result in substantial financial losses for both service providers and users. Consequently, the detection and mitigation of security vulnerabilities in smart contracts are critical to ensuring the security and reliability of blockchain platforms. Machine learning approaches are emerging as effective alternatives to traditional vulnerability detection methods, though many rely heavily on expert knowledge and primarily target familiar vulnerabilities. This chapter explores the creation of an AI-driven framework for detecting vulnerabilities in smart contracts, aimed at reducing risks and improving the reliability of blockchain systems. By incorporating advanced Machine Learning (ML) and Deep Learning (DL) techniques, the framework seeks to improve the accuracy and efficiency of vulnerability detection, addressing the shortcomings of traditional static and dynamic analysis methods. The proposed approach not only strengthens the security of smart contracts but also contributes to the broader goal of building more resilient and reliable blockchain ecosystems. Through an in-depth analysis of methodologies and case studies, this chapter highlights the essential role of AI in advancing the secure development and deployment of smart contracts.
The global digital identity landscape is undergoing an unprecedented crisis. Approximately 1.1 billion individuals worldwide lack any verifiable form of digital identity, while existing identity systems face existential threats from the industrialization of deepfake technology with injection attacks targeting biometric verification surging 900% since 2022 and occurring at a rate of once every five minutes in 2024. Simultaneously, conventional blockchain-based identity proposals that store biometric templates on-chain introduce critical privacy vulnerabilities incompatible with emerging regulatory frameworks including the EU AI Act (2024) and GDPR. This paper presents ZKP-GDIS (Zero-Knowledge Proof Global Decentralized Identity System), a novel, privacy-by-design identity architecture that fundamentally departs from prior work in three key dimensions. First, ZKP-GDIS never stores raw biometric data on-chain; instead, it employs zk-SNARK (Zero-Knowledge Succinct Non-Interactive Argument of Knowledge) cryptographic commitments that allow identity verification without any disclosure of underlying biometric features. Second, we introduce a Hybrid Deepfake-Resistant Liveness Pipeline (HDRLP) — a multi-modal anti-spoofing layer that fuses passive CNN-based texture analysis, photoplethysmography (PPG) heart-rate detection, and hardware-attested device fingerprinting to defeat both presentation and injection attack vectors. Third, the system adopts W3C Decentralized Identifier (DID) standards and implements a federated governance model, enabling cross-jurisdictional interoperability while respecting national digital sovereignty. We provide formal security proofs under the computational Diffie-Hellman hardness assumption, evaluate the system against the ISO/IEC 30107-3 Presentation Attack Detection benchmark, and report experimental results demonstrating 99.87% genuine acceptance rate, 0.004% false acceptance rate under deepfake attack, and 94% reduction in on-chain gas costs versus Ethereum mainnet through zkEVM Polygon deployment. ZKP-GDIS establishes a reproducible, standards- compliant, and audit-ready framework for the next generation of global digital identity infrastructure.
This study aims to comparatively examine the relationships between Bitcoin and Ethereum's energy consumption and price dynamics. Using daily frequency data, Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), ARDL cointegration tests, and Toda–Yamamoto causality analysis were applied to evaluate the effects of cryptocurrency markets on energy demand from both short-term and long-term perspectives. The analysis results indicate that there is a long-term cointegration relationship between energy consumption and prices for Bitcoin and a unidirectional causality from prices to energy consumption. In contrast, ARDL boundary test results for Ethereum revealed no long-term relationship, and causality analysis also failed to detect any directional causality between price and energy consumption. This indicates that with Ethereum's transition to a Proof-of-Stake mechanism, energy consumption has become independent of price movements. The findings reveal that the effects of cryptocurrency markets on the energy economy vary according to technology-specific structural characteristics.
Saber Zerhoudi, Kanishka Ghosh Dastidar, Felix Klement, Artur Romazanov · 12 authors
Moltbook is a social network where every participant is an AI agent. We analyze 1,312,238 posts, 6.7~million comments, and over 120,000 agent profiles across 5,400 communities, collected over 40 days (January 27 to March 9, 2026). We evaluate the platform through three layers. At the interaction layer, 91.4% of post authors never return to their own threads, 85.6% of conversations are flat (no reply ever receives a reply), the median time-to-first-comment is 55 seconds, and 97.3% of comments receive zero upvotes. Interaction reciprocity is 3.3%, compared to 22-60% on human platforms. An argumentation analysis finds that 64.6% of comment-to-post relations carry no argumentative connection. At the content layer, 97.9% of agents never post in a community matching their bio, 92.5% of communities contain every topic in roughly equal proportions, and over 80% of shared URLs point to the platform's own infrastructure. At the instruction layer, we use 41 Wayback Machine snapshots to identify six instruction changes during the observation window. Hard constraints (rate limit, content filters) produce immediate behavioral shifts. Soft guidance (``upvote good posts'', ``stay on topic'') is ignored until it becomes an explicit step in the executable checklist. The platform also poses technological risks. We document credential leaks (API keys, JWT tokens), 12,470 unique Ethereum addresses with 3,529 confirmed transaction histories, and attack discourse ranging from template-based SSH brute-forcing to multi-agent offensive security architectures. These persist unmoderated because the quality-filtering mechanisms are themselves non-functional. Moltbook is a socio-technical system where the technical layer responds to changes, but the social layer largely fails to emerge. The form of social media is reproduced in full. The function is absent.
Aleksei Adadurov, Sergey Barseghyan, Anton Chtepine, Antero Eloranta · 6 authors
We study optimal auction design for Maximum Extractable Value (MEV) auction markets on Ethereum. Using a dataset of 2.2 million transactions across three major orderflow providers, we establish three empirical regularities: extracted values follow a log-normal distribution with extreme right-tail concentration, competition intensity varies substantially across MEV types, and the standard Revenue Equivalence Theorem breaks down due to affiliation among searchers' valuations. We model this affiliation through a Gaussian common factor, deriving equilibrium bidding strategies and expected revenues for five auction formats, first-price sealed-bid, second-price sealed-bid, English, Dutch, and all-pay, across a fine grid of bidder counts $n$ and affiliation parameters $ρ$. Our simulations confirm the Milgrom-Weber linkage principle: English and second-price sealed-bid auctions strictly dominate Dutch and first-price sealed-bid formats for any $ρ> 0$, with a linkage gap of 14-28\% at moderate affiliation ($ρ=0.5$) and up to 30\% for small bidder counts. Applied to observed bribe totals, this gap corresponds to \$10-18 million in foregone revenue over the sample period. We also document a novel non-monotonicity: at large $n$ and high $ρ$, revenue peaks in the interior of the affiliation parameter space and declines thereafter, as near-perfect correlation collapses the order-statistic spread that drives competitive payments.