Optimizing asset exchanges on blockchain-driven platforms poses a novel and challenging graph query optimization problem. In this model, assets represent vertices and exchanges form edges, recasting the graph query task as a routing problem over a large-scale, dynamic graph. However, the existing solutions fail to solve the problem efficiently due to the non-linear nature of the edge weights defined by a concave swap function. To address the challenge, we propose PRIME, a two-stage iterative graph algorithm designed for the Token Graph Routing Problem (TGRP). The first stage employs a pruned graph search to efficiently identify a set of high-potential routing paths. The second stage formulates the allocation task as a strongly convex optimization problem, which we solve using our novel Adaptive Sign Gradient Method (ASGM) with a linear convergence rate. Extensive experiments on real-world Ethereum data confirm PRIME's advantages over industry baselines. PRIME consistently outperforms the widely-used Uniswap routing algorithm, achieving up to 8.42 basis points (bps) better execution prices on large trades while reducing computation up to 96.7%. The practicality of PRIME is further validated by its deployment in hedge fund production environments, demonstrating its viability as a scalable graph query processing solution for high-frequency decentralized markets.
This paper tackles the discovery of tMEV, that is, the Maximal Extractable Value on blockchains that arises from Token smart contracts. This scope differs from the existing MEV-discovery research, which analyzes application-layer contracts or attacker contracts, but ignores the wide and diverse range of token contracts. This paper presents a pipeline of techniques for tMEV discovery, including tSCAN, a static analysis tool for identifying non-standard supply-control functions in token contracts, and tSEARCH, a searcher that uncovers profitable tMEV opportunities by generating, refining, and solving token-specific constraints. By replaying real-world transactions, this paper demonstrates both the profitability of tMEV strategies and existing searchers' unawareness of them: the proposed tSEARCH extracts $10\times$ more profit than observed MEV activity on Ethereum. The practicality of tMEV searching is demonstrated through a prototype built on Slither, showing high effectiveness with low performance overhead.
This study provides an econometric investigation of Bitcoin’s return dynamics using daily data over 5.5 years from January 2020 to September 2025. This research deeply analyses the market behaviour of Bitcoin over other assets like Gold, Silver, Ethereum, Tether, Nifth50, BankNifty. In this analysis we used advanced time series and statistical models such as ARIMA, GARCH(1,1), Rolling GARCH, Half-Life estimation, and EGARCH models to evaluate conditional mean behavior, volatility clustering, persistence, asymmetric shock effects, and regime-dependent risk transmission. With the use of this models, rolling Garch reveals structural instability with persistence decline in later periods. EGARCH results asymmetric shock effects, where negative shocks increases volatility more than positive shocks. Forecasting models suggests that volatility will eventually return to its long term average, but risk is still expected to remain high for some time before normalizing. The analysis reveals strong conditional heteroskedasticity and near-integrated volatility persistence during crisis periods specific around the COVID-19 market collapse (2020), the FTX bankruptcy shock (2022), the April 2024 Bitcoin halving, and the 2025 Bybit exchange hack. Using various data visualizations, the analysis reveals high risky nature of Bitcoin trade with high returns compared to other assets. Deep learning model LSTM reveals the nature that closing price of next day is unpredictable as obvious in case of such high volatile nature of Bitcoin. These findings underline the importance and nature of trading in Bitcoin for individuals who are thinking to invest.
The most important problems of sustainable vegetable farming are pests, inefficient data-based decision making, and poor irrigation. Another threat to productivity and the environment is that the conventional methods will cause excess use of pesticides, over-irrigation and unreliable harvest. In this research, we suggest a Blockchain Ethereum-Backed IoT Platform to ensure that these problems are resolved and provide pest detection in real-time and smart irrigation control. IoT sensors are used to measure soil moisture, temperature, and humidity and edge devices with lightweight deep learning models can detect pest infestations with a high accuracy. The resulting data is encrypted and checked in the Ethereum blockchain smart contracts are used to run irrigation programs and send alerts to control pests. It uses Layer-2 solutions of Ethereum to reduce latency and transaction cost to achieve scalability and efficiency. It is scientifically proven that the proposed platform can detect pests with an accuracy of 93.8 %, use 35 % less water, and produce 20% more crops than traditional solutions. Moreover, surveys of farmers show that the level of trust and readiness to implement solutions based on blockchain has risen considerably. The contribution of this work is a secure and transparent and resource-efficient digital agriculture framework enabling the development of precision-based farming and supporting sustainable food production.
The emergent high-speed growth of precision agriculture requires solid frameworks that will improve sustainability, efficiency, and transparency in managing vineyards. The proposed research suggests a system that uses IoT sensor networks, machine learning models, and Ethereum-based blockchain to solve two important problems: pest identification and the optimization of water resources. The IoT layer was used to implement soil moisture, climate, and imaging sensors to gather real-time data about the vineyard. At the edge, preprocessing and deep learning algorithms were used with a blockchain-enabled Convolutional Neural Network (CNN) to provide correct pest detection. At the same time, the timing of irrigation was automated by IoT-enabled soil moisture monitoring, which greatly decreased the amount of wasted water. This ensured integrity of data and trust in the farmers as the Ethereum blockchain layer offered immutable storage, smart contracts to make decisions, and secure events logging. The results of the experiments revealed that the proposed system had a 95.8% pest detection accuracy, which was higher than the traditional and baseline machine learning methods. The efficiency of water management increased too by reducing water consumption by 55.2 % and doubling the crop productivity by 26.8 %t. Moreover, the blockchain application has scored high security of 0.93, confirming that it is a reliable solution even with moderate latency overhead. In this study, the authors emphasize the opportunities of converging the IoT with blockchain and transforming viticulture through sustainable practices, data safety, and efficiency.
Henrique Yassuyuki Tsuboi, Rafael Sousa Lima, Kleber Vasconcellos de Oliveira
Purpose This study aims to provide an alternative machine learning model to more quickly and efficiently detect addresses on the Ethereum network suspected of involvement in fraudulent activities. Design/methodology/approach This study performed a machine learning technique known as LightGBM. The machine learning model is trained by using a dataset that identifies licit or illicit addresses on the Ethereum network. This study then applies the trained model to predict the probability that a new transaction should be classified as suspicious for money laundering. Findings Through a set of performance metrics, we show that our model outperforms machine learning models from previous studies, better predicting suspicious money laundering activities. The most relevant attributes in identifying an illicit transaction are: (i) the time difference between the first and last activity of the crypto wallet (a short “lifetime” of the address); (ii) the total number of transactions (accounts used only once or a few times) and (iii) the difference in the distribution of values between the crypto wallets (low values). Research limitations/implications Machine learning techniques have great potential to contribute to the activities of government agents, regulatory authorities and accounting professionals. Practical implications This study adds another tool to combat money laundering, which could lead to improvements in auditing and forensic accounting procedures. This study may be of special interest to regulators and policymakers in their anti-money-laundering roles. Originality/value This study adopts a modern technique that can be considered a valuable tool in identifying and combating fraudulent activities on blockchain networks.
This research presents a decentralized medical data management system integrating a Flask-based backend, an SQLite relational database, and an Ethereum-compatible blockchain to enhance the security, integrity, and transparency of healthcare data. The system adopts a modular architecture using Flask Blueprints to manage authentication, hospital data retrieval, OTP verification, and prescription handling. Smart contracts developed with the Truffle framework ensure immutable and auditable storage of critical medical proofs, particularly prescription records, while Web3 enables secure interaction between the backend and the blockchain. Future improvements include replacing SQLite with cloud-native databases such as PostgreSQL or MongoDB for scalability, implementing advanced encryption with dynamic key rotation, and adopting decentralized identity (DID) for patient-centric access control. Additionally, integrating real-time analytics, AI-based anomaly detection, automated compliance auditing, and Layer-2 blockchain solutions can further enhance system performance, security, and efficiency.
This Systematization of Knowledge (SoK) provides a comprehensive historical analysis of Maximal Extractable Value (MEV) in blockchain systems, tracing its conceptual evolution through three distinct eras. We organize the fragmented literature on MEV into a unified chronological framework, beginning with Era~I (August 2014 - August 2020), which introduced Miner Extractable Value from pmcgoohan's seminal Reddit warning through the ``Dark Forest'' recognition, covering Proof-of-Work systems with public mempools and Priority Gas Auctions. Era~II (August 2020 - April 2024) marks the generalization to Maximal Extractable Value, encompassing formal taxonomies, Realized Extractable Value, Proposer-Builder Separation, the Ethereum Merge, MEV-Boost, and the integration of non-atomic and CEX-DEX arbitrage. Era~III (April 2024, present) addresses the frontier of Cross-Chain MEV, beginning with early studies on Layer-2 ecosystems, where value extraction spans multiple blockchains, rollups, bridges, and sequencers. We present a conceptual taxonomy distinguishing potential from realized extractable value, and single-domain from cross-domain phenomena. Our systematization identifies mitigations that emerged in response to each era, highlights measurement challenges, and proposes a research agenda for standardized metrics, detection benchmarks, and cross-chain infrastructure design.
The increasing incidents of forged academic certificates and the inefficiencies of traditional verification systems highlight the urgent need for a secure, transparent, and reliable credential management mechanism. Conventional systems are largely centralised, time-consuming, and prone to manipulation, resulting in high administrative overhead and verification delays. We prepared an AI-Based Decentralized Academic Credential Verification System that leverages blockchain technology, smart contracts, and artificial intelligence to provide a tamper-proof platform for issuing, storing, and validating academic records. Artificial Intelligence is integrated to perform anomaly detection during certificate issuance and AI-based facial authentication for students, enhancing security and preventing fraudulent entries before blockchain storage. Students gain permanent, secure access to their verified credentials, while verifiers, such as employers, can instantly authenticate certificates using blockchain records or QR code scanning, eliminating the need for intermediaries. By integrating Ethereum, Solidity, Web3.js, IPFS, React.js, and AI models, the proposed system delivers a decentralized, scalable, and cost-effective solution that enhances trust, reduces verification time, and effectively combats academic credential fraud.
Alinsha S, A Althaf, Chris P Reji, Fahad Mohammed A · 6 authors
Electronic voting techniques have gained popularity as a contemporary alternative to traditional paper-based elections because of their effectiveness and accessibility. The current electronic voting methods, however, have significant security flaws, such as multiple voting, identity theft, centralized control, and a lack of transparency. Despite the fact that blockchain technology is decentralized, immutable, and auditable, many blockchainbased voting systems merely employ cryptographic credentials and lack robust voter identification verification processes. The blockchain-based electronic voting system SecureVote, which incorporates multi-factor verification and facial biometric authentication, is proposed in this study. Ethereum smart contracts are used by the system to guarantee transparent result calculation and tamper-proof vote storage. SecureVote employs one-time password (OTP) validation as a secondary authentication method in conjunction with client-side facial recognition and deep learning-based feature extraction. The suggested design makes use of Web3.js and a decentralized application (DApp) concept for safe wallet-based transaction signing and blockchain interaction. High authentication reliability, avoidance of double voting, and effective transaction processing with low gas overhead are all demonstrated by the experimental results. SecureVote combines biometric multifactor authentication with blockchain immutability to enhance the reliability, transparency, and integrity of remote voting.
Open access
2 source records
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
The integrity and scalability of electoral processes within large-scale academic institutions are often compromised by centralized vulnerabilities and high computational overhead. This paper proposes a novel, hierarchical consortium blockchain framework designed for Indian university ecosystem to facilitate secure, transparent, and high-concurrency e-voting. By utilizing tiered architecture comprising establishment-level private sidechains and global university-wide Ethereum ledger, proposed system optimizes trade-off between voter anonymity and transactional throughput by integrating Linkable Ring Signatures and Zero- Knowledge Proofs to ensure the Secret Ballot principle while maintaining public auditability. Experimental evaluations on with N = 4000 participants demonstrate an average gas consumption of 15,580 units per voter and peak throughput of 181 TPS. Experimental results reveal 11.5% reduction in per-voter processing latency compared to state-of-the-art models, showing proposed framework efficacy for high-density academic environments.
The Ethereum ecosystem, which secures over $381 billion in assets, fundamentally relies on client APIs as the sole interface between users and the blockchain. However, these critical APIs suffer from widespread implementation inconsistencies, which can lead to financial discrepancies, degraded user experiences, and threats to network reliability. Despite this criticality, existing testing approaches remain manual and incomplete: they require extensive domain expertise, struggle to keep pace with Ethereum's rapid evolution, and fail to distinguish genuine bugs from acceptable implementation variations. We present APIDiffer, the first specification-guided differential testing framework designed to automatically detect API inconsistencies across Ethereum's diverse client ecosystem. APIDiffer transforms API specifications into comprehensive test suites through two key innovations: (1) specification-guided test input generation that creates both syntactically valid and invalid requests enriched with real-time blockchain data, and (2) specification-aware false positive filtering that leverages large language models to distinguish genuine bugs from acceptable variations. Our evaluation across all 11 major Ethereum clients reveals the pervasiveness of API bugs in production systems. APIDiffer uncovered 72 bugs, with 90.28% already confirmed or fixed by developers, including one critical error in the official specifications themselves. Beyond these raw numbers, APIDiffer achieves up to 89.67% higher code coverage than existing tools and reduces false positive rates by 37.38%. The Ethereum community's response validates our impact: developers have integrated our test cases, expressed interest in adopting our methodology, and escalated one bug to the official Ethereum Project Management meeting. By making APIDiffer open-source, we enable continuous validation of Ethereum client API implementations, thereby strengthening the foundational integrity of the entire Ethereum ecosystem.
Abstract Software development plays a central role in digital sustainability, yet developers’ role and engagement remains understudied. Here we analyse nearly a decade of developer discussions available on the code repository Github on Ethereum, a widely used open-source blockchain platform. Using topic modelling, with interpretation supported by large language models and a sustainability framework for software systems, we trace how economic, environmental, social, individual, and technical sustainability themes emerge and evolve over time. We find that sustainability awareness, particularly related to energy efficiency and cost, intensifies during key events such as the transition from proof-of-work to proof-of-stake consensus, which substantially reduced energy use. We identify influential contributors and thematic specialisation, providing a transferable framework for understanding sustainability in emerging developer communities. These findings highlight the role of developer discourse in shaping sustainable software ecosystems and integrating sustainability into open-source development.
Patrick Spiesberger, Nils Henrik Beyer, Hannes Hartenstein
Ethereum's ideals of decentralization and censorship resistance are undermined in practice, motivating ongoing efforts to reestablish these properties. Existing proposals for fairness mechanisms depend on the assumption that a sufficient fraction of block proposers adhere to Ethereum's protocols as intended. We refer to such proposers as altruistic, as this behavior may come at the cost of reduced revenue. Prior analyses indicate that a consistent share of 91 percent of proposers delegate block construction to centralized services, effectively signing externally constructed blocks blindly, and are thus not considered altruistic. To assess whether the remaining 9 percent of proposers genuinely exhibit altruistic behavior, we conducted an empirical analysis and found that an additional 6.1 percent also interact with such external services. Further, we found that less than 1.4 percent of proposers consistently acted in accordance with Ethereum's decentralization and censorship resistance objectives. These findings suggest that relying solely on the mere presence of altruistic proposers is insufficient to ensure that proposed fairness mechanisms reestablish Ethereum's ideals, highlighting the need for additional incentive- or penalty-based mechanisms.
Dr. A. Radhika, D. Avinash, D. Sowjanya, K. Karthik · 5 authors
The increasing use of digital communication has made it essential to maintain the confidentiality, integrity, and authenticity of sensitive information. Conventional image steganographic methods offer data hiding in digital images, but they fail to offer effective tamper proofing and secure ownership verification. To overcome these issues, this paper presents a Blockchain-Integrated Secure Image Steganography system using IPFS and Ethereum. In the proposed system, secret data is hidden within digital images using a Least Significant Bit (LSB) image steganographic method developed in Python. The stego images are then stored in the Inter Planetary File System (IPFS) for efficient and decentralized data storage. To ensure data integrity and secure access, the cryptographic hash values of the stego images and their corresponding IPFS Content Identifiers (CIDs) are securely stored on the Ethereum blockchain using smart contracts. The use of blockchain technology provides immutability, transparency, and tamper resistance, and IPFS provides decentralized storage without depending on centralized storage servers. The proposed system is validated to offer high image quality with negligible distortion and robust data security and traceability. This system is applicable for secure data sharing in confidential communication, digital forensics, and secure document transfer.
Open access
Advanced Steganography and Watermarking Techniques
Patrick Spiesberger, Nils Henrik Beyer, Hannes Hartenstein
Ethereum's ideal of censorship resistance, together with related fairness properties, is undermined in practice, motivating fairness mechanisms that aim to restore these properties. Several of these mechanisms hand control over block contents to a committee of proposers under a 1-of-n honest assumption: at least one committee member complies with the mechanism even when deviating would increase personal revenue. We refer to such proposers as altruistic. Yet prior work shows that roughly 91 percent of blocks are constructed by centralized block-building services that demonstrably take user-adverse actions for financial gain; the responsible proposers sign these blocks blindly, without any means of intervention. A common reading of this figure is that 9 percent of proposers forgo these gains and act altruistically. Our empirical analysis of the full year 2025 shows that this share is far smaller: at most 1.55 percent of proposers can plausibly be regarded as altruistic, whereas the remaining 98.45 percent of proposers exhibit observable non-altruistic behavior. We interpret 1.55 percent as an upper bound on the prevalence of altruistic proposers. These results imply that committee-based fairness mechanisms that rely on altruistic members would require substantially larger committees than currently proposed. This raises concerns about their practical viability and motivates mechanisms in which fair behavior is the rational choice.
Justin Wang, Andreas Bigger, Xiaohai Xu, Jiahao Lin · 8 authors
Smart contracts on public blockchains now manage large amounts of value, and vulnerabilities in these systems can lead to substantial losses. As AI agents become more capable at reading, writing, and running code, it is natural to ask how well they can already navigate this landscape, both in ways that improve security and in ways that might increase risk. We introduce EVMbench, an evaluation that measures the ability of agents to detect, patch, and exploit smart contract vulnerabilities. EVMbench draws on 117 curated vulnerabilities from 40 repositories and, in the most realistic setting, uses programmatic grading based on tests and blockchain state under a local Ethereum execution environment. We evaluate a range of frontier agents and find that they are capable of discovering and exploiting vulnerabilities end-to-end against live blockchain instances. We release code, tasks, and tooling to support continued measurement of these capabilities and future work on security.
The article provides a comprehensive study of the systemic transformation of corporate governance in the context of global digitalization, characterized by the transition from hierarchical models to decentralized structures. It is substantiated that blockchain technology emerges as a new institutional foundation, where traditional bureaucratic verification mechanisms are replaced by algorithms based on cryptographic protocols. A particular emphasis is placed on the distinctions between public (permissionless) and private (permissioned) blockchain networks regarding the immutability of records. The study examines the concept of decentralized governance and the functional specifics of Decentralized Autonomous Organizations (DAOs), where operational logic and management regulations are implemented directly into the software code of smart contracts. This minimizes the influence of traditional administrative management and mitigates "single point of failure" risks. The theoretical framework of the work builds upon classical theories, such as Oliver Williamson’s "Transaction Cost Theory," Michael Jensen and William Meckling’s "Principal-Agent Theory," and the scholarly works of Harold Demsetz. Blockchain is analyzed as a tool that renders market exchange more economically viable than hierarchy. The author proposes an original interpretation of a multi-tier blockchain model for enterprise management, encompassing the infrastructure, network, consensus, data, and application layers. The essence of consensus algorithms (PoW, PoS, DPoS) is disclosed through the prism of management. Special attention is devoted to international experience in legal regulation and the processes of implementing these standards within the legislative framework of Ukraine. The economic effect and practical aspects of the study are analyzed through successful case studies of global corporations (IBM, Amazon, Oracle, Walmart, Nestlé) and Ukrainian business initiatives (TASCOMBANK, SETAM, Agroxy, Softengi). These cases demonstrate a significant reduction in verification costs, lower operating expenses, and increased transparency in supply chains. The transition to an innovative "Management-as-a-Service" paradigm is justified, where blockchain serves not merely as software but as a new firm architecture. Conclusions are drawn regarding a shift in the management ontology – moving from "governance by humans" to algorithmic "governance by code," which ensures data immutability, cyber resilience, and the possibility of real-time preventive risk monitoring. References: 1. Kuzmina, T. O., Berezovskyi, Yu., Kalinskyi, Ye., Arliukova, Yu., & Trofymchuk, A. (2024). Innovatsiini elementy informatsiino-komunikatsiinykh tekhnologii u standartyzatsii materialiv ta vyrobiv lehkoi promyslovosti [Innovative elements of information and communication technologies in the standardization of materials and products of light industry]. Visnyk Khersonskoho natsionalnoho tekhnichnoho universytetu – Bulletin of the Kherson National Technical University, (2 (89)), 90–98. DOI: 10.35546/kntu2078-4481.2024.2.13. 2. Hordiienko, K. O., Nishchemenko, D. O., Hertsiuk, M. M., Aronov, A. O., & Havor, A. S. (2025). Masshtabovani detsentralizovani systemy na osnovi rozpodilenykh skhovyshch danykh [Scaled decentralized systems based on distributed data storages]. Naukovi zapysky Derzhavnoho universytetu informatsiino-komunikatsiinykh tekhnolohii – Scientific Notes of the State University of Information and Communication Technologies, (2), 102–108. DOI: 10.31673/2786-8362.2025.029186. 3. Shabir, Korotana (2025). Decentralized autonomous organizations: adapting legal structures and proposing a new model of DAOLLP, Capital Markets Law Journal, Volume 20, Issue 3, September 2025, kmaf011, 10.1093/cmlj/kmaf011. 4. Wang, S., Ding, W., Li, J., Yuan, Y., Ouyang, L., & Wang, F. Y. (2019). Decentralized autonomous organizations: Concept, model, and applications. IEEE Transactions on Computational Social Systems, 6(5), 870–878. DOI: 10.1109/TCSS.2019.2938190. 5. Alchian, A. A., & Demsetz, H. (1972). Production, information costs, and economic organization. The American Economic Review, 62(5), 777–795. 6. Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. 7. Williamson, O. E. (1979). Transaction-cost economics: the governance of contractual relations. The Journal of Law and Economics, 22(2), 233–261. 8. Davidson, S., De Filippi, P., & Potts, J. (2018). Blockchains and the economic institutions of capitalism. Journal of Institutional Economics, 14(4), 639-658. DOI: 10.1017/S1744137417000200 9. Wyoming State Legislature. (2021). Wyoming Decentralized Autonomous Organization Supplement. wyoleg.gov https://wyoleg.gov/2021/Enroll/SF0038.pdf (accessed January 31, 2026). 10. Law Commission (2024). Decentralised Autonomous Organisations (DAOs): Call for Evidence. lawcom.gov. https://surl.li/qejcyx (accessed March 01, 2026). 11. European Parliament and Council of the European Union. (2023). Regulation (EU) 2023/1114 of the European Parliament and of the Council of 31 May 2023 on markets in crypto-assets, and amending. Regulations No 1093/2010 and No 1095/2010 and Directives 2013/36/ and 2019/1937. eur-lex.europa.eu. https://surl.li/tkxtmn (accessed March 01, 2026). 12. European Parliament and Council of the European Union. (2022). Regulation (EU) 2022/858 of the European Parliament and of the Council of 30 May 2022 on a pilot regime for market infrastructures based on distributed ledger technology, and amending Regulations (EU) No 600/2014 and (EU) No 909/2014 and Directive 2014/65/EU. eur-lex.europa.eu. https://surl.li/bgrntl (accessed March 01, 2026). 13. European Parliament and Council of the European Union. (2023). Regulation (EU) 2023/2854 of the European Parliament and of the Council of 13 December 2023 on harmonised rules on fair access to and use of data and amending Regulation (EU) 2017/2394 and Directive (EU) 2020/1828 (Data Act). eur-lex.europa.eu. https://surl.li/gnzgew (accessed March 01, 2026). 14. Verkhovna Rada of Ukraine. (2022). Zakon Ukrainy Pro virtualni aktyvy [Law of Ukraine on Virtual Assets] (No. 2074-IX). zakon.rada.gov.ua. https://zakon.rada.gov.ua/laws/show/2074-20#Text (accessed March 01, 2026). 15. Catalini, C., & Gans, J. S. (2020). Some simple economics of the blockchain. Communications of the ACM, 63(7), 80–90. 16. Van Alstyne, M. W., Parker, G. G., & Choudary, S. P. (2016). Pipelines, platforms, and the new rules of strategy. Harvard business review, 94(4), 54–262. 17. Davidson, S., De Filippi, P., & Potts, J. (2018). Blockchains and the economic institutions of capitalism. Journal of institutional economics, 14(4), 639–658. DOI: 10.1017/S1744137417000200. 18. Gans, J. S., & Halaburda, H. (2015). Some economics of private digital currency. Economic analysis of the digital economy, 257–276. 19. Cong, L. W., & He, Z. (2019). Blockchain disruption and smart contracts. The Review of Financial Studies, 32(5), 1754–1797. DOI: 10.1093/rfs/hhz007. 20. Lumineau, F., Wang, W., & Schilke, O. (2021). Blockchain governance — A new way of organizing collaborations? Organization science, 32(2), 500-521. DOI: 10.1287/orsc.2020.1379. 21. Hsieh, Y. Y., Vergne, J. P., Anderson, P., Lakhani, K., & Reitzig, M. (2018). Bitcoin and the rise of decentralized autonomous organizations. Journal of Organization Design, 7(1), 1–16. 22. Chen, Y., & Bellavitis, C. (2020). Blockchain disruption and decentralized finance: The rise of decentralized business models. Journal of Business Venturing Insights, 13, e00151. DOI: 10.1016/j.jbvi.2019.e00151/. 23. Nikolić, I., et al. (2018). Finding The Greedy, Prodigal, and Suicidal Contracts at Scale. Proceedings of the 34th Annual Computer Security Applications Conference (ACSAC). https://surl.li/wicolv (accessed March 01, 2026). 24. Hajiali, M. (2020). Big data and sentiment analysis: A comprehensive and systematic literature review. Concurrency and Computation: Practice and Experience, 32(14), e5671. 25. Grech, N., Kong, M., Jurisevic, A., Brent, L., Scholz, B., & Smaragdakis, Y. (2018). Madmax: Surviving out-of-gas conditions in ethereum smart contracts. Proceedings of the ACM on Programming Languages, 2(OOPSLA), 1–27. DOI: 10.1145/3276486. 26. Brent, L., Jurisevic, A., Kong, M., Liu, E., Gauthier, F., Gramoli, V., ... & Scholz, B. (2018). Vandal: A scalable security analysis framework for smart contracts. arXiv preprint arXiv:1809.03981. 27. Nikolaiev, S. O., Voronenko, V. I., Kovalov, B. L., Hrytsenko, P. V., & Odevole, O. O. (2021). Blokchein yak faktor tsyfrovoi transformatsii ekonomiky Ukrainy [Blockchain as a factor of digital transformation of Ukraine’s economy]. Ekonomika ta derzhava – Economy and State, (10), 110–115. DOI: https://doi.org/10.21272/1817-9215.2021.2-2. 28. Chukut, S. A., & Buriachenko, K. O. (2018). Blokchein chy systema elektronnoho dokumentoobihu: suchasni tendentsii vprovadzhennia v orhanakh vykonavchoi vlady Ukrainy [Blockchain or electronic document management system: modern trends of implementation in the executive authorities of Ukraine]. Investytsii: praktyka ta dosvid – Investments: Practice and Experience, (1), 70–76. 29. Karpuntsov, V., & Veresha, R. (2023). Legal aspects of virtual assets regulation in Ukraine. Danube, 14(3), 235-252. DOI: 10.2478/danb-2023-0014. 30. Sytnyk, I. P., & Piuro, B. I. (2017). Analiz suchasnoho stanu ta perspektyv rozvytku kryptovaliuty BITCOIN v umovakh rozvytku informatsiinoi ekonomiky [Analysis of the current state and prospects of BITCOIN cryptocurrency development in the conditions of the information economy development]. Visnyk Odeskoho natsionalnoho universytetu. Seriia: Ekonomika – Herald of Odessa National University. Series: Economics, 22(1), 157–160. 31. Korytska, O., & Kalmuk, B. (2025). Detsentralizatsiia upravlinnia pidpryiemstvamy: klasyfikatsiia ta otsinka orhanizatsiinykh struktur [Decentralization of enterprise management: classification and assessment of organizational structures]. Ekonomika ta suspilstvo – Economy and Society, (74). DOI: 10.32782/2524-0072/2025-74-XX. 32. Al-Saqqa, S., & Sawalha, S. (2024). A comprehensive review of blockchain and smart contracts: Foundations, applications, and technical challenges. ResearchGate. https://surl.li/sstocc (accessed March 01, 2026). 33. Satoshi Nakamoto (2008). Bitcoin: A Peer-to-Peer Electronic Cash System. bitcoin.org. https://bitcoin.org/bitcoin.pdf (accessed March 01, 2026). 34. Fahim, S., Rahman, S. K., & Mahmood, S. (2023). Blockchain: A comparative study of consensus algorithms PoW, PoS, PoA, PoV. Int. J. Math. Sci. Comput, 3(1), 46-57. DOI: 10.5815/ijmsc.2023.03.04. 35. Konashevych, O. (2019). Why “Permissioned” and “Private” are not Blockchains. SSRN. DOI: 10.2139/ssrn.3496468. 36. Solat, S., Calvez, P., & Naït-Abdesselam, F. (2020). Permissioned vs. Permissionless Blockchain: How and Why There Is Only One Right Choice. Journal of Software, 16, 95–106. DOI: 10.17706/jsw.16.3.95-106. 37. Blockchain Council. (2024). Top 10 companies that have already adopted blockchain. blockchain-council.org. https://www.blockchain-council.org/blockchain/top-10-companies-that-have-already-adopted-blockchain/ (аccessed March 01, 2026). 38. Amazon Web Services (2026). Amazon Managed Blockchain: Create and manage scalable blockchain networks. aws.amazon.com. https://aws.amazon.com/managed-blockchain/ (accessed March 02, 2026). 39. Oracle (2026). Oracle Blockchain Platform: Integrating blockchain into Oracle ERP Cloud for finance and supply chain. oracle.com. https://www.oracle.com/a/ocom/docs/blockchain-integrated-with-erp-cloud.pdf (accessed March 01, 2026). 40. Accenture (2026). Blockchain and Web3 services: Accelerating the transition to decentralized systems and metaverse. accenture.com. https://surl.li/iygetq (accessed March 02, 2026). 41. Marr, B. (2018, March 23). How blockchain will transform the supply chain and logistics industry. Forbes. https://surl.li/exmhqn (accessed March 01, 2026). 42. Brainforge (2023, July 14). How Nestlé uses data analytics for market expansion. brainforge.ai. https://surl.lu/whemui (accessed March 01, 2026). 43. TASCOMBANK (2023). Report on the results of the pilot project "Issue of electronic money based on blockchain technology". tascombank.ua. https://surl.li/bhboxc (accessed March 02, 2026). 44. Кабінет Міністрів України. (2017, 20 квітня). Україна розпочинає масштабний проект по впровадженню Blockchain в державному управлінні. kmu.gov.ua. https://surl.li/oudjwr (accessed March 02, 2026). 45. The Recursive. (2022, June 21). The rise of AgriTech in Central and Eastern Europe: 15 startups to watch. therecursive.com. https://surl.li/lrrued (accessed March 2, 2026). 46. GoodFirms (2026). Top blockchain development companies in Ukraine. goodfirms.co. https://surl.li/zqlovu (accessed March 2, 2026). 47. Clutch (2026). Top blockchain developers in Ukraine. clutch.co. https://clutch.co/ua/developers/blockchain (accessed March 2, 2026).
Sunil Kumar R M, Raghavendra M Devadas, Mrutyunjaya M S, Praveen Gujjar J · 6 authors
Healthcare data infrastructures remain fragmented, insecure, and characterized by low trust, hindering interoperable, patient-centric care. Centralized architectures struggle with multi-stakeholder governance, fine-grained accountability, and performance at scale under real-world workloads. Distributed ledger technology (DLT) promises immutability, distributed trust, auditability, and strong provenance, yet most healthcare implementations focus on narrow use cases, ad-hoc governance choices, or limited performance benchmarking. This paper designs and empirically evaluates an integrated governance architecture and performance engineering framework for healthcare DLT ecosystems. The approach combines (i) a theoretical synthesis of DLT governance and health data governance, (ii) architecture and protocol design for permissioned and hybrid platforms (e.g., Corda, Hyperledger Fabric, Ethereum-based frameworks, edge–blockchain hybrids), and (iii) testbed implementations with multi-scenario benchmarking across local, cloud, and edge-integrated deployments. Results from representative use cases—EHR interoperability, m-health/IoT streaming, and self-sovereign identity and consent—show that DLT-enabled governance can strengthen integrity, accountability, and patient control while achieving acceptable throughput–latency envelopes compared with centralized baselines, subject to explicit trade-offs between decentralization, audit granularity, and scalability. The paper distils design guidelines and reusable governance–performance patterns for large-scale, interoperable DLT health ecosystems, clarifying when to Favor platform choices, consensus mechanisms, and data-management strategies given domain-specific requirements.
Innovative Approaches in Technology and Social Development
The pests and the ideal irrigation should be monitored simultaneously so that the crops can be efficiently managed to yield the maximum. The intended dual-purpose solution to the pest detection problem, which is proposed in this study, is the combination of IoT-enabled sensors with Ethereum smart contracts and a deep learning-based ResNeSt-DDETR pest detector. The ResNeSt backbone is able to improve the extraction of features with the help of split-attention mechanisms, whereas Deformable DETR pays attention to the areas which are of interest in order to achieve precise detection in the field under complex conditions. IoT sensors constantly check soil moisture, temperature, and humidity to adjust the irrigation patterns to control the water management accurately. The pest detections and irrigation logs are registered safely on the Ethereum blockchain and provide a solution with tamper-proof, transparent, and traceable data. The system is deployed on edge devices and implemented on Python with PyTorch, OpenCV, and Web3.py and works in real time. The experimental assessment of the IP102 data reveals that the model has a high accuracy (95.2%), precision (94.5%), recall (93.7%), F1-score (94.1%), and mAP 0.5:0.95 = 90.6% indicating that it is effective in integrated pest and irrigation management in precision agriculture.
Pest detection and soil moisture estimation models with little computation overhead are needed in resource-efficient pesticide monitoring of agricultural fields. This paper introduces a lightweight MobileViT-based system that is combined with IoT sensors and a hybrid Ethereum blockchain platform to offer secure and real-time pest and soil monitoring. MobileViT is a hybrid architecture that uses convolutional networks and transformer-based global features, which allow competition with detection accuracy and low computing needs. The IoT sensors are used to monitor soil moisture, temperature, and humidity to aid in making irrigation decisions with the key events being safely stored on the Ethereum blockchain to trace the events irrevocably. The system is executed in Python using PyTorch, OpenCV, and Web3.py and runs on edge devices and is fast in inference with low latency. The IP102 dataset includes the evaluation which proves that the model has high detection performance with accuracy 92.8%, precision 91.5%, recall 90.7%, F1-score 91.1%.
This study presents a blockchain-based enabling autonomous nursing professional development framework, known as BCeANPDF. The framework aims to enhance transparency, security, and professional autonomy in nursing credential management. It is grounded in the principles of competency-based human resource management. Blockchain and smart contract technologies are integrated to support independent recording, verification, and management of professional and non-professional credentials by nurses. At the same time, hospital human resource administrators continue to have the authority to conduct regulatory oversight and ensure compliance. The framework employs a three-layer architecture that includes controller, service, and repository components. These components coordinate access control, data processing, and blockchain-related operations. Seven smart contracts are designed within the framework. They automate credential ownership verification, credential updates, and compliance review processes. This design strengthens data integrity and reduces administrative workload. A prototype was implemented in a private blockchain environment to evaluate system performance. The results demonstrate stable and efficient operation. The average on-chain processing time per credential was 12.3 s. Median query latency ranged from 5 to 9 ms. These findings confirm that the framework achieves scalability and responsiveness comparable to Ethereum, while preserving data privacy and immutability. By combining decentralized trust mechanisms with credential management practices, the BCeANPDF framework offers a practical approach to supporting autonomous professional development. It also facilitates flexible management of the nursing workforce. Overall, the framework contributes to the development of transparent and competency-oriented healthcare institutions without increasing operational complexity.
Privacy disclosure from model parameters and malicious attacks are critical issues in federated learning (FL). Existing research has yet to effectively address the simultaneous need for efficient communication design, privacy protection, and attack detection, which impedes the widespread adoption of FL in mobile edge networks over 6G wireless communication. In this paper, we propose a trustworthy FL framework that can ensure privacy, robustness, accountability, fairness, and explainability in mobile edge networks. Specifically, we integrate authenticated zero-knowledge proofs (ZKPs) and Pedersen commitments into the FL process. Despite the lack of direct access between servers and mobile devices, the servers can still identify trustworthy clients for specific tasks. Clients can verify the authenticity of the received global model based on the provided proofs and commitments. Furthermore, we leverage Ethereum to act as the verifier and authenticator of models. This verification and authentication process enables the servers to detect abnormal local models and perform trust-based aggregations. Numerical results demonstrate that the proposed trustworthy FL framework significantly improves the global model's in terms of accuracy, convergence rate, and security.
Aleksandar Šević, Željko Šević, Athanasios Fassas, Panayiotis Tzeremes
There is a strong impetus to make cryptocurrencies more environmentally friendly, and in our study it is has been analyzed whether commodity price shocks have varying impacts on clean and dirty cryptocurrency interconnectedness before, during and after the COVID-19 pandemic. Using the decomposed and partial connectedness measure we evaluate the connectedness of oil price shocks, demand, supply and risk, as well as five clean and five dirty cryptocurrencies from October 2017 until April 2024. The spikes in demand and disruptions in oil supply lead to price increases. Oil shocks have the largest impact on sampled crypto products during the COVID-19 period, as opposed to pre- and post-pandemic years, and they demonstrate a stronger influence on selected cryptocurrencies than internal crypto-to-crypto dynamics. During the crisis, the difference between clean and dirty cryptocurrencies becomes less relevant when compared to no-crisis periods. We also find that clean cryptocurrencies are net recipients of shocks, while dirty counterparts, dominated by Bitcoin and Ethereum, are net transmitters, especially during the recovery phase. Our findings are relevant for supporting the transition to clean cryptocurrencies and contribute to a better understanding of dynamic interconnectedness. • Examines the decomposed and partial connectedness • Uses time-varying parameter vector autoregression (TVP-VAR) models • Highlights the heterogeneity in cryptos’ responses to oil price fluctuations • Total Connectedness Index peaks during the COVID-19 pandemic • The distinctions between clean and dirty cryptocurrencies reemerged post-COVID