Blockchain benefits are due to immutability, replication, and storage-and-execution of smart contracts on the blockchain. However, the benefits come at increased costs due to the blockchain size and execution. We address three fundamental issues that arise in transferring certain parts of a smart contract to be executed off-chain: (i) identifying which parts (patterns) of the smart contract should be considered for processing off-chain, (ii) under which conditions should a smart-contract pattern to be processed off-chain, and (iii) how to facilitate interaction between the computation off and on-chain. We use separation of concerns and FSM modeling to model a smart contract and generate its code. We then (i) use our algorithm to determine which parts (patterns) of the smart contract are to be processed off-chain; (ii) consider conditions under which to move the pattern off-chain; and (iii) provide model for automatically generating the interface between on and off-chain computation.
Krzysztof Gogol, Manvir Schneider, Claudio Tessone
We study the economics of transaction reverts on Ethereum rollups and show that they are not accidental failures but equilibrium outcomes of MEV strategies. Using execution traces from major L2s, we find that over 80% of reverted transactions are swaps, with half targeting USDC-WETH pools on Uniswap v3, v4. Clustering reveals distinct bot archetypes, including split-trade arbitrageurs, atomic duplicators, and end-of-block spammers, demonstrating that reverts follow systematic patterns rather than random noise. Empirically, we show that priority fee auctions on rollups do not allocate blockspace efficiently: transaction placement is mis-ordered, round-number bidding dominates, and duplication spam inflates base fees. As a result, reverted transactions contribute disproportionately more to sequencer fee revenues than to gas consumption, shifting welfare from users to sequencers. To explain these dynamics, we develop a model proving that trade-splitting and duplication strictly dominate one-shot execution under convex adversarial loss. Our findings establish reverts as a structural feature of rollup MEV microstructure and highlight the need for protocol-level reforms to sequencing, fee markets, and revert protection.
This paper presents a mathematically rigorous formal analysis of Simplified Payment Verification (SPV) clients, as specified in Section 8 of the original Bitcoin white paper, versus non-mining full nodes operated by home users. It defines security as resistance to divergence from global consensus and models transaction acceptance, enforcement capability, and divergence probability under adversarial conditions. The results demonstrate that SPV clients, despite omitting script verification, are cryptographically sufficient under honest-majority assumptions and topologically less vulnerable to attack than structurally passive, non-enforcing full nodes. The paper introduces new axioms on behavioral divergence and communication topology, proving that home-based full nodes increase systemic entropy without contributing to consensus integrity. Using a series of formally defined lemmas, propositions, and Monte Carlo simulation results, it is shown that SPV clients represent the rational equilibrium strategy for non-mining participants. This challenges the prevailing narrative that home validators enhance network security, providing formal and operational justifications for the sufficiency of SPV models.
Blockchain transaction data is inherently high dimensional, noisy, and entangled, posing substantial challenges for traditional clustering algorithms. While quantum enhanced clustering models have demonstrated promising performance gains, their interpretability remains limited, restricting their application in sensitive domains such as financial fraud detection and blockchain governance. To address this gap, we propose a two stage analysis framework that synergistically combines quantitative clustering evaluation with AI Agent assisted qualitative interpretation. In the first stage, we employ classical clustering methods and evaluation metrics including the Silhouette Score, Davies Bouldin Index, and Calinski Harabasz Index to determine the optimal cluster count and baseline partition quality. In the second stage, we integrate an AI Agent to generate human readable, semantic explanations of clustering results, identifying intra cluster characteristics and inter cluster relationships. Our experiments reveal that while fully trained Quantum Neural Networks (QNN) outperform random Quantum Features (QF) in quantitative metrics, the AI Agent further uncovers nuanced differences between these methods, notably exposing the singleton cluster phenomenon in QNN driven models. The consolidated insights from both stages consistently endorse the three cluster configuration, demonstrating the practical value of our hybrid approach. This work advances the interpretability frontier in quantum assisted blockchain analytics and lays the groundwork for future autonomous AI orchestrated clustering frameworks.
Montaser N.A. Ramadan, Mohammed A. H. Ali, Hadi Jaber, Mohammad Alkhedher
Air pollution in industrial zones significantly impacts environmental safety and worker health. This paper presents a novel decentralized IoT-federated learning (FL) framework, uniquely integrated with blockchain security, designed to provide a mechanistic understanding and accurate predictive modeling of air pollutant exposure in industrial environments. The novelty lies in the integration of a hybrid EMD-Transformer-BiLSTM prediction model with a blockchain-backed federated learning mechanism, providing secure, tamper-proof decentralized model updates. Three IoT-based sensing units, deployed across an industrial facility for five months, continuously monitored pollutants (PM2.5, PM10, CO₂, VOCs, CH₂O, CO, and O₃) and environmental factors (temperature, humidity). The innovative model improved prediction accuracy from 83.12 % to 92.5 % for short-term (5-minute) forecasts, stabilizing at 84.7 % for 60-minute predictions after 15 FL rounds. Model validation indicated strong predictive reliability (R² = 0.89), significantly reducing prediction errors (Mean Absolute Error and Root Mean Square Error). Blockchain integration successfully ensured data integrity, identifying and rejecting over 98.7 % of unauthorized updates. Additionally, a swarm intelligence approach optimized decentralized model aggregation, minimizing communication overhead despite increased security latency (FL rounds increased from 7.5 s to 13.5 s for 500 clients). Real-time RGB-based air quality index visualization and cloud-based spatio-temporal mapping provided actionable insights into pollutant dynamics. This study demonstrates a distinct advancement in air pollution monitoring by combining federated learning, blockchain technology, and real-time adaptive visualization for enhanced environmental safety in industrial settings.
This study examines the legal nature of cryptocurrency from the perspective of civil law, focusing on how cryptocurrencies can be integrated into the current Hungarian and Romanian private law systems. The author provides a detailed analysis of the historical and legal development of the concept of money, the functional characteristics of cryptocurrencies, and their applicability as a means of payment in contractual relations. The study highlights that cryptocurrencies are not recognized as legal tender and are often treated as barter instruments/ exchange rather than classical monetary payments. The paper aims to emphasize the legal challenges and the necessity of regulatory development regarding digital assets.
Deep learning and hybrid deep learning models are widely regarded as some of the most effective predictive modeling techniques to date. Their hierarchical architecture enables them to capture complex, non-linear relationships among features and uncover hidden patterns within data, making them particularly powerful for tasks involving high-dimensional and unstructured inputs. But, these models are computationally intensive and require substantial processing time. Moreover, their predictive efficiency is highly dependent on the availability of large-scale datasets. In this study, meta learning model is employed for the prediction of two financial markets: equity market and crypto market. NASDAQ and S&P 500 index has been taken for equity market prediction. On the other hand, Bitcoin & Ethereum are considered for crypto market. Three deep learning models: LSTM, GRU and CNN are trained for the prediction of these four indices and a hybrid deep learning model of GRU and CNN is also developed. Based on RMSE, MAE and R 2 values, it is observed that meta learning yields best results among all trained models with minimum time and using scarce computation resources based on small dataset.
Web-based credit card payments require complete disclosure of all payment card details for transaction authorization. The card’s CVV (Card Verification Value) is the secret code that authorizes card not presented transactions. Currently, all payment card details must be shared among various intermediaries involved in processing the transaction. To mitigate the risks associated with fraudulent transactions, industries have adopted security standards such as the PCI DSS. Credit card data confidentiality rests on all involved stakeholders adhering to best security practices, including data communication encryption, and do not misuse the payment information. However, this security posture does not prevent potential credit card data leaks. We propose an alternative method for conducting remote card payments that does not require disclosing the authorization code while ensuring high interoperability with existing payment networks. Our approach demonstrates how designated verifier Zero-Knowledge Proofs (ZKP) enable minimal disclosure of card details, particularly protecting the confidentiality of authorization codes.
Cloud identity management has evolved from a purely technical concern into a fundamental pillar of digital society, creating profound impacts that extend far beyond organizational boundaries. Modern cloud-based identity and access management systems serve as critical infrastructure enabling access to essential services including healthcare, education, government benefits, and financial services. These systems incorporate advanced technical mechanisms such as multi-factor authentication, single sign-on, zero trust architecture, and artificial intelligence-driven fraud detection to establish secure and inclusive digital environments. The transformation to cloud-based architectures addresses traditional limitations of on-premises systems while introducing new capabilities for digital inclusion through device-agnostic authentication, accessibility-first design, and multilingual support. However, this evolution presents significant challenges including privacy concerns arising from data aggregation, potential government surveillance, and algorithmic bias in automated decision-making systems. Strategic implementation through public-private partnerships, investment in open source components, and adoption of emerging technologies such as quantum-resistant cryptography and distributed ledger integration shapes the societal impact of these systems. The technical decisions made in designing and implementing cloud identity infrastructure have far-reaching implications for social equity, democratic participation, and economic opportunity in an increasingly digital world.
The aim of this study is to reveal the dynamics between climate policy uncertainty (CPU) and S&P Global Carbon Credit Index (CARBON), S&P Cryptocurrency DeFi Index (DeFi), and WilderHill New Energy Global Innovation Index (NEX) using data from December 2017 to March 2024 in the US. Fourier Bootstrap ARDL, Fourier Bootstrap quantile causality, and KRLS methods are used in the study. The findings reveal that there is a negative relationship between the CARBON and the CPU index in the long term. Although the DeFi does not have a statistically significant effect in the long term, it reveals that it has a negative effect on the CPU index in the short term. In contrast, the NEX has a positive relationship with the CPU index in both the short and long term. Moreover, there is a U-shaped non-linear relationship between the NEX and the CPU index, which weakens in moderate climate uncertainties and strengthens again in high uncertainty. Considering the causality results, there exists a causality from CARBON to CPU in the 2nd, 3rd, and 4th quantiles, and from CPU to CARBON in the 2nd and 3rd quantiles. Additionally, there is a causality from DeFi to CPU in the 8th quantile and from CPU to DeFi in the 1st quantile. Finally, there is a causal relationship from NEX to CPU in the 2nd, 3rd, 4th, and 5th quantiles and from CPU to NEX in the 9th quantile.
Daria Schumm, Cedric von Rauscher, Katharina Olga Emilia Müller, Burkhard Stiller
Transparency and immutability of blockchains can expose metadata and raise concerns about its classification as personal data under privacy regulations. This paper investigates privacy risks associated with metadata in blockchain-based identity systems. Additionally, two privacy-preserving mechanism designs, namely Zero-Knowledge Proof (ZKP) and Homomorphic Encryption (HE), to protect metadata are proposed. As a result, this work introduces the first use case of HE privacy-preserving mechanism in the context of Decentralized Identity (DI) and Self-Sovereign Identity (SSI) systems.
Alma Idah, R. Biroum Bernardianto, Suffianor Suffianor
This study offers a thorough summary of the state of research in the area of local government finance by conducting a systematic literature review. Drawing on 25 years of pertinent publications in the subject of public budgeting and finance, the study addresses a variety of topics, such as capital budgeting, budgeting and budget reform, intergovernmental finance, financial management, and alternative service delivery. Scopus was used to gather the data, and 580 articles were deemed suitable for additional examination. The data were analyzed using Bibliometric approach. The analysis highlights China, the United States, and the United Kingdom as dominant contributors, with a strong focus on topics such as fiscal decentralization, local government finance, and governance efficiency. The author collaboration network reveals fragmented clusters, with limited interconnections among researchers, emphasizing the need for broader global and interdisciplinary collaborations. Additionally, the findings underscore the growing importance of emerging themes such as sustainability, digital governance, and AI-driven fiscal management, which remain underexplored. Geographical imbalances in research output further highlight the need for greater representation from underrepresented regions, including Africa, South America, and parts of Asia. Policymakers and practitioners who want to keep up with the most recent advancements and industry best practices in local government finance will also benefit from it.
Henry Komakech, Lynn Atuyambe, Fadi El‐Jardali, Christopher Garimoi Orach
BACKGROUND: Low- and middle-income countries face several challenges in providing health services, particularly to displaced populations, during all phases of emergencies. However, little is known about how health services are organized to displaced populations following repatriation. This study examined the organization of health services following the repatriation of South Sudanese refugees from the three West Nile districts of Arua, Adjumani, and Moyo in Uganda. METHODS: We conducted a qualitative case study in three West Nile refugee hosting districts, Arua, Moyo, and Adjumani. We used the World Health Organization Health System Framework, focusing on four blocks: health services, financing, medicines and supplies, and human resources. We conducted in-depth interviews with 32 purposefully selected respondents, including health service providers, district civil leaders, local government staff, and non-government organization staff. The data were analyzed using content analysis. RESULTS: Following repatriation, the district health teams in the three districts assumed overall responsibility for planning, managing, and providing health services. Health services followed an integrated model within a decentralized framework in all three districts. Health services were available in most areas except for former refugee settlements where facilities were either closed or relocated. After repatriation, funding for health services was provided through the government's primary health care grant with minimal support from aid agencies. Districts, however, face several challenges, including shortages of medicines and essential supplies, inadequate health workers, and poor infrastructure. CONCLUSION: Refugee repatriation disrupted health service delivery in the refugee hosting districts, leading to a reduction in funding; inadequate skilled health workers and equipment; and the closure of some facilities. To ensure the continuity of health services, government and aid agencies should plan for repatriation and establish strategies to sustain health services in refugee-hosting areas.
Lending protocols have transformed the Decentralized Finance (DeFi) ecosystem, driving innovation while also introducing new risks. This study develops a machine learning framework to predict user behavior and assess factors influencing changes in health ratios within the Compound V2 protocol. By analyzing user historical data, position metrics, and market conditions, we propose machine learning-based models to predict whether users will adjust their positions or face liquidation. We find that Random Forest and XGBoost models excel in predicting these outcomes, with features like collateral values, historical risk exposure, and asset composition playing significant roles. Additionally, panel regression models reveal insights into health ratio dynamics over time and across asset types, as well as user sophistication. These findings offer a better understanding of user behavior, highlighting opportunities for improved risk modeling and adaptive strategies in DeFi lending.
Automated Market Makers (AMMs) with a conservative function, such as Uniswap, Balancer, Curve and others, are an integral part of decentralised finance. This article examines the effect of the exchange fees on the divergence losses of the automated market-making systems in public blockchain networks. The study consists of several parts: theoretical background, detailed description of the exchange mechanics, the derivation of explicit formulas, the results of modelling using the hyperparameters of pools from the Ethereum network and the analysis of the proposed approach using historical data. For the first time, the obtained closed formulas (Uniswap, Balancer) and modelling results (Uniswap, Balancer, Curve) indicate the presence of the impermanent gain for liquidity providers in the case of non-zero fees when the trading volume does not exceed a certain amount. The results indicate that the proposed methodology significantly affects the definition of ‘impermanent loss of a liquidity provider’ widely used in the blockchain community since there can always be a profitable range of values. As a practical part of the study, statistics on the share of trades with the effect of impermanent gain in Ethereum pools are provided, and the approach for managing the fee rate is considered during this observation. Explicit relationships for mostly used AMMs with non-zero trading fees are derived. The article may be useful for both practitioners and researchers in the field of decentralised finance seeking a deeper understanding of the dynamics of automated market-making in an ever-changing DeFi environment.
The convergence of blockchain and metaverse technologies is poised to redefine how Global Value Chains (GVCs) create, capture, and distribute value, yet scholarly insight into their joint impact remains scattered. Addressing this gap, the present study aims to clarify where, how, and under what conditions blockchain-enabled transparency and metaverse-enabled immersion enhance GVC performance. A systematic literature review (SLR), conducted according to PRISMA 2020 guidelines, screened 300 articles from ABI Global, Business Source Premier, and Web of Science records, yielding 65 peer-reviewed articles for in-depth analysis. The corpus was coded thematically and mapped against three theoretical lenses: transaction cost theory, resource-based view, and network/ecosystem perspectives. Key findings reveal the following: 1. digital twins anchored in immersive platforms reduce planning cycles by up to 30% and enable real-time, cross-border supply chain reconfiguration; 2. tokenized assets, micro-transactions, and decentralized finance (DeFi) are spawning new revenue models but simultaneously shift tax triggers and compliance burdens; 3. cross-chain protocols are critical for scalable trust, yet regulatory fragmentation—exemplified by divergent EU, U.S., and APAC rules—creates non-trivial coordination costs; and 4. traditional IB theories require extension to account for digital-capability orchestration, emerging cost centers (licensing, reserve backing, data audits), and metaverse-driven network effects. Based on these insights, this study recommends that managers adopt phased licensing and geo-aware tax engines, embed region-specific compliance flags in smart-contract metadata, and pilot digital-twin initiatives in sandbox-friendly jurisdictions. Policymakers are urged to accelerate work on interoperability and reporting standards to prevent systemic bottlenecks. Finally, researchers should pursue multi-case and longitudinal studies measuring the financial and ESG outcomes of integrated blockchain–metaverse deployments. By synthesizing disparate streams and articulating a forward agenda, this review provides a conceptual bridge for international business scholarship and a practical roadmap for firms navigating the next wave of digital GVC transformation.
Blockchain technology has become a vital foundation in the transformation of digital financial systems, particularly in supporting the growth of startupreneurs whorequire fast, secure, and transparent financial access. This study aims to analyze howtheimplementation ofblockchain technology can enhance operational efficiency and financial transparency in the development of digital startup businesses. Using a qualitative approach through literature review and best practice analysis, the research reveals that blockchain enables decentralized transactions, minimizes intermediaries, and ensures high data integrity ultimately strengthening investor and consumer trust in the startupreneur ecosystem. The adoption of smart contracts, immutable records, and automated verification also contributes to accelerating financial processes and mitigating fraud risks. However, challenges such as regulatory complexity, digital infrastructure readiness, and data security concerns remain obstacles to widespread blockchain adoption. These findings affirm that blockchain holds significant potential in creating a more inclusive, efficient, and sustainable digital financial model, in line with Sustainable Development Goals (SDGs) points 8 and 9. Recommendations are provided for startupreneurs and stakeholders to strategically integrate this technology into digital business development.
The evolution of blockchain technology, from its origins as a decentralized ledger for cryptocurrencies to its broader applications in areas like decentralized finance (DeFi), has significantly transformed financial ecosystems while introducing new challenges such as Maximum Extractable Value (MEV). This paper explores MEV on the Polygon blockchain, with a particular focus on Atomic Arbitrage (AA) transactions. We establish criteria for identifying AA transactions and analyze key factors such as searcher behavior, bidding dynamics, and token usage. Utilizing a dataset spanning 22 months and covering 23 million blocks, we examine MEV dynamics with a focus on Spam-based and Auction-based backrunning strategies. Our findings reveal that while Spam-based transactions are more prevalent, Auction-based transactions demonstrate greater profitability. Through detailed examples and analysis, we investigate the interactions between network architecture, transaction sequencing, and MEV extraction, offering comprehensive insights into the evolution and challenges of MEV in decentralized ecosystems. These results emphasize the need for robust transaction ordering mechanisms and highlight the implications of emerging MEV strategies for blockchain networks.
Hardhik Mohanty, Giovanni Zaarour, Bhaskar Krishnamachari
Everlasting options, a relatively new class of perpetual financial derivatives, have emerged to tackle the challenges of rolling contracts and liquidity fragmentation in decentralized finance markets. This paper offers an in-depth analysis of markets for everlasting options, modeled using a dynamic proactive market maker. We examine the behavior of funding fees and transaction costs across varying liquidity conditions. Using simulations and modeling, we demonstrate that liquidity providers can aim to achieve a net positive PnL by employing effective hedging strategies, even in challenging environments characterized by low liquidity and high transaction costs. Additionally, we provide insights into the incentives that drive liquidity providers to support the growth of everlasting option markets and highlight the significant benefits these instruments offer to traders as a reliable and efficient financial tool.
Joan Ferré-Queralt, Jordi Castellà‐Roca, Alexandre Viejo
Smart grid technology has transformed electricity generation , distribution, and consumption by incorporating advanced communication systems and distributed energy resources , including solar panels and energy storage solutions . This integration enables prosumers to actively participate in energy markets, benefiting from real-time monitoring, dynamic pricing , and load balancing. However, the detailed data collected during these processes raise significant privacy concerns, as it may expose sensitive information about users’ lifestyles. This work presents an innovative energy trading system operating within decentralized energy distribution networks . The system leverages blockchain-based hierarchical smart contracts to enhance privacy protection for users. It automates energy trades, ensures accurate transaction verification, and obscures user identities and energy consumption patterns through its hierarchical structure, preventing unauthorized profiling or data breaches. Additionally, mechanisms to detect and penalize dishonest behavior are incorporated, ensuring the integrity and fairness of the energy market. The feasibility of the proposed system is experimentally evaluated in an IoT environment through a small-scale implementation using actual IoT devices, yielding positive results in terms of scalability and privacy features. Lastly, a comparative analysis is presented to demonstrate the advantages of the proposed system over existing state-of-the-art solutions.
Blockchain technology has emerged as a transformative paradigm for secure, decentralized, and transparent data management. However, the rapid growth of decentralized applications (dApps), global transaction demands, and multi-chain ecosystems has exposed scalability bottlenecks in existing consensus mechanisms. Traditional models such as Proof of Work (PoW) and Proof of Stake (PoS), while effective in maintaining security, struggle with throughput, latency, and energy efficiency. Recent research highlights the potential of artificial intelligence (AI) to augment blockchain consensus by improving leader selection, optimizing validator participation, dynamically adjusting difficulty, and predicting network anomalies. This manuscript explores AI-assisted consensus mechanisms as a scalable alternative for next-generation blockchain systems. The paper conducts a comprehensive literature review of blockchain scalability challenges, outlines a methodology for integrating reinforcement learning (RL), deep learning, and predictive analytics into consensus protocols, and presents simulation-based results. Findings suggest that AI-enhanced consensus can achieve up to 70% improved throughput, reduce energy costs by 50%, and enhance fault tolerance by predicting malicious node behavior in advance. The study concludes that AI-assisted consensus mechanisms provide a sustainable path toward highly scalable, adaptive, and secure blockchain networks, with implications for finance, supply chains, IoT, and government applications.
Ethereum’s transaction pool (mempool) dynamics and fee market efficiency critically affect transaction inclusion, validator workload, and overall network performance. This research empirically analyzes gas price variations, mempool clearance rates, and block finalization times in Ethereum’s proof-of-stake ecosystem using real-time data from Geth and Prysm nodes. We observe that high-fee transactions are consistently prioritized, while low-fee transactions face delays or exclusion—despite EIP-1559’s intended improvements. Mempool congestion remains a key factor in validator efficiency and proposal latency. We provide empirical evidence of persistent fee-based disparities and show that extremely high fees do not always guarantee faster confirmation, revealing inefficiencies in the current fee market. To address these issues, we propose congestion-aware fee adjustments, reserved block slots for low-fee transactions, and improved handling of out-of-gas vulnerabilities. By mitigating prioritization bias and execution inefficiencies, our findings support more equitable transaction inclusion, enhance validator performance, and promote scalability. This work contributes to Ethereum’s long-term decentralization by reducing dependence on high transaction fees for network participation.
web3 wallets are key to managing user identity on blockchain. The main purpose of a web3 wallet application is to manage the private key for the user and provide an interface to interact with the blockchain. The key management scheme ( KMS ) used by the wallet to store and recover the private key can be either custodial, where the keys are permissioned and in custody of the wallet provider or noncustodial where the keys are in custody of the user. The existing non-custodial key management schemes tend to offset the burden of storing and recovering the key entirely on the user by asking them to remember seed-phrases. This creates onboarding hassles for the user and introduces the risk that the user may lose their assets if they forget or lose their seedphrase/private key. In this paper, we propose a novel method of backing up user keys using a non-custodial key management technique that allows users to save and recover a backup of their private key using any independent sign-in method such as google-oAuth or other 3P oAuth.