Abstract: Traditional financial systems commonly suffer from central control, opacity, and exclusion. Decentralized Finance (DeFi) solves this by employing blockchain technology such that users can interact directly without the need for intermediaries. Nonetheless, developing secure, equitable, and user-friendly staking systems is an enormous challenge. This paper introduces a DeFi token staking and rewards management automation platform based on Ethereum-based programmable contracts, React, and Node.js. By removing third-party control and offering transparent protocols, the system enables users to stake tokens securely. Smart contracts handle significant aspects such as the lock-up period, penalties, and reward distribution. With MetaMask support, authentication and transaction processing are seamless. A new dynamic reward mechanism optimizes payouts based on staking duration and network activity, promoting equitable rewards for small players. With OpenZeppelin for comprehensive audits, the platform provides increased security and reliability with a 40% reduction in gas fees compared to traditional models.Democratized passive income access and centralization risk elimination, this work facilitates the creation of scalable, user-centric DeFi applications. It offers a blueprint for the wider adoption of decentralized financial infrastructures, driving the creation of open and inclusive economic systems. Keywords: DeFi, Token Staking, Smart Contracts, Blockchain, Ethereum, Reward Distribution.
Parwat Singh Anjana, Srivatsan Ravi, Herlihy, Maurice
This paper presents a comprehensive analysis of historical data across two popular blockchain networks: Ethereum and Solana. Our study focuses on two key aspects: transaction conflicts and the maximum theoretical parallelism within historical blocks. We aim to quantify the degree of transaction parallelism and assess how effectively it can be exploited by systematically examining block-level characteristics, both within individual blocks and across different historical periods. In particular, this study is the first of its kind to leverage historical transactional workloads to evaluate conflict patterns. By offering a structured approach to analyzing these conflicts, our research provides valuable insights and an empirical basis for developing more efficient parallel execution techniques for smart contracts in the Ethereum and Solana. Our empirical analysis reveals that historical Ethereum blocks frequently achieve high independence, with over 50\% independent transactions in more than 50\% of blocks, while, on average, Solana blocks contain longer conflict chains $\sim$58\%, compared to $\sim$18\% in Ethereum, reflecting fundamentally different parallel execution dynamics.
Against the backdrop of increasingly severe global climate change and environmental challenges, green finance, as a bridge connecting capital and sustainable development, is facing problems such as information asymmetry, transaction frictions and regulatory challenges. This study systematically explores the innovative applications of blockchain technology in the field of green finance and its economic effects. Through literature research and case analysis, we found that blockchain technology, with its characteristics of decentralization, immutability and smart contracts, has shown significant advantages in improving transparency, reducing transaction costs and alleviating the phenomenon of "greenwashing". Research shows that the application of blockchain in areas such as green bonds, carbon trading, renewable energy certification and green supply chain finance has produced economic benefits such as reduced financing costs, improved market efficiency and enhanced information transparency. This paper constructs a comprehensive assessment framework to provide guidance for policymakers and market participants to promote the innovative development of green finance.
Marco Bellucci, Damiano Cesa Bianchi, Luca Bagnoli, Giacomo Manetti
Purpose This study aims to understand the impacts of nonfungible tokens (NFTs) on business models (BMs), particularly in terms of enabling decentralization and digitalization through innovations in products, customer interfaces, infrastructure management and financial aspects. Design/methodology/approach By adopting a conceptual approach based on the BM framework proposed by Osterwalder and Pigneur, this study adopts a qualitative methodology based on multiple case studies such as those of Christie’s, OpenSea, Uffizi Gallery and Ticketmaster. Findings Despite the bursting of the speculative bubble, the exploratory findings suggest that NFTs can foster digitalization and decentralization within existing BMs while also presenting opportunities for new BMs that focus on simplifying and securing technology for customers to serve as intermediaries. Originality/value This study contributes to the specialized literature on the relationship between digital NFT innovation and related BM changes in different market niches within the digital marketplace ecosystem. Furthermore, this study of NFTs also contributes to the growing body of research on accounting and finance related to cryptoassets and digital innovation.
1. Abstract The abstract introduces the growing issue of counterfeit products affecting global supply chains and consumer safety. It states that traditional methods—like barcodes, holograms, and watermarks—are increasingly ineffective due to technological advancements in forgery. To address this, the paper proposes a hybrid authentication framework combining the security of blockchain with the convenience and accessibility of QR codes (smart codes). It summarizes the methodology, highlights real-world examples, and touches on the system’s benefits, including enhanced traceability, consumer trust, and tamper-resistance. The abstract concludes by noting the paper’s focus on methodology, performance evaluation, future scope, and supporting case studies.
Information disorder has become a major societal challenge, impacting public discourse and democracy. This phenomenon has been exacerbated by the spread of social media platforms, affecting various areas, ranging from national elections to public health. Addressing fake news through a manual approach (e.g., human fact-checking) is unfeasible due to the rapid production of textual content. At the same time, applying automatic tools is equally challenging, primarily due to the ambiguity of natural language. In this paper, we addressed online information disorder from a different perspective by proposing a platform that supports trustworthy and reputable news producers and enhances awareness among readers across various social media. Specifically, the proposed platform enables news producers to automatically embed a unique watermark in the text they create, ensuring that the news cannot be manipulated or misattributed. The watermarking is embedded in a fine-grained way, allowing even small extracts of the news to be shared while preserving traceability. Additionally, the association between the watermark and the news item is recorded in a distributed ledger, preventing further manipulation that could arise from centralised management. The aim is to enable readers to make more informed decisions about the content they encounter, even when engaging with excerpts of the original document, minimising reliance on external fact-checking organisations.
This work develops a novel two-phase control framework that enables a swarm of compact spacecraft (agents), such as CubeSats and Nanosats, to autonomously capture tumbling and uncooperative targets. By leveraging decentralized, bio-inspired swarm behavior control and distributed coordination strategies, the proposed system enables fully interchangeable agents to achieve robust, leaderless self-organization. During the capture, flocking behavior guides agents towards the target, while anti-flocking behavior enforces uniform dispersion of agents around it to provide full surface coverage and effective encapsulation prior to capture. A consensus-based protocol synchronizes the capture action among agents by allowing all agents to agree on a common action time. In this process, each agent autonomously identifies available capture points and participates in an auction-based allocation algorithm to collectively allocate optimal capture positions among agents. Simulation results validate the effectiveness of the proposed framework in autonomously capturing targets of various shapes, sizes and motion patterns, and demonstrate scalability across different swarm sizes. Overall, the proposed approach shows significant potential for coordinated, efficient, and robust swarm-based capture of uncooperative targets in space, offering benefits in scalability, adaptability, robustness, and cost-effectiveness.
This research examines the incorporation of Artificial Intelligence (AI) in blockchain consensus algorithms, presenting an extensive overview of current improvements and anticipated effects. We conducted a thorough examination of a diverse array of academic sources, encompassing a broad spectrum of AI methodologies, such as machine learning, deep learning, and reinforcement learning, that have been applied to blockchain consensus mechanisms. The study highlights critical areas where AI can bolster blockchain performance, including enhancing effectiveness, dependability, and flexibility. Despite the promising benefits that AI integration offers, it also presents complexities and potential security risks, including data centralization and increased computational power requirements. In this analysis, we review the risks and examine the proposed mitigation strategies from existing studies, such as federated learning to preserve data privacy, secure multi-party computation to protect sensitive data, and decentralized AI marketplaces to distribute AI resources fairly. This study makes a significant contribution to the field by emphasizing the dual potential of AI to both improve and challenge blockchain systems. By advocating for balanced approaches that prioritize decentralization and security, our findings aim to provide direction for future research and practical applications in this multidisciplinary field.
Blockchain technology has rapidly expanded beyond its original use in cryptocurrencies to a broad range of applications, creating vast amounts of immutable, decentralized data. As blockchain adoption grows, so does the need for advanced data analytics techniques to extract insights for business intelligence, fraud detection, financial analysis and many more. While previous research has examined specific aspects of blockchain data analytics, such as transaction patterns, illegal activity detection, and data management, there remains a lack of comprehensive reviews that explore the full scope of blockchain data analytics. This study addresses this gap through a scoping literature review, systematically mapping the existing research landscape, identifying key topics, and highlighting emerging trends. Using established methodologies for literature reviews, we analyze 470 publications, clustering them into six major research themes: illegal activity detection, data management, financial analysis, user analysis, community detection, and mining analysis. Our findings reveal a strong focus on detecting illicit activities and financial applications, while holistic business intelligence use cases remain underexplored. This review provides a structured overview of blockchain data analytics, identifying research gaps and proposing future directions to enhance the field's impact.
Website information security has become a critical concern in the digital age. This article explores the evolution of website information security, examining its historical development, current practices, and future directions. The early beginnings from the 1960s to the 1980s laid the groundwork for modern cybersecurity, with the development of ARPANET, TCP/IP, public-key cryptography, and the first antivirus programs. The 1990s marked a transformative era, driven by the commercialization of the Internet and the emergence of web-based services. As the Internet grew, so did the range and sophistication of cyber threats, leading to advancements in security technologies such as the Secure Sockets Layer (SSL) protocol, password protection, and firewalls. Current practices in website information security involve a multi-layered approach, including encryption, secure coding practices, regular security audits, and user education. The future of website information security is expected to be shaped by emerging technologies such as artificial intelligence, blockchain, and quantum computing, as well as the increasing importance of international cooperation and standardization efforts. As cyber threats continue to evolve, ongoing research and innovation in website information security will be essential to protect sensitive information and maintain trust in the digital world.
Federated learning (FL) enables collaborative model training across distributed clients while preserving data locality. Although FedAvg pioneered synchronous rounds for global model averaging, slower devices can delay collective progress. Asynchronous FL (e.g., FedAsync) addresses stragglers by continuously integrating client updates, yet naive implementations risk client drift due to non-IID data and stale contributions. Some Blockchain-based FL approaches (e.g., BRAIN) employ robust weighting or scoring of updates to resist malicious or misaligned proposals. However, performance drops can still persist under severe data heterogeneity or high staleness, and synchronization overhead has emerged as a new concern due to its aggregator-free architectures. We introduce Fast-and-Reliable AI Network, FRAIN, a new asynchronous FL method that mitigates these limitations by incorporating two key ideas. First, our FastSync strategy eliminates the need to replay past model versions, enabling newcomers and infrequent participants to efficiently approximate the global model. Second, we adopt spherical linear interpolation (SLERP) when merging parameters, preserving models' directions and alleviating destructive interference from divergent local training. Experiments with a CNN image-classification model and a Transformer-based language model demonstrate that FRAIN achieves more stable and robust convergence than FedAvg, FedAsync, and BRAIN, especially under harsh environments: non-IID data distributions, networks that experience delays and require frequent re-synchronization, and the presence of malicious nodes.
Nakamoto Consensus achieves a decentralized ledger through a single-chain blockchain, assuming a maximum network delay, which limits block generation speed, resulting in low throughput. \cite{pg2018} (PG) enhances throughput using a blockDAG structure, but its probabilistic confirmation restricts smart contract applications. To address this, Mazzaroth proposes a Pow-based blockDAG consensus, employing a linear ordering algorithm to compute the \cite{eth} and achieve state finality, thereby supporting smart contracts. Its dynamic difficulty adjustment, independent of the assumption, adapts to network and hashrate fluctuations, ensuring state consistency via a head chain while maximizing throughput. Simulations validate Mazzaroth's efficient consensus performance. This paper presents the Mazzaroth ordering algorithm, the difficulty adjustment mechanism, and performance evaluation.
Solana is a rapidly evolving blockchain platform that has attracted an increasing number of users. However, this growth has also drawn the attention of malicious actors, with some phishers extending their reach into the Solana ecosystem. Unlike platforms such as Ethereum, Solana has distinct designs of accounts and transactions, leading to the emergence of new types of phishing transactions that we term SolPhish. We define three types of SolPhish and develop a detection tool called SolPhishHunter. Utilizing SolPhishHunter, we detect a total of 8,058 instances of SolPhish and conduct an empirical analysis of these detected cases. Our analysis explores the distribution and impact of SolPhish, the characteristics of the phishers, and the relationships among phishing gangs. Particularly, the detected SolPhish transactions have resulted in nearly \$1.1 million in losses for victims. We report our detection results to the community and construct SolPhishDataset, the \emph{first} Solana phishing-related dataset in academia.
Mutiullah Shaikh, Shafique Memon, Ali Ebrahimi, Uffe Kock Wiil
BACKGROUND: Healthcare information systems are hindered by delayed data sharing, privacy breaches, and lack of patient control over data. The growing need for secure, privacy-preserved access control interoperable in health informatics technology (HIT) systems appeals to solutions such as Blockchain (BC), which offers a decentralized, transparent, and immutable ledger architecture. However, its current adoption remains limited to conceptual or proofs-of-concept (PoCs), often relying on simulated datasets rather than validated real-world data or scenarios, necessitating further research into its pragmatic applications and their benchmarking. OBJECTIVE: This systematic literature review (SLR) aims to analyze BC-based healthcare implementations by benchmarking peer-reviewed studies and turning PoCs or production insights into real-world applications and their evaluation metrics. Unlike prior SLRs focusing on proposed or conceptual models, simulations, or limited-scale deployments, this review focuses on validating practical BC real-world applications in healthcare settings beyond conceptual studies and PoCs. METHODS: Adhering to PRISMA-2020 guidelines, we systematically searched five major databases (Scopus, Web of Science, PubMed, IEEE Xplore, and ScienceDirect) for high-precision relevant studies using MeSH terms related to BC in healthcare. The designed review protocol was registered with OSF, ensuring transparency in the review process, including study screening by independent reviewers, eligibility, quality assessment, and data extraction and synthesis. RESULTS: In total, 82 original studies fully met the eligibility criteria and narratively reported BC-based healthcare implementations with validated evaluation outcomes. These studies highlight the current challenges addressed by BC in healthcare settings, providing both qualitative and quantitative data synthesis on its effectiveness. CONCLUSIONS: BC-based healthcare implementations show both qualitative and quantitative effectiveness, with advancements in areas such as drug traceability (up to 100%) and fraud prevention (95% reduction). We also discussed the recent challenges of focusing more attention in this area, along with a discussion on the mythological consideration of our own work. Our future research should focus on addressing scalability, privacy-preservation, security, integration, and ethical frameworks for widespread BC adoption for data-driven healthcare.
The rapid growth of intelligent systems has raised significant concerns regarding data privacy and security. Traditional centralized machine learning approaches require data aggregation, increasing the risk of data breaches and regulatory violations. Federated Learning (FL) has emerged as a promising paradigm that enables collaborative model training while keeping data decentralized. This paper presents a comprehensive study of federated learning for privacy-preserving intelligent systems, highlighting its architecture, methodologies, applications, and challenges. The study also proposes an adaptive federated framework integrating secure aggregation and differential privacy. The findings demonstrate that federated learning significantly enhances privacy while maintaining model performance, making it suitable for healthcare, finance, and IoT applications.
As blockchain technology matures, the ecosystem is rapidly evolving into a multi-chain world, where different networks offer unique functionalities tailored to specific use cases. However, the inability of these networks to communicate directly with one another has created silos, hindering the full realization of blockchain's potential. This paper explores the critical issue of blockchain interoperability how distinct distributed ledger technologies can securely exchange data and value across platforms. We delve into current interoperability frameworks such as cross-chain bridges, relay chains, and interoperability protocols like Cosmos IBC, Polkadot, and Chainlink CCIP, analyzing their architectural models, security trade-offs, and practical applications. Additionally, the paper evaluates emerging standards and governance challenges in enabling a seamless and trust-minimized communication layer between blockchains. By mapping out both technical and conceptual frontiers, this study offers a comprehensive understanding of where the industry stands today and the pathways forward toward a truly interconnected decentralized web.
This research article introduces a deep learning (DL) for identifying vulnerabilities in the smart contracts, leveraging an optimized DL method. The proposed method, termed LogT BiLSTM, combines bidirectional long short-term memory (BiLSTM) with logistic chaos Tasmanian devil optimization (LogT) for enhancing detection of vulnerability. The evaluation of the suggested approach is conducted using publicly available datasets. Initially, preprocessing steps involve removing duplicate data and imputing missing data. Subsequently, the vulnerability detection process utilizes BiLSTM, with the optimization of the loss function achieved through LogT. Results indicate promising performance in identifying vulnerabilities in SC, highlighting the efficacy of the LogT-BiLSTM approach.
Efficient contract management is essential for ensuring sustainable and reliable supply chains; yet, traditional methods remain manual, error-prone, and inefficient, leading to delays, financial risks, and compliance challenges. AI and blockchain technology offer a transformative alternative, enabling the establishment of automated, transparent, and self-executing smart contracts that enhance efficiency and sustainability. As part of AI-driven smart contract automation, we previously implemented contractual clause extraction using question answering (QA) and named entity recognition (NER). This paper presents the next step in the information extraction process, relation extraction (RE), which aims to identify relationships between key legal entities and convert them into structured business rules for smart contract execution. To address RE in legal contracts, we present a novel hierarchical transformer model that captures sentence- and document-level dependencies. It incorporates global and segment-based attention mechanisms to extract complex legal relationships spanning multiple sentences. Given the scarcity of publicly available contractual datasets, we also introduce the contractual relation extraction (ContRE) dataset, specifically curated to support relation extraction tasks in legal contracts, that we use to evaluate the proposed model. Together, these contributions enable the structured automation of legal rules from unstructured contract text, advancing the development of AI-powered smart contracts.
The rise of decentralized finance (DeFi) has driven the demand for secure and efficient cross-chain transfers, enabling assets to seamlessly flow across different blockchain ecosystems. At the core of these innovations lie smart contracts, which facilitate trustless trading by automating transactions without relying on intermediaries. This paper explores the pivotal role that smart contracts play in enabling secure and transparent cross-chain transfers. We examine how these self-executing contracts eliminate counterparty risks, ensuring that transactions are immutable, verifiable, and executed only when predefined conditions are met. Furthermore, we investigate the challenges associated with cross-chain interoperability, including the complexities of maintaining security across different blockchain protocols. Through case studies of existing cross-chain protocols such as Polkadot, Cosmos, and Layer 2 solutions, we demonstrate how smart contracts are utilized to bridge disparate blockchains, fostering a more inclusive and accessible financial ecosystem. By providing a decentralized and trustless environment for asset transfer, smart contracts not only enhance security but also promote broader adoption of blockchain technology.
Mailson Teles-Borges, Rafael Z. Frantz, José Bocanegra, Sandro Sawicki · 5 authors
Smart cities take advantage of digital services to enhance citizens’ experiences. Integration processes facilitate interactions between these services, providing or improving functionalities. The integration can operate under specific restrictions, which can be represented through smart contracts deployed in a blockchain. Monitoring systems track interactions between integration processes and digital services by recording events from communication ports. In this paper, we argue that current monitoring tools lack the ability to observe these ports or invoke smart contracts. We propose a monitoring system to track integration processes, capture port-reported events, and invoke smart contracts on a blockchain platform.
The topic of financial resilience of territorial communities has gained significant relevance in the context of full-scale war, which has substantially affected public finances in Ukraine.The decentralization process granted new financial autonomy and decision-making possibilities to local powers.However, ongoing military actions have led to decreasing of financial flows, destruction of infrastructure and population displacement, which negatively influenced the financial sustainability of communities.This article aims to explore theoretical approaches to defining financial resilience of territorial communities, analyzing its key determinants and formulating an author's approach.The research identifies that financial resilience is a multidimensional concept incorporating self-financing capacity, effective resource management, debt burden control and adaptability to economic shocks.Additionally, resilience extends beyond financial stability to include proactive risk mitigation strategies that enhance the long-term sustainability of local powers.The morphological and functional analysis of the term "financial resilience" reveals its critical role in ensuring balanced economic and social development, particularly in crisis conditions.Key determinants influencing financial resilience include resource potential, financial inclusion, financial literacy, social capital, and strategic budget planning.Empirical observations suggest that communities with diversified income sources, sound financial planning, and efficient financial control mechanisms demonstrate higher levels of resilience.Moreover, interactions with external support systems, including governmental assistance and international funding, play an important role in maintaining financial stability.The study emphasizes that financial resilience is not solely dependent on internal financial management but also on broader socio-economic factors and governance efficiency.In the context of post-war recovery, financial resilience will serve as a foundation for rebuilding and strengthening local economies.Future 4(14) 2025 271 research should focus on refining financial resilience assessment models and developing targeted policies to support communities in navigating economic uncertainties and structural changes.
Digital Financial Architecture (DFA) has been invented to alter enterprise financial systems that offer agility, scalability, and operational efficiency through advanced technologies like Cloud Computing, Artificial Intelligence (AI), Blockchain, and API-driven platforms. The fact that this transformation has brought together traditional and rigid financial structures with modular and decentralized platforms to create the often real-time decision-making and compliance. Empirical evidence reveals that digital payment is positively related to financial inclusion, and 0.018% of the operational costs will be decreased with a 1% increase in digital transactions. Practices of use of AI and Cloud Computing have reduced the time that is required for making decisions while DevOps practice has decreased the time required for development cycles and deployment efficiency. It enhances the protection of transactions and supports the development of big platforms in finance while allowing data openness. By connecting ESB with EA, users experience better interconnection between systems while making their operations expandable. Numbers show exactly how dynamic resource management works and performance results from our continuous delivery methods. They will look at ways to broaden the previous systems, investigate the security issues surrounding data dissemination, and explore merging technology types with modern digital banking networks.
Ratih Fitria Putri, Robert Marbun, Wulandari Harjanti
The rapid development of cryptocurrency investment has raised concerns regarding its impact on economic stability, particularly in emerging markets. This study employs a qualitative approach through literature review and library research to analyze the risks associated with cryptocurrency investments and their implications for financial stability. This research identifies key risk factors, including market volatility, regulatory uncertainty, cybersecurity threats, and financial system disruptions by examining existing scholarly works, regulatory frameworks, and market trends. The findings indicate that cryptocurrency investments offer opportunities for financial inclusion and economic diversification but also pose significant risks to economic stability due to price fluctuations and speculative behavior. Furthermore, the lack of a unified regulatory framework across different countries exacerbates these risks, leading to potential financial instability in emerging economies. The study highlights the necessity of regulatory intervention and policy formulation to mitigate these risks while harnessing the benefits of cryptocurrency investments. Governments and financial institutions in emerging markets must establish robust risk management strategies and regulatory frameworks to balance innovation and financial stability. This research contributes to the academic discourse by providing a comprehensive understanding of the relationship between cryptocurrency investments and economic stability in emerging markets. Future research should focus on empirical case studies to further explore the long-term effects of cryptocurrency investments on financial stability.