This study proposes a hybrid model that integrates Wavelet frequency decomposition, convolutional neural networks (CNNs), and Transformers to predict correlation structures among eight major cryptocurrencies. The Wavelet module decomposes asset time series into short-, medium-, and long-term components, enabling multi-scale trend analysis. CNNs capture localized correlation patterns across frequency bands, while the Transformer models long-term temporal dependencies and global relationships. Ablation studies with three baselines (WaveletâCNN, WaveletâTransformer, and CNNâTransformer) confirm that the proposed WaveletâCNNâTransformer (WCT) consistently outperforms all alternatives across regression metrics (MSE, MAE, RMSE) and matrix similarity measures (Cosine Similarity and Frobenius Norm). The performance gap with the WaveletâTransformer highlights CNNâs critical role in processing frequency-decomposed features, and WCT demonstrates stable accuracy even during periods of high market volatility. By improving correlation forecasts, the model enhances portfolio diversification and enables more effective risk-hedging strategies than volatility-based approaches. Moreover, it is capable of capturing the impact of major events such as policy announcements, geopolitical conflicts, and corporate earnings releases on market networks. This capability provides a powerful framework for monitoring structural transformations that are often overlooked by traditional price prediction models.
This research developed a blockchain-enabled framework to enhance secure credentialing and access management for remote healthcare providers and patients across fragmented digital health platforms. Addressing inefficiencies in traditional systems such as lengthy verification delays and data silos, the study employed a design science approach, integrating Hyperledger Fabric and Ethereum smart contracts. Simulations using synthetic healthcare datasets demonstrated a significant improvement, including a 99.99% reduction in credential verification time (to 14 seconds), a 650% throughput increase (to 1,876 TPS), and a 94.7% reduction in security breaches, with 97.8% interoperability success across 234 systems. The framework achieved 99.93% authentication accuracy and 41% administrative cost savings. While results show strong potential, the reliance on simulations may not capture full real-world complexities, and high initial deployment costs remain a constraint. Regulatory compliance, particularly with evolving standards such as HIPAA, was considered essential for implementation. Future work will focus on real-world pilot deployments, AI-driven fraud detection, and the establishment of standardized protocols to support scalability and interoperability. Overall, this study advances secure and efficient healthcare delivery by enabling real-time credentialing and interoperable access, fostering patient-centric care in telemedicine.
Luis deâMarcos, AdriĂĄn DomĂnguezâDĂaz, Javier Junquera-SĂĄnchez, Carlos Cilleruelo ¡ 5 authors
The Dark Web, a hidden segment of the internet, has become a hub for illicit activities, facilitated by various forms of digital identification (IDs) such as email addresses, Telegram accounts, and cryptocurrency wallets. This study conducts a comprehensive analysis of the Dark Webâs identification and communication patterns, focusing on the roles of different ID types and their associated activities. Using a dataset of Dark Web documents, we construct and analyze a bipartite network to model the relationships between IDs and web documents, employing graphâtheoretical metrics such as degree centrality, closeness centrality, betweenness centrality, and k-core decomposition, while analyzing subnetworks formed by ID type. Our findings reveal that Telegram forms the backbone of the network, serving as the primary communication tool for hacking-related activities, particularly within Russian-speaking communities. In contrast, email plays a more decentralized role, facilitating financeâcrypto and other activities but with a high level of fragmentation and English as the predominant language. XMR (Monero) wallets emerge as a key component in financial transactions, forming a cohesive subnetwork focused on cryptocurrency-related activities. The analysis also highlights the modular and hierarchical nature of the Dark Web, with distinct clusters for hacking, financeâcrypto, and drugsânarcotics, often operating independently but with some cross-topic interactions. This study provides a foundation for understanding the Dark Webâs structure and dynamics, offering insights that can inform strategies for monitoring and mitigating its risks.
As healthcare ecosystems shift toward digital-first operations, personal health data faces unprecedented security and privacy risks from increasingly sophisticated cyber threats. This paper examines how the integration of Artificial Intelligence (AI), including Agentic AI, blockchain, and cloud computing, can establish an advanced security framework for resilient healthcare data management. Unlike traditional siloed systems, the proposed model leverages AI-driven anomaly detection, multi-agent orchestration, and explainable AI (XAI) for real-time threat prediction and adaptive defense. Blockchain contributes decentralized trust, tamper-proof auditability, and consent-enforcing smart contracts, while cloud platforms deliver elastic scalability, encrypted storage, and hybrid multi-cloud deployment models. The framework also incorporates federated learning, Model-Chaining Protocols (MCPs), and Zero-Knowledge Proofs (ZKPs) to enhance interoperability, preserve privacy, and enable verifiable compliance. Findings highlight significant improvements in confidentiality, integrity, and availability (CIA) of healthcare data, while simultaneously addressing regulatory obligations such as HIPAA and GDPR through embedded governance and risk orchestration layers. Despite challenges around system complexity and policy harmonization, the paper provides a state-of-the-art synthesis and proposes actionable best practices for healthcare practitioners and policymakers, including adopting continuous AI-powered risk monitoring, blockchain-based patient-centric data ownership, and automated compliance verification mechanisms. Overall, the convergence of AI, blockchain, and cloud technologiesâaugmented by governance-driven orchestrationâoffers a future-proof, cyber-resilient architecture for safeguarding personal health data in digital-first healthcare ecosystems.
The study addresses the intersection of indigenous food sovereignty and data sovereignty in the digital era by exploring community-governed digital infrastructures for indigenous bushfood systems. It explores the use of blockchain networks as a digital commons to safeguard transparent, tamper-proof records and ethical access to indigenosu data or knowledge. Through a participatory design approach embedded in cultural protocols and practices within the Australian bushfood sector, non-fungible tokens (NFTs) were designed to uphold indigenous sovereignty and collective benefit from the research, commerclisation and trade of bushfood species and derived products. This study presented a blockchain-enabled NFT infrastructure incorporating traditional owner tokens (TOTokens) for representing resource and cultural custodianship and enabling usage tracking, and authentic provenance tokens (APTokens) for tracing bushfood provenance and associated traditional ownership. This dual NFT infrastructure design enables the unique digital representation of bushfood and associated traditional ownership, while also provides a socio-economic mechanism to monetise traditional ownership across bushfood research and commerce scenarios. This dual NFT infrastructure is underpinned by smart contracts that enable the tradability and/or transferability of TOTokens and APTokens to automate governance rules, ethical access and collective benefit sharing, without reliance on external authorities. A proof-of-concept was piloted and tested on Polygon a public blockchain demonstrating its technical feasibility. The blockchain-based NFT infrastructure aligns with indigenous data sovereignty principles, CARE and FAIR data frameworks, and can integrate with Internet of things (IoTs), AI, machine learning and data analytics to conduct culturally grounded and ethics-controlled deep eResearch for business innovation and industry practice.
This critical review examines decentralised renewable energy (DRE) systems as game changers for sustainable energy access in Sub-Saharan Africa (SSA). Although rich in renewable resources, over 570 million people in rural communities lack electricity. Traditional energy models, shaped by colonial histories and marked by inefficiencies, have failed to meet the continent's diverse energy needs. DRE systems provide flexible, community-focused solutions that promote energy equity, foster economic growth, and enhance climate resilience. Using Critical Juncture Theory and the Rational Choice Model, this study examines factors influencing DRE adoption. Analyses show how DRE encourages energy democracy, local ownership, and aligns with Sustainable Development Goals, including SDG 7 (Clean Energy) and SDG 13 (Climate Action). However, these systems face obstacles like fragmented policies, insufficient funding, technical gaps, and governance issues. Case studies from Kenya, Nigeria, South Africa, and Ethiopia demonstrate implementation strategies, revealing supportive environments and challenges. This review synthesises policy discussions, highlights innovations like pay-as-you-go financing and digitalisation and outlines an integrated energy planning roadmap. Recommendations include regulatory reforms, blended financing models, capacity-building initiatives, and regional cooperation. This paper argues that decentralisation should be viewed not as a temporary measure but as a foundation for energy strategies. With visionary leadership, collaborative governance, and targeted investments, decentralised systems can transform Sub-Saharan Africa's energy future, prioritising equity, resilience, and sustainability. ⢠Decentralized renewable energy (DRE) is paving the way for fair energy access across Sub-Saharan Africa. ⢠ii. DRE systems are all about empowering communities, promoting energy democracy, and building resilience against climate change. ⢠iii. Unfortunately, there are policy, financial, and technical hurdles that hold back the widespread adoption of DRE in the area. ⢠iv. Various case studies showcase a range of DRE strategies and creative financing solutions. ⢠v. For a successful shift to sustainable energy, integrated policy reforms and regional collaboration are essential.
Smart contract vulnerability detection has attracted increasing attention due to billions of economic losses caused by vulnerabilities. Existing smart contract vulnerability detection methods have high false negative and high false positive rates. To address these issues, we present ByteEye, a bytecode level smart contract vulnerability detection framework with Graph Neural Networks (GNNs). ByteEye first constructs an edge-enhanced Control Flow Graph (CFG) to maintain rich information from the low-level bytecode with low latency. ByteEye also designs and incorporates both general information and vulnerability-specific information into its detection method as bytecode level features. Furthermore, ByteEye flexibly supports machine/deep learning models, especially with graph neural networks, which can facilitate vulnerability detection precisely. The extensive experimental results highlight that ByteEye outperforms the state-of-the-art approaches on all three types of vulnerability detection. ByteEye can achieve an average of 35.29%, 43.95%, and 6.38% higher on F1 than the bytecode level best-performed baseline on reentrancy vulnerability, timestamp dependency vulnerability, and integer overflow/underflow vulnerability, respectively. Moreover, ByteEye can detect 361 new vulnerabilities in real-world smart contracts, which are reported for the first time. ByteEye enhances control flow information, designs general bytecode-level features with expert knowledge, and flexibly supports deep learning models, particularly GNNs, thus achieving high detection effectiveness.
Addressing educational inequity in Sub-Saharan Africa, this research presents an autonomous agent-orchestrated framework for decentralized, culturally adaptive educational content generation on edge devices. The system leverages four specialized agents that work together to generate contextually appropriate educational content. Experimental validation on platforms including Raspberry Pi 4B and NVIDIA Jetson Nano demonstrates significant performance achievements. InkubaLM on Jetson Nano achieved a Time-To-First-Token (TTFT) of 129 ms, an average inter-token latency of 33 ms, and a throughput of 45.2 tokens per second while consuming 8.4 W. On Raspberry Pi 4B, InkubaLM also led with 326 ms TTFT and 15.9 tokens per second at 5.8 W power consumption. The framework consistently delivered high multilingual quality, averaging a BLEU score of 0.688, cultural relevance of 4.4/5, and fluency of 4.2/5 across tested African languages. Through potential partnerships with active community organizations including African Youth & Community Organization (AYCO) and Florida Africa Foundation, this research aims to establish a practical foundation for accessible, localized, and sustainable AI-driven education in resource-constrained environments. Keeping focus on long-term viability and cultural appropriateness, it contributes to United Nations SDGs 4, 9, and 10. Index Terms - Multi-Agent Systems, Edge AI Computing, Educational Technology, African Languages, Rural Education, Sustainable Development, UN SDG.
Chong Chen, Jiachi Chen, Lingfeng Bao, David F. Lo ¡ 10 authors
Smart contract vulnerabilities, particularly improper Access Control that allows unauthorized execution of restricted functions, have caused billions of dollars in losses. GitHub hosts numerous smart contract repositories containing source code, documentation, and configuration files-these serve as intermediate development artifacts that must be compiled and packaged before deployment. Third-party developers often reference, reuse, or fork code from these repositories during custom development. However, if the referenced code contains vulnerabilities, it can introduce significant security risks. Existing tools for detecting smart contract vulnerabilities are limited in their ability to handle complex repositories, as they typically require the target contract to be compilable to generate an abstract representation for further analysis. This paper presents TRACE, a tool designed to secure non-compilable smart contract repositories against access control vulnerabilities. TRACE employs LLMs to locate sensitive functions involving critical operations (e.g., transfer) within the contract and subsequently completes function snippets into a fully compilable contract. TRACE constructs a function call graph from the abstract syntax tree (AST) of the completed contract. It uses the control flow graph (CFG) of each function as node information. The nodes of the sensitive functions are then analyzed to detect Access Control vulnerabilities. Experimental results demonstrate that TRACE outperforms state-of-the-art tools on an open-sourced CVE dataset, detecting 14 out of 15 CVEs. In addition, it achieves 89.2% precision on 5,000 recent on-chain contracts, far exceeding the best existing tool at 76.9%. On 83 real-world repositories, TRACE achieves 87.0% precision, significantly surpassing DeepSeek-R1's 14.3%.
Digital transactions currently exceed trillions of dollars annually, yet traditional paper-based agreements remain a bottleneck for automation, enforceability, and dispute resolution. Natural language contracts introduce ambiguity, require manual processing, and lack computational verifiability, all of which hinder efficient digital commerce. Computable legal contracts, expressed in machine-readable formats, offer a potential solution by enabling automated execution and verification. Blockchain-based smart contracts further strengthen enforceability and accelerate dispute resolution; however, current implementations risk exposing sensitive agreement terms on public ledgers, raising serious privacy and competitive intelligence concerns that limit enterprise adoption. We introduce zk-agreements, a protocol designed to transition from paper-based trust to cryptographic trust while preserving confidentiality. Our design combines zero-knowledge proofs to protect private agreement terms, secure two-party computation to enable private compliance evaluation, and smart contracts to guarantee automated enforcement. Together, these components achieve both privacy preservation and computational enforceability, resolving the fundamental tension between transparency and confidentiality in blockchain-based agreements.
Shikah J. Alsunaidi, Hamoud Aljamaan, Mohammad Hammoudeh
Smart Contracts (SCs), self-executing programs on blockchain platforms, are transforming industries such as banking, healthcare, and supply chains through automated, trustless transactions. However, their inherent vulnerabilities have led to severe financial and operational losses, with large-scale exploits causing substantial economic damage. Machine Learning (ML) has emerged as a promising approach for SC vulnerability detection, yet its effectiveness, adaptability, and generalizability remain insufficiently explored. This article comprehensively classifies current Ethereum SC vulnerabilities and attacks. It also surveys 108 ML-based detection methods, covering both traditional models and a structured taxonomy of advanced approaches such as GNN-based, LLM-based, contrastive learning, ensemble, hybrid, meta-learning, and transfer learning techniques. The strengths, limitations, and practical challenges of these methods are systematically analyzed, with particular attention to factors such as detection stages, classification problems, dataset characteristics, feature engineering, performance evaluation, generalizability, detection capability, model aging, and ethical and privacy implications. Additionally, existing datasets on SC vulnerabilities are reviewed and consolidated. By integrating these insights, this work provides actionable guidelines and a foundation for building secure, resilient, and trustworthy SC ecosystems.
This paper addresses the challenge of designing secure and private digital credentialing systems by leveraging advanced mathematical primitives from applied cryptography. The core of our proposed solution is the application of Zero-Knowledge Proofs (ZKPs), a class of cryptographic protocols that allows for the verification of assertions without disclosing the underlying secret data. We introduce a formal, layered architecture that demonstrates how the mathematical properties of ZKPs can be systematically translated into a robust, large-scale information system. The framework's design is validated against the complex requirements of the academic domain, which serves as a rigorous testbed for our architectural approach. The primary contribution is a blueprint for integrating complex cryptographic protocols into practical system design, demonstrating how mathematical guarantees of privacy can be preserved in a distributed and verifiable manner. This work provides a novel contribution at the intersection of applied cryptography, system architecture, and information security.
This study explores the design and implementation of a blockchain-based system to enhance trust, transparency, and security in academic credentialing. Motivated by the growing distrust in centralized institutions and the inefficiencies of traditional credential verification processes, the research leverages the immutability, decentralization, and transparency of blockchain to develop a tamper-proof mechanism for academic record storage and validation. Using the Ethereum Sepolia test network and real-world student performance data from the Open University Learning Analytics Dataset (OULAD), the system securely issues, verifies, and revokes academic credentials through a custom smart contract developed in Solidity. Each credential is hashed using SHA-256 to ensure student privacy while enabling public, real-time verification. The implementation was conducted in a Google Colab environment using Web3.py and Infura, with batch processing mechanisms and a Web3 interface for seamless interaction. Empirical results reveal performance patterns across modules and highlight opportunities for academic intervention. The system not only demonstrates operational feasibility but also offers a scalable, interoperable, and ethical framework for higher education institutions to combat credential fraud and enhance institutional accountability. Future work will focus on privacy-enhancing cryptographic integrations and decentralized identity standards to further solidify blockchainâs role in education.
Covariance matrices estimated from short, noisy, and non-Gaussian financial time series are notoriously unstable. Empirical evidence suggests that such covariance structures often exhibit power-law scaling, reflecting complex, hierarchical interactions among assets. Motivated by this observation, we introduce a power-law covariance model to characterize collective market dynamics and propose a hybrid estimator that integrates Random Matrix Theory (RMT) with deep Residual Neural Networks (ResNets). The RMT component regularizes the eigenvalue spectrum in high-dimensional noisy settings, while the ResNet learns data-driven corrections that recover latent structural dependencies encoded in the eigenvectors. Monte Carlo simulations show that the proposed ResNet-based estimators consistently minimize both Frobenius and minimum-variance losses across a range of population covariance models. Empirical experiments on 89 cryptocurrencies over the period 2020-2025, using a training window ending at the local Bitcoin peak in November 2021 and testing through the subsequent bear market, demonstrate that a two-step estimator combining hierarchical filtering with ResNet corrections produces the most profitable and well-balanced portfolios, remaining robust across market regime shifts. Beyond finance, the proposed hybrid framework applies broadly to high-dimensional systems described by low-rank deformations of Wishart ensembles, where incorporating eigenvector information enables the detection of multiscale and hierarchical structure that is inaccessible to purely eigenvalue-based methods.
Michael J. Wooldridge, Attila Bagoly, Jonathan J. Ward, Emanuele La Malfa ¡ 5 authors
Recent surges in LLM-driven intelligent systems largely overlook decades of foundational multi-agent systems (MAS) research, resulting in frameworks with critical limitations such as centralization and inadequate trust and communication protocols. This paper introduces the Fetch.ai architecture, an industrial-strength platform designed to bridge this gap by facilitating the integration of classical MAS principles with modern AI capabilities. We present a novel, multi-layered solution built on a decentralized foundation of on-chain blockchain services for verifiable identity, discovery, and transactions. This is complemented by a comprehensive development framework for creating secure, interoperable agents, a cloud-based platform for deployment, and an intelligent orchestration layer where an agent-native LLM translates high-level human goals into complex, multi-agent workflows. We demonstrate the deployed nature of this system through a decentralized logistics use case where autonomous agents dynamically discover, negotiate, and transact with one another securely. Ultimately, the Fetch.ai stack provides a principled architecture for moving beyond current agent implementations towards open, collaborative, and economically sustainable multi-agent ecosystems.
Phishing attacks in Web3 ecosystems are increasingly sophisticated, exploiting deceptive contract logic, malicious frontend scripts, and token approval patterns. We present DeepTx, a real-time transaction analysis system that detects such threats before user confirmation. DeepTx simulates pending transactions, extracts behavior, context, and UI features, and uses multiple large language models (LLMs) to reason about transaction intent. A consensus mechanism with self-reflection ensures robust and explainable decisions. Evaluated on our phishing dataset, DeepTx achieves high precision and recall (demo video: https://youtu.be/4OfK9KCEXUM).
A blockchain-enabled Model integrates blockchain technology with Intrusion Detection Systems to enhance the security of Internet of Things (IoT) networks. It ensures data integrity, decentralization, and tamper-proof logging of intrusion detection. The approach improves trust, transparency, and real-time threat detection in distributed IoT environments. The existing blockchain-based IDS approaches, Blockchain Enabled (BCE-IoT), uniquely integrate blockchain consensus with federated-style local training, lightweight cryptography, and Shapley Additive Explanations (SHAP)-based explainability, ensuring both security and interpretability in IoT environments. The proposed work combines Blockchain technology with explainable artificial intelligence solutions to create a new cybersecurity Model that strengthens intrusion detection within IoT networks. The proposed model enhances transparency in tracking cyberattacks by combining blockchain security storage capabilities with SHAP, an explainable AI. This research utilises machine learning and artificial intelligence to detect threats in real-time, countering Distributed Denial of Service (DDoS), Denial of Service (DoS), scanning, Cross-Site Scripting (XSS), injection, password, and backdoor attacks. BCE-IoT delivers more precise security by combining blockchainâs permanent data features and AI anomaly detectors, thereby reducing security alert mistakes. The performance effectiveness of Blockchain-Enabled IoT surpasses that of the Content Integrity Detection System. It combines Blockchain and Software-Defined Networking to enhance security in network environments, utilising blockchain-based mutual confirmation for software-defined networking to detect and block cyber threats. The evaluation establishes BCE-IoT as an effective IoT network security solution that delivers strong cybersecurity features, is adaptable to modern connected environments, and offers interpretable security solutions. The performance evaluations demonstrate that BCE-IoT provides a robust, flexible, and interpretable cybersecurity solution suitable for modern IoT environments.
Mahran Morsidi, Sharul Tajuddin, S. H. Shah Newaz, Ravi Kumar Patchmuthu ¡ 5 authors
Blockchain technology, originally designed as a secure and immutable ledger, has expanded its applications across various domains. However, its scalability remains a fundamental bottleneck, limiting throughput, specifically Transactions Per Second (TPS) and increasing confirmation latency. Among the many proposed solutions, sharding has emerged as a promising Layer 1 approach by partitioning blockchain networks into smaller, parallelized components, significantly enhancing processing efficiency while maintaining decentralization and security. In this paper, we have conducted a systematic literature review, resulting in a comprehensive review of sharding. We provide a detailed comparative analysis of various sharding approaches and emerging AI-assisted sharding approaches, assessing their effectiveness in improving TPS and reducing latency. Notably, our review is the first to incorporate and examine the standardization efforts of the ITU-T and ETSI, with a particular focus on activities related to blockchain sharding. Integrating these standardization activities allows us to bridge the gap between academic research and practical standardization in blockchain sharding, thereby enhancing the relevance and applicability of our review. Additionally, we highlight the existing research gaps, discuss critical challenges such as security risks and inter-shard communication inefficiencies, and provide insightful future research directions. Our work serves as a foundational reference for researchers and practitioners aiming to optimize blockchain scalability through sharding, contributing to the development of more efficient, secure, and high-performance decentralized networks. Our comparative synthesis further highlights that while Bitcoin and Ethereum remain limited to 7â15 TPS with long confirmation delays, sharding-based systems such as Elastico and OmniLedger have reported significant throughput improvements, demonstrating shardingâs clear advantage over traditional Layer 1 enhancements. In contrast to other state-of-the-art scalability techniques such as block size modification, consensus optimization, and DAG-based architectures, sharding consistently achieves higher transaction throughput and lower latency, indicating its position as one of the most effective Layer 1 solutions for improving blockchain scalability.
This paper investigates how the evolution of interbank payments towards central bank settlement, and thus central bank money as a settlement asset, has affected the dynamics of bank crises.We take the cluster of bank defaults in the United States in 2023 as a starting example and show how, alongside fractional reserves and fast digital communication, centralized settlement in central bank money played a critical role in triggering swift bank failures.We argue that technical centralization has amplified banks' fragility in the development of confidence crises, making bank runs easier and expanding the role of central banks to a point where conflict of interest becomes nearly inevitable.While previous literature has emphasized the effects of fast news spread and online banking, the role of settlement technology in recent bank runs has been largely overlooked.Thus we describe the stability consequences of different settlement architectures in detail, and also discuss potential improvements to the current architecture, particularly decentralized approaches built on distributed ledgers, to mitigate financial instability and reduce the negative effects of centralization without reverting to inefficient legacy systems.
The effective management of electronic medical records is critical to deliver high-quality healthcare services. However, existing systems often suffer from issues such as fragmented data, lack of interoperability, and weak privacy protections, which hinder collaboration among healthcare stakeholders. This paper proposes a blockchain-based system to securely manage and share medical records in a decentralized and transparent manner. By leveraging smart contracts and access control policies, the system empowers patients with control over their data, ensures auditability of all interactions, and facilitates secure data sharing among patients, healthcare providers, insurance companies, and regulatory authorities. The proposed architecture is implemented using a private Ethereum blockchain and evaluated through a scenario-based comparison with the Prince Sultan Military Medical City system, as well as quantitative performance measurements of the blockchain prototype. Results demonstrate significant improvements in data security, access transparency, and system interoperability, with patients gaining the ability to track and control access to their records across multiple healthcare providers, while system performance remained practical for healthcare workflows.
The integration of smart contracts and AI agents in clinical trials fosters a new paradigm of transparent, efficient, and patient-centric research. Smart contractsâself-executing code on permissioned blockchainsâautomate protocol enforcement, consent capture, randomization, and remuneration. AI agents complement them by enabling intelligent participant matching, real-time safety monitoring, and adaptive analytics. In a simulated Phase II oncology trial and pilot implementations in rare-disease cohorts, this framework demonstrated up to 60% faster enrollment, 87% reduction in protocol deviations, near-instantaneous safety alerting, and near complete audit trails. This article details the system architecture, methodologies, results, discussion, and conclusions, with no speculation on future work.
Type of the article: Research ArticleAbstractThe rise of decentralized finance (DeFi) presents new opportunities for accessing modern financial services. Despite their transformative architecture, most DeFi applications are currently unregulated, which exposes market participants to unforeseen risks. Therefore, understanding the level of connectedness between DeFi and traditional finance (TradFi) is crucial, particularly in emerging Asian markets where the level of cryptocurrency acceptance is high. Applying the time-varying parameter vector autoregressive model, this study examines the return connectedness between leading DeFi assets and traditional financial sectors in Indonesia, India, and Vietnam â the top three countries in Asia for cryptocurrency adoption. By analyzing TradFi at the industry level, this study captures sector-specific spillover dynamics that are essential to the monitoring of systemwide risk. The empirical results reveal low, time-varying return spillovers between DeFi and traditional financial sectors in the selected emerging Asian markets. The emerging financial sectors exhibit stronger linkages with broader traditional market indicators than with DeFi, in which assets interact primarily with each other. Emerging financial sectors and gold are the recipients of return spillovers, and DeFi assets act as the return transmitters. The current low degree of integration between DeFi and TradFi offers policymakers a window of opportunity to develop a robust financial regulatory framework that addresses issues of market stability and consumer protection while promoting the advancement of financial innovation.AcknowledgmentsWe thank the editors and anonymous reviewers for their valuable and constructive feedback, which has contributed significantly to improving the quality of this manuscript.
Luca Olivieri, David Beste, Luca Negrini, Lea SchÜnherr ¡ 6 authors
Hyperledger Fabric (HF) is currently the one that made blockchain and smart contracts accessible to industries, providing highly customizable solutions for many enterprise use cases. Despite this, programmers are often discouraged from implementing smart contracts due to the high learning curve and security risks of naive smart contract implementations. At the same time, the advent of Large Language Models (LLMs) for code generation led to new possible scenarios such as creating new smart contract applications starting from natural language, allowing to reduce costs and development times. This paper investigates the maturity of LLMs for the code generation of HF smart contracts. In particular, we (i) generate smart contracts written in Go for HF starting from natural language descriptions, (ii) select state-of-the-art static analyzers of Go program, and (iii) perform a quality and security assessment of the generated smart contracts. Our empirical results show current LLMs do not produce high-quality smart contracts, and a relevant effort to debug and patch contracts containing bugs and possible vulnerabilities.