Decentralized Finance (DeFi) smart contracts manage billions of dollars, making them a prime target for exploits. Price manipulation vulnerabilities, often via flash loans, are a devastating class of attacks causing significant financial losses. Existing detection methods are limited. Reactive approaches analyze attacks only after they occur, while proactive static analysis tools rely on rigid, predefined heuristics, limiting adaptability. Both depend on known attack patterns, failing to identify novel variants or comprehend complex economic logic. We propose PMDetector, a hybrid framework combining static analysis with Large Language Model (LLM)-based reasoning to proactively detect price manipulation vulnerabilities. Our approach uses a formal attack model and a three-stage pipeline. First, static taint analysis identifies potentially vulnerable code paths. Second, a two-stage LLM process filters paths by analyzing defenses and then simulates attacks to evaluate exploitability. Finally, a static analysis checker validates LLM results, retaining only high-risk paths and generating comprehensive vulnerability reports. To evaluate its effectiveness, we built a dataset of 73 real-world vulnerable and 288 benign DeFi protocols. Results show PMDetector achieves 88% precision and 90% recall with Gemini 2.5-flash, significantly outperforming state-of-the-art static analysis and LLM-based approaches. Auditing a vulnerability with PMDetector costs just $0.03 and takes 4.0 seconds with GPT-4.1, offering an efficient and cost-effective alternative to manual audits.
The rapid digitization of commercial, governmental, and legal transactions has created an urgent need for efficient, secure, and transparent dispute resolution mechanisms. Traditional arbitration systems often fall short when handling the complexity and volume of digital evidence, smart contracts, and cross-border interactions. This study proposes a novel AI-powered digital arbitration framework that integrates smart contracts, blockchain-based evidence authentication, and explainable artificial intelligence (AI) to automate and modernize the arbitration process. The framework comprises three core layers: (i) a smart contract-based agreement layer that encodes legal terms and self-executing arbitration clauses; (ii) a blockchain-based evidence management layer that ensures the integrity, authenticity, and traceability of submitted evidence; and (iii) an AI-based arbitration engine that classifies, interprets, and evaluates evidence using transformer and LSTM models, supported by SHAP and LIME for interpretability. A controlled experimental setup was implemented using Ethereum and Hyperledger Fabric testnets, with AI models trained on 1,200 annotated arbitration cases. Results demonstrate a 99.5% reduction in arbitration time, a 92.4% agreement rate between AI and expert rulings, and a 99% accuracy in tampering detection. Furthermore, 87.3% of AI-generated decisions were rated as interpretable and acceptable by legal experts. These findings confirm the system's ability to deliver fast, accurate, and explainable arbitration decisions while complying with legal standards. This research contributes a foundational blueprint for deploying autonomous arbitration systems in digital governance, offering scalable solutions for future applications in smart contracts, e-commerce disputes, and algorithmic legal infrastructure.
To address the limitations of blockchain data storage capacity and uneven dis-tribution, this paper proposes a Chord dual-ring distributed storage method based on virtual nodes. Building upon the original Chord protocol, this approach introduces virtual rings to construct a âstorage ring-virtual ringâ dual-ring structure. Target virtual nodes are located through routing table lookups, and data is distributed across the storage ring via a name mapping mechanism. Sim-ulation experiments validate the proposed scheme's effectiveness by evalu-ating load balancing and query success rate across varying sharding granulari-ties. Results demonstrate that this approach not only efficiently achieves shard-ed storage for blockchain data but also ensures balanced distribution of block data.
Despite the popularity of Hashed Time-Locked Contracts (HTLCs) because of their use in wide areas of applications such as payment channels, atomic swaps, etc, their use in exchange is still questionable. This is because of its incentive incompatibility and susceptibility to bribery attacks. State-of-the-art solutions such as MAD-HTLC (Oakland'21) and He-HTLC (NDSS'23) address this by leveraging miners' profit-driven behaviour to mitigate such attacks. The former is the mitigation against passive miners; however, the latter works against both active and passive miners. However, they consider only two bribing scenarios where either of the parties involved in the transfer collude with the miner. In this paper, we expose vulnerabilities in state-of-the-art solutions by presenting a miner-collusion bribery attack with implementation and game-theoretic analysis. Additionally, we propose a stronger attack on MAD-HTLC than He-HTLC, allowing the attacker to earn profits equivalent to attacking naive HTLC. Leveraging our insights, we propose \prot, a game-theoretically secure HTLC protocol resistant to all bribery scenarios. \prot\ employs a two-phase approach, preventing unauthorized token confiscation by third parties, such as miners. In Phase 1, parties commit to the transfer; in Phase 2, the transfer is executed without manipulation. We demonstrate \prot's efficiency in transaction cost and latency via implementations on Bitcoin and Ethereum.
Large Language Models generate complex reasoning chains that reveal their decision-making, yet verifying the faithfulness and harmlessness of these intermediate steps remains a critical unsolved problem. Existing auditing methods are centralized, opaque, and hard to scale, creating significant risks for deploying proprietary models in high-stakes domains. We identify four core challenges: (1) Robustness: Centralized auditors are single points of failure, prone to bias or attacks. (2) Scalability: Reasoning traces are too long for manual verification. (3) Opacity: Closed auditing undermines public trust. (4) Privacy: Exposing full reasoning risks model theft or distillation. We propose TRUST, a transparent, decentralized auditing framework that overcomes these limitations via: (1) A consensus mechanism among diverse auditors, guaranteeing correctness under up to $30\%$ malicious participants. (2) A hierarchical DAG decomposition of reasoning traces, enabling scalable, parallel auditing. (3) A blockchain ledger that records all verification decisions for public accountability. (4) Privacy-preserving segmentation, sharing only partial reasoning steps to protect proprietary logic. We provide theoretical guarantees for the security and economic incentives of the TRUST framework. Experiments across multiple LLMs (GPT-OSS, DeepSeek-r1, Qwen) and reasoning tasks (math, medical, science, humanities) show TRUST effectively detects reasoning flaws and remains robust against adversarial auditors. Our work pioneers decentralized AI auditing, offering a practical path toward safe and trustworthy LLM deployment.
Gyuyeon Na, Minjung Park, Hyeonjeong Cha, Sangmi Chai
We present HCLA, a human-centered multi-agent system for anomaly detection in digital-asset transactions. The system integrates three cognitively aligned roles: Rule Abstraction, Evidence Scoring, and Expert-Style Justification. These roles operate in a conversational workflow that enables non-experts to express analytical intent in natural language, inspect structured risk evidence, and obtain traceable, context-aware reasoning. Implemented with an open-source, web-based interface, HCLA translates user intent into explicit analytical rules, applies classical anomaly detectors to quantify evidential risk, and reconstructs expert-style justifications grounded in observable transactional signals. Experiments on a cryptocurrency anomaly dataset show that, while the underlying detector achieves strong predictive accuracy, HCLA substantially improves interpretability, interaction, and decision transparency. Importantly, HCLA is not designed to explain a black-box model in the conventional XAI sense. Instead, we reconstruct a traceable expert reasoning process that aligns algorithmic evidence with regulatory and investigative judgment. By explicitly separating evidence scoring from expert-style justification, the framework emphasizes accountability beyond explainability and addresses practical requirements for regulatory, audit, and compliance-driven financial forensics. We describe the system architecture, closed-loop interaction design, datasets, evaluation protocol, and limitations. We argue that a human-in-the-loop reasoning reconstruction paradigm is essential for achieving transparency, accountability, and trust in high-stakes financial environments. Keywords: Human-Centered AI; LLM-Agent System; Multi-Agent Architecture; Anomaly Detection; Digital Asset Transactions; Cryptocurrency Forensics; Blockchain Analytics; Human-in-the-Loop; Explainable AI (XAI); Interpretability
Berikut ringkasan akademik dari tulisan âBitcoin dalam Ekonomi Syariah: Tinjauan di Pasar Muslimâ: Artikel ini mengkaji keamanan dan kepatuhan Bitcoin terhadap prinsip ekonomi syariah dalam konteks pasar Muslim, ditengah tren global kripto yang berkembang pesat. Kajian berangkat dari kebutuhan akan penilaian mendalam terkait kesesuaian Bitcoin dengan nilai maqasid al-shariah, khususnya keadilan, transparansi, dan kemaslahatan. Tujuan utama penelitian adalah mengevaluasi apakah Bitcoin dapat diadopsi dalam sistem keuangan Islam, dengan menyoroti aspek keamanan transaksi dan kepatuhan terhadap larangan riba, gharar, serta maysir. Penelitian menggunakan pendekatan mixed methods, menggabungkan survei kuantitatif dari pengguna Bitcoin di pasar Muslim serta kajian kualitatif atas literatur, fatwa, dan pendapat ulama. Hasil survei menunjukkan bahwa sebagian besar responden mengakui keunggulan teknologi blockchain dalam aspek keamanan dan transparansi, namun mengkhawatirkan volatilitas harga dan potensi spekulasi yang belum sesuai prinsip syariah. Analisis empiris dan wawancara ahli menemukan bahwa penerimaan Bitcoin secara syariah masih tergantung pada penguatan regulasi, pengawasan lembaga keuangan Islam, dan inovasi digital yang dapat mengeliminasi unsur spekulatif. Secara teoretis, penelitian berkontribusi dengan integrasi antara perspektif maqasid al-shariah dan analisis keamanan digitalâmemperluas pemahaman tentang potensi dan tantangan kripto dalam ekonomi Islam modern. Rekomendasi diberikan kepada regulator dan pelaku industri untuk mengembangkan instrumen kripto halal melalui smart contract, audit syariah, serta peningkatan literasi digital di kalangan masyarakat Muslim. Dengan landasan evidence-based dan pendekatan interdisipliner, artikel ini memperkuat wacana integrasi teknologi blockchain ke dalam prinsip keuangan syariah sebagai strategi inklusi dan inovasi di pasar global Muslim.
This study aims to: (1) examine the legal aspects of smart contracts in the Indonesian legal system and propose adaptations to existing laws and regulations to suit the characteristics of smart contract technology; (2) identify normative and implementative challenges in the legal adaptation process, such as normative gaps and the lack of a standard framework; and (3) formulate the direction of legal reform needed to form responsive and contextual smart contract regulations. Unlike previous studies which are generally descriptive and technological in nature, this study provides a legal contribution by mapping gaps in national contract law and presenting a comparative analysis as a basis for formulating a smart contract regulation model in Indonesia. This research uses a juridical-normative and empirical-qualitative approach, with a doctrinal legal analysis of the legislation, legal literature study, and in-depth interviews with legal practitioners and technology actors. The main findings of this research indicate that there is no legal framework that explicitly regulates the validity and execution of smart contracts, which creates legal uncertainty. Therefore, it is recommended that the principle of freedom of contract in the Civil Code be expanded to include digital contracts that are executed automatically. In addition, special regulations are needed in the form of derivative regulations or technical guidelines that bridge blockchain technology with national civil law principles. The practical contribution of this research is to provide a starting point for policymakers and academics in designing smart contract regulations in Indonesia that are comprehensive and responsive to technological developments, so that they can provide legal certainty while supporting digital innovation.
This study builds on the fiscal decentralization and promotion tournament theories. Utilizing 12 years of panel data from 108 cities in the Yangtze River Economic Belt, we measure the urban carbon productivity index through a super-efficient SBM model that incorporates undesired outputs. We then analyze the effect of local land finance strategy interaction on urban carbon productivity and its mechanism using a spatial self-lagging model. Our key findings reveal: (1) Local governments exhibit mimetic spatial strategy interactions in land finance behavior, both from a geographical distance perspective and when combining economic development levels with geographical distance factors; (2) the interactive behavior of local land finance strategy has a significant inhibitory effect on urban carbon productivity, thereby leading to a loss of urban carbon productivity; (3) the local land finance strategy interaction causes a reduction in urban carbon productivity by changing the cross-city foreign direct investment strategy interaction, environmental regulation strategy interaction, and industrial structure strategy interaction. To achieve these goals, China should foster healthy competition among cities regarding land finance and promote âthree-way synergyâ between opening up, environmental protection, and industrial upgrading. These coordinated efforts aim to boost urban carbon productivity in the Yangtze Basin while offering developing countries a fresh approach to watershed governance focused on carbon reduction goals.
Cryptocurrency price prediction poses significant challenges due to the inherent volatility and nonlineardynamics of the market. This study introduces a hybrid stacked modeling framework that integrates machine learning (ML) and deep learning (DL) techniques, capitalizing on their complementary strengths-ML models are effective at capturing nonlinearfeature interactions in structured data, while DL architectures are adept at modeling temporal dependencies in sequential data. The proposed model leverages historical price data, technical indicators, macroeconomic variables, and sentiment metrics, with feature engineering applied to enhance predictive capability. Empirical evaluation was conducted through two experimental setups: (i) short-term, monthly segment analysis and (ii) long-term generalization via five-fold cross-validation. The hybrid model outperformed individual baseline models, achieving up to 18.3% lower RMSE and 6.7% higher directional accuracy. Additionally, it yielded superior risk-adjusted returns, with Sharpe Ratios reaching 0.094 on the Ethereum dataset. Beyond technical improvements, this research offers foresight into digital financial markets, providing a robust tool for investors, institutions, and policymakers navigating the evolving cryptocurrency landscape. The model supports more informed decision-making, enhances market oversight, and contributes to the development of adaptive regulatory frameworks for digital finance.
This paper reconstructs zero-knowledge extensions on Solana as an architecture theory. Drawing on the existing ecosystem and on the author's prior papers and implementations as reference material, we propose a two-axis model that normalizes zero-knowledge (ZK) use by purpose (scalability vs. privacy) and by placement (on-chain vs. off-chain). On this grid we define five layer-crossing invariants: origin authenticity, replay-safety, finality alignment, parameter binding, and private consumption, which serve as a common vocabulary for reasoning about correctness across modules and chains. The framework covers the Solana Foundation's three pillars (ZK Compression, Confidential Transfer, light clients/bridges) together with surrounding components (Light Protocol/Helius, Succinct SP1, RISC Zero, Wormhole, Tinydancer, Arcium). From the theory we derive two design abstractions - Proof-Carrying Message (PCM) and a Verifier Router Interface - and a cross-chain counterpart, Proof-Carrying Interchain Message (PCIM), indicating concrete avenues for extending the three pillars.
INTRODUCTION: Somalia, the 44th largest country in the world by land area, struggles with a heavy burden of infectious diseases. Since 1991, populations have lacked essential health services, exacerbated by recurring infectious-disease outbreaks. Recurrent outbreaks of measles, cholera, and polio have devastated public health, generating significant morbidity and mortality. Despite improvements through new graduates, these issues remain unresolved. This study examines the impact of climate change on infectious-disease outbreaks in Somalia focusing on cholera, measles, and polio-to fill a gap in the literature by linking climate variability with outbreak dynamics and identifying weaknesses in Somalia's health system. The findings will inform targeted public-health strategies. METHOD: Following PRISMA guidelines, we undertook a narrative review of English-language literature (1990 - March 2025). Searches in PubMed, Scopus, Web of Science and Google Scholar combined terms for infectious-disease outbreaks, climate change and Somalia/Horn of Africa. Of 202 records identified, 74 met inclusion criteria. Two reviewers independently screened, extracted data and applied six-step inductive coding in NVivo 12, synthesizing findings into thematic domains. RESULTS: Four interlinked themes emerged. (1) Fragile health system: < 0.4 doctors, nurses and midwives per 10 000 population, poorly equipped facilities and patchy surveillance. (2) Control measures: routine immunization completeness â20%; limited oral-cholera-vaccine and WASH coverage sustain transmission. (3) Political instability and conflict: insecurity, decentralized coordination and ⼠2.6 million IDPs hamper rapid response. (4) Impact of climate change: drought-induced water scarcity and flood-related latrine breaches create year-round face-oral exposure, while climate shocks divert resources and swell susceptibility pools. CONCLUSION: Outbreak control in Somalia now hinges on integrating climate adaptation with health-system strengthening. Climate-proofed WASH infrastructure, mobile vaccination and surveillance linked to hydro-meteorological alerts, a National Outbreak Operations Centre, and ring-fenced financing are urgent priorities. Without such measures each extreme-weather event will erase hard-won gains; with them, Somalia can break the climate-outbreak feedback loop.
The Internet of Medical Things (IoMT) transforms healthcare by enabling real-time monitoring of patient vitals, such as heart rate and glucose levels, but faces significant challenges in securing sensitive data against cyber threats and ensuring reliability in resource-constrained wearable devices, like low-power biosensors with limited computational capacity. The rise of quantum computing, particularly Shor algorithm, threatens to break traditional cryptographic methods (e.g., RSA, ECC) within 5â10 years by efficiently solving their underlying mathematical problems, endangering patient data confidentiality. Post-quantum cryptography (PQC), such as lattice-based schemes, offers resilience but demands high computational resources, challenging IoMT scalability. Unlike other PQC IoMT frameworks, such as those using NTRU, which prioritize computational simplicity but lack advanced privacy mechanisms, Q-PRADAX pioneers a secure, adaptive data aggregation framework, integrating Ring-LWE-based PQC for quantum-resilient confidentiality, compact zk-SNARK proofs for tamper-proof verification of patient vitals, and adaptive clustering for enhanced network reliability and scalability. Evaluated using OMNeT + + 6.0.3 with INET 4.5, Q-PRADAX achieves 94.5% diagnostic accuracy on ECG datasets, 100% tampering detection, and 99.9% packet delivery across 1000 devices in its Baseline scenario, with a security latency of 12.2 ms/packet and energy consumption of 0.38 mJ/packet on ARM Cortex-M4 devices (200 mAh). Outperforming existing IoMT solutions in security and fault tolerance, Q-PRADAX establishes a global standard for a secure, patient-centric IoMT ecosystem, redefining reliable healthcare delivery.
Fowokemi Alaba Ogedengbe, Micheal Olajide Adelowotan
Research question/Issue Blockchain, as a disruptive technology, is reshaping organisational frameworks through its inherent immutability and robust security features. This offers significant potential to enhance corporate governance by fostering more efficient and effective governance models. This paper seeks to (1) critically assess the transformative influence of blockchain technology on corporate governance and (2) propose a âHolistic Governance Synthesis Frameworkâ that identifies and evaluates the features, factors, benefits, risks and impacts of blockchain integration in corporate governance using various theoretical perspectives. Research findings/Insight A thematic analysis of 106 peer-reviewed articles on blockchain and corporate governance was conducted, with dataset retrieved from Web of science and Scopus. Analysis was carried out through manual review of papers and the use of research analysis tools. Analysis tools employed include Microsoft Excel, Harzing Publish or Perish (for data organisation, analysis and thematic coding), VOS viewer and LaTeX (for data visualisation). Identified key themes include critical features (e.g., immutability, smart contract, and traceability), impacts (e.g., investment efficiency, improved firm performance, and audit quality), risks and challenges (e.g., regulatory uncertainty, technical limitation and ethical concerns) associated with blockchain integration in corporate governance are discussed. Based on these emergent themes, conceptual frameworks were proposed to guide future blockchain applications in governance contexts. Theoretical/Academic implications Theories such as agency, institutional, stakeholder and transaction-cost economies were used to provide valuable insights for stakeholders. This is aimed at equipping management and shareholders with a nuanced understanding of the internal and external dynamics of blockchain adoption in corporate governance. It also offers implications for institutional bodies to develop regulations that recognize blockchainâs role, thereby enhancing monitoring mechanisms and mitigating associated risks. Practitioner/Policy implications This study offers insight to policymakers by advocating for regulatory frameworks that recognize and guide the integration of disruptive technologies, including blockchain, into corporate governance. Such policies should address the use of digital currencies and blockchain activities, enhancing tax recognition and reducing evasion.
Problem Space: AI Vulnerabilities and Quantum Threats Generative AI vulnerabilities: model inversion, data poisoning, adversarial inputs. Quantum threats Shor Algorithm breaking RSA ECC encryption. Challenge Secure generative AI models against classical and quantum cyberattacks. Proposed Solution Collaborative Penetration Testing Suite Five Integrated Components: DAST SAST OWASP ZAP, Burp Suite, SonarQube, Fortify. IAST Contrast Assess integrated with CI CD pipeline. Blockchain Logging Hyperledger Fabric for tamper-proof logs. Quantum Cryptography Lattice based RLWE protocols. AI Red Team Simulations Adversarial ML & Quantum-assisted attacks. Integration Layer: Unified workflow for AI, cybersecurity, and quantum experts. Key Results 300+ vulnerabilities identified across test environments. 70% reduction in high-severity issues within 2 weeks. 90% resolution efficiency for blockchain-logged vulnerabilities. Quantum-resistant cryptography maintained 100% integrity in tests. Outcome: Quantum AI Security Protocol integrating Blockchain Quantum Cryptography AI Red Teaming.
The financial market is known to be highly sensitive to news. Therefore, effectively incorporating news data into quantitative trading remains an important challenge. Existing approaches typically rely on manually designed rules and/or handcrafted features. In this work, we directly use the news sentiment scores derived from large language models, together with raw price and volume data, as observable inputs for reinforcement learning. These inputs are processed by sequence models such as recurrent neural networks or Transformers to make end-to-end trading decisions. We conduct experiments using the cryptocurrency market as an example and evaluate two representative reinforcement learning algorithms, namely Double Deep Q-Network (DDQN) and Group Relative Policy Optimization (GRPO). The results demonstrate that our news-aware approach, which does not depend on handcrafted features or manually designed rules, can achieve performance superior to market benchmarks. We further highlight the critical role of time-series information in this process.
Distributed peer-to-peer (P2P) networking delivers the new blocks and transactions and is critical for the cryptocurrency blockchain system operations. Having poor P2P connectivity reduces the financial rewards from the mining consensus protocol. Previous research defines beneficalness of each Bitcoin peer connection and estimates the beneficialness based on the observations of the blocks and transactions delivery, which are after they are delivered. However, due to the infrequent block arrivals and the sporadic and unstable peer connections, the peers do not stay connected long enough to have the beneficialness score to converge to its expected beneficialness. We design and build Dynamic Peer Beneficialness Prediction (DyPBP) which predicts a peer's beneficialness by using networking behavior observations beyond just the block and transaction arrivals. DyPBP advances the previous research by estimating the beneficialness of a peer connection before it delivers new blocks and transactions. To achieve such goal, DyPBP introduces a new feature for remembrance to address the dynamic connectivity issue, as Bitcoin's peers using distributed networking often disconnect and re-connect. We implement DyPBP on an active Bitcoin node connected to the Mainnet and use machine learning for the beneficialness prediction. Our experimental results validate and evaluate the effectiveness of DyPBP; for example, the error performance improves by 2 to 13 orders of magnitude depending on the machine-learning model selection. DyPBP's use of the remembrance feature also informs our model selection. DyPBP enables the P2P connection's beneficialness estimation from the connection start before a new block arrives.
Dun Li, Dezhi Han, NoÍl Crespi, Roberto Minerva ¡ 8 authors
Digital twin (DT) technology integrates Internet of Things (IoT), communication networks, and sensor systems through high-fidelity modeling and multi-dimensional simulation, enabling dynamic mapping and real-time optimization of physical objects. However, DT development still faces several challenges, including cross-platform interoperability limitations, excessive latency in real-time scenarios, security vulnerabilities in distributed deployments, and the complexity of accurately modeling multi-modal systems. Blockchain (BC) enhances the security and functional scope of DTs across diverse applications. This survey begins by introducing the core principles of BC and DT, and then investigates the rationale and benefits behind their integration. From a data-centric perspective, we explore how Blockchain-empowered Digital Twins (BCDTs) enhance data storage, secure exchange, privacy protection, and system interoperability. The survey further explores the architecture of BCDT systems, covering network topology, functional modules, platform design, and representative prototypes, offering insights into real-world applications. In addition, we survey how BCDT supports the convergence of key Industry 4.0 technologies, including the Internet of Things, vehicle networks, unmanned aerial systems, artificial intelligence, federated learning, 5G mobile networks, and software-defined networking. Industrial-grade quality BCDT-supported applications are highlighted, providing a solid foundation for further research. Finally, we analyze the challenges faced by BCDT and offer some optimistic suggestions for further research in the field of BCDT.
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.