Search Results (391)

As governments increasingly explore digital financial instruments to diversify funding channels and expand citizen participation, Thailand’s G-Token represents an early attempt to integrate blockchain technology into sovereign debt issuance. This study examines its potential implications through a multi-dimensional risk and governance framework, situating the analysis within both domestic regulatory structures and international benchmarks. The evaluation considers macroeconomic effects—such as potential shifts in monetary policy transmission, bank disintermediation risks, and systemic liquidity impacts—alongside micro-level concerns involving investor protection, market integrity, and financial literacy. Using comparative analysis with the European Union, Singapore, and United States regulatory approaches, the paper identifies critical gaps in legal classification, oversight maturity, and structural safeguards. Findings indicate that while Thailand’s design—particularly its separation from payment systems—supports monetary coherence, its ad hoc legal integration, reliance on administrative investor protections, and early-stage market infrastructure pose vulnerabilities if adoption scales. The study concludes that achieving long-term viability will require explicit statutory authorization, enhanced disclosure and governance standards, strengthened interagency oversight, and inclusive market access strategies. These insights provide a structured basis for emerging economies seeking to adopt government-backed tokenized instruments without undermining financial stability or public trust. Full article

Detecting, tracking, and preventing cryptocurrency money laundering within blockchain systems is a major challenge for governments worldwide. This paper presents an anomaly detection model based on blockchain technology and machine learning to identify cryptocurrency money-laundering accounts within Ethereum blockchain networks. The proposed model employs Particle Swarm Optimization (PSO) to select optimal feature subsets. Additionally, three machine learning algorithms—XGBoost, Isolation Forest (IF), and Support Vector Machine (SVM)—are employed to detect suspicious accounts. A Genetic Algorithm (GA) is further applied to determine the optimal hyperparameters for each machine learning model. The evaluations demonstrate the superiority of the XGBoost algorithm over SVM and IF, particularly when enhanced with GA. It achieved accuracy, precision, recall, and F1-score values of 0.98, 0.97, 0.98, and 0.97, respectively. After applying GA, XGBoost’s performance metrics improved to 0.99 across all categories. Full article

This study explores the influence of governance structures board diversity, public shareholding, and managerial ownership on CSR disclosure among LQ45 companies in Indonesia during 2021–2023. Using panel data regression analysis, the research identifies significant relationships between these governance variables and CSR practices. The study highlights the critical role of board diversity in fostering inclusivity and aligning corporate strategies with societal expectations, supported by stakeholder and legitimacy theories. Public shareholding and managerial ownership also play pivotal roles in enhancing transparency and aligning managerial incentives with organizational goals. The integration of advanced technologies, such as blockchain and AI, is discussed as a means to improve CSR practices through enhanced transparency and efficiency. These findings provide actionable insights for policymakers, corporate leaders, and investors, emphasizing the need for inclusive governance and technological innovation to advance sustainable business practices. Full article

Natural disasters often strike unexpectedly, leaving thousands of victims and affected individuals each year. Effective disaster preparedness is critical to reducing these consequences and accelerating recovery. This paper presents Eghatha, a blockchain-based decentralized system designed to optimize humanitarian aid delivery during crises. By enabling secure and transparent transfers of donations and relief from donors to beneficiaries, the system enhances trust and operational efficiency. All transactions are immutably recorded and verified on a blockchain network, reducing fraud and misuse while adapting to local contexts. The platform is volunteer-driven, coordinated by civil society organizations with humanitarian expertise, and supported by government agencies involved in disaster response. Eghatha’s design accounts for disaster-related constraints—including limited mobility, varying levels of technological literacy, and resource accessibility—by offering a user-friendly interface, support for local currencies, and integration with locally available technologies. These elements ensure inclusivity for diverse populations. Aligned with Morocco’s “Digital Morocco 2030” strategy, the system contributes to both immediate crisis response and long-term digital transformation. Its scalable architecture and contextual sensitivity position the platform for broader adoption in similarly affected regions worldwide, offering a practical model for ethical, decentralized, and resilient humanitarian logistics. Full article

Ownership rights related to land and property represent a highly contentious matter in areas across Pakistan because female inheritors struggle to assert their property rights due to cultural practices along with unclear procedures and traditional document systems. The present government-controlled systems demonstrate inadequate proficiency along with safety protocols to execute fair inheritance distribution, mainly impacting marginalized populations. This research introduces a blockchain system known as the Land Registration and Inheritance Automation System (LRIAS) which prioritizes the female protection of inheritance privileges. The proposed system includes digitalizing the traditional paper-based land registration and inheritance process. The system ensures blockchain security through the implementation of MetaMask together with Web3.js for Ethereum transactions. The blockchain system distributes inheritances through programmed agreements which follow Shariah validation rules. The LRIAS establishes permanent and free-version records that show who owns land and who the legal heirs are. The system enables women to access their inheritance records through verifiable reliable data which cannot be altered. Through the system, authorities can verify inheritance claims and execute them without bureaucratic interference, which minimizes both legal disputes and family conflicts. Experimental tests show that the LRIAS succeeds in safeguarding women’s land inheritance claims and increasing confidence in legal inheritance procedures. Full article

Blockchain is described as a technology of trust. Its design relies on cryptography, decentralization, and immutability to ensure secure and transparent transactions. Yet users frequently report confusion, frustration, and skepticism when engaging with blockchain applications. This tension is the blockchain trust paradox: while trust is engineered into the technology, trust is not always experienced by its users. Our article examines the paradox through three theoretical perspectives. Socio-Technical Systems (STS) theory highlights how trust emerges from the interaction between technical features and social practices; Technology Acceptance models (TAM and UTAUT) emphasize how perceived usefulness and ease of use shape adoption. Ostrom’s commons governance theory explains how legitimacy and accountability affect trust in decentralized networks. Drawing on recent research in experience design, human–computer interaction, and decentralized governance, the article identifies the barriers that undermine user confidence. These include complex key management, unpredictable transaction costs, and unclear processes for decision-making and dispute resolution. The article offers an integrated framework that links engineered trust with experienced trust. Seven propositions are developed to guide future research and practice. The conclusion argues that blockchain technologies will gain traction if design and governance evolve alongside technical protocols to create systems that are both technically secure and trustworthy in experience. Full article

The global construction industry faces growing pressure to minimize environmental impact while maintaining durable, high-performance building materials. Fly ash-based geopolymer concrete (GPC) provides a sustainable, low-carbon, durable, and high-performance alternative to ordinary Portland cement (OPC). However, challenges remain in accurately predicting its structural behavior, particularly flexural strength, under varying compositional and curing conditions. This study integrates a Blockchain-assisted Gene Expression Programming Framework (B-GEPF) to enhance reliability and traceability in durability assessments of fly ash-based GPC. Focusing on the silica modulus of alkaline activators, the framework aims to improve predictive accuracy for flexural strength and optimize durability performance. Flexural strength was evaluated under controlled alkaline activator conditions (8M sodium hydroxide with sodium silicate) and varying fine aggregate ratios (1:1.5, 1:2, 1:3). The predictive model captures complex nonlinear relationships among silica modulus, fly ash content, and flexural behavior. Results indicate that higher activator concentrations increase flexural strength, while fly ash improves workability, reduces heat of hydration, and sustains long-term strength through secondary reactions. The B-GEPF framework demonstrates potential to accelerate GPC formulation optimization, ensuring reproducibility, reliability, and industrial scalability. By combining AI-driven predictions with blockchain-based validation, this approach supports sustainable construction, quality assurance, regulatory compliance, and transparent stakeholder collaboration. The study highlights dual benefits of environmental sustainability and digital trust, positioning fly ash-based GPC as a durable, low-carbon, and verifiable solution for resilient infrastructure. This convergence of AI predictive modeling and blockchain-secured data governance offers a robust, scalable tool for designing, validating, and deploying eco-friendly construction materials. Full article

In this paper, we design, implement, and empirically evaluate a tamper-evident, blockchain-secured solar energy logging system for resource-constrained edge Internet of Things (IoT) devices. Using a Merkle tree batching approach in conjunction with threshold-triggered blockchain anchoring, the system combines high-frequency local logging with energy-efficient, cryptographically verifiable submissions to the Ethereum Sepolia testnet, a public Proof-of-Stake (PoS) blockchain. The logger captured and hashed cryptographic chains on a minute-by-minute basis during a continuous 135 h deployment on a Raspberry Pi equipped with an INA219 sensor. Thanks to effective retrial and daily rollover mechanisms, it committed 130 verified Merkle batches to the blockchain without any data loss or unverifiable records, even during internet outages. The system offers robust end-to-end auditability and tamper resistance with low operational and carbon overhead, which was tested with comparative benchmarking against other blockchain logging models and conventional local and cloud-based loggers. The findings illustrate the technical and sustainability feasibility of digital audit trails based on blockchain technology for distributed solar energy systems. These audit trails facilitate scalable environmental, social, and governance (ESG) reporting, automated renewable energy certification, and transparent carbon accounting. Full article

The convergence of decentralized artificial intelligence (DAI), blockchain technology, and smart contracts is reshaping the design and governance of intelligent systems. As these technologies rapidly evolve, addressing privacy within their architecture, usage models, and associated risks has become increasingly critical. This systematic literature review examines architectural patterns, governance frameworks, real-world applications, and persistent challenges in DAI systems. It identifies prevailing designs such as federated learning integrated with consensus protocols, smart contract-based incentive mechanisms, and decentralized verification methods. Drawing from a diverse body of recent literature, the review highlights implementations across sectors, including healthcare, finance, IoT, autonomous systems, and intelligent infrastructure, each demonstrating significant contributions to privacy, security, and collaborative innovation. Despite these advancements, DAI systems face ongoing obstacles such as scalability limitations, privacy trade-offs, and difficulties with regulatory compliance. The review emphasizes the need for integrative governance approaches that balance transparency, accountability, incentive alignment, and ethical oversight. These elements are proposed as co-evolving pillars essential to establishing trustworthiness in decentralized AI ecosystems. This work offers a comprehensive review for understanding the current landscape and guiding the development of responsible and effective DAI systems in the Web3 era. Full article

The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents—trained using centralized training with decentralized execution (CTDE)—handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems. Full article

Cross-border data flows are critical to the operation of global supply chains, particularly for digital enterprises such as Alibaba and Infosys. However, these flows introduce substantial challenges related to digital supply chain risk and cybersecurity management. This study examines how blockchain technology addresses these challenges within the operational contexts of Alibaba and Infosys. Unlike earlier research that often focused on sector-specific implementations or conceptual models, this study positions its findings within broader empirical evidence on blockchain-enabled supply chain governance, offering a comparative perspective that has been largely absent in prior work. Using an explanatory mixed-methods approach, the research combines thematic analysis of 85 peer-reviewed studies with in-depth case evaluations of the two firms. NVivo-based qualitative coding was applied to supporting sources, including GDPR audit reports, blockchain transaction records, and company disclosures. The findings demonstrate that blockchain adoption reduces cybersecurity breaches, enhances data integrity, and improves supply chain resilience. The study further shows how blockchain integration strengthens digital collaboration and regulatory alignment, enabling secure and uninterrupted data flows that support operational continuity and innovation. Overall, the research offers practical insights for digital enterprises and contributes to a deeper understanding of blockchain’s strategic role in cross-border data risk management. Full article

This paper explores how blockchain technology, widely known as the backbone of cryptocurrencies, can be harnessed to address limitations of traditional water quality monitoring (WQM) systems. Blockchain offers a decentralized, tamper-proof ledger that enables secure, transparent, and traceable data management across distributed networks. When applied to water quality monitoring, blockchain facilitates real-time data acquisition, enhances data integrity, and enables smart contracts for automated regulatory compliance and alerts. These features not only improve the accuracy and efficiency of WQM systems but also build public trust in the reported data. Key insights from current research and pilot applications highlight blockchain’s capacity to integrate with IoT devices for real-time sensing, support adaptive water governance, and empower local stakeholders through decentralized control and transparent access to information. The implications for policy and practice are significant: blockchain-based WQM can support stronger regulatory enforcement, encourage cross-sector collaboration, and provide a robust digital foundation for sustainable water management in smart cities and rural areas alike. As such, this review paper positions blockchain as a transformative tool in the digital transition toward more resilient and equitable water management systems. Full article

In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent transport system (ITS) without human intervention. The integration of these agents into autonomous vehicles and their deployment across distributed cloud networks have increased significantly. These systems, which include drones, ground vehicles, and aircraft, are used to perform a wide range of tasks such as delivering passengers and packages within defined operational boundaries. Despite their growing utility, practical implementations face significant challenges stemming from the heterogeneity of network resources, as well as persistent issues related to security, privacy, and processing costs. To overcome these challenges, this study proposes a novel blockchain-enabled self-autonomous intelligent transport system designed for drone workflow applications. The proposed system architecture is based on a remote method invocation (RMI) client–server model and incorporates a serverless computing framework to manage processing costs. Termed the self-autonomous blockchain-enabled cost-efficient system (SBECES), the framework integrates a client and system agent mechanism governed by Q-learning and deep-learning-based policies. Furthermore, it incorporates a blockchain-based hash validation and fault-tolerant (HVFT) mechanism to ensure data integrity and operational reliability. A deep reinforcement learning (DRL)-enabled adaptive scheduler is utilized to manage drone workflow execution while meeting quality of service (QoS) constraints, including deadlines, cost-efficiency, and security. The overarching objective of this research is to minimize the total processing costs that comprise execution, communication, and security overheads, while maximizing operational rewards and ensuring the timely execution of drone-based tasks. Experimental results demonstrate that the proposed system achieves a 30% reduction in processing costs and a 29% improvement in security and privacy compared to existing state-of-the-art solutions. Full article

The global transition toward renewable energy and the electrification of transportation is imposing unprecedented power quality (PQ) challenges on modern distribution networks, rendering traditional governance models inadequate. To bridge the existing research gap of the lack of a holistic analytical framework, this review first establishes a systematic diagnostic methodology by introducing the “Triadic Governance Objectives–Scenario Matrix (TGO-SM),” which maps core objectives—harmonic suppression, voltage regulation, and three-phase balancing—against the distinct demands of high-penetration photovoltaic (PV), electric vehicle (EV) charging, and energy storage scenarios. Building upon this problem identification framework, the paper then provides a comprehensive review of advanced mitigation technologies, analyzing the performance and application of key ‘unit operations’ such as static synchronous compensators (STATCOMs), solid-state transformers (SSTs), grid-forming (GFM) inverters, and unified power quality conditioners (UPQCs). Subsequently, the review deconstructs the multi-timescale control conflicts inherent in these systems and proposes the forward-looking paradigm of “Distributed Dynamic Collaborative Governance (DDCG).” This future architecture envisions a fully autonomous grid, integrating edge intelligence, digital twins, and blockchain to shift from reactive compensation to predictive governance. Through this structured approach, the research provides a coherent strategy and a crucial theoretical roadmap for navigating the complexities of modern distribution grids and advancing toward a resilient and autonomous future. Full article

Education 4.0 promotes active, personalized, and competency-based learning aligned with the Sustainable Development Goals (SDGs), yet most current platforms rely on centralized architectures that restrict access, agency, and adaptability. To address this problem, Web3 technologies—including blockchain, decentralized identifiers (DIDs), peer-to-peer storage, and smart contracts—enable the creation of platforms that uphold equity, data sovereignty, and pedagogical flexibility. This paper investigates how the convergence of Education 4.0 and Web3 technologies can drive the development of sustainable, inclusive, and learner-centered digital education systems. We examine two decentralized education platforms, EtherLearn and DeLMS, to assess their design affordances and limitations. Building on these insights, we propose a layered architectural framework grounded in sustainability principles. Our analysis shows that decentralized infrastructures can expand access in underserved regions, increase credential portability, empower learners with greater autonomy, and foster participatory governance through decentralized voting, token-based incentives, and community moderation. Despite these advantages, significant challenges remain around usability, energy efficiency, and regulatory compliance. We conclude by identifying key research priorities at the intersection of sustainable educational technology, digital equity, and decentralized system design. Full article