Blockchain technology has created a major transformation in the digital world with features such as decentralization, security, transparency, and traceability. Emerging with Nakamoto's introduction of Bitcoin in 2009, this technology finds applications in various sectors, primarily finance, supply chain, healthcare, and public services. The fundamental principles of blockchain are based on elements such as hashing, distributed ledger technology (DLT), and consensus mechanisms. Innovative technologies such as smart contracts and side chains increase the potential of this system. Cryptocurrencies are an application area of blockchain technology and come across different types such as bitcoin, altcoins, and NFTs. APIs facilitate software development processes and enable integrating systems with blockchain. In the investigative phases, blockchain traceability provides a significant advantage in solving crimes like crypto fraud. This study aims to automate the manual tracking of crypto-related criminal transactions by implementing an algorithm based on the FIFO principle.
Samuel A. Oyenuga, Brendan Ubochi, Okechi Onuoha, Nnamdi Nwulu
The rapid growth in IoT applications has brought enormous challenges especially with achieving scalability and security in communicating devices.Traditional centralized security models are inadequate for managing the vast volume of data and diverse communication protocols in IoT environments, making them vulnerable to attacks such as Distributed Denial of Service (DDoS) and unauthorized access.Blockchain technology offers a decentralized alternative with its inherent properties of immutability, transparency, and decentralized consensus, providing a robust security solution for IoT communication.This paper presents a novel blockchain-based framework designed to secure IoT communication by addressing key challenges such as data integrity, privacy, and scalability.The proposed system integrates Ethereum's blockchain, Zero Knowledge (ZK)-Rollups for Layer 2 scaling, and edge computing to optimise both performance and energy efficiency in large-scale IoT networks.The framework achieves a transaction throughput of 2,500 transactions per second with a median latency of 850 milliseconds.ZK-Rollups ensure that 99.8% of transactional data remains off-chain, improving privacy while reducing computational overhead.The system maintains 99.7% uptime during DDoS attacks and reduces energy consumption by 95% compared to traditional Proof of Work (PoW) blockchain systems.These findings indicate that the proposed blockchain-based framework is scalable, energyefficient, and secure, making it a promising solution for large-scale IoT deployments in sectors such as smart cities, industrial automation, and healthcare.
With the rise of modern deep learning, neural networks have become an essential part of virtually every artificial intelligence system, making it difficult even to imagine different models for intelligent behavior. In contrast, nature provides us with many different mechanisms for intelligent behavior, most of which we have yet to replicate. One of such underinvestigated aspects of intelligence is embodiment and the role it plays in intelligent behavior. In this work, we focus on how the simple and fixed behavior of constituent parts of a simulated physical body can result in an emergent behavior that can be classified as cognitive by an outside observer. Specifically, we show how simulated voxels with fixed behaviors can be combined to create a robot such that, when presented with an image of an MNIST digit zero, it moves towards the left; and when it is presented with an image of an MNIST digit one, it moves towards the right. Such robots possess what we refer to as âmorphological cognitionâ â the ability to perform cognitive behavior as a result of morphological processes. To the best of our knowledge, this is the first demonstration of a high-level mental faculty such as image classification performed by a robot without any neural circuitry. We hope that this work serves as a proof-of-concept and fosters further research into different models of intelligence.
Abstract The role of central banks in advancing sustainable (âgreenâ) digital finance is becoming increasingly significant, positioning them as both facilitators and key actors. This chapter begins by examining how climate-related financial risks may require adjustments to the operational frameworks of central bank policy tools, and highlights recent initiatives undertaken by central banks in response. It then reviews specific cases of sustainable digital finance in the central banking context, including: (1) the BISâs Project Genesis, which integrates the green bond market and carbon markets through digital technologies; and (2) the collaboration between the Bank of Korea (BOK) and the Korea Exchange (KRX) to explore the application of distributed ledger technology and central bank digital currency (CBDC) in carbon trading.
Abstract Emerging digital financial technology has already had a significant impact on financial development and holds significant potential to advance the sustainable finance agenda. Various challenges limit the assessment of environmental risks, as well as the mobilisation of sustainable finance. Digital technology (including artificial intelligence, distributed ledger technologies, cloud computing, the Internet of Things and big data) can help address the risk identification and mobilisation challenges and can at the same time help promote financial inclusion and energy justice. This chapter highlights the potential of digital solutions and presents six proposals to enhance digital technologies to mobilise capital, assess environmental risks and enhance financial inclusion.
As cryptocurrencies began with the launch of Bitcoin in 2009, a technological and financial revolution has created a fundamental menace to worldwide banking infrastructure by its presence. The article is a thorough exposition of the increasing use of cryptocurrencies and its compounding implications to the conventional banking systems. We mention the principles of decentralized finance (DeFi) which explicitly challenge the role between banks, payments, and settlements, lending, and borrowing, and even, the custody of assets. This paper adopts a conceptual and comparative analysis research design to consolidate a number of general layers of scholarly articles, industrial reports and regulation books to develop an overall structure against which to understand this dynamic relationship in a holistic manner. It is analyzed by means of the two-sided impact that semi-protects the traditional bank axiom on one hand, the cryptocurrencies and the DeFi systems are actively disintermediating the traditional banking operations which made delivery of cross-border remittances, P2P lending protocols, and self-custody opportunities faster, cheaper, and more convenient. This is putting competitive pressure on the existing institutions threatening the existence of fee based revenue systems and customer relations. One other, but equally, is that the boarding cryptocurrencies over technological resolutions, namely blockchain and distributed ledger technology (DLT), is borrowed even by the banking sector itself. Banks are learning about DLT to automatize their back-office business, create new digital assets, and the crypto threat establishes their stance through two forms Central Bank Digital Currency (CBDCs) and regulated stablecoins. The implication of this change is evaluated critically depending on the discussion of the potential increase of financial inclusion and financial efficiency in addition to the high level of security risks and the great uncertainty of regulation and the threat of volatility, systemic financial stability. The conclusion of this paper is that crash belongs more to cryptocurrency than to its replacement, and the old banking structures will have to make use of it to be creative, and develop a new value proposal in a more and more decentralized financial system. The future has been defined as requiring a hybrid solution of centralization and decentralization of systems in which they would co exist, compete, and converge.
El Mehdi Badre, SAMIR Kourtite, Slimane Sidouna, MâHammed Ziane ¡ 5 authors
Motivated by the growing need for secure and efficient cryptographic solutions in blockchain technology, this study explores the cryptographic potential of binary Huff curves defined over the non-local ring <img src=image/13442212_01.gif>, introducing novel group structures for advanced blockchain applications. The research aims to enhance the security and efficiency of cryptographic primitives by leveraging the algebraic properties of these curves, particularly for resource-constrained devices in blockchain ecosystems. We establish a bijection between the Huff curve <img src=image/13442212_02.gif> and the product <img src=image/13442212_03.gif>, enabling an efficient group law that increases the complexity of the discrete logarithm problem (DLP). Methodologically, we define arithmetic operations in <img src=image/13442212_04.gif>, prove the bijection, and derive addition formulas for the curve. These results are applied to adapt the Yak key exchange protocol, enhancing its resistance to DLP-based attacks through the non-local ring's structure. Principal findings demonstrate that <img src=image/13442212_02.gif> achieves approximately <img src=image/13442212_05.gif> group order, doubling the DLP security to <img src=image/13442212_06.gif>-bit compared to <img src=image/13442212_06.gif>/2-bit for standard curves over <img src=image/13442212_07.gif>, with computational efficiency suitable for Internet of Things (IoT) devices. The study contributes to cryptography by proposing a robust framework for blockchain transaction security and secure data management, notably in multi-party computation and zero-knowledge proofs. Key conclusions highlight the curves' potential to secure blockchain validators and IoT nodes, as exemplified in supply chain applications. Novel aspects include the non-local ring's algebraic constraints and the Yak protocol's adaptation for blockchain. Limitations include the need for practical implementation and benchmarking against curves like secp256k1. Practical implications involve improved transaction security and data privacy in blockchain, while social implications include enabling secure, decentralized systems for healthcare and supply chain tracking. Future research should validate performance in real-world blockchain environments and assess resistance to side-channel attacks.
With the advancement of edge intelligence technology and the acceleration of urbanization, intelligent transportation systems (ITS) have experienced rapid development. Vehicle-road-cloud (VRC) collaboration was enabled through the coordinated sharing of vehicle-to-vehicle (V2V), vehicle-to-road (V2R), and vehicle-to-cloud (V2C) data in the Internet of vehicles, thereby constructing a more efficient cooperative intelligent transportation system (C-ITS). However, numerous security threats in VRC collaboration were found to severely impede the development of cooperative autonomous driving. The development status of VRC collaboration was first summarized, and the history of autonomous driving and the VRC-based autonomous driving environment were elaborated. Subsequently, attacks and security defense technologies in VRC collaboration were systematically categorized into two types: classical information security mechanisms and defense technologies, which were detailed from five aspectsâinformation availability, integrity, confidentiality, authenticity, and non-repudiation; and machine learning-based security threats and defense technologies, which were analyzed from both centralized and distributed perspectives. Finally, future development directions and research priorities of VRC collaborative security technologies were forecasted, primarily covering federated learning, blockchain technology, secure multi-party computation, zero-knowledge proof, and differential privacy technology.
This study examines insider trading within decentralized capital markets, focusing on its legal implications. The primary objective is to understand how insider trading affects market integrity, investor trust, and the broader regulatory environment in decentralized finance (DeFi). Using a qualitative approach, the research analyzes case studies of reported insider trading incidents in blockchain-based platforms and evaluates existing legal frameworks governing such activities. Data is collected through literature reviews, legal document analysis, and expert interviews. The findings reveal significant gaps in the regulatory oversight of insider trading in decentralized markets due to the pseudonymous nature of blockchain and the absence of centralized regulatory authorities. Furthermore, the study highlights that while blockchain technology offers transparency, it also poses challenges in identifying and prosecuting offenders. The research concludes that strengthening international cooperation and creating adaptive legal frameworks are essential to mitigate insider trading risks in decentralized markets. Such measures will enhance investor protection, foster trust, and promote fair market practices in the rapidly growing DeFi ecosystem.
Eugenio Felipe Merlano, Glenn Parry, Abubakar-Sadiq Shehu, Steve Schneider
Global labour markets face significant disruption from the rapid advance of artificial intelligence (AI) and automation. Digital or gig economy workers, like freelancers and online independent contractors, are more exposed to the disruptive impacts of technological changes due to their flexible working conditions, which often come with flexible contracts, less robust legal agreements and other unstable working conditions. This study explores how gig economy workers benefit from alternative privacy-enhancing decentralised reputation systems and technologies that enable them to manage information like education, certifications, credentials, and professional experience, both by collecting and sharing information with employers. We propose a blockchain framework comprising three components: (1) Self-sovereign identity (SSI) enabling cryptographically secured, portable control over credentials via decentralised storage; (2) Immutable reputation registries leveraging consensus mechanisms to secure tamper-proof work histories; and (3) Privacy-preserving signalling using zero-knowledge proofs (ZKPs) to let workers selectively disclose reputation metrics without revealing sensitive details. We combine Signalling Theory (ST) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to empirically assess real workersâ intentions to use this type of decentralised reputation system. Our framework enhances transparency, worker autonomy, and privacy in the digital economy.
This paper presents a novel health data analysis platform for improved individualized risk forecasting of permanent pacemaker implantation (PPI) following transcatheter aortic valve replacement (TAVR) procedures. Specifically, we introduce XGBoost-federated adaptive interpolation transfer learning (XG-FedAIT)âa platform that integrates heterogeneous hospital data sets via adaptive output-level interpolation and performance-weighted model ensembling. This approach facilitates federated learning across institutions with different feature spaces and prediction targets, such as time-to-event and binary models, eliminating structural and semantic mismatches prevalent in regular federated transfer learning. To ensure secure, privacy-preserving, and general data protection regulation (GDPR)-compliant data exchange, we propose a dual-chain blockchain architecture integrating proof of authority (PoA), zero-knowledge proofs (ZKPs), and chain-specific smart contracts, and off-chain encrypted storage through Filecoin. Experimental evaluation proves that the PrimaryChain handles over 300 tx/s with a median latency of 500 ms, and the SecondaryChain offers 99.8% data availability and decentralized access control. The system is scalable to handle up to 2,500 transactions/hour, and the federated learning pipeline classifies PPI risk with an F1-score of 0.85 and AUROC of 0.91. These results support the effectiveness of our system in delivering real-time, regulation-compliant, and clinically actionable cardiac care in distributed environments.
Open access
Artificial Intelligence in Healthcare and Education
Inna Kovalchuk, Victoria Melnyk, Tamara Novak, Anna Pakhomova ¡ 5 authors
The article examines innovative approaches to state support for agribusiness through the implementation of virtual asset technologies in the Ukrainian legal field, taking into account international experience. The relevance of the topic is due to the need to modernize the existing mechanisms for financing the agricultural sector in the context of the digital transformation of the economy and the development of the global crypto-asset market. The authors analyzed the current state of legal regulation of virtual assets in Ukraine, in particular in the context of the Law of Ukraine "On Virtual Assets" and its implementation. Also, it was outlined the main problems and obstacles to the introduction of innovative financial instruments in the agricultural sector including: instability of the regulatory framework, insufficient integration of digital solutions into state support programs, as well as low technological readiness of small and medium-sized agricultural producers. The authors studied the international experience of using blockchain technologies to support the agricultural sector in countries such as the USA, Australia, Singapore and the countries of the European Union. In particular, the authors paid attention to the analysis of legal models of tokenization of agricultural assets and the use of smart contracts to optimize the processes of state subsidies. Based on the analysis, the authors proposed a comprehensive model of integration of virtual asset technologies into the mechanisms of state support for agribusiness in Ukraine. The key elements of this model are: the creation of special legal regimes for agricultural tokens and NFTs, the formation of infrastructure for the digital interaction of farmers with state institutions, the implementation of blockchain solutions for the transparent distribution of subsidies, as well as the development of a legal framework for agricultural digital cooperatives. The authors substantiated the need to amend the legislation of Ukraine, in particular the Law of Ukraine "On State Support of Agriculture of Ukraine", the Tax Code and relevant regulations on virtual assets. Also, the authors identified the potential risks and advantages of the proposed innovations for both agricultural producers and the state. The results of the research are of theoretical and practical importance for the formation of state policy in the field of the agro-industrial complex of Ukraine, taking into account the global trends in digitalization and the use of distributed ledger technologies to ensure the efficiency, transparency and accessibility of state support for agribusiness. Keywords: virtual assets, blockchain, smart contracts, state support for agriculture, tokenization of agricultural assets, agricultural sector, digital transformation.
Jeongin Lee, Geunyeong Choi, Jihyo Han, Jungheum Park
Monero, a privacy-preserving cryptocurrency, employs advanced cryptographic techniques to obfuscate transaction participants and amounts, thereby achieving strong untraceability. However, digital forensic approach can still reveal sensitive information by examining off-chain artifacts such as memory and wallet files. In this work, we conduct an in-depth forensic analysis of Monero's wallet application, focusing on the handling of public and private keys and the wallet's data storage formats. We reveal how these keys are managed in memory and develop a memory scanning algorithm capable of identifying key-related data structures. Furthermore, we analyze the wallet keys and cache files, presenting a method for decrypting and interpreting serialized keys and transaction data encrypted with a user-specified passphrase. Our approach is implemented as an open-source Volatility3 plugin and a set of decryption scripts. Finally, we discuss the applicability of our methodology to multi-cryptocurrency wallets that incorporate Monero components, thereby validating the generalizability of our techniques.
Mohammad Vahidpour, Amir Daneshvar, Mohsen Amini Khouzani, Mahdi Homayounfar
Purpose This study aims to enhance cryptocurrency price and trend prediction by applying advanced machine learning (ML) techniques. Given the marketâs high volatility and complexity, the research identifies effective models for different conditions, providing insights for investors and risk management. Design/methodology/approach This study proposes a six-stage framework for cryptocurrency price prediction, integrating advanced ML techniques. Data from ten cryptocurrencies are processed, extracting 37 key features, including return, the Fear and Greed Index and various technical indicators. The model employs Deep Q-Networks (DQN), Long Short-Term Memory (LSTM) and multiple regression methods such as linear regression, support vector regression, ridge, LASSO, decision tree, Random Forest, multi-layer perceptron, stochastic gradient descent, elastic net and Bayesian regression. Model performance is evaluated using trading strategies and metrics like accuracy, sensitivity, recall, MSE, MAE and F1-score. Findings The results indicate that complex models like DQN and LSTM excel in volatile markets due to their ability to capture intricate price patterns, whereas simpler models such as linear regression and ridge regression perform better in stable conditions. The multi-layered parallel design enhances computational efficiency, enabling independent asset evaluation. These findings highlight the potential of artificial intelligence in improving prediction accuracy and supporting informed investment decisions. Originality/value This research introduces a novel six-stage ML framework incorporating diverse predictive models and key features for cryptocurrency forecasting. The multi-layered parallel approach enhances computational efficiency, setting this study apart from existing research. The comparative analysis of models offers valuable guidance for investors, traders and financial analysts navigating volatile cryptocurrency markets.
With the rapid advancement of Vehicle-to-Everything (V2X) communication systems, ensuring robust security has become a critical challenge. Various studies have proposed several protocols; however, many of them may suffer from potential threats, such as impersonation attacks. This paper presents an efficient and lightweight authentication protocol for V2X communication. Our scheme uses zero-knowledge proof to perform mutual authentication without revealing the real identities in V2X communication. The proposed protocol ensures that all vehicles on the network are authenticated without compromising the integrity. Hence, the proposed scheme protects V2X communication against possible attacks. The preliminary result shows that the proposed scheme is efficient in terms of computational complexity.
This study investigates the specific factors affecting blockchain or the usage intention of distributed ledger technology (DLT), specifically availability, diversity, and economic value, from the perspective of a unified theory of technology acceptance. Users of DLT in public and private sectors were surveyed. Using a structural equation model, the results indicate that availability and economic value affect performance expectancy, while availability, diversity, and economic value have an influence on effort expectancy. Performance expectancy and transparency have a positive effect on the intention to use DLT, which in turn exerts a positive effect on usage behavior. This study provides implications for researchers in that it attempts to investigate the factors directly (like performance expectancy and transparency) or indirectly (like availability and economic value) affecting the usage intention of DLT based on the extended unified theory of acceptance and encompassing diverse industries that adopt DLT, such as the public, IT, financial, service medical, and logistics sectors.
This policy paper reviews the evolution of research from 2016 to 2024 on the role of digital assets in illicit financial flows and evaluates the effectiveness of existing detection and prevention strategies. It highlights two central policy imperatives for Latin America: (i) fostering regional cooperation among central banks to harmonize anti-money-laundering standards and address regulatory arbitrage, and (ii) integrating supervisory technologies (SupTech and RegTech) into compliance frameworks to improve detection capacity. The paper concludes that combining technological innovation with coordinated regional regulation is essential to safeguard financial integrity in the digital-asset era.
The rapid growth of the AI-driven digital economy has intensified the demand for scalable optimization techniques and secure computational frameworks across industries such as logistics, finance, healthcare, and autonomous systems. Classical computational frameworks are becoming inadequate for addressing the inherent combinatorial complexity of AI processes and for maintaining robust cryptographic protections suitable for decentralized and sensitive data environments. This paper explores how emerging quantum computing paradigms, particularly the quantum approximate optimization algorithm and quantum-state-based cryptographic protocols, can provide effective solutions to these dual challenges. As fully fault-tolerant quantum systems remain on the horizon, hybrid quantum-classical architectures provide a practical, near-term solution. These systems embed quantum modules into classical AI pipelines, enabling enhanced optimization capabilities and quantum-resilient communication within the constraints of current noisy intermediate-scale quantum devices. We discuss a potential hybrid architecture intended to support complex decision-making and secure data exchange in practical settings. The study includes comparative analyses of classical, post-quantum, and quantum-state-based techniques and evaluates their applicability across key sectors. Rather than replacing existing systems, quantum methods are positioned as complementary technologies, offering domain-specific advantages in computational efficiency, data security, and decentralized functionality, which are essential capabilities for the future AI-enabled digital infrastructure.
The study aims to examine how blockchain is used in the multimodal and interdisciplinary metaverse as a nexus of education and training, accounting, banking and finance, entertainment and media, marketing and e-commerce, and retail, healthcare, and wellness. It seeks to evaluate the influence of blockchain combined with artificial intelligence, the Internet of Things, and other emerging technologies in the metaverse, so evaluating the challenges and concerns in the field, new business prospects, and sustainable development paths corresponding to Sustainable Development Goals. Using a Systematic Literature Review (SLR) technique, this article addresses the problem from a commercial viewpoint. The study investigates the topic structure of the literature by means of Biblioshiny for R combined with VOSviewer version 1.6.20. Furthermore, increasing the analytical depth is a bibliographic coupling method used on a dataset of 172 Scopus (2024) items. The study underlines the economic rationality and direct consequences of the characteristics of the blockchainâNFTs, DeFi, cryptocurrencies, transparency, decentralization, and securityâon the relevance of business models in the metaverse. The information provided here is a vital literature study on how the blockchain addresses issues in constructing and metamorphosing the metaverse from several sectors and angles. In the framework of the metaverse, this article offers a thorough theoretical study of the possibilities and possible challenges in blockchain integration.