This paper explores the emerging paradigm of Blockchain-as-a-Service (BaaS) and its implementation of distributed ledger technology in cloud environments. We examine the key characteristics, benefits, and challenges of BaaS platforms, analyze different architectural approaches and deployment models, and evaluate performance considerations for blockchain networks in the cloud. Through a comprehensive literature review and analysis of existing BaaS offerings, we provide insights into the current state of the technology and identify promising research directions. Our findings indicate that BaaS has significant potential to accelerate enterprise blockchain adoption by reducing complexity and costs, but also faces hurdles related to security, scalability, and standardization that need to be addressed as the field matures.
This study compares the performance of the Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) models in predicting the price of Ethereum, which is important to support cryptocurrency investment strategies. With the high volatility of the cryptocurrency market, an accurate and reliable prediction model is needed. In this study, Ethereum's daily closing price data over four years was analyzed using TCN and GRU models to evaluate its predictive capabilities. Model accuracy is measured using Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Squared Error (MSE). The results showed that the TCN model excelled in average accuracy with lower MAE and MAPE values, while the GRU model showed excellence in reducing the impact of large errors with smaller MSE values. This reflects TCN's superiority in capturing the overall pattern of price movements, while the GRU is more responsive to short-term price fluctuations. These findings demonstrate the potential of both models in cryptocurrency price forecasting, with their respective advantages. This research provides valuable information for investors and researchers in developing predictive strategies in dynamic financial markets. A combination of TCN and GRU models can also be explored to improve prediction performance in the future.
Non-Fungible Token (NFT) merupakan salah satu inovasi dalam dunia ekonomi berupa aset digital yang memiliki keunikan dalam aspek identitas dan kepemilikan yang menggunakan teknologi blockchain untuk menyimpan dan memverifikasi data. Penelitian ini bertujuan untuk melihat peran NFT dalam ekonomi digital, apakah NFT benar-benar menjembatani kesenjangan dalam digitalisasi ekonomi atau justru menambah lapisan kompleksitas yang kontraproduktif. Penelitian ini menggunakan metode Systematic Literature Review (SLR) untuk mengumpulkan data dari artikel-artikel yang berkaitan dengan topik NFT. Hasil penelitian ini menunjukkan bahwa peran NFT dapat memiliki potensi ganda sebagai jembatan kesenjangan sekaligus sumber kompleksitas kontraproduktif. Teknologi NFT mampu meningkatkan transparansi, memperluas akses ke pasar global, melindungi hak kekayaan intelektual, dan mendukung inovasi di bidang pendidikan, administrasi pertanahan, dan pembiayaan program SDGs. Namun, NFT juga menghadapi tantangan multidimensi seperti ketidakjelasan regulasi yang berpotensi memicu penipuan, pencucian uang, dan ketimpangan akses yang diperburuk oleh volatilitas pasar, spekulasi berlebihan, dan kesenjangan infrastruktur digital di negara-negara berkembang.
Web3 grant programs are evolving mechanisms aimed at supporting innovation within the blockchain ecosystem, yet little is known on about their effectiveness. This paper proposes the concept of maturity to fill this gap and introduces the Grant Maturity Framework (GMF), a mixed-methods model for evaluating the maturity of Web3 grant programs. The GMF provides a systematic approach to assessing the structure, governance, and impact of Web3 grants, applied here to four prominent Ethereum layer-two (L2) grant programs: Arbitrum, Optimism, Mantle, and Taiko. By evaluating these programs using the GMF, the study categorizes them into four maturity stages, ranging from experimental to advanced. The findings reveal that Arbitrum's Long-Term Incentive Pilot Program (LTIPP) and Optimism's Mission Rounds show higher maturity, while Mantle and Taiko are still in their early stages. The research concludes by discussing the user-centric development of a Web3 grant management platform aimed at improving the maturity and effectiveness of Web3 grant management processes based on the findings from the GMF. This work contributes to both practical and theoretical knowledge on Web3 grant program evaluation and tooling, providing a valuable resource for Web3 grant operators and stakeholders.
Blockchain technology promises to democratize finance and promote social equity through decentralization, but questions remain about whether current implementations advance or hinder these goals.Through a mixed-methods study combining semi-structured interviews with 13 diverse blockchain stakeholders and analysis of over 3,000 cryptocurrency discussions on Reddit, we examine how trust manifests in cryptocurrency ecosystems despite their decentralized architecture.Our findings uncover that users actively seek out and create centralized trust anchors, such as established exchanges, prominent community figures, and recognized development teams, contradicting blockchain's fundamental promise of trustless interactions.We identify how this contradiction arises from users' mental need for accountability and their reluctance to shoulder the full responsibility of self-custody.The study also reveals how these centralized trust patterns disproportionately impact different user groups, with newer and less technical users showing stronger preferences for centralized intermediaries.This work contributes to our understanding of the inherent tensions between theoretical decentralization and practical implementation in cryptocurrency systems, highlighting the persistent role of centralized trust in supposedly trustless environments. CCS Concepts Security and privacy Social aspects of security and pri-
Autonomous Vehicles (AV) proliferation brings important and pressing security and reliability issues that must be dealt with to guarantee public safety and help their widespread adoption. The contribution of the proposed research is towards achieving more secure, reliable, and trustworthy autonomous transportation system by providing more capabilities for anomaly detection, data provenance, and real-time response in safety critical AV deployments. In this research, we develop a new framework that combines the power of Artificial Intelligence (AI) for real-time anomaly detection with blockchain technology to detect and prevent any malicious activity including sensor failures in AVs. Through Long Short-Term Memory (LSTM) networks, our approach continually monitors associated multi-sensor data streams to detect anomalous patterns that may represent cyberattacks as well as hardware malfunctions. Further, this framework employs a decentralized platform for securely storing sensor data and anomaly alerts in a blockchain ledger for data incorruptibility and authenticity, while offering transparent forensic features. Moreover, immediate automated response mechanisms are deployed using smart contracts when anomalies are found. This makes the AV system more resilient to attacks from both cyberspace and hardware component failure. Besides, we identify potential challenges of scalability in handling high frequency sensor data, computational constraint in resource constrained environment, and of distributed data storage in terms of privacy.
Persistent Homology (PH) is a fundamental tool in computational topology, designed to uncover the intrinsic geometric and topological features of data across multiple scales. Originating within the broader framework of Topological Data Analysis (TDA), PH has found diverse applications ranging from protein structure and knot analysis to financial domains such as Bitcoin behaviour and stock market dynamics. Despite its growing relevance, there remains a lack of accessible resources that bridge the gap between theoretical foundations and practical implementation for beginners. This paper offers a clear and comprehensive introduction to persistent homology, guiding readers from core concepts to real-world applications. Specifically, we illustrate the methodology through the analysis of a 3-1 supercoiled DNA structure. The paper is tailored for readers without prior exposure to algebraic topology, aiming to demystify persistent homology and foster its broader adoption in data analysis tasks.
This article discusses the current topic of the use of blockchain technologies in the digital economy. The study is based on an analysis of existing research and publications, as well as on the consideration of examples of the use of blockchain in various business areas. The main purpose of the work is to identify the challenges and prospects of using blockchain, as well as to assess its impact on economic processes. The advantages and disadvantages of using blockchain are discussed in detail, as well as recommendations for its effective implementation. In conclusion, the conclusions about the potential of blockchain technologies for the transformation of the digital economy and identifies possible areas for further research.
bstract In order to give users instant access to market data, portfolio management features, and analytical tools, this research paper introduces CryptoTracker, a feature-rich real-time cryptocurrency tracking application. The system makes use of contemporary web technologies to provide a responsive interface that is constantly updated to reflect the state of the market. In addition to outlining the system's functionality, architecture, and implementation specifics, we also go into the difficulties in creating trustworthy cryptocurrency tracking tools in a volatile market. Keywords: Cryptocurrency, Real-time tracking, Portfolio management, Web technologies, Financial analysis, Data visualization
This article presents a comprehensive framework for applying blockchain technology to secure data integration challenges in multi-cloud and hybrid-cloud environments. This article examines how distributed ledger technology creates a trust layer that addresses key vulnerabilities in traditional integration approaches while maintaining performance characteristics suitable for enterprise deployments. This article's architecture leverages permissioned blockchain networks, smart contracts, and cryptographic verification mechanisms to ensure data integrity, enforce governance policies, and provide immutable audit trails across heterogeneous cloud platforms. Our performance evaluation demonstrates viable throughput and latency characteristics compared to traditional integration methods, while offering enhanced security properties. Through case studies in financial services, healthcare, supply chain, and critical infrastructure protection, we illustrate practical implementations and quantifiable benefits. Despite challenges in scalability, energy consumption, legacy system integration, regulatory compliance, and organizational adoption, the architecture shows promising results for high-value data workflows. The research contributes to the emerging intersection of blockchain and multi-cloud computing by providing both theoretical foundations and practical implementation guidance for organizations seeking to enhance security posture across distributed cloud environments.
Minh Tri Le, O. M. Harris, Charlotte Bennett, Fiona Greene
With the deep penetration of blockchain technology across various fields, its security system faces severe challenges, and fraudulent activities are becoming increasingly frequent. This study focuses on the problem of fraud detection in blockchain and proposes an innovative model, FraudGNN, based on Graph Neural Networks (GNN). The model constructs a dynamic transaction graph, where transaction addresses are treated as nodes and asset transfer relationships as edges, incorporating time-series features. A Graph Attention Network (GAT) is used to extract behavioral features from node neighborhoods. In addition, a Bidirectional Long Short-Term Memory network (Bi-LSTM) is introduced to capture behavioral paths across block-level transactions, enabling accurate classification and prediction of abnormal accounts within blockchain networks. Experiments conducted on an Ethereum transaction dataset—containing approximately 3.6 million transaction records and 40,000 labeled addresses—show that the FraudGNN model significantly outperforms traditional methods such as Random Forest and Graph Convolutional Networks (GCN) in key metrics, achieving 91.2% precision, 87.5% recall, and an F1-score of 89.3%. In particular, the model demonstrates stronger generalization and reasoning capabilities when identifying previously unseen addresses, offering solid technical support for improving blockchain security systems.
Vaiva Pakštaitė, Ernestas Filatovas, Mindaugas Juodis, Remigijus Paulavičius
Bitcoin’s role in global finance has rapidly expanded with increasing institutional participation, prompting new questions about its linkage to macroeconomic variables. This study thoughtfully integrates a Bayesian Markov Chain Monte Carlo (MCMC) covariate selection process within homogeneous and non-homogeneous Hidden Markov Models (HMMs) to analyze 16 macroeconomic and Bitcoin-specific factors from 2016 to 2024. The proposed method integrates likelihood penalties to refine variable selection and employs a rolling-window bootstrap procedure for 1-, 5-, and 30-step-ahead forecasting. Results indicate a fundamental shift: while early Bitcoin pricing was primarily driven by technical and supply-side factors (e.g., halving cycles, trading volume), later periods exhibit stronger ties to macroeconomic indicators such as exchange rates and major stock indices. Heightened volatility aligns with significant events—including regulatory changes and institutional announcements—underscoring Bitcoin’s evolving market structure. These findings demonstrate that integrating Bayesian MCMC within a regime-switching model provides robust insights into Bitcoin’s deepening connection with traditional financial forces.
ImageChain is a Python application that combines image steganography, blockchain, and Interplanetary File System (IPFS) to create a secure, decentralized system for image metadata management. The system allows users to hide secret messages in images using steganography, keeping data confidential and authentic. The images are then stored on IPFS, which creates a distributed storage hash. IPFS hash and title, description metadata are stored in a local Ethereum blockchain through Solidity-written smart contracts. Updating and retrieving metadata, as well as transferring ownership among Ethereum addresses through Web3.py, are supported by the platform. Image processing is carried out through OpenCV. With these technologies together, transparency, traceability, and tamper-evident record-keeping across the life cycle of the digital image are provided. ImageChain illustrates the value of the integration of steganography, decentralized storage, and blockchain technology to create secure systems for use in digital rights management, forensic processing, and secure communication.
Blockchain technology now stands as a revolutionary power that changes multiple businesses and breaks down established centralized operations.The following research studies blockchain development as it transforms different business sectors through decentralized systems mechanisms.The analysis evaluates the distinct properties of blockchain technology, including distributed ledger systems and immutable design, as well as peer-to-peer architecture that resolves recurring challenges within data protection, privacy, and security domains.Financial institutions are set up to adopt new ways of doing business through blockchain networks, which provide superior models for transaction processes, asset control systems, and regulatory compliance functions.The paper explores extended industry transformations created by blockchain technology, which goes beyond finance into alternate sectors, including energy systems, as well as internet decentralization through Web3.The paper uses extensive research from academic publications and industry documents to clarify blockchain development alongside its substantial transformations for business operations and societal delivery.
Abstract Pixel-Web3 Wallet is a hierarchical deterministic (HD) wallet designed for secure and decentralized asset management across multiple blockchain networks, including Ethereum and Solana. Unlike traditional wallets that depend on browser extensions or centralized servers, Pixel offers a web-based solution with user-controlled security through locally stored seed phrases. This paper explores the wallet’s architecture, security framework, and innovative features, such as real-time balance updates and flexible recovery options. Additionally, the research evaluates the scalability of Pixel and its potential expansion to support more blockchain networks. By eliminating reliance on third-party services, Pixel enhances accessibility while maintaining strong security, making it a promising solution for blockchain enthusiasts, traders, and developers. Keywords: Blockchain, HD Wallet, Cryptocurrency,Web3,Ethereum,Solana, Security
Objective: The combination of 5G connectivity and edge computing known as 5G Edge, lifts the limitations of the Internet of Medical Things (IoMT) and enables a plethora of authentic healthcare services, including access to medical information and diagnosis. Additionally, there is a chance that malicious insiders using the 5G Edge platform could compromise the security and confidentiality of IoMT data. As a result, end users cannot trust 5G Edge data. Materials and methods: This paper imagines a new hierarchical blockchain edge of things (HBEoT) architecture that would facilitate healthcare applications managed by blockchain at the network edge. Additionally, the article delves into how HBEoT can offer security services such as authenticating users, protecting data, detecting attacks, and managing trust. First, the consensus method PoS generates a block for healthcare data, and its hash values are kept in the blockchain. The Zero Knowledge Proof (ZKP) protocol stores secret information in the blockchain, authenticating healthcare systems for enhanced privacy and security. Results: To ensure immutable data storage, the suggested architecture incorporates 5G Edge servers into a blockchain platform. It prevents unauthorised users from accessing healthcare services by acting as an anonymous authenticator. We assessing the proposed methods efficiency in terms of latency, throughput, communication cost, energy consumption cost and the validation between ZKP prover and verifier. Conclusion: We conduct multiple experiments to evaluate the effectiveness of the suggested framework. To further investigate the quality measures, such as authenticate latency, throughput rate was examined. To fulfil the requirements for a 5G-enabled healthcare system, the research shows that the suggested HBEoT-ZKP performs a decrease in latency of around 1.16 s and an improvement in throughput of approximately 339 tps. Communication and energy cost is evaluated and compared with other conventional methods.
Emerging technologies, such as artificial intelligence (AI), blockchain, and fintech, have profoundly reshaped the financial sector driving unprecedented innovation and creating transformative opportunities for development. However, they also pose significant challenges to long-term sustainability. While the existing literature provides valuable insights into their influence, a broader scope is necessary to reflect their role in advancing sustainable finance. This study conducts a bibliometric analysis of 2,446 publications from the Web of Science (1996–2024) to map the evolving nexus between emerging technologies and finance. Our findings reveal an expanding research landscape, with key themes including the application of emerging technologies in solving financial problems, the integration of technologies with behavioural and regulatory frameworks, financial innovation for promoting development, risk management and financial stability, digital currencies and blockchain, digital transformation challenges, and sustainable finance. The analysis highlights the dual nature of emerging technologies: while they enhance financial efficiency, transparency, and inclusion, and offer significant opportunities to advance sustainable finance, they also introduce risks such as cybersecurity threats, algorithmic bias, regulatory challenges, and critical barriers to long-term sustainability. To address these challenges, we propose a research agenda prioritizing ethical governance, stress-testing AI models under economic crises, securing decentralized systems, mitigating greenwashing risks, and fostering globally aligned regulatory standards. Interdisciplinary collaboration is essential to tackle ethical, security, and inclusivity concerns. It is imperative for policymakers, regulators, and financial institutions to align technological innovation with sustainability objectives to ensure that advancements contribute to the development of an equitable, resilient, and inclusive financial ecosystem.
The article argues for the sociocultural contextualization of Web3 affordances by examining play-to-earn gaming in the Philippines. It first outlines how socioeconomic factors promote blockchain technology and cryptocurrency. Against this background, and based on scholarship in cultural communication, anthropology, and critical platform studies, the article illustrates how sociocultural frames shape the interpretation and enactment of blockchain-based gameplay affordances. A Grounded Theory analysis of interviews and documents reveals that players identify persistent access and ownership as technical affordances, performing them through the cultural frame of cockfighting and its digital economy version, the side hustle. The study challenges universalist notions of Web3 adoption, highlighting how technical affordances both support and disrupt sociocultural and economic reproduction through narratives of family, competition, and inclusivity. The research calls for comparative studies on how platform corporations structure societies in emerging economies, how platforms exploit culture as use value, and how adopters strategically utilize Web3 technologies.
Giuseppe Cavaliere, Thomas Mikosch, Anders Rahbek, Frederik Vilandt
Integrated autoregressive conditional duration (ACD) models serve as natural counterparts to the well-known integrated GARCH models used for financial returns. However, despite their resemblance, asymptotic theory for ACD is challenging and also not complete, in particular for integrated ACD. Central challenges arise from the facts that (i) integrated ACD processes imply durations with infinite expectation, and (ii) even in the non-integrated case, conventional asymptotic approaches break down due to the randomness in the number of durations within a fixed observation period. Addressing these challenges, we provide here unified asymptotic theory for the (quasi-) maximum likelihood estimator for ACD models; a unified theory which includes integrated ACD models. Based on the new results, we also provide a novel framework for hypothesis testing in duration models, enabling inference on a key empirical question: whether durations possess a finite or infinite expectation. We apply our results to high-frequency cryptocurrency ETF trading data. Motivated by parameter estimates near the integrated ACD boundary, we assess whether durations between trades in these markets have finite expectation, an assumption often made implicitly in the literature on point process models. Our empirical findings indicate infinite-mean durations for all the five cryptocurrencies examined, with the integrated ACD hypothesis rejected -- against alternatives with tail index less than one -- for four out of the five cryptocurrencies considered.
Faced with multiple societal challenges, the healthcare sector has been compelled to leverage recent and emerging technologies to adapt. Blockchain is one of the leading technologies, offering transparency, process automation, immutability of traces and the ability to scale up in terms of both the volume of processes and the number of players interacting. The goal of the paper is to show the potential of blockchain technology - alone or merged with other technologies - to help the healthcare system evolve and provide scalable, efficient and secure solutions to four healthcare applications: electronic health record (EHR) storage, health data sharing, remote patient monitoring, and pharmaceutical supply chains. After identifying the functional and security requirements of healthcare systems, the paper conducts an in-depth review of the literature. The survey assesses the effectiveness of blockchain-based solutions in meeting functional, privacy and security needs. It is completed by an analysis of the synergies that can be expected between blockchain and emerging technologies, e.g. artificial intelligence, federated learning, the Internet of Things (IoT), and Large Language Models (LLM), to the benefit of security or privacy in healthcare.
Blockchain technology is reshaping supply chain management (SCM) by enhancing transparency, traceability, and trust among stakeholders. This study reviews recent applications of blockchain in SCM, focusing on its integration with IoT, smart contracts, and distributed ledger technologies. Key benefits include real-time data sharing, tamper-proof records, and improved coordination across supply chain actors. The article proposes a conceptual blockchain-based model that supports secure data exchange, automated verification, and tokenized asset tracking, aimed at reducing fraud and inefficiencies. Despite its potential, blockchain implementation faces challenges such as scalability, regulatory uncertainty, and interoperability. Through analysis of recent case studies and pilot projects, the paper identifies practical limitations and future directions, including the development of lightweight consensus algorithms and ethical frameworks for decentralized systems. The findings underscore blockchain’s capacity to modernize supply chains, provided that technical and institutional barriers are addressed.
This article examines the influence of the major stock indices' value on Bitcoin price fluctuations. The study focuses on the correlation between the absolute value of Bitcoin and the daily closing prices of leading stock market indices — namely, the S&P 500, Nasdaq Composite, and Dow Jones Industrial Average — based on daily time series data from 2020 to 2025. The paper spotlights the correlation analysis between the dynamics of daily changes (increases or decreases) in the closing prices of these indices and the corresponding changes in the price of Bitcoin over the same time frame. The Pearson correlation coefficient was employed to quantify the strength and direction of linear relationships between Bitcoin and each stock market index for each year within selected timeframe. Furthermore, the correlation results were interpreted using Chaddock’s scale to assess their practical significance and to identify the stock asset with the highest correlation to Bitcoin. The methodological framework of the study includes quantitative analysis methods, observation, and comparative analysis. The findings reveal the presence of a variable, time-dependent correlation between Bitcoin and the selected stock indices, with stronger interconnections observed during periods of global financial turmoil. This may reflect a growing integration of the cryptocurrency market into the broader financial system. At the same time, during relatively stable periods, both the cryptocurrency and traditional stock markets tend to show a degree of independence from each other. These results support the hypothesis of Bitcoin's complex and hybrid nature as a financial asset, which may simultaneously serve speculative purposes and display certain hedging characteristics. The results obtained may be of practical importance for the development of investment strategies, portfolio diversification, cryptocurrency price forecasting, and risk assessment in conditions of increasing financial market uncertainty. Consequently, the research contributes to a deeper understanding of Bitcoin’s role within the global financial landscape and its potential responsiveness to systemic risks.
This article examines the transformative landscape of financial data integration technologies and their collective impact on the financial services industry. The comprehensive exploration covers three pivotal developments reshaping the sector: Open Banking and API standardization, real-time data streaming with AI analytics, and data fabric architecture. Each innovation addresses specific challenges within the financial ecosystem while contributing to a more connected, intelligent, and responsive financial infrastructure. The article details how regulatory frameworks drive adoption, technical standards ensure implementation success, and emerging architectures enable unprecedented capabilities. By investigating the convergence of these technologies and emerging trends, including semantic interoperability, quantum computing applications, and decentralized finance integration, the article provides a forward-looking perspective on how financial institutions can leverage integrated data solutions to gain competitive advantages while navigating complex regulatory requirements and evolving customer expectations.