Vaccines play a crucial role in the prevention and control of infectious diseases. However, the vaccine supply chain faces numerous challenges that hinder its efficiency. To address these challenges and enhance public health outcomes, many governments provide subsidies to support the vaccine supply chain. This study analyzes a government-subsidized, three-tier vaccine supply chain within a continuous-time differential game framework. The model incorporates dynamic system equations that account for both vaccine quality and manufacturer goodwill. The research explores the effectiveness and characteristics of different government subsidy strategies, considering factors such as price sensitivity, and provides actionable managerial insights. Key findings from the analysis and numerical simulations include the following: First, from a long-term perspective, proportional subsidies for technological investments emerge as a more strategic approach, in contrast to the short-term focus of volume-based subsidies. Second, when the public is highly sensitive to vaccine prices and individual vaccination benefits closely align with government objectives, a volume-based subsidy policy becomes preferable. Finally, the integration of blockchain technology positively impacts the vaccine supply chain, particularly by improving vaccine quality and enhancing the profitability of manufacturers in the later stages of production.
Cryptocurrencies have revolutionized the financial landscape, introducing decentralized digital assets like Bitcoin and Ethereum. Their growth has spurred interest in statistical methods for monitoring and analyzing transactions, especially in the context of traditional financial systems like SWIFT (Society for Worldwide Interbank Financial Telecommunication). Statistical methods play a crucial role in identifying patterns, anomalies, and potential risks associated with cryptocurrency transactions. These methods involve data analysis, clustering, and machine learning algorithms to detect fraudulent activities, money laundering, and market trends. The integration of blockchain technology ensures transparency and immutability, enhancing statistical analysis accuracy.On the other hand, SWIFT transactions, widely used for cross-border payments, rely on statistical techniques to track and validate international fund transfers. These methods aid in fraud detection, regulatory compliance, and transaction efficiency. Combining the statistical prowess of cryptocurrencies and SWIFT transactions offers a comprehensive approach to secure and efficient global finance.In conclusion, cryptocurrencies have emerged as a disruptive force in the world of finance, offering decentralized, secure, and borderless transactions. Their popularity has grown exponentially, attracting both enthusiasts and skeptics. They have disrupted traditional finance, offering decentralized digital assets like Bitcoin and Ethereum. Statistical methods are crucial for monitoring and securing transactions on the SWIFT network, the backbone of global financial messaging. One of the recommendations was that advanced data analytics to detect anomalies, trend analysis for fraud prevention, and machine learning algorithms for predictive modeling.
Samsudin Samsudin, Muhammad Dedi Irawan, Muhammad Irwan Padli Nasution, Raissa Amanda Putri
Bitcoinâs extreme price volatility has long posed challenges for both investors and researchers seeking reliable forecasting models. Conventional financial approaches often fail to capture the highly complex, nonlinear, and fast-moving nature of cryptocurrency markets. To address this gap, this study develops a Bitcoin price prediction model using Random Forest Regression based on on-chain market data. The dataset was obtained from publicly available historical Bitcoin daily trading records spanning more than five years. Key features include opening price, daily high and low ranges, trading volume, and percentage change. The research was carried out in several stages. First, data preprocessing was conducted through normalization, handling of missing values, and feature engineering. Second, model training was performed with Random Forest, including parameter tuning to optimize predictive accuracy. Third, model evaluation employed R² and Mean Absolute Percentage Error (MAPE) as primary performance indicators. Fourth, visualization was implemented using interactive charts to allow users to observe short-term price fluctuations and long-term market patterns. The system development followed an iterative methodology inspired by the Streamlit Framework, which is an open-source Python library that simplifies building interactive web applications for data science and machine learning. This approach provides flexibility, enabling rapid experimentation and adaptation to evolving market conditions. The results show that the proposed model achieves near-perfect R² values (approaching 1.0) with consistently low MAPE, highlighting its reliability. Beyond predictive performance, the framework is designed to be scalable, supporting future integration with deep learning methods such as LSTM and external macroeconomic indicators, thus offering both practical utility for investors and academic contributions to decentralized finance research.
Amy Thomas, Maria-Jose Schmidt-Kessen, Simon Karlin
This chapter explores the role of intellectual property (IP) in the commercialisation and regulation of sports and eSports, focussing on copyright, trade marks, and image rights. It outlines how these rights enable key stakeholders - such as sports organisers, players and fans - to assert control over various aspects of sporting content and performances. Though comparative analysis of legal frameworks in Germany, the EU, and the UK, the chapter highlights significant jurisdictional differences in the protection and interpretation of these rights, particularly in relation to the use of player likenesses and ownership of performance outputs. The chapter also investigates how new technologies, including generative artificial intelligence (AI) and Non-Fungible Token (NFTs), might complicate rights-based relationships in both fields. A central theme is the imbalance of rights and bargaining power among stakeholders, especially players, whose creative contributions are often excluded from IP protection. In doing so, the chapter raises normative questions and critical reflections on fairness, enforcement, and contractual practices in the regulation of sports and eSports content.
Puguh Hiskiawan, Jovan William, Louis Feliepe Tio Jansel
Bitcoin, a highly volatile and decentralized digital asset, presents considerable challenges for accurate price forecasting. This study proposes an applied data science framework that compares traditional statistical approaches with modern Artificial Intelligence (AI)-based models to predict Bitcoinâs daily closing price. Using BTC-USD historical data from January 2020 to December 2024, we converted prices into Indonesian Rupiah (IDR) to increase local relevance. Our forecasting horizon is 30 days, based on a 60-day lookback window. We evaluate six models: Linear Regression, ARIMA, and Prophet as traditional techniques, alongside Random Forest, XGBoost, and Long Short-Term Memory (LSTM) networks as AI approaches. All models were trained using lag-based or sequence-based time series features and evaluated using MAE, RMSE, R², MAPE, and SMAPE. Results show that AI models, particularly LSTM and XGBoost, offer better performance in capturing short-term non-linear dynamics compared to traditional models. LSTM provides high accuracy, though with greater computational demand, while XGBoost strikes a balance between speed and precision. Prophet and ARIMA remain effective for quick and interpretable forecasts but struggle with abrupt trend shift common in cryptocurrency markets. In addition to performance metrics, we include a robustness analysis based on median absolute error and outlier detection to assess model stability under extreme variations. Visual analyticsâincluding forecast curves, error distributions, and uncertainty boundsâhelp interpret and communicate model behavior. This comprehensive evaluation offers practical insights for investors, analysts, and fintech practitioners, and the pipeline can be extended to other volatile assets.
This paper explores the integration of Blockchain technology into Structural Health Monitoring (SHM) to enhance data traceability, integrity, and automation in infrastructure asset management. Traditional SHM approaches, including Digital Twin-based systems, often face limitations related to data tampering, sensor unreliability, and the lack of transparent and verifiable data workflows. To address these challenges, the SHERPA framework is proposed. SHERPA leverages decentralized storage via the InterPlanetary File System and three Smart Contracts dedicated to data validation, anomaly flagging, and automated workflow execution. Rather than focusing on the structural interpretation of data, SHERPA establishes a secure and auditable backbone for SHM data governance. A prototype implementation on the Canalone Viaduct in Italy demonstrated the feasibility of the system, showcasing automated response to threshold violations and immutable data registration. The framework proved effective in enhancing transparency, traceability, and stakeholder confidence, positioning SHERPA as a promising enabler of more trustworthy and accountable SHM systems.
Proof-of-stake blockchains require consensus protocols that support Dynamic Availability and Reconfiguration (so-called DAR setting), where the former means that the consensus protocol should remain live even if a large number of nodes temporarily crash, and the latter means it should be possible to change the set of operating nodes over time. State-of-the-art protocols for the DAR setting, such as Ethereum, Cardano's Ouroboros, or Snow White, require unrealistic additional assumptions, such as social consensus, or that key evolution is performed even while nodes are not participating. In this paper, we identify the necessary and sufficient adversarial condition under which consensus can be achieved in the DAR setting without additional assumptions. We then introduce a new and realistic additional assumption: honest nodes dispose of their cryptographic keys the moment they express intent to exit from the set of operating nodes. To add reconfiguration to any dynamically available consensus protocol, we provide a bootstrapping gadget that is particularly simple and efficient in the common optimistic case of few reconfigurations and no double-spending attempts.
The rapid advancement of blockchain technology has precipitated the widespread adoption of Ethereum and smart contracts across a variety of sectors. However, this has also given rise to numerous fraudulent activities, with many speculators embedding Ponzi schemes within smart contracts, resulting in significant financial losses for investors. Currently, there is a lack of effective methods for identifying and analyzing such new types of fraudulent activities. This paper categorizes these scams into four structural types and explores the intrinsic characteristics of Ponzi scheme contract source code from a program analysis perspective. The Mythril tool is employed to conduct static and dynamic analyses of representative cases, thereby revealing their vulnerabilities and operational mechanisms. Furthermore, this paper employs shell scripts and command patterns to conduct batch detection of open-source smart contract code, thereby unveiling the common characteristics of Ponzi scheme smart contracts.
Blockchain technology has become one of the most disruptive innovations of the 21st century, reshaping industries such as finance, supply chain management, healthcare, and governance. However, the conventional blockchain ecosystemâparticularly models based on Proof of Work (PoW)âhas been widely criticized for its excessive energy consumption and ecological footprint. As societies move toward sustainability and carbon-neutral goals, the exploration of energy-efficient blockchain models becomes not just an academic pursuit but also an ethical imperative. This manuscript investigates the evolution of energy-efficient consensus mechanisms and their integration into âgreen smart contracts,â which enable automated, verifiable, and sustainable digital agreements. It highlights consensus algorithms such as Proof of Stake (PoS), Delegated Proof of Stake (DPoS), Proof of Authority (PoA), Proof of Space-Time (PoST), Practical Byzantine Fault Tolerance (PBFT), and emerging hybrid mechanisms. The manuscript offers a comprehensive literature review, outlines statistical insights comparing energy and performance trade-offs, and proposes methodologies for integrating eco-friendly smart contract architectures. The results emphasize that while PoW-based systems consume up to 99% more energy than PoS-based models, hybrid approaches demonstrate a promising balance between security, decentralization, and efficiency. The study concludes that energy-efficient blockchain models, when strategically aligned with sustainability frameworks, can redefine smart contract ecosystems to meet global climate commitments while maintaining reliability, transparency, and scalability.
The growth of freelancing exposed several drawbacks in the platforms that are now in use, including high service costs, fraud risks, and late payments [1]. A well-designed system architecture is necessary to develop a freelance platform to guarantee scalability, security, and effectiveness. A modular architecture was chosen to give the required flexibility, scalability, and ease of maintenance to overcome these issues. This paper presents the design of a microservices-based architecture for a crypto freelance exchange platform, which uses Domain-Driven Design principles [2]. The architecture is built to support decentralized transactions, smart contract integration, and secure user authentication. It also ensures high availability and fault tolerance. The system uses a multi-layered architecture incorporating PostgreSQL, MongoDB, Redis, and Web3.js. The main components are a web application, KrakenD API Gateway, auth microservice, files microservice, and main microservice for managing transactions, orders, and payments. They are designed to meet critical non-functional requirements such as scalability, security, and maintainability. Each service can be independently deployed, updated, and scaled according to transaction volumes. With an emphasis on security, the platform uses JWT token techniques and multi-factor authentication to authenticate users. Also, the integration of blockchain technology enhances transparency, enabling freelancers and clients to have a trusted record of all transactions and reducing the risk of fraud. The architecture is visualized through UML and C4 model diagrams showing component interactions and service orchestration. This paper also discusses the rationale behind the chosen technologies, security mechanisms, and the benefits of using a modular microservices approach for building a crypto freelance platform.
This work focuses on the study of distributed ledger applications, presenting proposals of fair and decentralised applications to counter scenarios in which centralisation of wealth and power are the norm. The first contribution is a novel architecture for a decentralised data market, in which participants crowd-source data and receive a fair share of the reward. The market is shown to be resilient against a number of adversarial behaviours. Subsequently, an algorithm to prove one's location is presented. This algorithm is a key component necessary to the functioning of the data market. In contrast to prior approaches, the design does not require assumptions of honest participation, nor dependence on an external ground truth to identify malicious actors. It is fully peer-to-peer, robust in highly adversarial settings, and compatible with privacy-preserving techniques. The security and reliability of the algorithm are evaluated empirically and characterised mathematically. The protocol is then generalised into a consensus mechanism applicable beyond location verification. An extended mathematical model is developed for this case, and its performance under varying operational conditions is systematically characterised. Finally, a study of governance vulnerabilities in Distributed Ledger Technologies is presented. This work provides a taxonomy of formalised properties necessary for good governance, solutions to implement them and an evaluation of how the absence of these cause severe vulnerabilities. The analysis is then extended to realm of Decentralised Autonomous Organisations (DAOs), which are a class of applications implemented on Distributed Ledger Technologies. The findings anticipated several governance exploits that later materialised, incurring losses in the scale of millions for multiple DAOs. Overall, this thesis aims to contribute to the technological development of distributed ledger applications with the goal of furthering social good, presenting architectures, algorithms, and governance properties that prioritise fairness, decentralisation, and resilience.
In recent years, significant research efforts have focused on improving blockchain throughput and confirmation speeds without compromising security. While decreasing the time it takes for a transaction to be included in the blockchain ledger enhances user experience, a fundamental delay still remains between when a transaction is issued by a user and when its inclusion is confirmed in the blockchain ledger. This delay limits user experience gains through the confirmation uncertainty it brings for users. This inherent delay in conventional blockchain protocols has led to the emergence of preconfirmation protocols -- protocols that provide users with early guarantees of eventual transaction confirmation. This article presents a Systematization of Knowledge (SoK) on preconfirmations. We present the core terms and definitions needed to understand preconfirmations, outline a general framework for preconfirmation protocols, and explore the economics and risks of preconfirmations. Finally, we survey and apply our framework to several implementations of real-world preconfirmation protocols, bridging the gap between theory and practice.
Ambi Rachel Alex, Syed Hassan Imam Gardezi, P S Krishnendu, P. Aruna ¡ 5 authors
The rapid expansion of the Internet of Things (IoT) has led to an unprecedented rise in interconnected devices, generating vast volumes of sensitive data that demand robust security and trust mechanisms. Traditional centralized architectures often struggle to ensure integrity, privacy, and resilience against single points of failure, making them unsuitable for next-generation IoT ecosystems. This paper proposes a blockchain-enabled decentralized trust framework to strengthen the security, transparency, and reliability of IoT networks. By integrating distributed ledger technology with lightweight consensus protocols, the framework establishes immutable device identities, secure data exchange, and automated access control without dependence on centralized authorities. The proposed approach enhances interoperability among heterogeneous IoT devices while minimizing latency and computational overhead. Experimental evaluation and comparative analysis demonstrate that the blockchain-based trust model effectively mitigates common threats such as data tampering, spoofing, and unauthorized access, paving the way for a scalable and trustworthy foundation for future IoT applications.
The industrial internet of things (IIoT) expanded fast as physical devices and systems were connected to the internet. However, this interconnectedness made IIoT systems vulnerable to hackers. Intrusion detection systems (IDSs) were put in place to detect and prevent such assaults. Nonetheless, attackers might circumvent IDSs by forging identities or interfering with recorded data. The article intended to improve IIoT security by achieving system confidentiality, integrity, availability, scalability, performance, and security. For IIoT security, the article developed a secure federated learning access control framework (SecureFLACF) linked with a blockchain-based IDS. SecureFLACF used blockchain to secure data collected by IDS, AES-256 encryption to secure stored data, zero-knowledge proof (ZKP) to validate user identities and manage data access, and a federated learning access control framework (FLACF) to train a machine learning model for intrusion detection. SecureFLACF developed as a viable solution for improving IIoT security, providing strong assurances for IDS data and access control using blockchainâs tamper-proof structure and AES-256 encryption. Furthermore, FLACFâs design allows private machine learning model training, ensuring data privacy as well as model fidelity. The frameworkâs usefulness was highlighted by its application in real-world circumstances, making it a cost-effective option for organisations of all sizes. This method not only strengthened IIoT systems against a wide range of cyber threats, but also stressed their dependability as a safeguard. SecureFLACF exhibited considerable promise for improving IIoT security across several dimensions by encapsulating practicability, cost-effectiveness, and dependability.
Decentralization has emerged as a prominent strategy for health sector reform in low- and middle-income countries (LMICs), aiming to enhance service quality, efficiency, equity, and responsiveness. This study systematically reviews literature published between 2021 and 2025 to explore the role of decentralized health systems in shaping healthcare service quality across LMICs. Using PRISMA 2020 guidelines, 20 eligible studies were identified and analyzed from databases including PubMed, Scopus, Web of Science, and Google Scholar. Thematic synthesis of findings reveals mixed outcomes: while decentralization improves local responsiveness, enhances community engagement, and strengthens health system performance in some settings, it also exacerbates disparities in others due to uneven institutional capacity, limited fiscal resources, and fragmented coordination. Key performance areas identified include human resource deployment, financing, access to services, and equity in service delivery. The study emphasizes the significance of local capacity-building, efficient resource allocation, and integrated planning in attaining sustainable and equitable healthcare improvements within decentralized systems. This review provides practical insights for policymakers aiming to align decentralization strategies with health equity and service quality objectives.
Khaleel Radhi Hasan Alzlzly, Basim Abdullah Kadhim, Rahim Raad Hameed, Hussein Basim Furaij
"The objective of this study is to analyze the impact of real-time public procurement disclosure through distributed ledger technology (DLT) on reducing the cost of bank financing for public projects in Iraq The importance of this research stems from the growing need to increase financial transparency and reduce information asymmetry between government entities and the banking sector, thereby reducing credit risk and funding a descriptive research." methodology such as An applied field design combining quantitative and qualitative approaches is supported. Data were collected through a structured questionnaire from a sample of 165 senior and middle managers from three major Iraqi banks (Al-Rafidin, Al-Rashid and Trade Bank of Iraq) that finance public projects. Used multiple linear regression and F/T tests to validate the study hypotheses. Had gone The findings show that real-time disclosure via DLT significantly reduces funding costs (ι ⤠0.05) by improving transparency and shortening contract verification cycles. Furthermore the availability of immutable, time-stamped purchasing data increased banks' trust in public agencies The study recommends that Iraq's Ministry of Finance and public procurement authorities improve the security of digital data and government and adopts a pilot DLT-based tender and contract management system with a legal framework to integrate banking platforms
Distributed ledger technology has already been integrated into areas like supply chain management. However, other areas such as e -commerce have been largely neglected in research. For this reason, three application areas-customer experience, transactions and security mechanisms-of distributed ledger technology in e -commerce are identified and described through a systematic literature review. The results are then discussed by highlighting recurring themes and observed potentials. Three possible research areas can ultimately be der ived from this: decoupling from traditional platforms and marketplaces; integrating DLT into e -Commerce systems; new e -commerce business models with DLT that can be considered in the face of new and traditional e -commerce organisations
An estimated 1.3 billion people worldwide (roughly 16% of the global population) are affected by various forms of disability. As highlighted by the World Health Organization, many face mobility-related challenges that significantly restrict their ability to participate fully in social and communal life. These restrictions hinder communication and reduce opportunities for linguistic and social enrichment. This study addresses these challenges by proposing Fairverse, an accessible metaverse designed to enhance socialization and inclusivity for people with physical disabilities. Using virtual reality (VR), blockchain and game technologies, Fairverse initially operates as a VR environment but also supports web-based access to ensure broader usability without specialized hardware. As a proof of concept, a customizable virtual room was developed that integrates Ready Player Me avatars and barrier-free avatars that can be controlled by voice commands. To ensure sustainable governance, a Decentralized Autonomous Organization (DAO) is proposed underpinned by a token economy, facilitating sponsorships and donations to incentivize content creators and virtual-world developers. By fostering an inclusive digital ecosystem, Fairverse aims to remove accessibility barriers in the virtual world, empowering users with disabilities to participate fully in the metaverse.
AndrĂŠs FernĂĄndezâMiguel, Susana OrtĂz-Marcos, Mariano JimĂŠnez, Alfonso Pedro FernĂĄndez del Hoyo ¡ 6 authors
This study advances toward establishing the theoretical foundations of Industry 6.0 by developing a comprehensive framework that integrates artificial intelligence (AI), decentralized control systems, and cyberâphysical production environments for intelligent, sustainable, and adaptive manufacturing. The research employs a tri-modal methodology (deductive, inductive, and abductive reasoning) to construct a theoretical architecture grounded in five interdependent constructs: advanced technology integration, decentralized organizational structures, mass customization and sustainability strategies, cultural transformation, and innovation enhancement. Unlike prior conceptualizations of Industry 6.0, the proposed framework explicitly emphasizes the cyclical feedback between innovation and organizational design, as well as the role of cultural transformation as a binding element across technological, organizational, and strategic domains. The resulting framework demonstrates that AI-driven decentralized control systems constitute the cornerstone of Industry 6.0, enabling autonomous real-time decision-making, predictive zero-defect manufacturing, and strategic organizational agility through distributed intelligent control architectures. This work contributes foundational theory and actionable guidance for transitioning from centralized control paradigms to AI-driven distributed intelligent manufacturing control systems, establishing a conceptual foundation for the emerging Industry 6.0 paradigm.
Wencheng Chen, Jun Wang, JengâShyang Pan, R. Simon Sherratt ¡ 5 authors
The explosive growth of Internet of Things (IoT) data demands secure and reliable storage, where traditional centralized solutions often fall short. Blockchain offers decentralization and tamper-resistance, making it a promising foundation for IoT. However, IoT blockchain systems based on Delegated Proof-of-Stake (DPoS) face challenges such as weak node incentives, unfair reward distribution, and low consensus efficiency. This paper proposes a fairness-aware incentive mechanism that accounts for both node capability and effort under information asymmetry. By incorporating fairness preferences into the contract design, the mechanism improves participation and motivates sustained effort. Theoretical analysis and simulation results show that our approach enhances throughput by about 15%, while achieving revenue fairness, incentive compatibility, and stronger consensus performance. The mechanismâs adaptability makes it suitable for diverse IoT application scenarios.
Kypros Iacovou, Georgia M. Kapitsaki, Evangelia Vanezi
Open Source Software (OSS) is widely used and carries licenses that indicate the terms under which the software is provided for use, also specifying modification and distribution rules. Ensuring that users are respecting OSS license terms when creating derivative works is a complex process. Compliance issues arising from incompatibilities among licenses may lead to legal disputes. At the same time, the blockchain technology with immutable entries offers a mechanism to provide transparency when it comes to licensing and ensure software changes are recorded. In this work, we are introducing an integration of blockchain and license management when creating derivative works, in order to tackle the issue of OSS license compatibility. We have designed, implemented and performed a preliminary evaluation of FOSS-chain, a web platform that uses blockchain and automates the license compliance process, covering 14 OSS licenses. We have evaluated the initial prototype version of the FOSS-chain platform via a small scale user study. Our preliminary results are promising, demonstrating the potential of the platform for adaptation on realistic software systems.