Blockchain, while originally conceptualised to develop cryptocurrencies, may have broad applicational value across higher education. This study is the first scoping review to consider how blockchain has been used in healthcare higher education credentialling. It was designed using a Context-Input-Process-Product conceptual framework and adhered to PRISMA-ScR and JBI guidelines. The protocol was digitally registered [INPLASY2025120005] on 2nd December 2025, prior to the commencement of screening. EBSCO, Scopus and MEDLINE (Ovid) were searched using key terms related to healthcare, higher education, credentialling, and blockchain.Only five papers met all inclusion criteria, and the extant literature is predominantly theoretical. Public blockchain architectures were discussed however alternative private and consortium blockchains were underexplored despite possible sustainability, scalability, and privacy benefits. Only one paper discussed a governance model (autonomous self-governance). While ten themes of positive outcomes were theorised, no negative outcomes were posed and only one outcome (time efficiency) was empirically evidenced.This is a nascent field. Future research on blockchain-enabled should be based on advanced architectural modelling and evaluate governance and implementation strategies.
Nourhaine Nefzi, A. Melki, Sahar Loukil, Ahmed Jeribi
Abstract This study investigates the dynamic connectedness within the cryptocurrency market by analyzing four distinct cryptomarket blocks: Bitcoin and Ethereum (conventional cryptocurrencies); PAXG, DGX, and GLC (gold-backed cryptocurrencies); LINK and MNK (decentralized finance); and THETA and MANA (nonfungible tokens). Using the time-varying parameter quantile vector autoregressive (TVP-Quantile VAR) model for the period 2019–2023, our analysis reveals significant insights into the risk transmission dynamics among cryptocurrencies. Both conventional cryptocurrencies exhibit a consistent net transmitter effect in extreme periods, whereas decentralized finance (DeFi) and nonfungible tokens (NFTs) shift between a net shock transmitter and a net shock receiver over time and quantiles. Moreover, our results shed light on the hedging and safe haven properties of these assets. By linking the dynamic connectedness findings with established literature on hedging and safe haven functions, we elucidate how these cryptocurrencies perform under varying market conditions. Specifically, we report that the role of LINK, MNK, THETA, and MANA as reliable safe-haven assets is contingent upon the observed period. We also observe the hedge and safe haven properties of selected gold-backed cryptocurrencies within the network. Overall, our findings suggest that, despite the dynamic connectedness of the cryptocurrency market, investors have the flexibility to diversify across these digital assets.
Description of the Invention The present invention relates to the field of cybersecurity and artificial intelligence, and more particularly to a method and system for local autonomous internal penetration testing using artificial intelligence agents augmented by retrieval-based knowledge mechanisms. The invention discloses a technical solution in which one or more autonomous artificial intelligence agents operate within an internal enterprise environment to continuously assess the security posture of information systems, networks, services, and configurations. Unlike conventional penetration testing approaches that rely on manual effort, predefined scripts, or static rule-based scanners, the proposed system dynamically adapts its behavior based on observed system states, retrieved security knowledge, and learned experience. The system employs reinforcement learning to model penetration testing as a sequential decision-making problem, wherein an agent observes an environment state, selects an action from a defined action space, receives a reward based on the outcome, and updates its policy to maximize an expected cumulative reward associated with discovering security weaknesses, misconfigurations, or policy violations. The environment may include hosts, network topology, authentication mechanisms, access control rules, and security monitoring components. To overcome the limitations of fixed context windows and static knowledge in machine learning models, the invention integrates retrieval-augmented mechanisms. At each decision step, the agent retrieves relevant contextual information from a locally stored vector database containing embeddings of security documentation, vulnerability descriptions, configuration policies, compliance requirements, historical findings, and system metadata. The retrieval process is performed using approximate nearest neighbor search, enabling low-latency access to relevant knowledge at scale. The retrieved contextual data is used to augment the agent’s internal state representation and, in some embodiments, to construct augmented prompts for a local large language model responsible for high-level planning, reasoning, or task decomposition. This combination enables the agent to make informed decisions grounded in both learned behavior and up-to-date domain-specific knowledge. In certain embodiments, the system supports multiple cooperating agents, each specializing in a subset of penetration testing activities, such as reconnaissance, vulnerability identification, exploitation simulation, lateral movement analysis, or compliance auditing. Coordination between agents is achieved through a centralized planner or critic during training, while execution may occur in a decentralized manner. A key feature of the invention is that all data processing, retrieval, learning, and inference are performed locally within the organization’s infrastructure, without transmitting sensitive information to external systems. This design preserves confidentiality, complies with regulatory and organizational privacy requirements, and enables deployment in restricted or high-security environments. The disclosed method and system provide continuous, adaptive, and knowledge-informed internal security assessment, improving the detection of security weaknesses while reducing reliance on manual testing and static tools.
This study examines how Decentralized Autonomous Organisations (DAOs) could be incorporated into municipal administration to improve citizen participation and transparency. As DAOs in governance are attracting growing academic and practical attention, this research uses scenarios to analyze the conditions for their application at municipal and regional levels. It takes a conceptual, scenario-based approach to develop a model for DAO-based e-participation, identifying key concepts and their relationships to explain how DAOs operate as self-regulated systems for digital participation. The research is structured into three phases: First, in the foundational phase, this study synthesizes existing research on DAOs and participatory governance models, contrasting blockchain-based and traditional processes to establish a framework for DAO integration. Second, the application phase uses illustrative scenarios to explore how DAO mechanisms might influence participation and decision-making in municipalities. Third, the evaluation and recommendation phase consolidates insights into a structured model for implementation, highlighting task characteristics, contextual conditions, and organizational capacities that shape DAO feasibility. The analysis suggest that DAOs may enable new forms of participation and more transparent procedures, but only when antecedent conditions such as digital literacy, administrative capacity, and infrastructure stability are sufficiently met. Future work should look at long-term effects, compare cases across municipalities, and examine the role of legal and regulatory frameworks.
Anderson Melo de Morais, Fernando Lins, Nelson Souto Rosa
The Internet of Things (IoT) drives technological advances across various sectors by enabling seamless communication among smart devices. However, significant challenges remain regarding the integrity and reliability of the data stored by these devices. Traditional blockchain solutions, such as those based on Proof of Work (PoW), are generally unsuitable for IoT applications due to their high computational resource demands. Although approaches combining multiple consensus algorithms have emerged as alternatives to optimise performance and security, determining the best combination for each scenario remains an open problem. This paper proposes a strategy based on Genetic Algorithms (GAs) to adaptively select and combine consensus algorithms, thus improving blockchain efficiency in IoT environments. The approach was evaluated on a test blockchain, OmniBlock, implemented using a Directed Acyclic Graph (DAG) and designed specifically for evaluation purposes in IoT applications. OmniBlock supports multiple consensus algorithms, including Proof of Authority (PoA), Proof of Stake (PoS), Proof of Work (PoW), Practical Byzantine Fault Tolerance (PBFT), Raft, and others. The consensus algorithm combination is chosen based on performance attributes. All combinations of consensus algorithms evaluated in this work were suggested by GAs; the practical feasibility of each is analyzed empirically. Experimental results indicate that the evolutionary optimization-based strategy performs better across most of the combinations suggested by the GAs.
We present BAZINGA, a novel distributed system that achieves unification of artificial intelligence and blockchain through a new consensus mechanism called Proof-of-Boundary (PoB). Unlike traditional approaches that treat AI and blockchain as separate layers ("AI on blockchain"), BAZINGA demonstrates that AI and blockchain are Subject and Object of a single system, with consensus emerging from the boundary between them. The key discovery is that blockchain consensus can be achieved through understanding rather than computational work or financial stake. Nodes validate blocks by demonstrating comprehension via a mathematical boundary condition: the ratio of Physical to Geometric measures must equal φ⁴ ≈ 6.854 (where φ is the golden ratio). Key results: • 70 billion times more energy-efficient than Bitcoin • Sybil-resistant without financial stake • Unified with federated learning for distributed AI training • Validated through mathematical understanding rather than arbitrary computation The system includes four integration layers (Trust Oracle, Knowledge Ledger, Gradient Validator, Inference Market) that bind AI intelligence with blockchain validation. Fully implemented as open-source software (MIT License). Software: https://pypi.org/project/bazinga-indeed/ Source: https://github.com/0x-auth/bazinga-indeed Demo: https://huggingface.co/spaces/bitsabhi/bazinga
Decentralized autonomous organizations (DAOs) are transforming how people collaborate and manage collective ventures, yet the law has lagged behind this innovative form of organization. Because DAOs rely on smart contracts rather than conventional organizational infrastructure, it is often unclear how they fit into existing laws. This paper defines DAOs, outlines the current regulatory frameworks governing them, and demonstrates where those frameworks fall short. It argues that DAOs should be recognized as a distinct form of corporate entity and that a tailored exemption from federal securities laws is necessary for them to operate effectively. The proposed model offers a practical path to granting DAOs legal recognition while preserving core principles of transparency, accountability, and investor protection.
This paper presents the implementation oriented development of an interactive web platform designed to bring transparency and trust to charitable giving through the use of blockchain technology. This DApp integrates both Web2 and Web3 components: Here organizations create verified charity campaigns so that donors contribute directly through Meta-Mask a cryptocurrency wallet, with all transactions immutably recorded on the blockchain for public auditability. The backend (Web2) manages user data, campaign verification, and document storage, enforcing legitimacy through decentralized storage (IPFS). Also with the use of web2 has helped to create a more user friendly and attractive user interface layouts. Etherium Smart contracts are used to handle and release donations based on predefined conditions. A Merkle Tree algorithm is implemented to provide cryptographic proof for inclusion of donations in charities. This platform solves common challenges of traditional charity systems, such as mismanagement, high intermediary fees, and mainly donor mistrust, by offering a secure, decentralized, and automated donation ecosystem.
NFTs, kurz für Non-Fungible Tokens, sind digitale Zertifikate auf einer Blockchain, einer unveränderlichen Datenbank. Sie zeigen, wem ein digitales Objekt gehört, beispielsweise eine Grafik, Audio-Dateien, In-Game-Items oder Tweets. Anders als klassische Dateien, die beliebig kopiert werden können, dienen NFTs als Eigentumsnachweis. Die eigentliche Datei liegt meist auf externen Servern. NFTs sind Teil der Vision des Web3, eines dezentralen Internets, in dem Nutzer*innen über Inhalte, Besitz und digitale Identitäten selbst bestimmen sollen. Das Konzept eröffnet neue Möglichkeiten in virtuellen Welten, Spielen und Community-Projekten, stößt aber an Grenzen, weil Plattformen, Wallets und Anbieter letztlich entscheiden, wer teilnehmen darf. Bekannt wurden NFTs ab 2020 im Kunst- und Sammlermarkt, beispielsweise Beeples Everydays oder Sammlungen wie CryptoPunks. In Games und virtuellen Welten wie Axie Infinity oder Decentraland lassen sich Besitzrechte, Handelsmechanismen und Community-Dynamiken praktisch nachvollziehen. Nach dem anfänglichen Boom sank ab 2023 der Wert vieler NFTs, Plattformen verschwanden und technische wie rechtliche Fragen blieben offen. Für die Medienpädagogik bieten NFTs zahlreiche Ansatzpunkte: Fachkräfte können mit Lernenden über digitale Besitzformen, Wertzuschreibung, Marktmechanismen und soziale Dynamiken diskutieren. Eigene Experimente – Tokens erstellen, Sammlungen aufbauen, Spielobjekte gestalten – machen Logik, FOMO-Effekte und Machtstrukturen erfahrbar. Gleichzeitig lassen sich Nachhaltigkeit, langfristige Verfügbarkeit von Daten und Zugangshürden kritisch reflektieren. NFTs bieten so einen Einstieg, um digitale Wertlogiken, Teilhabe und Verantwortung im Web3 zu hinterfragen.
Censorship resistance is a core value of Web3, yet practical access to decentralized websites remains dependent on centralized gateways such as ipfs.io, .link, and .limo, which are susceptible to regulatory takedowns and availability limitations. This paper investigates the technical barriers to truly censorship-resistant access in decentralized web architectures and presents an engineering-driven analysis of dweb3.wtf, a dedicated rendering gateway developed within the Web3Compass infrastructure. The system directly interfaces with decentralized name systems such as ENS and Unstoppable Domains, autonomously resolves content hashes via on-chain resolvers, and renders the associated IPFS-hosted sites via self-hosted infrastructure. By eliminating reliance on third-party APIs and centralized frontends, the gateway offers a robust alternative to Web2-style intermediaries. This paper presents the architecture, implementation, and performance characteristics of dweb3.wtf, evaluating its effectiveness in ensuring access continuity, domain coverage, and reduced external dependency.
Samela Kivilo, Alex Norta, Marie Hattingh, Sowelu Avanzo · 5 authors
In recent years, tokenomic systems, decentralized systems that use cryptographic tokens to represent value and rights, have evolved considerably. Growing complexity in incentive structures has expanded the applicability of blockchain beyond purely transactional use. Existing research predominantly examines token economies within specific use cases, proposes conceptual frameworks, or studies isolated aspects such as governance, incentive design, and tokenomics. However, the literature offers limited empirically grounded, end-to-end guidance that integrates these dimensions into a coherent, step-by-step design approach informed by concrete token-economy development efforts. To address this gap, this paper presents the Token Economy Design Method (TEDM), a design-science artifact that synthesizes stepwise design propositions for token-economy design across incentives, governance, and tokenomics. TEDM is derived through an iterative qualitative synthesis of prior contributions and refined through a co-designed case. The artifact is formatively evaluated via the Currynomics case study and additional expert interviews. Currynomics is an ecosystem that maintains the Redcurry stablecoin, using real estate as the underlying asset. TEDM is positioned as reusable design guidance that facilitates the analysis of foundational requirements of tokenized ecosystems. The specificity of the proposed approach lies in the focus on the socio-technical context of the system and early stages of its design.
Allen Clement, Natacha Crooks, Neil Giridharan, Alex Shamis
Production state-machine replication (SMR) implementations are complex, multi-layered architectures comprising data dissemination, ordering, execution, and reconfiguration components. Existing research consensus protocols rarely discuss reconfiguration. Those that do tightly couple membership changes to a specific algorithm. This prevents the independent upgrade of individual building blocks and forces expensive downtime when transitioning to new protocol implementations. Instead, modularity is essential for maintainability and system evolution in production deployments. We present Gauss, a reconfiguration engine designed to treat consensus protocols as interchangeable modules. By introducing a distinction between a consensus protocol's inner log and a sanitized outer log exposed to the RSM node, Gauss allows engineers to upgrade membership, failure thresholds, and the consensus protocol itself independently and with minimal global downtime. Our initial evaluation on the Rialo blockchain shows that this separation of concerns enables a seamless evolution of the SMR stack across a sequence of diverse protocol implementations.
This thesis explores the doctrinal and practical challenges of applying the principle of party autonomy (lex voluntatis) to smart contracts and transactions governed by Artificial Intelligence (AI). The decentralized and immutable nature of Distributed Ledger Technology (DLT) fundamentally disrupts traditional private international law connecting factors, such as "place of performance" or "habitual residence." The author analyzes how the Rome I Regulation and the Hague Principles on Choice of Law can be adapted to "code-is-law" ecosystems where enforcement is automated and often bypasses state judicial mechanisms. Special attention is paid to the tension between algorithmic execution and "overriding mandatory provisions" (lois de police), questioning whether an AI can recognize and apply mandatory public policy norms that usually override the chosen law. The paper proposes a hybrid regulatory approach "Lex Cryptographia" that embeds choice of law clauses directly into the smart contract's metadata to ensure legal certainty.
Cryptocurrencies constitute a fast-evolving, disruptive technological development. Their proliferation and mainstreaming are undermining national security in several ways. By exploring emblematic cases, this paper examines how decentralised digital assets challenge sovereign functions, complicate law enforcement efforts, and give rise to security challenges. It explores different state-level responses to these developments by drawing on policy documents, reports, and guidance from multilateral regulatory authorities, alongside literature from finance, security studies, international relations, and technology governance. Strategic considerations spanning areas of illicit finance, sanctions evasion, great power rivalry, and state co-option by means of issuing Central Bank Digital Currencies and establishing cryptocurrency strategic reserves are delineated. A comprehensive mapping of the actual impact of cryptocurrencies across several strategic domains is carried out, synthesising insights from previously siloed technical, legal, and international relations literatures into an integrative national-security analytical lens. Specific recommendations are provided for policymakers and planners to navigate this fast-evolving threat landscape. • Synthesizes siloed literature to elucidate how Cryptocurrencies impact national security. • Cryptocurrencies are reshaping threat landscapes and emerging as a domain for great power competition. • Stablecoins strain monetary policy in fragile economies, while Privacy coins hinder AML/KYC enforcement and counter-terror efforts. • Sovereign actors are already using cryptocurrency to circumvent sanctions, fund weapon programmes and covert operations. • CBDCs seek to harness the benefits of cryptocurrencies while re-asserting sovereignty, reflecting divergent geopolitical strategies.
Energy poverty remains a critical barrier to socioeconomic development in rural Africa, where millions lack access to reliable electricity. This study explores the state of rural electrification, the consequences of dependence on traditional energy sources, and the potential of solar energy as a viable solution. Using a qualitative secondary research methodology, the study synthesizes data from scholarly articles, institutional reports, and case studies across various African nations, including Kenya, Rwanda, and Tanzania. Findings reveal that decentralized solar solutions, such as mini-grids and standalone solar home systems, offer scalable and cost effective alternatives to grid expansion. However, challenges such. Ydf as high upfront costs, weak regulatory frameworks, and limited financing mechanisms hinder widespread adoption. Innovative financing models, including pay-as-you-go (PAYG) schemes and microcredit financing, have demonstrated success in increasing energy affordability, while public-private partnerships (PPPs) have facilitated large-scale solar electrification projects. The study concludes that achieving universal energy access in rural Africa requires strengthened institutional support, policy harmonization, and increased investment in decentralized renewable energy solutions. Policy recommendations include government-led subsidy programs, tax incentives for solar enterprises, and enhanced regulatory frameworks to encourage private sector participation. This research contributes to the ongoing discourse on sustainable energy transitions by providing policy insights and strategic recommendations for accelerating rural electrification efforts in Africa.
The article provides a comprehensive study of the fundamental transformation of the nature of financial crises in the conditions of rapid digitalization of the global economy. It is shown that technological changes not only modify the toolkit of financial transactions, but also radically change the dynamics, speed and mechanisms of the spread of crisis phenomena. Special attention is paid to the evolution of banking panics: from traditional physical queues near branches to the phenomenon of "bank sprint", characterized by instantaneous, synchronized and mass withdrawal of liquidity through digital channels. This form of panic differs significantly from classical models in that the time lag between the appearance of negative information and the reaction of depositors is reduced from days or hours to minutes, which significantly complicates the possibilities of regulatory intervention. Based on historical analysis of the collapse of Continental Illinois (1984) and Silicon Valley Bank (2023), it is demonstrated that the digitalization of financial services combined with information synchronization through social networks creates conditions for an exponential acceleration of the spread of financial shocks. Particular attention is paid to new systemic risk vectors in the decentralized finance sector (DeFi), in particular the problem of the absence of automatic market fuses (circuit breakers) and threats of algorithmic cascading liquidations by smart contracts. The influence of artificial intelligence and large language models on market behavior, which contributes to the emergence of the "digital herding" effect, is considered. The need to change the regulatory paradigm is substantiated: the transition from static liquidity standards to dynamic management of operational stability. In this context, the unique experience of the Ukrainian Power Banking network was analyzed, which ensured the continuity of financial services in the conditions of large-scale crisis challenges caused by war and energy attacks. It is shown that the creation of a physically and energetically autonomous infrastructure of bank branches can be an effective tool for increasing the operational stability of the financial system.
The article provides a comprehensive study of the fundamental transformation of the nature of financial crises in the conditions of rapid digitalization of the global economy. It is shown that technological changes not only modify the toolkit of financial transactions, but also radically change the dynamics, speed and mechanisms of the spread of crisis phenomena. Special attention is paid to the evolution of banking panics: from traditional physical queues near branches to the phenomenon of "bank sprint", characterized by instantaneous, synchronized and mass withdrawal of liquidity through digital channels. This form of panic differs significantly from classical models in that the time lag between the appearance of negative information and the reaction of depositors is reduced from days or hours to minutes, which significantly complicates the possibilities of regulatory intervention. Based on historical analysis of the collapse of Continental Illinois (1984) and Silicon Valley Bank (2023), it is demonstrated that the digitalization of financial services combined with information synchronization through social networks creates conditions for an exponential acceleration of the spread of financial shocks. Particular attention is paid to new systemic risk vectors in the decentralized finance sector (DeFi), in particular the problem of the absence of automatic market fuses (circuit breakers) and threats of algorithmic cascading liquidations by smart contracts. The influence of artificial intelligence and large language models on market behavior, which contributes to the emergence of the "digital herding" effect, is considered. The need to change the regulatory paradigm is substantiated: the transition from static liquidity standards to dynamic management of operational stability. In this context, the unique experience of the Ukrainian Power Banking network was analyzed, which ensured the continuity of financial services in the conditions of large-scale crisis challenges caused by war and energy attacks. It is shown that the creation of a physically and energetically autonomous infrastructure of bank branches can be an effective tool for increasing the operational stability of the financial system.
This article examines the revolutionary impact of Artificial Intelligence (AI) on transforming cryptocurrency trading, a sector characterised by extreme volatility, dynamism, and nonlinear data. Through a rigorous bibliometric analysis based on the Web of Science database, this study examines a sample of 555 scientific papers published between 2016 and 2025, utilising the PRISMA protocol for systematic selection, and tools such as VOSviewer and MS Excel. The analysis identifies five major thematic clusters: (1) blockchain infrastructure and AI integration in decentralised ecosystems, (2) data analysis and practical applicability in crypto markets, (3) financial and social data analysis—machine learning algorithms, (4) algorithmic trading and automation, and (5) prediction and modelling of crypto market developments. The originality of this study lies in providing an overview of the implementation stage of these technologies by integrating the results into a map of Technology Readiness Levels (TRLs). The findings highlight a clear transition from traditional statistical methods to autonomous decision-making systems capable of processing massive volumes of data for portfolio optimisation. This study’s limitation is that it may require periodic updates, as the AI and cryptocurrency landscape are constantly evolving.
We show that a decidable promise problem has a non-interactive statistical zero-knowledge proof system if and only if it is randomly reducible via an honest polynomial-time reduction to a promise problem for Kolmogorov-random strings, with a superlogarithmic additive approximation term. This extends work by Saks and Santhanam (CCC 2022). (Saks and Santhanam showed that promise problems that can be reduced in this way to such an approximation of the Kolmogorov-random strings have (possibly interactive) zero-knowledge proof systems, and they did not address the converse implication.) We build on this to give new characterizations of Statistical Zero Knowledge SZK , as well as the related classes NISZK L and SZK L .
Lihki Rubio, Keyla Alba, Carlos E Velásquez, Filipe R. Ramos
Accurately forecasting Bitcoin’s conditional variance is essential for reliable Value-at-Risk (VaR) estimation yet remains challenging due to nonlinear dynamics, volatility clustering, and heavy-tailed return distributions. This study developed a novel stacking ensemble that integrates econometric and machine-learning models through XGBoost meta-learning to produce improved variance forecasts. Hybrid ML–GARCH specifications are incorporated separately to enrich the comparative analysis. All estimators are trained with time-aware cross-validation to ensure temporal coherence and prevent look-ahead bias. Using Bitcoin data from 2014 to 2020, the empirical results show that the stacking ensemble consistently outperforms both standalone and hybrid alternatives in conditional variance forecasting and VaR accuracy, including during periods of severe market stress such as the COVID-19 episode. Residual diagnostics confirm that the ensemble effectively captures persistent temporal dependencies in volatility dynamics. Overall, the proposed methodology offers an innovative and interpretable risk-management tool for financial institutions, combining statistical rigor with the adaptability of machine-learning techniques in digital asset markets.
Blockchain technology, with its characteristics of decentralization, immutability, auditability, and traceability, has gradually become a core infrastructure in the digital economy era, demonstrating great potential in fields such as finance, government services, and the Internet of Things (IoT). However, as the scale of blockchain networks expands and data volumes surge, issues such as full-node storage redundancy, limited transaction throughput, and inefficient synchronization of historical data have become increasingly prominent, severely restricting the large-scale application of blockchain systems. The storage scalability problem faced by blockchain is therefore becoming more critical. To address the challenge in which on-chain storage expansion still cannot meet the demand for large-scale data storage, a storage method combining the InterPlanetary File System (IPFS) with blockchain, referred to as IPFS-BC, is proposed. In IPFS-BC, large-scale raw data are stored in the decentralized and content-addressable IPFS network, while the blockchain only retains the unique content identifier (CID) hash and related metadata. Through smart contracts enabling dynamic permission management and fine-grained access control, efficient interaction and collaborative storage between on-chain and off-chain systems are achieved. In this work, file upload simulation experiments were conducted, and two evaluation indicators—storage space consumption and storage performance (file read/write time and speed)—were used to compare three storage approaches: Distributed Hash Table (DHT)-based off-chain storage, Financial Blockchain Shenzhen Open Source (FISCO BCOS) on-chain storage, and the IPFS-BC on-chain/off-chain collaborative storage model. Experimental results show that the IPFS-BC model reduces storage space consumption by approximately 75% compared with FISCO BCOS blockchain storage when storing file data, significantly decreasing data redundancy. Moreover, IPFS-BC ensures system security during the on-chain process, and through the automated management and auditing provided by smart contracts, it effectively enhances system security and realizes scalable on-chain/off-chain collaborative storage.
Necati Altemur, İbrahim Halil Ekşi̇, Rizky Yudaruddin
Purpose This study aims to provide a comprehensive examination of the nonlinear and asymmetric relationships between global uncertainty indicators, namely, gold (GOLD), the US Dollar Index (DXY) and the Volatility Index (VIX), and major cryptocurrencies, including Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Cardano (ADA) and Binance Coin (BNB). It particularly focuses on how these dynamics evolve across different market conditions and the extent to which certain cryptocurrencies function as alternative safe-haven assets. Design/methodology/approach The analysis uses weekly data from January 2018 to June 2025, covering five major cryptocurrencies (BTC, ETH, XRP, ADA and BNB). To capture the dynamic and nonlinear relationships between global uncertainty indicators and cryptocurrency markets, the Quantile-on-Quantile Regression (QQR) approach is applied. Furthermore, the Quantile-on-Quantile Kernel-Based Regularized Least Squares (QQKRLS) technique is used as a robustness check to validate the findings. Findings The results demonstrate that the relationship between global uncertainty indicators and cryptocurrencies is neither linear, stationary nor unidirectional. Instead, it exhibits complex and asymmetric interactions that vary across quantiles and market conditions. Significant and predominantly inverse relationships are identified between the DXY, the VIX and cryptocurrencies, particularly at lower (0.05–0.30) and higher (0.70+) quantile levels. These findings suggest that investor behavior is influenced not only by economic fundamentals but also by uncertainty, market dynamics and risk perceptions. Originality/value This study is the first to apply QQR and QQKRLS methods to analyze the nonlinear and asymmetric linkages between global uncertainty indicators and major cryptocurrencies. It provides novel evidence on how these relationships shift across market conditions, offering fresh insights into the potential safe-haven role of cryptocurrencies.