Alexander Kropiunig, Svetlana Kremer, Bernhard Haslhofer
Crypto Key Opinion Leaders (KOLs) shape Web3 narratives and retail investment behaviour. In volatile, high-risk markets, their credibility becomes a key determinant of their influence on followers. Yet prior research has focused on lifestyle influencers or generic financial commentary, leaving crypto KOLs' understandings of motivation, credibility, and responsibility underexplored. Drawing on interviews with 13 KOLs and self-determination theory (SDT), we examine how psychological needs are negotiated alongside monetisation and community expectations. Whereas prior work treats finfluencer credibility as a set of static credentials, our findings reveal it to be a self-determined, ethically enacted practice. We identify four community-recognised markers of credibility: self-regulation, bounded epistemic competence, accountability, and reflexive self-correction. This reframes credibility as socio-technical performance, extending SDT into high-risk crypto ecosystems. Methodologically, we employ a hybrid human-LLM thematic analysis. The study surfaces implications for designing credibility signals that prioritise transparency over hype.
Fatemeh Shoaei, Mohammad Pishdar, Mozafar Bag-Mohammadi, Mojtaba Karami
Smart contract-based ecosystems enable decentralized applications without trusted intermediaries, but their immutability and permissionless design also facilitate large-scale fraud. One of the most prevalent attacks is the rug pull, where project operators abruptly withdraw liquidity after artificially inflating token value. Existing detection methods primarily rely on reactive on-chain signals and often suffer from temporal data leakage, limiting their real-world reliability. This paper proposes a leakage-aware framework for early rug-pull detection that integrates on-chain behavioral metrics with temporally aligned Open Source Intelligence (OSINT) signals. We construct a hand-labeled dataset of 1,000 token projects, spanning DeFi and non-DeFi settings, with all features extracted strictly prior to any liquidity withdrawal to preserve causal validity. The dataset combines structural on-chain indicators with external attention signals derived from social media activity and search trends. Within this framework, TabPFN is employed as a core modeling component for learning from multimodal tabular data under strict temporal constraints. Experimental results show that the proposed framework achieves strong discriminative performance and improved probability calibration compared to classical baselines, while maintaining low false-negative rates. By framing rug-pull detection as a causal, multimodal forecasting problem, this work emphasizes the necessity of leakage-resilient evaluation and calibrated risk estimation for deployment in blockchain security systems.
This editorial addresses the critical intersection of artificial intelligence (AI) and blockchain technologies, highlighting their contrasting tendencies toward centralization and decentralization, respectively. While AI, particularly with the rise of large language models (LLMs), exhibits a strong centralizing force due to data and resource monopolization by large corporations, blockchain offers a counterbalancing mechanism through its inherent decentralization, transparency, and security. The editorial argues that these technologies are not mutually exclusive but possess complementary strengths. Blockchain can mitigate AI's centralizing risks by enabling decentralized data management, computation, and governance, promoting greater inclusivity, transparency, and user privacy. Conversely, AI can enhance blockchain's efficiency and security through automated smart contract management, content curation, and threat detection. The core argument calls for the development of ``decentralized intelligence'' (DI) -- an interdisciplinary research area focused on creating intelligent systems that function without centralized control.
Artificial Intelligence (AI) has become a critical driver of firm survival in the banking industry, particularly for deposit money banks (DMBs) facing increasing challenges such as economic volatility, regulatory compliance, cybersecurity threats, and rising customer expectations. This study explores the role of AI in enhancing operational efficiency, risk management, fraud detection, customer experience, and financial resilience in the banking sector. AI-powered technologies, including machine learning, predictive analytics, robotic process automation (RPA), and natural language processing (NLP), are transforming how banks analyze financial risks, detect fraudulent transactions, automate operations, and provide personalized banking services. Research findings indicate that AI adoption has led to a 35% reduction in loan defaults, a 40% improvement in operational efficiency, and a 60% decline in financial fraud cases, highlighting its transformative potential in ensuring the survival and competitiveness of DMBs. Despite these advancements, AI adoption in the banking sector is hindered by high implementation costs, cybersecurity vulnerabilities, workforce resistance, and regulatory uncertainties. Many banks, particularly in developing economies like Nigeria, struggle with legacy banking systems, lack of AI governance frameworks, and concerns over algorithmic bias in lending decisions. Additionally, AI-driven financial innovations, such as blockchain integration, decentralized finance (DeFi), and AI-powered ESG compliance solutions, are reshaping the banking industry, yet require strategic policy alignment and investment to maximize their benefits. The study identifies gaps in existing literature, including the need for empirical research on AI’s long-term impact on firm survival, its role in financial inclusion, and the ethical challenges of AI governance in banking. To bridge these gaps, future research should focus on developing AI implementation models suited to the challenges of emerging economies, exploring AI’s potential in expanding financial access to underserved populations, and strengthening AI-driven sustainability and ESG compliance frameworks in banking. As AI continues to evolve, deposit money banks must embrace a balanced approach that integrates AI innovation with regulatory oversight, cybersecurity safeguards, and workforce upskilling to ensure long-term survival and competitiveness in the digital financial landscape
The paper systematizes current regulatory and legal approaches across various jurisdictions, as well as theoretical and methodological recommendations proposed by scholars regarding the identification of different types of digital assets. It substantiates the hierarchical relationship among the concepts of "digital assets", "virtual assets" and "crypto-assets", which describe forms of digital value. The procedure for recognizing digital assets on the balance sheet is clarified. A three-tier classification of digital assets is proposed based on the following criteria: the mode of existence and circulation of digital value, the use of distributed ledger technology, and the mechanism for ensuring value stability. The study develops a sequence for accounting recognition of a digital asset as an intangible asset, a commodity, or a financial instrument, in compliance with accounting standards. It also justifies the classification of certain types of digital assets functionally similar to digital securities, which are recognized as financial instruments.
Abdullah Melhem, Ahmed Aleroud, Abdullah Al-Mamun, Mohamed I. Ibrahem · 5 authors
The use of web-enabled healthcare analytics has broadened access to machine learning (ML)- and AI-driven cloud models, but it has also created privacy and security challenges. Federated learning (FL) has been used to address data privacy issues; however, deployments of current FL architectures rely on centralized aggregation approaches, thereby creating a single point of failure (SPoF), as a successful adversarial attack on the global model during training or inference can compromise the entire system. These approaches also assume homogeneous data distributions across clients and overlook the constraints and diversity of web-based analytics. To address those limitations, traditional blockchain-based FL systems incorporated distributed ledgers to record model updates and artifacts. However, using the chain as a data ledger to record model artifacts and logs increases consensus overhead and coordination costs. This paper introduces Blockchain-based Clustered Federated Learning (BCFL), an architecture-diverse and cluster-based FL framework. Our approach is coordinated by a lightweight permissioned ledger that eliminates the trusted central aggregator while preserving utility, robustness, and verifiable provenance in web-based healthcare analytics. BCFL records compact provenance metadata on-chain while keeping model parameters off-chain. In addition, by distributing trust across clusters, the design reduces the transfer of adversarial attacks across models by limiting the impact of malicious updates during training and improving reliability at inference time. Experiments on real-world healthcare data and other benchmarks show that BCFL improves the performance of trained AI/ML models and reduces attack success rates compared with several FL baselines.
This study develops and evaluates a hybrid Compliance–AI cybersecurity model for unified protection of traditional banking and decentralized finance (DeFi) systems in Brazil. Using publicly available data from the NIST Cybersecurity Framework, DeFi exploit repositories (REKT and DeFiLlama), Elliptic crypto-transaction graphs, IEEE-CIS fraud data, DARPA Transparent Computing datasets, and Monte Carlo–simulated cross-domain attack scenarios, the research applies hierarchical clustering, supervised learning, Markov chain modeling, and stochastic simulation. Results show that 45% of banking controls are transferable or hybridizable to DeFi, that embedding machine-readable compliance features improves ROC–AUC from 0.842 to 0.914 and reduces false positives by nearly 47%, and that bidirectional orchestration lowers escalation probability by over 54%. Monte Carlo analysis further indicates a 62% reduction in tail financial risk under the hybrid architecture. The study recommends machine-readable regulation, compliance-aware AI deployment, orchestrated enforcement layers, and expanded RegTech and SupTech adoption to strengthen systemic financial cybersecurity. Keywords: Compliance–AI Integration, Financial Cybersecurity, Decentralized Finance, Machine-Readable Regulation, Systemic Cyber Risk.
BACKGROUND: Noncommunicable diseases (NCDs), including hypertension and diabetes, account for approximately 27% of all deaths in Kenya, with 26% of adults having elevated blood pressure. Despite devolution of health services to county governments in 2013, financing for NCD management at the primary health care (PHC) level remains weak. This study examines financial determinants shaping hypertension and diabetes care in PHC facilities within a devolved county health system in rural Kisumu County, Kenya. METHODS: We conducted a convergent parallel mixed-methods cross-sectional study in seven public PHC facilities in Seme Sub-County, providing new facility-level evidence on how the interaction between devolution’s financing architecture, facility-level financial autonomy constraints, and resource allocation mechanisms shapes chronic disease care effectiveness in rural Kenya. Quantitative data were collected via structured questionnaires and retrospective document review of financial records (January–August 2024). Qualitative data were gathered through key informant interviews (n = 7) with facility in-charges exploring planning, budgeting, and resource allocation. Descriptive statistics were produced in STATA v16; qualitative data were analyzed thematically in R. RESULTS: All seven facilities prepared annual workplans and budgets, but none achieved comprehensive NCD-specific planning (workplan + budget + dedicated NCD budget line). Funding sources were narrow: 71.4% (n = 5) of the facilities depended on NHIF reimbursements and donor support, while only 28.6% (n = 2) received direct county funding; 57.1% (n = 4) of the facilities relied on only two funding streams. Although all facilities held bank accounts, none had formal financial autonomy and expenditures required county-level approval, typically taking 3–4 weeks (57.1%, n = 4) to over two months (28.6%, n = 2). Combined with unreliable central supplies, this lack of autonomy meant facilities could not procure locally when stockouts occurred; consequently 85.7% (n = 6) of the facilities reported frequent medication stockouts. Facility in-charges attributed these failures to inadequate, unpredictable funding and centralized approval processes that prevented timely local procurement. CONCLUSIONS: Rural PHC facilities operate under structural governance failures in Kenya’s devolved health financing system that systematically undermine effective NCD care. The centralization of financial authority at county level, absence of ring-fenced NCD budgets, and misalignment between planning processes and resource allocation represent system-level policy contradictions rather than facility-level operational deficiencies. Addressing these governance failures requires not only increased funding but constitutional fiscal decision-space for facilities, mandatory NCD budget protection, and reformed disbursement mechanisms essential for equitable chronic care under Kenya’s UHC agenda. The sustainability of chronic care depends fundamentally on facility decision space, not only on funding volume. These findings are transferable to other Kenyan counties under the same devolved framework and to decentralized health systems in sub-Saharan Africa facing similar tensions between fiscal accountability and operational autonomy for chronic disease management.
Andrii Pecheniuk, Igor Harasymchuk, Pavlo Potapskyi, Mykola Vusatyi
The article provides a comprehensive substantiation of the conceptual foundations and strategic priorities for the transformation of power supply systems in the agricultural sector amid contemporary global challenges. It is demonstrated that the traditional paradigm of energy supply in the agro-industrial complex has exhausted its potential, necessitating a transition to a qualitatively new management model based on the principles of decentralization, decarbonization, and intellectualization. A thorough diagnosis of the critical state of obsolete network infrastructure has been conducted, and an algorithm for its systemic reengineering is proposed. Particular attention is paid to the development and implementation of the Microgrid concept as a key tool for ensuring local energy resilience. The mechanisms for transitioning agricultural enterprises to autonomous operating modes are investigated, guaranteeing the continuity of critical technological processes even under conditions of destabilization of the national power system. The scientific novelty of the study is expanded through the substantiation of the synergistic effect resulting from the integration of phytotechnologies and renewable energy sources. The role of energy crops and the bioconversion of agricultural waste are described as a basis for forming a closed-loop energy consumption cycle, allowing agribusiness to act as an active subject of energy generation. The specifics of the “green transition” are detailed, where carbon footprint management is considered not merely as an environmental standard but as a strategic asset. The shift in the structure of capital and operating expenditures is analyzed, a methodology for the life cycle assessment of energy facilities is substantiated, and a direct correlation between the decarbonization of power supply and the investment attractiveness of enterprises is identified. The article emphasizes the social and organizational aspects of this transformation. The role of human resources and the necessity of forming new professional competencies among personnel in the context of power facility modernization are highlighted. The advantages of energy cooperation as a form of collective resource management, contributing to the socio-economic revival of rural areas, are substantiated.
EVMbench, released by OpenAI, Paradigm, and OtterSec, is the first large-scale benchmark for AI agents on smart contract security. Its results -- agents detect up to 45.6% of vulnerabilities and exploit 72.2% of a curated subset -- have fueled expectations that fully automated AI auditing is within reach. We identify two limitations: its narrow evaluation scope (14 agent configurations, most models tested on only their vendor scaffold) and its reliance on audit-contest data published before every model's release that models may have seen during training. To address these, we expand to 26 configurations across four model families and three scaffolds, and introduce a contamination-free dataset of 22 real-world security incidents postdating every model's release date. Our evaluation yields three findings: (1) agents' detection results are not stable, with rankings shifting across configurations, tasks, and datasets; (2) on real-world incidents, no agent succeeds at end-to-end exploitation across all 110 agent-incident pairs despite detecting up to 65% of vulnerabilities, contradicting EVMbench's conclusion that discovery is the primary bottleneck; and (3) scaffolding materially affects results, with an open-source scaffold outperforming vendor alternatives by up to 5 percentage points, yet EVMbench does not control for this. These findings challenge the narrative that fully automated AI auditing is imminent. Agents reliably catch well-known patterns and respond strongly to human-provided context, but cannot replace human judgment. For developers, agent scans serve as a pre-deployment check. For audit firms, agents are most effective within a human-in-the-loop workflow where AI handles breadth and human auditors contribute protocol-specific knowledge and adversarial reasoning. Code and data: https://github.com/blocksecteam/ReEVMBench/.
Decentralized autonomous organizations (DAOs) are designed to disperse control, yet recent evidence shows that effective governance is often concentrated in a small number of participants. This note studies one simple mechanism behind that pattern. Because decentralized governance is monitor-intensive, rising proposal flow may eventually outpace the capacity of broad-based participation. Using a DAO--quarter panel, I estimate a fixed-effects kink model with DAO and quarter fixed effects and find a statistically significant decline in the marginal responsiveness of active voters once proposal activity crosses an interior threshold. I then study realized voting concentration using kink specifications with data-driven cutoffs. Across specifications, decentralization gains do not persist indefinitely once governance workload becomes sufficiently high, and load-based measures show especially clear evidence of a transition toward more concentrated realized control. The results provide reduced-form evidence consistent with a ``too big to monitor'' mechanism in DAO governance: when proposal flow grows faster than broad participation can keep up, effective control may drift toward a smaller set of highly active participants.
The "Cardano Foundation Cardano Blockchain Certified Associate (CBCA) PDF" serves as an official guide and resource for individuals seeking certification in the Cardano blockchain ecosystem. This comprehensive document outlines the essential knowledge and skills required to understand and operate within the Cardano blockchain framework. It includes detailed sections on blockchain technology fundamentals, Cardano's unique proof-of-stake mechanism, smart contracts, and the broader implications of blockchain on various industries. Additionally, the PDF features exam preparation tips, recommended reading materials, and case studies to enhance learning. This certification aims to empower professionals and enthusiasts to harness the potential of Cardano, promoting proficiency and credibility in the blockchain space. Ideal for those looking to deepen their expertise or advance their careers in a rapidly evolving digital landscape. Original Page: https://www.certification-exam.com/en/pdf/cardano-foundation-pdf/cbca1-pdf/
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This paper proposes a thermodynamic architecture for distributed ledger computing based on the concepts of Logical Grounding and phase-coherent synchronization. Conventional computing dissipates computational entropy as waste heat and relies on amplitude-based control and clock synchronization, which limits energy efficiency and scalability. The proposed framework treats unused computational resources as logical entropy sinks and redirects entropy flow through potential gradients into these regions. The absorbed signals are transformed into deep resonance signals that maintain global synchronization via phase coherence rather than amplitude control. By combining logical grounding, negative-pressure information circulation, and phase-coherent synchronization engines, the architecture suggests a new entropy-aware computing paradigm that may improve energy efficiency, distributed scalability, and system resilience in large-scale computing environments.
Abstract Decentralized finance (DeFi) platforms have gained in popularity over the last few years, as they offer a wide range of accessible, innovative, and complex financial services. Because they evolve quickly under limited regulation, it is easy for malicious parties to target them for profit when they notice a vulnerability in these emergent protocols. Existing work has focused on understanding typical attack flows and securing the technology to alleviate crime. However, little is known about what other attributes, beyond technical vulnerabilities, may put DeFi actors at risk. Drawing on Cook’s (Crime Justice 7:1–27, 1986) crime opportunity framework of target attractiveness, this study investigates which attributes are associated with an increase or a decrease in the likelihood of DeFi victimization. We compare actors victimized in 2022 with those that were not across several target dimensions: propinquity, vulnerability, potential payoff, main area of operation, and self-protection activities. Results show that being listed on a popular centralized exchange, operating on a layer-2 blockchain, offering lending services, and having high trading volumes are associated with an increased likelihood of victimization, while operating a dApp and having experienced past victimization are associated with a decrease. By contrast, self-protection measures such as publicly disclosed audits, and bug bounty programs show no measurable effect, likely reflecting variation in their quality and implementation or the fact that undisclosed audits could not be observed. By integrating criminological theory into DeFi security research, this study provides a holistic framework for understanding crime opportunities in this novel ecosystem, while informing potential prevention strategies to reduce associated harms.
Decentralized finance (DeFi) systems currently rely on static parameters and reactive mechanisms that fail to adapt to rapidly changing market conditions. These limitations contribute to systemic inefficiencies including yield instability, capital fragmentation, and the extraction of value through adversarial mechanisms such as maximal extractable value (MEV). This paper introduces The Aeon Protocol, a control-theoretic framework for adaptive financial infrastructure. The protocol models decentralized liquidity management as a closed-loop control system in which economic variables are continuously monitored, predicted, and regulated through feedback mechanisms derived from classical control theory. The Aeon architecture integrates four primary system layers: • KENDRA — predictive forecasting and regime detection from on-chain data streams• NOEMA — model predictive control for economic orchestration• AURA — ethical routing layer that captures and redistributes MEV through sealed-bid auctions• LEIA — liquidity management engine governing protocol-owned liquidity across decentralized markets At the core of the system is a PID-controlled adaptive yield mechanism designed to regulate total value locked (TVL) and stabilize protocol yield within bounded ranges. A complementary Burn-and-Mint Equilibrium (BME) mechanism dynamically adjusts token supply to maintain long-term economic balance. A central implication of the Aeon architecture is the emergence of a self-reinforcing liquidity ecosystem. By integrating predictive forecasting, control optimization, and ethical MEV capture into a closed-loop economic system, the protocol continuously identifies inefficiencies in decentralized markets and redirects the associated value back into the protocol’s liquidity layer. This process transforms otherwise extractive market dynamics into a productive feedback cycle, where captured value is redistributed through liquidity provisioning, treasury reserves, and reflection mechanisms. Empirical simulations and historical replay experiments demonstrate that this feedback architecture materially increases capital utilization across the system. In controlled Monte Carlo simulations spanning 10,000 market scenarios, the protocol achieved improvements of 50–180% in capital efficiency, while redirecting approximately 68% of extractable value to protocol participants rather than external arbitrage actors. These results suggest that adaptive control systems can convert structural market inefficiencies into a persistent source of liquidity and yield generation, enabling decentralized financial networks to operate as self-regulating economic environments rather than static rule-based infrastructures. Formal analysis establishes asymptotic stability conditions for the controller using the Routh–Hurwitz criterion and Lyapunov stability methods, providing theoretical guarantees that the system converges toward equilibrium under defined parameter constraints. Collectively, the results demonstrate that control-theoretic economic architectures can provide a principled foundation for designing stable, transparent, and adaptive decentralized financial infrastructure. The Aeon Protocol represents a broader research direction toward autonomous economic systems, where financial networks operate as self-regulating feedback environments capable of maintaining equilibrium under dynamic market conditions.
Purpose: to develop a model of a payment token based on the analysis of its key economic characteristics, such as backing, price stability, liquidity, and volatility. Methods: heterodox, systemic, structural-functional, institutional, cybernetic, and pragmatic methodological approaches to the formation of a cross-border payment infrastructure based on distributed ledger technology; methods of mathematical modeling: descriptive statistics method, index method, normalization method, numerical optimization methods. Results: a payment token model based on a stable currency basket has been developed. Optimal quantitative characteristics of the payment token for use in a cross-border payment infrastructure based on distributed ledger technology have been determined. For the cross-border payment space of the BRICS countries, it is advisable to use a payment token backed by a stable currency basket consisting of the Chinese yuan, Russian ruble, Indian rupee, and Brazilian real. By minimizing the optimization function, the coefficients of the national currencies in the stable basket backing the payment token of the BRICS cross-border payment space have been determined. An alternative model of a payment token backed by a currency basket of Western countries – the US dollar, euro, pound sterling, and yen – has been developed. It is shown that the volatility of tokens based on currency baskets with weights optimized within the proposed model is significantly lower than the volatility of any single currency. Conclusions and Relevance: in the money circulation subsystem of a cross-border payment infrastructure based on distributed ledger technology, it is rational to employ a payment token that meets the economic characteristics of backing, price stability, liquidity, and low volatility. Such a token will ensure the uninterrupted functioning and accessibility of cross-border settlements for economic agents of the BRICS cross-border payment area. The application of an invariant index of the currency value in the design of the payment token model provides a methodological foundation, independent of the choice of a base currency, for optimizing the numerical indicators of its economic characteristics.
Ameer Majeed Dahdouh Al-Alili, Zaid Salam Abdullah
This research aims to clarify the actual works of criminal liability for crimes committed using cryptocurrency and to highlight the flaws of Iraqi legislation about this modern type of crime. Accordingly, an attempt has been made to analyse the elements, kinds, and difficulties of evidence, leading up to determining the legal system in which to protect from and suppress this type of crime, of which cyberspace is a part. This research is descriptive-analytical in nature, where legislation has been examined. The research indicates that the wide scope of risks involving cryptocurrency crimes makes it difficult to subject them to existing laws on movable property, especially since the legislator has omitted the criminalization of certain attacks like wallet hacking, while the sophisticated nature of making inquiries and collecting evidence complicates establishing a definitive link between the perpetrator and the transaction. All in all, this study finishes off with the need to develop or amend legislation to extend the definition of digital assets and criminalise attacks against them, strengthen investigative capacity in electronic tracking, establish units for cryptocurrency crimes, and regulate digital seizures and confiscation mechanisms. This further highlights the importance of modernising legislation in light of the criminal threat’s cryptocurrencies pose to upholding economic and legal security.
This study seeks to fill a gap in the understanding of how blockchain smart contracts may improve logistics service quality and investigate the drivers of blockchain smart contracts.Semistructured interviews were carried out with ten logistics professionals to collect data.According to the findings, the drivers of using blockchain smart contracts in the logistics industry comprised the strongest predictor amongst security, traceability, decentralization, transparency, efficient information sharing, and automation factors.The identified drivers of blockchain smart contracts could be used by logistics practitioners as a "road map" for the development of appropriate solutions to successfully strengthen logistics service quality within the logistics industry.The results indicate that blockchain smart contracts enhance payment transaction security, increase end-to-end visibility, and improve delivery timeliness.Moreover, this technology optimizes routing, advances fleet management, and reduces logistics costs.The existing literature focuses on the approaches to applying theoretical, technical, or operational benefits of blockchain smart contracts, but fails to propose a deep dive into logistics service quality.This research appraises blockchain smart contracts by assessing and suggesting how they can enhance logistics service quality.
Abstract This study examines bubble dynamics in the S&P 500 Index and Bitcoin, with particular emphasis on the role of gold as a proxy for market-wide stress. We apply the GSADF bubble test, time-varying Granger causality, and multifractal detrended fluctuation analysis to both original and gold-filtered price series. The results reveal a pronounced asymmetry between equity and cryptocurrency markets. Bitcoin exhibits statistically significant and persistent bubble behavior in both raw and filtered data, accompanied by multifractal persistence consistent with self-reinforcing speculative dynamics. In contrast, the S&P 500 shows no consistent evidence of sustained bubble behavior, and its multifractal properties remain aligned with short memory and rapid information absorption. The causality analysis indicates a stable, state-dependent predictive relationship from gold to equity prices, suggesting sensitivity to global risk sentiment, while no comparable persistent linkage is observed for Bitcoin. Overall, the findings suggest that equity price dynamics remain connected to market-wide stress conditions, whereas Bitcoin’s behavior appears to be driven primarily by asset-specific speculative forces.
Since the launch of Bitcoin in 2008, social scientists have sought to clarify the relationship between cryptocurrency and money. A dominant conclusion – particularly among approaches employing a commodity theory of money – has been that cryptocurrency is not money but an instrument of speculation whose activities are confined to circulation. This paper deepens the analysis of cryptocurrency and money by drawing on shifts in Marxist theory from the 1970s in the work of Diane Elson and Suzanne de Brunhoff. These developments enable a conceptualisation of money in capitalism not simply as the sum of its functions, but as taking on a particular social form. Building upon this, I develop a novel conceptualisation of cryptocurrency as ‘fictitious money’, a social form anchored in the general equivalent that facilitates the circulation and valorisation of new digital assets in the total circuit of money capital. Situating cryptocurrency in a value-form approach attendant to the unities of the functions of money and of production and circulation reveals productive entanglements and trajectories for it that must be taken seriously within IPE.
In today’s world the way people handle money and carry transactions is changing. For centuries, people were dependent on physical coins and paper notes issued by governments. Now, we are moving towards digital banking, using apps and various platforms. But a new innovation called cryptocurrency is now gaining importance. It is promising a massive change in the global financial system.In simple words, cryptocurrency is a form of digital money. It does not exist as physical coins or bills but it relies entirely on the internet. it uses "cryptography" (a way of using complex math to keep information secret and secure) and "blockchain" (a digital record-book that everyone can see but no one can easily change). The money in your bank account is controlled by a central authority like a bank or a government, many cryptocurrencies are "decentralized." This means they are run by a global network of computers instead of one single boss.Because more people are shopping online and sending money to other countries, everyone is looking for a faster, cheaper, and safer way to pay.
The Y.I.N. Governance Framework is a comprehensive 15-domain policy integration system that transforms fragmented AI governance requirements into a unified operational architecture. Unlike existing frameworks that organize compliance checklists, the Y.I.N. Governance Framework is specifically designed to be cryptographically enforceable through the 26-layer Y.I.N. Mazari Architecture. This framework addresses the critical gap identified by the OECD Responsible AI Due Diligence Guidance (2026): organizations face over 100 overlapping governance regimes with no systematic method to integrate and enforce them simultaneously. The Y.I.N. Governance Framework integrates the EU AI Act, ISO/IEC 42001:2023, OECD AI Principles, NIST AI Risk Management Framework, G7 Hiroshima AI Process Code of Conduct, IEEE 7000-2021, UN Guiding Principles on Business and Human Rights, GDPR, EU DORA, NIS2, HIPAA, NY Senate Bill S.7263, and over 50 additional regulatory frameworks worldwide. Key Innovation: Each policy requirement in the framework maps directly to cryptographic enforcement mechanisms in the Y.I.N. Mazari Architecture, creating the world's first governance system where compliance is mathematically provable, not procedurally documented. The framework comprises 15 integrated domains: (1) Regulatory Compliance, (2) Risk Classification & Management, (3) Privacy & Data Protection, (4) Security & Resilience, (5) Transparency & Explainability, (6) Human Oversight & Accountability, (7) Bias & Fairness, (8) Safety & Reliability, (9) Data Governance, (10) Model Governance, (11) Ethical Principles, (12) Professional Practice, (13) Incident Response & Remediation, (14) Third-Party & Supply Chain, (15) Continuous Monitoring & Improvement. Each domain maps to specific layers of the Y.I.N. Mazari Architecture for cryptographic enforcement through differential privacy, zero-knowledge proofs, homomorphic encryption, hardware-enforced finite state machines, and blockchain-anchored audit trails. This publication establishes the complete Y.I.N. governance solution: Framework (policy layer) + Architecture (cryptographic enforcement layer).