Abstract The evolution of art from traditional material media to contemporary digital forms reveals historically specific interconnections among discourse, reproduction, and materiality. This study traces art as a historically situated cultural practice shaped by shifting technologies, ideologies, postmodern theory, and institutional conditions. Informed by a Foucauldian account of power–knowledge, it examines how artistic meaning, visibility, cultural legitimacy, and value are organized within different historical formations. As digital technologies expand, blockchain and non-fungible tokens challenge established hierarchies by reconfiguring authenticity, originality, and ownership through mechanisms such as traceable provenance and artificial scarcity. At the same time, these developments are accompanied by new modalities of control, including platform infrastructures and algorithmic governance that influence circulation, attention, and valuation. The resulting tension between dematerialization and re-materialization highlights the ways digital art loosens its dependence on physical substrates while remaining embedded in technical, economic, and institutional arrangements. Overall, debates over the virtual and the actual, the copy and the original, continue to structure contemporary discussions of art and its changing conditions of production and value.
The scrum is one of the most iconic and demanding phases of rugby union. Opposing eight players from each team, it represents both a major tactical challenge and a complex biomechanical context, characterized by high levels of physical effort and an increased risk of serious injury. This phase, therefore, carries a dual performance and health stake, requiring tools capable of finely characterizing the forces produced by players. Yet existing studies are often dated and generally provide global measurements obtained under conditions far from actual on-field scrummaging conditions.It is in this context that the present thesis was conducted, in collaboration with Stade Toulousain, with the primary objective of developing and validating an embedded instrumentation system enabling the simultaneous estimation of individual 3D pushing forces for each of the eight players in a pack, under real scrummaging conditions.Attention was focused on ground reaction forces (GRF) as the primary mechanical quantity for quantifying individual pushing efforts. Among the available solutions for measuring GRF in field conditions, instrumented insoles were identified as the most suitable given the constraints imposed by the context of this thesis. However, these sensors only measure forces perpendicular to their surface and exhibit an overestimation bias when highly flexed inside the shoe, as is the case during scrummaging. A Machine Learning (ML) approach was therefore adopted to correct this bias and estimate all three components of the GRF. A proof of concept was first established on an initial group of subjects, demonstrating the feasibility of estimating three-dimensional pushing forces during scrummaging with errors comparable to those reported in the literature for walking and running.A comparative study of several ML architectures (Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Random Forest (RF)) and training strategies was then conducted, including elite development players from Stade Toulousain. A personalized MLP model trained on data from these players achieved the best performance, with body-weight-normalized errors below 8.5% across all axes, including during critical phases such as foot repositioning. By extending the training dataset to both subject groups, model generalization was improved with no significant loss of performance. At the end of this study, two operational models, one generic and one personalized, were provided and are directly applicable to Stade Toulousain players.These models were then deployed during three experimental field sessions under real scrummaging conditions, enabling an analysis of pushing forces at three levels: collective, individual, and intra-individual. The results illustrate the potential of this tool for performance monitoring and the preservation of players' physical integrity in a professional context.In summary, this thesis proposes a comprehensive and validated methodological framework for the synchronized, individualized measurement of 3D pushing forces during rugby scrummaging under real conditions, combining embedded instrumentation and ML. It thereby contributes new elements to the biomechanical analysis of the scrum and opens the way for future developments, in particular the integration of individual kinematic information for an even more complete characterization of the mechanisms underlying collective and individual scrum performance.
Automated Market Makers (AMMs), as a core infrastructure of decentralized finance (DeFi), uniquely drive on-chain asset pricing through a deterministic reserve ratio mechanism. Unlike traditional markets, AMM price dynamics is triggered largely by on-chain events (e.g., swap) that change the reserve ratio, rather than by continuous responses to off-chain information. This makes event-level analysis crucial for understanding price formation mechanisms in AMMs. However, existing research generally neglects the micro-structural dynamics at the AMMs level, lacking both a comprehensive dataset covering multiple protocols with fine-grained event classification and an effective framework for event-aware modeling. To fill this gap, we construct a dataset containing 8.9 million on-chain event records from four representative AMMs protocols: Pendle, Uniswap v3, Aave and Morpho, with precise annotations of transaction type and block height timestamps. Furthermore, we propose an Uncertainty Weighted Mean Squared Error (UWM) loss function, which incorporates the block interval regression term into the traditional Temporal Point Process (TPP) objective function by weighting the uncertainty with homoscedasticity. Extensive experiments on eight advanced TPP architectures across four representative DeFi protocols demonstrate that this loss function reduces the time prediction error by an average of 31.17% while maintaining the accuracy of event (transaction) type prediction, establishing a robust benchmark for event-aware prediction in the AMMs ecosystem. This work provides the necessary data foundation and methodological framework for modeling the discreteness and event-driven characteristics of on-chain price discovery. All datasets and source code are publicly available. https://github.com/finbrain-lab-hkustgz/Deep-AMM-Events
With the rapid advancement of virtual reality technology, its application in the judicial field has become increasingly widespread. However, the determination of the legal validity of VR evidence still faces numerous challenges, including issues related to technical reliability, evidentiary authenticity, and legal adaptability. This paper systematically examines the criteria for determining the legal validity of VR evidence in criminal proceedings, the rules for its evaluation, and practical dilemmas. It aims to address theoretical gaps in the current legal framework and provide actionable guidelines for judicial practice. The research not only facilitates the deep integration of technology and law but also offers valuable insights for refining evidence rules in the digital era and safeguarding judicial fairness. Therefore, the publication of this paper holds significant academic and practical value.
Abstract This chapter explores the integration of artificial general intelligence (AGI) and blockchain in circular manufacturing within Industry 5.0, emphasising sustainability and efficiency. AGI optimises resource use and waste reduction through advanced reasoning to improve data from internet of things (IoT) sensors and blockchain-based digital product passports. Blockchain ensures transparent, immutable tracking of material life cycles with smart contracts and tokenised models, enhancing automation and stakeholder trust. Despite challenges like cybersecurity, regulatory gaps and algorithmic bias, innovations such as zero-knowledge proofs and proof-of-stake consensus address these issues. The collaboration of AGI and blockchain drives human-centric systems, circular economy goals and sustainable manufacturing practices.
Wahida Mansouri, Abdulrahman M. Qahtani, M. M. Kamruzzaman, Anandhavalli Muniasamy · 6 authors
The evolution of 6G networks brings unprecedented connectivity and processing capabilities to Internet of Things environments, yet it also raises more challenges in data security, intrusion detection, and computational efficiency. In this paper, a complete end-to-end framework with the inclusion of Artificial Intelligence, blockchain technology, and novel encryption techniques is proposed to address these challenges in 6G-IoT networks. Then, the AI-Powered Cybersecurity Events Dataset is utilized first, wherein network traffic is normalized through Min-Max normalization to normalize heterogeneous features to a uniform scale. A Gated Recurrent Unit-based neural network is then trained on this normalized data to detect real-time intrusions by learning complex temporal dependencies. Upon detecting anomalies, a blockchain layer is called to execute smart contracts that apply automatic security measures, e.g., quarantining affected nodes. For efficient processing, the architecture accommodates GRU-assisted task offloading optimization on a multi-factor delay, energy, and network load optimization model. Blockchain smart contracts manage load balancing and delegation verification autonomously without any central authority. Homomorphic encryption and proxy re-encryption also ensure data confidentiality in edge computing and secure multi-party computation. All encryption and task offloading operations are permanently stored onto the blockchain, ensuring system-wide transparency and auditability. Federated learning support is built-in to enable privacy-preserving decentralized AI training from distributed edge nodes. The entire framework is implemented in Python using TensorFlow/Keras for the GRU model and web3.py for blockchain-related interactions. Experimental measurements demonstrated the efficiency and stability of the system by exhibiting a percentage of accuracy of 98.9% in intrusion detection, justifying the applicability for future 6G-IoT secure and scalable operations. The experiments utilize the AI-Powered Cybersecurity Events Dataset containing approximately 1.5 million network flow records with class distribution of 52% benign traffic, 21% DoS, 15% probing, and 12% privilege escalation events.
A Groth16 zero-knowledge proof is published certifying the existence of a 152-bit Slater-determinant occupation string for the standard FeMoco active-space Hamiltonian (113 electrons, 76 orbitals) whose Hamiltonian expectation value on the public LLDUC FCIDUMP [1] — evaluated in the fixed split-localised orbital basis of [1] without orbital optimisation — is −22053.164626725997 Ha. The string satisfies 58 alpha + 55 beta = 113 electrons and MS = 3/2, matching the active-space constraints of [1]. The proof is verifiable in under one second by any party in possession of the proof artifact and verification key, with no access to the FCIDUMP or the occupation string itself.
Munaganti Anjali, Arra Nanda Kishore, Sirnapally Mithesh, Mr Lodangi Prabhat
Abstract—The crowdfunding sites that are in use today make use of a centralized system in which the funding and validation of crowdfunding campaigns are done by an authority figure. This has resulted in higher transaction fees and a lack of transparency in the way the funding is being done by donors. This paper aims to propose a Decentralized Crowdfunding Platform using blockchain technology to overcome the current challenges. A new crowdfunding system based on smart contracts using Ethereum is proposed. This new system will ensure a reliable environment for executing smart contracts. A new crowdfunding system based on smart contracts using Ethereum is proposed. This system will have a Startup Verifi-cation process and an Iteration-Based Fund Release process. In the current crowdfunding system, crowdfunding sites release the funds in bulk to the startup. In the new system that is being proposed, the funds will be released in stages. A new system of voting will also be implemented in the new system. This will enable donors to vote based on the cryptographic proofs and reports that are presented by the startup. The software development framework of the platform is based on an effective decentralized technology stack such as Solidity for smart contract development and execution, Hardhat for software development and testing, and React.js combined with Web3.js for front-end interface development. The incorporation of the blockchain technology stack guarantees that all financial transactions are transparent, secure, and tamper-proof. The efficacy of the proposed decentralized approach is measured by how effectively it avoids the costs of intermediaries, is auditable in real-time, and is democratic in nature for all donors. Index Terms—Blockchain Technology, Ethereum, Smart Con-tracts, Solidity, Decentralized Finance (DeFi), Crowdfunding, Decentralized Autonomous Organization (DAO), Iteration-Based Funding, Milestone Verification, Web3.js, Hardhat, Metamask, Trustless Execution, Cryptographic Transparency, Digital Wallet Authentication.
This research presents a blockchain-enabled freelancing platform that integrates smart contract-based escrow, decentralized identity, and intelligent freelancer matching to promote trust, transparency, and automation in digital labor markets. The system uses an Ethereum-compatible smart contract called Freelance Escrow, which manages the funding of projects securely, restricts interactions between employers and freelancers to a few specific roles, and automates the release of payments based on the verifiable completion of work. A Python-based blockchain interface developed using Web3.py is used to deploy contracts, sign transactions, and retrieve the current states, while a Streamlit front end provides authentication for user, project, and wallet operations. The platform includes a TF-IDF similarity model that matches freelancers to projects based on relevant skills and semantic similarity, as well as a structured database using SQLite, in which all users, profiles, and project metadata are stored. Comprehensive analysis reveals that the application has strengths in automation, transparency, and enforcement of workflow, while addressing privacy concerns around private key handling, file path inconsistencies, and Web3 library compatibility. The research demonstrates a working end-to-end architecture for decentralized freelance contracting and establishes a foundation for building further secure, scalable, and trust-preserving digital marketplaces.
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This case study examines the transformative impact of Decentralized Finance (DeFi) on India’s traditional banking sector through a multi-stakeholder perspective. Drawing on both quantitative performance indicators and qualitative stakeholder insights, the study explores how DeFi influences operational efficiency, financial inclusion, and regulatory compliance. The findings indicate that while DeFi enhances transaction efficiency and expands access to credit, its integration into India’s financial ecosystem is constrained by regulatory ambiguity, cybersecurity concerns, and infrastructural disparities. The case highlights the need for a hybrid financial architecture supported by collaborative governance and adaptive regulatory frameworks.
The rapid expansion of blockchain infrastructures, Web3 architectures, and immersive metaverse environments has reignited calls for a Universal Code of Digital Ethics. International organizations, technology leaders, and multilateral forums increasingly advocate for global standards capable of guiding decentralized innovation toward inclusion, transparency, and social good. Yet the normative ambition of universality confronts a structural contradiction: digital access, connectivity quality, and technological literacy remain profoundly unequal across the Global South.Ethical frameworks for Web3 and the metaverse often presuppose a baseline of connectivity, computational capacity, and institutional stability that large segments of humanity do not possess. The COVID-19 pandemic amplified these asymmetries, accelerating digital transformation in high-income countries while deepening infrastructural gaps elsewhere. Concurrently, geopolitical tensions and emerging techno-nationalist strategies have repoliticized digital infrastructure as a domain of strategic competition rather than global solidarity.This Opinion article argues that any claim to a "universal" digital ethics framework is normatively fragile unless it incorporates a structural critique of global capitalism, technological acceleration, and asymmetrical power. Drawing on critical theory-particularly Herbert Marcuse's concept of the "one-dimensional man"-as well as Marxian analyses of technology and capital, I contend that ethical discourse risks becoming ideologically functional to market expansion if it fails to address material inequalities in connectivity and digital capability (Marcuse, 1972;Marx, 2005;García Ramírez, 2021).proposals but also problematizes the concept of universality itself as a normative and political construct.Global ethics initiatives often frame digital transformation as inherently democratizing. Blockchain is described as decentralized, Web3 as user-empowering, and the metaverse as participatory. However, decentralization at the protocol level does not necessarily translate into equitable access at the societal level.In regions of the Global South, access to stable broadband remains limited, mobile data costs are disproportionate to income, and digital literacy gaps persist. Under such conditions, the ethical vocabulary of autonomy, self-sovereign identity, and tokenized participation becomes aspirational rather than operative.Marx's analysis of technology as a force embedded within relations of production remains instructive. Technology is not neutral; it is shaped by capital accumulation dynamics (Marx, 2005). Fumikazu (1983) similarly emphasized that technological evolution must be understood historically and politically. When applied to Web3 ecosystems, this suggests that blockchain infrastructures operate within global financial logics that may reproduce, rather than dissolve, structural dependency.Contemporary Science and Technology Studies (STS) and critical philosophy of technology further reinforce this perspective. Feenberg (1999) argues that technology is not merely instrumental but socially constructed and politically conditioned, shaped by dominant interests yet open to democratic transformation. Similarly, Yuk Hui (2020) challenges the presumed universality of technological rationality, proposing the concept of "technodiversity" to account for plural technological trajectories rooted in different cultural and cosmological traditions. These perspectives suggest that any ethical framework for digital technologies must recognize the multiplicity of socio-technical realities rather than assume a homogeneous global condition.Thus, a universal code of digital ethics risks functioning as what critical theory would describe as ideological abstraction-detached from the material preconditions required for ethical agency.Herbert Marcuse's One-Dimensional Man (1972) provides a compelling lens through which to interpret contemporary digital governance. Marcuse argued that advanced industrial societies generate a form of technological rationality that integrates dissent by absorbing it into the logic of efficiency and consumption.In the context of Web3 and the metaverse, ethical discourse may become one-dimensional when it focuses on procedural compliance (privacy standards, transparency metrics, algorithmic audits) while neglecting structural exclusion. The language of inclusion becomes embedded within market expansion strategies. As Daum (2018) argues, digital capitalism increasingly converts users into capital itself-data, attention, and participation become monetizable assets.From a constructivist perspective, technologies such as blockchain are not inherently emancipatory but acquire meaning through their social embedding (Bijker, 1995). This implies that ethical claims about decentralization and empowerment must be evaluated in relation to the socio-economic contexts in which these technologies are deployed. Without such contextualization, ethical discourse risks overstating the transformative potential of technological architectures. This dynamic is particularly visible when global institutions promote digital entrepreneurship and blockchain adoption in developing regions without parallel investments in public infrastructure, education, and regulatory sovereignty. The rhetoric of empowerment may conceal asymmetric dependency.Cañas Quirós (2023) emphasizes that ethics cannot be separated from political structures; morality detached from power analysis risks legitimizing unjust arrangements. In this sense, digital ethics frameworks must confront the political economy of connectivity rather than merely codify behavioral norms for technology developers.Digital governance is increasingly entangled with geopolitical competition. Infrastructure financing, cloud sovereignty, semiconductor supply chains, and cybersecurity alliances shape technological ecosystems. In this context, international organizations often advocate regulatory harmonization to "facilitate innovation" and "reduce market friction".While harmonization may lower barriers for cross-border digital services, it can simultaneously constrain policy autonomy in developing nations. Ethical frameworks that prioritize market efficiency risk subordinating universal connectivity goals to investor confidence and capital mobility.García Ramírez (2021) calls for a "Marx in the South," reinterpreting technology through the lens of rights, education, and structural inequality. From this perspective, Web3 adoption without universal broadband is analogous to building virtual property rights atop infrastructural scarcity. The promise of decentralized finance or immersive governance becomes utopian-or dystopian-when foundational digital rights remain unrealized.The acceleration principle of technological evolution -where innovation in wealthy nations compounds exponentially-renders access in peripheral regions inversely proportional to global advancement. Ethical frameworks that ignore this asymmetry may inadvertently normalize a tiered digital citizenship.The assumption that a single set of ethical principles can be universally applicable across diverse socio-cultural and technological contexts has been widely debated. From decolonial and pluralist perspectives, ethical frameworks are historically situated and epistemically conditioned (Escobar, 2018). What is considered "ethical" in one context may not translate directly into another, particularly when technological infrastructures, cultural values, and political systems differ significantly.In this sense, the concept of a Universal Code of Digital Ethics may be inherently paradoxical. Rather than a fixed and homogeneous set of principles, digital ethics may need to be understood as a plural and adaptive framework, capable of accommodating different technological ontologies and social priorities. This does not imply abandoning normative aspirations, but rather rethinking universality as negotiated, situated, and contingent.If a global digital ethics framework is to be normatively defensible-whether universal or plural-it must integrate structural and contextual dimensions:If universality is to be normatively defensible, digital ethics must integrate at least five structural components:If universality is to be normatively defensible, digital ethics must integrate at least five structural components:1.Material Preconditions Clause: Ethical standards should explicitly recognize connectivity, affordability, and digital literacy as prerequisites for meaningful participation.Political Economy Transparency: Frameworks must disclose how market incentives shape technological deployment.Geopolitical Reflexivity: Digital governance should account for power asymmetries between states and corporations.Public Infrastructure Commitment: Ethical guidelines must prioritize universal broadband as a public good, not merely a commercial opportunity.Critical Participation Mechanisms: Inclusion must extend beyond tokenized representation toward substantive decision-making capacity.5.6. Epistemic Pluralism: Ethical frameworks must recognize diverse knowledge systems, cultural values, and technological imaginaries, particularly from the Global South.Such principles align ethics with transformative social justice rather than technocratic governance.The aspiration to develop a Universal Code of Digital Ethics for Web3 and the metaverse is commendable. However, without structural critique, universality risks becoming rhetorical. Critical theory reminds us that technological systems embed power relations (Marcuse, 1972). Marxian perspectives reveal how capital shapes technological deployment (Marx, 2005). Contemporary analyses from the Global South highlight the need to situate ethics within historical and geopolitical realities (García Ramírez, 2021;Cañas Quirós, 2023). The ethical question is not merely how to regulate blockchain or ensure transparency in virtual worlds. It is whether digital transformation reproduces one-dimensional rationality-where market logic absorbs ethical discourse-or fosters multidimensional emancipation grounded in material equality.A truly global digital ethics must therefore begin not with code, but with conditions. Without universal connectivity and critical capacity, Web3 and the metaverse risk becoming architectures of selective participation. Ethics, to be universal, must first be infrastructural. Ethics, whether conceived as universal or plural, must first be infrastructural, contextual, and politically grounded.
The rapid collapse of decentralized game economies, often characterized by the \textit{death spiral,} remains the most formidable barrier to the mass adoption of Web3 gaming. This paper proposes that the sustainability of an open game economy is predicated on three necessary and sufficient conditions: Anti-Sybil Resilience, Anti-Capital Dominance, and Anti-Inflationary Saturation. The first section establishes a theoretical proof of these conditions, arguing that the absence of any single dimension leads to systemic failure. The second section explores the dialectical relationship between these dimensions, illustrating how unchecked automation and capital-driven monopolies accelerate asset hyperinflation. In the third section, we introduce the Identity-Bound Asset Integrity Model (IBAIM) as a comprehensive technical solution. IBAIM utilizes Zero-Knowledge (ZK) biometric hashing and Account Abstraction (AA) to anchor asset utility to unique human identities through a privacy-preserving and regulatory-compliant architecture. By exogenizing biometric verification to trusted local environments and utilizing Zero-Knowledge Proofs of Identity (zk-PoI), the model ensures absolute user privacy. Furthermore, by implementing an Asymmetric Utility Decay (AUD) engine-whereby assets suffer a vertical 50% utility cliff upon secondary transfer-and an entropy-driven thermodynamic degradation mechanism., the model successfully decouples financial speculation from in-game merit. Finally, we apply this framework to analyze prominent historical failures in the GameFi sector, demonstrating that their collapse was an inevitable consequence of violating these core economic constraints. Our findings suggest that trading a degree of asset liquidity for system integrity is the only viable path toward long-term economic viability in decentralized virtual worlds.
The rise of IoT devices and the uptake of cloud computing have informed a new era of data-driven intelligence. Traditional centralized machine learning models that require a large volume of data to be stored in a single location have therefore become more susceptible to data breaches, privacy violations, and regulatory non-compliance. This report presents a thorough examination of the merging of Federated Learning (FL) and blockchain technology in a cloud-edge setting, demonstrating it as an effective solution to the stated concerns. We are proposing a detailed four-dimensional architectural categorization that meticulously assesses coordination frameworks, consensus algorithms, data storage practices, and trust models that are significant to these integrated systems. The manuscript presents a comprehensive comparative examination of two cutting-edge frameworks: the Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB), which is designed for intelligent transportation systems, and the Federated Blockchain-IoT Framework for Sustainable Healthcare Systems (FBCI-SHS), elucidating their distinctive contributions and inherent limitations. Lastly, we engage in a thorough evaluation of the literature that integrates a comparative perspective on current frameworks to discern the singular nature of this research within existing knowledge systems. The manuscript culminates in delineating the principal challenges and offering a strategic framework for prospective research trajectories, emphasizing the advancement of adaptive, resilient, and standardized BCFL systems across diverse application domains.
The consensus that GCN, GraphSAGE, GAT, and EvolveGCN outperform feature-only baselines on the Elliptic Bitcoin Dataset is widely cited but has not been rigorously stress-tested under a leakage-free evaluation protocol. We perform a seed-matched inductive-versus-transductive comparison and find that this consensus does not hold. Under a strictly inductive protocol, Random Forest on raw features achieves F1 = 0.821 and outperforms all evaluated GNNs, while GraphSAGE reaches F1 = 0.689 +/- 0.017. A paired controlled experiment reveals a 39.5-point F1 gap attributable to training-time exposure to test-period adjacency. Additionally, edge-shuffle ablations show that randomly wired graphs outperform the real transaction graph, indicating that the dataset's topology can be misleading under temporal distribution shift. Hybrid models combining GNN embeddings with raw features provide only marginal gains and remain substantially below feature-only baselines. We release code, checkpoints, and a strict-inductive protocol to enable reproducible, leakage-free evaluation.
The Lightning Network (LN) is a rapidly evolving payment channel network that enables scalable, off-chain transactions on top of Bitcoin. While prior research has documented its topological structure and liquidity concentration, the joint relationships between node lifetime, connectivity, and capacity remain insufficiently understood. This study provides a comprehensive empirical analysis of these relationships using a large-scale dataset of LN topology snapshots spanning the period 2019-2023. We examine whether node lifetime influences shared channel capacity, and whether this effect is mediated and moderated by node degree. In addition, we account for hierarchical geographic structure and explore the role of country-level economic conditions. The results show that node lifetime has a positive but relatively modest direct effect on capacity. This relationship is largely mediated by node degree, indicating that liquidity accumulation primarily occurs through increased connectivity. Furthermore, the interaction between lifetime and degree reveals significant heterogeneity, with stronger effects observed among highly connected and high-capacity nodes. Mixed-level models demonstrate superior explanatory power, highlighting the importance of country- and region-level variation. The inclusion of GDP per capita confirms that broader economic conditions significantly influence capacity distribution. Overall, the findings suggest that liquidity in the LN emerges from the interplay of temporal dynamics, network structure, and economic context. This study contributes to a more integrated understanding of payment channel networks and provides a foundation for future research on their evolution and efficiency.
Shaoyu Li, Chaoyu Zhang, Hexuan Yu, Y. Thomas Hou · 5 authors
Autonomous AI agents live or die by the API tokens they consume: without paid inference capacity they cannot reason, act, or delegate. Compute-token cost has become the binding resource of the emerging agent economy, yet it is non-transferable: it is account-bound, vendor-specific, and absent from on-chain ledgers. Existing payment rails such as x402 move fiat-backed value between agents, but they do not represent the quantity agents actually burn. As a result, agents can transport purchasing power but cannot quote, escrow, or settle workflows in a unit aligned with compute cost. We present ClawCoin, a tokenized, compute-cost-indexed unit of account and settlement asset for decentralized agent economies. ClawCoin combines four layers: a robust basket index over standardized prices; an oracle publishing signed fresh attestations; a NAV-based mint/redeem vault with coverage thresholds and rate limits; and an on-chain settlement layer for multi-hop delegations. We implement a prototype on an Ethereum-compatible L2 and evaluate it using a multi-agent simulator and the OpenClaw testbed. Across single-agent, multi-agent, workflow, and procurement experiments, ClawCoin stabilizes execution capacity under cost shocks, reduces cross-agent quote dispersion, eliminates partial settlements, and sustains cooperative market dynamics that fiat-denominated baselines cannot. These results suggest that compute-indexed units of account can improve decentralized agent coordination.
Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to support early diagnosis and risk assessment. While traditional machine learning models perform well on structured clinical data, large language models (LLMs) present new possibilities to interpret medical information expressed in natural language. In this work, we develop a hybrid framework that bridges structured clinical data and natural-language representations for CAD prediction. Using a publicly available dataset of 1,190 patient records with 11 clinical attributes, structured variables are converted into interpretable feature representations and synthetic clinical narratives using LLMs. A validation pipeline performs reverse extraction of clinical variables and computes a consistency score with the original records, achieving an average fidelity of 94.61%. We then evaluate four conventional machine learning models and compare their performance with LLM-based classification under zero-shot and few-shot prompting settings. We use two LLMs here, GPT and Gemini. Experimental results show that Random Forest achieves the highest accuracy. Despite this advantage, LLM-based classification remains beneficial in real-world clinical settings. This is because LLMs operate directly on natural language patient descriptions, meaning that sensitive numerical patient data such as exact lab values, blood pressure readings, and diagnostic codes are kept private. Findings suggest that combining structured clinical data with LLM-generated narratives can enable new directions for hybrid clinical prediction systems.
Large language models (LLMs) have advanced code generation, yet code repair remains difficult due to complex multi-step reasoning, strict correctness requirements, limited data, and real-time constraints. Smart contracts, as immutable blockchain programs, are an important case for repair. This paper presents FlowMemRep, a memory-based workflow for smart contract vulnerability repair. It extracts repair logic from a few-shot prompts, retrieves knowledge with gas cost profiles, version constraints, and deployment rules. In addition, outputs are stored in short-term memory, and then verified information is transferred to long-term memory for accurate reuse. Experiments on 205 real-world vulnerabilities show that FlowMemRep reaches 75 percent remediation accuracy, surpassing prior methods by 20 percent.
Blockchain technology has become a game-changing way to solve long-standing problems with data security, integrity, and trust in distributed environments. Its decentralized structure, unchangeability, and cryptographic features make it a strong way to protect sensitive data from unauthorized access, tampering, and cyberattacks. This study investigates the function of blockchain in fortifying contemporary data security frameworks, analyzing the ways in which consensus algorithms, smart contracts, and distributed ledgers improve confidentiality, availability, and accountability. This study emphasizes blockchain's capacity to transform data governance through an in-depth examination of existing applications, limitations, and case studies, while also identifying the technical and operational challenges that must be resolved for widespread implementation. This study also highlights the role of the CoreDaoVip Global Curriculum in strengthening blockchain-based data security frameworks by integrating decentralization, cryptographic mechanisms, smart contracts, and ethical governance. The curriculum adopts a security-by-design approach aligned with the Satoshi 3.0 philosophy, enabling the development of tamper-resistant, transparent, and privacy-preserving data architectures. By bridging theoretical foundations with practical implementation, CoreDaoVip prepares a globally competent workforce capable of addressing emerging data security challenges across critical digital ecosystems.