This paper reviews the innovative applications of AI and Web3 in metaverse social platforms. It first analyzes the foundational roles of AI (e.g., virtual avatar generation, intelligent interaction, personalized recommendation) and Web3 (e.g., blockchain, NFTs, decentralized identity) in enabling immersive, secure, and user-centric social interactions. It then examines their synergies, with case studies of Decentraland and The Sandbox illustrating practical integrations. The research identifies key challenges, including technical bottlenecks (e.g., AI realism, blockchain scalability), user-related issues (e.g., awareness, privacy concerns), and industry-level hurdles (e.g., regulatory ambiguities, homogenization). Finally, it proposes future directions: advancing AI/Web3 technologies, expanding application scenarios across education and entertainment, and implementing strategic recommendations to foster inclusive and sustainable metaverse social ecosystems.
Efficient energy sharing among solar-based microgrids was crucial for enhancing grid reliability, scalability, and sustainability in modern energy systems. This research presents a novel blockchain-powered decentralized energy trading framework that integrates Raspberry Pi 4, IoT-driven real-time monitoring, and Ethereum-based smart contracts to facilitate seamless and secure peer-to-peer (P2P) energy exchange. The proposed system enables real-time data acquisition and transmission of critical energy parameters, including current, voltage, and power generation, from five interconnected solar microgrids. Raspberry Pi 4 serves as the centralized edge computing node, aggregating and transmitting real-time energy data to the ThingSpeak IoT platform, where advanced AI-driven analytics optimize grid efficiency. Blockchain technology, specifically Ethereum with Ganache, was employed to create a tamper-proof, transparent, and trustless energy marketplace, eliminating reliance on centralized energy intermediaries. The incorporation of Solidity-based smart contracts automates transactions, ensuring secure, immutable, and fair energy trading while enabling dynamic pricing models based on real-time demand-supply conditions. Python, integrated with Web3.py, facilitates seamless interaction between Raspberry Pi 4 and the blockchain network, ensuring low-latency transaction execution and verifiable trade settlements. Through the integration of IoT-enabled smart grids, blockchain-based energy transactions, and AI-driven predictive analytics, the proposed system offers a scalable, autonomous, and energy-efficient solution for decentralized energy management. Experimental validation confirms the system's effectiveness, demonstrating its ability to achieve real-time energy balancing, seamless P2P trading, and enhanced security through blockchain immutability. This cutting-edge approach significantly advances the adoption of renewable energy sources, optimizes microgrid autonomy, and reinforces the resilience of next-generation smart power networks, paving the way for a sustainable and decentralized energy economy.
Stablecoin -Stable-Caucasus (STCAUX)Technical Whitepaper v1.0 Multi-Asset Commodity-Backed Stablecoin for the South Caucasus Region1. Executive Summary StableCaucasus is a multi-component stable digital asset based on the economic foundation of the South Caucasus and pegged to: ● regional currencies (AZN, GEL, AMD) oriented toward USD exchange rates; ● commodity indices: gold, agriculture, metals mining, oil and gas; ● the region’s energy and resource potential; ● Logistics ● uranium as a strategic component and future driver of global energy The goal of StableCaucasus is to create a regional, resilient digital asset that reflects the real economic potential of the Caucasus and provides users with stability, liquidity, and protection against currency volatility. 2. Mission & VisionMissionTo create the world’s first multi-commodity stable asset representing the South Caucasus as a center of: ● agricultural production, ● energy, ● gold mining, ● Logistics and strategic natural resources Vision To position StableCaucasus as: ● a settlement unit for cross-border trade between the Caucasus, GCC, EU, and Central Asia; ● a protective asset for investors; ● a digital instrument for governments, banks, logistics operators, and exporters. 3. Value Proposition StableCaucasus provides: ✔ Stability — the multi-index model reduces dependence on any single asset. ✔ Trust -each part of the formula is based on public financial and macroeconomic data. ✔ Regional relevance- partially reflects the real economy of the South Caucasus. ✔ Collateralization - the asset is backed by auditable and transparent baskets. ✔ Web3 infrastructure - BNB Chain and Ethereum compatible.
The rapid development of digital infrastructures demands new approaches to managing born-digital data and enabling knowledge valorisation. Rather than constructing another digital Library of Alexandria, the SODHA-team is developing Project BLU, a framework for building a distributed information network. Drawing inspiration from Web3 principles—decentralisation, interoperability, and user-driven discovery—the project explores methods for metadata exchange between archives. This project engages with established metadata standards like the Data Documentation Initiative (DDI), exploring how they can support or be extended to enable interoperability. Instead of centralising data, Project BLU enables a shared, federated catalogue based on metadata-exchange accessible from any entry point. Researchers will navigate this network to discover and link data across disciplines, reducing access barriers and opening new avenues for metadata specialists. These include promoting and, where needed, upgrading DDI standards, linking interdisciplinary data, and bridging vocabularies across scientific fields. The interconnected nature of the system reinforces adherence to FAIR principles, improves access to both open and restricted data, and enhances knowledge reuse at national and international levels (e.g. linking CESSDA and DDI partners). This presentation outlines the conceptual foundations of Project BLU and its vision for transforming metadata into dynamic, actionable links across the research landscape.
Modern states rely on legacy financial infrastructures—paper currency, centralized taxation, and compliance-heavy fiscal administration—that impose massive operational costs and produce structural inequities across generations. This paper proposes a circular, future-oriented national operating system grounded in tensional theory and value-flow mechanics. The system integrates Burn Mandate (automatic burn of violence-derived value), ChildChain (a value-distribution model prioritizing future generations), and a post-currency architecture that replaces cash-based infrastructures with programmatic smart contracts. The framework reveals (a) the cognitive and ethical limitations of full DAO governance, (b) the unsustainable tension generated by centralization-dependent money systems, and (c) the possibility of a hybrid model where governments maintain responsibility while adopting web3-derived automation. This study argues that value circulation—not accumulation—is the fundamental determinant of social stability. Integrating tensional theory with computational governance yields a normative model for a nation-state where negative externalities are burned, positive value is propagated to children, and fiscal administration becomes fully autonomous, transparent, and circular.
This research paper presents the design, architecture, and evaluation of a Hybrid Blockchain Database System, a decentralized–distributed model that integrates blockchain’s immutability with the high-performance data processing capabilities of traditional database systems. The proposed hybrid architecture addresses key limitations of fully on-chain storage—such as scalability constraints, high transaction costs, and limited query efficiency—by combining on-chain verification layers with off-chain distributed storage engines. The system introduces a multi-layered architectural model consisting of: (1) a blockchain consensus and validation layer for ensuring trust and tamper-resistant auditability; (2) a distributed database layer for high-speed read/write operations; (3) an interoperability layer that synchronizes state between on-chain and off-chain data; and (4) an IPFS/Filecoin-based decentralized storage framework for storing large datasets and metadata. This design enables secure state anchoring, cryptographic data integrity, and scalable data indexing across heterogeneous storage environments. A detailed performance evaluation is conducted using a combination of Ethereum-compatible smart contracts, IPFS storage, and distributed NoSQL/SQL databases. Key metrics such as transaction throughput, read/write latency, storage overhead, and consistency verification time are analyzed under varying workloads. Experimental results demonstrate that the hybrid system improves data scalability by 70–85%, reduces storage costs by over 90%, and maintains strong auditability through periodic blockchain state commitments. Security analysis is performed using static and dynamic testing tools to identify common vulnerabilities such as broken access control, inconsistent state updates, or missing verification boundaries, ensuring robustness of the hybrid synchronization logic. Additionally, the paper discusses design trade-offs involving decentralization vs. performance, on-chain transparency vs. gas efficiency, and consistency vs. latency across distributed components. This work provides a comprehensive blueprint for the development of real-world hybrid blockchain database systems suitable for supply chain management, enterprise data systems, government registries, financial applications, and Web3 infrastructure. The results highlight how hybrid architectures can bridge the gap between decentralized trust and scalable data management in next-generation digital ecosystems.
This comprehensive technical specification details the ADN-NChain protocol, a first-of-its-kind Distributed Ledger Technology (DLT). It introduces a revolutionary self-repairing neural blockchain architecture, leveraging biologically-inspired genetic algorithms for state management and unprecedented network resilience to systemic and quantum threats. The whitepaper thoroughly covers the robust system design, the novel Proof-of-Resonance (PoR) consensus mechanism, and a future-proof post-quantum cryptography implementation (CRYSTALS-Dilithium) essential for securing the next generation of crypto-assets and decentralized applications (dApps). Performance metrics confirm high scalability (12,000+ TPS) and exceptional data integrity with a 99.9% automatic corruption repair success rate. This work is critical for researchers, investors, and developers focused on next-generation blockchain, Web3, cryptocurrency security, and Decentralized Finance (DeFi) solutions.
The global Web3 Payments Market is projected to grow from USD 2.83 billion in 2023 to USD 44.98 billion by 2030, driven by decentralized blockchain adoption and AI integration. Web3 payments enable secure, transparent, and cost-efficient financial transactions for businesses worldwide. To access the full report, visit - https://www.nextmsc.com/report/web3-payments-market
Access control in P2P network is extremely challenging due to several reasons such as decentrailized network, anonymous connectivity, high churn, resource constraints and large attack surface. The number of attributes to be tracked grows exponentially as P2P networks scale, exacerbating issues in traditional approaches such as RBAC, ABAC, RuBAC. This paper presents a novel blockchain-based access control framework for P2P networks using Ethereum smart contracts to offer an adaptable, decentralized, and scalable solution. Our token economy framework incentivizes nodes that demonstrate consistent compliance and penalize malicious actions using fungible ERC-20 tokens. Hierarchical static role-based structure consisting of regular members, primary and secondary group is the foundational layer of role management. Dynamic state management through a finite state machine classifies peers as benign, suspicious, or malicious mapping their behavior to the STRIDE threat model. We have used standard blockchain tool stack consisting of OpenZeppelin (contract library), Mocha (testing), and Web3.js (deployment). Our framework is tested with eight test cases, such as privilege escalation and denial-of-service attacks, and built using three types of Ethereum smart contracts. Our focus is on collecting four metrics gas cost, latency, execution time, simultaneous requests that provides a comprehensive view of realistic network activity. This study goes beyond conceptual nature of state-of-art architecture to showcase the realistic mapping between resources, peer roles and standard threats in a P2P network. The experimental results shows low gas costs (upto 1.4 million gas) and fast execution times (80–550 ms), confirming the design efficiency. The framework handles up to five concurrent requests with minimal performance impact, demonstrating strong scalability across roles and threat conditions. This framework enables scalable, behavior-driven governance in P2P networks, enhancing IoT and Web3 security.
Aleksei Olkhovikov, Yash Madhwal, Arsen Andrian, Hamza Imran · 8 authors
• Prototype system with Raspberry Pi and dual ultrasonic sensors for data acquisition. • Real-time data signing and blockchain submission using web3.py and EVM chain. • Smart contract for secure data logging, access control, and gas-efficient events. • Frontend with Streamlit MVP and Vue3 dashboard supporting secure user login. • Experiments on 15M-record dataset to evaluate gas cost, batching, and scalability. Integrity and traceability of sensor data in oilfield operations are essential for safe, efficient, and compliant resource extraction. This paper presents a blockchain-enabled proof-of-concept (PoC) IoT framework that facilitates decentralized, tamper-evident monitoring of oil extraction infrastructure. The system integrates field-deployed sensors with a Raspberry Pi-based edge controller to capture, buffer, and cryptographically sign telemetry data, which is then submitted to an EVM-compatible blockchain using smart contracts. The PoC demonstrates historical and real-time data visualization through a web-based dashboard that authenticates and displays blockchain event streams. A real-world drilling data set comprising more than 15 million records is used for the experimental evaluation of the prototype. Gas consumption metrics are analyzed under varying payload sizes and batching strategies, revealing linear scalability with respect to parameter volume and significant efficiency gains through transaction batching. These results demonstrate measurable improvements in resource utilization and operational cost, confirming the framework’s efficiency and robustness for large-scale industrial telemetry. The architecture supports secure access control, structured metadata annotation, and transparent logging without reliance on centralized intermediaries. By addressing key challenges in data authenticity and operational visibility, the proposed solution establishes a scalable foundation for secure telemetry in oil and gas operations, with potential applicability to other critical infrastructure domains such as energy grids, mining, and water resource management. Unlike prior blockchain-IoT frameworks focusing primarily on architectural design or off-chain coordination, the proposed system demonstrates an end-to-end implementation directly linking field-level sensors to on-chain storage and visualization. Through large-scale validation on a 15 M-record drilling dataset, this work provides one of the first empirical analyses of gas-efficient, real-time telemetry submission in industrial settings.
Web3, referring to the next-generation decentralized web, has gained extensive attention from industry and academia. Its popularity drives considerable demand for continuous development and delivery of web3 applications and services. Blockchain is an essential technology that enables web3 but comes with considerable operational cost. Blockchain-as-a-service (BaaS) is considered as a promising solution for supporting web3 applications; however, existing BaaS platforms are conceptual, built for specific applications, or not optimized for emerging web3 applications. This work analyzes the primary features and fundamental requirements of decentralized web3 applications and articulates the critical role of blockchain for web3. To fill the gap, we introduce DAPPaaS, the first customizable BaaS platform specializing in web3 applications, with carefully designed goals and principles to meet the goals. Our key technical contributions lie on the approaches enabling component modularization, communication efficiency, automated scaling and performance monitoring, distributed deployment, and resource optimization. We implement a real-world application based on DAPPaaS and conduct extensive performance evaluations to validate its utility and efficiency.
This deposit formalizes a decentralized framework for certifying and archiving AI models, scientific content, and intellectual creations using NFTs and blockchain metadata. It includes a ready-to-use NFT smart contract (ERC-721), structured metadata, digital certificate with SHA256 hash, and a minting interface. The DOI reference is embedded for scientific traceability. All files are timestamped, signed (Anne Povie), and packed for archival or minting purposes. This submission is intended to support researchers, developers, and creators in producing immutable, verifiable scientific artifacts using Web3 infrastructur e.
Zero-knowledge proofs (ZKPs) have evolved from foundational interactive proof systems to highly efficient, scalable, and trusted-setup-free constructions powering today’s privacy-preserving and blockchain applications. The field began with the seminal works of Goldwasser, Micali, and Rackoff (GMR) and Goldreich, Micali, Wigderson (GMW) in the 1980s, which introduced interactive proofs, knowledge complexity, and showed that all NP languages admit zero-knowledge proofs. The 1990s brought non-interactive ZK (NIZK) via the CRS model (Blum–Feldman–Micali) and practical sigma-protocols like Schnorr proofs, establishing foundational tools still used today. From the 2000s through early 2010s, research integrated pairings, PCPs, and cryptographic soundness, culminating in pairing-based NIZKs and early succinct argument systems. The SNARK revolution accelerated with QAP-based zk-SNARKs (Gennaro–Gentry–Parno), practical implementations like Pinocchio and libsnark, and the highly efficient Groth16 proving system that became a blockchain standard. Since 2018, the field has shifted toward transparent, universal, and post-quantum-secure systems. Innovations include Bulletproofs (short proofs without trusted setup), zk-STARKs (scalable and PQ-secure), PLONK (universal/updatable setup), and Halo/Halo2 enabling recursive proofs without trusted setup. These advances underpin modern Zcash deployments, zk-rollups, and privacy-preserving scaling systems across Web3. Overall, the ZKP landscape has progressed from theoretical constructs to practical, scalable, and secure systems central to modern cryptography and decentralized computation.
Stablecoin ENTISQ (ENUR TAGA ISTIQARAR) Technical Whitepaper v1.1 1. Executive Summary ENTISQ (Energy + Nur + Taqa + Istiqarar) is an innovative digital asset backed by the economic fundamentals of the GCC energy sector and synthetically pegged to AED and SAR. ENTISQ creates a new class of stable assets by combining currency stability with the region’s energy foundation. Objective: Provide a reliable stablecoin for cross-border payments, B2B transactions, energy contract settlements, and Web3 integrations within the GCC. 2. Mission & Vision Mission: Deliver a stable, transparent, and predictable digital asset for the GCC linking currency and energy markets. Vision: ENTISQ aims to become the benchmark digital currency of the region, serving as a foundation for a sustainable economy and energy sector.
ASES — Arabian Sustainable Energy StablecoinTechnical Whitepaper v1.1 1. Executive Summary ASES (Arabian Sustainable Energy Stablecoin) is an innovative digital asset backed by the economic fundamentals of the GCC energy sector and synthetically pegged to AED and SAR. ASES creates a new class of stable assets by combining currency stability with the region’s energy foundation. Objective: Provide a reliable stablecoin for cross-border payments, B2B transactions, energy contract settlements, and Web3 integrations within the GCC. 2. Mission & Vision Mission: Deliver a stable, transparent, and predictable digital asset for the GCC linking currency and energy markets. Vision: ASES aims to become the benchmark digital currency of the region, serving as a foundation for a sustainable economy and energy sector.
Abstract This study aims to sociologically understand the ideas about organizations, mechanisms to realize those contained in Decentralized Autonomous Organization (DAO), and Web3. It specifically focuses on the unique relationship between people and objects in this context. In this study, DAO refers to an organization on the Internet that uses blockchain technology and that has no specific administrator (that is decentralized and flat), whereas Web3 refers to the way that the Web is organized premised on such an organization. A DAO attempts to reduce uncertain elements and create a firm, flat organization by introducing the physical technology of blockchain into an organization composed of people. However, its operation is more intricate. For example, blockchain is not purely an object but humans are embedded within it and their desires are harnessed as its driving force. This study attempts to describe such an intricate construct and specifically focuses on the effort needed to create a “flat” organization. It is not simply realized mechanically through the blockchain but people are also substantially involved, including in the pre-discussion process (off-chain). For example, whether a DAO becomes a flat organization may depend on the original relationships between its members. The workings of the blockchain are more dependent on the people participating in it and their relationships than they appear. Recently, some DAOs have placed less emphasis on protocols, such as on the automatic execution of the content of contracts, and more emphasis on community. However, irrespective of where they place the emphasis, no essential difference exists between them on the point that people are involved in them to a greater or lesser extent.
Memecoins, emerging from internet culture and community-driven narratives, have rapidly evolved into a unique class of crypto assets. Unlike technology-driven cryptocurrencies, their market dynamics are primarily shaped by viral social media diffusion, celebrity influence, and speculative capital inflows. To capture the distinctive vulnerabilities of these ecosystems, we present the first Memecoin Ecosystem Fragility Framework (ME2F). ME2F formalizes memecoin risks in three dimensions: i) Volatility Dynamics Score capturing persistent and extreme price swings together with spillover from base chains; ii) Whale Dominance Score quantifying ownership concentration among top holders; and iii) Sentiment Amplification Score measuring the impact of attention-driven shocks on market stability. We apply ME2F to representative tokens (over 65% market share) and show that fragility is not evenly distributed across the ecosystem. Politically themed tokens such as TRUMP, MELANIA, and LIBRA concentrate the highest risks, combining volatility, ownership concentration, and sensitivity to sentiment shocks. Established memecoins such as DOGE, SHIB, and PEPE fall into an intermediate range. Benchmark tokens ETH and SOL remain consistently resilient due to deeper liquidity and institutional participation. Our findings provide the first ecosystem-level evidence of memecoin fragility and highlight governance implications for enhancing market resilience in the Web3 era.
This research examines how emerging forms of digital sovereignty, decentralized infrastructures, and anticipatory AI governance are reshaping nationhood in the algorithmic age. Drawing on the conceptual framework of Algorithmic Nations (Calzada 2018) and incorporating new empirical insights from embedded action research (2022–2025), the study analyses the Basque Country as a paradigmatic case of a “small stateless nation” navigating the global reconfiguration of power between states, corporations, and communities. The presentation synthesizes three competing post-Westphalian paradigms—Network States (Srinivasan 2022), Network Sovereignties (De Filippi 2024), and Algorithmic Nations (Calzada 2018)—as shown in the comparative table on page 19, highlighting their differing assumptions regarding governance, identity, participation, and technological control. Building on the diagnostic indicators of Europe’s digital dependence (page 10) and the transition from Gaia-X to EuroStack (page 11), the study evaluates the strategic implications of digital public infrastructures, data cooperatives, federated architectures, and Web3 ecosystems for stateless nations. Through comparative analysis of the Global North (e.g., Scotland, Quebec, Flanders), the Global South (e.g., Kurdistan, Sámi, Tamil, Amazigh), and the Basque Country (pages 16–17), the work demonstrates how communities with diverse geopolitical constraints can articulate forms of AI sovereignty grounded in rights-based, culturally rooted, and community-driven governance. The Basque case illustrates how fragmented digital systems (.eus, EJIE/Izenpe, Osakidetza, MUBIL, etc.) can evolve toward an interoperable, multi-scalar technopolitical architecture, aligning linguistic, territorial, and infrastructural dimensions. The analysis argues that AI-driven infrastructures, data governance, and decentralized architectures are not merely technical layers but emerging geopolitical terrains where stateless, indigenous, diasporic, and minority nations can renegotiate autonomy. The concept of Algorithmic Nations provides a framework for understanding how community sovereignty can be built through data commons, federated systems, and anticipatory governance, particularly in multilingual and culturally distinct territories such as the Basque Country. Overall, the study contributes to debates on global digital governance, digital sovereignty, and the future of nationhood by proposing that algorithmic infrastructures are becoming central to political organization. It calls for democratic, inclusive, and community-oriented models of AI governance capable of avoiding techno-authoritarianism, Big Tech dependency, and “sovereignty washing,” while enabling emancipatory, culturally anchored, and future-oriented forms of collective self-determination.
Behavioral finance has emerged as a critical framework for understanding market dynamics beyond traditional rational agent models. This research presents a comprehensive multimodal approach to behavioral finance analysis, integrating market data, macroeconomic indicators, news sentiment, cryptocurrency metrics, Web3 analytics, GitHub development activity, and social sentiment to test five advanced hypotheses regarding behavioral pattern identification and market anomaly detection. The study employs an ultra-comprehensive data pipeline processing 30,400 samples across seven distinct data sources, generating 91 engineered features representing behavioral biases, investment patterns, and market psychology. Advanced machine learning techniques including Principal Component Analysis, t-Distributed Stochastic Neighbor Embedding, Variational Autoencoders, K-Means, Hierarchical Clustering, DBSCAN, Isolation Forest, One-Class SVM, and Elliptic Envelope are applied to identify behavioral structures and detect anomalies. Statistical validation through chi-square tests, ANOVA, Granger causality analysis, and lagged correlation studies demonstrates that three of five hypotheses (60%) achieve statistical significance at p < 0.05. Key findings reveal that behavioral structures exist and correspond to canonical biases (chi-square = 3406.780, p < 0.001), cluster assignments maintain moderate stability across market regimes (Jaccard similarity = 0.300), and sentiment and macroeconomic factors exhibit 65 significant causal relationships with behavioral patterns. However, multimodal data integration does not uniformly improve clustering quality (Silhouette score decrease of 0.116), and cluster-conditioned anomaly detection fails to outperform global methods (F1-score decrease of 0.017). These findings contribute to behavioral finance theory while providing practical applications for investment management, fraud detection, and regulatory compliance.
Permissionless blockchains have evolved beyond cryptocurrency into foundations for Web3 applications, decentralized finance (DeFi), and digital asset ownership, yet this rapid expansion has intensified privacy vulnerabilities. This study provides a comprehensive review of recent trends, emerging privacy threats, and mitigation strategies in permissionless blockchain ecosystems. We examine six developments reshaping the landscape: meme coin proliferation on high-throughput networks, real-world asset tokenization linking on-chain activity to regulated identities, perpetual derivatives exposing trading strategies, institutional adoption concentrating holdings under regulatory oversight, prediction markets creating permanent records of beliefs, and blockchain–AI integration enabling both privacy-preserving analytics and advanced deanonymization. Through this work and forensic analysis of documented incidents, we analyze seven critical privacy threats grounded in verifiable 2024–2025 transaction data: dust attacks, private key management failures, transaction linking, remote procedure call exposure, maximal extractable value extraction, signature hijacking, and smart contract vulnerabilities. Blockchain exploits reached $2.36 billion in 2024 and $2.47 billion in the first half of 2025, with over 80% attributed to compromised private keys and signature vulnerabilities. We evaluate privacy-enhancing technologies, including zero-knowledge proofs, ring signatures, and stealth addresses, identifying the gap between academic proposals and production deployment. We further propose a Secure Development Lifecycle framework incorporating measurable security controls validated against incident data. This work bridges the disconnect between privacy research and industrial practice by synthesizing current trends, providing insights, documenting real-world threats with forensic evidence, and providing actionable insights for both researchers advancing privacy-preserving techniques and developers building secure blockchain applications.
This thematic issue examines how artificial intelligence, metaverse imaginaries, and decentralized Web3 systems have become arenas for states to build infrastructures, set technical standards, and project geopolitical power. It reconceptualizes technology not merely as an object of regulation but as a medium of statecraft through which sovereignty, security, and leadership are contested and remade in a multipolar digital order. This issue analyzes three interconnected dimensions: (a) the impact of global AI competition on state-making processes, enhancing coercive, extractive, delivery, and informational capacities similar to earlier state formation phases; (b) the nature of technological leadership as a relational and dynamic process influenced by interactions between leading and following states; and (c) the role of security logics in transforming external rivalry and internal governance through securitization. Through comparative analysis of the US, China, the EU, and emerging economies, this issue explores how diverse political systems encode openness, sovereignty, and accountability into their technological regimes, demonstrating that technological governance is inseparable from state-making. The contributions map competing logics—sovereign, liberal, entrepreneurial—showing that digital governance emerges not as convergence toward a singular model but as recursive entanglements of imagination and infrastructure.
This paper investigates how Web3 technologies, such as blockchain, NFTs, and the metaverse, can drive Business Model Innovation (BMI) by enabling new forms of value creation, delivery, and capture. While the strategic potential of Web3 has been widely discussed, there remains a lack of operational tools to guide its implementation in real-world business contexts. To address this gap, we introduce the Web3 Value Exploitation De sign Model (Web3 VEDM), a step-by-step framework grounded in the GUEST methodology. The model is designed to support engineering managers in assessing Web3 readiness, aligning stakeholders, and developing decentralized business models. The framework is empirically validated through a real-world case study in the agri-food sector, offering actionable insights into how organizations can leverage Web3 to transition from centralized to decentralized, participatory ecosystems. The study contributes both theoretically and practically by bridging the gap between conceptual exploration and structured application of Web3 in business transformation.
The convergence of artificial intelligence (AI) and decentralized web technologies represents a pivotal shift in digital infrastructure, giving rise to the concept of AI-native protocols. These protocols integrate AI capabilities directly into their fundamental design, moving beyond mere application-level AI to create intelligent, adaptive, and autonomous decentralized systems. This paper explores the transformative potential of AI-native protocols in reshaping the decentralized web, often referred to as Web3. We delve into the architectural implications, key benefits such as enhanced security, efficiency, and scalability, and the profound societal impact of such a paradigm shift. Through a comprehensive literature review, we identify existing challenges in both AI and blockchain domains that AI-native protocols are uniquely positioned to address, including algorithmic bias, data privacy, and consensus mechanism inefficiencies. We propose a conceptual framework for designing these protocols, emphasizing core components like intelligent consensus, autonomous agents, and AI-powered smart contracts. Furthermore, the paper discusses the ethical considerations inherent in embedding AI within decentralized governance structures and outlines future research directions for fostering responsible innovation. Our findings suggest that AI-native protocols are not merely an incremental improvement but a foundational evolution that promises to unlock unprecedented levels of intelligence and autonomy across the decentralized digital landscape, fostering a more robust, equitable, and resilient internet.
Behavioral finance explores the psychological influences and cognitive biases that affect investor behavior and financial decision-making, including herding, the disposition effect, overconfidence, and others. Algorithmic trading is a method that uses computer programs to automatically execute buy and sell orders based on predefined mathematical models and trading strategies. With the continuous development of modern technology, the advent of the Web3 era, and the gradual evolution of artificial intelligence, algorithmic trading is becoming increasingly prevalent and garnering significant attention. While algorithmic trading is automated and may seem immune to human cognitive biases, the opposite is often true. This study aims to review the main findings of existing research from the perspective of the stock market, exploring the interactive relationship between behavioral finance and algorithmic trading and how cognitive biases such as herding and the disposition effect can influence algorithm performance. The results emphasize the importance of behavioral finance in both the research and practice of algorithmic trading, while also proposing the potential for using machine learning techniques to advance the field of behavioral finance. By integrating existing theories, this study contributes to a deeper understanding of the relationship between behavioral finance and algorithmic trading and offers new perspectives for its future development.