Blockchain technology has emerged as a transformative force across various industries by providing a decentralized, transparent, and secure digital infrastructure. Central to this transformation are smart contracts, which are self-executing agreements that autonomously enforce and execute contractual terms without the need for intermediaries. While smart contracts offer significant advantages in terms of efficiency and security, their inherent rigidity and limited adaptability pose challenges in dynamic and complex environments. This situation prompts the critical question: How Far Should We Go from Smart Contracts to Smarter Contractors? Driven by the necessity to overcome these limitations, this systematic review investigates the evolution of smart contracts into smarter contractors through the integration of artificial intelligence (AI) and machine learning (ML) technologies. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, an extensive literature search was performed across multiple academic databases, identifying and analyzing 276 relevant studies published between 2015 and 2024. The analysis, structured around six key research questions, reveals that the incorporation of AI and ML has significantly enhanced the functionality, security, and adaptability of smart contracts throughout their lifecycle. These enhancements include automated code generation, formal verification, real-time monitoring, and adaptive management. Despite these advancements, persistent challenges such as scalability, interoperability, data privacy, and computational overhead continue to hinder the full realization of smarter contractors. Additionally, the advent of Large Language Models (LLMs) has further expanded the capabilities of smart contracts, enabling more sophisticated code generation, vulnerability detection, and intelligent auditing. This review underscores the pivotal role of AI and ML in addressing the limitations of traditional smart contracts, highlighting their transformative impact on the broader blockchain ecosystem and facilitating the development of more advanced and intelligent decentralized applications. Finally, we propose future research directions that emphasize the necessity for standardized frameworks, enhanced interoperability protocols, and robust security measures to support the ongoing advancement of intelligent smart contracts.
This paper proposes a dynamic risk assessment model for cross-border e-commerce data that integrates genetic algorithms with zero-knowledge proofs. The study first constructs a dynamic evaluation framework based on multidimensional feature extraction and employs an improved genetic algorithm featuring an adaptive crossover and mutation operator to efficiently optimize risk assessment parameters. Subsequently, a lightweight zero-knowledge proof protocol is designed to verify data integrity and the trustworthiness of the risk evaluation logic, while ensuring the confidentiality of the original data. Experimental results on a simulated cross-border e-commerce dataset demonstrate that the proposed model achieves 92.7% accuracy on a simulated dataset, surpassing traditional SVM and Random Forest by 15.3% and 7.5%, respectively. Additionally, the proof generation time is maintained at the millisecond level, significantly outperforming existing homomorphic encryption schemes. This research offers an innovative solution to the “security-efficiency-compliance” trilemma in cross-border e-commerce data flows and holds substantial practical significance for constructing a trusted data element circulation system.
Abdul Muheet Chowdhary, Kuldeep Sharma, Kolawole Omole
Abstract The growth of crypto assets-related transactions is not short of phenomenal. One of the concerns of governments is their potential impact on financial and economic stability arising from illicit financial flows and reduced visibility of tax administration over crypto transactions. The United Nations Tax Committee is developing a toolkit for jurisdictions to be able to evaluate crypto asset risks. The chapter seeks to contribute to the development of this toolkit by analysing the challenges posed by the decentralized nature of crypto assets as well as the use of cryptocurrency as a medium of exchange. A survey is suggested that could be employed by governments to evaluate those challenges. The adoption of blockchain technology is continuously evolving, resulting in the formation of Decentralized Autonomous Organizations (DAOs) and Decentralized Finance (DeFi). The chapter discusses their features at length and suggests another survey to evaluate their risks as well as measures which tax administrations may consider to tackle the tax challenges arising from crypto assets, DAOs and DeFi.
PURCAREA LIVIU, RADULESCU CARMEN VALENTINA, Mănescu Andreea Maria
Blockchain technology has rapidly transformed the way decentralized systems operate, offering new possibilities for transparency, security, and autonomy. However, these benefits come with a notable drawback: the significant environmental cost associated with blockchain consensus mechanisms — particularly Proof of Work (PoW) Möser et all (2021). This paper examines the environmental impact of blockchain and investigates more sustainable alternatives, such as Proof of Stake (PoS) and other energy-efficient models. Using Ethereum’s transition from PoW to PoS as a central case study, along with examples such as Algorand and Chia, we explore how different architectural decisions affect energy consumption. Our analysis, based on recent academic research and technical data, suggests that sustainable blockchain models are technically viable—but their success depends on broader systemic changes, including clear regulations, governance reforms, and industry-wide engagement. In conclusion, blockchain can evolve into a sustainable technology, but only through a responsible and coordinated effort
Luigi Coppolino, Giovanni Maria Cristiano, Salvatore D’Antonio, Jonah Giglio · 6 authors
Ensuring the reliability and accuracy of information is a critical challenge in sectors like finance, media, and health. The reliance on centralized verification systems introduces risks of bias, manipulation, and limited transparency. To address these issues, we propose VeriNet , a decentralized framework for third-party content verification leveraging blockchain technology and the Ethereum Attestation Service. VeriNet integrates on-chain and off-chain attestations to ensure privacy, transparency, and accountability, supported by a Decentralized Data Warehouse and cryptographic Proof-of-SQL mechanisms. The framework includes Contributors , who submit content, and Verifiers , who assess its authenticity. We carried out two Proof-of-Concept implementations, namely in deepfake detection and fintech credit scoring, to demonstrate the efficiency of VeriNet and its adaptability to diverse domains. Moreover, we conducted an experimental evaluation focusing on various parameters, such as costs and execution time, to demonstrate framework feasibility, scalability, and potential to establish a trusted ecosystem for collaborative verification.
The rapid co-evolution of Artificial Intelligence (AI) and blockchain technology has exposed a persistent gap between intelligence-the ability to extract insight from data-and trust-the assurance that data, models, and decisions are transparent, verifiable, and tamper-proof.This study introduces the Unified Trust-Intelligence Framework (UTIF), an end-to-end architecture that natively fuses AI and distributed-ledger technologies to deliver auditable, privacy-preserving, and energy-aware intelligent services.A systematic review compliant with PRISMA guidelines (167 peer--reviewed sources, 2018-2024) reveals four critical deficiencies in the current literature: (i) the lack of formal on-chain model certification, (ii) opaque immutability of operational logs, (iii) limited cross-chain and cross-domain interoperability, and (iv) sub--optimal energy footprints.UTIF addresses these gaps through: On-chain algorithmic certification that fingerprints model weights and training metadata via cryptographic hashing.Federated data governance that combines privacy-preserving federated learning with zero-knowledge proofs (ZK-SNARKs) for regulatory compliance (GDPR, EU AI Act).An AI-assisted hybrid PoS-BFT consensus that dynamically tunes fault-tolerance parameters under varying network conditions.A self-verifiable MLOps pipeline deployed on Hyperledger Fabric with Layer-2 rollups, providing continuous integration, delivery, and audit trails.Experimental validation uses two open-access benchmarks-MIMIC-IV (clinical) and ECB-SDW (financial)-executed on a 20-node heterogeneous testbed.UTIF reduces transaction latency by 38 % and operational energy consumption by 27 % compared with Fabric 2.x and PoA baselines, while enhancing adversarial ro-bustness (F1 + 12 %) through on-chain model attestation.A perception survey of 46 domain experts reports a statistically significant boost in trustability (+1.27 0.31 on a 5-point Likert scale, p < 0.01).Stress tests show 98 % valid throughput under Sybil scenarios with 1,000 malicious nodes, maintaining a carbon footprint below 0.25 kg CO e per 1,000 transactions.The findings demonstrate that deep, native convergence of AI and blockchain can simultaneously achieve measurable trust guarantees, competitive performance, and sustainability.The article concludes with regulatory implications, identified limitations (network scale, oracle dependencies), and a research roadmap toward edge-to--cloud, 6G-ready, Web3-compliant intelligent infrastructures.
The unique International Mobile Equipment Identity (IMEI) number is essential for identifying mobile devices and blacklisting stolen ones within networks. Current solutions are limited to local blacklists and lack a global mechanism for information exchange among operators. Efforts by the Global System for Mobile Communications Association (GSMA) to implement a common blacklist have been constrained by costs, resulting in fragmented and ineffective IMEI management systems. To address these challenges, we have developed a blockchain-based framework that uses the decentralized consensus and tamper-proof nature of distributed ledger technology to enable a unified and globally accessible IMEI blacklist. The framework is implemented on a permissioned blockchain deployed on the Sepolia testnet, utilizing the Proof of Authority (PoA) consensus mechanism to ensure fast and secure validation in a multi-stakeholder environment. Our solution includes a Decentralized Application (DApp) for user interaction, with smart contracts deployed using a Web3 wallet and connected via the Alchemy API to enable efficient communication between the front end and blockchain. Smart contracts automate device status verification, theft reporting, and transaction recording, enhancing transparency, accountability, and security in mobile device management. To validate IMEI numbers, the system uses the Luhn algorithm, a widely accepted checksum method. The framework also collaborates with law enforcement and insurance companies to improve theft verification and claims processing. Experimental results demonstrate the framework's scalability, achieving low latency of under 1 second at transaction rates up to 1,000 TPS and reducing transaction processing time by 30% compared to a traditional centralized database-based system. Performance outcomes were validated through 30 independent test runs to account for variability, underscoring the framework's robustness and potential for widespread adoption. These results set a new standard for global mobile device security through industry-wide collaboration.
Danqi Chen, Yicheng Wang, Ahmed Muneeb Mehta, Muhammad Asif · 6 authors
Decentralization has become a central theme in debates on economic growth, governance effectiveness, and sustainable development. This study undertakes a bibliometric analysis of 1,322 articles published between 2005 and 2024, retrieved from the Web of Science (WoS) and Scopus databases. Using VOSviewer and Bibliometrix (R-package), the analysis maps research productivity, collaboration networks, and thematic clusters. The results reveal two dominant clusters: fiscal decentralization and its socio-economic and environmental impacts and determinants of decentralization and economic growth . High-ranked journals such as Sustainability and Environmental Science and Pollution Research emerge as leading outlets, while the most productive countries include the China and United States . The findings highlight underexplored areas such as decentralization’s role in renewable energy transitions and green finance. Framed within fiscal federalism and endogenous growth theory, this study contributes by identifying emerging trends, regional gaps, and policy implications for designing decentralization strategies that foster inclusive and sustainable economic growth. However, as a bibliometric study, it is limited in capturing case-specific or contextual details, which could be addressed in future qualitative or mixed-method research.
Love Allen Chijioke Ahakonye, Cosmas Ifeanyi Nwakanma, Jae Min Lee, Dong‐Seong Kim
The growing complexity of distributed industrial IoT systems heightens cybersecurity risks, exposing the limitations of centralized ML-based intrusion detection. Federated Learning (FL) enables decentralized, privacy-preserving model training but remains susceptible to adversarial threats and system-level failures. This study introduces PureChain, a decentralized ledger using a proof-of-authority and association (PoA 2 ) consensus mechanism to enhance FL-based IDS security. The study offers insight into the mathematical model of the PureChain-enhanced FL, which integrates blockchain-inspired consensus protocols for collaborative intrusion detection across organizations, ensuring data privacy while providing tamper-proof logs and automated responses through smart contracts. It incorporates dynamic fault tolerance, poisoning resistance, and privacy preservation with FL, enhancing security and performance in decentralized systems. Experimentation with varying client subsets demonstrates its adaptability with a TPS range of 312 . 5 − 1178 . 3 and a low latency range of 0 . 0008484 − 0 . 0032 . The framework ensures comprehensive security, reliability, and privacy, providing a scalable solution for decentralized, secure systems.
Background and Research Aims Community Resource Management Areas (CREMAs) are Ghana’s decentralized approach to biodiversity conservation and sustainable natural resource governance. Despite their institutional legality and community support, CREMAs face persistent financial instability, largely due to donor dependency and the absence of sustainable internal funding mechanisms. In contrast, Village Savings and Loan Associations (VSLAs) have emerged as resilient, community-driven financial systems that promote local livelihoods. This study asks: Can communities sustainably finance their own conservation through grassroots mechanisms like VSLAs? Grounded in Community-Based Natural Resource Management (CBNRM), collective action theory, and informal rural finance systems, the aim is to explore the feasibility of integrating VSLAs into CREMA governance as a model for localized conservation finance. Methods A qualitative case study was employed across four CREMAs in Ghana’s Black Volta River and Western Wildlife corridors. Sixteen focus group discussions were conducted with CREMA executive members, community leaders, VSLA participants, and women and youth groups. Thematic analysis identified perceptions, challenges, and opportunities for financing CREMA activities through VSLAs. Results Findings indicate strong community acceptance of CREMAs, but significant underfunding limits their conservation impact. Conversely, VSLAs were described as trusted, inclusive, and capable of supporting household and community needs. Participants advocated integrating VSLAs into CREMA governance with transparency safeguards and shared control mechanisms. Women and youth, central to VSLA operations, were identified as key stakeholders for advancing inclusive conservation finance. Conclusion Integrating VSLAs into CREMA structures presents a promising model for bottom-up, sustainable financing of conservation activities in Ghana. It builds on existing community trust systems, enhances participation, and reduces reliance on external donors. Implications for Conservation This study contributes a novel community-based green financing framework that links informal rural finance with decentralized conservation governance. It offers replicable insights for scaling localized conservation finance in other resource-dependent, tropical contexts across Sub-Saharan Africa.
Open access
Conservation, Biodiversity, and Resource Management
This paper addresses the critical systemic risk of AI Safety Arbitrage, where users exploit inconsistent safety standards across jurisdictions to access restricted capabilities. Through a controlled red-team test, we demonstrate how current frameworks fail to prevent the extraction of hazardous procedural knowledge, leaving these failures unreported and without legal consequence. To resolve this, we propose a Global Socio-Technical Architecture for AI Accountability based on distributed ledger technology (DLT). This infrastructure creates a protocol network that is conceptually similar to TCP/IP but for accountability designed to align incentives through transparency and cryptographic verification. Key Contributions & ArchitectureThe proposed solution rests on four pillars designed to replace trust relationships with cryptographic verification: Globally Unique Model Registration: Establishes digital identities (DIDs) for AI systems with value chain provenance. Independent Auditor Certification: Licensed validators stake economic value on certification accuracy, removing the need to trust model provider claims. Hardware-Backed Attestation: Tamper-resistant verification ensures deployed systems adhere to registered specifications. Continuous Reputation Monitoring: Oracle networks provide ongoing assessment of compliance with automated penalties for fraud. Technical & Governance Implementation Zero-Knowledge Proofs (ZKP): We illustrate technical viability using zkEVM technology. This allows auditors to prove compliance with safety standards without revealing proprietary training data or model architectures, resolving the tension between accountability and Intellectual Property protection. The AIAO Framework: Inspired by the International Civil Aviation Organization (ICAO), we propose the AI Accountability Coordination Organization (AIAO). This body defines "red-line" safety primitives that nations voluntarily adopt, allowing for regulatory sovereignty while ensuring global interoperability. ConclusionBy breaking the "regulatory arbitrage cycle," this framework enables a transition from safety theater to verifiable safety. It supports open-source innovation through graduated oversight and reputation systems, ensuring that AI development remains both agile and accountable.
В статье рассматриваются аспекты применения технологии блокчейн в сфере образования. Описаны основные принципы работы блокчейна, его механизмы безопасности и преимущества децентрализации. Особое внимание уделено смарт-контрактам и их использованию для автоматизации образовательных процессов. Также рассмотрены примеры применения технологии для хранения дипломов, сертификации, учета академических достижений и управления стипендиями. Обсуждаются преимущества и вызовы внедрения блокчейна, а также перспективы его развития в образовательной среде на основе мирового опыта. Практическая значимость данного исследования заключается в применении технологии блокчейн для упрощения проверки подлинности документов, автоматизации образовательного процесса и обеспечения безопасности данных. В данной статье рассмотрены теоретические основы и конкретные примеры применения технологии блокчейн в сфере образования, разработан элективный курс по информатике «Технологии Web3».
Abstract Increased and sustained access to the formal financial system enables communities to better meet their basic needs, prepare for and respond to unexpected shocks, and achieve broader economic growth. Yet many emerging economies like those across Latin America continue to face barriers that prevent individuals, households, and businesses from accessing the formal financial system. Financial inclusion efforts that leverage technological innovation are well suited to address some of them. Specifically, fintech products built on blockchains offer new ways to access financial services by creating pathways that do not rely on traditional infrastructure. Blockchain-based services offer easier financial access, innovative credit opportunities, and capital formation in a decentralized financial environment. While blockchain and DeFi are still in their early stages, these services have the potential to close some persistent gaps that stand in the way of more inclusive Latin American economies.
Umna Iftikhar, Hafiz Muhammad Attaullah, Inam Ullah Khan, Muhammad Mansoor Alam · 6 authors
Verification of a qualification, achievement, quality, or aspect of a person’s background is one of the biggest problems nowadays as we have seen many platforms where students can get fake credentials. Every organization must select professional and academically qualified employees to give quality service. As a result, corporations rely on academic certifications to confirm and measure their prospective employees’ academic qualifications. On the other hand, these employers lack a standardized process for confirming the legitimacy of academic certificates or degrees. Because the present procedures for verifying educational certifications are time-consuming, exhausting, and costly, just a few employers verify certificates for prospective employees. This research examines the issues that are related to the smart verification of someone’s credentials. To make the process of verifying digital credentials quicker, simpler, and more cost-effective, we suggest decentralized architecture. We present the prototype, design, and implementation of the proposed framework.
1. Introduction and ObjectiveInformation asymmetry has long been recognized as a critical challenge within financial markets, where unequal access to information between contracting parties can lead to inefficient outcomes. In the context of Islamic finance, this issue manifests most prominently through adverse selection and moral hazard, both of which are substantially intensified in profit-and-loss sharing arrangements. Participatory contracts such as Mushārakah and Muḍārabah rely on mutual trust, transparency, and aligned incentives. However, empirical evidence from Islamic banking practice—particularly in Iran—shows that actual utilization of these contracts remains limited. Banks frequently avoid participatory financing and shift toward fixed-return modes (such as Murābaḥah), mainly due to the heightened risk of borrower default arising from information asymmetry, insufficient visibility into business operations, and difficulties in monitoring managerial behavior. Within this environment, adverse selection emerges before contract formation when the bank cannot accurately distinguish between high-quality and low-quality project proposals or entrepreneurs. This may result in the unintended approval of risky proposals, thereby elevating the likelihood of non-performing financing. The problem is further accentuated by limitations in credit assessment processes, inadequate transparency in project data, and disparity in profit expectations and execution approaches between banks and entrepreneurs. Recent advances in decentralized technologies—particularly Web3 architectures incorporating blockchain, decentralized identity frameworks, distributed ledgers, and programmable smart contracts—provide new opportunities to address these long-standing informational and contractual challenges. Web3 offers a structural shift from centralized information control to transparent and verifiable records shared within a network of stakeholders. Such transparency can diminish information asymmetry, reduce opportunities for misrepresentation, automate contract enforcement, and improve the reliability of credit histories. The primary objective of this research is to assess the feasibility of reducing default risk caused by adverse selection in Islamic participatory contracts through the application of Web3 technology. The study aims to: (1) Identify the core factors that generate adverse selection in participatory financing, (2) Evaluate the strength and direction of their influence on default risk, and (3) Analyze how Web3 mechanisms can mitigate these factors and enhance the practical viability of participatory contracts in Islamic banking systems.2. Methods and MaterialsThis research adopts a mixed-methods exploratory–confirmatory design. Owing to the complexity and conceptual novelty of integrating Web3 systems with Islamic financial contracts, the study began with a qualitative phase followed by quantitative model testing. Qualitative Phase: Delphi MethodThe qualitative stage employed a three-round Delphi process to identify and validate the principal determinants of adverse selection in participatory financing. The expert panel comprised university scholars in Islamic economics, senior managers of credit and risk departments in Iranian banks, and professional consultants in Islamic financial technology. The first round used open-ended questionnaires to collect diverse expert insights, resulting in an initial list of thirteen candidate factors. In the second round, a structured Likert-scale survey assessed the significance of the proposed factors. Consensus criteria were set at mean ≥ 3.5 and standard deviation ≤ 1, consistent with established Delphi methodology. In the final round, experts confirmed the final factor set, which consolidated into three primary constructs: (1) Lack of transparency in customer information, (2) Insufficient evaluation of the entrepreneur’s technical competence, and (3) Misalignment of objectives between financing partners. These validated constructs provided the basis for the structural model.Quantitative Phase: PLS-SEM AnalysisIn the second phase, a structured questionnaire was administered to 289 participants representing the same expert categories. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS software. PLS-SEM was chosen due to:- The predictive and exploratory nature of the research,- The inclusion of higher-order and formative construct, and- Potential non-normality in expert response distributions. Model evaluation followed established metrics, including reliability (Cronbach’s alpha and composite reliability), convergent validity (AVE), discriminant validity, and structural path significance (t-statistics and p-values). Multi-collinearity was assessed using VIF values, all of which were below the acceptable threshold. Confidence levels were set at 95% with corresponding significance thresholds of p < 0.05.3. Research FindingsThe results of the structural model confirm that adverse selection exerts a direct and significant positive effect on default risk in participatory contracts (β = 0.299, p < 0.01). The components of adverse selection are strongly driven by:- Lack of transparency in customer information (β = 0.932, p < 0.001),- Misalignment of objectives between partners (β = 0.887, p < 0.001), and- Insufficient assessment of entrepreneurial competence (β = 0.885, p < 0.001). This highlights that default risk in participatory financing is not merely a result of financial capacity constraints, but is deeply rooted in information imbalances and strategic behavior at the contract initiation stage. The model further demonstrates that Web3 technologies have a significant mitigating influence. The path coefficient for Web3’s direct effect on reducing default risk is negative and statistically meaningful (β = −0.214, p < 0.01). Additionally, Web3 reduces the negative effects of adverse selection and information asymmetry, as shown by reversed and weakened path effects in the Web3-enhanced environment. Key Web3 mechanisms enabling this outcome include:- Real-time transparency and immutable information records,- Smart contracts that automate profit-sharing and enforce commitments,- Decentralized digital identity (DID) systems that support reliable, tamper-proof credit histories,- Tokenization of collateral and tangible/ intangible assets, enabling verifiable and liquid security guarantees,- Reduced monitoring and enforcement costs due to auditability of on-chain transactions. 4. Discussion and ConclusionThe findings of this research indicate that the primary barrier to effective participatory financing in Islamic banking is not merely structural or regulatory, but fundamentally informational. Adverse selection emerges where transparency, competence assessment, and goal alignment are weak. Conventional mechanisms—such as collateralization and post-contract supervisory audits—provide only partial and reactive mitigation. In contrast, Web3 offers a proactive and systemic solution by embedding transparency, verifiability, and automated compliance directly into the contract infrastructure. By shifting the reliance from personal trust to systemic trust, Web3 supports the original normative philosophy of Islamic finance: equitable profit-and-loss sharing, partnership-based financing, and ethical allocation of capital. From a policy perspective, adopting Web3 frameworks may substantially increase the feasibility and attractiveness of participatory financing modes for Islamic banks that currently avoid them due to high default exposure. This study contributes to the academic discourse on risk management in Islamic finance by demonstrating a structural linkage between information theory, contract design, and emerging decentralized technological capabilities. Practically, the research proposes a hybrid risk-management strategy, integrating traditional credit evaluation frameworks with Web3-based transparency, identity assurance, and automated enforcement. Future work should examine regulatory, Shariah governance, cybersecurity considerations, and interoperability standards needed to implement Web3-based participatory financing systems at scale. Nonetheless, the present results indicate that intelligent and compliant adoption of Web3 can significantly reduce default risk and enable the revival of participatory financing models in Islamic banking.
Stefan Kitzler, Masarah Paquet-Clouston, Bernhard Haslhofer
The Decentralized Finance (DeFi) ecosystem has experienced over \$10 billion in direct losses due to crime events. Beyond these immediate losses, such events often trigger broader market reactions, including price declines, trading activity changes, and reductions in market capitalization. Decentralized Autonomous Organizations (DAOs) govern DeFi applications through tradable governance assets that function like corporate shares for voting and decision-making. Leveraging DeFi's granular trading data, we conduct an event study on 22 crime events between 2020 and 2022 to assess their economic impact on governance asset prices, trading volumes, and market capitalization. Using a dynamic difference-in-differences (DiD) framework with counterfactual governance assets, we aim for causal inference of intraday temporal effects. Our results show that 55% of crime events lead to significant negative price impacts, with an average decline of about 14%. Additionally, 68% of crime events lead to increased governance asset trading volume. Based on these impacts, we estimate indirect economic losses of over $1.3 billion in DAO market capitalization, far exceeding direct victim costs and accounting for 74% of total losses. Our study provides valuable insights into how crime events shape market dynamics and affect DAOs. Moreover, our methodological approach is reproducible and applicable beyond DAOs, offering a framework to assess the indirect economic impact on other cryptoassets.
Web3 is a fundamental change from a centralized, platform-centric internet to a decentralized, user-owned ecosystem powered by an open-source technology called Blockchain. This shift is not just a technological evolution, but also changes significantly how value is created, delivered, and captured. It enables new products, innovations, and business models. These products are often powered by tokens, whose value is described by their underlying tokenomics. They are designed to engage the community and incentivize the people who interact with the project. The users of Web3 often come together in decentralized autonomous organizations and democratically decide on the next steps and essential developments of the organization. The business models that are now possible because of the capabilities of a decentralized web have different success factors and reasons why they fail. This thesis conducts a systematic literature review and qualitative expert interviews to identify the success factors of Web3 business models. It first lists and categorizes the prevalent Web3 business models, their structures, and revenue models, and then highlights the success factors for Web3 business models. The findings reveal the difference between Web2 and Web3 business models, highlighting the importance of community, decentralization, governance, and robust token economics.
Sep 30, 2025·In Proceedings of the 21st Annual IFIP International Conference on Network and Parallel Computing (NPC 2025), Nha Trang, Vietnam, 14-16 November 2025
Modern blockchains need fast, reliable propagation to balance security and throughput. Virtual-coordinate methods speed dissemination but rely on slow iterative updates, leaving nodes out of sync. We present BlockSDN-VC, a transaction-broadcast protocol that centralises coordinate computation and forwarding control in an SDN controller, delivering global consistency, minimal path stretch and rapid response to churn or congestion. In geo-distributed simulations, BlockSDN-VC cuts median latency by up to 62% and accelerates convergence fourfold over state-of-the-art schemes with under 3% control-plane overhead. In a real blockchain environment, BlockSDN-VC boosts confirmed-transaction throughput by 17% under adversarial workloads, requiring no modifications to existing clients.
Dominik Apel, Zeta Avarikioti, Matteo Maffei, Yuheng Wang
Rollup protocols have recently received significant attention as a promising class of Layer 2 (L2) scalability solutions. By utilizing the Layer 1 (L1) blockchain solely as a bulletin board for a summary of the executed transactions and state changes, rollups enable secure off-chain execution while avoiding the complexity of other L2 mechanisms. However, to ensure data availability, current rollup protocols require the plaintext of executed transactions to be published on-chain, resulting in inherent privacy limitations. In this paper, we address this problem by introducing Calyx, the first privacy-preserving multi-token optimistic-Rollup protocol. Calyx guarantees full payment privacy for all L2 transactions, revealing no information about the sender, recipient, transferred amount, or token type. The protocol further supports atomic execution of multiple multi-token transactions and introduces a transaction fee scheme to enable broader application scenarios while ensuring the sustainable operation of the protocol. To enforce correctness, Calyx adopts an efficient one-step fraud-proof mechanism. We analyze the security and privacy guarantees of the protocol and provide an implementation and evaluation. Our results show that executing a single transaction costs approximately $0.06 (0.00002 ETH) and incurs only constant-size on-chain cost in asymptotic terms.
Federico P. Cortese, Antonio Pievatolo, Elisa Maria Alessi
Statistical jump models have been recently introduced to detect persistent regimes by clustering temporal features and discouraging frequent regime changes. However, they are limited to hard clustering and thereby do not account for uncertainty in state assignments. This work presents an extension of the statistical jump model that incorporates uncertainty estimation in cluster membership. Leveraging the similarities between statistical jump models and the fuzzy c-means framework, our fuzzy jump model sequentially estimates time-varying state probabilities. Our approach offers high flexibility, as it supports both soft and hard clustering through the tuning of a fuzziness parameter, and it naturally accommodates multivariate time series data of mixed types. Through a simulation study, we evaluate the ability of the proposed model to accurately estimate the true latent-state distribution, demonstrating that it outperforms competing approaches under high cluster assignment uncertainty. We further demonstrate its utility on two empirical applications: first, by automatically identifying co-orbital regimes in the three-body problem, a novel application with important implications for understanding asteroid behavior and designing interplanetary mission trajectories; and second, on a financial dataset of five assets representing distinct market sectors (equities, bonds, foreign exchange, cryptocurrencies, and utilities), where the model accurately tracks both bull and bear market phases.
Healthcare data management in cloud environments faces critical challenges in balancing security, privacy, operational efficiency, and regulatory compliance, as traditional access control mechanisms are inadequate for addressing the complex, multi-tenant, and dynamic nature of modern cloud infrastructures handling sensitive Electronic Health Records (EHRs). This paper introduces a novel hybrid framework that integrates Attribute-Based Access Control (ABAC) with Ethereum blockchain technology to establish a comprehensive multi-layered security architecture for cloud-based healthcare systems, combining advanced cryptographic techniques including double encryption using AES and RSA algorithms, Shamir's Secret Sharing for key management, smart contracts for automated policy enforcement, and multi-party computation protocols to ensure privacy preservation without compromising system performance. Extensive experimental evaluation conducted using 4,412 anonymized clinical records with 12 distinct attributes demonstrates exceptional efficiency with average record insertion time of 1.90 ms, retrieval time of 0.14 ms, blockchain mining time of 0.01 seconds, and hybrid architecture throughput of 562.94 records/second with 1.78 ms latency, significantly outperforming traditional centralized systems (337.17 rec/s, 2.97 ms) and pure blockchain implementations (3.37 rec/s, 296.98 ms). Security assessments reveal 100% operational reliability, 97.3% policy violation detection accuracy, 89% reduction in unauthorized access attempts, and comprehensive regulatory compliance with HIPAA (97%), GDPR (92%), ISO 27001 (94%), and SOC 2 (89%) standards, while maintaining optimized system resource utilization with CPU usage at 35.6%, memory at 48.0%, and network I/O at 34.1%. The framework addresses key limitations of existing approaches by providing seamless integration across multiple cloud layers, eliminating single points of failure, ensuring data immutability through distributed consensus mechanisms, maintaining comprehensive audit trails, and offering practical implications for healthcare organizations seeking secure, scalable, and compliant data sharing mechanisms in cloud-native environments, thereby contributing to the advancement of secure healthcare informatics by bridging the gap between theoretical access control models and practical implementation requirements in distributed cloud infrastructures.
We step outside the P = NP vs. P ≠ NP dichotomy and, following a co-evolutionary, hypothesis-first program, we frame evidence by the accounting constraint P(L, t) + NP(L, t) = 1, where t indexes registered time windows and L indexes structural layers of analysis. The credit assigned to constructive computation P(L, t) versus certificate-based reasoning NP(L, t) may shift across windows and layers, but their sum is conserved by design. Within this multilayer, time-indexed lens, we propose a test object for proof in 3-SAT: a small, auditable branching set K. Our operational hypothesis is that, within controlled experimental windows, there exists K ⊆ V(F) with |K| ≤ c·log n such that, for every partial assignment α: K → {0,1}, the restricted formula F ∣ α terminates in polynomial time and emits a publicly verifiable certificate (a satisfying assignment or a DRAT/DRUP-style unsatisfiability proof). Because 2^|K| = n^O(1), exhaustive branching over K is polynomial inside the window, enabling artifact-backed constructive behavior without asserting a universal algorithm. We (i) define auditable objects and falsifiable hypotheses, (ii) sketch a π-rounds normalization pipeline that contracts structure while logging transformations, (iii) posit a finite catalog of local obstructions with radius-2 witnesses, (iv) outline a greedy hitting-set routine to assemble K, and (v) introduce protection mechanisms against recovery of K by an adversary (commitments and zero-knowledge). Evidence will be supplied via reproducible artifacts (DRAT logs, commitments, run ledgers) and transport tests across registered windows and layers, and will be interpreted under the constraint P(L, t) + NP(L, t) = 1, in a manner consistent with kernelization barriers and sparsification limits.
Ruben M Nayve Jr, Ferdinand Timbang, Mark Anthony Pelegrin
This study investigates the role of network effects in shaping economic value creation within cryptocurrency ecosystems. While cryptocurrencies have evolved from speculative assets into complex digital platforms that facilitate transactions, decentralized finance, and governance, their economic sustainability is fundamentally driven by the dynamics of user adoption and interconnectivity. Drawing upon theories of network externalities and digital platform economics, this research employs a mixed-methods approach that integrates quantitative econometric modeling with qualitative analysis of policy and industry practices. Quantitative data, including market capitalization, transaction volume, wallet addresses, and hash rate, are analyzed to measure the correlation between network growth and value creation. Complementary qualitative insights are derived from literature reviews and expert interviews to contextualize the findings within broader regulatory and technological frameworks. The results reveal that network size and user activity exert significant positive effects on value creation, with evidence of nonlinear threshold dynamics: once a critical mass of adoption is reached, economic value accelerates disproportionately. Comparative analysis across major ecosystems such as Bitcoin and Ethereum highlights differences in how network effects interact with technological innovation and governance structures. The findings contribute to advancing theoretical understanding of digital network economies and provide practical insights for stakeholders, including developers, investors, and policymakers. By identifying both opportunities and risks, particularly regarding volatility and regulatory challenges, this study offers a comprehensive framework for evaluating the long-term sustainability of cryptocurrency ecosystems.
Objective: This study aims to comprehensively analyze the transformative potential of asset tokenization for Micro, Small, and Medium Enterprises (MSMEs) within the Decentralized Finance (DeFi) ecosystem, with a focus on opportunities, risks, and the hybrid governance architecture required. Research Design & Methods: This study uses a qualitative approach, combining a systematic literature review and case studies with thematic content analysis of secondary data from credible sources to identify patterns and key themes. Findings: Findings show that tokenization of MSME assets offers significant opportunities, such as broader and faster access to funding through fractionalization of ownership, increased asset liquidity, operational efficiency, and enhanced transparency and security. However, this implementation is accompanied by various risks, including smart contract vulnerabilities, cyber attacks, blockchain scalability challenges, regulatory uncertainty, market volatility, and operational risks related to reliance on off-chain third parties. A hybrid governance architecture that integrates on-chain and off-chain elements is essential to mitigate these risks. Implications & Recommendations: This study indicates that tokenization can be an essential bridge for MSMEs to enter the global digital economy, but it requires improved digital literacy and technological readiness. For investors, it offers new diversification opportunities with inherent risks. Policy recommendations include adaptive regulation, simplified compliance, digital education, infrastructure strengthening, utilization of regulatory sandboxes, and cross-sector collaboration and standardization. Contribution & Value Added: This study presents a comprehensive framework for tokenizing MSME assets, offering practical guidance for various stakeholders to promote financial inclusion and equitable economic growth.