Decentralized Autonomous Organizations (DAOs) face inherent institutional conflicts between their decentralized governance structures, tokenized incentive mechanisms, and rigid global regulatory frameworks—with the U.S. regulatory landscape (SEC, OFAC, FinCEN) emerging as the most stringent and impactful. In 2024, 7 U.S.-based DAOs were subject to SEC investigations (aggregate penalties of $12.8 million), 18% incurred FinCEN sanctions for OFAC-sanctioned address interactions, and 68% of Base chain DAOs were denied institutional capital due to inadequate compliance documentation. Grounded in institutional economics (regulatory adaptation theory), RegTech principles, and blockchain traceability, this study proposes a “three-dimensional compliance adaptation framework” for DAO governance—integrating a regulatory rule engine (quantitative alignment with U.S. rules), automated on-chain audit report generation (transparency assurance), and dynamic governance optimization (securities risk mitigation). Drawing on the development of the “DAO Shield Pro” system and empirical testing across 7 representative U.S. Base chain DAOs (3 AI-focused, 2 meme-based, 2 investment-focused) over a 6-month period (March–August 2025), the framework achieves: (1) a 67.9% reduction in average compliance risk scores (from 3.8 to 0.98), (2) a 45.6-percentage-point increase in U.S. institutional investor participation (from 7.8% to 53.4%), (3) a 100% SEC regulatory inquiry acceptance rate, and (4) a 64.2% reduction in monthly compliance labor costs (from $19,200 to $6,870). This research fills critical gaps in DAO compliance scholarship by providing a theoretically rigorous, technically actionable, and empirically validated solution tailored to U.S. regulatory requirements (SEC Howey Test, OFAC sanctions screening, PCAOB auditing standards). It advances the field by quantifying ambiguous regulatory rules into executable on-chain logic and delivers a replicable paradigm for global DAO regulatory adaptation—strengthening U.S. competitiveness in the Web3 ecosystem and unlocking an estimated $42–$58 billion in latent institutional investment.
the paper examines the phenomenon of decentralized finance (DeFi) as one of the most promising and at the same time controversial areas of the digital economy. DeFi is defined as an ecosystem of protocols and applications based on blockchain and smart contracts that allows financial transactions to be carried out without the intermediation of traditional institutions. It is noted that the key advantages of the technology are transparency, automation, reduction of transaction costs, and expansion of the accessibility of financial services. Simultaneously, risks associated with the lack of unified regulatory approaches, high vulnerability of smart contracts, the use of DeFi for unlawful purposes, and the uncertainty of legal liability are emphasized. Particular attention is paid to AML/KYC problems, as well as the use of DeFi platforms for money laundering of criminal proceeds.
Abdullah Ayub Khan, Asif Ali Laghari, Hamad Almansour, Teerath Kumar · 7 authors
Wearable health technology has revolutionized remote monitoring and personalized healthcare by allowing real-time surveillance of patient health measurements and vital signs. However, their widespread acceptance is hampered by issues with security, privacy preservation, data protection, and interoperability. Blockchain Technology (BT), in particular Zero-Knowledge Proofs (ZKPs) and smart contracts, present a viable way to enhance the privacy, provenance, and integrity of wearable health data. This paper proposes a BT-enabled system that guarantees decentralized, unforged data management, transparency, immutability, and dynamic traceability for wearable health devices, particularly smartwatches with biosensors. To evaluate the effectiveness of the proposed work, the main performance-related metrics-latency, throughput, computational overhead, security robustness, and scalability-are looked at. The experiment's simulated findings show that BT integration is effective, with a 99.33% improvement in data integrity and protection. Automated access control protocols demonstrate data protection by utilizing smart contracts, and ZKPs guarantee verifiable data exchanges without jeopardizing patient privacy. These results demonstrate improved interoperability, decreased processing time, and increased security in comparison to comparable cutting-edge centralized platforms.
Blockchain-based financial systems: Trust, transparency, and the future of decentralized financeBlockchain technology is increasingly recognized as one of the most transformative innovations in contemporary finance (Andronie et al., 2024).By embedding verification, trust, and transparency into decentralized digital infrastructures, it challenges conventional assumptions regarding the organization, regulation, and governance of financial systems (Turek et al., 2023;Balcerzak & Valaskova, 2024).The conceptual foundations and practical implications of blockchain-based financial systems are examined, with particular emphasis on three interrelated dimensions: the reconfiguration of trust, the emergence of transparency as a systemic aFribute, and the evolving architecture of decentralized finance (Lzroiu et al.,
Toqeer Ali Syed, Mohammad Riyaz Belgaum, Salman Jan, Asadullah Abdullah Khan · 5 authors
The software supply chain attacks are becoming more and more focused on trusted development and delivery procedures, so the conventional post-build integrity mechanisms cannot be used anymore. The available frameworks like SLSA, SBOM and in toto are majorly used to offer provenance and traceability but do not have the capabilities of actively identifying and removing vulnerabilities in software production. The current paper includes an example of agentic artificial intelligence (AI) based on autonomous software supply chain security that combines large language model (LLM)-based reasoning, reinforcement learning (RL), and multi-agent coordination. The suggested system utilizes specialized security agents coordinated with the help of LangChain and LangGraph, communicates with actual CI/CD environments with the Model Context Protocol (MCP), and documents all the observations and actions in a blockchain security ledger to ensure integrity and auditing. Reinforcement learning can be used to achieve adaptive mitigation strategies that consider the balance between security effectiveness and the operational overhead, and LLMs can be used to achieve semantic vulnerability analysis, as well as explainable decisions. This framework is tested based on simulated pipelines, as well as, actual world CI/CD integrations on GitHub Actions and Jenkins, including injection attacks, insecure deserialization, access control violations, and configuration errors. Experimental outcomes indicate better detection accuracy, shorter mitigation latency and reasonable build-time overhead than rule-based, provenance only and RL only baselines. These results show that agentic AI can facilitate the transition to self defending, proactive software supply chains rather than reactive verification ones.
Self-Sovereign Identity is a transformative paradigm in digital identity management, empowering individuals with full control over their credentials. However, the coexistence of diverse SSI ecosystems, such as the European Digital Identity and the European Blockchain Services Infrastructure, poses significant challenges for cross-ecosystem interoperability due to technological and trust framework differences. This paper introduces \textit{interID}, a modular credential verification application that addresses this fragmentation by orchestrating ecosystem-specific verifier services. Our key contributions include: (1) an ecosystem-agnostic orchestration layer that interfaces with multiple SSI verification services, (2) a unified API that abstracts underlying protocol complexities for service providers, and (3) a practical implementation that bridges three major SSI ecosystems: Hyperledger Indy/Aries, EBSI, and EUDI. Evaluation results demonstrate that interID successfully verifies credentials across all tested wallets with minimal performance overhead, while maintaining a flexible architecture that can be extended to accept credentials from additional SSI ecosystems. This work offers both a technical solution and architectural pattern for achieving interoperability in SSI verifier implementations.
Production platforms that serve large language models (LLMs) must continuously decide how many GPU replicas to keep online. Conventional autoscalers inherited from microservice stacks scale on request rate or coarse utilization, but LLM requests differ by up to three orders of magnitude in the number of tokens they process, arrive in extreme bursts, and run on replicas whose cold start takes minutes and whose idle power remains a large fraction of peak. We present TEAS (Token- and Energy-Aware Scaling), a horizontal autoscaler for LLM serving that (i) measures load in phase-weighted effective tokens rather than requests, (ii) forecasts load over the replica cold-start horizon with an empirical-residual safety margin, (iii) adapts its utilization headroom to the measured burstiness of the arrival process, and (iv) applies an energy-aware asymmetric hysteresis that delays scale-in until the projected idle-energy waste exceeds the energy cost of a replica restart. We evaluate TEAS in a trace-driven cluster simulator with a calibrated power and cost model, replaying real production traces from Azure LLM inference services alongside synthetic diurnal and composition-drift workloads derived from the real token distributions. Across all four workloads TEAS is the only dynamic policy that sustains at least 96% SLO attainment; on a bursty production code-assistant trace it improves attainment by 32 percentage points over a Kubernetes-HPA-style baseline and by 11 points over static peak provisioning, and on a 24-hour diurnal workload it meets a 95% SLO target at 11.6% lower monetary cost than peak provisioning. We release the simulator, policies, and experiment scripts.
This technical dossier presents the comprehensive software architecture for implementing Ternary Moral Logic (TML) as a deterministic enforcement layer on EVM-compatible blockchain platforms. Unlike traditional "Code is Law" models, TML introduces a "Logic is Constitution" paradigm, where ethical axioms (Prohibit -1, Pause 0, Permit +1) are embedded directly into the execution bytecode. This dataset contains three technical specifications: 01_TML_System_Architecture_and_Ecosystem.pdf: Defines the high-level ecosystem, including the "Lantern Signal" (proof of hesitation), the "Hybrid Shield" (cross-chain redundancy), and the "Goukassian Promise." 02_TML_Technical_Specification_and_FSM.pdf: Details the rigorous Finite State Machine (FSM) logic, Solidity interfaces (ITMLEnforcer), and the "Sacred Zero" epistemic hold mechanism. 03_TML_Security_Audit_and_Adversarial_Analysis.pdf: Provides a deep adversarial analysis and mathematical verification of the "No God Mode" principle, proving that no administrative key can override a constitutional integrity freeze. Status: Released for educational review and technical standardization.
<p>Resumo <br>Este artigo investiga a viabilidade econômica e comportamental da criação de criptomoedas <br>personalizadas (Fan Coins) atreladas à performance de jogadores de futebol, utilizando métodos <br>quantitativos em Econometria, com foco em arrecadação por bilheteria, patrocínios e apostas esportivas. A <br>análise inclui os casos de Diego Ribas (Flamengo), Neymar (Santos), e artilheiros do São Paulo e Palmeiras. <br>A proposta é analisar como a reputação e a performance esportiva podem ser transformadas em ativos <br>digitais de valor mensurável. Ainda, propõe-se a utilização de sistemas de inteligência artificial para gestão <br>de carteiras de patrocinadores e um aplicativo de fan clube com sistema de assinaturas para fomentar um <br>novo modelo de negócios esportivos baseado em dados e personalização. <br>Palavras-chave: Fan Coin; Criptomoeda; Econometria; Economia do Esporte; Teoria dos <br>Jogos; Finanças Comportamentais; Apostas Esportivas; Inteligência Artificial; Modelagem <br>Financeira; Patrocínio Digital.</p>
This study systematically reviews scientific research on predicting cryptocurrency markets. A total of 790 articles obtained from the Web of Science database were included in the analysis, and the structure of the literature was evaluated using bibliometric methods. The preliminary investigation indicated that studies examining the prediction of cryptocurrencies have undergone a substantial increase since 2016. While a significant proportion of the extant literature pertains to Bitcoin, the first cryptocurrency, it is evident that other cryptocurrencies, such as Ethereum, have also attracted the attention of researchers over the years. The analysis yielded four primary categories: machine learning-based prediction methods, financial risk and volatility analyses, behavioral and technical determinants, and finally, advanced deep learning methods. In the context of cryptocurrency prediction, studies have underscored the significance of attributes, emphasizing their role in enhancing the efficacy of prediction models. These studies have also highlighted the impact of integrating machine learning and deep learning-based models with conventional methods in enhancing the performance of established models. The study emphasizes the necessity to direct future research towards the integration of behavioral indicators and the examination of multiple market relationships.
Ameeta Jaiswal‐Dale, Romain M. Lorentz, Ernest L. Owens, Bhuvanesh Singh
This study presents a practical framework for implementing blockchain technology, specifically smart contracts, to optimise operations and enhance financial performance in the Consumer Packaged Goods (CPG) sector. It identifies best practices for operational efficiency and outlines the structural flow and challenges of implementing smart contracts in a small-scale CPG company. While blockchain is often associated with cryptocurrency, its value lies in enhancing core business processes such as vendor selection, procurement and legal compliance monitoring. The framework integrates blockchain-enabled smart contracts with project management lifecycle updates to streamline operations, enhance cash flow and reduce the Cost of Goods Sold (COGS). It highlights how procurement processes, legal requirements and vendor management can be streamlined through smart contracts, providing transparency, reducing delays and ensuring regulatory compliance. Blockchain is a decentralised database, and its applications span procurement, production processes and inventory management. However, leveraging blockchain effectively requires smart contracts. Integrating these contracts with project management tools ensures efficient operations and measurable financial metrics. This interdisciplinary approach combines technology, business law and project management to deliver actionable insights. The study highlights how modest operational efficiencies can drive profitability in low-margin industries, such as CPG, and establishes a foundation for future implementation studies across other sectors.
AbstractThis article investigates the economic and behavioral feasibility of creating personalizedcryptocurrencies (Fan Coins) linked to the performance of soccer players, using quantitative methods inEconometrics, focusing on box office revenue, sponsorships and sports betting. The analysis includes theThe cases of Diego Ribas (Flamengo), Neymar (Santos), and top scorers from São Paulo and Palmeiras.proposal is to analyze how reputation and sports performance can be transformed into digital assets withmeasurable value. Furthermore, it is proposed to use artificial intelligence systems to manage sponsorportfolios and a fan club application with a subscription system to foster a new sports business model basedon data and personalization.Keywords: Fan Coin; Cryptocurrency; Econometrics; Sports Economics; Game Theory;Behavioral Finance; Sports Betting; Artificial Intelligence; Financial Modeling; Digital Sponsorship
Dockerised blockchain solution can mitigate the low levels of distributed technology adoption in small and medium enterprises. It can be done via designing and implementing an environment which inherits ease of deployment and scalability of containerized systems with safety and transparency of distributed applications. Practical implementation of a dockerized blockchain solution designed as a demonstrative implementation for existing client–server architecture is described in this paper. This solution uses Docker containers to simplify the setup and deployment of a private blockchain network, a mediator server and a reverse proxy. Implementation of this system on a low scale demonstrates feasibility of integrating blockchain technology into existing business processes without fundamental architectural changes and acknowledges deployment and maintaining challenges that usually accompany distributed systems using private blockchain. Discussed implementation is a demonstration of designed architecture being potentially a reproducible and easily maintainable environment for logging and validating data through an immutable ledger on a smaller scale. Proof of concept successfully validates the core idea. The implementation shows a mediator server intercepting client request, recording them on a private Ethereum blockchain via a JSON-RPC interface, and then forwarding them to the original server. This confirms the solution’s ability to introduce a trusted, intermediate layer for data immutability. The project demonstrates a working framework for embedding distributed ledger technologies into client–server ecosystems. While the current Proof of Work consensus mechanism presents scalability limitations, the architecture provides a strong foundation for future research, including migrating to more efficient consensus mechanisms and integrating smart contracts.
Dockerised blockchain solution can mitigate the low levels of distributed technology adoption in small and medium enterprises. It can be done via designing and implementing an environment which inherits ease of deployment and scalability of containerized systems with safety and transparency of distributed applications. Practical implementation of a dockerized blockchain solution designed as a demonstrative implementation for existing client–server architecture is described in this paper. This solution uses Docker containers to simplify the setup and deployment of a private blockchain network, a mediator server and a reverse proxy. Implementation of this system on a low scale demonstrates feasibility of integrating blockchain technology into existing business processes without fundamental architectural changes and acknowledges deployment and maintaining challenges that usually accompany distributed systems using private blockchain. Discussed implementation is a demonstration of designed architecture being potentially a reproducible and easily maintainable environment for logging and validating data through an immutable ledger on a smaller scale. Proof of concept successfully validates the core idea. The implementation shows a mediator server intercepting client request, recording them on a private Ethereum blockchain via a JSON-RPC interface, and then forwarding them to the original server. This confirms the solution’s ability to introduce a trusted, intermediate layer for data immutability. The project demonstrates a working framework for embedding distributed ledger technologies into client–server ecosystems. While the current Proof of Work consensus mechanism presents scalability limitations, the architecture provides a strong foundation for future research, including migrating to more efficient consensus mechanisms and integrating smart contracts.
Voting is a cornerstone of democracy, enabling individuals to choose their leaders and influence decisions shaping their communities and future. Traditional voting systems, however, face numerous challenges, such as long queues, paper-based inefficiencies, and security vulnerabilities. To address these issues, blockchain technology has emerged as a transformative solution, leveraging its decentralized and secure infrastructure. This paper presents a blockchain-based e-voting system aimed at enhancing accessibility, security, and efficiency. By utilizing the Ethereum blockchain and smart contracts, the system ensures transparency, immutability, and tamper-proof vote recording. Furthermore, the integration of AI-powered facial recognition technology reinforces identity verification, guaranteeing that only authorized voters participate. Comprehensive testing, including simulations and stress analyses, confirms that the proposed model enhances the voting process by offering a secure, user-friendly, and reliable digital platform. This research highlights the potential of combining blockchain and AI to modernize voting systems, fostering trust and inclusivity in democratic processes. This paper introduces a blockchain-based e-voting system that simplifies the voting process while ensuring maximum security and trust. By using the Ethereum blockchain and smart contracts, votes are recorded and verified securely, leaving no room for manipulation. To make the system even more robust, AI-powered facial recognition is integrated to confirm voter identity, ensuring only eligible individuals can participate. The system has been tested extensively to ensure it’s not only secure but also easy to use, providing a seamless experience for voters. This combination of blockchain and AI has the potential to revolutionize voting, making it fairer, more inclusive, and efficient for everyone.
An effective healthcare data system must safeguard individual privacy, foster public trust, and enhance societal resilience. To achieve this, access to critical health information must be provided in an ethical, secure, and reliable manner. This paper proposes a blockchain-based healthcare management framework designed to improve security, privacy, and transparency in healthcare administration. The architecture incorporates smart contracts, multi-signature wallets, and zero-knowledge proofs (ZKPs) to securely facilitate key operations such as patient registration, policy updates, and medical device management on a decentralized platform. Multi-signature wallets require authorization from multiple stakeholders for sensitive transactions, while ZKPs enable identity or access verification without disclosing confidential information. A built-in performance monitoring module collects key metrics, including transaction latency, gas consumption, and block time, which are visualized using JavaScript. Overall, the proposed system offers a secure, transparent, and privacy-preserving approach to decentralized healthcare management.
The transition of Micro, Small, and Medium Enterprises (MSMEs) toward decentralized rooftop solar is critical for sustainable industrial growth in emerging economies, yet commercial adoption remains sluggish despite grid parity. This study empirically investigates MSME preferences for solar financing architectures using a Choice-Based Conjoint (CBC) experiment grounded in Random Utility Theory. Primary data were collected from 100 MSMEs in India’s National Capital Region, generating 1,000 discrete choice observations under strictly controlled load conditions (50–60 kW). A Conditional Logit Model was employed to estimate part-worth utilities across capital structures, tariff mechanisms, and performance risk allocation. Contradicting standard market assumptions, the aggregate choices revealed a 77.6% rejection rate of standard solar offerings. The econometric results demonstrate severe utility penalties for upfront capital and fixed repayment obligations . Crucially, the requirement for firm-assumed maintenance risk generated perfect separation , acting as an absolute barrier to adoption. However, market simulations isolating an optimized financing package—combining zero-upfront OPEX, pay-per-unit tariffs, and developer-assumed risk—resulted in the adoption rate increasing to 76.3%. The findings indicate that the current stagnation in commercial solar diffusion is driven primarily by suboptimal risk allocation and product mismatch, rather than a lack of underlying economic viability. To accelerate deployment, policymakers and financial institutions must pivot from capital-subsidy models toward standardizing and de-risking third-party "Energy-as-a-Service" frameworks.
We present the Mazari Bidirectional Architecture, a cryptographic framework providing end-to-end privacy guarantees for computation systems employing homomorphic encryption (HE), differential privacy (DP), and zero-knowledge proofs (ZK). The architecture comprises two complementary orderings: Y.I.N. Mazari Ordering (DP→ZK→HE) for secure data submission and Y.A.N.G. Mazari Ordering (VERIFY→DISAGGREGATE→DECRYPT→DENOISE) for secure result retrieval. Through exhaustive permutation analysis, we prove that among all possible orderings for each direction, exactly one ordering in each direction is secure—and these orderings are mathematical duals. The Y.I.N. ordering prevents information leakage during data submission, while the Y.A.N.G. ordering prevents timing side-channel attacks during result retrieval. This paper provides comprehensive coverage of all implementation variations, security proofs, and robustness analysis to establish technical foundations for privacy-preserving computation research.
Abstract In this paper, we address the problem of coordinating multiple robots to explore large-scale underground areas covered with low-bandwidth communication. Based on the evaluation of existing coordination methods, we found that well-performing methods rely on exchanging significant amounts of data, including maps. Such extensive data exchange becomes infeasible using only low-bandwidth communication, which is suitable for underground environments. Therefore, we propose a coordination method that satisfies low-bandwidth constraints by sharing only the robot’s positions. The proposed method employs a fully decentralized principle called Cross-rank that computes how to distribute robots uniformly at intersections and subsequently orders exploration waypoints based on the traveling salesman problem formulation. The proposed principle has been evaluated based on exploration time, traveled distance, and coverage in five large-scale simulated subterranean environments and a real-world deployment with three quadruped robots. The results suggest that the proposed approach provides a suitable tradeoff between the required communication bandwidth and the time needed for exploration.
Autonomous financial agents, powered by the convergence of artificial intelligence and blockchain technology, represent a paradigm shift in decentralized finance. These self-operating entities now possess capabilities to hold cryptocurrency wallets, execute complex transactions, and even launch tokens without human oversight. The architectural framework supporting these agents integrates specialized language models, secure wallet management systems, and persistent on-chain identities. From market-making to yield optimization, these agents demonstrate remarkable efficacy across various financial operations, creating novel market dynamics when interacting with both human participants and other autonomous systems. Essential to mainstream adoption are sophisticated reputation frameworks combining algorithmic assessment with social consensus mechanisms. However, significant challenges exist, including market manipulation vulnerabilities, spam production, and regulatory complexity. As these autonomous agents continue evolving, appropriate governance models tailored to agent characteristics become critical for balancing innovation with market integrity in this emerging financial landscape.
Madina Konyrova, Katipa Chezhimbayeva, Abdul Razaque, Dina S.M. Hassan
The integration of renewable resources and prosumers into smart grids poses challenges related to scalability, transparency, and transmission efficiency. Centralized routing frequently depends on expensive technology and experiences significant losses. This study presents a blockchain-based smart contract system (BSCS) that reduces transmission losses while guaranteeing secure and decentralized energy transfers. The smart grid is represented as a weighted directed graph, with edges denoting actual power losses. Dijkstra’s shortest path algorithm generates optimal paths from the generator to the consumer with minimal loss. These optimal pathways are permanently documented and regulated by permissioned blockchain smart contracts, ensuring tamper-proof and verifiable energy settlement. Validation is performed using an enhanced IEEE 58-bus test system, which is based on the standard IEEE 57-bus network, by incorporating an additional synthetic consumer node (Bus 58) linked to Bus 12 to simulate a flexible prosumer load of 1.5 MW + 0.5 Mvar. Additionally, the synthetic consumer node employs Ganache, Truffle, and Solidity for its implementation. This modification facilitates the assessment of dynamic energy routing and decentralized transaction settlement in extended topology scenarios. The proposed BSCS demonstrates substantial enhancements compared to baseline blockchain systems. Active power losses in transmission lines are diminished, gas consumption declines by approximately 12%, latency is enhanced by as much as 21%, and throughput increases by more than 30%. The rapid deployment and execution of smart contracts within sub-second intervals validate the system's appropriateness for real-time grid operations. The proposed technique combines graph-theoretic optimization with blockchain governance to provide a safe, scalable, and hardware-independent framework for decentralized energy markets.
Вячеслав Осадчий, Катерина Осадча, Світлана Симоненко
Швидкий прогрес у цифрових технологіях сприяє глибинній трансформації системи вищої освіти, відкриваючи нові можливості для інтерактивного та персоналізованого навчання. Одним із найбільш перспективних напрямів є використання Metaverse, який поєднує технології віртуальної, доповненої, мішаної та розширеної реальності, блокчейну, цифрових двійників та non-fungible token. Завдяки цьому Metaverse пропонує захоплюючий, індивідуалізований та гнучкий освітній досвід, що робить його потужним інструментом у сучасній академічній практиці.Метою даного дослідження є здійснення комплексного аналізу наукових джерел, які висвітлюють можливості інтеграції Metaverse із технологіями штучного інтелекту (ШІ) для удосконалення вищої освіти. Для реалізації поставленої мети було проведено відбір наукових праць у трьох міжнародних наукометричних базах (Scopus, Web of Science, ERIC), сформовано пошукову стратегію на основі ключових запитів та визначених критеріїв включення й виключення. Подальший бібліометричний аналіз із використанням програмного забезпечення VOSviewer дозволив здійснити мережеву візуалізацію та проаналізувати спільне використання ключових слів у відібраних публікаціях.Наративний огляд обраних праць дав змогу окреслити основні напрями поєднання Metaverse та ШІ у вищій освіті. Серед ключових шляхів виділено: створення зручних та корисних для користувача освітніх середовищ і їх розширення до віртуальних університетських кампусів; використання інструментів ШІ для забезпечення безпеки та захисту даних у Metaverse; формування персоналізованих навчальних траєкторій на основі поведінкових моделей та індивідуальних уподобань студентів; сприяння комунікації та співпраці в режимі реального часу; генерація навчального контенту для Metaverse; інтеграція фізичного та цифрового світу; реалізація концепції ШІ-тьюторства для підтримки індивідуального навчання.Результати дослідження підтверджують значний потенціал Metaverse у поєднанні з технологіями ШІ для модернізації вищої освіти, підвищення її якості, доступності та інноваційності.
Rob J. Lewis, Jonas Lembrechts, P. D. Walker, Chunli Li · 5 authors
Background and Rationale Despite decades of progress in ecological monitoring, primary biodiversity and environmental data remain unevenly mobilised and poorly interoperable (Hampton et al. 2015, Poisot et al. 2019). Datasets, often gathered with public funds, frequently remain inaccessible or insufficiently described, limiting their reuse in global syntheses (Culina et al. 2018). Ecologists’ concerns about trust, transparency, and control of shared data persist, particularly where data production is resource-intensive or socially embedded. These concerns echo the foundational properties of distributed ledgers, where ownership and governance are distributed across peer networks rather than centralized repositories (Lewis et al. 2023). Forests exemplify both the potential and the challenge of such decentralised infrastructures. As globally significant carbon and biodiversity reservoirs, forests are also deeply fragmented across ownership and jurisdictional boundaries. In Europe alone, over half of forested land is privately owned, yet these actors often lack mechanisms to derive tangible value from stewardship. At the same time, digital twins (macroecological models) that integrate in situ and remotely sensed data, are becoming central to forest policy and monitoring frameworks (e.g., Food and Agriculture Organization of the United Nations (FAO), Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), Global Biodiversity Framework (GBF)). ForestWeb3 (FW3) hypothesizes that a decentralised, Findable Accessible, Interoperable, Reusable (FAIR; Wilkinson et al. 2016, Nosek et al. 2022)-aligned data network can unlock the latent value of underused biodiversity data while building trust and incentives for participation.. Objectives Mobilisation and harmonisation of forest biodiversity and environmental data (Objective 1): to spearhead a shift from data curation to data stewardship through a decentralised data infrastructure built on open-source blockchain frameworks. Incentivisation and uptake (Objective 2): to design transnational pathways through which private forest owners and local communities can be economically rewarded for verifiable ecological data via nature-backed digital assets and ReFi mechanisms. Together, these objectives align technical innovation (Objective 1) with behavioural and economic motivation (Objective 2), establishing the groundwork for distributed biodiversity observatories capable of sustaining long-term ecological data flows. Methodological Approach WP 1 develops a blockchain-based data ledger with smart contracts that autonomously manage data registration, access control, and reuse. Metadata and identifiers are immutably recorded on-chain, while primary datasets remain decentralised on contributor-managed nodes. This architecture enables contributors to retain data sovereignty while ensuring transparency and traceability in reuse transactions. WP 2 extends the infrastructure to real-time environmental sensing through the integration of modular Internet ofThings (IoiT)-based microclimate sensors. These devices stream environmental data at high temporal resolution directly into the distributed ledger, forming a Decentralized Physical Infrastructure Network (DePIN) for ecological data. WP 3 links these data streams to the creation of digital twins of forest ecosystems, combining in situ biodiversity observations with satellite and climate datasets to model ecosystem integrity. These models underpin the valuation of nature-backed digital assets, a form of tokenised evidence for ecological performance, providing the data foundation for voluntary biodiversity and carbon markets. Finally, WP4 investigates forest owners’ perceptions, motivations, and barriers to adopting regenerative finance (ReFi)-based conservation mechanisms. Through interviews and a pan-European survey, it explores how varying sociocultural and institutional contexts shape engagement with emerging biodiversity credit schemes, drawing parallels to established Payment for Ecosystem Services frameworks (Kaiser et al. 2021). Significance and Legacy FW3 exemplifies the convergence of data decentralisation, digital sensing, and regenerative economics, a triad capable of transforming how ecological knowledge is produced, verified, and valued. By embedding data provenance and attribution within the infrastructure itself, we addresses long-standing issues of trust and recognition in ecological data sharing. Its incentive mechanisms offer pathways to decouple conservation finance from traditional public funding, potentially scaling stewardship and democratizing data mobilisation across millions of hectares of privately owned forest land. The project’s legacy lies in demonstrating that data infrastructures can be both scientific and economic commons, capable of sustaining biodiversity monitoring through distributed participation. Beyond its immediate technical deliverables, ForestWeb3 contributes to a broader vision of dynamic, self-sustaining ecological data ecosystems that power both global biodiversity frameworks and locally grounded conservation action.
The rapid expansion of cryptocurrencies and decentralized finance (DeFi) has redefined global financial systems, creating new challenges in asset pricing, risk measurement, and systemic stability. This study conducts a comprehensive review of 93 peer-reviewed articles published between 2019 and 2024 to consolidate the fragmented literature on mathematical models applied to cryptocurrencies and DeFi platforms. Using a mixed bibliometric–systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review integrates performance indicators, conceptual mapping, and qualitative synthesis to identify methodological advances and research trends. The findings reveal a progressive convergence between econometric models, such as the Generalized Autoregressive Conditional Heteroskedasticity (GARCH), stochastic volatility, and Lévy processes, and data-driven approaches based on machine learning (ML), deep learning (DL), and reinforcement learning (RL). These hybrid frameworks enhance predictive accuracy and adaptability in high-frequency and non-linear blockchain markets. The review also highlights optimization-based decision models that integrate Conditional Value-at-Risk (CVaR), network theory, and portfolio analytics for decentralized finance operations. However, interpretability, governance, and environmental sustainability remain underexplored dimensions. The study contributes by classifying mathematical approaches to pricing, volatility, and risk propagation, identifying methodological gaps, and recommending future research on explainable artificial intelligence (AI), environmental and cyber-risk modeling, and real-time validation for transparent and resilient decentralized financial ecosystems. • Review 93 studies analyzing mathematical models in cryptocurrency and digital finance systems. • Identify emerging methods for pricing, risk, and portfolio decisions under high volatility. • Compare deep learning models to traditional methods for forecasting and risk evaluation. • Evaluate decision models that include environmental, risk, and governance factors. • Recommend future research on interpretable tools for real-time decision-making.