The development of technology around us is going through a rapid and significant state that is almost causing a technological revolution, so one of the most important problems facing us in the current technological era is the management of warehouse data and the growth occurring in the volume of data that is dealt with on a daily basis, whether in terms of its storage or security, especially if the data is huge and large. Therefore, we developed a proposed model in our study that provides security in addition to storage/warehouse management. In our proposed model, the El-Gamal and GLUON functions address the security problem. In addition to supporting other security methods, such as GLUON, which is secure and fast, for encryption. Hybrid Blockchain technology is used in our proposed model to deal with the storage of this type of huge data, and also for the purpose of organizing warehouse storage. Data is exposed to intrusion or loss when using any traditional, centralized technology or when storing it in databases, so we chose the hybrid Blockchain to be an integrated fit with our proposed model, and also because it allows the distribution of data across public and private domains. Our proposed model, upon examination, shows that it effectively dealt with defending against attacks such as NotPetya, GoldenEye, WannaCry, Emotet, Trickbot, Conti, and DarkSide. In addition, the results of lightweight GLUON and El-Gamal showed that the performance analysis of our model was very successful, where the time it takes to create a block was between 0.01 ns and not more than 0.09 ns which is considered too fast for such a system that deals with a big data. As a result, we were able to gain an effective model for data repository control, security, performance, and management.
Faozi A. Almaqtari, Ali Thabit Yahya, Nahad Al-Maskari, Najib H.S. Farhan · 5 authors
In a digitalized business, blockchain technology, fintech, AI, and IT governance are crucial for reducing risks and aligning with organizational goals. IT governance ensures smooth and efficient adoption of fintech solutions and AI. Blockchain introduces trust and security through smart contracts, enhancing sustainability performance. Thus, in today’s rapidly evolving digital environment, the integration of these technologies has become critical to organizational resilience in the long-term. The present study aims to explore how the integrated role of IT governance, fintech, and blockchain technologies can enhance sustainability practices to mitigate organizational risks. The study utilized a questionnaire survey to assess the impact of IT governance, fintech, and blockchain technologies on sustainability performance in Oman. The sample included commercial, industrial, and service companies, including banks. A non-probability sampling approach, including convenience and snowball sampling, was used. Software tools such as SPSS and Smart PLS were used to estimate quantitative data analysis and structural modeling results. The study concludes that IT governance dimensions alone have an insignificant impact on sustainability. Importantly, the integrated effect of IT governance (alignment, policies, and committees) improves sustainability. The results also report that IT governance significantly enhances fintech adoption, but it has an insignificant influence on blockchain adoption in organizations. The results reveal that the respondents perceive that sustainability is positively and significantly improved by IT governance strategic alignment and the steering committee. The study offers a unique perspective on the impact of blockchain, IT governance, and fintech technologies on sustainability, filling existing literature gaps and urging policymakers to achieve the Omani Vision 2040.
Muhammad Mukhlis Kamarul Zaman, Zahari Md Rodzi, Yusrina Andu, Nur Aima Shafie · 7 authors
Blockchain integration in microfinance is beginning to reshape the scenario of financial inclusion and economic empowerment in emerging markets. To support a strategic decision on adoption, the study introduces the Adaptive Utility Ranking Algorithm (AURA), a newly established Multi-Criteria Decision-Making (MCDM) method to be used in evaluating blockchain-based alternatives relevant to microfinance in Malaysia. AURA stands apart from traditional MCDM techniques in that it has a distance function that is flexible and a normalization scheme that is dynamic by nature, thereby making it capable of offering the decision maker more leverage in terms of adaptability to actual economic conditions. For demonstrating the methodology, a simulated dataset based on eight blockchain-modeled alternatives and six criteria considered important in economic performance was constructed. These criteria were used for sensitivity analysis; the application of comparative evaluation of well-known MCDM methods such as TOPSIS, VIKOR, and COBRA; and robustness checks with the simulation methodology, all of which helped attest to the reliability of AURA. Even though it was based on synthetic data, the study has provided strong conceptual insight into the possibility of financial institutions being able to prioritize options from the technology perspective under complex economic constraints. Portraying AURA as a competitive decision-support tool for technology evaluation in microfinance will certainly make an impact.
This paper is dedicated to addressing the management challenge of fake transaction behavior in recommendation systems and proposes a novel solution that integrates smart contracts and blockchain technology. By delving into the decentralized nature of blockchain, the immutability of its data, and the automatic execution advantages of smart contracts—especially the anonymous transaction environment provided by the latter—it not only effectively protects the personal privacy of participants but also reduces transaction biases caused by differences in identity, thereby promoting a more fair and transparent transaction process. Taking the anti-fake transaction mechanism of e-commerce platforms as an example, this paper demonstrates the significant effectiveness of this technological combination in enhancing data reliability, enabling dynamic rule adjustments, and facilitating cross-platform collaboration. The study finds that this method can reduce the false transaction detection omission rate by 30% to 50%, while also enhancing the fairness of the recommendation system and the level of user trust. In addition, the paper discusses potential future directions for technological integration, such as privacy computing and federated learning, and provides corresponding legal compliance recommendations.
Abstract: The Financial Technology (Fintech) sector is changing at a swift pace, as artificial intelligence (AI) is extending its influence. Greater complexity and global linkages are going to demand from fintech the power to rethink the integrity of its cybersecurity mechanisms and fraud tactics that have gotten intense up to a growing extent. The paper argues for the necessity of an "Algorithmic Fortress," an AI-driven cybernetic system incorporating all possible technologies targeted at securing digital financial networks against cyber-attacks and acts of financial fraud. The article delves into AI/ML, deep learning, anomaly detection through generative adversarial networks, etc., scope to predict battle, detect and fight problems. It does address adverse effects of AI risk, threatened system independence through synthetic identity fraud, application of AI for fraud detection in decentralized finance, DeFi, as well as the threat-hunting models that need to become autonomous. Supervised learning, unsupervised learning, and reinforcement learning are examination methodologies that are being applied in taking high recourse to the preservation of cybersecurity amongst their uncertainties. Our analysis will involve different experimentations of Python-based simulated attack scenarios to compare the two forms of cybersecurity. Also brought in are SmartArt visual representations revealed in multi-tier defensive architectures, combined with some strategic recommendations destined to protect future-facing fintech infrastructures from doing illicit deeds of algorithms. This study sketches possible solutions for securing the future-ready, trustworthy, and resilient fintech ecosystems once assisted by AI-enhanced, digital fortresses.
Autonomous vehicles have attracted considerable attention from researchers and organizations, with artificial intelligence (AI) playing a key role in this technology. For AI models in autonomous vehicles to be reliable, the integrity of the training data is crucial, resulting in the development of various blockchain-based management systems. However, conventional blockchain systems incur significant time delays when processing training data transactions, posing challenges in autonomous vehicle environments that require real-time processing. In this study, we propose a hashgraph-based training data management system for trusted AI. To validate our system, we conducted simulations using the CARLA simulator and compared its performance to a conventional blockchain-based system. The simulation results show that Hedera achieved significantly lower latencies and better scalability than Ethereum, confirming its suitability for secure and efficient AI data verification in autonomous systems.
Theorem proving serves as a major testbed for evaluating complex reasoning abilities in large language models (LLMs). However, traditional automated theorem proving (ATP) approaches rely heavily on formal proof systems that poorly align with LLMs' strength derived from informal, natural language knowledge acquired during pre-training. In this work, we propose DeepTheorem, a comprehensive informal theorem-proving framework exploiting natural language to enhance LLM mathematical reasoning. DeepTheorem includes a large-scale benchmark dataset consisting of 121K high-quality IMO-level informal theorems and proofs spanning diverse mathematical domains, rigorously annotated for correctness, difficulty, and topic categories, accompanied by systematically constructed verifiable theorem variants. We devise a novel reinforcement learning strategy (RL-Zero) explicitly tailored to informal theorem proving, leveraging the verified theorem variants to incentivize robust mathematical inference. Additionally, we propose comprehensive outcome and process evaluation metrics examining proof correctness and the quality of reasoning steps. Extensive experimental analyses demonstrate DeepTheorem significantly improves LLM theorem-proving performance compared to existing datasets and supervised fine-tuning protocols, achieving state-of-the-art accuracy and reasoning quality. Our findings highlight DeepTheorem's potential to fundamentally advance automated informal theorem proving and mathematical exploration.
Morena Barboni, Guglielmo De Angelis, Andrea Morichetta, Andrea Polini
Blockchain technology is increasingly adopted in scenarios requiring trust and data integrity. On the Ethereum blockchain, the proxy pattern has become increasingly popular because it allows smart contract code to evolve while preserving stored data. However, a key challenge remains ensuring that such upgrades do not introduce breaking changes or cause disruptions to other contracts and off-chain systems. In this article, we introduce Catana , a framework that leverages historical transactions for Capture-Replay testing of proxy-based Upgradeable Smart Contracts (USCs). Catana assesses the potential impact of an upgrade by comparing the outcomes of replayed transactions with those from the previous version deployed on the main network. Additionally, it extracts and decodes contract state variables, providing deeper insights into how code changes affect the contract state, and helping developers mitigate issues before deployment. Experiments demonstrate that analyzing storage data accounts for the majority (about 86.5%) of detected disruptive upgrades. We also evaluate different policies for building replay test suites from historical transactions. Results identify a strategy that maximizes effectiveness while requiring a small number of replay test executions. Even a test suite containing just one transaction per each invoked method achieved good effectiveness (about 60%) in detecting disruptive upgrades.
This study examines how decentralized financial flows - including blockchain technologies, cryptocurrencies, fintech platforms, and central bank digital currencies - have influenced economic integration in post-Soviet countries from 2000 to 2025. Framed within the broader processes of digital transformation and institutional change, the research addresses the demand for alternative financial infrastructure in transitional economies. Employing a mixed-methods approach, it combines cross-country analysis with case studies to assess digital infrastructure, regulatory adaptation, financial inclusion, and DeFi adoption. Findings show that while digital connectivity has increased, decentralized finance usage varies based on institutional and socio-economic conditions. Countries facing financial shocks often adopted DeFi from the bottom up, while others pursued top-down regulatory strategies. The study concludes that decentralized finance is already enhancing integration by improving access to payments, savings, and public services.
This study examines the impact of the Financial Stress Index (FSI) and US Equity Market Uncertainty (EMU) on cryptocurrencies. We analyse the short and long-run impact of FSI on prices of the top five cryptos using the Nonlinear ARDL (NARDL) framework to assess the alternative asset suitability of these cryptocurrencies during different financial market stress events. Our analysis finds a statistically significant impact of FSI and EMU on the cryptocurrency returns for both short-run and long-run. While the impact of FSI is asymmetric in the long run, we find that in the short run, the impact is symmetric. Thus, a rise in the FSI has a larger impact on the returns when compared to a fall in the FSI in the long run. The findings across various subperiods suggest that FSI and EMU affect the returns of cryptos differently. While in some periods, we see that a surge in financial stress leads to an increase in returns for some of the cryptos, for others, it leads to a decrease in returns. This indicates that the investors do not have the same preference for all the cryptos during periods of heightened financial stress and they may not be considered equal safe havens. Our results have clear policy implications for investors, regulators, and policymakers.
In the rapidly evolving decentralized finance (DeFi) ecosystem, ensuring efficient and interoperable transaction mechanisms is a critical challenge. This paper introduces a strategic optimization model for a blockchain-based token exchange platform, leveraging Coincidence of Wants (CoWs), multi-chain Automated Market Makers (AMMs), and an on-chain solver auction mechanism to enhance transaction efficiency and cross-chain interoperability in DeFi. In our model, users specify their transaction intents, while solvers, selected through a competitive auction based on game theory principles, compete to find the most efficient execution pathways, considering liquidity availability and market constraints. This approach not only facilitates seamless cross-chain transaction flows, but also optimizes the efficiency of existing solvers and reduces the reliance on centralized mechanisms. Our model’s effectiveness is validated through extensive simulation experiments, where performance with various order inputs and AMM constraints demonstrates a transaction completion rate increase ranging from 26.1% to 46.1% compared to the CoWs-only model, thereby enhancing user welfare and market fairness. The proposed model offers broad applicability for efficient, interoperable cross-chain transactions, positioning it to make a significant impact on the DeFi landscape.
Giovanni Farina, Alexander Kocian, Gianluca Brunori, Stefano Chessa · 21 authors
Traceability plays a critical role in ensuring the quality, safety, and transparency of supply chains, where transportation stakeholders are fundamental to the efficient movement of goods. However, the diversity of actors involved poses significant challenges to achieving these goals. Each organization typically operates its own information system, tailored to manage internal data, but often lacks the ability to communicate effectively with external systems. Moreover, when data exchange between different systems is required, it becomes critical to maintain full control over the shared data and to manage access rights precisely. In this work, we propose the concept of interoperable traceability. We present a model that enables the seamless integration of data from sensors, IoT devices, data management platforms, and distributed ledger technologies (DLT) within a newly designed data space architecture. We also demonstrate a practical implementation of this concept by applying it to real-world scenarios in the agri-food sector, with direct implications for transportation systems and all stakeholders in a supply chain. Our demonstrator supports the secure exchange of traceability data between existing systems, providing stakeholders with a novel approach to managing and auditing data with increased transparency and efficiency.
Mubarak A. Khan, Md Mofijul Islam, Istiaq Ahmed, Md Masud Karim Rabbi · 10 authors
Peer-to-peer trading and the move to decentralized grids have reshaped the energy markets in the United States. Notwithstanding, such developments lead to new challenges, mainly regarding the safety and authenticity of energy trade. This study aimed to develop and build a secure, intelligent, and efficient energy transaction system for the decentralized US energy market. This research interlinks the technological prowess of blockchain and artificial intelligence (AI) in a novel way to solve long-standing challenges in the distributed energy market, specifically those of security, fraudulent behavior detection, and market reliability. The dataset for this research is comprised of more than 1.2 million anonymized energy transaction records from a simulated peer-to-peer (P2P) energy exchange network emulating real-life blockchain-based American microgrids, including those tested by LO3 Energy and Grid+ Labs. Each record contains detailed fields of transaction identifier, timestamp, energy volume (kWh), transaction type (buy/sell), unit price, prosumer/consumer identifier (hashed for privacy), smart meter readings, geolocation regions, and settlement confirmation status. The dataset also includes system-calculated behavior metrics of transaction rate, variability of energy production, and historical pricing patterns. The system architecture proposed involves the integration of two layers, namely a blockchain layer and artificial intelligence (AI) layer, each playing a unique but complementary function in energy transaction securing and market intelligence improvement. The machine learning models used in this research were specifically chosen for their established high performance in classification tasks, specifically in the identification of energy transaction fraud in decentralized markets. To guarantee the reliability and accuracy of the used machine learning models, an extensive battery of evaluation metrics was utilized. The plot demonstrates clearly that XG-Boost obtained the highest accuracy out of the three models, Random Forest was slightly lower, and conversely, Logistic Regression was the lowest of the three models. Integrating blockchain technology with AI can increase the transparency, security, and efficiency of the energy sector in the U.S. Blockchain's decentralized and immutable ledger can make energy transactions traceable and resistant to tampering, and it becomes extremely hard for malicious actors to manipulate prices or fake records. In the future, the integration of deep learning methodologies and real-time integration of data from the Internet of Things (IoT) holds promising implications for future improvements. Deep learning models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) can detect strongly nonlinear patterns of fraud, which conventional models may not identify, particularly for the usage of multivariate time-series data from smart meters, sensors, and distributed energy resources.
Stephan Rabanser, Ali Shahin Shamsabadi, Olive Franzese, Xiao Wang · 6 authors
Cautious predictions -- where a machine learning model abstains when uncertain -- are crucial for limiting harmful errors in safety-critical applications. In this work, we identify a novel threat: a dishonest institution can exploit these mechanisms to discriminate or unjustly deny services under the guise of uncertainty. We demonstrate the practicality of this threat by introducing an uncertainty-inducing attack called Mirage, which deliberately reduces confidence in targeted input regions, thereby covertly disadvantaging specific individuals. At the same time, Mirage maintains high predictive performance across all data points. To counter this threat, we propose Confidential Guardian, a framework that analyzes calibration metrics on a reference dataset to detect artificially suppressed confidence. Additionally, it employs zero-knowledge proofs of verified inference to ensure that reported confidence scores genuinely originate from the deployed model. This prevents the provider from fabricating arbitrary model confidence values while protecting the model's proprietary details. Our results confirm that Confidential Guardian effectively prevents the misuse of cautious predictions, providing verifiable assurances that abstention reflects genuine model uncertainty rather than malicious intent.
Cryptocurrency mining, especially Bitcoin's Proof of Work (PoW), significantly impacts the environment through high energy consumption, carbon footprint, and e-waste. Ethereum's adoption of Proof of Stake (PoS) in 2022 offers a potential solution to reduce these effects. This study compares the environmental impacts of PoW and PoS, focusing on energy consumption, mining efficiency, hash rate, and carbon footprint. Using regression analysis and t-tests on data from Bitcoin (PoW) and Ethereum (before and after PoS) from 2017 to 2024, the results show that PoS significantly reduces energy consumption, carbon footprint, and e-waste, while improving mining efficiency. The findings highlight that transitioning to PoS can mitigate the environmental impact of cryptocurrency mining and encourage its broader adoption to align with global sustainability goals.
BACKGROUND: Nepal has been undergoing demographic and epidemiological transitions, marked by an increasing burden of non-communicable diseases (NCDs) and injuries. These transitions have led to financial implications, including rising out-of-pocket (OOP) expenses. This study reviews and synthesizes evidence on the status, issues and challenges in health financing system, policies, and programs to achieve universal health coverage (UHC) in Nepal. METHODS: We conducted a scoping review of literature on Nepal's health financing system, policies, and programs. A search strategy was developed using keywords related to two core concepts: health financing and universal health coverage. Grey literature was identified from the web pages of relevant ministries and organizations. A total of 148 studies/policy documents published in Nepali and English up to 31 December 2024 were included. Policies and content related to the health financing system were reviewed to understand the status, issues and challenges of health financing functions, and UHC . A framework-guided deductive content analysis approach was employed, and findings were interpreted using the three UHC components: service coverage, population coverage, and financial coverage. RESULTS: Nepal's health policy documents prioritize financial protection for low-income people and target groups through social health protection programs/schemes. However, multiple social health protection schemes coexist with fragmented risk pooling and low efficiency in health financing. OOP expenditure is high at 54.2%, with 10% of the population facing catastrophic health expenditures. Injuries and chronic morbidities contribute significantly to this burden, with 70% of injury-related and 62% of NCD-related expenses borne through OOP payments. Despite efforts to improve financial risk protection, the National Health Insurance Program (NHIP) suffers from low population coverage (28%), low renewal rate (54%), and financial sustainability issues (as provider payments exceed revenue collection). The UHC service coverage index, though improving, was only 54 out of 100 in 2021 reflecting limited health system capacity and insufficient readiness to address health challenges, including those posed by shifting demographics and the growing burden of NCDs. Nepal's total health expenditure remains around 2% of GDP, with persistent inefficiencies in resource allocation, fiscal decentralization, and budget absorption. CONCLUSIONS: Nepal's health financing policies align with UHC goals, yet critical gaps remain in multiple dimensions . Issues such as inefficiencies, underfunding, and fragmented social health protection schemes limit equitable access to quality health care. Therefore, comprehensive structural reforms-spanning legal, institutional, and policy frameworks-are urgently needed. Key reforms include: (1) merging or harmonizing existing social health protection schemes for efficient pooling and purchasing; (2) enhancing domestic health financing through increased health funding (≥5% of GDP) via payroll contributions, progressive taxation, and earmarked sin taxes; (3) reforming NHIP to mandatory enrollment starting from formal sector, subsidizing premium for informal sector and free coverage for disadvantaged groups, alongside strengthening policy implementation including accrediting of health facilities, ensuring service quality, prioritising and expanding coverage packages with strategic purchasing from all public and private health facilities; and (4) equitable public financing to ensure needs-based allocation across government levels that respond to demographic and epidemiological patterns. Further research is needed to assess hybrid tax and premium based insurance models, strategic purchasing optimization, and digital health innovations for financial sustainability.
Jadyn Kimber, Enrico Branca, Andrei Natadze, Natalia Stakhanova
The increasing number of Ethereum scams is causing significant concern within the blockchain community, costing users millions of dollars annually. Yet, our understanding of how these scams operate remains limited. In this study, we present the first end-to-end analysis of crypto scams using a large set of malicious Ethereum accounts as a case study. We examine the tactics these scams employ on social media platforms to deceive users and convince them to transfer funds to malicious accounts. Our analysis explores the full life cycle of these scams, considering both their distribution through social media and their activity on the Ethereum blockchain. We identify several unique aspects of Ethereum phishing scams that have not been documented in prior literature and find that these scams generally persist significantly longer and result in greater financial losses compared to traditional phishing scams studied in earlier research.
Open access
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Blockchain Technology Applications and Security
Advanced Malware Detection Techniques
Advanced Steganography and Watermarking Techniques
Independent Researcher, USA, Damodar Bihani, Bright Chibunna Ubamadu, Signal Alliance Technology Holding, Nigeria · 6 authors
The integration of blockchain technology into the tokenization of real-world assets (RWAs) is revolutionizing how value is stored, transferred, and accessed globally. This paper proposes a scalable framework for cross-functional collaboration in Web3 product development focused on blockchain-based tokenized RWAs. Tokenization enables physical assets such as real estate, commodities, and intellectual property to be digitized into blockchain-based tokens, allowing for fractional ownership, increased liquidity, and enhanced accessibility. However, the successful development and deployment of such Web3 products require an interdisciplinary approach that combines technological innovation, legal compliance, financial modeling, and user experience design. Our framework addresses these needs by enabling seamless collaboration between developers, legal experts, financial analysts, and UX/UI designers throughout the product lifecycle. We present a modular architecture built on interoperable blockchain protocols such as Ethereum and Polkadot, integrating smart contracts, decentralized identifiers (DIDs), and oracles for real-time asset verification. The framework emphasizes agile product development practices and leverages decentralized autonomous organization (DAO) structures to facilitate decision-making and community governance. Furthermore, we explore how regulatory-compliant token standards, such as ERC-1400, can be incorporated to ensure adherence to jurisdiction-specific asset ownership and transfer laws. This study includes a case analysis of cross-functional product teams building tokenized real estate platforms and carbon credit marketplaces, demonstrating how scalable collaboration can accelerate time-to-market and improve transparency, trust, and user adoption. Our findings highlight that such a collaborative framework significantly reduces technical debt and improves legal and financial risk mitigation. The framework also enhances stakeholder alignment through integrated project management tools and on-chain documentation. By offering a structured, scalable, and adaptable approach, this framework positions Web3 product teams to unlock the full potential of tokenized RWAs in a decentralized economy. It serves as a critical guide for developers, entrepreneurs, regulators, and investors aiming to leverage blockchain technology in building trustworthy, scalable, and cross-functional Web3 applications.
Modern blockchain applications are often constrained by a trade-off between user experience and trust. Chainless Apps present a new paradigm of application architecture that separates execution, trust, bridging, and settlement into distinct compostable layers. This enables app-specific sequencing, verifiable off-chain computation, chain-agnostic asset and message routing via Agglayer, and finality on Ethereum - resulting in fast Web2-like UX with Web3-grade verifiability. Although consensus mechanisms have historically underpinned verifiable computation, the advent of zkVMs and decentralized validation services opens up new trust models for developers. Chainless Apps leverage this evolution to offer modular, scalable applications that maintain interoperability with the broader blockchain ecosystem while allowing domain-specific trade-offs.
This article explores the potential of Web3 and decentralized applications (dApps) to revolutionize digital banking. It explores how blockchain technology transforms traditional banking through distributed ledger systems that enhance transparency, security, and user autonomy. The technical architecture of Web3 banking solutions is detailed, including blockchain networks, smart contracts, and emerging applications like decentralized lending protocols, cross-border payment systems, and asset tokenization. While these technologies address significant limitations in conventional banking systems, such as excessive fees, settlement delays, and centralized control, challenges persist in regulatory compliance, scalability, interoperability, and user experience. The integration of artificial intelligence and quantum-resistant cryptography represents promising developments that could further enhance decentralized financial systems. As the blockchain ecosystem matures, financial institutions that embrace these technologies stand to gain competitive advantages through operational efficiencies and enhanced customer value.
Motivated by applications such as cloud computing, gig platforms, and blockchain auctions, we study optimal selling mechanisms for dynamic markets with stochastic supply and demand. In our model, buyers with private valuations and homogeneous goods arrive stochastically and can be held in queues at a cost. The optimal mechanism pairs allocative efficiency with dynamic admission control: goods are assigned to the highest-value buyer, while entry is restricted by value thresholds that strictly increase with the queue length and decrease with available inventory. This policy smooths competitive pressure across time and is implemented in dominant strategies via auctions with dynamic reserve prices.
This paper investigates the dynamic relationship between geopolitical uncertainty and decentralized finance (DeFi) token prices using a nonlinear, time-series-based framework. Leveraging the GPRD index as a proxy for global risk sentiment, the study examines seven prominent DeFi tokens representing diverse functional roles within the ecosystem. Through a layered empirical strategy - including Transfer Entropy, Mutual Information, Kernel-based Granger Causality, and Structural Time Series. Modeling - the analysis identifies both predictive and structural dependencies between GPRD and token valuations. The results reveal that tokens associated with financial-layer functions such as lending, collateralization, and liquidity rebalancing (e.g., Maker, Aave, BAL) exhibit stronger and more persistent exposure to geopolitical shocks than exchange-layer tokens like Uniswap or PancakeSwap. Kernel Granger causality confirms significant nonlinear predictive power of GPRD across all tokens, while structural decomposition shows that GPRD systematically depresses the long-term trend component of financial DeFi tokens. These findings indicate that global uncertainty operates not only through shortterm volatility, but also as a sustained driver of DeFi asset repricing. By combining information-theoretic and structural techniques, the study provides a comprehensive empirical lens through which to evaluate systemic risk transmission into DeFi markets. The results underscore the heterogeneous macro-financial sensitivity of decentralized protocols and suggest the need for differentiated risk assessment frameworks in crypto-asset research and governance.
The article examines the main regulatory provisions governing digital legal relations, including the norms of civil legislation and legislation on digital assets. Particular attention is paid to the definition of the features of digital rights that distinguish them from other objects of turnover, as well as to the analysis of the conditions for the emergence, exercise and transfer of such rights in the framework of distributed registers. The legal peculi-arities of smart contracts as software constructions replacing traditional forms of contractual interaction and ensuring the fulfillment of obligations without the participation of the parties after the activation of the algorithm are considered. Legal risks arising in the absence of normative regulation of smart contracts are substantiated, including the impossibility of judicial correction of performance, loss of access to digital assets and uncertainty of identification of subjects. Measures to improve legislation are proposed, including the regulatory consolida-tion of the concept of a smart contract, the establishment of a presumption of ownership of a digital right for a person with access to a key, as well as the recognition of the legal significance of blockchain records in judicial protection. Сonclusion is made about the the necessity of systemic adaptation of civil legislation to new forms of digital interaction and technological autonomy of turnover.