The exponential growth of IoT data demands efficient, secure, and scalable storage solutions on one hand, and efficient data migration and retrieval on the other hand are essential for the systems to be practical and acceptable for different applications. The traditional cloud-based models face latency, security, and high operational costs, while existing bi-directional data storage and retrieval-based IPFS models are not computationally efficient and incur high gas costs at the cost of a necessary blockchain deployment. To overcome the challenges of efficient data migration, we initially developed a 2-way data storage and retrieval system as well as a scalable framework that dynamically monitors and transfers device-generated data to IPFS, records the content identifier(CID) on a blockchain, and enables secure, real-time access via smart contracts. Experimental results demonstrate that the existing work achieved an average data upload time of 117.12 sec for a file size of 500 MB; our framework achieves a faster upload time of 7.63 sec, marking a 93.47% improvement. We further optimize the proposed framework to reduce the file upload time incurred from the smart contracts by introducing a blockchain-inspired, lightweight, and customizable Python framework that replicates the storage and retrieval functionalities of a traditional blockchain, where the file upload time is 4.2 sec, further optimized by 45% from our previous approach, thus demonstrating its efficiency, security and suitability for deploy ment in real-time and critical IoT applications and outperforming the existing IPFS-smart contract based solutions.
Luigi D'Amico, Daniel De Rosso, Ninad Dixit, Raul Salles de Padua · 9 authors
In the rapidly evolving domain of financial technology, the detection of illicit transactions within blockchain networks remains a critical challenge, necessitating robust and innovative solutions. This work proposes a novel approach by combining Quantum Inspired Graph Neural Networks (QI-GNN) with flexibility of choice of an Ensemble Model using QBoost or a classic model such as Random Forrest Classifier. This system is tailored specifically for blockchain network analysis in anti-money laundering (AML) efforts. Our methodology to design this system incorporates a novel component, a Canonical Polyadic (CP) decomposition layer within the graph neural network framework, enhancing its capability to process and analyze complex data structures efficiently. Our technical approach has undergone rigorous evaluation against classical machine learning implementations, achieving an F2 score of 74.8% in detecting fraudulent transactions. These results highlight the potential of quantum-inspired techniques, supplemented by the structural advancements of the CP layer, to not only match but potentially exceed traditional methods in complex network analysis for financial security. The findings advocate for a broader adoption and further exploration of quantum-inspired algorithms within the financial sector to effectively combat fraud.
This study explores how national institutional environments shape entrepreneurial activity in the context of decentralized finance. Focusing on Initial Coin Offerings (ICOs), we use fuzzy-set Qualitative Comparative Analysis (fsQCA) on data from 2,709 ICOs across 42 countries to identify institutional configurations associated with high and low ICO activity. Drawing on institutional theory and heuristic-driven decision-making, we find that entrepreneurs and investors navigate uncertainty through diverse, context-specific institutional combinations. The findings contribute to entrepreneurship research by uncovering multiple pathways through which macro-level regulatory, cultural, financial, and technological conditions enable or constrain token-based fundraising across national ecosystems.
The article is devoted to the study of the problem of harmonization of Ukrainian legislation in the field of crypto-asset market regulation in the context of the implementation of the provisions of the new European Regulation 2023/1114 of May 31, 2023. Given Ukraine’s status as a candidate for membership in the European Union, the task of unifying legal approaches to the definition and classification of digital assets is becoming increasingly relevant. The article provides a comparative analysis of the evolution of the conceptual and categorical apparatus in the European Union, using the provisions of Directive 2018/843, which focuses mainly on combating money laundering, and Regulation 2023/1114, and examines the transition from the term “virtual currency” to the systematic, expanded, and functionally oriented concept of “crypto-asset,” which includes both digital value and digital rights. Particular attention is also paid to the analysis of Ukrainian legislation and recent legislative initiatives, in particular the Law of Ukraine “On Virtual Assets” No. 2074-IX and draft laws No. 10225 and No. 10225-1. In the context of these documents, a detailed comparison of the definitions of “virtual asset” used is carried out and attempts to gradually adapt the Ukrainian conceptual framework to European standards are revealed, including by referring to the technological criterion (use of distributed ledger technology) and expanding the functional content of assets. Discrepancies between the Ukrainian and European approaches have been identified in both the basic terminology and the classification system for crypto-assets. A comparison of classification models has been carried out: the basic three-level structure enshrined in Regulation 2023/1114, which includes asset- referenced tokens, electronic money tokens, and other tokens, and the options proposed in Ukrainian draft laws, which attempt to adapt European categories to national specifics. Attempts to directly transpose the classification model of Regulation 2023/1114 into the Ukrainian legal system and the challenges associated with adapting certain categories of crypto-assets, taking into account the existing legal regime in Ukraine, are analyzed. Proposals are made on the advisability of revising the terminology and further work on the development of a national classification of crypto assets in line with European Union legislation.
Introduction Lassa fever remains endemic in Nigeria, yet diagnostics, treatment, and hospitalization are excluded from the National Health Insurance Scheme (NHIS), leaving most patients to cover costs out-of-pocket. With NHIS coverage below 10%, both epidemic preparedness and financial protection are compromised. The 2022 National Health Insurance Authority Act offers a policy window to integrate Lassa fever services into NHIS and advance Universal Health Coverage (UHC). Methods A systematic desk review of national health policy, epidemic preparedness, and financing documents published between 2010 and 2024 was conducted using the PRISMA framework. Key sources included the NHIS Operational Guidelines (2012), National Health Policy (2016), NHIA Act (2022), Nigeria’s UHC Roadmap (2020–2030), and NCDC Lassa fever Incident Action Plans (2023–2024). Screening identified 62 unique records, 31 full texts were assessed, and 17 documents met inclusion criteria. Thematic analysis explored gaps in benefit design, financing barriers, and the roles of the Basic Health Care Provision Fund and the COVID-19 Preparedness and Response Project funds. Results The review revealed that NHIS benefit packages omit Lassa fever services and that primary health centers in endemic states lack accreditation. Analysis of the 2023 Incident Action Plan showed that only 6 of 38 (16%) priority activities were fully implemented, 7 of 38 (18%) were partially implemented, and 25 of 38 (66%) were largely not conducted. In 2024, flexible, decentralized financing markedly improved Emergency Operations Centre activation and case reporting. Conclusion Achieving resilient and equitable outbreak response in Nigeria requires more than emergency activation—it demands structural reform. Integrating Lassa fever services into NHIS benefit packages is not just a policy option; it is a public health imperative. Strategic actions such as expanding NHIS accreditation to endemic PHCs, institutionalizing flexible subnational financing, and operationalizing joint NHIA–NCDC accountability frameworks can transform underfunded response plans into sustainable national capacity. These reforms will not only improve the execution of IAPs but also serve as a model for embedding epidemic preparedness within UHC systems across West Africa.
Within the financial transparency and auditing sector, blockchain technology is re-inventing transaction-based verification, since it has shifted towards the concept of decentralized, permanent and real-time verifications in transactions. This innovation will drive greater trust, less fraud, and more compliance as audit trails become tamper proof, as processes are automatized in terms of smart contracts, and can be audited continuously. The most notable applications are fraud prevention, anti-money laundering (AML) tracking and supply chain transparency, with scalability, legal uncertainty and privacy concerns still being a problem. With such widespread adoption, blockchain is the oft-rewarded path to a more efficient, provable ecosystem of money and many forms of finance, paired with AI and central bank digital currencies (CBDCs). This paper is going to discuss the effect of blockchain on auditing, its advantages, limitations, and the future of blockchain in transforming financial accountability.
Energy access and utilization remain highly unequal across sub-Saharan Africa (SSA) countries, despite the region's vast natural resources and growing energy needs. This study examines the differential inequalities in energy access and utilization in selected SSA countries, focusing on demand and supply-side constraints, technological opportunities, and the role of government and private sector interventions. The review paper highlights the persistent energy poverty affecting over 600 million people in SSA, particularly in rural areas, where reliance on traditional biomass remains prevalent. Infrastructure deficiencies, high energy costs, and inadequate policy frameworks further exacerbate these inequalities. The paper underscores the critical role of governments in formulating effective energy policies, implementing subsidies, and fostering public-private partnerships to expand sustainable energy access. The private sector's involvement in financing and deploying decentralized energy solutions is identified as a key driver of progress. Recommendations include strengthening policy and regulatory frameworks, expanding regional power pools, investing in decentralized energy solutions, and promoting financial inclusion through innovative funding mechanisms. By addressing these challenges, SSA can move towards achieving equitable and sustainable energy access, fostering economic growth, and improving overall quality of life as key objectives of achieving the sustainable development goals (SDGs).
Pri razvoju decentraliziranih aplikacij (dApps) se tradicionalni razvojni procesi pogosto izkažejo za nezadostne. Tovrstne rešitve zahtevajo večji poudarek na tehničnih, varnostnih in uporabniških vidikih kakovosti aplikacij, kot smo jih sicervajeni pri razvoju klasičnih rešitev. Ker je spreminjanje pametnih pogodb po namestitvi v omrežje verig blokov zahtevno oziroma nemogoče, sta temeljito testiranje ter presoja programske kode ključnega pomena za uspešen razvoj tovrstnih rešitev. Optimizacija stroškov goriva, nujna za izvrševanje programov v javnih omrežjih, predstavlja enega ključnih razvojnih izzivov, ki ga je potrebnoustrezno obravnavati. Poleg tega specifično okolje omrežij veriženja blokov zahteva ustrezne ukrepe za obvladovanje tveganj povezanih z ranljivostmi aplikacij in morebitnimi povezanimi finančnimi izgubami. Nespremenljivost, stroški goriva in zagotavljanje varnosti so le nekateri izmed ključnih razvojnih izzivov, ki jih je treba uspešno nasloviti pri izgradnji kakovostnih in stabilnih decentraliziranih aplikacij. Prispevek obravnava izzive, sodobne pristope in strategije razvoja decentraliziranih aplikacij ter podaja priporočila za njihov zanesljivejši in učinkovitejši razvoj, s čimer naslavlja ključne izzive uvajanja tehnologij veriženja blokov v industrijska okolja ter razvoja pametnih pogodb. Poseben poudarek je namenjen pametnim pogodbam, ki temeljijo na omrežju Ethereum.
Pri razvoju decentraliziranih aplikacij (dApps) se tradicionalni razvojni procesi pogosto izkažejo za nezadostne.Tovrstne rešitve zahtevajo večji poudarek na tehničnih, varnostnih in uporabniških vidikih kakovosti
This paper provides a systematic review and synthesis of two converging financial literatures: mortgage-backed securities (MBS) and decentralized finance (DeFi). I trace the evolution of MBS research from the 1970s through the 2008 financial crisis to present day, while examining how blockchain innovations create new possibilities for real estate finance. The methodology combines traditional literature review techniques with bibliometric analysis, utilizing Google Scholar and Google Trends data to document the shifting research landscape and public interest in these topics over time. The findings reveal that while MBS research peaked following the 2008 financial crisis, DeFi and real estate tokenization research show more recent growth trajectories since 2016. The synthesis highlights how blockchain technology offers potential improvements in transparency, transaction costs, and liquidity for real estate markets, while acknowledging significant regulatory and governance challenges. This review contributes to understanding the current state of research at the intersection of traditional real estate finance and emerging blockchain applications, providing a foundation for future empirical investigations.
Digital payments play a pivotal role in the burgeoning digital economy. Moving forward, the enhancement of digital payment systems necessitates programmability, going beyond just efficiency and convenience, to meet the evolving needs and complexities. Smart contract platforms like Central Bank Digital Currency (CBDC) networks and blockchains support programmable digital payments. However, the prevailing paradigm of programming payment logics involves coding smart contracts with programming languages, leading to high costs and significant security challenges. A novel and versatile method for payment programming on DLTs was presented in this paper - transforming digital currencies into token streams, then pipelining smart contracts to authorize, aggregate, lock, direct, and dispatch these streams efficiently from source to target accounts. By utilizing a small set of configurable templates, a few specialized smart contracts could be generated, and support most of payment logics through configuring and composing them. This approach could substantially reduce the cost of payment programming and enhance security, self-enforcement, adaptability, and controllability, thus hold the potential to become an essential component in the infrastructure of digital economy.
This study investigates the mediating role of organizational commitment in the relationship between budget participation, leadership style, motivation, job satisfaction, and managerial performance in the regional government apparatus (OPDs) of Merauke, South Papua. As one of Indonesia's newly autonomous provinces, South Papua faces significant administrative challenges, especially in implementing decentralization policies and managing special autonomy funds effectively. This research employs a quantitative explanatory design with data collected from 386OPD employees through structured questionnaires and analyzed using Structural Equation Modeling (SEM) with SmartPLS 4.0. The findings reveal that budget participation, motivation, job satisfaction, and organizational commitment have significant direct effects on managerial performance. In contrast, leadership style does not directly influence performance. Motivation, while negatively affecting performance directly, contributes positively to organizational commitment, which subsequently enhances performance. Organizational commitment also plays a significant mediating role in the relationship between budget participation, motivation, and job satisfaction with managerial performance. However, it does not significantly mediate the effect of leadership style on performance.This study is grounded in Organizational Behavior Theory, Path-Goal Theory, Expectancy Theory, and Contingency Theory, offering a robust theoretical lens to understand how internal organizational dynamics affect public sector performance. The results underscore the importance of participatory leadership, strategic human resource management, and job satisfaction in fostering emotional attachment and performance-enhancing behaviors among civil servants.Theoretically, the research enriches public administration literature; practically, it informs policymakers and administrators seeking to optimize human capital in underdeveloped regions with unique cultural and political contexts.
Giacomo Vella, Luca Gastaldi, Francesco Paolo Appio
The rise of Decentralized Applications (DApps) represents a significant shift in how digital services are developed and governed, utilizing blockchain technology to eliminate central oversight and facilitate peer-to-peer interactions. While blockchain's algorithmic governance mechanisms are designed to enforce transparency and decentralization, human-driven processes—such as leadership roles, community engagement, and social norms—continue to play a pivotal role in shaping governance outcomes. This study investigates how these non-algorithmic factors influence the decentralization of DApp governance. Through a multiple case study of seven Decentralized Finance DApps, we analyze the governance structures, decision-making processes, and power dynamics at play. Our findings reveal that, despite the technological promise of decentralization, human-driven processes can reintroduce centralization risks, impacting inclusivity and decision-making. We propose an integrated governance framework that emphasizes human-driven mechanisms, contributing to the discourse on the practical realities of decentralized governance in DApps. The study offers theoretical and empirical insights into how decentralization is enacted and challenged in blockchain-based ecosystems. • Human-driven processes in DApps governance can reintroduce centralization risks despite tech promises. • Proposes an integrated framework emphasizing human-driven governance to address limits of algorithms. • Core teams often retain major decision power, impacting decentralization and creating power imbalances. • Misaligned incentives hinder participation, concentrating power and affecting DApps' decentralization.
Blockchain technology, once limited to niche technological communities, has seen widespread global adoption in recent years, with the potential to reshape financial and social systems. Launched in July 2015, the Ethereum blockchain introduced programmable Smart Contracts. This innovation enabled the creation of user-defined crypto-assets adhering to the ERC-20 standard, supporting a wide range of decentralized applications beyond simple value transfer. We present a large-scale, temporally annotated dataset of ERC-20 token transactions recorded on the Ethereum blockchain. Spanning from November 2015 to December 2024, the dataset encapsulates the trading activity of 216,336,529 users trading 1,138,136 unique tokens, offering a detailed view of crypto-market activity over time. Uniquely, it enables the analysis of a financial ecosystem from its inception, providing rare insights into its structural evolution, participant dynamics, and emergent behaviors. As the largest publicly available resource of its kind, it supports research in blockchain analytics, market dynamics and temporal network analysis. The full dataset and accompanying code are released for public use.
Abdul Razzaq, Ahmed B. Altamimi, Wilayat Khan, Mohammad Alsaffar · 7 authors
CONTEXT: Metaverse is an emerging technology that synchronizes physical and virtual things. It is used to communicate and simulate the virtual world with the physical world through human actions in real-life scenarios. Combining blockchain and metaverse technologies produces an archetype shift in the educational technology domain regarding online certification, largely due to the impact of synchronizing educational technologies. The combined technology elevates the security measure, ensures transparency, enhances accountability, and reduces costs for the online certification process. Proposed Solution: The suggested solution (MetaEduTech) accelerates the certificate verification process by (i) extenuating the risks of misuse by leveraging decentralized storage of the InterPlanetary File System (IPFS), (ii) securing the certificate, and (iii) providing the metaverse environment for certification. We perform experiments and evaluate the MetaEduTech solution by deploying a blockchain-based smart contract model on Ethereum on the Microsoft Windows platform. RESULTS AND IMPLICATIONS: The evaluation results show (i) the efficiency of the query response (5 ms-50 ms), (ii) and the performance of the query execution (CPU utilization between 2%-6%). The findings in this research underscore the effectiveness of the proposed solution with the potential to modernize the certification exam process. The proposed solution and its evaluation can provide insights into how to address the persistent issues surrounding certificate authenticity related to academic verification in a metaverse environment.
Jane Carney, Kushal Upreti, Gaby G. Dagher, Tim Andersen
Federated learning enhances traditional deep learning by enabling the joint training of a model with the use of IoT device's private data. It ensures privacy for clients, but is susceptible to data poisoning attacks during training that degrade model performance and integrity. Current poisoning detection methods in federated learning lack a standardized detection method or take significant liberties with trust. In this paper, we present \Sys, a novel blockchain-enabled poison detection framework in federated learning. The framework decentralizes the role of the global server across participating clients. We introduce a judge model used to detect data poisoning in model updates. The judge model is produced by each client and verified to reach consensus on a single judge model. We implement our solution to show \Sys is robust against data poisoning attacks and the creation of our judge model is scalable.
Proof-of-work allows Bitcoin to boast security amidst arbitrary fluctuations in participation of miners throughout time, so long as, at any point in time, a majority of hash power is honest. In recent years, however, the pendulum has shifted in favor of proof-of-stake-based consensus protocols. There, the sleepy model is the most prominent model for handling fluctuating participation of nodes. However, to date, no protocol in the sleepy model rivals Bitcoin in its robustness to drastic fluctuations in participation levels, with state-of-the-art protocols making various restrictive assumptions. In this work, we present a new adversary model, called external adversary. Intuitively, in our model, corrupt nodes do not divulge information about their secret keys. In this model, we show that protocols in the sleepy model can meaningfully claim to remain secure against fully fluctuating participation, without compromising efficiency or corruption resilience. Our adversary model is quite natural, and arguably naturally captures the process via which malicious behavior arises in protocols, as opposed to traditional worst-case modeling. On top of which, the model is also theoretically appealing, circumventing a barrier established in a recent work of Malkhi, Momose, and Ren.
We propose a hybrid estimation procedure to estimate global fixed parameters and subject-specific random effects in a mixed fractional Black-Scholes model based on discrete-time observations. Specifically, we consider $N$ independent stochastic processes, each driven by a linear combination of standard Brownian motion and an independent fractional Brownian motion, and governed by a drift term that depends on an unobserved random effect with unknown distribution. Based on $n$ discrete time statistics of process increments, we construct parametric estimators for the Brownian motion volatility, the scaling parameter for the fractional Brownian motion, and the Hurst parameter using a generalized method of moments. We establish their strong consistency under the two-step regime where the observation frequency $n$ and then the sample size $N$ tend to infinity, and prove their joint asymptotic normality when $H \in \big(\frac12, \frac34\big)$. Then, using a plug-in approach, we consistently estimate the random effects, and we study their asymptotic behavior under the same sequential asymptotic regime. Finally, we construct a nonparametric estimator for the distribution function of these random effects using a Lagrange interpolation at Chebyshev-Gauss nodes based method, and we analyze its asymptotic properties as both $n$ and $N$ increase. We illustrate the theoretical results through a numerical simulation framework. We further demonstrate the efficiency performance of the proposed estimators in an empirical application to crypto returns data, analyzing five major cryptocurrencies to uncover their distinct volatility structures and heterogeneous trend behaviors.
This paper proposes a novel architectural framework for robust security within dynamic multi-cloud environments, addressing the limitations of traditional perimeter defenses. It establishes and elaborates upon core Zero-Trust principles, including stringent identity validation, fine-grained access control, and perpetual operational vigilance, to counter contemporary cyber threats such as lateral infiltration and cloud-native attack vectors. The contribution details a systematic approach to fortifying distributed cloud workloads through the enforcement of least-privilege access and micro-segmentation strategies. Furthermore, the paper critically examines advanced policy enforcement mechanisms, enhanced identity management solutions, and the strategic integration of cryptographic and distributed ledger technologies to achieve superior defensive postures. This work delivers actionable insights for designing resilient security postures across diverse cloud infrastructures.
This paper presents a framework for analyzing and modeling validator behavior in dynamic consensus protocols. A discrete state-based model is proposed in order to represent four key validator states: majority, non-faulty minority, faulty minority, and non-validator, enabling systematic behavioral analysis through three complementary metrics: Jensen-Shannon Divergence (JSD) for entropy-based behavioral differences, the Bhattacharyya Coefficient for distribution similarity, and Wasserstein distance for state transition costs. To identify coherent validator groups and detect outliers, an HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) clustering is used since it is well-suited for detecting clusters in data with varying densities. Using JSD-based similarity measures in HDBSCAN, transient convergence patterns and stable behavioral clusters are uncovered, even in decentralized networks with diverse fault conditions. Simulation results on a 50-node network demonstrate the framework’s effectiveness, providing insights into system dynamics and offering tools for validator selection, fault detection, and stability monitoring in distributed ledger systems. This approach is particularly relevant, as consensus protocols evolve beyond traditional PBFT (Practical Byzantine Fault Tolerance) implementations, combining theoretical metrics with clustering techniques to enhance consensus robustness.
A blockchain is a distributed ledger that allows users to exchange information without a centralized authority. This technology enables users to send and receive tokens among other applications, such as transactions, product management, and elections. It is possible to send data and tokens inside a single blockchain, but a method to efficiently share the data and tokens among different blockchains has not yet been con structed. Cross-chain communication, the focal point of several recent research efforts, is a scheme for sending data or tokens among different blockchains. In existing studies, a trusted third party (TTP) is used to ensure fair rates of token exchange among different blockchains. However, because blockchains are originally designed with a policy that does not incorporate the use of TTPs, the fair exchange rate should not be determined by TTPs, but rather by the market price of tokens among users. When exchange rates are determined from quotes among users, the preferred scheme is to determine the exchange rate offered by many users as an auction. Here, some existing cross-chain communication systems use smart contracts that automatically execute arbitrary processes on the blockchain. However, such schemes require a gas fee each time a smart contract is executed. Thus, implementing an auction scheme that determines the fair exchange rate among different blockchains would necessitate each user to pay a fee for each new token offered, which would result in high gas fees. In this study, we propose a scheme to deter mine exchange rates from quotes among users with a relatively low gas fee. Using a first-price sealed-bid auction and commit ment scheme, the user with the highest token value can be identified without revealing the other users' token offer values. In our scheme, the largest token value among users is determined as the exchange rate using an external Smart Contract (SC) instead of a TTP. We further modify the existing insert key-value com mitment scheme to aggregate the commitment values of token offers. Our scheme is based on the generalized RSA assumption. By proving that it satisfies the key-binding property, we prove that the token sender cannot act maliciously. We further implement the proposed scheme and demonstrate that the gas fees and data space required to implement the proposed scheme are practically feasible.
V. Ananthakrishna, Bajrang Lal, Chandra Shekhar Yadav
The increasing demand for security and privacy-preserving collaboration among healthcare institutions presents significant challenges in data sharing, consent enforcement, and diagnostic automation, especially considering emerging quantum threats. This paper introduces PQ-FedCare, an innovative federated system architecture that incorporates post-quantum cryptography, zero-knowledge proofs, and smart contract–governed diagnostics to facilitate verifiable and privacy-compliant clinical collaboration. The proposed framework supports decentralized identity validation, encrypted consent delegation, and encrypted rule execution across blockchain-connected healthcare nodes. Using CRYSTALS-Kyber and SPHINCS+ for quantum-resistant security and zk-SNARKs for proof generation, PQ-FedCare ensures zero data exposure while enabling real-time, cross-institutional medical decision support. Evaluation on real-world clinical datasets (MIMIC-III, TCGA, and GEO GSE12102) demonstrates superior performance over recent baselines in diagnostic accuracy (94.5%), privacy leakage (0%), and proof verification time (92 ms). Additional stress tests confirm the system’s robustness against missing data and scalability across federated nodes. The findings establish PQ-FedCare as a forward-compatible infrastructure for secure, accountable, and future-proof federated healthcare diagnostics. The proposed work is particularly suited for high-stakes clinical environments demanding transparency, regulatory compliance, and resistance to quantum-era attacks.
Blockchain-based digital assets represent a new stage in the evolution of monetary systems. However, the mechanisms of seigniorage – the revenue derived from issuing these digital assets – remain insufficiently studied, creating a research gap. The author addresses this topic to analyze how seigniorage is transformed in the context of digitalization and what new forms it assumes. The objective of the work is to investigate the evolution and mechanisms of seigniorage in digital assets, including cryptocurrencies, stablecoins, and central bank digital currencies (CBDCs). The tasks include analyzing the historical development of digital assets, comparing seigniorage mechanisms (Proof-of-Work and Proof-of-Stake), and assessing the prospects for implementing the digital ruble in Russia. Research methods: analysis of historical data, comparison of seigniorage mechanisms in blockchain projects (Bitcoin, Ethereum, MakerDAO), and evaluation of the economic aspects of central bank digital currencies. The study utilizes open data, legislative acts, and scientific publications. The results demonstrate that seigniorage in digital assets takes on new forms, such as mining, staking, and algorithmic governance, which contribute to the creation of significant financial value. The implementation of the digital ruble, despite high costs, presents opportunities to enhance the efficiency of the financial system. The scope of application for the results includes developing regulatory approaches to digital assets and optimizing seigniorage mechanisms.
Leonardo Soares dos Santos, Ana Paula Neutzling Gomes, P Rupino
The widespread adoption of distributed energy resources poses challenges to the operation and management of electricity grids. The intrinsic characteristics of such resources, such as variability and dispatchability, require increased flexibility in power systems. Demand-side flexibility is expected to play a significant role in future power systems, necessitating a more active role for consumers and prosumers in the energy system. To effectively operationalize flexibility and accommodate the growth of distributed generation, there is an urgent need for active and automated local management of energy resources alongside local transactions and energy exchanges. Technologies like blockchain and smart contracts offer significant potential for facilitating energy transactions within decentralized systems, mainly due to their capacity to facilitate secure microtransactions over time. Although their potential is recognized and review works exist, a detailed understanding of their characteristics and functionalities is lacking, which is critical for the deployment of those technologies. In this regard, the authors utilize the Prisma protocol to conduct a comprehensive analysis of current developments, identify primary innovative contract functionalities, quantify their utilization, and uncover potential gaps in their application in local energy transactions. They were analyzed 197 smart contracts, where 179 indicated at least one functionality. The findings suggest that most of these functionalities focus on energy transactions without details. They were identified and characterized in terms of the type of blockchain on which these smart contracts were developed. The conclusions show that they primarily work on a private Ethereum, promoting transactions between two peers in real-time and in the day ahead. The study culminates in an inclusive conclusion that spans the range of smart contract functionalities across different aspects of blockchain technology and temporal trade dynamics. This analysis reveals a significant gap in the transaction approach involving multiple sellers and buyers, underscoring the need for further exploration. This gap presents an exciting opportunity for future research and development in energy management, particularly in the context of blockchain's potential to facilitate local energy transactions. • Review of smart contracts for energy trading based on 127 reviewed articles. • Presentation of the smart contract's functionalities for energy trading. • Critical features overview of reviewed energy trading platforms. • Identification of the challenges in applying smart contracts in energy transactions. • Recommendations to consider when implementing smart contracts for energy trading.