High-Frequency Trading (HFT) is pivotal in cryptocurrency markets, demanding rapid decision-making. Social media platforms like Reddit offer valuable, yet underexplored, information for such high-frequency, short-term trading. This paper introduces \textbf{PulseReddit}, a novel dataset that is the first to align large-scale Reddit discussion data with high-frequency cryptocurrency market statistics for short-term trading analysis. We conduct an extensive empirical study using Large Language Model (LLM)-based Multi-Agent Systems (MAS) to investigate the impact of social sentiment from PulseReddit on trading performance. Our experiments conclude that MAS augmented with PulseReddit data achieve superior trading outcomes compared to traditional baselines, particularly in bull markets, and demonstrate robust adaptability across different market regimes. Furthermore, our research provides conclusive insights into the performance-efficiency trade-offs of different LLMs, detailing significant considerations for practical model selection in HFT applications. PulseReddit and our findings establish a foundation for advanced MAS research in HFT, demonstrating the tangible benefits of integrating social media.
The present dissertation addresses the problem of fairly distributing shared resources in non-commercial blockchain networks. Blockchains are distributed systems that order and timestamp records of a given network of users, in a public, cryptographically secure, and consensual way. The records, which may in kind be events, transaction orders, sets of rules for structured transactions etc. are placed within well-defined datastructures called blocks, and they are linked to each other by the virtue of cryptographic pointers, in a total ordering which represents their temporal relations of succession. The ability to operate on the blockchain, and/or to contribute a record to the content of a block are shared resources of the blockchain systems. In commercial networks, these resources are exchanged in return for fiat money, and consequently, fairness is not a relevant problem in terms of computer engineering. In non-commercial networks, however, monetary solutions are not available, by definition. The present non-commercial blockchain networks employ trivial distribution mechanisms called faucets, which offer fixed amounts of free tokens (called cryptocurrencies) specific to the given network. This mechanism, although simple and efficient, is prone to denial of service (DoS) attacks and cannot address the fairness problem. In the present dissertation, the faucet mechanism is adapted for fair distribution, in line with Max-min Fairness scheme. In total, we contributed 6 distinct Max-min Fair algorithms as efficient blockchain faucets. The algorithms we contribute are resistant to DoS attacks, low-cost in terms of blockchain computation economics, and they also allow for different user weighting policies.
Mirco Peron, Nicolò Saporiti, Rossella Pozzi, Maria Pia Ciano
The pharmaceutical sector is experiencing rapid growth in global medicine usage and spending. As the sector expands, so do risks. Blockchain technology promises significant benefits for addressing key issues in the pharmaceutical supply chains, such as enhancing traceability, preventing counterfeit drugs, and securing sensitive data. However, its adoption is not without criticism, facing challenges such as high implementation costs, regulatory uncertainties, and even skepticism about its purported benefits. This study provides the first empirical analysis of blockchain adoption challenges and countermeasures in the pharmaceutical industry. The research aims to identify key challenges and propose effective solutions to support broader implementation. A three-step methodology was employed: a systematic literature review to identify challenges, followed by a Delphi study to assess their relevance, and finally, a questionnaire collecting practical countermeasures. Seventeen major challenges and eight sets of countermeasures were identified and prioritized, with "IT security" being ranked as the most critical challenge and "Education and training" as the most effective countermeasure as it tackles more pressing challenges. The results were then analyzed using the Technology-Organization-Environment (TOE) framework and the Stakeholder Theory. The use of these complementary frameworks allowed to shed light on how different stakeholders can address the different challenges based on their roles, emphasizing the importance of their collective and collaborative efforts. This integration of theoretical frameworks provides valuable practical insights for addressing blockchain-related challenges and accelerating its adoption. Moreover, this research compares the pharmaceutical sector with other application areas, extending insights into blockchain adoption across industries.
Background and aims: Intensive care units (ICUs) face growing challenges with cybersecurity, data interoperability, medication safety, and resource management in an increasingly digital healthcare environment. This review explores how blockchain technology can address these issues and improve critical care delivery. Data sources: Relevant literature was sourced from peer-reviewed journals, healthcare cybersecurity reports, and studies on blockchain applications in medical settings. Study selection: Included works focused on blockchain's role in enhancing data security, drug traceability, consent management, and integration with AI tools in ICU contexts. Data synthesis: Blockchain offers tamper-proof health records, decentralized data sharing, and automated smart contracts, potentially transforming ICU operations. Benefits include improved patient safety, resource efficiency, and decision support. However, challenges such as scalability, regulatory concerns, and implementation costs remain. Conclusion: Blockchain holds strong potential to enhance ICU workflows and patient outcomes. Realizing its impact will require collaborative efforts and further research to overcome current limitations. How to cite this article: Gondode P, Dass C, Kumar S, Malviya A, Ashwin M, Khanna P. Blockchain in Critical Care. Indian J Crit Care Med 2025;29(6):525-530.
Muhammad Izzul Syahmi Zulkepli, Suffian Haqiem Nor Azelan, Nur Bakri Abdul Hamid, Hazrul Hizam Karim · 5 authors
A smart contract is a script that encodes and executes the terms of a contract or transaction on a blockchain platform. This technology offers a practical mechanism for delivering Islamic banking products. Smart contracts can be utilized for Islamic home financing, specifically to implement the diminishing partnership (musharakah mutanaqisah) structure. This arrangement has seen a decline in use, largely due to the rise of tawarruq contracts which benefit from more developed systems and facilities. Reassessing and redeploying the diminishing partnership model via smart contracts could reduce the industry’s heavy reliance on tawarruq in Islamic banking operations. Accordingly, this study evaluates the potential of smart contract technology to execute diminishing partnership agreements, especially within the context of housing finance offerings. We adopt a qualitative approach to achieve this aim, drawing on thematic analysis of prior studies and synthesizing findings through narrative methods. Our results indicate that smart contracts can serve as an effective operational mechanism for diminishing partnership-based home financing, thanks to automated workflows, enhanced transaction transparency, and improved risk management. However, these advantages can only be fully realized if the technological, operational, and Sharia-related risks are properly identified and controlled.
Objective: The key objective of this paper is to provide comprehensive trends in the topic of cryptocurrency and the financial sector. Utilizing bibliometric analysis, this study examined 1293 papers published between November 2014 and 2024 to ascertain the scope of research conducted over this period. Research Design and Methods: Data was collected from the Lens database, producing 1,293 papers. Utilizing VOSviewer, we investigated key elements of literature, including well-known organizations, authors, nations, and journals. We also looked at co-authorship, bibliographic coupling, and co-occurrence of terms. Findings: According to the findings, the greatest number of publications, largely from journals, were discovered in 2023. The most active authors were Elie Bouri, Andreas Hani, Horst Treiblmaier, and Jochen Michaelis and Julapa Jagtiani with seven documents each. The most common topics of investigation are cryptocurrency and business. The SSRN electronic journal topped the list. The top three productive countries in the field include the United States, China, and the United Kingdom. As evidenced by the findings, research on cryptocurrencies is expanding rapidly, yet there is still a deficiency of international author partnerships and scientific exchanges. Implications and Recommendations: The current study provides a comprehensive perspective and a thorough analysis of the relationship between cryptocurrencies and the financial sector, which form the theoretical foundations of the cryptocurrency deployment. Contribution and Value Added: Summing up 10 years of academic articles on the relationship between the financial sector and cryptocurrencies, this is one of the first bibliometric studies in the field. The bibliometric analysis provides a thorough knowledge base that enables scholars to make well-informed conclusions about industrial practices and policy formation. These results highlight important knowledge gaps and provide interesting directions for further study, consolidating the present understanding of cryptocurrency research in the financial industry. Received: 23 December 2024 | Revised: 13 March 2025 | Accepted: 8 May 2025 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the Lens at https://www.lens.org. Author Contribution Statement Karima Sayari: Conceptualization, Methodology, Data curation, Writing – original draft, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Mayssa Ben Belgacem: Conceptualization, Methodology, Writing – review & editing, Visualization.
This article explores the potential of integrating blockchain technologies into public management of Ukraine's agricultural sector for post-war recovery through the lens of ESG principles. The authors analyze limitations of traditional regulatory models, particularly information asymmetry, lack of transparency in state support distribution, and limited access to financing for small and medium agricultural producers. The proposed concept envisions transforming state institutions from monopolistic regulators into infrastructural participants of network partnerships. The research reveals significant shortcomings in existing financing mechanisms, where approximately 75% of small agricultural enterprises face loan rejections, while traditional international payment instruments are characterized by high costs and low efficiency. The authors propose implementing agricultural asset tokenization mechanisms based on distributed ledger technology, enabling transparent, automated, and inclusive mechanisms for state support and private financing. A comparative analysis of traditional and innovative blockchain-based public management models is developed according to criteria of transparency, trust-building mechanisms, operational efficiency, and adaptability to ESG requirements. The study examines mechanisms for tokenizing agricultural assets with integrated ESG parameters, ensuring programmable responsibility of producers for compliance with environmental, social, and governance standards. Methods for implementing ESG standards are presented through: tokenization of ESG obligations, decentralized ESG certification, and programmed ESG regulation. Practical implementation of the proposed instruments is illustrated using examples of a decentralized blockchain platform for pre-ordering food poppy products and carp farming. The authors demonstrate that integrating distributed ledger technology creates conditions for democratizing market access, supply chain transparency, and automated verification of ESG criteria compliance, transforming them from regulatory requirements into competitive advantages. Implementing the proposed model requires not only technological modernization but also institutional transformation of regulatory bodies and development of specialized digital inclusion programs to bridge the digital divide in rural areas.
The existing methods do not effectively meet the security and performance demands for Internet of Vehicles (IoV) applications. They also do not provide low-latency, secure edge-computing solutions for end-users in vehicular environments. The study presented in this paper proposes a blockchain-based edge computing framework that utilises Double Deep Q-Network (DDQN) for reinforcement learning and lightweight Practical Byzantine Fault Tolerance (PBFT) consensus for simultaneously optimising latency, energy consumption, and security. For efficient microservice orchestration and task off-loading, the containerised architecture utilises Kubernetes with Hyperledger Fabric. The experiments conducted in urban, suburban, and highway scenarios confirmed that the proposed framework outperformed baseline algorithms with end-to-end latency reduction of 30–45% while also lowering energy consumption by up to 55% under moderate-to-heavy loads. With less than 1.2 seconds per block on the blockchain consensus, the system also maintained task completion rates exceeding 95% during peak conditions. The framework demonstrates consistent performance across various vehicular densities and consumes zero-knowledge proofs with attribute-based encryption for data against cybersecurity threats. These results confirm that the integration of DDQN and blockchain technology effectively tackles primary obstacles IoV faces by providing secure edge computing for next generation vehicular networks.
In the contemporary digital age, education is no longer limited to traditional educational environments. Many educational institutions shifted to depend on the smart learning process but expressed concern about this solution due to its various challenges in securing the learning process and learners' data. By virtue of the most recent technologies like blockchain and artificial intelligence, which played a significant role in solving many challenges that faced the educational sector and overcoming issues like fake certificates, manipulation, tracking learners' activities, and predicting learners' academic performance. The study proposed a smart framework based on blockchain and deep learning to enhance smart learning processes and provide solutions for challenges in the field. The framework is intended to store the learner's data on the blockchain through the interplanetary file system and reap the benefits of securing the learner's data and ensuring its integrity, as well as ensuring the confidentiality and authentication of the users through the wallets that are created on the Ethereum private blockchain platform. Then apply the deep learning model to this secured data to predict the learner's performance. The smart contract functions also play a role in enabling the university to issue learners' certificates that are stored on the blockchain to be available and verifiable by all the nodes in the network. Based on the experimental results, deep neural networks were used to model the encrypted data that was stored on the blockchain and predict the learner's performance and achieved a high degree of accuracy (91.29%) and low loss (about 0.18) in comparison to other studies that depended on the centralized nature of the data. As well, the university blockchain's functionality was tested, and it successfully returned all the functional requirements and showed its legitimacy.
Suharyanto H. Soro, Anik Susanti, Nur Endah Sariningsih
Financing education is vital components in organization education quality , but implementation transparency reporting Still face various challenges in the era of decentralization Education . Research This aiming to study existence power education in create transparency reporting financing education at Informatics Vocational School Integrated Bandung. Using approach qualitative descriptive , data collected through observation , interview in-depth , and study documentation . Research results show power education play a role strategic through mechanism structural with implementation transparency realized through display information finance and ARKAS applications . Research This also reveals factor Supporter in the form of framework regulation , support leadership , and implementation technology information , while factor inhibitor covers delay disbursement of funds and limitations infrastructure technology . Research This confirm importance strengthening capacity power education and optimization technology information For increase transparency reporting financing in schools vocational that has complexity more tall in management finance Because need development skills vocational
Purpose: This study aims to examines advanced portfolio management techniques using Long Short-Term Memory (LSTM) networks, the study was applied to investing in cryptocurrencies whose markets are characterized by high-frequency trading, and using behavioral finance models based on the concept of return-risk and deep learning based on the work of artificial neural networks (ANN) and long-term memory (LSTM) algorithms Design/Methodology/Approach: This study adopts quantitative approach. Moreover, A random portfolio consisting of 25 cryptocurrencies was selected based on the database of the website: https://finance.yahoo.com/crypto/ during the period 2021-2024 AD and programming the Python language. And an attempt to evaluate the performance of the models used in accurately predicting the optimal relative weights of the investment portfolio, which proved the relative effectiveness of deep learning models by estimating the values of the mean square error (MSE) at a level of 0.0218% to predict the optimal portfolio weights for 5 days based on training 80% and testing 20% of the study data. Findings: The second hypothesis of this study was accepted, which states the effectiveness of deep learning algorithms to predict the weights of optimal portfolios with a return estimated at 1.7239% and a risk of 1.1219% and a Sharpe index value estimated at 1.5365%, while the Markowitz return-risk model portfolio came with a return rate estimated at 31.15% and a risk of 39.05%. With no diversification of investment on all portfolio assets and a Sharpe index value of 0.7978%. Practical Implications: This study provides important insights that machine learning offers significant advantages in portfolio optimization, from improved forecasting of asset returns to dynamic rebalancing, better risk management, and automation. The ability to handle high-dimensional, non-linear, and non-stationary data makes ML an ideal tool for optimizing portfolios in complex and fast-moving markets; especially in cryptocurrency markets. However, challenges like data quality, overfitting, and interpretability must be addressed to ensure effective deployment of ML in real-world portfolio. Originality/Value: This study provides an original and timely contribution to understanding the use of deep learning for portfolio optimization represents a significant advancement over traditional financial models by offering several original and valuable benefits. These include the ability to capture complex non-linear relationships, dynamic rebalancing in response to real-time data, processing of unstructured data (like sentiment analysis), advanced risk management, and the integration of high-dimensional data. The combination of these capabilities enables more accurate, adaptive, and robust portfolio optimization, ultimately enhancing portfolio performance and reducing risk.
This article explores the symbiosis of Industry 5.0 technologies and regulatory mechanisms that radically transform the interaction between the banking sector and public governance with a perspective extending to 2045. The research identifies key mechanisms of mutual influence between advanced technologies (artificial intelligence, distributed ledgers, quantum computing) and regulatory processes that define the evolution of banks from traditional financial intermediaries into integrated ecosystems of socio-economic development. The theoretical novelty of the study lies in substantiating the concept of «hybrid financial entities», in which technological infrastructure and regulatory processes function as inseparable components of a unified self-adapting system based on principles of proactive compliance and algorithmic trust. The paper demonstrates that the transformation of public governance models in the financial sector is moving from hierarchical structures towards platform solutions («Government as a Platform», GaaP), creating preconditions for regulatory landscape fragmentation and the formation of «regulatory microclimates». The integration of ESG principles into banking regulation redefines the very nature of financial intermediation, transforming banks into institutions of sustainable development. Special attention is paid to ensuring «digital sovereignty» and «safe degradation» of financial systems in conditions of geopolitical instability and risks of global conflicts. The study proposes a methodological table of banking institutions’ characteristics within four scenarios of financial system evolution: «Technological Hegemony», «Regulatory Revenge», «Decentralised Autonomy», and «Hybrid Convergence». The paper argues that the most probable scenario for banking sector evolution by 2045 is «Hybrid Convergence», characterised by a multi-level financial system where centralised and decentralised elements, state regulation and market self-regulation coexist within a unified ecosystem.
This paper examines the phenomenon of digital transformation in socio-economic systems as an object of public governance through the transition from an instrumental to a substantional understanding of digital technologies’ role. Using an interdisciplinary approach, the research systematizes theoretical and methodological concepts of digital transformation and identifies its seven fundamental properties: transversality, recursiveness, emergence, accelerativity, cognitive-transformational potential, institutional reconfigurability, and ontological hybridity. These properties form the methodological foundation for a new understanding of digital transformation, where technologies are viewed not as external optimization tools but as constitutive elements of a new institutional reality. The study develops a matrix of digital transformation’s impact on components of the socio-economic system, structuring the nature of changes, influence mechanisms, and resulting effects for economic, social, political-administrative, innovative, and informational subsystems. The research analyzes the evolution of public governance instruments for digitalization—from e-government to digital statehood models focused on digital resilience and platform-based approaches. Based on analysis of real-world cases of public sector digital modernization across different countries, the study confirms that effective public governance of digital transformation requires a comprehensive approach that accounts for the systemic nature of transformational processes. The research demonstrates that the transitive model of digital statehood creates a methodological foundation for proactive responses to digital era challenges through vertical integration of artificial intelligence systems and distributed ledgers into mechanisms of strategic planning and institutional adaptation.
The energy sector underwent a significant transformation with increasing demand for efficiency, transparency, and sustainability. The traditional or conventional system often faces several challenges, such as inefficient energy trading, a lack of transparency in renewable energy generation verification, and complex regulatory guidelines that affect its widespread adoption. Thus, blockchain technology has emerged as a potential solution to overcome these challenges, as it is known for its transparent, secure, and decentralized nature. However, despite the promising application of blockchain, its integration into the energy supply chain (ESC) is underexplored. The purpose of this research is to analyze the potential applications of blockchain technology in ESC in order to enhance efficiency, transparency, and sustainability in energy systems. The aim is to investigate the integration of blockchain with emerging technologies (such as IoTs, smart contracts, and P2P energy trading) in order to optimize energy production, distribution, and consumption. Furthermore, by comparing different blockchain platforms (like Ethereum, Solana, Hedera, and Hyperledger Fabric), this study discusses the security and scalability challenges of using blockchain in energy systems. It also examines the practical use cases of blockchain for the tokenization of RECs, dynamic energy pricing, and P2P energy trading by providing the Energy Web Foundation and Power Ledger as real-world examples. The article concludes that blockchain technology has the potential to transform ESC by enabling decentralized energy trading, which subsequently enhances transparency in energy transactions and the verification of renewable energy generation. It also identifies smart contracts and tokenization of energy assets as key parameters for dynamic pricing models and efficient trading mechanisms. However, regulatory and scalability challenges remain significant obstacles to its widespread adoption. Finally, this study provides the basis for future advancement in the adoption of blockchain technology in ESC, which offers a valuable resource for industry professionals, regulating authorities, and researchers.
Efficiently verifying mathematical proofs and computations has been a heavily researched topic within Computer Science. Particularly, even repetitive steps within a proof become much more complex and inefficient to validate as proof sizes grow. To solve this problem, we suggest viewing it through the lens of Incrementally Verifiable Computation (IVC). However, many IVC methods, including the state-of-the-art Nova recursive SNARKs, require proofs to be linear and for each proof step to be identical. This paper proposes Lova, a novel framework to verify mathematical proofs end-to-end that solves these problems. Particularly, our approach achieves a few novelties alongside the first-of-its-kind implementation of Nova: (i) an innovative proof splicing mechanism to generate independent proof sequences, (ii) a system of linear algorithms to verify a variety of mathematical logic rules, and (iii) a novel multiplexing circuit allowing non-homogeneous proof sequences to be verified together in a single Nova proof. The resulting Lova pipeline has linear prover time, constant verifying capability, dynamic/easy modification, and optional zero-knowledge privacy to efficiently validate mathematical proofs. We offer potential use cases for Lova to secure entire Cyber-Physical Systems (CPS) pipelines, as well as localized CPS systems in automotive and healthcare devices. Code is available at https://github.com/noelkelias/lova.
The article analyzes the foundations of Ukraine's state regional policy for digital transformation, including the regulatory framework, strategic documents, and regional informatization programs. It is determined that implementing digital technologies is a key direction of state policy aimed at creating conditions for the development of an information society, integrating modern technologies into public administration, and ensuring the competitiveness of regions. Considering the decentralization of power, conditions are being created to align regional development policies with key areas of digital transformation, including the digitalization of regional governance, the development of the digital economy, the implementation of innovative technologies, process automation, the expansion of e-government, and the formation of e-democracy tools, among others. The study found that the primary tool for ensuring digital transformation at the regional level is regional informatization programs developed by the standard project for such programs approved by the Ministry of Digital Transformation of Ukraine. As of January 1, 2025, 16 regions of Ukraine have adopted these programs, which cover the implementation of e-government, digital infrastructure, cybersecurity, digital literacy, and more. Disparities in financing and levels of digital maturity among regions have been identified, requiring improved coordination mechanisms and ensuring the balanced development of digital infrastructure at the regional level. The study's results emphasize the need to synchronize regional programs with national priorities, integrate European digitalization standards, and implement a monitoring system to assess program effectiveness. These aspects are crucial for the harmonious development of a digital state, ensuring equal access to modern technologies and improving the efficiency of public administration.
M Vishwas Bharadwaj, M Vishwas Bharadwaj, P Shreyas Gowda, Sanjana Desh · 5 authors
The rapid rise of cryptocurrencies has impacted the global socio-economic landscape, encouraging investors to seek income through crypto trading. Due to the market’s volatility and complex interdependencies, researchers have built various prediction models using machine learning, deep-learning, and sentiment-based hybrid algorithms. Notably, the DLCFS (Deep Learning Cryptocurrency Forecasting considering Sentiment) framework incorporates market features, trading volume, and sentiment from Reddit to improve price predictions for Bitcoin, Ethereum, and Litecoin—achieving high accuracy when compared to traditional machine learning models. Alongside forecasting, sentiment analysis plays an important role in understanding market trends and investor behavior. With growing user-generated content across different platforms like social media and news sites, extracting public sentiment through NLP has become essential. Recent works explore advanced models and datasets tailored to the unique linguistic features of crypto-related content, highlighting the need for robust and adaptive sentiment analysis techniques in this dynamic domain.
The PISTIS project, funded by the EU, aims to develop a federated platform for secure, trusted, and controlled data trading. By integrating Distributed Ledger Technologies (DLTs) and non-fungible tokens (NFTs), PISTIS enables seamless data management, peer-to-peer transfers, on-chain contracts, and systematic data valuation. It establishes a decentralized ecosystem governed by a cloud-based infrastructure and peer-to-peer connected local resources, allowing stakeholders to manage and monetize their data assets securely. The PISTIS Monetary Ledger, leveraging the IOTA protocol, ensures scalable, feeless, and efficient financial transactions, while the Digital DLT FIAT Wallet facilitates stable fiat-to-token exchanges. Additionally, the NFT Generator enables data tokenization, promoting a transparent and value-driven data economy. This paper outlines the aforementioned parts of the platform’s functional and nonfunctional requirements, system architecture, and future developments.
Paul-Cristian VASILE, Alexandru Georgescu, Daniela Munteanu
Space systems have become key components in a wide variety of critical infrastructures and some would say that they are critical infrastructures in themselves.They provide data gathering, communications, navigation, positioning and timing services that enable an important variety of critical services affecting billions of users.Increasingly, distributed ledger technology has begun to be integrated into the functioning of space systems, both in support of their roles and also to enable new roles.This article traces the main applications, discusses the issues inherent in blockchain use for space systems and underscores the potential that the technology provides across the space sector.
Zhaowei Hu, Jin Rui-fang, HangYi Quan, Shiyun Ni · 5 authors
To address privacy leakage risks arising from low collaborative user engagement, third-party trust deficits, and insufficient collaboration timeliness in location-based services (LBS), this paper proposes a dual-protection framework integrating blockchain technology and threshold cryptography for safeguarding location privacy. The framework employs asymmetric encryption with Shamir's (t, n) secret sharing to encrypt user queries, distributing decryption key fragments to collaborative users while generating n anonymous service requests through location generalization strategies. A temporary private blockchain constructed using smart contracts ensures confidential data transmission, supported by a dynamic privacy parameter configuration system based on Byzantine fault tolerance. The framework implements a priority-response consensus mechanism through Token-based equity proof-of-stake, prioritizing service for users with higher Token values. To mitigate privacy breaches caused by unresponsive collaborators, a competitive incentive mechanism ensures timely information submission. Through ciphertext fragment verification algorithms and Lagrange interpolation-based key reconstruction, the framework enables secure query decryption and service matching in untrusted third-party environments, guaranteeing information security, integrity, and non-repudiation. Experimental validation using real-world datasets confirms the framework's feasibility and operational effectiveness.
This article substantiates a new paradigm for managing state target programs based on distributed logic implemented through blockchain technologies and artificial intelligence (AI). The theoretical section analyzes the limitations of the centralized model for implementing state target programs, particularly high transaction costs, corruption risks, and management inefficiencies inherent in hierarchical systems. The research demonstrates that a distributed approach based on blockchain ensures data immutability and implementation transparency (through smart contracts and decentralized autonomous organizations), while AI application enables predictive analytics and dynamic resource planning, enhancing program adaptability. The authors propose a conceptual model of a «Distributed Target-Program» (DTP), which combines decentralized autonomous organizations (DAOs) for program management, smart contracts with KPIs for automatic performance control, a network of AI agents for monitoring and forecasting, and funding mechanisms through digital funds The methodological section provides a systematic analysis of experiences implementing distributed logic elements in Ukraine and worldwide. Examples examined include Estonia’s blockchain-based integrity of state registries, UN/World Food Programme (WFP) projects using blockchain for humanitarian assistance, digitalization of public services in Ukraine (the «Diia» mobile application), and blockchain solution experiments in the public sector. The research includes comparative case analyses of Ukrainian examples, such as blockchain implementation in social assistance provision (WFP Building Blocks project in Ukraine), the scale of digital aid payments through «Diia» (eSupport program), and DAO technology application in public administration. Special attention is given to assessing the regulatory compliance of the proposed solutions. The analysis covers current Ukrainian legislation in digital governance and public finance, as well as AI regulatory requirements according to the EU AI Act 2024/1689. The research demonstrates that implementing the DTP model requires legal framework adaptation (particularly defining DAO status and ensuring transparency and oversight of AI algorithms). This work is valuable for researchers and practitioners in public administration as it combines an interdisciplinary approach (public administration, information technology, legal aspects) and offers an innovative vision for the digital transformation of state programs.
Verifiable Delay Function (VDF) is a cryptographic concept that ensures a minimum delay before output through sequential processing, which is resistant to parallel computing. One of the significant VDF protocols academically reviewed is the VDF protocol proposed by Pietrzak. However, for the blockchain environment, the Pietrzak VDF has drawbacks including long proof size and recursive protocol computation. In this paper, we present an implementation study of Pietrzak VDF verification on Ethereum Virtual Machine (EVM). We found that the discussion in the Pietrzak's original paper can help a clear optimization in EVM where the costs of computation are predefined as the specific amounts of gas. In our results, the cost of VDF verification can be reduced from 4M to 2M gas, and the proof length can be generated under 8 KB with the 2048-bit RSA key length, which is much smaller than the previous expectation.